Compare commits

..
Author SHA1 Message Date
Hona b530418425 fix(session-ui): share timeline tool headers 2026-08-30 01:48:49 +00:00
1273 changed files with 25103 additions and 59311 deletions
+3 -2
View File
@@ -47,7 +47,7 @@ jobs:
- uses: ./.github/actions/setup-bun
- name: Deploy update service
if: github.ref_name == 'v2'
if: github.ref_name == 'v2' || github.ref_name == 'beta'
working-directory: packages/updates
run: bun run deploy
env:
@@ -417,6 +417,7 @@ jobs:
- uses: actions/checkout@f43a0e5ff2bd294095638e18286ca9a3d1956744 # v3.6.0
- uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4.3.0
if: github.ref_name == 'beta'
with:
name: opencode-preview-cli
path: packages/cli/dist
@@ -479,7 +480,7 @@ jobs:
OPENCODE_VERSION: ${{ needs.version.outputs.version }}
OPENCODE_CHANNEL: ${{ (github.ref_name == 'beta' && 'beta') || 'prod' }}
OPENCODE_CLI_TARGET: ${{ matrix.settings.target }}
OPENCODE_CLI_DIST: ${{ github.workspace }}/packages/cli/dist
OPENCODE_CLI_DIST: ${{ (github.ref_name == 'beta' && format('{0}/packages/cli/dist', github.workspace)) || '' }}
- name: Build
run: bun run build
+1 -2
View File
@@ -3,7 +3,7 @@
- Current implementation changes belong in `packages/core`, `packages/cli`, `packages/server`, `packages/protocol`, `packages/schema`, and related generated client surfaces when required.
- This repository does not use Changesets. Do not add `.changeset` files; follow the existing release workflow instead.
- The default branch in this repo is `v2`.
- Default new branches and worktrees to `v2`, or `origin/v2` when the local `v2` ref is unavailable, and default pull requests to target `v2`. Use another base or target branch when the requester explicitly instructs it.
- Base all new branches and worktrees on `v2`, or `origin/v2` when the local `v2` ref is unavailable. Do not base them on `dev`.
- Local `main` ref may not exist; use `v2` or `origin/v2` for diffs.
## Live V2 TUI Testing
@@ -46,7 +46,6 @@ Examples: `fix(tui): simplify thinking toggle styling`, `docs: update contributi
### General Principles
- Keep things in one function unless composable or reusable
- Validate unknown values once at the boundary that owns them. Pass typed values inward instead of repeating `typeof value === "object"` and property-existence checks. Do not defensively revalidate values already guaranteed by a schema, constructor, or internal type.
- Do not extract single-use helpers preemptively. Inline the logic at the call site unless the helper is reused, hides a genuinely complex boundary, or has a clear independent name that improves the caller.
- Before adding complexity for a speculative or vanishingly unlikely race or security edge case, explain the concrete failure mode, likelihood, and complexity cost to the user and get their buy-in. Do not silently expand scope for theoretical robustness.
- Avoid `try`/`catch` where possible
+68 -100
View File
@@ -54,7 +54,6 @@
"name": "@opencode-ai/app",
"version": "1.18.15",
"dependencies": {
"@corvu/drawer": "catalog:",
"@dnd-kit/abstract": "0.5.0",
"@dnd-kit/dom": "0.5.0",
"@dnd-kit/helpers": "0.5.0",
@@ -184,7 +183,6 @@
"@typescript/native-preview": "catalog:",
"effect": "catalog:",
"solid-js": "catalog:",
"zod": "catalog:",
},
"peerDependencies": {
"effect": "4.0.0-rc.112",
@@ -418,7 +416,7 @@
"electron-window-state": "^5.0.3",
},
"devDependencies": {
"@brendonovich/vite-plugin-opencode": "0.1.3",
"@brendonovich/vite-plugin-opencode": "0.1.1",
"@effect/platform-node": "catalog:",
"@lydell/node-pty": "catalog:",
"@opencode-ai/app": "workspace:*",
@@ -432,7 +430,6 @@
"@types/bun": "catalog:",
"@types/node": "catalog:",
"@typescript/native-preview": "catalog:",
"app-builder-lib": "26.15.7",
"drizzle-orm": "catalog:",
"effect": "catalog:",
"electron": "42.10.1",
@@ -575,7 +572,6 @@
"@opencode-ai/client": "workspace:*",
"@opencode-ai/protocol": "workspace:*",
"@opencode-ai/schema": "workspace:*",
"@opencode-ai/util": "workspace:*",
"@standard-schema/spec": "catalog:",
"effect": "catalog:",
"zod": "catalog:",
@@ -586,7 +582,6 @@
"@opentui/solid": "catalog:",
"@tsconfig/bun": "catalog:",
"@tsconfig/node22": "catalog:",
"@types/bun": "catalog:",
"@types/node": "catalog:",
"@typescript/native-preview": "catalog:",
"solid-js": "catalog:",
@@ -594,8 +589,8 @@
},
"peerDependencies": {
"@opencode-ai/theme": "workspace:*",
"@opentui/core": ">=0.5.10",
"@opentui/solid": ">=0.5.10",
"@opentui/core": ">=0.5.9",
"@opentui/solid": ">=0.5.9",
"solid-js": ">=1.9.0",
},
"optionalPeers": [
@@ -732,7 +727,6 @@
"@types/bun": "catalog:",
"@typescript/native-preview": "catalog:",
"vite": "catalog:",
"vite-plugin-solid": "catalog:",
},
},
"packages/simulation": {
@@ -972,7 +966,7 @@
"mime-types": "3.0.2",
"minimatch": "10.2.5",
"npm-package-arg": "13.0.2",
"pacote": "21.5.1",
"resolve.exports": "catalog:",
},
"devDependencies": {
"@tsconfig/bun": "catalog:",
@@ -981,7 +975,6 @@
"@types/node": "catalog:",
"@types/npm-package-arg": "6.1.4",
"@types/npmcli__arborist": "6.3.3",
"@types/pacote": "11.1.8",
"@typescript/native-preview": "catalog:",
},
},
@@ -1046,13 +1039,11 @@
"@pierre/trees@1.0.0-beta.4": "patches/@pierre%2Ftrees@1.0.0-beta.4.patch",
"@tanstack/virtual-core@3.17.8": "patches/@tanstack%2Fvirtual-core@3.17.8.patch",
"@modelcontextprotocol/sdk@1.29.0": "patches/@modelcontextprotocol%2Fsdk@1.29.0.patch",
"ghostty-web@github:anomalyco/ghostty-web#83c0a07": "patches/ghostty-web@0.3.0.patch",
"@standard-community/standard-openapi@0.2.9": "patches/@standard-community%2Fstandard-openapi@0.2.9.patch",
"@ai-sdk/mistral@3.0.51": "patches/@ai-sdk%2Fmistral@3.0.51.patch",
"@npmcli/agent@4.0.2": "patches/@npmcli%2Fagent@4.0.2.patch",
"@silvia-odwyer/photon-node@0.3.4": "patches/@silvia-odwyer%2Fphoton-node@0.3.4.patch",
"solid-js@1.9.15": "patches/solid-js@1.9.15.patch",
"vite@8.2.2": "patches/vite@8.2.2.patch",
"@ff-labs/fff-bun@0.10.5": "patches/@ff-labs%2Ffff-bun@0.10.5.patch",
"@ai-sdk/google@3.0.73": "patches/@ai-sdk%2Fgoogle@3.0.73.patch",
"@dnd-kit/dom@0.5.0": "patches/@dnd-kit%2Fdom@0.5.0.patch",
@@ -1082,9 +1073,9 @@
"@npmcli/arborist": "9.4.0",
"@octokit/rest": "22.0.0",
"@openauthjs/openauth": "0.0.0-20250322224806",
"@opentui/core": "0.5.10",
"@opentui/keymap": "0.5.10",
"@opentui/solid": "0.5.10",
"@opentui/core": "0.5.9",
"@opentui/keymap": "0.5.9",
"@opentui/solid": "0.5.9",
"@pierre/diffs": "1.2.10",
"@playwright/test": "1.59.1",
"@sentry/solid": "10.71.0",
@@ -1125,6 +1116,7 @@
"opentui-spinner": "0.0.7",
"remeda": "2.26.0",
"remend": "1.3.1",
"resolve.exports": "2.0.3",
"semver": "7.7.4",
"shiki": "4.4.3",
"solid-js": "1.9.15",
@@ -1598,7 +1590,7 @@
"@braintree/sanitize-url": ["@braintree/sanitize-url@7.1.2", "", {}, "sha512-jigsZK+sMF/cuiB7sERuo9V7N9jx+dhmHHnQyDSVdpZwVutaBu7WvNYqMDLSgFgfB30n452TP3vjDAvFC973mA=="],
"@brendonovich/vite-plugin-opencode": ["@brendonovich/vite-plugin-opencode@0.1.3", "", { "dependencies": { "@babel/core": "^7.29.0", "@opencode-ai/client": "0.0.0-beta-18050" }, "peerDependencies": { "vite": "^6.0.0 || ^7.0.0 || ^8.0.0" } }, "sha512-iiIwlNoycOMUiaUzL1ZirLapCG3WY8cg9hSj4KgE/JYIWgqauoHikIlfUt7SdMqqGIkb4iZu6I1MxgDDJaRfBA=="],
"@brendonovich/vite-plugin-opencode": ["@brendonovich/vite-plugin-opencode@0.1.1", "", { "dependencies": { "@babel/core": "^7.29.0", "@opencode-ai/client": "0.0.0-beta-18050" }, "peerDependencies": { "vite": "^6.0.0 || ^7.0.0 || ^8.0.0" } }, "sha512-aPG0ct8ctxAqndbNOx7NW0GhU6QY6sOUfi/DaKqH9c5WdxICSsUop6uSkJwPDHP9WpN9eg0dd2D2qwYpG6UdHw=="],
"@bruits/satteri-darwin-arm64": ["@bruits/satteri-darwin-arm64@0.9.5", "", { "os": "darwin", "cpu": "arm64" }, "sha512-iw4nZgx9v30lWo/MTngQqi1pI78KI0DnkSm+lVJGYdmPLgAyDNJigVhpG42/Iq55A6c1Ll8q66ljyyRiQUxwow=="],
@@ -1648,10 +1640,6 @@
"@cloudflare/workers-types": ["@cloudflare/workers-types@4.20251008.0", "", {}, "sha512-dZLkO4PbCL0qcCSKzuW7KE4GYe49lI12LCfQ5y9XeSwgYBoAUbwH4gmJ6A0qUIURiTJTkGkRkhVPqpq2XNgYRA=="],
"@corvu/dialog": ["@corvu/dialog@0.2.4", "", { "dependencies": { "@corvu/utils": "~0.4.2", "solid-dismissible": "~0.1.1", "solid-focus-trap": "~0.1.8", "solid-presence": "~0.2.0", "solid-prevent-scroll": "~0.1.10" }, "peerDependencies": { "solid-js": "^1.8" } }, "sha512-n54vJq+fOy8GVrnYBdJpD6JXNuyx7LOeMrRxwzAvZnYGpW8+AA12tnb/P/2emJj/HjOO5otheGKb0breshdFlA=="],
"@corvu/drawer": ["@corvu/drawer@0.2.4", "", { "dependencies": { "@corvu/dialog": "~0.2.4", "@corvu/utils": "~0.4.2", "@solid-primitives/memo": "^1.4.1", "solid-transition-size": "~0.1.4" }, "peerDependencies": { "solid-js": "^1.8" } }, "sha512-7jQoGZ8ROB9CmXam2nMY2wEskU3IoFwZQywkF/7vrBc/edGsPv7mOVQ1GN6G+4nd7nrMZ3UtqHAhmRh9V0azlw=="],
"@corvu/utils": ["@corvu/utils@0.4.2", "", { "dependencies": { "@floating-ui/dom": "^1.6.11" }, "peerDependencies": { "solid-js": "^1.8" } }, "sha512-Ox2kYyxy7NoXdKWdHeDEjZxClwzO4SKM8plAaVwmAJPxHMqA0rLOoAsa+hBDwRLpctf+ZRnAd/ykguuJidnaTA=="],
"@cspotcode/source-map-support": ["@cspotcode/source-map-support@0.8.1", "", { "dependencies": { "@jridgewell/trace-mapping": "0.3.9" } }, "sha512-IchNf6dN4tHoMFIn/7OE8LWZ19Y6q/67Bmf6vnGREv8RSbBVb9LPJxEcnwrcwX6ixSvaiGoomAUvu4YSxXrVgw=="],
@@ -1690,7 +1678,7 @@
"@electron/fuses": ["@electron/fuses@1.8.0", "", { "dependencies": { "chalk": "^4.1.1", "fs-extra": "^9.0.1", "minimist": "^1.2.5" }, "bin": { "electron-fuses": "dist/bin.js" } }, "sha512-zx0EIq78WlY/lBb1uXlziZmDZI4ubcCXIMJ4uGjXzZW0nS19TjSPeXPAjzzTmKQlJUZm0SbmZhPKP7tuQ1SsEw=="],
"@electron/get": ["@electron/get@3.1.0", "", { "dependencies": { "debug": "^4.1.1", "env-paths": "^2.2.0", "fs-extra": "^8.1.0", "got": "^11.8.5", "progress": "^2.0.3", "semver": "^6.2.0", "sumchecker": "^3.0.1" }, "optionalDependencies": { "global-agent": "^3.0.0" } }, "sha512-F+nKc0xW+kVbBRhFzaMgPy3KwmuNTYX1fx6+FxxoSnNgwYX6LD7AKBTWkU0MQ6IBoe7dz069CNkR673sPAgkCQ=="],
"@electron/get": ["@electron/get@5.1.0", "", { "dependencies": { "debug": "^4.1.1", "env-paths": "^3.0.0", "graceful-fs": "^4.2.11", "progress": "^2.0.3", "semver": "^7.6.3", "sumchecker": "^3.0.1" }, "optionalDependencies": { "undici": "^7.24.4" } }, "sha512-3kSBtG8ObcTVfXanm5vVJ6UnBLEVmVsRk1M+vGqCuMBV+XLCbJYuWQful+yIy0GQDsSlK0kHEriEHn7SPk4EnA=="],
"@electron/notarize": ["@electron/notarize@2.5.0", "", { "dependencies": { "debug": "^4.1.1", "fs-extra": "^9.0.1", "promise-retry": "^2.0.1" } }, "sha512-jNT8nwH1f9X5GEITXaQ8IF/KdskvIkOFfB2CvwumsveVidzpSc+mvhhTMdAGSYF3O+Nq49lJ7y+ssODRXu06+A=="],
@@ -2224,27 +2212,27 @@
"@opentelemetry/semantic-conventions": ["@opentelemetry/semantic-conventions@1.43.0", "", {}, "sha512-eSYWTm620tTk45EKSedaUL8MFYI8hW164hIXsgIHyxu3VobUB3fFCu5t0hQby6OoWRPsG1KkKUG2M5UadiLiVg=="],
"@opentui/core": ["@opentui/core@0.5.10", "", { "dependencies": { "bun-ffi-structs": "0.3.1", "diff": "9.0.0", "marked": "17.0.1", "string-width": "7.2.0", "strip-ansi": "7.1.2" }, "optionalDependencies": { "@opentui/core-darwin-arm64": "0.5.10", "@opentui/core-darwin-x64": "0.5.10", "@opentui/core-linux-arm64": "0.5.10", "@opentui/core-linux-arm64-musl": "0.5.10", "@opentui/core-linux-x64": "0.5.10", "@opentui/core-linux-x64-musl": "0.5.10", "@opentui/core-win32-arm64": "0.5.10", "@opentui/core-win32-x64": "0.5.10" }, "peerDependencies": { "web-tree-sitter": "0.25.10" } }, "sha512-C3a2UbmefeAjIxAgm4BqjuSxKT4oqutfvYFwVvUgMxmGRHkNbBc/s7sukV0JgwcxFcV3uMFrXxo+E+BQtvuOiw=="],
"@opentui/core": ["@opentui/core@0.5.9", "", { "dependencies": { "bun-ffi-structs": "0.3.1", "diff": "9.0.0", "marked": "17.0.1", "string-width": "7.2.0", "strip-ansi": "7.1.2" }, "optionalDependencies": { "@opentui/core-darwin-arm64": "0.5.9", "@opentui/core-darwin-x64": "0.5.9", "@opentui/core-linux-arm64": "0.5.9", "@opentui/core-linux-arm64-musl": "0.5.9", "@opentui/core-linux-x64": "0.5.9", "@opentui/core-linux-x64-musl": "0.5.9", "@opentui/core-win32-arm64": "0.5.9", "@opentui/core-win32-x64": "0.5.9" }, "peerDependencies": { "web-tree-sitter": "0.25.10" } }, "sha512-d0EWYyp6djitu1N1R0o75NrLl4TxY3oJEmRNrX9vSKKC5/jriGQdSV6lJmwiB77O0cxtBnPzWztAV79vA1J2fA=="],
"@opentui/core-darwin-arm64": ["@opentui/core-darwin-arm64@0.5.10", "", { "os": "darwin", "cpu": "arm64" }, "sha512-Vyb+nTbhab8ZcRy5gg1loEEGwRcIbjAeVRIBfHBcbFDqmITBOg7x2gqJ+x/TnoOy4uwMhCmICUN2wiyREw3r1Q=="],
"@opentui/core-darwin-arm64": ["@opentui/core-darwin-arm64@0.5.9", "", { "os": "darwin", "cpu": "arm64" }, "sha512-YcpWGD8GwoO3UYYw0kLDI4qof3ElSwWW5M3fNG+Kw87BSjCa5frZqak5xebdZ/XU5Xsk+BjgbBsxbzo9yrIjZQ=="],
"@opentui/core-darwin-x64": ["@opentui/core-darwin-x64@0.5.10", "", { "os": "darwin", "cpu": "x64" }, "sha512-tTFLcM7Oj1gTyhm/bUdAt3C6grZdCxPk6+/g2azcZBUlI3/62LwbeRS6HbQKFFmm+1fUmX8cq6kWrtul885mVg=="],
"@opentui/core-darwin-x64": ["@opentui/core-darwin-x64@0.5.9", "", { "os": "darwin", "cpu": "x64" }, "sha512-xgdZwgcwlDCqi0WPCS1d0GVSd+oKDFX+UVE1ZPei8u8W3pirXGSExxVHNku7QbEYCRvpCeMtoKIa6xN9cVTsoQ=="],
"@opentui/core-linux-arm64": ["@opentui/core-linux-arm64@0.5.10", "", { "os": "linux", "cpu": "arm64" }, "sha512-ncJXcgudhBf2GdJyF3xVQN/Ec+1F7GOL+pRrURmgBYSj2v1w6EyoDQFAACtPTK2c3R38W6fvZwL4JSLlm4EFXQ=="],
"@opentui/core-linux-arm64": ["@opentui/core-linux-arm64@0.5.9", "", { "os": "linux", "cpu": "arm64" }, "sha512-vHBiJp69wMaL+fRMDpnXCCIpPyQRPZEWCWBatnblUUGIWUseqeQlaPow6ljHSbgAd/GjygAf4tFCyTqNT2ezZA=="],
"@opentui/core-linux-arm64-musl": ["@opentui/core-linux-arm64-musl@0.5.10", "", { "os": "linux", "cpu": "arm64" }, "sha512-dGMphDKexSdeYqwl0wgoFBP88Ta/cdi1Zc1mk29/ENkSCGz+74zlCHgqTHRNGLmI8W5TfuUtCyktQH11/Z+TBQ=="],
"@opentui/core-linux-arm64-musl": ["@opentui/core-linux-arm64-musl@0.5.9", "", { "os": "linux", "cpu": "arm64" }, "sha512-A7GW4NCTInj2vy3X2gVO2Sm8jllgdLxwPdOklPqyl+xqivQtyG9qwS5YXoCk9fBJt10ORhYKETEjYEnIE5xvMQ=="],
"@opentui/core-linux-x64": ["@opentui/core-linux-x64@0.5.10", "", { "os": "linux", "cpu": "x64" }, "sha512-5qtYaOgwVycZD1GaGshTRsi0rXPAmVExO03N1JQaHu+NYxK/vXSOc7Bu4QW0sPXx3Sp0SpzpP+FHjXABfoK66g=="],
"@opentui/core-linux-x64": ["@opentui/core-linux-x64@0.5.9", "", { "os": "linux", "cpu": "x64" }, "sha512-rdw7QVopVHwj6fMrJc7hPGVaYg2V5NBlW3MUfkrQBlUlo/87OC4aYDAh5oRIL1CIFD9LjDdh7hwmUNbn/K9Pcw=="],
"@opentui/core-linux-x64-musl": ["@opentui/core-linux-x64-musl@0.5.10", "", { "os": "linux", "cpu": "x64" }, "sha512-Oj4H9hApuvuTKPWxh4SoZAgGJorR7vbvnrZA/cAkSMAk2VGSoHRRcqeXQbcH8IcdjVZ0KFpv8Zkl/D5Ye+2mew=="],
"@opentui/core-linux-x64-musl": ["@opentui/core-linux-x64-musl@0.5.9", "", { "os": "linux", "cpu": "x64" }, "sha512-J4wQs1OMPZ4hR93Op1C/BLFmIta2mUJm4M7djevlgcWcal4RSNO4V8QvUXfogWGci+ADZqEmX2osQQDY5NVJpw=="],
"@opentui/core-win32-arm64": ["@opentui/core-win32-arm64@0.5.10", "", { "os": "win32", "cpu": "arm64" }, "sha512-A9VhgvTxQoUdZ+8LmUumEng1sQNbj9QQQT3NYG9mSxI54qTANi7vOWNSphMiY6RMVsr22pgm6nUvSSvJXv7Jog=="],
"@opentui/core-win32-arm64": ["@opentui/core-win32-arm64@0.5.9", "", { "os": "win32", "cpu": "arm64" }, "sha512-OnmEsGLWwsHrm6sTsWSSesu4LGjSV1JNWqCsEItbuitWaqH6rXdSahSc5jMFsS57bZxUyaRz01yAqiZzUSXnVQ=="],
"@opentui/core-win32-x64": ["@opentui/core-win32-x64@0.5.10", "", { "os": "win32", "cpu": "x64" }, "sha512-u3KHa7kEeWrmKVDRJYpxSGO+g5E9cMGlrmTsPN3GVPHUmQMiREUawLXUvsU8+IHaQnqG3Q5nuE1yf4fPBzS+Qw=="],
"@opentui/core-win32-x64": ["@opentui/core-win32-x64@0.5.9", "", { "os": "win32", "cpu": "x64" }, "sha512-/CnAIfKL7+ZeGLyZoXV5zS71Nd8Zn97RUir2DAIY05MJozmfg5s7XOAdUNYuK1cg5mbwlAuqeAsfbuWUAEe15g=="],
"@opentui/keymap": ["@opentui/keymap@0.5.10", "", { "dependencies": { "@opentui/core": "0.5.10" }, "peerDependencies": { "@opentui/react": "0.5.10", "@opentui/solid": "0.5.10", "react": ">=19.2.0", "solid-js": "1.9.12" }, "optionalPeers": ["@opentui/react", "@opentui/solid", "react", "solid-js"] }, "sha512-8vDJF+ltXscSnLEv3rgCa4m7PcoYZeUT9BngugpFCmVoNevbaRtYijjdfiUuLmXfT61lO5QbR6nEhn2RZMK8ow=="],
"@opentui/keymap": ["@opentui/keymap@0.5.9", "", { "dependencies": { "@opentui/core": "0.5.9" }, "peerDependencies": { "@opentui/react": "0.5.9", "@opentui/solid": "0.5.9", "react": ">=19.2.0", "solid-js": "1.9.12" }, "optionalPeers": ["@opentui/react", "@opentui/solid", "react", "solid-js"] }, "sha512-ZcRNeuCDv+LJ89BS5xBw90e0S1Etn6j41jflg6LZlWj4bnqjCU55dq0+zERUdkkC4rqD4SikM8q7V3ZfkwzyyQ=="],
"@opentui/solid": ["@opentui/solid@0.5.10", "", { "dependencies": { "@babel/core": "7.28.0", "@babel/preset-typescript": "7.27.1", "@opentui/core": "0.5.10", "babel-plugin-module-resolver": "5.0.2", "babel-preset-solid": "1.9.12", "entities": "7.0.1", "s-js": "^0.4.9" }, "peerDependencies": { "solid-js": "1.9.12" } }, "sha512-KrmMIsHiKBHOABTC0brOwqWm+sGq1ZX2sGCAx6WgtBbE3STMup9n8TAy/6gUYhwcjC9zugT53ytfSVwCwVWZUg=="],
"@opentui/solid": ["@opentui/solid@0.5.9", "", { "dependencies": { "@babel/core": "7.28.0", "@babel/preset-typescript": "7.27.1", "@opentui/core": "0.5.9", "babel-plugin-module-resolver": "5.0.2", "babel-preset-solid": "1.9.12", "entities": "7.0.1", "s-js": "^0.4.9" }, "peerDependencies": { "solid-js": "1.9.12" } }, "sha512-zGSP/ia9ww+TTMvQMZjJW2+h7fg9YkVRIM723EgvP+v7ZBZ/0vMzPNh7FJ0jET3ysw0h5hsbvcYISs4sUcf+UA=="],
"@oslojs/asn1": ["@oslojs/asn1@1.0.0", "", { "dependencies": { "@oslojs/binary": "1.0.0" } }, "sha512-zw/wn0sj0j0QKbIXfIlnEcTviaCzYOY3V5rAyjR6YtOByFtJiT574+8p9Wlach0lZH9fddD4yb9laEAIl4vXQA=="],
@@ -2786,7 +2774,7 @@
"@silvia-odwyer/photon-node": ["@silvia-odwyer/photon-node@0.3.4", "", {}, "sha512-bnly4BKB3KDTFxrUIcgCLbaeVVS8lrAkri1pEzskpmxu9MdfGQTy8b8EgcD83ywD3RPMsIulY8xJH5Awa+t9fA=="],
"@sindresorhus/is": ["@sindresorhus/is@4.6.0", "", {}, "sha512-t09vSN3MdfsyCHoFcTRCH/iUtG7OJ0CsjzB8cjAmKc/va/kIgeDI/TxsigdncE/4be734m0cvIYwNaV4i2XqAw=="],
"@sindresorhus/is": ["@sindresorhus/is@7.2.0", "", {}, "sha512-P1Cz1dWaFfR4IR+U13mqqiGsLFf1KbayybWwdd2vfctdV6hDpUkgCY0nKOLLTMSoRd/jJNjtbqzf13K8DCCXQw=="],
"@smithy/config-resolver": ["@smithy/config-resolver@4.7.2", "", { "dependencies": { "@smithy/core": "^3.33.2", "tslib": "^2.6.2" } }, "sha512-Y1XfSefHIOub9762qm3ShafdlEE/Va8h3kLUeMq765fNeWeNLcOP2YUPr86H1SlyGwZTOqQ67RlBZPZ3k9Djgg=="],
@@ -2886,8 +2874,6 @@
"@solid-primitives/media": ["@solid-primitives/media@2.3.6", "", { "dependencies": { "@solid-primitives/event-listener": "^2.4.6", "@solid-primitives/rootless": "^1.5.4", "@solid-primitives/static-store": "^0.1.4", "@solid-primitives/utils": "^6.4.1" }, "peerDependencies": { "solid-js": "^1.6.12" } }, "sha512-pk49gPOq/UMRUJ+pTSrOfBiR8xJjRYHXIf1iR/jSnyQ/KroU+ZXhkZzavC7hvfp2vJeOTW5k2/HN0r3Q1VJ7Pw=="],
"@solid-primitives/memo": ["@solid-primitives/memo@1.5.1", "", { "dependencies": { "@solid-primitives/scheduled": "^1.5.3", "@solid-primitives/utils": "^6.4.1" }, "peerDependencies": { "solid-js": "^1.6.12" } }, "sha512-VDPrkl9epp0tbby9MvsqphGFCYCtDRC5J8FKzTqHbQiG5hhR8n6xv4MfjhTW231IaBxxPHLxS43EE8c5Q23mSQ=="],
"@solid-primitives/props": ["@solid-primitives/props@3.2.4", "", { "dependencies": { "@solid-primitives/utils": "^6.4.1" }, "peerDependencies": { "solid-js": "^1.6.12" } }, "sha512-MXXdvi2TSB6d+0N6ueA/HP1j/Kh9SEc4WdF1ZDMLwPagxW6pIHHSS9xbRO0RjqSVpNP4j0nxCWT1hx3CkJNhNA=="],
"@solid-primitives/refs": ["@solid-primitives/refs@1.1.4", "", { "dependencies": { "@solid-primitives/utils": "^6.4.1" }, "peerDependencies": { "solid-js": "^1.6.12" } }, "sha512-bLjwIs6ZPu8NQnuw04sU3Zc8qKSpbc0umUU/O4SHf6oWOdO4+dHY8vb1T7C4b/Tg103+9WGr6sEE0+NFlbaB/A=="],
@@ -3216,6 +3202,8 @@
"@upstash/redis": ["@upstash/redis@1.38.0", "", { "dependencies": { "uncrypto": "^0.1.3" } }, "sha512-wu+dZBptlLy0+MCUEoHmzrY/TnmgDey3+c7EbIGwrLqAvkP8yi5MWZHYGIFtAygmL4Bkz2TdFu+eU0vFPncIcg=="],
"@valibot/to-json-schema": ["@valibot/to-json-schema@1.6.0", "", { "peerDependencies": { "valibot": "^1.3.0" } }, "sha512-d6rYyK5KVa2XdqamWgZ4/Nr+cXhxjy7lmpe6Iajw15J/jmU+gyxl2IEd1Otg1d7Rl3gOQL5reulnSypzBtYy1A=="],
"@vercel/oidc": ["@vercel/oidc@3.2.0", "", {}, "sha512-UycprH3T6n3jH0k44NHMa7pnFHGu/N05MjojYr+Mc6I7obkoLIJujSWwin1pCvdy/eOxrI/l3uDLQsmcrOb4ug=="],
"@vitejs/plugin-react": ["@vitejs/plugin-react@4.7.0", "", { "dependencies": { "@babel/core": "^7.28.0", "@babel/plugin-transform-react-jsx-self": "^7.27.1", "@babel/plugin-transform-react-jsx-source": "^7.27.1", "@rolldown/pluginutils": "1.0.0-beta.27", "@types/babel__core": "^7.20.5", "react-refresh": "^0.17.0" }, "peerDependencies": { "vite": "^4.2.0 || ^5.0.0 || ^6.0.0 || ^7.0.0" } }, "sha512-gUu9hwfWvvEDBBmgtAowQCojwZmJ5mcLn3aufeCsitijs3+f2NsrPtlAWIR6OPiqljl96GVCUbLe0HyqIpVaoA=="],
@@ -3544,7 +3532,7 @@
"chromium-pickle-js": ["chromium-pickle-js@0.2.0", "", {}, "sha512-1R5Fho+jBq0DDydt+/vHWj5KJNJCKdARKOCwZUen84I5BreWoLqRLANH1U87eJy1tiASPtMnGqJJq0ZsLoRPOw=="],
"ci-info": ["ci-info@4.3.1", "", {}, "sha512-Wdy2Igu8OcBpI2pZePZ5oWjPC38tmDVx5WKUXKwlLYkA0ozo85sLsLvkBbBn/sZaSCMFOGZJ14fvW9t5/d7kdA=="],
"ci-info": ["ci-info@4.4.0", "", {}, "sha512-77PSwercCZU2Fc4sX94eF8k8Pxte6JAwL4/ICZLFjJLqegs7kCuAsqqj/70NQF6TvDpgFjkubQB2FW2ZZddvQg=="],
"classnames": ["classnames@2.3.2", "", {}, "sha512-CSbhY4cFEJRe6/GQzIk5qXZ4Jeg5pcsP7b5peFSDpffpe1cqjASH/n9UTjBwOp6XpMSTwQ8Za2K5V02ueA7Tmw=="],
@@ -3824,7 +3812,7 @@
"dot-prop": ["dot-prop@8.0.2", "", { "dependencies": { "type-fest": "^3.8.0" } }, "sha512-xaBe6ZT4DHPkg0k4Ytbvn5xoxgpG0jOS1dYxSOwAHPuNLjP3/OzN0gH55SrLqpx8cBfSaVt91lXYkApjb+nYdQ=="],
"dotenv": ["dotenv@16.6.1", "", {}, "sha512-uBq4egWHTcTt33a72vpSG0z3HnPuIl6NqYcTrKEg2azoEyl2hpW0zqlxysq2pK9HlDIHyHyakeYaYnSAwd8bow=="],
"dotenv": ["dotenv@17.4.2", "", {}, "sha512-nI4U3TottKAcAD9LLud4Cb7b2QztQMUEfHbvhTH09bqXTxnSie8WnjPALV/WMCrJZ6UV/qHJ6L03OqO3LcdYZw=="],
"dotenv-expand": ["dotenv-expand@11.0.7", "", { "dependencies": { "dotenv": "^16.4.5" } }, "sha512-zIHwmZPRshsCdpMDyVsqGmgyP0yT8GAgXUnkdAoJisxvf33k7yO6OuoKmcTGuXPWSsm8Oh88nZicRLA9Y0rUeA=="],
@@ -3896,7 +3884,7 @@
"entities": ["entities@7.0.1", "", {}, "sha512-TWrgLOFUQTH994YUyl1yT4uyavY5nNB5muff+RtWaqNVCAK408b5ZnnbNAUEWLTCpum9w6arT70i1XdQ4UeOPA=="],
"env-paths": ["env-paths@2.2.1", "", {}, "sha512-+h1lkLKhZMTYjog1VEpJNG7NZJWcuc2DDk/qsqSTRRCOXiLjeQ1d1/udrUGhqMxUgAlwKNZ0cf2uqan5GLuS2A=="],
"env-paths": ["env-paths@3.0.0", "", {}, "sha512-dtJUTepzMW3Lm/NPxRf3wP4642UWhjL2sQxc+ym2YMj1m/H2zDNQOlezafzkHwn6sMstjHTwG6iQQsctDW/b1A=="],
"err-code": ["err-code@2.0.3", "", {}, "sha512-2bmlRpNKBxT/CRmPOlyISQpNj+qSeYvcym/uT0Jx2bMOlKLtSy1ZmLuVxSEKKyor/N5yhvp/ZiG1oE3DEYMSFA=="],
@@ -4218,7 +4206,7 @@
"hono-openapi": ["hono-openapi@1.1.2", "", { "peerDependencies": { "@hono/standard-validator": "^0.2.0", "@standard-community/standard-json": "^0.3.5", "@standard-community/standard-openapi": "^0.2.9", "@types/json-schema": "^7.0.15", "hono": "^4.8.3", "openapi-types": "^12.1.3" }, "optionalPeers": ["@hono/standard-validator", "hono"] }, "sha512-toUcO60MftRBxqcVyxsHNYs2m4vf4xkQaiARAucQx3TiBPDtMNNkoh+C4I1vAretQZiGyaLOZNWn1YxfSyUA5g=="],
"hosted-git-info": ["hosted-git-info@4.1.0", "", { "dependencies": { "lru-cache": "^6.0.0" } }, "sha512-kyCuEOWjJqZuDbRHzL8V93NzQhwIB71oFWSyzVo+KPZI+pnQPPxucdkrOZvkLRnrf5URsQM+IJ09Dw29cRALIA=="],
"hosted-git-info": ["hosted-git-info@9.0.3", "", { "dependencies": { "lru-cache": "^11.1.0" } }, "sha512-Hc+ghLoSt6QaYZUv0WBiIvmMDZuZZ7oaDvdH8MbfOO4lOsxdXLEvuC6ePoGs9H1X9oCLyq6+NVN0MKqD+ydxyg=="],
"html-entities": ["html-entities@2.3.3", "", {}, "sha512-DV5Ln36z34NNTDgnz0EWGBLZENelNAtkiFA4kyNOG2tDI6Mz1uSWiq1wAKdyjnJwyDiDO7Fa2SO1CTxPXL8VxA=="],
@@ -5172,6 +5160,8 @@
"resolve-pkg-maps": ["resolve-pkg-maps@1.0.0", "", {}, "sha512-seS2Tj26TBVOC2NIc2rOe2y2ZO7efxITtLZcGSOnHHNOQ7CkiUBfw0Iw2ck6xkIhPwLhKNLS8BO+hEpngQlqzw=="],
"resolve.exports": ["resolve.exports@2.0.3", "", {}, "sha512-OcXjMsGdhL4XnbShKpAcSqPMzQoYkYyhbEaeSko47MjRP9NfEQMhZkXL1DoFlt9LWQn4YttrdnV6X2OiyzBi+A=="],
"responselike": ["responselike@2.0.1", "", { "dependencies": { "lowercase-keys": "^2.0.0" } }, "sha512-4gl03wn3hj1HP3yzgdI7d3lCkF95F21Pz4BPGvKHinyQzALR5CapwC8yIi0Rh58DEMQ/SguC03wFj2k0M/mHhw=="],
"restructure": ["restructure@3.0.2", "", {}, "sha512-gSfoiOEA0VPE6Tukkrr7I0RBdE0s7H1eFCDBk05l1KIQT1UIKNc5JZy6jdyW6eYH3aR3g5b3PuL77rq0hvwtAw=="],
@@ -5310,10 +5300,6 @@
"socks-proxy-agent": ["socks-proxy-agent@8.0.5", "", { "dependencies": { "agent-base": "^7.1.2", "debug": "^4.3.4", "socks": "^2.8.3" } }, "sha512-HehCEsotFqbPW9sJ8WVYB6UbmIMv7kUUORIF2Nncq4VQvBfNBLibW9YZR5dlYCSUhwcD628pRllm7n+E+YTzJw=="],
"solid-dismissible": ["solid-dismissible@0.1.1", "", { "dependencies": { "@corvu/utils": "~0.4.1" }, "peerDependencies": { "solid-js": "^1.8" } }, "sha512-9kcKBJIMdS+586cA1g63HYWxKh3h89leeNHbPZ1csYjuni+NvPBtNr11l0iEX2AKKEt6FHk6qNhc/gjoYAW1pA=="],
"solid-focus-trap": ["solid-focus-trap@0.1.9", "", { "dependencies": { "@corvu/utils": "~0.4.2" }, "peerDependencies": { "solid-js": "^1.8" } }, "sha512-LTyNki6GUJPRLXV5uMWPkYClB07SUMubbr2EkAddiR0CJCF/I283txilMU9RURSr/P8EewMfXWu2o3aWrK7A5A=="],
"solid-js": ["solid-js@1.9.15", "", { "dependencies": { "csstype": "^3.1.0", "seroval": "~1.5.4", "seroval-plugins": "~1.5.4" } }, "sha512-EeiY2xfpZJqPLjXspVEKjAII4yv8NyG//NxZ3IpOFHdUNnnTyL0uJOeS9LWGvA7cFCz5y94cjFwYlmw5Luncsg=="],
"solid-list": ["solid-list@0.3.0", "", { "dependencies": { "@corvu/utils": "~0.4.0" }, "peerDependencies": { "solid-js": "^1.8" } }, "sha512-t4hx/F/l8Vmq+ib9HtZYl7Z9F1eKxq3eKJTXlvcm7P7yI4Z8O7QSOOEVHb/K6DD7M0RxzVRobK/BS5aSfLRwKg=="],
@@ -5328,8 +5314,6 @@
"solid-stripe": ["solid-stripe@0.8.1", "", { "peerDependencies": { "@stripe/stripe-js": ">=1.44.1 <8.0.0", "solid-js": "^1.6.0" } }, "sha512-l2SkWoe51rsvk9u1ILBRWyCHODZebChSGMR6zHYJTivTRC0XWrRnNNKs5x1PYXsaIU71KYI6ov5CZB5cOtGLWw=="],
"solid-transition-size": ["solid-transition-size@0.1.4", "", { "dependencies": { "@corvu/utils": "~0.3.2" }, "peerDependencies": { "solid-js": "^1.8" } }, "sha512-ocHVnbfy23CgfaH4cEUR/AFg0Y3CEL8Oh3n9Qv8OHFJgPh+zkmERKZQfi/xH5XvxDCizg8VjPrVUhiHB1Gza8g=="],
"solid-use": ["solid-use@0.9.1", "", { "peerDependencies": { "solid-js": "^1.7" } }, "sha512-UwvXDVPlrrbj/9ewG9ys5uL2IO4jSiwys2KPzK4zsnAcmEl7iDafZWW1Mo4BSEWOmQCGK6IvpmGHo1aou8iOFw=="],
"sort-keys": ["sort-keys@1.1.2", "", { "dependencies": { "is-plain-obj": "^1.0.0" } }, "sha512-vzn8aSqKgytVik0iwdBEi+zevbTYZogewTUM6dtpmGwEcdzbub/TX4bCzRhebDCRC3QzXgJsLRKB2V/Oof7HXg=="],
@@ -5452,6 +5436,8 @@
"supports-preserve-symlinks-flag": ["supports-preserve-symlinks-flag@1.0.0", "", {}, "sha512-ot0WnXS9fgdkgIcePe6RHNk1WA8+muPa6cSjeR3V8K27q9BB1rTE3R1p7Hv0z1ZyAc8s6Vvv8DIyWf681MAt0w=="],
"sury": ["sury@11.0.0-alpha.4", "", { "peerDependencies": { "rescript": "12.x" }, "optionalPeers": ["rescript"] }, "sha512-oeG/GJWZvQCKtGPpLbu0yCZudfr5LxycDo5kh7SJmKHDPCsEPJssIZL2Eb4Tl7g9aPEvIDuRrkS+L0pybsMEMA=="],
"svgo": ["svgo@4.0.2", "", { "dependencies": { "commander": "^11.1.0", "css-select": "^5.1.0", "css-tree": "^3.0.1", "css-what": "^6.1.0", "csso": "^5.0.5", "picocolors": "^1.1.1", "sax": "^1.5.0" }, "bin": "./bin/svgo.js" }, "sha512-ekx94z1rRc5LDi6oSUaeRnYhd0UOJxdtQCL2rF8xpWxD3TPAsISWOrxezqGovqS38GRZOdpDfvQe3ts6F7nsng=="],
"tagged-tag": ["tagged-tag@1.0.0", "", {}, "sha512-yEFYrVhod+hdNyx7g5Bnkkb0G6si8HJurOoOEgC8B/O0uXLHlaey/65KRv6cuWBNhBgHKAROVpc7QyYqE5gFng=="],
@@ -5666,6 +5652,8 @@
"uuid": ["uuid@14.0.2", "", { "bin": { "uuid": "dist-node/bin/uuid" } }, "sha512-xZe/16rV4aa+HGSOCiY2YeLT1OybRLrrkL/Rqaq7p7GMVXjFh+6wN4oMYgjFmnSnhY8t6Xpdl2l9qmnHYuMHwQ=="],
"valibot": ["valibot@1.4.2", "", { "peerDependencies": { "typescript": ">=5" }, "optionalPeers": ["typescript"] }, "sha512-gjdCvJ6d3RyHAneqxMYMW9QMCwYMb3jpOO0IyHZV1bnRHFBHrX3VkIILt5XYR0WhwHiH7Mty8ovuPZ/O3gamrg=="],
"validate-npm-package-name": ["validate-npm-package-name@7.0.2", "", {}, "sha512-hVDIBwsRruT73PbK7uP5ebUt+ezEtCmzZz3F59BSr2F6OVFnJ/6h8liuvdLrQ88Xmnk6/+xGGuq+pG9WwTuy3A=="],
"vary": ["vary@1.1.2", "", {}, "sha512-BNGbWLfd0eUPabhkXUVm0j8uuvREyTh5ovRa/dyow/BqAbZJyC+5fU+IzQOzmAKzYqYRAISoRhdQr3eIZ/PXqg=="],
@@ -5814,7 +5802,9 @@
"xml-naming": ["xml-naming@0.3.0", "", {}, "sha512-ghig2TBE/H11aOVgmahA3MhimvkBr6JIYknH/Dhdk10nXwdbIqBJsbfMxpvFPG8bAw77gN29aQWvKpmVoPlvPQ=="],
"xmlbuilder": ["xmlbuilder@15.1.1", "", {}, "sha512-yMqGBqtXyeN1e3TGYvgNgDVZ3j84W4cwkOXQswghol6APgZWaff9lnbvN7MHYJOiXsvGPXtjTYJEiC9J2wv9Eg=="],
"xml2js": ["xml2js@0.5.0", "", { "dependencies": { "sax": ">=0.6.0", "xmlbuilder": "~11.0.0" } }, "sha512-drPFnkQJik/O+uPKpqSgr22mpuFHqKdbS835iAQrUC73L2F5WkboIRd63ai/2Yg6I1jzifPFKH2NTK+cfglkIA=="],
"xmlbuilder": ["xmlbuilder@11.0.1", "", {}, "sha512-fDlsI/kFEx7gLvbecc0/ohLG50fugQp8ryHzMTuW9vSa1GJ0XYWKnhsUx7oie3G98+r56aTQIUB4kht42R3JvA=="],
"xmlhttprequest-ssl": ["xmlhttprequest-ssl@2.1.2", "", {}, "sha512-TEU+nJVUUnA4CYJFLvK5X9AOeH4KvDvhIfm0vV1GaQRtchnG0hgK5p8hw/xjv8cunWYCsiPCSDzObPyhEwq3KQ=="],
@@ -5822,7 +5812,7 @@
"y18n": ["y18n@5.0.8", "", {}, "sha512-0pfFzegeDWJHJIAmTLRP2DwHjdF5s7jo9tuztdQxAhINCdvS+3nGINqPd00AphqJR/0LhANUS6/+7SCb98YOfA=="],
"yallist": ["yallist@5.0.0", "", {}, "sha512-YgvUTfwqyc7UXVMrB+SImsVYSmTS8X/tSrtdNZMImM+n7+QTriRXyXim0mBrTXNeqzVF0KWGgHPeiyViFFrNDw=="],
"yallist": ["yallist@4.0.0", "", {}, "sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A=="],
"yaml": ["yaml@2.9.0", "", { "bin": { "yaml": "bin.mjs" } }, "sha512-2AvhNX3mb8zd6Zy7INTtSpl1F15HW6Wnqj0srWlkKLcpYl/gMIMJiyuGq2KeI2YFxUPjdlB+3Lc10seMLtL4cA=="],
@@ -5846,6 +5836,8 @@
"zod": ["zod@4.1.8", "", {}, "sha512-5R1P+WwQqmmMIEACyzSvo4JXHY5WiAFHRMg+zBZKgKS+Q1viRa0C1hmUKtHltoIFKtIdki3pRxkmpP74jnNYHQ=="],
"zod-openapi": ["zod-openapi@5.4.6", "", { "peerDependencies": { "zod": "^3.25.74 || ^4.0.0" } }, "sha512-P2jsOOBAq/6hCwUsMCjUATZ8szkMsV5VAwZENfyxp2Hc/XPJQpVwAgevWZc65xZauCwWB9LAn7zYeiCJFAEL+A=="],
"zod-to-json-schema": ["zod-to-json-schema@3.25.2", "", { "peerDependencies": { "zod": "^3.25.28 || ^4" } }, "sha512-O/PgfnpT1xKSDeQYSCfRI5Gy3hPf91mKVDuYLUHZJMiDFptvP41MSnWofm8dnCm0256ZNfZIM7DSzuSMAFnjHA=="],
"zod-to-ts": ["zod-to-ts@1.2.0", "", { "peerDependencies": { "typescript": "^4.9.4 || ^5.0.2", "zod": "^3" } }, "sha512-x30XE43V+InwGpvTySRNz9kB7qFU8DlyEy7BsSTCHPH1R0QasMmHWZDCzYm6bVXtj/9NNJAZF3jW8rzFvH5OFA=="],
@@ -5968,8 +5960,6 @@
"@astrojs/starlight/js-yaml": ["js-yaml@4.3.1", "", { "dependencies": { "argparse": "^2.0.1" }, "bin": { "js-yaml": "bin/js-yaml.js" } }, "sha512-CY6crGq313MX8GkwvB7tzgp99vjQxY1++5y10/BKN/GUfHqWaOGQMNZkBvqSzsZKWk/ijwHlWzzkLulsGHhjWQ=="],
"@astrojs/telemetry/ci-info": ["ci-info@4.4.0", "", {}, "sha512-77PSwercCZU2Fc4sX94eF8k8Pxte6JAwL4/ICZLFjJLqegs7kCuAsqqj/70NQF6TvDpgFjkubQB2FW2ZZddvQg=="],
"@astrojs/telemetry/is-docker": ["is-docker@4.0.0", "", { "bin": { "is-docker": "cli.js" } }, "sha512-LHE+wROyG/Y/0ZnbktRCoTix2c1RhgWaZraMZ8o1Q7zCh0VSrICJQO5oqIIISrcSBtrXv0o233w1IYwsWCjTzA=="],
"@aws-crypto/crc32/@aws-sdk/types": ["@aws-sdk/types@3.974.4", "", { "dependencies": { "@smithy/types": "^4.16.1", "tslib": "^2.6.2" } }, "sha512-dSFDNG00MEz0/xl5gxL62giLd1iYyJsTxZ1I1DOj6lC+bbgLB4TRsYClJg3b62dhXT1uATzsTNXPnC+33EJV3A=="],
@@ -6124,9 +6114,7 @@
"@electron/fuses/fs-extra": ["fs-extra@9.1.0", "", { "dependencies": { "at-least-node": "^1.0.0", "graceful-fs": "^4.2.0", "jsonfile": "^6.0.1", "universalify": "^2.0.0" } }, "sha512-hcg3ZmepS30/7BSFqRvoo3DOMQu7IjqxO5nCDt+zM9XWjb33Wg7ziNT+Qvqbuc3+gWpzO02JubVyk2G4Zvo1OQ=="],
"@electron/get/fs-extra": ["fs-extra@8.1.0", "", { "dependencies": { "graceful-fs": "^4.2.0", "jsonfile": "^4.0.0", "universalify": "^0.1.0" } }, "sha512-yhlQgA6mnOJUKOsRUFsgJdQCvkKhcz8tlZG5HBQfReYZy46OwLcY+Zia0mtdHsOo9y/hP+CxMN0TU9QxoOtG4g=="],
"@electron/get/semver": ["semver@6.3.1", "", { "bin": { "semver": "bin/semver.js" } }, "sha512-BR7VvDCVHO+q2xBEWskxS6DJE1qRnb7DxzUrogb71CWoSficBxYsiAGd+Kl0mmq/MprG9yArRkyrQxTO6XjMzA=="],
"@electron/get/undici": ["undici@7.29.0", "", {}, "sha512-IDxfleLmmbSskfWSUATiN1nfn2rDuvnMOqb5CWR92iIfojA0Ud+ulOAAEQ57LPr9rWmsreUyf5lwyao+7GNNVw=="],
"@electron/notarize/fs-extra": ["fs-extra@9.1.0", "", { "dependencies": { "at-least-node": "^1.0.0", "graceful-fs": "^4.2.0", "jsonfile": "^6.0.1", "universalify": "^2.0.0" } }, "sha512-hcg3ZmepS30/7BSFqRvoo3DOMQu7IjqxO5nCDt+zM9XWjb33Wg7ziNT+Qvqbuc3+gWpzO02JubVyk2G4Zvo1OQ=="],
@@ -6162,12 +6150,6 @@
"@modelcontextprotocol/sdk/jose": ["jose@6.2.9", "", {}, "sha512-XrchZOFZUl/T3vTwRe8XK+cJrGtMF4th1ARnDfwbBXFKThGhlsxEE4Zu03AD/bjJSt/9jT/mxrOCkJWOg77aPA=="],
"@npmcli/arborist/hosted-git-info": ["hosted-git-info@9.0.3", "", { "dependencies": { "lru-cache": "^11.1.0" } }, "sha512-Hc+ghLoSt6QaYZUv0WBiIvmMDZuZZ7oaDvdH8MbfOO4lOsxdXLEvuC6ePoGs9H1X9oCLyq6+NVN0MKqD+ydxyg=="],
"@npmcli/config/ci-info": ["ci-info@4.4.0", "", {}, "sha512-77PSwercCZU2Fc4sX94eF8k8Pxte6JAwL4/ICZLFjJLqegs7kCuAsqqj/70NQF6TvDpgFjkubQB2FW2ZZddvQg=="],
"@npmcli/package-json/hosted-git-info": ["hosted-git-info@9.0.3", "", { "dependencies": { "lru-cache": "^11.1.0" } }, "sha512-Hc+ghLoSt6QaYZUv0WBiIvmMDZuZZ7oaDvdH8MbfOO4lOsxdXLEvuC6ePoGs9H1X9oCLyq6+NVN0MKqD+ydxyg=="],
"@npmcli/query/postcss-selector-parser": ["postcss-selector-parser@7.1.5", "", { "dependencies": { "cssesc": "^3.0.0", "util-deprecate": "^1.0.2" } }, "sha512-KvvtD7SrlBP7dlgkBghEE3r84CABm5SmV2aNcG4oCA+qDnJ/tvKonFVvwWAyyWUEwxuNawdfEAZKP9zM3oZ2Uw=="],
"@octokit/auth-app/@octokit/request": ["@octokit/request@10.0.14", "", { "dependencies": { "@octokit/endpoint": "^11.0.3", "@octokit/request-error": "^7.1.1", "@octokit/types": "^17.0.0", "content-type": "^2.0.0", "json-with-bigint": "^3.5.12", "universal-user-agent": "^7.0.2" } }, "sha512-bgWgiSfFS689/AxQDarU+b3Qu1FYewfVr5vI/jhV84s7GQ3C2OsdRxukGGYKB37MCgwcS50UrfBLlHzhiG13sw=="],
@@ -6256,8 +6238,6 @@
"@opencode-ai/session-ui/vite": ["vite@7.3.6", "", { "dependencies": { "esbuild": "^0.27.0 || ^0.28.0", "fdir": "^6.5.0", "picomatch": "^4.0.3", "postcss": "^8.5.6", "rollup": "^4.43.0", "tinyglobby": "^0.2.15" }, "optionalDependencies": { "fsevents": "~2.3.3" }, "peerDependencies": { "@types/node": "^20.19.0 || >=22.12.0", "jiti": ">=1.21.0", "less": "^4.0.0", "lightningcss": "^1.21.0", "sass": "^1.70.0", "sass-embedded": "^1.70.0", "stylus": ">=0.54.8", "sugarss": "^5.0.0", "terser": "^5.16.0", "tsx": "^4.8.1", "yaml": "^2.4.2" }, "optionalPeers": ["@types/node", "jiti", "less", "lightningcss", "sass", "sass-embedded", "stylus", "sugarss", "terser", "tsx", "yaml"], "bin": { "vite": "bin/vite.js" } }, "sha512-4XP60spRGjSZFf1qYH+dJIkK2znL3zQfl9KkOV9MkkRR/3Dls0dxaBsQPTloEc5BLXWPL9vsOxopxyKoMmDueg=="],
"@opencode-ai/session-ui/vite-plugin-solid": ["vite-plugin-solid@2.11.10", "", { "dependencies": { "@babel/core": "^7.23.3", "@types/babel__core": "^7.20.4", "babel-preset-solid": "^1.8.4", "merge-anything": "^5.1.7", "solid-refresh": "^0.6.3", "vitefu": "^1.0.4" }, "peerDependencies": { "@testing-library/jest-dom": "^5.16.6 || ^5.17.0 || ^6.*", "solid-js": "^1.7.2", "vite": "^3.0.0 || ^4.0.0 || ^5.0.0 || ^6.0.0 || ^7.0.0" }, "optionalPeers": ["@testing-library/jest-dom"] }, "sha512-Yr1dQybmtDtDAHkii6hXuc1oVH9CPcS/Zb2jN/P36qqcrkNnVPsMTzQ06jyzFPFjj3U1IYKMVt/9ZqcwGCEbjw=="],
"@opencode-ai/stats-app/vite": ["vite@7.3.6", "", { "dependencies": { "esbuild": "^0.27.0 || ^0.28.0", "fdir": "^6.5.0", "picomatch": "^4.0.3", "postcss": "^8.5.6", "rollup": "^4.43.0", "tinyglobby": "^0.2.15" }, "optionalDependencies": { "fsevents": "~2.3.3" }, "peerDependencies": { "@types/node": "^20.19.0 || >=22.12.0", "jiti": ">=1.21.0", "less": "^4.0.0", "lightningcss": "^1.21.0", "sass": "^1.70.0", "sass-embedded": "^1.70.0", "stylus": ">=0.54.8", "sugarss": "^5.0.0", "terser": "^5.16.0", "tsx": "^4.8.1", "yaml": "^2.4.2" }, "optionalPeers": ["@types/node", "jiti", "less", "lightningcss", "sass", "sass-embedded", "stylus", "sugarss", "terser", "tsx", "yaml"], "bin": { "vite": "bin/vite.js" } }, "sha512-4XP60spRGjSZFf1qYH+dJIkK2znL3zQfl9KkOV9MkkRR/3Dls0dxaBsQPTloEc5BLXWPL9vsOxopxyKoMmDueg=="],
"@opencode-ai/storybook/@tailwindcss/vite": ["@tailwindcss/vite@4.1.11", "", { "dependencies": { "@tailwindcss/node": "4.1.11", "@tailwindcss/oxide": "4.1.11", "tailwindcss": "4.1.11" }, "peerDependencies": { "vite": "^5.2.0 || ^6 || ^7" } }, "sha512-RHYhrR3hku0MJFRV+fN2gNbDNEh3dwKvY8XJvTxCSXeMOsCRSr+uKvDWQcbizrHgjML6ZmTE5OwMrl5wKcujCw=="],
@@ -6310,16 +6290,12 @@
"@pierre/trees/react-dom": ["react-dom@19.2.8", "", { "dependencies": { "scheduler": "^0.27.0" }, "peerDependencies": { "react": "^19.2.8" } }, "sha512-rVprimfGBG3DR+Tq0IQG2DT5PxKth1WIGDmj5yPmlzr4YBe7uyE+Du4oVqTDXZSHGGGXRtTJEGSSePyQCMBglQ=="],
"@poppinss/dumper/@sindresorhus/is": ["@sindresorhus/is@7.2.0", "", {}, "sha512-P1Cz1dWaFfR4IR+U13mqqiGsLFf1KbayybWwdd2vfctdV6hDpUkgCY0nKOLLTMSoRd/jJNjtbqzf13K8DCCXQw=="],
"@poppinss/dumper/supports-color": ["supports-color@10.2.2", "", {}, "sha512-SS+jx45GF1QjgEXQx4NJZV9ImqmO2NPz5FNsIHrsDjh2YsHnawpan7SNQ1o8NuhrbHZy9AZhIoCUiCeaW/C80g=="],
"@protobuf-ts/plugin/typescript": ["typescript@3.9.10", "", { "bin": { "tsc": "bin/tsc", "tsserver": "bin/tsserver" } }, "sha512-w6fIxVE/H1PkLKcCPsFqKE7Kv7QUwhU8qQY2MueZXWx5cPZdwFupLgKK3vntcK98BtNHZtAF4LA/yl2a7k8R6Q=="],
"@rollup/pluginutils/estree-walker": ["estree-walker@2.0.2", "", {}, "sha512-Rfkk/Mp/DL7JVje3u18FxFujQlTNR2q6QfMSMB7AvCBx91NGj/ba3kCfza0f6dVDbw7YlRf/nDrn7pQrCCyQ/w=="],
"@sentry/bundler-plugins/dotenv": ["dotenv@17.4.2", "", {}, "sha512-nI4U3TottKAcAD9LLud4Cb7b2QztQMUEfHbvhTH09bqXTxnSie8WnjPALV/WMCrJZ6UV/qHJ6L03OqO3LcdYZw=="],
"@sentry/bundler-plugins/glob": ["glob@13.0.6", "", { "dependencies": { "minimatch": "^10.2.2", "minipass": "^7.1.3", "path-scurry": "^2.0.2" } }, "sha512-Wjlyrolmm8uDpm/ogGyXZXb1Z+Ca2B8NbJwqBVg0axK9GbBeoS7yGV6vjXnYdGm6X53iehEuxxbyiKp8QmN4Vw=="],
"@sentry/cli/https-proxy-agent": ["https-proxy-agent@5.0.1", "", { "dependencies": { "agent-base": "6", "debug": "4" } }, "sha512-dFcAjpTQFgoLMzC2VwU+C/CbS7uRL0lWmxDITmqm7C+7F0Odmj6s9l6alZc6AELXhrnggM2CeWSXHGOdX2YtwA=="],
@@ -6392,6 +6368,14 @@
"anymatch/picomatch": ["picomatch@2.3.2", "", {}, "sha512-V7+vQEJ06Z+c5tSye8S+nHUfI51xoXIXjHQ99cQtKUkQqqO1kO/KCJUfZXuB47h/YBlDhah2H3hdUGXn8ie0oA=="],
"app-builder-lib/@electron/get": ["@electron/get@3.1.0", "", { "dependencies": { "debug": "^4.1.1", "env-paths": "^2.2.0", "fs-extra": "^8.1.0", "got": "^11.8.5", "progress": "^2.0.3", "semver": "^6.2.0", "sumchecker": "^3.0.1" }, "optionalDependencies": { "global-agent": "^3.0.0" } }, "sha512-F+nKc0xW+kVbBRhFzaMgPy3KwmuNTYX1fx6+FxxoSnNgwYX6LD7AKBTWkU0MQ6IBoe7dz069CNkR673sPAgkCQ=="],
"app-builder-lib/ci-info": ["ci-info@4.3.1", "", {}, "sha512-Wdy2Igu8OcBpI2pZePZ5oWjPC38tmDVx5WKUXKwlLYkA0ozo85sLsLvkBbBn/sZaSCMFOGZJ14fvW9t5/d7kdA=="],
"app-builder-lib/dotenv": ["dotenv@16.6.1", "", {}, "sha512-uBq4egWHTcTt33a72vpSG0z3HnPuIl6NqYcTrKEg2azoEyl2hpW0zqlxysq2pK9HlDIHyHyakeYaYnSAwd8bow=="],
"app-builder-lib/hosted-git-info": ["hosted-git-info@4.1.0", "", { "dependencies": { "lru-cache": "^6.0.0" } }, "sha512-kyCuEOWjJqZuDbRHzL8V93NzQhwIB71oFWSyzVo+KPZI+pnQPPxucdkrOZvkLRnrf5URsQM+IJ09Dw29cRALIA=="],
"app-builder-lib/js-yaml": ["js-yaml@4.3.1", "", { "dependencies": { "argparse": "^2.0.1" }, "bin": { "js-yaml": "bin/js-yaml.js" } }, "sha512-CY6crGq313MX8GkwvB7tzgp99vjQxY1++5y10/BKN/GUfHqWaOGQMNZkBvqSzsZKWk/ijwHlWzzkLulsGHhjWQ=="],
"app-builder-lib/semver": ["semver@7.7.4", "", { "bin": { "semver": "bin/semver.js" } }, "sha512-vFKC2IEtQnVhpT78h1Yp8wzwrf8CM+MzKMHGJZfBtzhZNycRFnXsHk6E5TxIkkMsgNS7mdX3AGB7x2QM2di4lA=="],
@@ -6402,8 +6386,6 @@
"astro/@clack/prompts": ["@clack/prompts@1.7.0", "", { "dependencies": { "@clack/core": "1.4.3", "fast-string-width": "^3.0.2", "fast-wrap-ansi": "^0.2.0", "sisteransi": "^1.0.5" } }, "sha512-y7/yvZ2TPAnR9+jnc00klvNNLkJiXFFrQA/hlLCcxA9a2A4zQIOimyFQ9XfwYKiGD1fb5GY8vbKIIgO8d5Tb2A=="],
"astro/ci-info": ["ci-info@4.4.0", "", {}, "sha512-77PSwercCZU2Fc4sX94eF8k8Pxte6JAwL4/ICZLFjJLqegs7kCuAsqqj/70NQF6TvDpgFjkubQB2FW2ZZddvQg=="],
"astro/diff": ["diff@8.0.3", "", {}, "sha512-qejHi7bcSD4hQAZE0tNAawRK1ZtafHDmMTMkrrIGgSLl7hTnQHmKCeB45xAcbfTqK2zowkM3j3bHt/4b/ARbYQ=="],
"astro/es-module-lexer": ["es-module-lexer@2.3.2", "", {}, "sha512-poHGpORABojJJucnV9KbOavETW8lBVnphkW77ER5/BQ5Fz7oXSoCNek7IH3vR5nRjdsEz926ibFYX8KtLQmdyw=="],
@@ -6448,8 +6430,6 @@
"conf/dot-prop": ["dot-prop@10.2.0", "", { "dependencies": { "type-fest": "^5.0.0" } }, "sha512-BTJ9aZYL3vCfZlZOBLy9v8TUqWGQ0pzFnygKwFZt5udj6viBoFIBviKPUoZLDCPn1FoXffv6McQFDenrm5Krfw=="],
"conf/env-paths": ["env-paths@3.0.0", "", {}, "sha512-dtJUTepzMW3Lm/NPxRf3wP4642UWhjL2sQxc+ym2YMj1m/H2zDNQOlezafzkHwn6sMstjHTwG6iQQsctDW/b1A=="],
"config-chain/ini": ["ini@1.3.8", "", {}, "sha512-JV/yugV2uzW5iMRSiZAyDtQd+nxtUnjeLt0acNdw98kKLrvuRVyB80tsREOE7yvGVgalhZ6RNXCmEHkUKBKxew=="],
"cross-spawn/which": ["which@2.0.2", "", { "dependencies": { "isexe": "^2.0.0" }, "bin": { "node-which": "./bin/node-which" } }, "sha512-BLI3Tl1TW3Pvl70l3yq3Y64i+awpwXqsGBYWkkqMtnbXgrMD+yj7rhW0kuEDxzJaYXGjEW5ogapKNMEKNMjibA=="],
@@ -6476,16 +6456,14 @@
"dot-prop/type-fest": ["type-fest@3.13.1", "", {}, "sha512-tLq3bSNx+xSpwvAJnzrK0Ep5CLNWjvFTOp71URMaAEWBfRb9nnJiBoUe0tF8bI4ZFO3omgBR6NvnbzVUT3Ly4g=="],
"dotenv-expand/dotenv": ["dotenv@16.6.1", "", {}, "sha512-uBq4egWHTcTt33a72vpSG0z3HnPuIl6NqYcTrKEg2azoEyl2hpW0zqlxysq2pK9HlDIHyHyakeYaYnSAwd8bow=="],
"duplexer2/readable-stream": ["readable-stream@2.3.8", "", { "dependencies": { "core-util-is": "~1.0.0", "inherits": "~2.0.3", "isarray": "~1.0.0", "process-nextick-args": "~2.0.0", "safe-buffer": "~5.1.1", "string_decoder": "~1.1.1", "util-deprecate": "~1.0.1" } }, "sha512-8p0AUk4XODgIewSi0l8Epjs+EVnWiK7NoDIEGU0HhE7+ZyY8D1IMY7odu5lRrFXGg71L15KG8QrPmum45RTtdA=="],
"editorconfig/commander": ["commander@10.0.1", "", {}, "sha512-y4Mg2tXshplEbSGzx7amzPwKKOCGuoSRP/CjEdwwk0FOGlUbq6lKuoyDZTNZkmxHdJtp54hdfY/JUrdL7Xfdug=="],
"editorconfig/minimatch": ["minimatch@9.0.9", "", { "dependencies": { "brace-expansion": "^2.0.2" } }, "sha512-OBwBN9AL4dqmETlpS2zasx+vTeWclWzkblfZk7KTA5j3jeOONz/tRCnZomUyvNg83wL5Zv9Ss6HMJXAgL8R2Yg=="],
"electron/@electron/get": ["@electron/get@5.1.0", "", { "dependencies": { "debug": "^4.1.1", "env-paths": "^3.0.0", "graceful-fs": "^4.2.11", "progress": "^2.0.3", "semver": "^7.6.3", "sumchecker": "^3.0.1" }, "optionalDependencies": { "undici": "^7.24.4" } }, "sha512-3kSBtG8ObcTVfXanm5vVJ6UnBLEVmVsRk1M+vGqCuMBV+XLCbJYuWQful+yIy0GQDsSlK0kHEriEHn7SPk4EnA=="],
"electron-builder/ci-info": ["ci-info@4.4.0", "", {}, "sha512-77PSwercCZU2Fc4sX94eF8k8Pxte6JAwL4/ICZLFjJLqegs7kCuAsqqj/70NQF6TvDpgFjkubQB2FW2ZZddvQg=="],
"electron-publish/mime": ["mime@2.6.0", "", { "bin": { "mime": "cli.js" } }, "sha512-USPkMeET31rOMiarsBNIHZKLGgvKc/LrjofAnBlOttf5ajRvqiRA8QsenbcooctK6d6Ts6aqZXBA+XbkKthiQg=="],
"electron-store/type-fest": ["type-fest@5.8.0", "", { "dependencies": { "tagged-tag": "^1.0.0" } }, "sha512-YGYEVz3Fm5iy/AybuA0oyNFq7H4CgQNfRp/qfe8nurE1kuCeNm3/vfm9X4Mtl+qLyaKJUh5xrFZwogr41SMjYA=="],
@@ -6532,7 +6510,7 @@
"globby/ignore": ["ignore@5.3.2", "", {}, "sha512-hsBTNUqQTDwkWtcdYI2i06Y/nUBEsNEDJKjWdigLvegy8kDuJAS8uRlpkkcQpyEXL0Z/pjDy5HBmMjRCJ2gq+g=="],
"hosted-git-info/lru-cache": ["lru-cache@6.0.0", "", { "dependencies": { "yallist": "^4.0.0" } }, "sha512-Jo6dJ04CmSjuznwJSS3pUeWmd/H0ffTlkXXgwZi+eq1UCmqQwCh+eLsYOYCwY991i2Fah4h1BEMCx4qThGbsiA=="],
"got/@sindresorhus/is": ["@sindresorhus/is@4.6.0", "", {}, "sha512-t09vSN3MdfsyCHoFcTRCH/iUtG7OJ0CsjzB8cjAmKc/va/kIgeDI/TxsigdncE/4be734m0cvIYwNaV4i2XqAw=="],
"html-minifier-terser/commander": ["commander@10.0.1", "", {}, "sha512-y4Mg2tXshplEbSGzx7amzPwKKOCGuoSRP/CjEdwwk0FOGlUbq6lKuoyDZTNZkmxHdJtp54hdfY/JUrdL7Xfdug=="],
@@ -6578,9 +6556,9 @@
"nitro/undici": ["undici@7.29.0", "", {}, "sha512-IDxfleLmmbSskfWSUATiN1nfn2rDuvnMOqb5CWR92iIfojA0Ud+ulOAAEQ57LPr9rWmsreUyf5lwyao+7GNNVw=="],
"node-gyp/undici": ["undici@6.28.0", "", {}, "sha512-LIY910g9TI13YS95lrMFrs8Rm/u/irgHeTWoKCoteeJ04CUJ92eEfj0rVn+7VKMPBpUPiUoBKfhNyLI23EE/KA=="],
"node-gyp/env-paths": ["env-paths@2.2.1", "", {}, "sha512-+h1lkLKhZMTYjog1VEpJNG7NZJWcuc2DDk/qsqSTRRCOXiLjeQ1d1/udrUGhqMxUgAlwKNZ0cf2uqan5GLuS2A=="],
"npm-package-arg/hosted-git-info": ["hosted-git-info@9.0.3", "", { "dependencies": { "lru-cache": "^11.1.0" } }, "sha512-Hc+ghLoSt6QaYZUv0WBiIvmMDZuZZ7oaDvdH8MbfOO4lOsxdXLEvuC6ePoGs9H1X9oCLyq6+NVN0MKqD+ydxyg=="],
"node-gyp/undici": ["undici@6.28.0", "", {}, "sha512-LIY910g9TI13YS95lrMFrs8Rm/u/irgHeTWoKCoteeJ04CUJ92eEfj0rVn+7VKMPBpUPiUoBKfhNyLI23EE/KA=="],
"openid-client/jose": ["jose@4.15.9", "", {}, "sha512-1vUQX+IdDMVPj4k8kOxgUqlcK518yluMuGZwqlr44FS1ppZB/5GWh4rZG89erpOBOJjU/OBsnCVFfapsRz6nEA=="],
@@ -6602,6 +6580,8 @@
"playwright/fsevents": ["fsevents@2.3.2", "", { "os": "darwin" }, "sha512-xiqMQR4xAeHTuB9uWm+fFRcIOgKBMiOBP+eXiyT7jsgVCq1bkVygt00oASowB7EdtpOHaaPgKt812P9ab+DDKA=="],
"plist/xmlbuilder": ["xmlbuilder@15.1.1", "", {}, "sha512-yMqGBqtXyeN1e3TGYvgNgDVZ3j84W4cwkOXQswghol6APgZWaff9lnbvN7MHYJOiXsvGPXtjTYJEiC9J2wv9Eg=="],
"postcss-css-variables/balanced-match": ["balanced-match@1.0.2", "", {}, "sha512-3oSeUO0TMV67hN1AmbXsK4yaqU7tjiHlbxRDZOpH0KW9+CeX4bRAaX0Anxt0tx2MrpRpWwQaPwIlISEJhYU5Pw=="],
"postcss-css-variables/escape-string-regexp": ["escape-string-regexp@1.0.5", "", {}, "sha512-vbRorB5FUQWvla16U8R/qgaFIya2qGzwDrNmCZuYKrbdSUMG6I1ZCGQRefkRVhuOkIGVne7BQ35DSfo1qvJqFg=="],
@@ -6636,8 +6616,6 @@
"slice-ansi/is-fullwidth-code-point": ["is-fullwidth-code-point@4.0.0", "", {}, "sha512-O4L094N2/dZ7xqVdrXhh9r1KODPJpFms8B5sGdJLPy664AgvXsreZUyCQQNItZRDlYug4xStLjNp/sz3HvBowQ=="],
"solid-transition-size/@corvu/utils": ["@corvu/utils@0.3.2", "", { "dependencies": { "@floating-ui/dom": "^1.6.7" }, "peerDependencies": { "solid-js": "^1.8" } }, "sha512-ZWlyWEE8qV9+CB9OAyo2bTrZGXQN9ZeM+JfYv89zoR+lRACKTDuoOZEdiyL8Uc7U5dUSH1uTqKhTTnaHWb+wZA=="],
"sort-keys/is-plain-obj": ["is-plain-obj@1.1.0", "", {}, "sha512-yvkRyxmFKEOQ4pNXCmJG5AEQNlXJS5LaONXo5/cLdTZdWvsZ1ioJEonLGAosKlMWE8lwUy/bJzMjcw8az73+Fg=="],
"source-map-support/source-map": ["source-map@0.6.1", "", {}, "sha512-UjgapumWlbMhkBgzT7Ykc5YXUT46F0iKu8SGXq0bcwP5dz/h0Plj6enJqjz1Zbq2l5WaqYnrVbwWOWMyF3F47g=="],
@@ -6664,6 +6642,8 @@
"svgo/commander": ["commander@11.1.0", "", {}, "sha512-yPVavfyCcRhmorC7rWlkHn15b4wDVgVmBA7kV4QVBsF7kv/9TKJAbAXVTxvTnwP8HHKjRCJDClKbciiYS7p0DQ=="],
"tar/yallist": ["yallist@5.0.0", "", {}, "sha512-YgvUTfwqyc7UXVMrB+SImsVYSmTS8X/tSrtdNZMImM+n7+QTriRXyXim0mBrTXNeqzVF0KWGgHPeiyViFFrNDw=="],
"temp/rimraf": ["rimraf@2.6.3", "", { "dependencies": { "glob": "^7.1.3" }, "bin": { "rimraf": "./bin.js" } }, "sha512-mwqeW5XsA2qAejG46gYdENaxXjx9onRNCfn7L0duuP4hCuTIi/QO7PDK07KJfp1d+izWPrzEJDcSqBa0OZQriA=="],
"tempy/type-fest": ["type-fest@0.16.0", "", {}, "sha512-eaBzG6MxNzEn9kiwvtre90cXaNLkmadMWa1zQMs3XORCXNbsH/OewwbxC5ia9dCxIxnTAsSxXJaa/p5y8DlvJg=="],
@@ -6806,8 +6786,6 @@
"@astrojs/starlight/astro/acorn": ["acorn@8.18.0", "", { "bin": { "acorn": "bin/acorn" } }, "sha512-lGq+9yr1/GuAWaVYIHRjvvySG5/4VfKIvC8EWxStPdcDh/Ka7FG3twP6v4d5BkravUilhIAsG4Qj83t02LWUPQ=="],
"@astrojs/starlight/astro/ci-info": ["ci-info@4.4.0", "", {}, "sha512-77PSwercCZU2Fc4sX94eF8k8Pxte6JAwL4/ICZLFjJLqegs7kCuAsqqj/70NQF6TvDpgFjkubQB2FW2ZZddvQg=="],
"@astrojs/starlight/astro/common-ancestor-path": ["common-ancestor-path@1.0.1", "", {}, "sha512-L3sHRo1pXXEqX8VU28kfgUY+YGsk09hPqZiZmLacNib6XNTCM8ubYeT7ryXQw8asB1sKgcU5lkB7ONug08aB8w=="],
"@astrojs/starlight/astro/cookie": ["cookie@1.1.1", "", {}, "sha512-ei8Aos7ja0weRpFzJnEA9UHJ/7XQmqglbRwnf2ATjcB9Wq874VKH9kfjjirM6UhU2/E5fFYadylyhFldcqSidQ=="],
@@ -6930,8 +6908,6 @@
"@electron/fuses/fs-extra/jsonfile": ["jsonfile@6.2.1", "", { "dependencies": { "universalify": "^2.0.0" }, "optionalDependencies": { "graceful-fs": "^4.1.6" } }, "sha512-zwOTdL3rFQ/lRdBnntKVOX6k5cKJwEc1HdilT71BWEu7J41gXIB2MRp+vxduPSwZJPWBxEzv4yH1wYLJGUHX4Q=="],
"@electron/get/fs-extra/universalify": ["universalify@0.1.2", "", {}, "sha512-rBJeI5CXAlmy1pV+617WB9J63U6XcazHHF2f2dbJix4XzpUF0RS3Zbj0FGIOCAva5P/d/GBOYaACQ1w+0azUkg=="],
"@electron/notarize/fs-extra/jsonfile": ["jsonfile@6.2.1", "", { "dependencies": { "universalify": "^2.0.0" }, "optionalDependencies": { "graceful-fs": "^4.1.6" } }, "sha512-zwOTdL3rFQ/lRdBnntKVOX6k5cKJwEc1HdilT71BWEu7J41gXIB2MRp+vxduPSwZJPWBxEzv4yH1wYLJGUHX4Q=="],
"@electron/universal/fs-extra/jsonfile": ["jsonfile@6.2.1", "", { "dependencies": { "universalify": "^2.0.0" }, "optionalDependencies": { "graceful-fs": "^4.1.6" } }, "sha512-zwOTdL3rFQ/lRdBnntKVOX6k5cKJwEc1HdilT71BWEu7J41gXIB2MRp+vxduPSwZJPWBxEzv4yH1wYLJGUHX4Q=="],
@@ -7154,8 +7130,6 @@
"@opencode-ai/web/astro/acorn": ["acorn@8.18.0", "", { "bin": { "acorn": "bin/acorn" } }, "sha512-lGq+9yr1/GuAWaVYIHRjvvySG5/4VfKIvC8EWxStPdcDh/Ka7FG3twP6v4d5BkravUilhIAsG4Qj83t02LWUPQ=="],
"@opencode-ai/web/astro/ci-info": ["ci-info@4.4.0", "", {}, "sha512-77PSwercCZU2Fc4sX94eF8k8Pxte6JAwL4/ICZLFjJLqegs7kCuAsqqj/70NQF6TvDpgFjkubQB2FW2ZZddvQg=="],
"@opencode-ai/web/astro/common-ancestor-path": ["common-ancestor-path@1.0.1", "", {}, "sha512-L3sHRo1pXXEqX8VU28kfgUY+YGsk09hPqZiZmLacNib6XNTCM8ubYeT7ryXQw8asB1sKgcU5lkB7ONug08aB8w=="],
"@opencode-ai/web/astro/cookie": ["cookie@1.1.1", "", {}, "sha512-ei8Aos7ja0weRpFzJnEA9UHJ/7XQmqglbRwnf2ATjcB9Wq874VKH9kfjjirM6UhU2/E5fFYadylyhFldcqSidQ=="],
@@ -7260,6 +7234,14 @@
"ansi-align/string-width/strip-ansi": ["strip-ansi@6.0.1", "", { "dependencies": { "ansi-regex": "^5.0.1" } }, "sha512-Y38VPSHcqkFrCpFnQ9vuSXmquuv5oXOKpGeT6aGrr3o3Gc9AlVa6JBfUSOCnbxGGZF+/0ooI7KrPuUSztUdU5A=="],
"app-builder-lib/@electron/get/env-paths": ["env-paths@2.2.1", "", {}, "sha512-+h1lkLKhZMTYjog1VEpJNG7NZJWcuc2DDk/qsqSTRRCOXiLjeQ1d1/udrUGhqMxUgAlwKNZ0cf2uqan5GLuS2A=="],
"app-builder-lib/@electron/get/fs-extra": ["fs-extra@8.1.0", "", { "dependencies": { "graceful-fs": "^4.2.0", "jsonfile": "^4.0.0", "universalify": "^0.1.0" } }, "sha512-yhlQgA6mnOJUKOsRUFsgJdQCvkKhcz8tlZG5HBQfReYZy46OwLcY+Zia0mtdHsOo9y/hP+CxMN0TU9QxoOtG4g=="],
"app-builder-lib/@electron/get/semver": ["semver@6.3.1", "", { "bin": { "semver": "bin/semver.js" } }, "sha512-BR7VvDCVHO+q2xBEWskxS6DJE1qRnb7DxzUrogb71CWoSficBxYsiAGd+Kl0mmq/MprG9yArRkyrQxTO6XjMzA=="],
"app-builder-lib/hosted-git-info/lru-cache": ["lru-cache@6.0.0", "", { "dependencies": { "yallist": "^4.0.0" } }, "sha512-Jo6dJ04CmSjuznwJSS3pUeWmd/H0ffTlkXXgwZi+eq1UCmqQwCh+eLsYOYCwY991i2Fah4h1BEMCx4qThGbsiA=="],
"app-builder-lib/js-yaml/argparse": ["argparse@2.0.1", "", {}, "sha512-8+9WqebbFzpX9OR+Wa6O29asIogeRMzcGtAINdpMHHyAg10f05aSFVBbcEqGf/PXw1EjAZ+q2/bEBg3DvurK3Q=="],
"app-builder-lib/which/isexe": ["isexe@3.1.5", "", {}, "sha512-6B3tLtFqtQS4ekarvLVMZ+X+VlvQekbe4taUkf/rhVO3d/h0M2rfARm/pXLcPEsjjMsFgrFgSrhQIxcSVrBz8w=="],
@@ -7278,8 +7260,6 @@
"astro-expressive-code/astro/acorn": ["acorn@8.18.0", "", { "bin": { "acorn": "bin/acorn" } }, "sha512-lGq+9yr1/GuAWaVYIHRjvvySG5/4VfKIvC8EWxStPdcDh/Ka7FG3twP6v4d5BkravUilhIAsG4Qj83t02LWUPQ=="],
"astro-expressive-code/astro/ci-info": ["ci-info@4.4.0", "", {}, "sha512-77PSwercCZU2Fc4sX94eF8k8Pxte6JAwL4/ICZLFjJLqegs7kCuAsqqj/70NQF6TvDpgFjkubQB2FW2ZZddvQg=="],
"astro-expressive-code/astro/common-ancestor-path": ["common-ancestor-path@1.0.1", "", {}, "sha512-L3sHRo1pXXEqX8VU28kfgUY+YGsk09hPqZiZmLacNib6XNTCM8ubYeT7ryXQw8asB1sKgcU5lkB7ONug08aB8w=="],
"astro-expressive-code/astro/cookie": ["cookie@1.1.1", "", {}, "sha512-ei8Aos7ja0weRpFzJnEA9UHJ/7XQmqglbRwnf2ATjcB9Wq874VKH9kfjjirM6UhU2/E5fFYadylyhFldcqSidQ=="],
@@ -7414,10 +7394,6 @@
"electron-winstaller/fs-extra/universalify": ["universalify@0.1.2", "", {}, "sha512-rBJeI5CXAlmy1pV+617WB9J63U6XcazHHF2f2dbJix4XzpUF0RS3Zbj0FGIOCAva5P/d/GBOYaACQ1w+0azUkg=="],
"electron/@electron/get/env-paths": ["env-paths@3.0.0", "", {}, "sha512-dtJUTepzMW3Lm/NPxRf3wP4642UWhjL2sQxc+ym2YMj1m/H2zDNQOlezafzkHwn6sMstjHTwG6iQQsctDW/b1A=="],
"electron/@electron/get/undici": ["undici@7.29.0", "", {}, "sha512-IDxfleLmmbSskfWSUATiN1nfn2rDuvnMOqb5CWR92iIfojA0Ud+ulOAAEQ57LPr9rWmsreUyf5lwyao+7GNNVw=="],
"esbuild-plugin-copy/chokidar/readdirp": ["readdirp@3.6.0", "", { "dependencies": { "picomatch": "^2.2.1" } }, "sha512-hOS089on8RduqdbhvQ5Z37A0ESjsqz6qnRcffsMU3495FuTdqSm+7bhJ29JvIOsBDEEnan5DPu9t3To9VRlMzA=="],
"filelist/minimatch/brace-expansion": ["brace-expansion@2.1.4", "", { "dependencies": { "balanced-match": "^1.0.0" } }, "sha512-hGfVzPxthbf3+2yjg/RBs60cB0FhqBS/zvdV/4wn4/BmN0bNMMHPc4V/BbFieqf1TKAGGAHnY4eSjajCl0f2Xg=="],
@@ -7426,8 +7402,6 @@
"gcp-metadata/gaxios/node-fetch": ["node-fetch@3.3.2", "", { "dependencies": { "data-uri-to-buffer": "^4.0.0", "fetch-blob": "^3.1.4", "formdata-polyfill": "^4.0.10" } }, "sha512-dRB78srN/l6gqWulah9SrxeYnxeddIG30+GOqK/9OlLVyLg3HPnr6SqOWTWOXKRwC2eGYCkZ59NNuSgvSrpgOA=="],
"hosted-git-info/lru-cache/yallist": ["yallist@4.0.0", "", {}, "sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A=="],
"js-beautify/glob/jackspeak": ["jackspeak@3.4.3", "", { "dependencies": { "@isaacs/cliui": "^8.0.2" }, "optionalDependencies": { "@pkgjs/parseargs": "^0.11.0" } }, "sha512-OGlZQpz2yfahA/Rd1Y8Cd9SIEsqvXkLVoSw/cgwhnhFMDbsQFeZYoJJ7bIZBS9BcamUW96asq/npPWugM+RQBw=="],
"js-beautify/glob/minimatch": ["minimatch@9.0.9", "", { "dependencies": { "brace-expansion": "^2.0.2" } }, "sha512-OBwBN9AL4dqmETlpS2zasx+vTeWclWzkblfZk7KTA5j3jeOONz/tRCnZomUyvNg83wL5Zv9Ss6HMJXAgL8R2Yg=="],
@@ -7476,12 +7450,6 @@
"miniflare/sharp/@img/sharp-win32-x64": ["@img/sharp-win32-x64@0.33.5", "", { "os": "win32", "cpu": "x64" }, "sha512-MpY/o8/8kj+EcnxwvrP4aTJSWw/aZ7JIGR4aBeZkZw5B7/Jn+tY9/VNwtcoGmdT7GfggGIU4kygOMSbYnOrAbg=="],
"minipass-flush/minipass/yallist": ["yallist@4.0.0", "", {}, "sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A=="],
"minipass-pipeline/minipass/yallist": ["yallist@4.0.0", "", {}, "sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A=="],
"openid-client/lru-cache/yallist": ["yallist@4.0.0", "", {}, "sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A=="],
"p-locate/p-limit/yocto-queue": ["yocto-queue@0.1.0", "", {}, "sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q=="],
"pkg-dir/find-up/locate-path": ["locate-path@5.0.0", "", { "dependencies": { "p-locate": "^4.1.0" } }, "sha512-t7hw9pI+WvuwNJXwk5zVHpyhIqzg2qTlklJOf0mVxGSbe3Fp2VieZcduNYjaLDoy6p9uGpQEGWG87WpMKlNq8g=="],
@@ -7508,8 +7476,6 @@
"toolbeam-docs-theme/astro/acorn": ["acorn@8.18.0", "", { "bin": { "acorn": "bin/acorn" } }, "sha512-lGq+9yr1/GuAWaVYIHRjvvySG5/4VfKIvC8EWxStPdcDh/Ka7FG3twP6v4d5BkravUilhIAsG4Qj83t02LWUPQ=="],
"toolbeam-docs-theme/astro/ci-info": ["ci-info@4.4.0", "", {}, "sha512-77PSwercCZU2Fc4sX94eF8k8Pxte6JAwL4/ICZLFjJLqegs7kCuAsqqj/70NQF6TvDpgFjkubQB2FW2ZZddvQg=="],
"toolbeam-docs-theme/astro/common-ancestor-path": ["common-ancestor-path@1.0.1", "", {}, "sha512-L3sHRo1pXXEqX8VU28kfgUY+YGsk09hPqZiZmLacNib6XNTCM8ubYeT7ryXQw8asB1sKgcU5lkB7ONug08aB8w=="],
"toolbeam-docs-theme/astro/cookie": ["cookie@1.1.1", "", {}, "sha512-ei8Aos7ja0weRpFzJnEA9UHJ/7XQmqglbRwnf2ATjcB9Wq874VKH9kfjjirM6UhU2/E5fFYadylyhFldcqSidQ=="],
@@ -8188,6 +8154,8 @@
"ansi-align/string-width/strip-ansi/ansi-regex": ["ansi-regex@5.0.1", "", {}, "sha512-quJQXlTSUGL2LH9SUXo8VwsY4soanhgo6LNSm84E1LBcE8s3O0wpdiRzyR9z/ZZJMlMWv37qOOb9pdJlMUEKFQ=="],
"app-builder-lib/@electron/get/fs-extra/universalify": ["universalify@0.1.2", "", {}, "sha512-rBJeI5CXAlmy1pV+617WB9J63U6XcazHHF2f2dbJix4XzpUF0RS3Zbj0FGIOCAva5P/d/GBOYaACQ1w+0azUkg=="],
"archiver-utils/glob/jackspeak/@isaacs/cliui": ["@isaacs/cliui@8.0.2", "", { "dependencies": { "string-width": "^5.1.2", "string-width-cjs": "npm:string-width@^4.2.0", "strip-ansi": "^7.0.1", "strip-ansi-cjs": "npm:strip-ansi@^6.0.1", "wrap-ansi": "^8.1.0", "wrap-ansi-cjs": "npm:wrap-ansi@^7.0.0" } }, "sha512-O8jcjabXaleOG9DQ0+ARXWZBTfnP4WNAqzuiJK7ll44AmxGKv/J2M4TPjxjY3znBCfvBXFzucm1twdyFybFqEA=="],
"archiver-utils/glob/minimatch/brace-expansion": ["brace-expansion@2.1.4", "", { "dependencies": { "balanced-match": "^1.0.0" } }, "sha512-hGfVzPxthbf3+2yjg/RBs60cB0FhqBS/zvdV/4wn4/BmN0bNMMHPc4V/BbFieqf1TKAGGAHnY4eSjajCl0f2Xg=="],
-5
View File
@@ -87,11 +87,6 @@ stdenv.mkDerivation (finalAttrs: {
cd packages/desktop
export OPENCODE_CLI_DIST="$TMPDIR/desktop-cli"
cli_package=$(bun -e 'import { getCurrentCli } from "./scripts/utils.ts"; console.log(getCurrentCli().package.replace("@opencode-ai/", ""))')
mkdir -p "$OPENCODE_CLI_DIST/$cli_package/bin"
cp ${lib.getExe opencode} "$OPENCODE_CLI_DIST/$cli_package/bin/opencode2"
bun run build
npx electron-builder --dir \
--config electron-builder.config.ts \
+4 -4
View File
@@ -1,8 +1,8 @@
{
"nodeModules": {
"x86_64-linux": "sha256-I2sHU46Bdp1DmW/ygNKKxF6U53oNIeVeAS8TmMdtvzw=",
"aarch64-linux": "sha256-aQUEBmfHhaR/YdsRyw0u6hsEo0TEmXHBMyiGnOFRCeI=",
"aarch64-darwin": "sha256-40Y3HYSjs8S0M3SFNUrU8lMYaHy7vpmiW+7b6Y9pKzs=",
"x86_64-darwin": "sha256-7iGegI/1pXcR5W0UBX0wAWAga+F34fhj9lbAHotVwEo="
"x86_64-linux": "sha256-No3mCuG2tGQauX1HUpO+rebiWh+rrpSHUCC7rtXFu1s=",
"aarch64-linux": "sha256-8joWv1iDkc6TejEukGBEX0wW8DPs55wJEPY7+9+HDdM=",
"aarch64-darwin": "sha256-4MWGFQUIP1Ae4dujztTb4G8/uDJx2wiOoypcPSUWDbw=",
"x86_64-darwin": "sha256-BtvnraCJmVagtA3Iv+EbWodjFG74sTd2Purqgo7Wkr4="
}
}
+8 -8
View File
@@ -5,10 +5,10 @@
"version": "0.0.0",
"private": true,
"type": "module",
"packageManager": "bun@1.4.0",
"packageManager": "bun@1.3.14",
"scripts": {
"dev": "bun run --cwd packages/cli --conditions=browser src/index.ts",
"dev:live": "sh -c 'OPENCODE_TUI_CHANNEL=dev OPENCODE_PASSWORD=\"$(opencode2 service get password)\" exec bun run dev \"$@\" --server \"$(opencode2 service status)\"' --",
"dev:live": "OPENCODE_TUI_CHANNEL=dev OPENCODE_PASSWORD=\"$(opencode2 service get password)\" bun run dev --server \"$(opencode2 service status)\"",
"dev:desktop": "bun --cwd packages/desktop dev",
"dev:web": "bun --cwd packages/app dev",
"dev:console": "ulimit -n 10240 2>/dev/null; bun run --cwd packages/console/app dev",
@@ -27,6 +27,7 @@
"upgrade-opentui": "bun run script/upgrade-opentui.ts",
"postinstall": "bun run --cwd packages/core fix-node-pty",
"prepare": "husky",
"reserve-packages": "bun script/reserve-package-names.ts",
"random": "echo 'Random script'",
"sso": "aws sso login --sso-session=opencode --no-browser",
"test": "echo 'do not run tests from root' && exit 1"
@@ -48,9 +49,9 @@
"@octokit/rest": "22.0.0",
"@hono/standard-validator": "0.2.0",
"@hono/zod-validator": "0.4.2",
"@opentui/core": "0.5.10",
"@opentui/keymap": "0.5.10",
"@opentui/solid": "0.5.10",
"@opentui/core": "0.5.9",
"@opentui/keymap": "0.5.9",
"@opentui/solid": "0.5.9",
"@tanstack/solid-virtual": "3.13.37",
"@shikijs/stream": "4.4.3",
"@standard-schema/spec": "1.1.0",
@@ -92,6 +93,7 @@
"@typescript/native-preview": "7.0.0-dev.20251207.1",
"zod": "4.1.8",
"remeda": "2.26.0",
"resolve.exports": "2.0.3",
"sst": "4.13.1",
"shiki": "4.4.3",
"solid-list": "0.3.0",
@@ -176,8 +178,6 @@
"@pierre/trees@1.0.0-beta.4": "patches/@pierre%2Ftrees@1.0.0-beta.4.patch",
"@modelcontextprotocol/sdk@1.29.0": "patches/@modelcontextprotocol%2Fsdk@1.29.0.patch",
"@tanstack/virtual-core@3.17.8": "patches/@tanstack%2Fvirtual-core@3.17.8.patch",
"@ff-labs/fff-bun@0.10.5": "patches/@ff-labs%2Ffff-bun@0.10.5.patch",
"ghostty-web@github:anomalyco/ghostty-web#83c0a07": "patches/ghostty-web@0.3.0.patch",
"vite@8.2.2": "patches/vite@8.2.2.patch"
"@ff-labs/fff-bun@0.10.5": "patches/@ff-labs%2Ffff-bun@0.10.5.patch"
}
}
+1 -102
View File
@@ -241,115 +241,14 @@ Constructing `stream()` or `generate()` does not record a request, invoke a resp
Each execution does. An exhausted queue without a fallback defects immediately rather than waiting for a
future reply.
Generation responses remain canonical event arrays or arbitrary `Stream<LLMEvent, AIError>` values. The client consumes
Responses remain canonical event arrays or arbitrary `Stream<LLMEvent, AIError>` values. The client consumes
supplied streams directly, preserving failure identity, finalizers, incomplete output, and post-finish tails;
it does not repair or truncate them.
For explicit compaction, script a `CompactionResponse` through `push`, `always`, or `serve`. Its `replacement` contains the next context window, including retained user messages. The client returns that result and usage directly, with the same lazy request recording and gates. Generation and compaction reject fixtures for the wrong operation instead of converting between response shapes.
The published legacy `Service`, `layer`, `clientLayer`, and module-level controls remain available as adapters
over the same implementation, including the legacy live `requests` array. New tests should use `Test` and
`testLayer`.
## Provider compaction
Compaction is opt-in. The package supports automatic compaction in OpenAI/Azure Responses and Anthropic Messages (including Claude on Vertex), and explicit compaction calls in OpenAI/Azure/xAI Responses. Model and deployment support still depends on the provider. Bedrock compaction is deferred to a separate follow-up.
This is different from prompt caching, server-side history storage, or truncation. Compaction returns provider-owned context that must be replayed to continue the conversation.
### Explicit compaction
`LLMClient.compact(request)` is the caller-controlled operation for OpenAI, Azure, and xAI Responses. It performs exactly one HTTP call to `/responses/compact`, using the selected route's endpoint, credentials, query, and HTTP middleware. It returns a `CompactionResponse` with `replacement: Message[]` and optional `usage`, not a normal generation response.
Prefer this operation, where supported, when the application owns compaction policy and durable context updates.
```ts
const result = yield * LLMClient.compact(request)
const next = LLMRequest.update(request, {
messages: result.replacement,
})
const response = yield * LLMClient.generate(next)
```
`replacement` replaces the complete input window. Do not append it to the original transcript or extract only the encrypted item: the provider may retain additional messages in its output. Retained user and assistant messages remain ordinary messages with typed text, media, or reasoning parts, in their original order. Provider-specific message IDs, status, and phase use `providerMetadata`, not a raw output array hidden in an assistant message. Unsupported returned item types fail explicitly.
The selected model carries explicit-compaction capability through request construction and updates. Calls using unsupported routes fail type checking. When the model is selected dynamically, narrow the request with `LLMClient.canCompact(request)` before calling `LLMClient.compact`; a model or route switch does not inherit the old capability. Runtime validation still rejects unsupported calls from untyped consumers. Capability describes the route's API, not whether every model or custom deployment supports the operation.
Generation-only body overlays such as `stream` and `store` are not sent to the compact endpoint. Supported compact controls such as service tier and prompt-cache settings preserve request defaults and HTTP-overlay precedence. Retained image and file detail settings survive serialization and replay.
The input must still fit the model's context window. Explicit compaction is not an overflow-recovery operation. Anthropic does not expose this operation in this package; its in-band compaction remains available below. Compatible routes do not inherit an explicit compact endpoint simply because they use a Responses protocol.
### Advanced: in-band compaction
`providerOptions.contextManagement` lets the provider decide when to compact during an ordinary `generate` or `stream` call. This is an advanced option for callers that own persistence and recovery: persist the complete assistant message, including its checkpoint, before continuing. Enabling the option does not provide durable checkpoint storage, interruption recovery, or model-switch policy. Keep the prior context until a successful checkpoint has been persisted.
Inside an `Effect.gen`, enable OpenAI compaction with typed provider options:
```ts
import { LLM, LLMClient, LLMRequest, Message } from "@opencode-ai/ai"
import { OpenAI } from "@opencode-ai/ai/providers"
const request = LLM.request({
model: OpenAI.configure({ apiKey }).responses("gpt-5.3-codex"),
messages,
providerOptions: {
contextManagement: [{ type: "compaction", compactThreshold: 200_000 }],
},
})
const response = yield * LLMClient.generate(request)
const next = LLMRequest.update(request, {
messages: [...request.messages, response.message, Message.user("Continue")],
})
```
`store: false` remains the default. Keep the entire `response.message`, not just `response.text`. Compaction events become ordered `CompactionPart`s alongside text and reasoning. The conversation contains everything needed to continue; there is no separate replay object or hidden provider transcript.
A compaction part has `provider` and exactly one representation: `encrypted` for Responses, or `text` for Anthropic. Responses also preserves the optional checkpoint `id`. These fields survive message serialization without becoming visible assistant text. Sending a checkpoint to another provider or an incompatible API fails rather than silently losing context.
```ts
import { CompactionPart, ProviderID } from "@opencode-ai/ai"
CompactionPart.make({ provider: ProviderID.make("openai"), id: "cmp_123", encrypted: "..." })
CompactionPart.make({ provider: ProviderID.make("anthropic"), text: "Summary of the conversation..." })
```
For Anthropic, use:
```ts
providerOptions: {
contextManagement: {
edits: [{
type: "compact_20260112",
trigger: { type: "input_tokens", value: 150_000 },
pauseAfterCompaction: true,
instructions: "Summarize the task and decisions. Do not call tools while summarizing.",
}],
},
}
```
- The trigger is optional (provider default: 150,000 tokens), with a minimum of 50,000.
- Custom instructions replace Anthropic's default summarization instructions.
- The route adds `compact-2026-01-12` to existing beta headers, including when replaying a checkpoint without enabling new compactions.
- A pause is exposed as `response.finishReason.raw === "compaction"`. It occurs only if the threshold triggers compaction: `pauseAfterCompaction` does not mean "compact now". The caller explicitly issues the next request; the package never automatically resumes.
- Anthropic can return a compaction block with `content: null` when summarization fails. This becomes a compaction part with `text: null`, which is **not** a successful replacement for prior history. The package never prunes history automatically.
- `Usage` totals include all reported Anthropic `usage.iterations`, including compaction. `contextTokens` separately reports the final message iteration's inclusive input size, when available. A compaction-only pause does not report a post-compaction context size. Raw iteration usage remains in `providerMetadata`.
### Ownership and verification
The AI package transports options and typed conversation parts. It does not schedule compaction, persist Session checkpoints, select history, switch providers, or replace Core's existing local compaction policy. Native compaction is not enabled for OpenCode Sessions by this feature; Session integration must persist these parts before enabling it. The AI SDK bridge rejects native compaction parts rather than dropping them. Provider-executed tool APIs and persistence changes are a separate follow-up.
Tests cover serialized round trips, real local HTTP plus a tool loop, WebSocket recovery, provider errors, malformed blocks, and usage accounting. Live provider tests are gated by `RECORD=true` and the relevant API keys:
```sh
# Run from packages/ai. Only records the selected new cassette group.
RECORD=true RECORDED_PREFIX=openai-compaction bun test test/provider/compaction.recorded.test.ts
RECORD=true RECORDED_PREFIX=xai-compaction bun test test/provider/compaction.recorded.test.ts
RECORD=true RECORDED_PREFIX=anthropic-compaction bun test test/provider/compaction.recorded.test.ts
```
Provider references: [OpenAI](https://developers.openai.com/api/docs/guides/compaction), [Azure](https://learn.microsoft.com/en-us/azure/foundry/openai/how-to/responses#server-side-compaction), [Anthropic](https://platform.claude.com/docs/en/build-with-claude/compaction), [xAI](https://docs.x.ai/developers/advanced-api-usage/context-compaction).
## Caching
Prompt caching is **on by default**. Every `LLMRequest` resolves to `cache: "auto"` unless the caller opts out with `cache: "none"`. Each protocol translates `CacheHint`s to its wire format (`cache_control` on Anthropic, `cachePoint` on Bedrock; OpenAI and Gemini do implicit caching server-side and don't need inline markers — auto is a no-op there).
+32 -198
View File
@@ -6,12 +6,8 @@ import { Auth } from "../route/auth.js"
import { Endpoint } from "../route/endpoint.js"
import { Framing } from "../route/framing.js"
import { Protocol } from "../route/protocol.js"
import { Headers } from "effect/unstable/http"
import { HttpTransport } from "../route/transport/index.js"
import {
AIError,
HttpOptions,
LLMRequest,
LLMEvent,
mergeJsonRecords,
Usage,
@@ -19,6 +15,7 @@ import {
type FinishReasonDetails,
type FinishReason,
type JsonSchema,
type LLMRequest,
type MediaPart,
type ProviderMetadata,
type ToolCallPart,
@@ -64,8 +61,6 @@ export type ThinkingInput =
))
export interface OptionsInput {
/** Advanced in-band compaction. The caller owns checkpoint persistence and recovery. */
readonly contextManagement?: ContextManagement
readonly [key: string]: unknown
readonly thinking?: ThinkingInput
readonly effort?: string
@@ -94,23 +89,6 @@ export interface OptionsInput {
export type ProviderOptionsInput = OptionsInput
export const ContextManagement = Schema.Struct({
edits: Schema.Array(
Schema.Struct({
type: Schema.Literal("compact_20260112"),
trigger: Schema.optional(
Schema.Struct({
type: Schema.Literal("input_tokens"),
value: Schema.Int.check(Schema.isGreaterThanOrEqualTo(50000)),
}),
),
pauseAfterCompaction: Schema.optional(Schema.Boolean),
instructions: Schema.optional(Schema.String),
}),
),
})
export type ContextManagement = typeof ContextManagement.Type
// =============================================================================
// Request Body Schema
// =============================================================================
@@ -258,12 +236,7 @@ const AnthropicUserBlock = Schema.Union([
AnthropicToolResultBlock,
])
type AnthropicUserBlock = Schema.Schema.Type<typeof AnthropicUserBlock>
const AnthropicCompactionBlock = Schema.Struct({
type: Schema.Literal("compaction"),
content: Schema.NullOr(Schema.String),
})
const AnthropicAssistantBlock = Schema.Union([
AnthropicCompactionBlock,
AnthropicTextBlock,
AnthropicThinkingBlock,
AnthropicRedactedThinkingBlock,
@@ -339,18 +312,6 @@ const AnthropicContainer = Schema.Union([
])
const AnthropicBodyFields = {
context_management: Schema.optional(
Schema.Struct({
edits: Schema.Array(
Schema.Struct({
type: Schema.Literal("compact_20260112"),
trigger: ContextManagement.fields.edits.value.fields.trigger,
pause_after_compaction: Schema.optional(Schema.Boolean),
instructions: Schema.optional(Schema.String),
}),
),
}),
),
model: Schema.String,
system: optionalArray(AnthropicTextBlock),
messages: Schema.Array(AnthropicMessage),
@@ -374,7 +335,7 @@ const AnthropicBodyFields = {
export const AnthropicMessagesBody = Schema.Struct(AnthropicBodyFields)
export type AnthropicMessagesBody = Schema.Schema.Type<typeof AnthropicMessagesBody>
const AnthropicIterationUsage = Schema.StructWithRest(
const AnthropicUsage = Schema.StructWithRest(
Schema.Struct({
input_tokens: optionalNull(Schema.Number),
output_tokens: Schema.optional(Schema.Number),
@@ -393,13 +354,6 @@ const AnthropicIterationUsage = Schema.StructWithRest(
}),
[Schema.Record(Schema.String, Schema.Unknown)],
)
const AnthropicUsage = Schema.StructWithRest(
Schema.Struct({
...AnthropicIterationUsage.schema.fields,
iterations: Schema.optional(Schema.Array(AnthropicIterationUsage)),
}),
[JsonObject],
)
type AnthropicUsage = Schema.Schema.Type<typeof AnthropicUsage>
const AnthropicStreamBlock = Schema.Struct({
@@ -423,7 +377,6 @@ type AnthropicStreamBlock = Schema.Schema.Type<typeof AnthropicStreamBlock>
const decodeAnthropicStreamBlock = Schema.decodeUnknownOption(AnthropicStreamBlock)
const AnthropicStreamDelta = Schema.Struct({
content: optionalNull(Schema.String),
type: Schema.optional(Schema.String),
text: Schema.optional(Schema.String),
thinking: Schema.optional(Schema.String),
@@ -453,8 +406,6 @@ const AnthropicEvent = Schema.Struct({
type AnthropicEvent = Schema.Schema.Type<typeof AnthropicEvent>
interface ParserState {
readonly provider: LLMRequest["model"]["provider"]
readonly compactions: Readonly<Record<number, string | null>>
readonly providerMetadataKey: string
readonly tools: ToolStream.State<number>
readonly reasoningSignatures: Readonly<Record<number, string>>
@@ -880,7 +831,6 @@ const lowerMessages = Effect.fn("AnthropicMessages.lowerMessages")(function* (
const content: AnthropicUserBlock[] = []
for (const part of message.content) {
if (part.type === "text") {
if (part.text.trim().length === 0) continue
content.push({ type: "text", text: part.text, cache_control: cacheControl(breakpoints, part.cache) })
continue
}
@@ -890,21 +840,14 @@ const lowerMessages = Effect.fn("AnthropicMessages.lowerMessages")(function* (
}
return yield* ProviderShared.unsupportedContent("Anthropic Messages", "user", ["text", "media"])
}
if (content.length > 0) messages.push({ role: "user", content })
messages.push({ role: "user", content })
continue
}
if (message.role === "assistant") {
const content: AnthropicAssistantBlock[] = []
for (const part of message.content) {
if (part.type === "compaction") {
if (part.provider !== request.model.provider || part.text === undefined)
return yield* invalid("Compaction state must be replayed to its originating provider and API")
content.push({ type: "compaction", content: part.text })
continue
}
if (part.type === "text") {
if (part.text.trim().length === 0) continue
content.push({ type: "text", text: part.text, cache_control: cacheControl(breakpoints, part.cache) })
continue
}
@@ -948,7 +891,7 @@ const lowerMessages = Effect.fn("AnthropicMessages.lowerMessages")(function* (
`Anthropic Messages assistant messages only support text, reasoning, and tool-call content for now`,
)
}
if (content.length > 0) messages.push({ role: "assistant", content })
messages.push({ role: "assistant", content })
continue
}
@@ -1058,9 +1001,6 @@ const resolveThinking = Effect.fn("AnthropicMessages.resolveThinking")(function*
})
const fromRequest = Effect.fn("AnthropicMessages.fromRequest")(function* (request: LLMRequest) {
const management = yield* ProviderShared.validateWith(
Schema.decodeUnknownEffect(Schema.UndefinedOr(ContextManagement)),
)(request.providerOptions?.contextManagement)
const generation = request.generation
const toolSchemaCompatibility = request.model.compatibility?.toolSchema
// Allocate the 4-breakpoint budget in invalidation order: tools → system →
@@ -1079,11 +1019,10 @@ const fromRequest = Effect.fn("AnthropicMessages.fromRequest")(function* (reques
)
// Anthropic rejects tool_choice when tools are absent; "none" is only meaningful with tools present.
const toolChoice = tools === undefined || !request.toolChoice ? undefined : yield* lowerToolChoice(request.toolChoice)
const systemParts = request.system.filter((part) => part.text.length > 0)
const system =
systemParts.length === 0
request.system.length === 0
? undefined
: systemParts.map((part) => ({
: request.system.map((part) => ({
type: "text" as const,
text: part.text,
cache_control: cacheControl(breakpoints, part.cache),
@@ -1095,7 +1034,7 @@ const fromRequest = Effect.fn("AnthropicMessages.fromRequest")(function* (reques
)
}
const options = yield* resolveOptions(request)
const body = {
return {
model: request.model.id,
system,
messages,
@@ -1116,18 +1055,6 @@ const fromRequest = Effect.fn("AnthropicMessages.fromRequest")(function* (reques
metadata: options.metadata,
service_tier: options.service_tier,
}
if (!management) return body
return {
...body,
context_management: {
edits: management.edits.map((edit) => ({
type: edit.type,
trigger: edit.trigger,
pause_after_compaction: edit.pauseAfterCompaction,
instructions: edit.instructions,
})),
},
}
})
// =============================================================================
@@ -1149,31 +1076,18 @@ const mapFinishReason = (reason: string | null | undefined): FinishReason => {
// expose that subset through `output_tokens_details.thinking_tokens`.
const mapUsage = (usage: AnthropicUsage | undefined, providerMetadataKey: string): Usage | undefined => {
if (!usage) return undefined
const iterations = usage.iterations?.length ? usage.iterations : [usage]
const last = usage.iterations?.at(-1)
const nonCached = ProviderShared.sumTokens(...iterations.map((item) => item.input_tokens ?? undefined))
const cacheRead = ProviderShared.sumTokens(...iterations.map((item) => item.cache_read_input_tokens ?? undefined))
const cacheWrite = ProviderShared.sumTokens(
...iterations.map((item) => item.cache_creation_input_tokens ?? undefined),
)
const nonCached = usage.input_tokens ?? undefined
const cacheRead = usage.cache_read_input_tokens ?? undefined
const cacheWrite = usage.cache_creation_input_tokens ?? undefined
const inputTokens = ProviderShared.sumTokens(nonCached, cacheRead, cacheWrite)
const outputTokens = ProviderShared.sumTokens(...iterations.map((item) => item.output_tokens))
return new Usage({
inputTokens,
outputTokens,
contextTokens:
last?.type === "message"
? ProviderShared.sumTokens(
last.input_tokens ?? undefined,
last.cache_read_input_tokens ?? undefined,
last.cache_creation_input_tokens ?? undefined,
)
: undefined,
outputTokens: usage.output_tokens,
nonCachedInputTokens: nonCached,
cacheReadInputTokens: cacheRead,
cacheWriteInputTokens: cacheWrite,
reasoningTokens: ProviderShared.sumTokens(...iterations.map((item) => item.output_tokens_details?.thinking_tokens)),
totalTokens: ProviderShared.totalTokens(inputTokens, outputTokens, undefined),
reasoningTokens: usage.output_tokens_details?.thinking_tokens,
totalTokens: ProviderShared.totalTokens(inputTokens, usage.output_tokens, undefined),
providerMetadata: { [providerMetadataKey]: usage },
})
}
@@ -1195,7 +1109,6 @@ const mergeUsage = (left: Usage | undefined, right: Usage | undefined, providerM
return new Usage({
inputTokens,
outputTokens,
contextTokens: right.contextTokens ?? left.contextTokens,
nonCachedInputTokens,
cacheReadInputTokens,
cacheWriteInputTokens,
@@ -1254,6 +1167,7 @@ const onContentBlockStart = (
event: AnthropicEvent & { readonly content_block: AnthropicStreamBlock },
): StepResult => {
const block = event.content_block
if (!block) return [state, NO_EVENTS]
if (block.type === "tool_use" || block.type === "server_tool_use") {
if (event.index === undefined || !block.id) return [state, NO_EVENTS]
@@ -1348,16 +1262,7 @@ const onContentBlockDelta = Effect.fn("AnthropicMessages.onContentBlockDelta")(f
) {
const delta = event.delta
if (delta.type === "compaction_delta") {
if (event.index === undefined || !(event.index in state.compactions) || delta.content === undefined)
return yield* ProviderShared.eventError(ADAPTER, "Compaction delta is missing its block or content")
return [
{ ...state, compactions: { ...state.compactions, [event.index]: delta.content } },
NO_EVENTS,
] satisfies StepResult
}
if (delta.type === "text_delta" && delta.text) {
if (delta?.type === "text_delta" && delta.text) {
if (!state.lifecycle.text.has(`text-${event.index ?? 0}`)) return [state, NO_EVENTS] satisfies StepResult
const events: LLMEvent[] = []
return [
@@ -1366,7 +1271,7 @@ const onContentBlockDelta = Effect.fn("AnthropicMessages.onContentBlockDelta")(f
] satisfies StepResult
}
if (delta.type === "thinking_delta" && delta.thinking) {
if (delta?.type === "thinking_delta" && delta.thinking) {
if (!state.lifecycle.reasoning.has(`reasoning-${event.index ?? 0}`)) return [state, NO_EVENTS] satisfies StepResult
const events: LLMEvent[] = []
return [
@@ -1378,7 +1283,7 @@ const onContentBlockDelta = Effect.fn("AnthropicMessages.onContentBlockDelta")(f
] satisfies StepResult
}
if (delta.type === "signature_delta" && delta.signature) {
if (delta?.type === "signature_delta" && delta.signature) {
const index = event.index ?? 0
if (!state.lifecycle.reasoning.has(`reasoning-${index}`)) return [state, NO_EVENTS] satisfies StepResult
return [
@@ -1390,7 +1295,7 @@ const onContentBlockDelta = Effect.fn("AnthropicMessages.onContentBlockDelta")(f
] satisfies StepResult
}
if (delta.type === "input_json_delta" && event.index !== undefined) {
if (delta?.type === "input_json_delta" && event.index !== undefined) {
if (!delta.partial_json) return [state, NO_EVENTS] satisfies StepResult
if (!state.tools[event.index]) return [state, NO_EVENTS] satisfies StepResult
const result = ToolStream.appendExisting(
@@ -1415,18 +1320,6 @@ const onContentBlockStop = Effect.fn("AnthropicMessages.onContentBlockStop")(fun
event: AnthropicEvent,
) {
if (event.index === undefined) return [state, NO_EVENTS] satisfies StepResult
if (event.index in state.compactions) {
const { [event.index]: content, ...compactions } = state.compactions
const events: LLMEvent[] = []
const lifecycle = Lifecycle.stepStart(state.lifecycle, events)
events.push(
LLMEvent.compaction({
provider: state.provider,
text: content,
}),
)
return [{ ...state, compactions, lifecycle }, events] satisfies StepResult
}
const result = yield* ToolStream.finish(ADAPTER, state.tools, event.index)
const events: LLMEvent[] = []
const resultEvents = result.events ?? []
@@ -1478,23 +1371,11 @@ const onMessageDelta = (
}
const onMessageStop = Effect.fn("AnthropicMessages.onMessageStop")(function* (state: ParserState) {
if (Object.keys(state.compactions).length)
return yield* ProviderShared.eventError(ADAPTER, "Response ended with an incomplete compaction block")
const result = yield* ToolStream.finishAll(ADAPTER, state.tools)
const events: LLMEvent[] = []
const lifecycle = result.events.length ? Lifecycle.stepStart(state.lifecycle, events) : state.lifecycle
events.push(...result.events)
const closed = Object.entries(state.reasoningSignatures).reduce(
(current, [index, signature]) =>
Lifecycle.reasoningEnd(
current,
events,
`reasoning-${index}`,
providerMetadata(state.providerMetadataKey, { signature }),
),
lifecycle,
)
const finished = Lifecycle.finish(closed, events, {
const finished = Lifecycle.finish(lifecycle, events, {
reason: state.pendingFinish?.reason ?? {
normalized: "unknown",
raw: undefined,
@@ -1524,21 +1405,16 @@ const onError = (event: AnthropicEvent) => {
)
}
const STREAM_BLOCK_TYPES = new Set([
"compaction",
"text",
"thinking",
"redacted_thinking",
"tool_use",
"server_tool_use",
])
const STREAM_DELTA_TYPES = new Set([
"compaction_delta",
"text_delta",
"thinking_delta",
"signature_delta",
"input_json_delta",
])
const isKnownStreamBlockType = (type: string) =>
type === "text" ||
type === "thinking" ||
type === "redacted_thinking" ||
type === "tool_use" ||
type === "server_tool_use" ||
isServerToolResultType(type)
const isKnownStreamDeltaType = (type: string) =>
type === "text_delta" || type === "thinking_delta" || type === "signature_delta" || type === "input_json_delta"
const invalidStreamEvent = (event: AnthropicEvent) =>
Effect.fail(
@@ -1567,16 +1443,7 @@ const step = (state: ParserState, event: AnthropicEvent) => {
if (event.type === "content_block_start") {
if (!ProviderShared.isRecord(event.content_block) || typeof event.content_block.type !== "string")
return invalidStreamEvent(event)
if (event.content_block.type === "compaction") {
const decoded = Schema.decodeUnknownOption(AnthropicCompactionBlock)(event.content_block)
if (event.index === undefined || Option.isNone(decoded)) return invalidStreamEvent(event)
return Effect.succeed<StepResult>([
{ ...state, compactions: { ...state.compactions, [event.index]: decoded.value.content } },
NO_EVENTS,
])
}
if (!STREAM_BLOCK_TYPES.has(event.content_block.type) && !isServerToolResultType(event.content_block.type))
return Effect.succeed<StepResult>([state, NO_EVENTS])
if (!isKnownStreamBlockType(event.content_block.type)) return Effect.succeed<StepResult>([state, NO_EVENTS])
const decoded = decodeAnthropicStreamBlock(event.content_block)
if (Option.isNone(decoded)) return invalidStreamEvent(event)
const block = decoded.value
@@ -1590,7 +1457,7 @@ const step = (state: ParserState, event: AnthropicEvent) => {
}
if (event.type === "content_block_delta") {
if (!ProviderShared.isRecord(event.delta)) return invalidStreamEvent(event)
if (typeof event.delta.type === "string" && !STREAM_DELTA_TYPES.has(event.delta.type))
if (typeof event.delta.type === "string" && !isKnownStreamDeltaType(event.delta.type))
return Effect.succeed<StepResult>([state, NO_EVENTS])
const decoded = decodeAnthropicStreamDelta(event.delta)
if (Option.isNone(decoded)) return invalidStreamEvent(event)
@@ -1624,8 +1491,6 @@ export const protocol = Protocol.make({
stream: {
event: Protocol.jsonEvent(AnthropicEvent),
initial: (request) => ({
provider: request.model.provider,
compactions: {},
providerMetadataKey: request.model.route.providerMetadataKey ?? String(request.model.provider),
tools: ToolStream.empty<number>(),
reasoningSignatures: {},
@@ -1635,37 +1500,6 @@ export const protocol = Protocol.make({
},
})
export const transport = <Body extends Pick<AnthropicMessagesBody, "messages" | "context_management">>() => {
const http = HttpTransport.httpJson<Body, string>({ framing })
return {
...http,
prepare: (input: Parameters<typeof http.prepare>[0]) => {
if (
!input.body.context_management?.edits.length &&
!input.body.messages.some((message) => message.content.some((block) => block.type === "compaction"))
)
return http.prepare(input)
const headers = Headers.fromInput(input.request.http?.headers)
const betas = new Set(
(headers["anthropic-beta"] ?? "")
.split(",")
.map((item) => item.trim())
.filter(Boolean),
)
betas.add("compact-2026-01-12")
return http.prepare({
...input,
request: LLMRequest.update(input.request, {
http: new HttpOptions({
...input.request.http,
headers: { ...headers, "anthropic-beta": [...betas].join(",") },
}),
}),
})
},
}
}
export const route = Route.make({
id: ADAPTER,
provider: "anthropic",
@@ -1675,7 +1509,7 @@ export const route = Route.make({
baseURL: DEFAULT_BASE_URL,
}),
auth: Auth.none,
transport: transport<AnthropicMessagesBody>(),
framing,
headers: () => ({ "anthropic-version": "2023-06-01" }),
})
+88 -149
View File
@@ -1,4 +1,4 @@
import { Effect, Encoding, Schema } from "effect"
import { Effect, Schema } from "effect"
import { Route } from "../route/client.js"
import { Endpoint } from "../route/endpoint.js"
import { Protocol } from "../route/protocol.js"
@@ -262,28 +262,24 @@ const providerMetadata = (key: string, metadata: Record<string, unknown>): Provi
const reasoningSignature = (part: ReasoningPart, providerMetadataKey: string) => {
const metadata = part.providerMetadata?.[providerMetadataKey]
if (part.encrypted !== undefined) return part.encrypted
if (ProviderShared.isRecord(metadata) && typeof metadata.signature === "string") return metadata.signature
return (
part.encrypted ??
(ProviderShared.isRecord(metadata) && typeof metadata.signature === "string" ? metadata.signature : undefined)
)
}
const reasoningRedactedData = (part: ReasoningPart, providerMetadataKey: string) => {
const metadata = part.providerMetadata?.[providerMetadataKey]
if (ProviderShared.isRecord(metadata) && typeof metadata.redactedData === "string") return metadata.redactedData
}
const removeEmptyToolInputKeys = (input: unknown): unknown => {
if (Array.isArray(input)) return input.map(removeEmptyToolInputKeys)
if (!ProviderShared.isRecord(input)) return input
return Object.fromEntries(
Object.entries(input).flatMap(([key, value]) => (key === "" ? [] : [[key, removeEmptyToolInputKeys(value)]])),
)
return ProviderShared.isRecord(metadata) && typeof metadata.redactedData === "string"
? metadata.redactedData
: undefined
}
const lowerToolCall = (part: ToolCallPart): BedrockToolUseBlock => ({
toolUse: {
toolUseId: part.id,
name: part.name,
input: removeEmptyToolInputKeys(part.input),
input: part.input,
},
})
@@ -418,12 +414,7 @@ const lowerMessages = Effect.fn("BedrockConverse.lowerMessages")(function* (
const lowerSystem = (
breakpoints: BedrockCache.Breakpoints,
system: ReadonlyArray<LLMRequest["system"][number]>,
) => {
const content = system
.filter((part) => part.text.length > 0)
.flatMap((part) => textWithCache(breakpoints, part.text, part.cache))
return content.length === 0 ? undefined : content
}
): BedrockSystemBlock[] => system.flatMap((part) => textWithCache(breakpoints, part.text, part.cache))
const fromRequest = Effect.fn("BedrockConverse.fromRequest")(function* (request: LLMRequest) {
const toolChoice = request.toolChoice ? yield* lowerToolChoice(request.toolChoice) : undefined
@@ -431,42 +422,38 @@ const fromRequest = Effect.fn("BedrockConverse.fromRequest")(function* (request:
// Bedrock-Claude shares Anthropic's 4-breakpoint cap. Spend the budget in
// tools → system → messages order to favour the highest-impact prefixes.
const breakpoints = BedrockCache.breakpoints()
const toolConfig = (() => {
if (request.tools.length === 0) return undefined
return {
tools: lowerTools(request.model.compatibility?.toolSchema, breakpoints, request.tools),
// Converse has no native "none". Keep definitions stable for prompt
// caching and omit only the unsupported choice.
toolChoice,
}
})()
const system = lowerSystem(breakpoints, request.system)
const toolConfig =
request.tools.length > 0
? {
tools: lowerTools(request.model.compatibility?.toolSchema, breakpoints, request.tools),
// Converse has no native "none". Keep definitions stable for prompt
// caching and omit only the unsupported choice.
toolChoice,
}
: undefined
const system = request.system.length === 0 ? undefined : lowerSystem(breakpoints, request.system)
const messages = yield* lowerMessages(request, breakpoints)
if (breakpoints.dropped > 0) {
yield* Effect.logWarning(
`Bedrock Converse: dropped ${breakpoints.dropped} cache breakpoint(s); the API allows at most ${BedrockCache.BEDROCK_BREAKPOINT_CAP} per request.`,
)
}
const inferenceConfig = (() => {
if (
generation?.maxTokens === undefined &&
generation?.temperature === undefined &&
generation?.topP === undefined &&
(generation?.stop === undefined || generation.stop.length === 0)
)
return undefined
return {
maxTokens: generation?.maxTokens,
temperature: generation?.temperature,
topP: generation?.topP,
stopSequences: generation?.stop,
}
})()
return {
modelId: request.model.id,
messages,
system,
inferenceConfig,
inferenceConfig:
generation?.maxTokens === undefined &&
generation?.temperature === undefined &&
generation?.topP === undefined &&
(generation?.stop === undefined || generation.stop.length === 0)
? undefined
: {
maxTokens: generation?.maxTokens,
temperature: generation?.temperature,
topP: generation?.topP,
stopSequences: generation?.stop,
},
toolConfig,
// Converse's base inferenceConfig has no topK; Anthropic/Nova accept it
// as a model-specific field, so it goes through additionalModelRequestFields.
@@ -482,6 +469,7 @@ const mapFinishReason = (reason: string): FinishReason => {
if (reason === "max_tokens" || reason === "model_context_window_exceeded") return "length"
if (reason === "tool_use") return "tool-calls"
if (reason === "content_filtered" || reason === "guardrail_intervened") return "content-filter"
if (reason === "malformed_model_output" || reason === "malformed_tool_use") return "error"
return "unknown"
}
@@ -510,23 +498,12 @@ interface ParserState {
readonly tools: ToolStream.State<number>
readonly finishedTools: ReadonlySet<number>
// Bedrock splits the finish into `messageStop` (carries `stopReason`) and
// `metadata` (carries usage). Hold both in state so `onHalt` can emit exactly
// one finish after both chunks have had a chance to arrive.
readonly finishReason: FinishReasonDetails | undefined
readonly usage: Usage | undefined
// `metadata` (carries usage). Hold the terminal event in state so `onHalt`
// can emit exactly one finish after both chunks have had a chance to arrive.
readonly pendingFinish: { readonly reason: FinishReasonDetails; readonly usage?: Usage } | undefined
readonly hasToolCalls: boolean
readonly lifecycle: Lifecycle.State
readonly reasoningSignatures: Readonly<Record<number, string>>
readonly reasoningRedactedContent: Readonly<Record<number, ReadonlyArray<Uint8Array>>>
}
const encodeRedactedContent = (chunks: ReadonlyArray<Uint8Array>) => {
const bytes = new Uint8Array(chunks.reduce((total, chunk) => total + chunk.length, 0))
chunks.reduce((offset, chunk) => {
bytes.set(chunk, offset)
return offset + chunk.length
}, 0)
return Encoding.encodeBase64(bytes)
}
const step = (state: ParserState, event: BedrockEvent) =>
@@ -574,46 +551,23 @@ const step = (state: ParserState, event: BedrockEvent) =>
const index = event.contentBlockDelta.contentBlockIndex
const reasoning = event.contentBlockDelta.delta.reasoningContent
const events: LLMEvent[] = []
const redactedChunks = yield* (() => {
if (reasoning.redactedContent === undefined) return Effect.succeed(undefined)
return Effect.fromResult(Encoding.decodeBase64(reasoning.redactedContent)).pipe(
Effect.map((chunk) => [...(state.reasoningRedactedContent[index] ?? []), chunk]),
Effect.mapError((cause) =>
ProviderShared.eventError(
ADAPTER,
"Bedrock Converse reasoningContent.redactedContent contains invalid base64 data",
undefined,
cause,
),
),
)
})()
const redactedData = redactedChunks === undefined ? reasoning.data : encodeRedactedContent(redactedChunks)
const metadata = (() => {
if (reasoning.signature) return providerMetadata(state.providerMetadataKey, { signature: reasoning.signature })
if (redactedData !== undefined) return providerMetadata(state.providerMetadataKey, { redactedData })
})()
const lifecycle = (() => {
if (reasoning.text === undefined && metadata === undefined) return state.lifecycle
return Lifecycle.reasoningDelta(state.lifecycle, events, `reasoning-${index}`, reasoning.text ?? "", metadata)
})()
const reasoningRedactedContent = (() => {
if (redactedChunks !== undefined) return { ...state.reasoningRedactedContent, [index]: redactedChunks }
if (reasoning.data === undefined) return state.reasoningRedactedContent
return Object.fromEntries(
Object.entries(state.reasoningRedactedContent).filter(([key]) => key !== String(index)),
)
})()
const reasoningSignatures = (() => {
if (!reasoning.signature) return state.reasoningSignatures
return { ...state.reasoningSignatures, [index]: reasoning.signature }
})()
const redactedData = reasoning.redactedContent ?? reasoning.data
const metadata = reasoning.signature
? providerMetadata(state.providerMetadataKey, { signature: reasoning.signature })
: redactedData !== undefined
? providerMetadata(state.providerMetadataKey, { redactedData })
: undefined
const lifecycle =
reasoning.text !== undefined || metadata !== undefined
? Lifecycle.reasoningDelta(state.lifecycle, events, `reasoning-${index}`, reasoning.text ?? "", metadata)
: state.lifecycle
return [
{
...state,
lifecycle,
reasoningSignatures,
reasoningRedactedContent,
reasoningSignatures: reasoning.signature
? { ...state.reasoningSignatures, [index]: reasoning.signature }
: state.reasoningSignatures,
},
events,
] as const
@@ -641,24 +595,16 @@ const step = (state: ParserState, event: BedrockEvent) =>
const result = yield* ToolStream.finish(ADAPTER, state.tools, index)
const events: LLMEvent[] = []
const resultEvents = result.events ?? []
const lifecycle = (() => {
if (resultEvents.length) return Lifecycle.stepStart(state.lifecycle, events)
const metadata = (() => {
const signature = state.reasoningSignatures[index]
if (signature) return providerMetadata(state.providerMetadataKey, { signature })
const redactedContent = state.reasoningRedactedContent[index]
if (redactedContent)
return providerMetadata(state.providerMetadataKey, {
redactedData: encodeRedactedContent(redactedContent),
})
})()
return Lifecycle.reasoningEnd(
Lifecycle.textEnd(state.lifecycle, events, `text-${index}`),
events,
`reasoning-${index}`,
metadata,
)
})()
const lifecycle = resultEvents.length
? Lifecycle.stepStart(state.lifecycle, events)
: Lifecycle.reasoningEnd(
Lifecycle.textEnd(state.lifecycle, events, `text-${index}`),
events,
`reasoning-${index}`,
state.reasoningSignatures[index]
? providerMetadata(state.providerMetadataKey, { signature: state.reasoningSignatures[index] })
: undefined,
)
events.push(...resultEvents)
return [
{
@@ -672,30 +618,21 @@ const step = (state: ParserState, event: BedrockEvent) =>
reasoningSignatures: Object.fromEntries(
Object.entries(state.reasoningSignatures).filter(([key]) => key !== String(index)),
),
reasoningRedactedContent: Object.fromEntries(
Object.entries(state.reasoningRedactedContent).filter(([key]) => key !== String(index)),
),
},
events,
] as const
}
if (event.messageStop) {
if (
event.messageStop.stopReason === "malformed_model_output" ||
event.messageStop.stopReason === "malformed_tool_use"
)
return yield* ProviderShared.eventError(
ADAPTER,
`Bedrock Converse stopped with ${event.messageStop.stopReason}`,
ProviderShared.encodeJson(event),
)
return [
{
...state,
finishReason: {
normalized: mapFinishReason(event.messageStop.stopReason),
raw: event.messageStop.stopReason,
pendingFinish: {
reason: {
normalized: mapFinishReason(event.messageStop.stopReason),
raw: event.messageStop.stopReason,
},
usage: state.pendingFinish?.usage,
},
},
[],
@@ -703,11 +640,14 @@ const step = (state: ParserState, event: BedrockEvent) =>
}
if (event.metadata) {
const usage = mapUsage(event.metadata.usage, state.providerMetadataKey) ?? state.usage
const usage = mapUsage(event.metadata.usage, state.providerMetadataKey) ?? state.pendingFinish?.usage
return [
{
...state,
usage,
pendingFinish: {
reason: state.pendingFinish?.reason ?? { normalized: "stop" },
usage,
},
},
[],
] as const
@@ -730,22 +670,23 @@ const step = (state: ParserState, event: BedrockEvent) =>
const framing = BedrockEventStream.framing(ADAPTER)
const onHalt = (state: ParserState): ReadonlyArray<LLMEvent> => {
if (!state.finishReason) return []
const normalized = (() => {
if (state.finishReason.normalized === "stop" && state.hasToolCalls) return "tool-calls"
return state.finishReason.normalized
})()
const events: LLMEvent[] = []
Lifecycle.finish(state.lifecycle, events, {
reason: {
...state.finishReason,
normalized,
},
usage: state.usage,
})
return events
}
const onHalt = (state: ParserState): ReadonlyArray<LLMEvent> =>
state.pendingFinish
? (() => {
const events: LLMEvent[] = []
Lifecycle.finish(state.lifecycle, events, {
reason: {
...state.pendingFinish.reason,
normalized:
state.pendingFinish.reason.normalized === "stop" && state.hasToolCalls
? "tool-calls"
: state.pendingFinish.reason.normalized,
},
usage: state.pendingFinish.usage,
})
return events
})()
: []
// =============================================================================
// Protocol And Bedrock Route
@@ -766,12 +707,10 @@ export const protocol = Protocol.make({
providerMetadataKey: request.model.route.providerMetadataKey ?? String(request.model.provider),
tools: ToolStream.empty<number>(),
finishedTools: new Set<number>(),
finishReason: undefined,
usage: undefined,
pendingFinish: undefined,
hasToolCalls: false,
lifecycle: Lifecycle.initial(),
reasoningSignatures: {},
reasoningRedactedContent: {},
}),
step,
onHalt: (state) => Effect.succeed(onHalt(state)),
@@ -1,7 +1,7 @@
import { EventStreamCodec } from "@smithy/eventstream-codec"
import { fromUtf8, toUtf8 } from "@smithy/util-utf8"
import { Effect, Encoding, Stream } from "effect"
import { AIError, AIErrorReason, InvalidProviderOutputError } from "../schema/index.js"
import { AIError, AIErrorReason } from "../schema/index.js"
import { Framing } from "../route/framing.js"
import { ProviderShared } from "./shared.js"
@@ -22,10 +22,6 @@ interface FrameBufferState {
const initialFrameBuffer: FrameBufferState = { buffer: new Uint8Array(0), offset: 0 }
type FrameInput = { readonly _tag: "Chunk"; readonly bytes: Uint8Array } | { readonly _tag: "End" }
const endOfStream: FrameInput = { _tag: "End" }
const appendChunk = (state: FrameBufferState, chunk: Uint8Array): FrameBufferState => {
const remaining = state.buffer.length - state.offset
// Compact: drop the consumed prefix and append the new chunk in one alloc.
@@ -37,23 +33,9 @@ const appendChunk = (state: FrameBufferState, chunk: Uint8Array): FrameBufferSta
return { buffer: next, offset: 0 }
}
const consumeFrames = (route: string) => (state: FrameBufferState, input: FrameInput) =>
const consumeFrames = (route: string) => (state: FrameBufferState, chunk: Uint8Array) =>
Effect.gen(function* () {
if (input._tag === "End") {
const remaining = state.buffer.subarray(state.offset)
if (remaining.length > 0)
return yield* new AIError({
reason: new InvalidProviderOutputError({
route,
classification: "incomplete-stream",
message: `Incomplete Bedrock Converse event-stream frame: ${remaining.length} buffered bytes remain at end of stream`,
body: Encoding.encodeBase64(remaining),
}),
})
return [state, []] as const
}
let cursor = appendChunk(state, input.bytes)
let cursor = appendChunk(state, chunk)
const out: object[] = []
while (cursor.buffer.length - cursor.offset >= 4) {
const view = cursor.buffer.subarray(cursor.offset)
@@ -131,12 +113,7 @@ const consumeFrames = (route: string) => (state: FrameBufferState, input: FrameI
export const framing = (route: string): Framing.Definition<object> => ({
id: "aws-event-stream",
body: (frame) => ("rawBody" in frame && typeof frame.rawBody === "string" ? frame.rawBody : undefined),
frame: (bytes) =>
bytes.pipe(
Stream.map((bytes): FrameInput => ({ _tag: "Chunk", bytes })),
Stream.concat(Stream.succeed(endOfStream)),
Stream.mapAccumEffect(() => initialFrameBuffer, consumeFrames(route)),
),
frame: (bytes) => bytes.pipe(Stream.mapAccumEffect(() => initialFrameBuffer, consumeFrames(route))),
})
export * as BedrockEventStream from "./bedrock-event-stream.js"
+26 -91
View File
@@ -240,10 +240,6 @@ interface ParserState {
readonly lifecycle: Lifecycle.State
readonly reasoningSignature?: string
readonly textSignature?: string
readonly reasoningId?: string
readonly textId?: string
readonly nextReasoningId: number
readonly nextTextId: number
readonly seenCallIds?: ReadonlySet<string>
}
@@ -575,23 +571,19 @@ const finish = (state: ParserState): ReadonlyArray<LLMEvent> => {
const events: LLMEvent[] = []
let lifecycle = state.lifecycle
if (state.reasoningId !== undefined)
if (state.reasoningSignature !== undefined)
lifecycle = Lifecycle.reasoningEnd(
lifecycle,
events,
state.reasoningId,
state.reasoningSignature === undefined
? undefined
: providerMetadata(state.providerMetadataKey, { thoughtSignature: state.reasoningSignature }),
"reasoning-0",
providerMetadata(state.providerMetadataKey, { thoughtSignature: state.reasoningSignature }),
)
if (state.textId !== undefined)
if (state.textSignature !== undefined)
lifecycle = Lifecycle.textEnd(
lifecycle,
events,
state.textId,
state.textSignature === undefined
? undefined
: providerMetadata(state.providerMetadataKey, { thoughtSignature: state.textSignature }),
"text-0",
providerMetadata(state.providerMetadataKey, { thoughtSignature: state.textSignature }),
)
Lifecycle.finish(lifecycle, events, {
reason: {
@@ -609,27 +601,18 @@ const finish = (state: ParserState): ReadonlyArray<LLMEvent> => {
}
const step = (state: ParserState, event: GeminiEvent) => {
if (ProviderShared.isRecord(event.error)) {
if (ProviderShared.isRecord(event.error) && typeof event.error.message === "string") {
const body = ProviderShared.encodeJson(event)
return Effect.fail(
new AIError({
reason: classifyProviderFailure({
message:
typeof event.error.message === "string" && event.error.message.length > 0
? event.error.message
: typeof event.error.status === "string" && event.error.status.length > 0
? event.error.status
: "Gemini provider error",
message: event.error.message,
status: typeof event.error.code === "number" ? event.error.code : undefined,
rawBody: body,
}),
}),
)
}
if ("error" in event)
return Effect.fail(
ProviderShared.eventError(state.route, `Invalid ${state.route} stream event`, ProviderShared.encodeJson(event)),
)
const nextState = {
...state,
promptFeedback: event.promptFeedback ?? state.promptFeedback,
@@ -649,10 +632,6 @@ const step = (state: ParserState, event: GeminiEvent) => {
let lifecycle = nextState.lifecycle
let reasoningSignature = nextState.reasoningSignature
let textSignature = nextState.textSignature
let reasoningId = nextState.reasoningId
let textId = nextState.textId
let nextReasoningId = nextState.nextReasoningId
let nextTextId = nextState.nextTextId
// Supplier ids must be tracked across chunks of the same response, not just within one event's parts.
const seenCallIds = new Set(nextState.seenCallIds)
@@ -678,51 +657,27 @@ const step = (state: ParserState, event: GeminiEvent) => {
else if (signature !== undefined && "text" in part) textSignature = signature
if ("text" in part && part.text.length > 0) {
if (part.thought) {
if (textId !== undefined) {
lifecycle = Lifecycle.textEnd(
lifecycle,
events,
textId,
textSignature
? providerMetadata(state.providerMetadataKey, { thoughtSignature: textSignature })
: undefined,
)
textId = undefined
textSignature = undefined
}
if (reasoningId === undefined) {
reasoningId = `reasoning-${nextReasoningId}`
nextReasoningId += 1
}
lifecycle = Lifecycle.reasoningDelta(
lifecycle,
events,
reasoningId,
"reasoning-0",
part.text,
signature ? providerMetadata(state.providerMetadataKey, { thoughtSignature: signature }) : undefined,
)
continue
}
if (reasoningId !== undefined) {
lifecycle = Lifecycle.reasoningEnd(
lifecycle,
events,
reasoningId,
reasoningSignature
? providerMetadata(state.providerMetadataKey, { thoughtSignature: reasoningSignature })
: undefined,
)
reasoningId = undefined
reasoningSignature = undefined
}
if (textId === undefined) {
textId = `text-${nextTextId}`
nextTextId += 1
}
lifecycle = Lifecycle.reasoningEnd(
lifecycle,
events,
"reasoning-0",
reasoningSignature
? providerMetadata(state.providerMetadataKey, { thoughtSignature: reasoningSignature })
: undefined,
)
lifecycle = Lifecycle.textDelta(
lifecycle,
events,
textId,
"text-0",
part.text,
textSignature ? providerMetadata(state.providerMetadataKey, { thoughtSignature: textSignature }) : undefined,
)
@@ -740,28 +695,14 @@ const step = (state: ParserState, event: GeminiEvent) => {
const duplicate = supplied !== undefined && seenCallIds.has(supplied)
if (supplied !== undefined) seenCallIds.add(supplied)
const id = supplied !== undefined && !duplicate ? supplied : `tool_${crypto.randomUUID().replaceAll("-", "")}`
if (reasoningId !== undefined) {
lifecycle = Lifecycle.reasoningEnd(
lifecycle,
events,
reasoningId,
reasoningSignature
? providerMetadata(state.providerMetadataKey, { thoughtSignature: reasoningSignature })
: undefined,
)
reasoningId = undefined
reasoningSignature = undefined
}
if (textId !== undefined) {
lifecycle = Lifecycle.textEnd(
lifecycle,
events,
textId,
textSignature ? providerMetadata(state.providerMetadataKey, { thoughtSignature: textSignature }) : undefined,
)
textId = undefined
textSignature = undefined
}
lifecycle = Lifecycle.reasoningEnd(
lifecycle,
events,
"reasoning-0",
reasoningSignature
? providerMetadata(state.providerMetadataKey, { thoughtSignature: reasoningSignature })
: undefined,
)
lifecycle = Lifecycle.stepStart(lifecycle, events)
events.push(
LLMEvent.toolCall({
@@ -784,10 +725,6 @@ const step = (state: ParserState, event: GeminiEvent) => {
lifecycle,
reasoningSignature,
textSignature,
reasoningId,
textId,
nextReasoningId,
nextTextId,
seenCallIds,
finishReason: candidate.finishReason ?? nextState.finishReason,
},
@@ -815,8 +752,6 @@ export const protocol = Protocol.make({
providerMetadataKey: request.model.route.providerMetadataKey ?? String(request.model.provider),
hasToolCalls: false,
lifecycle: Lifecycle.initial(),
nextReasoningId: 0,
nextTextId: 0,
}),
step,
onHalt: (state) => Effect.succeed(finish(state)),
-1
View File
@@ -1,7 +1,6 @@
export * as AnthropicMessages from "./anthropic-messages.js"
export * as BedrockConverse from "./bedrock-converse.js"
export * as Gemini from "./gemini.js"
export * as MistralChat from "./mistral-chat.js"
export * as OpenAIChat from "./openai-chat.js"
export * as OpenAIImages from "./openai-images.js"
export * as OpenAICompatibleChat from "./openai-compatible-chat.js"
-780
View File
@@ -1,780 +0,0 @@
import { Effect, Schema } from "effect"
import { Auth } from "../route/auth.js"
import { Route } from "../route/client.js"
import { Endpoint } from "../route/endpoint.js"
import { Framing } from "../route/framing.js"
import { Protocol } from "../route/protocol.js"
import { HttpTransport } from "../route/transport/index.js"
import {
AIError,
InvalidProviderOutputError,
LLMEvent,
Usage,
type FinishReasonDetails,
type LLMRequest,
type MediaPart,
type ToolCallPart,
type ToolDefinition,
} from "../schema/index.js"
import { classifyProviderFailure } from "../provider-error.js"
import { JsonObject, optionalArray, optionalNull, ProviderShared } from "./shared.js"
import { Lifecycle } from "./utils/lifecycle.js"
import { ToolStream } from "./utils/tool-stream.js"
const ADAPTER = "mistral-chat"
const DONE = "[DONE]" as const
const TOOL_ID = /^[A-Za-z0-9]{9}$/
export const DEFAULT_BASE_URL = "https://api.mistral.ai/v1"
export const PATH = "/chat/completions"
const MistralTextContent = Schema.Struct({
type: Schema.Literal("text"),
text: Schema.String,
})
const MistralThinkingUnit = Schema.StructWithRest(
Schema.Struct({
type: Schema.optional(Schema.String),
text: Schema.optional(Schema.String),
}),
[Schema.Record(Schema.String, Schema.Unknown)],
)
type MistralThinkingUnit = Schema.Schema.Type<typeof MistralThinkingUnit>
const MistralThinkingContent = Schema.StructWithRest(
Schema.Struct({
type: Schema.Literal("thinking"),
thinking: Schema.Array(MistralThinkingUnit),
}),
[Schema.Record(Schema.String, Schema.Unknown)],
)
type MistralThinkingContent = Schema.Schema.Type<typeof MistralThinkingContent>
const isMistralThinkingContent = Schema.is(MistralThinkingContent)
const MistralUserContent = Schema.Union([
MistralTextContent,
Schema.Struct({ type: Schema.Literal("image_url"), image_url: Schema.String }),
Schema.Struct({ type: Schema.Literal("document_url"), document_url: Schema.String }),
])
type MistralUserContent = Schema.Schema.Type<typeof MistralUserContent>
const MistralAssistantToolCall = Schema.Struct({
id: Schema.String,
type: Schema.Literal("function"),
function: Schema.Struct({ name: Schema.String, arguments: Schema.String }),
})
type MistralAssistantToolCall = Schema.Schema.Type<typeof MistralAssistantToolCall>
const MistralMessage = Schema.Union([
Schema.Struct({ role: Schema.Literal("system"), content: Schema.String }),
Schema.Struct({
role: Schema.Literal("user"),
content: Schema.Union([Schema.String, Schema.Array(MistralUserContent)]),
}),
Schema.Struct({
role: Schema.Literal("assistant"),
content: Schema.Union([Schema.String, Schema.Array(Schema.Union([MistralTextContent, MistralThinkingContent]))]),
tool_calls: optionalArray(MistralAssistantToolCall),
prefix: Schema.optional(Schema.Literal(true)),
}),
Schema.Struct({
role: Schema.Literal("tool"),
tool_call_id: Schema.String,
name: Schema.String,
content: Schema.Union([Schema.String, Schema.Array(MistralUserContent)]),
}),
]).pipe(Schema.toTaggedUnion("role"))
type MistralMessage = Schema.Schema.Type<typeof MistralMessage>
const MistralTool = Schema.Struct({
type: Schema.Literal("function"),
function: Schema.Struct({
name: Schema.String,
description: Schema.String,
parameters: JsonObject,
strict: Schema.Literal(false),
}),
})
type MistralTool = Schema.Schema.Type<typeof MistralTool>
const MistralOptions = Schema.Struct({
safePrompt: Schema.optional(Schema.Boolean),
documentImageLimit: Schema.optional(Schema.Number),
documentPageLimit: Schema.optional(Schema.Number),
parallelToolCalls: Schema.optional(Schema.Boolean),
reasoningEffort: Schema.optional(Schema.String),
promptMode: Schema.optional(Schema.Literal("reasoning")),
promptCacheKey: Schema.optional(Schema.String),
})
export type ReasoningEffort = "none" | "minimal" | "low" | "medium" | "high" | "xhigh" | (string & {})
export type ProviderOptionsInput = {
readonly safePrompt?: boolean
readonly documentImageLimit?: number
readonly documentPageLimit?: number
readonly parallelToolCalls?: boolean
readonly reasoningEffort?: ReasoningEffort
readonly promptMode?: "reasoning"
readonly promptCacheKey?: string
readonly [key: string]: unknown
}
const MistralBody = Schema.Struct({
model: Schema.String,
messages: Schema.Array(MistralMessage),
tools: optionalArray(MistralTool),
tool_choice: Schema.optional(
Schema.Union([
Schema.Literals(["auto", "none", "any"]),
Schema.Struct({ type: Schema.Literal("function"), function: Schema.Struct({ name: Schema.String }) }),
]),
),
stream: Schema.Literal(true),
max_tokens: Schema.optional(Schema.Number),
random_seed: Schema.optional(Schema.Number),
temperature: Schema.optional(Schema.Number),
top_p: Schema.optional(Schema.Number),
frequency_penalty: Schema.optional(Schema.Number),
presence_penalty: Schema.optional(Schema.Number),
stop: optionalArray(Schema.String),
prompt_cache_key: Schema.optional(Schema.String),
safe_prompt: Schema.optional(Schema.Boolean),
document_image_limit: Schema.optional(Schema.Number),
document_page_limit: Schema.optional(Schema.Number),
parallel_tool_calls: Schema.optional(Schema.Boolean),
reasoning_effort: Schema.optional(Schema.String),
prompt_mode: Schema.optional(Schema.Literal("reasoning")),
})
export type MistralBody = Schema.Schema.Type<typeof MistralBody>
const MistralUsageDetails = Schema.StructWithRest(Schema.Struct({ cached_tokens: optionalNull(Schema.Number) }), [
Schema.Record(Schema.String, Schema.Unknown),
])
const MistralUsage = Schema.StructWithRest(
Schema.Struct({
prompt_tokens: optionalNull(Schema.Number),
completion_tokens: optionalNull(Schema.Number),
total_tokens: optionalNull(Schema.Number),
num_cached_tokens: optionalNull(Schema.Number),
prompt_token_details: optionalNull(MistralUsageDetails),
prompt_tokens_details: optionalNull(MistralUsageDetails),
}),
[Schema.Record(Schema.String, Schema.Unknown)],
)
const MistralOutputContent = Schema.StructWithRest(
Schema.Struct({
type: Schema.String,
text: optionalNull(Schema.String),
thinking: optionalNull(Schema.Unknown),
}),
[Schema.Record(Schema.String, Schema.Unknown)],
)
type MistralOutputContent = Schema.Schema.Type<typeof MistralOutputContent>
const MistralToolDelta = Schema.Struct({
index: optionalNull(Schema.Number),
id: optionalNull(Schema.String),
function: optionalNull(
Schema.Struct({
name: optionalNull(Schema.String),
arguments: optionalNull(Schema.Union([Schema.String, JsonObject])),
}),
),
})
type MistralToolDelta = Schema.Schema.Type<typeof MistralToolDelta>
const MistralChoice = Schema.StructWithRest(
Schema.Struct({
delta: optionalNull(
Schema.StructWithRest(
Schema.Struct({
content: optionalNull(Schema.Union([Schema.String, Schema.Array(MistralOutputContent)])),
tool_calls: optionalNull(Schema.Array(MistralToolDelta)),
}),
[Schema.Record(Schema.String, Schema.Unknown)],
),
),
finish_reason: optionalNull(Schema.String),
}),
[Schema.Record(Schema.String, Schema.Unknown)],
)
const MistralError = Schema.StructWithRest(
Schema.Struct({
message: Schema.String,
code: optionalNull(Schema.Union([Schema.String, Schema.Number])),
}),
[Schema.Record(Schema.String, Schema.Unknown)],
)
const MistralEvent = Schema.StructWithRest(
Schema.Struct({
choices: optionalNull(Schema.Array(MistralChoice)),
usage: optionalNull(MistralUsage),
error: optionalNull(MistralError),
}),
[Schema.Record(Schema.String, Schema.Unknown)],
)
type MistralEvent = Schema.Schema.Type<typeof MistralEvent>
const MistralStreamEvent = Schema.Union([Schema.Literal(DONE), Protocol.jsonEvent(MistralEvent)])
const hashID = (value: string) => {
const hash = (seed: number) => {
let result = seed
for (const char of value) result = Math.imul(result ^ char.charCodeAt(0), 16777619)
return (result >>> 0).toString(36)
}
return `${hash(2166136261).padStart(7, "0")}${hash(2246822519).padStart(7, "0")}`.slice(-9)
}
const toolIDNormalizer = (request: LLMRequest) => {
const ids = request.messages.flatMap((message) =>
message.content.flatMap((part) => (part.type === "tool-call" || part.type === "tool-result" ? [part.id] : [])),
)
const used = new Set(ids.filter((id) => TOOL_ID.test(id)))
const normalized = new Map<string, string>()
return (id: string) => {
if (TOOL_ID.test(id)) return id
const previous = normalized.get(id)
if (previous) return previous
let attempt = 0
let candidate = hashID(id)
while (used.has(candidate)) candidate = hashID(`${id}:${++attempt}`)
used.add(candidate)
normalized.set(id, candidate)
return candidate
}
}
const lowerMedia = Effect.fn("MistralChat.lowerMedia")(function* (part: MediaPart) {
const media = ProviderShared.normalizeMedia(part)
const url = typeof part.data === "string" && /^(?:https?:|data:)/.test(part.data) ? part.data : media.dataUrl
if (media.mime.startsWith("image/")) return { type: "image_url" as const, image_url: url }
if (media.mime === "application/pdf") return { type: "document_url" as const, document_url: url }
return yield* ProviderShared.invalidRequest(`Mistral Chat does not support media type ${part.mediaType}`)
})
const lowerUser = Effect.fn("MistralChat.lowerUser")(function* (message: LLMRequest["messages"][number]) {
const content: MistralUserContent[] = []
for (const part of message.content) {
if (part.type === "text") {
content.push({ type: "text", text: part.text })
continue
}
if (part.type === "media") {
content.push(yield* lowerMedia(part))
continue
}
return yield* ProviderShared.unsupportedContent("Mistral Chat", "user", ["text", "media"])
}
if (content.every((part) => part.type === "text"))
return { role: "user" as const, content: content.map((part) => part.text).join("") }
return { role: "user" as const, content }
})
const lowerToolCall = (part: ToolCallPart, normalizeID: (id: string) => string): MistralAssistantToolCall => ({
id: normalizeID(part.id),
type: "function",
function: { name: part.name, arguments: ProviderShared.encodeJson(part.input) },
})
const lowerAssistant = Effect.fn("MistralChat.lowerAssistant")(function* (
message: LLMRequest["messages"][number],
normalizeID: (id: string) => string,
prefix: boolean,
) {
const structured = message.content.some(
(part) => part.type === "reasoning" && isMistralThinkingContent(part.providerMetadata?.mistral?.thinking),
)
const content: Array<Schema.Schema.Type<typeof MistralTextContent> | MistralThinkingContent> = []
const text: string[] = []
const toolCalls: MistralAssistantToolCall[] = []
for (const part of message.content) {
if (part.type === "text") {
if (structured) content.push({ type: "text", text: part.text })
else text.push(part.text)
continue
}
if (part.type === "reasoning") {
const native = part.providerMetadata?.mistral?.thinking
if (structured && isMistralThinkingContent(native)) content.push(native)
else if (structured) content.push({ type: "text", text: part.text })
else text.push(part.text)
continue
}
if (part.type === "tool-call") {
toolCalls.push(lowerToolCall(part, normalizeID))
continue
}
return yield* ProviderShared.unsupportedContent("Mistral Chat", "assistant", ["text", "reasoning", "tool-call"])
}
return {
role: "assistant" as const,
content: structured ? content : text.join(""),
...(toolCalls.length > 0 ? { tool_calls: toolCalls } : {}),
...(prefix ? { prefix: true as const } : {}),
}
})
const lowerToolResults = Effect.fn("MistralChat.lowerToolResults")(function* (
message: LLMRequest["messages"][number],
normalizeID: (id: string) => string,
) {
const output: MistralMessage[] = []
for (const part of message.content) {
if (part.type !== "tool-result")
return yield* ProviderShared.unsupportedContent("Mistral Chat", "tool", ["tool-result"])
if (part.result.type !== "content") {
output.push({
role: "tool",
tool_call_id: normalizeID(part.id),
name: part.name,
content: ProviderShared.toolResultText(part),
})
continue
}
const content: MistralUserContent[] = []
for (const item of part.result.value) {
if (item.type === "text") {
content.push({ type: "text", text: item.text })
continue
}
content.push(yield* lowerMedia({ type: "media", mediaType: item.mime, data: item.uri, filename: item.name }))
}
output.push({
role: "tool",
tool_call_id: normalizeID(part.id),
name: part.name,
content: content.some((item) => item.type !== "text")
? content
: content.map((item) => (item.type === "text" ? item.text : "")).join(""),
})
}
return output
})
const lowerMessages = Effect.fn("MistralChat.lowerMessages")(function* (request: LLMRequest) {
const normalizeID = toolIDNormalizer(request)
const messages: MistralMessage[] =
request.system.length === 0 ? [] : [{ role: "system", content: ProviderShared.joinText(request.system) }]
for (const message of request.messages) {
if (message.role === "system") {
const update = yield* ProviderShared.wrappedSystemUpdate("Mistral Chat", message)
messages.push({
role: "user",
content: update.text,
})
continue
}
if (message.role === "user") {
messages.push(yield* lowerUser(message))
continue
}
if (message.role === "assistant") {
const hasToolCalls = message.content.some((part) => part.type === "tool-call")
const hasNativeThinking = message.content.some(
(part) => part.type === "reasoning" && isMistralThinkingContent(part.providerMetadata?.mistral?.thinking),
)
const text = message.content
.flatMap((part) => (part.type === "text" || part.type === "reasoning" ? [part.text] : []))
.join("")
if (!hasToolCalls && !hasNativeThinking && text.trim() === "") continue
messages.push(yield* lowerAssistant(message, normalizeID, !hasToolCalls && message === request.messages.at(-1)))
continue
}
messages.push(...(yield* lowerToolResults(message, normalizeID)))
}
return messages
})
const lowerTool = (tool: ToolDefinition): MistralTool => ({
type: "function",
function: { name: tool.name, description: tool.description, parameters: tool.inputSchema, strict: false },
})
export const fromRequest = Effect.fn("MistralChat.fromRequest")(function* (request: LLMRequest) {
const options = yield* ProviderShared.validateWith(Schema.decodeUnknownEffect(MistralOptions))(
request.providerOptions ?? {},
)
const selected = request.toolChoice?.type === "tool" ? request.toolChoice.name : undefined
if (request.toolChoice?.type === "tool" && !selected)
return yield* ProviderShared.invalidRequest("Mistral Chat tool choice requires a tool name")
if (options.reasoningEffort !== undefined && options.promptMode !== undefined)
return yield* ProviderShared.invalidRequest(
"Mistral Chat reasoningEffort and promptMode provider options are mutually exclusive",
)
const toolChoice = request.toolChoice
? yield* ProviderShared.matchToolChoice("Mistral Chat", request.toolChoice, {
auto: () => "auto" as const,
none: () => "none" as const,
required: () => "any" as const,
tool: (name) => ({ type: "function" as const, function: { name } }),
})
: undefined
return {
model: request.model.id,
messages: yield* lowerMessages(request),
tools: request.tools.length > 0 ? request.tools.map(lowerTool) : undefined,
tool_choice: toolChoice,
stream: true as const,
max_tokens: request.generation?.maxTokens,
random_seed: request.generation?.seed,
temperature: request.generation?.temperature,
top_p: request.generation?.topP,
frequency_penalty: request.generation?.frequencyPenalty,
presence_penalty: request.generation?.presencePenalty,
stop: request.generation?.stop,
prompt_cache_key: request.cache === "none" ? undefined : (options.promptCacheKey ?? request.promptCacheKey),
safe_prompt: options.safePrompt,
document_image_limit: options.documentImageLimit,
document_page_limit: options.documentPageLimit,
parallel_tool_calls:
options.parallelToolCalls ?? (request.toolChoice?.disableParallelToolUse === true ? false : undefined),
reasoning_effort: options.reasoningEffort,
prompt_mode: options.promptMode,
}
})
type ToolKey = string | number
interface PendingTool {
readonly id: string
readonly name?: string
readonly input: string
}
interface ActiveContent {
readonly type: "text" | "reasoning"
readonly id: string
readonly thinking?: MistralThinkingContent
}
export interface ParserState {
readonly tools: ToolStream.State<ToolKey>
readonly pendingTools: Partial<Record<ToolKey, PendingTool>>
readonly toolIDs: ReadonlyMap<string, string>
readonly usedToolIDs: ReadonlySet<string>
readonly completedTools: ReadonlyArray<LLMEvent>
readonly latestToolKey?: ToolKey
readonly generatedTools: number
readonly lifecycle: Lifecycle.State
readonly active?: ActiveContent
readonly nextContent: number
readonly usage?: Usage
readonly finishReason?: FinishReasonDetails
}
const mapUsage = (usage: MistralEvent["usage"]): Usage | undefined => {
if (!usage) return undefined
const input = usage.prompt_tokens ?? undefined
const reported =
usage.num_cached_tokens ??
usage.prompt_tokens_details?.cached_tokens ??
usage.prompt_token_details?.cached_tokens ??
undefined
const cached = input === undefined || reported === undefined ? undefined : Math.max(0, Math.min(input, reported))
const output = usage.completion_tokens ?? undefined
return new Usage({
inputTokens: input,
outputTokens: output,
nonCachedInputTokens: ProviderShared.subtractTokens(input, cached),
cacheReadInputTokens: cached,
totalTokens: ProviderShared.totalTokens(input, output, usage.total_tokens ?? undefined),
providerMetadata: { mistral: usage },
})
}
const mapFinishReason = (reason: string) => {
switch (reason) {
case "stop":
return "stop" as const
case "length":
case "model_length":
return "length" as const
case "tool_calls":
return "tool-calls" as const
case "content_filter":
return "content-filter" as const
case "error":
case "network_error":
return "error" as const
default:
return "unknown" as const
}
}
const thinkingUnits = (value: unknown): ReadonlyArray<MistralThinkingUnit> => {
if (typeof value === "string") return [{ type: "text", text: value }]
if (!Array.isArray(value)) return []
return value.filter(Schema.is(MistralThinkingUnit))
}
const thinkingText = (thinking: ReadonlyArray<MistralThinkingUnit>) =>
thinking.flatMap((unit) => (typeof unit.text === "string" ? [unit.text] : [])).join("")
const thinkingMetadata = (thinking: MistralThinkingContent) => ({ mistral: { thinking } })
const closeActive = (state: ParserState, events: LLMEvent[]) => {
if (!state.active) return state
const lifecycle =
state.active.type === "text"
? Lifecycle.textEnd(state.lifecycle, events, state.active.id)
: Lifecycle.reasoningEnd(
state.lifecycle,
events,
state.active.id,
thinkingMetadata(state.active.thinking ?? { type: "thinking", thinking: [] }),
thinkingText(state.active.thinking?.thinking ?? []),
)
return { ...state, lifecycle, active: undefined }
}
const appendText = (state: ParserState, events: LLMEvent[], text: string) => {
if (text.length === 0) return state
const current = state.active?.type === "text" ? state : closeActive(state, events)
const active = current.active ?? { type: "text" as const, id: `text-${current.nextContent}` }
return {
...current,
lifecycle: Lifecycle.textDelta(current.lifecycle, events, active.id, text),
active,
nextContent: current.active ? current.nextContent : current.nextContent + 1,
}
}
const appendThinking = (state: ParserState, events: LLMEvent[], part: MistralOutputContent) => {
const current = state.active?.type === "reasoning" ? state : closeActive(state, events)
const units = thinkingUnits(part.thinking)
const active = current.active ?? { type: "reasoning" as const, id: `reasoning-${current.nextContent}` }
const thinking = {
...active.thinking,
...part,
type: "thinking" as const,
thinking: [...(active.thinking?.thinking ?? []), ...units],
}
const text = thinkingText(units)
return {
...current,
lifecycle:
text.length > 0
? Lifecycle.reasoningDelta(current.lifecycle, events, active.id, text, thinkingMetadata(thinking))
: Lifecycle.reasoningStart(current.lifecycle, events, active.id, thinkingMetadata(thinking)),
active: { ...active, thinking },
nextContent: current.active ? current.nextContent : current.nextContent + 1,
}
}
const appendContent = (
state: ParserState,
events: LLMEvent[],
content: string | ReadonlyArray<MistralOutputContent>,
) => {
if (typeof content === "string") return appendText(state, events, content)
return content.reduce((current, part) => {
if (part.type === "text") return appendText(current, events, part.text ?? "")
if (part.type === "thinking") return appendThinking(current, events, part)
return closeActive(current, events)
}, state)
}
const normalizeStreamToolID = (state: ParserState, source: string) => {
if (TOOL_ID.test(source))
return { id: source, state: { ...state, usedToolIDs: new Set([...state.usedToolIDs, source]) } }
const previous = state.toolIDs.get(source)
if (previous) return { id: previous, state }
let attempt = 0
let id = hashID(source)
while (state.usedToolIDs.has(id)) id = hashID(`${source}:${++attempt}`)
return {
id,
state: {
...state,
toolIDs: new Map([...state.toolIDs, [source, id]]),
usedToolIDs: new Set([...state.usedToolIDs, id]),
},
}
}
const toolText = (tool: MistralToolDelta) => {
const value = tool.function?.arguments
if (typeof value === "string") return value
return value === null || value === undefined ? "" : ProviderShared.encodeJson(value)
}
const appendTools = Effect.fn("MistralChat.appendTools")(function* (
initial: ParserState,
events: LLMEvent[],
deltas: ReadonlyArray<MistralToolDelta>,
) {
if (deltas.length === 0) return initial
let state = closeActive(initial, events)
for (const [position, delta] of deltas.entries()) {
const wireID = delta.id?.trim() || undefined
const providedID = wireID === "null" ? undefined : wireID
const key =
delta.index ??
(providedID
? `id:${providedID}`
: deltas.length > 1
? `position:${position}`
: (state.latestToolKey ?? `missing:${state.generatedTools}`))
const existing = state.tools[key]
const pending = state.pendingTools[key]
const source = providedID ?? `generated:${String(key)}`
const normalized =
existing || pending ? { id: existing?.id ?? pending?.id ?? "", state } : normalizeStreamToolID(state, source)
state = normalized.state
const name = existing?.name ?? pending?.name ?? (delta.function?.name?.trim() || undefined)
const text = `${pending?.input ?? ""}${toolText(delta)}`
if (!name) {
state = {
...state,
pendingTools: { ...state.pendingTools, [key]: { id: normalized.id, input: text } },
latestToolKey: key,
generatedTools: state.generatedTools + (!providedID && !pending ? 1 : 0),
}
continue
}
const result = ToolStream.appendOrStart(
ADAPTER,
state.tools,
key,
{ id: normalized.id, name, text },
"Mistral Chat tool call delta is missing a name",
)
if (ToolStream.isError(result)) return yield* result
if (result.events.length > 0) state = { ...state, lifecycle: Lifecycle.stepStart(state.lifecycle, events) }
events.push(...result.events)
const pendingTools = { ...state.pendingTools }
delete pendingTools[key]
state = {
...state,
tools: result.tools,
pendingTools,
latestToolKey: key,
generatedTools: state.generatedTools + (!providedID && !existing && !pending ? 1 : 0),
}
}
return state
})
const hasLateContent = (event: MistralEvent) => {
const delta = event.choices?.[0]?.delta
if (typeof delta?.content === "string" && delta.content.length > 0) return true
if (Array.isArray(delta?.content) && delta.content.length > 0) return true
return (delta?.tool_calls ?? []).some(
(tool) => Boolean(tool.id) || Boolean(tool.function?.name) || tool.function?.arguments !== undefined,
)
}
const step = Effect.fn("MistralChat.step")(function* (state: ParserState, event: MistralEvent) {
if (event.error) {
const body = ProviderShared.encodeJson(event)
return yield* new AIError({
reason: classifyProviderFailure({
message: event.error.message,
status: typeof event.error.code === "number" ? event.error.code : undefined,
rawBody: body,
}),
})
}
const events: LLMEvent[] = []
const usage = mapUsage(event.usage) ?? state.usage
if (state.finishReason) {
if (hasLateContent(event))
return yield* ProviderShared.eventError(
ADAPTER,
"Mistral Chat received content after the finish reason",
ProviderShared.encodeJson(event),
)
return [{ ...state, usage }, events] as const
}
const choice = event.choices?.[0]
const withContent = choice?.delta?.content == null ? state : appendContent(state, events, choice.delta.content)
const withTools = yield* appendTools(withContent, events, choice?.delta?.tool_calls ?? [])
if (!choice?.finish_reason) return [{ ...withTools, usage }, events] as const
const finishReason = {
normalized: mapFinishReason(choice.finish_reason),
raw: choice.finish_reason,
}
const incomplete = finishReason.normalized === "length" || finishReason.normalized === "content-filter"
if (!incomplete && Object.keys(withTools.pendingTools).length > 0)
return yield* ProviderShared.eventError(
ADAPTER,
"Mistral Chat tool call delta is missing a name",
ProviderShared.encodeJson(event),
)
const finished =
!incomplete && Object.keys(withTools.tools).length > 0
? yield* ToolStream.finishAll(ADAPTER, withTools.tools)
: undefined
return [
{
...withTools,
tools: finished?.tools ?? withTools.tools,
completedTools: finished?.events ?? withTools.completedTools,
usage,
finishReason,
},
events,
] as const
})
const finishEvents = Effect.fn("MistralChat.finishEvents")(function* (state: ParserState) {
if (!state.finishReason)
return yield* new AIError({
reason: new InvalidProviderOutputError({
message: "Mistral Chat stream ended without finish_reason",
classification: "incomplete-stream",
route: ADAPTER,
}),
})
const events: LLMEvent[] = []
const closed = closeActive(state, events)
const lifecycle = closed.completedTools.length > 0 ? Lifecycle.stepStart(closed.lifecycle, events) : closed.lifecycle
events.push(...closed.completedTools)
const reason =
state.finishReason.normalized === "stop" && closed.completedTools.some(LLMEvent.is.toolCall)
? { ...state.finishReason, normalized: "tool-calls" as const }
: state.finishReason
Lifecycle.finish(lifecycle, events, { reason, usage: closed.usage })
return events
})
export const protocol = Protocol.make({
id: ADAPTER,
body: { schema: MistralBody, from: fromRequest },
stream: {
event: MistralStreamEvent,
initial: (): ParserState => ({
tools: ToolStream.empty<ToolKey>(),
pendingTools: {},
toolIDs: new Map(),
usedToolIDs: new Set(),
completedTools: [],
generatedTools: 0,
lifecycle: Lifecycle.initial(),
nextContent: 0,
}),
step: (state: ParserState, event) => (event === DONE ? Effect.succeed([state, []] as const) : step(state, event)),
terminal: (event) => event === DONE,
onHalt: finishEvents,
},
})
export const framing = Framing.sseWithDone
export const httpTransport = HttpTransport.sseJson.with<MistralBody>().with({ framing })
export const route = Route.make({
id: ADAPTER,
provider: "mistral",
providerMetadataKey: "mistral",
protocol,
endpoint: Endpoint.path(PATH, { baseURL: DEFAULT_BASE_URL }),
auth: Auth.none,
transport: httpTransport,
})
export * as MistralChat from "./mistral-chat.js"
+156 -240
View File
@@ -32,19 +32,17 @@ export const PATH = "/responses"
// =============================================================================
// Request Body Schema
// =============================================================================
export const OpenResponsesInputText = Schema.Struct({
const OpenResponsesInputText = Schema.Struct({
type: Schema.tag("input_text"),
text: Schema.String,
})
export const OpenResponsesInputImage = Schema.Struct({
const OpenResponsesInputImage = Schema.Struct({
type: Schema.tag("input_image"),
image_url: Schema.String,
detail: Schema.optional(Schema.String),
})
export const OpenResponsesInputFile = Schema.Struct({
const OpenResponsesInputFile = Schema.Struct({
type: Schema.tag("input_file"),
filename: Schema.String,
detail: Schema.optional(Schema.String),
file_data: Schema.optional(Schema.String),
file_url: Schema.optional(Schema.String),
})
@@ -56,7 +54,7 @@ const MediaInput = Schema.Union([OpenResponsesInputImage, OpenResponsesInputFile
export type MediaInput = Schema.Schema.Type<typeof MediaInput>
const OpenResponsesInputContent = Schema.Union([OpenResponsesInputText, MediaInput])
export const OpenResponsesOutputText = Schema.Struct({
const OpenResponsesOutputText = Schema.Struct({
type: Schema.tag("output_text"),
text: Schema.String,
})
@@ -64,13 +62,6 @@ export const OpenResponsesOutputText = Schema.Struct({
export const MessagePhase = Schema.NullOr(Schema.Literals(["commentary", "final_answer"]))
type MessagePhase = Schema.Schema.Type<typeof MessagePhase>
export const MessageMetadata = Schema.Struct({
itemId: Schema.optional(Schema.String),
type: Schema.optional(Schema.Literal("message")),
status: Schema.optional(Schema.String),
phase: Schema.optional(MessagePhase),
})
const messagePhase = (value: unknown): MessagePhase | undefined => {
if (value === null || value === "commentary" || value === "final_answer") return value
return undefined
@@ -81,7 +72,7 @@ const OpenResponsesReasoningSummaryText = Schema.Struct({
text: Schema.String,
})
export const OpenResponsesReasoningItem = Schema.Struct({
const OpenResponsesReasoningItem = Schema.Struct({
type: Schema.tag("reasoning"),
id: Schema.optionalKey(Schema.String),
summary: Schema.Array(OpenResponsesReasoningSummaryText),
@@ -158,30 +149,16 @@ const OpenResponsesFunctionCallOutput = Schema.Union([
Schema.Array(OpenResponsesFunctionCallOutputContent),
])
export const CompactionItem = Schema.Struct({
type: Schema.Literal("compaction"),
id: optionalNull(Schema.String),
encrypted_content: Schema.String,
})
export const InputItem = Schema.Union([
CompactionItem,
Schema.Struct({ role: Schema.tag("system"), content: Schema.String }),
Schema.Struct({ role: Schema.tag("developer"), content: Schema.String }),
Schema.Struct({
role: Schema.tag("user"),
content: Schema.Array(OpenResponsesInputContent),
type: Schema.optional(Schema.Literal("message")),
id: Schema.optional(Schema.String),
status: Schema.optional(Schema.String),
}),
Schema.Struct({ role: Schema.tag("user"), content: Schema.Array(OpenResponsesInputContent) }),
Schema.Struct({
type: Schema.tag("message"),
id: Schema.optionalKey(Schema.String),
role: Schema.tag("assistant"),
content: Schema.Array(OpenResponsesOutputText),
phase: Schema.optionalKey(MessagePhase),
status: Schema.optional(Schema.String),
}),
OpenResponsesReasoningItem,
Schema.Struct({
@@ -199,14 +176,14 @@ export const InputItem = Schema.Union([
HostedToolItem,
])
type OpenResponsesInputItem = Schema.Schema.Type<typeof InputItem>
export type HostedToolReplayItem = {
export type ExtendedHostedToolItem = {
readonly type: string
readonly id: string
readonly [key: string]: unknown
}
type LoweredInputItem =
| OpenResponsesInputItem
| HostedToolReplayItem
| ExtendedHostedToolItem
| {
readonly type: "message"
readonly id?: string
@@ -290,7 +267,7 @@ const OpenResponsesBody = Schema.Struct({
})
export type OpenResponsesBody = Schema.Schema.Type<typeof OpenResponsesBody>
export const OpenResponsesUsage = Schema.Struct({
const OpenResponsesUsage = Schema.Struct({
input_tokens: Schema.optional(Schema.Number),
input_tokens_details: optionalNull(
Schema.Struct({
@@ -304,11 +281,6 @@ export const OpenResponsesUsage = Schema.Struct({
})
type OpenResponsesUsage = Schema.Schema.Type<typeof OpenResponsesUsage>
// The spec requires `id` on every output item, but some gateways drop it from
// later item events (Bedrock Mantle renames it to `item_id` on
// `output_item.done` and `response.completed.output`). Decode it as optional
// and let `normalize` recover or mint it once before the parser runs.
// https://www.openresponses.org/specification#extending-items
export const StreamItem = Schema.StructWithRest(
Schema.Struct({
type: Schema.String,
@@ -321,7 +293,6 @@ export const StreamItem = Schema.StructWithRest(
[Schema.Record(Schema.String, Schema.Unknown)],
)
export type StreamItem = Schema.Schema.Type<typeof StreamItem>
export type OutputItem = StreamItem & { readonly id: string }
// The Responses schema puts streaming error details at the top level and
// response failures under `response.error`. WebSocket failures use an
@@ -401,9 +372,8 @@ export const Event = Schema.StructWithRest(
[Schema.Record(Schema.String, Schema.Unknown)],
)
export type Event = Schema.Schema.Type<typeof Event>
export type NormalizedEvent = Event & { readonly item?: OutputItem | null }
export interface ProviderAdapter {
export interface Extension {
readonly id: string
readonly name: string
readonly lowerMedia?: (input: {
@@ -411,18 +381,18 @@ export interface ProviderAdapter {
readonly media: ProviderShared.NormalizedMedia
readonly request: LLMRequest
}) => MediaInput | undefined
readonly restoreHostedToolItem?: (item: unknown) => HostedToolReplayItem | undefined
readonly lowerHostedToolItem?: (item: unknown) => ExtendedHostedToolItem | undefined
}
const BASE_ADAPTER: ProviderAdapter = { id: ADAPTER, name: NAME }
const BASE: Extension = { id: ADAPTER, name: NAME }
export interface ParserState {
readonly provider: LLMRequest["model"]["provider"]
readonly completedCompactions: ReadonlySet<string>
readonly id: string
readonly name: string
readonly providerMetadataKey: string
readonly tools: ToolStream.State<string>
// Call ids stay independent of item ids, which may be omitted or reused.
readonly completedTools: ReadonlySet<string>
readonly hasFunctionCall: boolean
readonly lifecycle: Lifecycle.State
readonly outputItems: Readonly<Record<number, string>>
@@ -512,15 +482,12 @@ const lowerReasoning = (part: ReasoningPart, providerMetadataKey: string): OpenR
const lowerMedia = Effect.fn("OpenResponses.lowerMedia")(function* (
part: MediaPart,
request: LLMRequest,
adapter: ProviderAdapter,
extension: Extension,
target: "message" | "tool-result",
) {
const media = ProviderShared.normalizeMedia(part)
const providerMedia = adapter.lowerMedia?.({ part, media, request })
if (providerMedia) return providerMedia
const detail = yield* ProviderShared.validateWith(Schema.decodeUnknownEffect(OpenResponsesInputImage.fields.detail))(
part.providerMetadata?.[metadataKey(request.model)]?.detail,
)
const extended = extension.lowerMedia?.({ part, media, request })
if (extended) return extended
const url =
typeof part.data === "string" && (part.data.startsWith("https://") || part.data.startsWith("http://"))
? part.data
@@ -531,31 +498,26 @@ const lowerMedia = Effect.fn("OpenResponses.lowerMedia")(function* (
return {
type: "input_file" as const,
filename: part.filename ?? (media.mime === "application/pdf" ? "document.pdf" : "file"),
detail,
...(url ? { file_url: url } : { file_data: media.dataUrl }),
}
}
return {
type: "input_image" as const,
image_url: url ?? media.dataUrl,
detail,
}
return { type: "input_image" as const, image_url: url ?? media.dataUrl }
})
const lowerUserContent = Effect.fnUntraced(function* (
part: LLMRequest["messages"][number]["content"][number],
request: LLMRequest,
adapter: ProviderAdapter,
extension: Extension,
) {
if (part.type === "text") return { type: "input_text" as const, text: part.text }
if (part.type === "media") return yield* lowerMessageMedia(part, request, adapter)
return yield* ProviderShared.unsupportedContent(adapter.name, "user", ["text", "media"])
if (part.type === "media") return yield* lowerMessageMedia(part, request, extension)
return yield* ProviderShared.unsupportedContent(extension.name, "user", ["text", "media"])
})
const lowerMessageMedia = Effect.fnUntraced(function* (part: MediaPart, request: LLMRequest, adapter: ProviderAdapter) {
const lowered = yield* lowerMedia(part, request, adapter, "message")
const lowerMessageMedia = Effect.fnUntraced(function* (part: MediaPart, request: LLMRequest, extension: Extension) {
const lowered = yield* lowerMedia(part, request, extension, "message")
if (lowered.type === "input_video")
return yield* ProviderShared.invalidRequest(`${adapter.name} user messages do not support input_video`)
return yield* ProviderShared.invalidRequest(`${extension.name} user messages do not support input_video`)
return lowered
})
@@ -564,13 +526,13 @@ const lowerMessageMedia = Effect.fnUntraced(function* (part: MediaPart, request:
const lowerToolResultContentItem = Effect.fnUntraced(function* (
item: Content,
request: LLMRequest,
adapter: ProviderAdapter,
extension: Extension,
) {
if (item.type === "text") return { type: "input_text" as const, text: item.text }
return yield* lowerMedia(
{ type: "media", mediaType: item.mime, data: item.uri, filename: item.name },
request,
adapter,
extension,
"tool-result",
)
})
@@ -578,52 +540,47 @@ const lowerToolResultContentItem = Effect.fnUntraced(function* (
const lowerHostedToolResultContentItem = Effect.fnUntraced(function* (
item: Content,
request: LLMRequest,
adapter: ProviderAdapter,
extension: Extension,
) {
if (item.type === "text") return { type: "input_text" as const, text: item.text }
return yield* lowerMessageMedia(
{ type: "media", mediaType: item.mime, data: item.uri, filename: item.name },
request,
adapter,
extension,
)
})
const lowerToolResultOutput = Effect.fnUntraced(function* (
part: ToolResultPart,
request: LLMRequest,
adapter: ProviderAdapter,
extension: Extension,
) {
// Text/json/error results are encoded as a plain string for backward
// compatibility with existing cassettes and provider expectations.
if (part.result.type !== "content") return ProviderShared.toolResultText(part)
// Preserve the narrowed array element type when compiled through a consumer package.
const content: ReadonlyArray<Content> = part.result.value
return yield* Effect.forEach(content, (item) => lowerToolResultContentItem(item, request, adapter))
return yield* Effect.forEach(content, (item) => lowerToolResultContentItem(item, request, extension))
})
const lowerMessages = Effect.fn("OpenResponses.lowerMessages")(function* (
request: LLMRequest,
adapter: ProviderAdapter,
) {
const lowerMessages = Effect.fn("OpenResponses.lowerMessages")(function* (request: LLMRequest, extension: Extension) {
const input: LoweredInputItem[] = []
const providerMetadataKey = metadataKey(request.model)
const providerMetadataKey = request.model.route.providerMetadataKey ?? "openresponses"
for (const message of request.messages) {
const metadata = yield* ProviderShared.validateWith(
Schema.decodeUnknownEffect(Schema.UndefinedOr(MessageMetadata)),
)(message.providerMetadata?.[providerMetadataKey])
if (message.role === "system") {
input.push({
role: "developer",
content: ProviderShared.joinText(yield* ProviderShared.systemUpdateText(adapter.name, message)),
content: ProviderShared.joinText(yield* ProviderShared.systemUpdateText(extension.name, message)),
})
continue
}
if (message.role === "user") {
const content = yield* Effect.forEach(message.content, (part) => lowerUserContent(part, request, adapter))
if (content.length > 0)
input.push({ role: "user", content, type: metadata?.type, id: metadata?.itemId, status: metadata?.status })
input.push({
role: "user",
content: yield* Effect.forEach(message.content, (part) => lowerUserContent(part, request, extension)),
})
continue
}
@@ -636,10 +593,9 @@ const lowerMessages = Effect.fn("OpenResponses.lowerMessages")(function* (
const groups = content.reduce<
Array<{ id: string | undefined; phase: MessagePhase | null | undefined; parts: TextPart[] }>
>((groups, part) => {
const partMetadata = part.providerMetadata?.[providerMetadataKey]
const id = itemID(part.providerMetadata, providerMetadataKey) ?? metadata?.itemId
const partPhase = messagePhase(partMetadata?.phase)
const phase = partPhase === undefined ? metadata?.phase : partPhase
const metadata = part.providerMetadata?.[providerMetadataKey]
const id = itemID(part.providerMetadata, providerMetadataKey)
const phase = ProviderShared.isRecord(metadata) ? messagePhase(metadata.phase) : undefined
const group = groups.at(-1)
if (group && group.id === id && group.phase === phase) group.parts.push(part)
else groups.push({ id, phase, parts: [part] })
@@ -650,7 +606,6 @@ const lowerMessages = Effect.fn("OpenResponses.lowerMessages")(function* (
type: "message" as const,
...(group.id === undefined ? {} : { id: group.id }),
role: "assistant" as const,
status: metadata?.status,
content: group.parts.map((part) => ({ type: "output_text" as const, text: part.text })),
...(group.phase === undefined ? {} : { phase: group.phase }),
})),
@@ -658,15 +613,6 @@ const lowerMessages = Effect.fn("OpenResponses.lowerMessages")(function* (
content.splice(0, content.length)
}
for (const part of message.content) {
if (part.type === "compaction") {
flushText()
if (part.provider !== request.model.provider || part.encrypted === undefined)
return yield* ProviderShared.invalidRequest(
"Compaction state must be replayed to its originating provider and API",
)
input.push({ type: "compaction", id: part.id, encrypted_content: part.encrypted })
continue
}
if (part.type === "text") {
content.push(part)
continue
@@ -700,7 +646,7 @@ const lowerMessages = Effect.fn("OpenResponses.lowerMessages")(function* (
? undefined
: Schema.is(HostedToolItem)(part.result.value)
? part.result.value
: adapter.restoreHostedToolItem?.(part.result.value)
: extension.lowerHostedToolItem?.(part.result.value)
if (id !== undefined && hosted?.id === id) {
if (!hostedToolItems.has(id)) {
input.push(hosted)
@@ -714,11 +660,13 @@ const lowerMessages = Effect.fn("OpenResponses.lowerMessages")(function* (
: [{ type: "text", text: ProviderShared.toolResultText(part) }]
input.push({
role: "user",
content: yield* Effect.forEach(content, (item) => lowerHostedToolResultContentItem(item, request, adapter)),
content: yield* Effect.forEach(content, (item) =>
lowerHostedToolResultContentItem(item, request, extension),
),
})
continue
}
return yield* ProviderShared.unsupportedContent(adapter.name, "assistant", [
return yield* ProviderShared.unsupportedContent(extension.name, "assistant", [
"text",
"reasoning",
"tool-call",
@@ -731,11 +679,11 @@ const lowerMessages = Effect.fn("OpenResponses.lowerMessages")(function* (
for (const part of message.content) {
if (!ProviderShared.supportsContent(part, ["tool-result"]))
return yield* ProviderShared.unsupportedContent(adapter.name, "tool", ["tool-result"])
return yield* ProviderShared.unsupportedContent(extension.name, "tool", ["tool-result"])
input.push({
type: "function_call_output",
call_id: part.id,
output: yield* lowerToolResultOutput(part, request, adapter),
output: yield* lowerToolResultOutput(part, request, extension),
})
}
}
@@ -743,30 +691,13 @@ const lowerMessages = Effect.fn("OpenResponses.lowerMessages")(function* (
return input
})
export const lowerConversation = Effect.fn("OpenResponses.lowerConversation")(function* (
request: LLMRequest,
adapter: ProviderAdapter,
) {
const instructions = ProviderShared.joinText(request.system)
return {
model: request.model.id,
input: yield* lowerMessages(request, adapter),
...(instructions ? { instructions } : {}),
}
})
export const lowerGeneration = (request: LLMRequest) => {
const lowerOptions = (request: LLMRequest) => {
const options = OpenResponsesOptions.resolve(request)
const generation = request.generation
const instructions = ProviderShared.joinText(request.system)
const cacheKey = ProviderShared.promptCacheKey(request)
const parallelToolCalls = resolveParallelToolCalls(request)
return {
stream: true as const,
max_output_tokens: generation?.maxTokens,
temperature: generation?.temperature,
top_p: generation?.topP,
presence_penalty: generation?.presencePenalty,
frequency_penalty: generation?.frequencyPenalty,
...(instructions ? { instructions } : {}),
...(options.store !== undefined ? { store: options.store } : {}),
...(options.metadata ? { metadata: options.metadata } : {}),
...(options.safetyIdentifier ? { safety_identifier: options.safetyIdentifier } : {}),
@@ -794,7 +725,7 @@ export const resolveParallelToolCalls = (request: LLMRequest) => {
return disabled === undefined ? undefined : !disabled
}
export const allowedToolChoice = (request: LLMRequest) => {
const allowedToolChoice = (request: LLMRequest) => {
const allowed = OpenResponsesOptions.resolve(request).allowedTools
if (!allowed) return undefined
return {
@@ -804,34 +735,42 @@ export const allowedToolChoice = (request: LLMRequest) => {
}
}
export const fromRequestWithAdapter = Effect.fn("OpenResponses.fromRequestWithAdapter")(function* (
export const fromRequestWithExtension = Effect.fn("OpenResponses.fromRequestWithExtension")(function* (
request: LLMRequest,
adapter: ProviderAdapter,
extension: Extension,
) {
const generation = request.generation
const toolSchemaCompatibility = request.model.compatibility?.toolSchema
return {
...(yield* lowerConversation(request, adapter)),
...lowerGeneration(request),
model: request.model.id,
input: yield* lowerMessages(request, extension),
tools:
request.tools.length === 0
? undefined
: yield* Effect.forEach(request.tools, (tool) =>
lowerTool(
adapter.name,
extension.name,
tool,
ToolSchemaProjection.modelCompatibility(tool.inputSchema, toolSchemaCompatibility),
),
),
tool_choice:
allowedToolChoice(request) ??
(request.toolChoice ? yield* lowerToolChoice(adapter.name, request.toolChoice) : undefined),
(request.toolChoice ? yield* lowerToolChoice(extension.name, request.toolChoice) : undefined),
stream: true as const,
max_output_tokens: generation?.maxTokens,
temperature: generation?.temperature,
top_p: generation?.topP,
presence_penalty: generation?.presencePenalty,
frequency_penalty: generation?.frequencyPenalty,
...lowerOptions(request),
}
})
const decodeBody = ProviderShared.validateWith(Schema.decodeUnknownEffect(OpenResponsesBody))
export const fromRequest = Effect.fn("OpenResponses.fromRequest")(function* (request: LLMRequest) {
return yield* decodeBody(yield* fromRequestWithAdapter(request, BASE_ADAPTER))
return yield* decodeBody(yield* fromRequestWithExtension(request, BASE))
})
// =============================================================================
@@ -841,7 +780,7 @@ export const fromRequest = Effect.fn("OpenResponses.fromRequest")(function* (req
// cached-read and cache-write subsets, and `output_tokens` (inclusive total)
// with a `reasoning_tokens` subset. Pass the totals through and derive the
// non-cached breakdown.
export const mapUsage = (usage: OpenResponsesUsage | null | undefined, providerMetadataKey: string) => {
const mapUsage = (usage: OpenResponsesUsage | null | undefined, providerMetadataKey: string) => {
if (!usage) return undefined
const cached = usage.input_tokens_details?.cached_tokens
const cacheWrite = usage.input_tokens_details?.cache_write_tokens
@@ -871,12 +810,13 @@ const mapFinishReason = (event: Event, hasFunctionCall: boolean): FinishReason =
return hasFunctionCall ? "tool-calls" : "unknown"
}
export const metadataKey = (model: LLMRequest["model"]) => model.route.providerMetadataKey ?? "openresponses"
export const providerMetadata = (state: ParserState, metadata: Record<string, unknown>): ProviderMetadata => ({
[state.providerMetadataKey]: metadata,
})
const isReasoningItem = (item: StreamItem): item is StreamItem & { type: "reasoning"; id: string } =>
item.type === "reasoning" && typeof item.id === "string"
export type StepResult = readonly [ParserState, ReadonlyArray<LLMEvent>]
const NO_EVENTS: StepResult["1"] = []
@@ -920,34 +860,9 @@ const joinReasoningText = (parts: ReadonlyArray<string | undefined>) => {
return parts.filter((part) => part !== undefined).join("\n\n")
}
const outputItemID = (state: ParserState, event: Event) =>
export const outputItemID = (state: ParserState, event: Event) =>
event.output_index === undefined ? event.item_id : (state.outputItems[event.output_index] ?? event.item_id)
const ITEM_ID_PREFIX: Readonly<Record<string, string>> = {
message: "msg",
reasoning: "rs",
function_call: "fc",
compaction: "cmp",
}
// An item without an id adopts the id already open in its output slot,
// otherwise it gets a locally minted one.
const resolveItem = (state: ParserState, item: StreamItem, index: number | undefined): OutputItem => ({
...item,
id:
item.id ??
(index === undefined ? undefined : state.outputItems[index]) ??
`${ITEM_ID_PREFIX[item.type] ?? "item"}_${crypto.randomUUID().replaceAll("-", "")}`,
})
// Registered output slots are authoritative for `item_id` routing, and items
// are resolved here so everything downstream can rely on `item.id`.
export const normalize = (state: ParserState, input: Event): NormalizedEvent => ({
...input,
item_id: input.item_id === undefined ? undefined : outputItemID(state, input),
item: input.item ? resolveItem(state, input.item, input.output_index) : input.item,
})
const startReasoningSummaryPart = (state: ParserState, itemID: string, index: number): StepResult => {
const item = state.reasoningItems[itemID]
if (!item?.open || index === 0 || item.summaryParts[index] !== undefined) return [state, NO_EVENTS]
@@ -1021,7 +936,7 @@ export const onReasoningDone = (state: ParserState, event: Event, itemID: string
return onReasoningDelta(state, { ...event, delta: event.text }, itemID)
}
const reasoningMetadata = (state: ParserState, item: OutputItem) =>
const reasoningMetadata = (state: ParserState, item: StreamItem & { id: string }) =>
providerMetadata(state, { itemId: item.id, reasoningEncryptedContent: item.encrypted_content ?? null })
// Responses APIs normally stream reasoning items in this order:
@@ -1034,15 +949,15 @@ const reasoningMetadata = (state: ParserState, item: OutputItem) =>
// `onOutputItemAdded` seeds the per-item entry, while each later part start is
// also an implicit boundary for the previous part. This keeps the common event
// lifecycle ordered when a compatible provider omits or delays a part-done event.
const onOutputItemAdded = (state: ParserState, event: NormalizedEvent): StepResult => {
const onOutputItemAdded = (state: ParserState, event: Event): StepResult => {
const item = event.item
if (!item) return [state, NO_EVENTS]
if (item.type === "message") {
if (item?.type === "message" && item.id !== undefined) {
const itemID = item.id
const phase = messagePhase(item.phase)
// A new message closes earlier messages, including ones that never streamed.
const events: LLMEvent[] = []
const lifecycle = [...state.lifecycle.text]
.filter((id) => id !== item.id)
.filter((id) => id !== itemID)
.reduce((lifecycle, id) => {
const openPhase = state.message?.id === id ? state.message.phase : undefined
return Lifecycle.textEnd(
@@ -1057,14 +972,14 @@ const onOutputItemAdded = (state: ParserState, event: NormalizedEvent): StepResu
...state,
lifecycle,
message: {
id: item.id,
phase: phase === undefined && state.message?.id === item.id ? state.message.phase : phase,
id: itemID,
phase: phase === undefined && state.message?.id === itemID ? state.message.phase : phase,
},
},
events,
]
}
if (item.type === "reasoning") {
if (item && isReasoningItem(item)) {
if (state.reasoningItems[item.id] !== undefined) return [state, NO_EVENTS]
const events: LLMEvent[] = []
return [
@@ -1084,16 +999,18 @@ const onOutputItemAdded = (state: ParserState, event: NormalizedEvent): StepResu
events,
]
}
if (item.type !== "function_call" || !item.call_id) return [state, NO_EVENTS]
if (state.tools[item.id] !== undefined) return [state, NO_EVENTS]
const metadata = providerMetadata(state, { itemId: item.id })
if (item?.type !== "function_call" || !item.call_id) return [state, NO_EVENTS]
const id = item.id ?? item.call_id
if (Object.values(state.tools).some((tool) => tool?.id === item.call_id) || state.completedTools.has(item.call_id))
return [state, NO_EVENTS]
const metadata = item.id !== undefined ? providerMetadata(state, { itemId: item.id }) : undefined
const events: LLMEvent[] = []
const lifecycle = Lifecycle.stepStart(state.lifecycle, events)
return [
{
...state,
lifecycle,
tools: ToolStream.start(state.tools, item.id, {
tools: ToolStream.start(state.tools, id, {
id: item.call_id,
name: item.name ?? "",
input: item.arguments ?? "",
@@ -1165,33 +1082,14 @@ const onFunctionCallArgumentsDelta = Effect.fn("OpenResponses.onFunctionCallArgu
const onOutputItemDone = Effect.fn("OpenResponses.onOutputItemDone")(function* (
state: ParserState,
item: NormalizedEvent["item"],
item: Event["item"],
) {
if (!item) return [state, NO_EVENTS] satisfies StepResult
if (item.type === "compaction") {
if (typeof item.encrypted_content !== "string")
return yield* ProviderShared.eventError(state.id, "Compaction output is missing its encrypted content")
if (state.completedCompactions.has(item.id)) return [state, NO_EVENTS] satisfies StepResult
const events: LLMEvent[] = []
const lifecycle = Lifecycle.stepStart(state.lifecycle, events)
events.push(
LLMEvent.compaction({
provider: state.provider,
id: item.id,
encrypted: item.encrypted_content,
}),
)
return [
{ ...state, lifecycle, completedCompactions: new Set([...state.completedCompactions, item.id]) },
events,
] satisfies StepResult
}
if (item.type === "message") {
const active = state.message?.id === item.id
if (item.type === "message" && item.id !== undefined) {
const message = state.message?.id === item.id ? state.message : undefined
const itemPhase = messagePhase(item.phase)
const phase = itemPhase === undefined && active ? state.message?.phase : itemPhase
const phase = itemPhase === undefined ? message?.phase : itemPhase
const parts: ReadonlyArray<unknown> = Array.isArray(item.content) ? item.content : []
const content: string[] = []
for (const part of parts) {
@@ -1202,12 +1100,13 @@ const onOutputItemDone = Effect.fn("OpenResponses.onOutputItemDone")(function* (
const text = content.length > 0 ? content.join("") : undefined
const metadata = providerMetadata(state, { itemId: item.id, ...(phase === undefined ? {} : { phase }) })
const events: LLMEvent[] = []
const lifecycle = text ? Lifecycle.textStart(state.lifecycle, events, item.id, metadata) : state.lifecycle
const lifecycle =
message && text ? Lifecycle.textStart(state.lifecycle, events, item.id, metadata) : state.lifecycle
return [
{
...state,
lifecycle: Lifecycle.textEnd(lifecycle, events, item.id, metadata, text),
message: active ? undefined : state.message,
message: message ? undefined : state.message,
},
events,
] satisfies StepResult
@@ -1215,22 +1114,36 @@ const onOutputItemDone = Effect.fn("OpenResponses.onOutputItemDone")(function* (
if (item.type === "function_call") {
if (!item.call_id || !item.name) return [state, NO_EVENTS] satisfies StepResult
const metadata = providerMetadata(state, { itemId: item.id })
const registered = state.tools[item.id] !== undefined
const tools = registered
? state.tools
: ToolStream.start(state.tools, item.id, { id: item.call_id, name: item.name, providerMetadata: metadata })
const callID = item.call_id
if (state.completedTools.has(callID)) return [state, NO_EVENTS] satisfies StepResult
const metadata = item.id !== undefined ? providerMetadata(state, { itemId: item.id }) : undefined
const fallback = item.id ?? callID
// Match the pending tool by call id so item events that disagree on
// whether `item.id` is present still resolve the same call.
const registered =
state.tools[fallback] !== undefined
? fallback
: Object.keys(state.tools).find((key) => state.tools[key]?.id === callID)
const id = registered ?? fallback
const tools =
registered !== undefined
? state.tools
: ToolStream.start(state.tools, id, {
id: callID,
name: item.name,
providerMetadata: metadata,
})
const result =
item.arguments === undefined
? yield* ToolStream.finish(state.id, tools, item.id)
: yield* ToolStream.finishWithInput(state.id, tools, item.id, item.arguments)
? yield* ToolStream.finish(state.id, tools, id)
: yield* ToolStream.finishWithInput(state.id, tools, id, item.arguments)
const events: LLMEvent[] = []
const finished = result.events ?? []
// A done-only call never streamed a start event, so open its lifecycle here.
const resultEvents =
registered || finished.length === 0
registered !== undefined || finished.length === 0
? finished
: [LLMEvent.toolInputStart({ id: item.call_id, name: item.name, providerMetadata: metadata }), ...finished]
: [LLMEvent.toolInputStart({ id: callID, name: item.name, providerMetadata: metadata }), ...finished]
const lifecycle = resultEvents.length ? Lifecycle.stepStart(state.lifecycle, events) : state.lifecycle
events.push(...resultEvents)
return [
@@ -1241,12 +1154,13 @@ const onOutputItemDone = Effect.fn("OpenResponses.onOutputItemDone")(function* (
resultEvents.some((event) => LLMEvent.is.toolCall(event) || LLMEvent.is.toolInputError(event)) ||
state.hasFunctionCall,
tools: result.tools,
completedTools: new Set([...state.completedTools, callID]),
},
events,
] satisfies StepResult
}
if (item.type === "reasoning") {
if (isReasoningItem(item)) {
if (state.reasoningItems[item.id]?.open === false) return [state, NO_EVENTS] satisfies StepResult
const metadata = reasoningMetadata(state, item)
const summaryParts: ReadonlyArray<unknown> = Array.isArray(item.summary) ? item.summary : []
@@ -1330,35 +1244,27 @@ const onResponseFinish = Effect.fn("OpenResponses.onResponseFinish")(function* (
let current = state
const events: LLMEvent[] = []
if (event.type === "response.completed") {
// An output item's array position is its output index.
for (const item of (event.response?.output ?? []).map((item, index) => resolveItem(state, item, index))) {
// Terminal recovery cannot insert a checkpoint before already-emitted content.
if (item.type === "compaction" && state.lifecycle.stepStarted && !state.completedCompactions.has(item.id))
return yield* ProviderShared.eventError(
state.id,
"Cannot recover a compaction checkpoint after output has been emitted",
)
const recoverable =
item.type === "compaction" || (item.type === "function_call" && current.tools[item.id] !== undefined)
if (!recoverable) continue
for (const item of event.response?.output ?? []) {
const id = item.id ?? (item.type === "function_call" ? item.call_id : undefined)
if (id === undefined) continue
if (item.type !== "function_call" || !current.tools[id]) continue
const [next, emitted] = yield* onOutputItemDone(current, item)
current = next
events.push(...emitted)
}
// Some compatible providers omit output_item.done even after completing the response.
const pending = yield* ToolStream.finishAll(current.id, current.tools)
current = {
...current,
tools: pending.tools,
hasFunctionCall:
current.hasFunctionCall ||
pending.events.some((event) => LLMEvent.is.toolCall(event) || LLMEvent.is.toolInputError(event)),
}
events.push(...pending.events)
}
// Some compatible providers omit output_item.done even after completing the response.
const pending =
event.type === "response.completed"
? yield* ToolStream.finishAll(current.id, current.tools)
: { tools: current.tools, events: NO_EVENTS }
events.push(...pending.events)
const hasFunctionCall =
pending.events.some((event) => LLMEvent.is.toolCall(event) || LLMEvent.is.toolInputError(event)) ||
current.hasFunctionCall
const lifecycle = Lifecycle.finish(current.lifecycle, events, {
reason: {
normalized: mapFinishReason(event, current.hasFunctionCall),
normalized: mapFinishReason(event, hasFunctionCall),
raw: event.response?.incomplete_details?.reason,
},
usage: mapUsage(event.response?.usage, current.providerMetadataKey),
@@ -1370,7 +1276,7 @@ const onResponseFinish = Effect.fn("OpenResponses.onResponseFinish")(function* (
})
: undefined,
})
return [{ ...current, lifecycle }, events] satisfies StepResult
return [{ ...current, lifecycle, hasFunctionCall, tools: pending.tools }, events] satisfies StepResult
})
// Build the prettiest summary available from whatever the provider supplied.
@@ -1407,9 +1313,12 @@ export const providerFailure = (event: Event, fallback: string, body = ProviderS
return new AIError({ reason })
}
// Callers must pass events through `normalize` first. The OpenAPI requires
// string IDs but imposes no minLength; empty is not missing.
export const step = (state: ParserState, event: NormalizedEvent) => {
export const step = (state: ParserState, input: Event) => {
// The OpenAPI requires string IDs but imposes no minLength; empty is not missing.
const event =
input.item_id !== undefined && outputItemID(state, input) !== input.item_id
? { ...input, item_id: outputItemID(state, input) }
: input
if (event.type === "response.output_text.delta" || event.type === "response.output_text.done") {
if (event.item_id === undefined) return ProviderShared.eventError(state.id, `${event.type} is missing item_id`)
return Effect.succeed(
@@ -1449,16 +1358,20 @@ export const step = (state: ParserState, event: NormalizedEvent) => {
? Effect.succeed(onReasoningSummaryPartDone(state, event))
: ProviderShared.eventError(state.id, `${event.type} is missing item_id`)
if (event.type === "response.output_item.added") {
if (event.item?.type === "message" && event.item.id === undefined)
return ProviderShared.eventError(state.id, `${event.type} message is missing id`)
if (
event.item?.type === "reasoning" &&
event.item &&
isReasoningItem(event.item) &&
state.reasoningItems[event.item.id] === undefined &&
state.lifecycle.reasoning.size > 0
)
return ProviderShared.eventError(state.id, `${event.type} started reasoning before the previous item ended`)
const id = event.item?.id ?? (event.item?.type === "function_call" ? event.item.call_id : undefined)
return Effect.succeed(
onOutputItemAdded(
event.output_index !== undefined && event.item
? { ...state, outputItems: { ...state.outputItems, [event.output_index]: event.item.id } }
event.output_index !== undefined && id !== undefined
? { ...state, outputItems: { ...state.outputItems, [event.output_index]: id } }
: state,
event,
),
@@ -1468,7 +1381,11 @@ export const step = (state: ParserState, event: NormalizedEvent) => {
return event.item_id !== undefined
? onFunctionCallArgumentsDelta(state, event)
: ProviderShared.eventError(state.id, `${event.type} is missing item_id`)
if (event.type === "response.output_item.done") return onOutputItemDone(state, event.item)
if (event.type === "response.output_item.done") {
if (event.item?.type === "message" && event.item.id === undefined)
return ProviderShared.eventError(state.id, `${event.type} message is missing id`)
return onOutputItemDone(state, event.item)
}
if (event.type === "response.completed" || event.type === "response.incomplete") return onResponseFinish(state, event)
if (event.type === "response.failed") return providerFailure(event, `${state.name} response failed`)
if (event.type === "error")
@@ -1493,14 +1410,13 @@ export const step = (state: ParserState, event: NormalizedEvent) => {
* The provider-neutral Open Responses protocol. Provider-specific Responses
* implementations compose this baseline with their own tools and event variants.
*/
export const initial = (request: LLMRequest, adapter: ProviderAdapter = BASE_ADAPTER): ParserState => ({
provider: request.model.provider,
completedCompactions: new Set<string>(),
id: adapter.id,
name: adapter.name,
providerMetadataKey: metadataKey(request.model),
export const initial = (request: LLMRequest, extension: Extension = BASE): ParserState => ({
id: extension.id,
name: extension.name,
providerMetadataKey: request.model.route.providerMetadataKey ?? "openresponses",
hasFunctionCall: false,
tools: ToolStream.empty<string>(),
completedTools: new Set<string>(),
lifecycle: Lifecycle.initial(),
outputItems: {},
message: undefined,
@@ -1516,7 +1432,7 @@ export const protocol = Protocol.make({
stream: {
event: Protocol.jsonEvent(Event),
initial,
step: (state: ParserState, event: Event) => step(state, normalize(state, event)),
step,
terminal,
},
})
+17 -34
View File
@@ -5,14 +5,13 @@ import { Auth } from "../route/auth.js"
import { Endpoint } from "../route/endpoint.js"
import { Protocol } from "../route/protocol.js"
import { HttpTransport } from "../route/transport/index.js"
import type { LLMRequest, JsonSchema, ToolDefinition } from "../schema/index.js"
import { LLMRequest, type JsonSchema, type ToolDefinition } from "../schema/index.js"
import { OpenResponses } from "./open-responses.js"
import { JsonObject, optionalArray, optionalNull, ProviderShared } from "./shared.js"
import { OpenAIImage } from "./utils/openai-image.js"
import { ResponsesHostedTools } from "./utils/responses-hosted-tools.js"
import { ToolSchemaProjection } from "./utils/tool-schema.js"
import { OpenResponsesChannel } from "./open-responses-channel.js"
import { ResponsesCompaction } from "./utils/responses-compaction.js"
const ADAPTER = "openai-responses"
const NAME = "OpenAI Responses"
@@ -21,14 +20,6 @@ const WEBSOCKET_ROTATE_AFTER_MS = 55 * 60 * 1000
export const DEFAULT_BASE_URL = "https://api.openai.com/v1"
export const PATH = OpenResponses.PATH
export const ContextManagement = Schema.Array(
Schema.Struct({
type: Schema.Literal("compaction"),
compactThreshold: Schema.optional(Schema.Int.check(Schema.isGreaterThan(0))),
}),
)
export type ContextManagement = typeof ContextManagement.Type
const OpenAIResponsesImageGenerationTool = Schema.Struct({
type: Schema.tag("image_generation"),
action: Schema.optional(Schema.Literals(["auto", "generate", "edit"])),
@@ -87,14 +78,6 @@ const OpenAIResponsesCoreFields = {
input: Schema.Array(Schema.Union([OpenResponses.InputItem, OpenAIResponsesHostedToolItem])),
tools: optionalArray(OpenAIResponsesTools),
tool_choice: Schema.optional(OpenAIResponsesToolChoice),
context_management: Schema.optional(
Schema.Array(
Schema.Struct({
type: Schema.Literal("compaction"),
compact_threshold: Schema.optional(Schema.Int.check(Schema.isGreaterThan(0))),
}),
),
),
}
const OpenAIResponsesBody = Schema.Struct({
@@ -103,11 +86,11 @@ const OpenAIResponsesBody = Schema.Struct({
})
export type OpenAIResponsesBody = Schema.Schema.Type<typeof OpenAIResponsesBody>
const adapter = {
const extension = {
id: ADAPTER,
name: NAME,
restoreHostedToolItem: (item: unknown) => (Schema.is(OpenAIResponsesHostedToolItem)(item) ? item : undefined),
} satisfies OpenResponses.ProviderAdapter
lowerHostedToolItem: (item: unknown) => (Schema.is(OpenAIResponsesHostedToolItem)(item) ? item : undefined),
} satisfies OpenResponses.Extension
const nativeImageToolInput = (tool: ToolDefinition) => {
const native = tool.native?.openai
@@ -142,14 +125,15 @@ const lowerToolChoice = (toolChoice: NonNullable<LLMRequest["toolChoice"]>, tool
const decodeBody = ProviderShared.validateWith(Schema.decodeUnknownEffect(OpenAIResponsesBody))
const fromRequest = Effect.fn("OpenAIResponses.fromRequest")(function* (request: LLMRequest) {
const management = yield* ProviderShared.validateWith(
Schema.decodeUnknownEffect(Schema.UndefinedOr(ContextManagement)),
)(request.providerOptions?.contextManagement)
const body = yield* OpenResponses.fromRequestWithExtension(
LLMRequest.update(request, { tools: [], toolChoice: undefined }),
extension,
)
const toolSchemaCompatibility = request.model.compatibility?.toolSchema
const parallelToolCalls = OpenResponses.resolveParallelToolCalls(request)
return yield* decodeBody({
...(yield* OpenResponses.lowerConversation(request, adapter)),
...OpenResponses.lowerGeneration(request),
context_management: management?.map((edit) => ({ type: edit.type, compact_threshold: edit.compactThreshold })),
...body,
...(parallelToolCalls === undefined ? {} : { parallel_tool_calls: parallelToolCalls }),
tools:
request.tools.length === 0
? undefined
@@ -157,8 +141,7 @@ const fromRequest = Effect.fn("OpenAIResponses.fromRequest")(function* (request:
lowerTool(tool, ToolSchemaProjection.modelCompatibility(tool.inputSchema, toolSchemaCompatibility)),
),
tool_choice:
OpenResponses.allowedToolChoice(request) ??
(request.toolChoice ? yield* lowerToolChoice(request.toolChoice, request.tools) : undefined),
body.tool_choice ?? (request.toolChoice ? yield* lowerToolChoice(request.toolChoice, request.tools) : undefined),
})
})
@@ -201,11 +184,12 @@ const HOSTED_TOOLS = {
},
} as const satisfies ResponsesHostedTools.Definitions
const step = (state: OpenResponses.ParserState, input: OpenResponses.Event) => {
const event = OpenResponses.normalize(state, input)
const step = (state: OpenResponses.ParserState, event: OpenResponses.Event) => {
if (event.type === "response.reasoning_text.delta")
return event.item_id !== undefined
? Effect.succeed(OpenResponses.onReasoningDelta(state, event, event.item_id))
? Effect.succeed(
OpenResponses.onReasoningDelta(state, event, OpenResponses.outputItemID(state, event) ?? event.item_id),
)
: ProviderShared.eventError(ADAPTER, `${event.type} is missing item_id`)
if (event.type === "response.output_item.done" && event.item && ResponsesHostedTools.isItem(event.item, HOSTED_TOOLS))
return ResponsesHostedTools.onDone(state, event.item, HOSTED_TOOLS)
@@ -220,7 +204,7 @@ export const protocol = Protocol.make({
},
stream: {
event: OpenResponses.protocol.stream.event,
initial: (request) => OpenResponses.initial(request, adapter),
initial: (request) => OpenResponses.initial(request, extension),
step,
terminal: OpenResponses.terminal,
},
@@ -239,7 +223,6 @@ export const transport = channelTransport({
})
export const route = Route.make({
compact: ResponsesCompaction.make(adapter),
id: ADAPTER,
provider: "openai",
providerMetadataKey: "openai",
-25
View File
@@ -6,13 +6,11 @@ import { Headers, HttpClientRequest, HttpClientResponse } from "effect/unstable/
import {
InvalidProviderOutputError,
InvalidRequestError,
UnsupportedOperationError,
AIError,
HttpContext,
type ContentPart,
type LLMRequest,
type MediaPart,
type ProviderID,
type TextPart,
type ToolResultPart,
} from "../schema/index.js"
@@ -256,29 +254,6 @@ export const invalidRequest = (message: string, cause?: unknown) =>
reason: new InvalidRequestError({ message, cause }),
})
/**
* Canonical constructor for operations the selected route does not implement.
* Prefer this over `invalidRequest` when the failure is a missing route
* capability rather than a malformed caller input, so consumers can branch on
* `reason._tag` plus `reason.operation` instead of matching message text.
*/
export const unsupportedOperation = (input: {
readonly operation: string
readonly message: string
readonly provider?: ProviderID
readonly route?: string
readonly cause?: unknown
}) =>
new AIError({
reason: new UnsupportedOperationError({
operation: input.operation,
message: input.message,
provider: input.provider,
route: input.route,
cause: input.cause,
}),
})
export const imageResponse = Effect.fn("ProviderShared.imageResponse")(function* (
route: string,
name: string,
@@ -1,4 +1,4 @@
import { Effect, Encoding, Schema } from "effect"
import { Effect, Schema } from "effect"
import type { MediaPart } from "../../schema/index.js"
import { ProviderShared } from "../shared.js"
@@ -57,16 +57,6 @@ const documentBlock = (name: string, format: DocumentFormat, bytes: string): Doc
},
})
const mediaBase64 = Effect.fn("BedrockMedia.mediaBase64")(function* (part: MediaPart) {
const media = ProviderShared.normalizeMedia(part)
const bytes = yield* Effect.fromResult(Encoding.decodeBase64(media.base64)).pipe(
Effect.mapError((cause) =>
ProviderShared.invalidRequest("Bedrock Converse media data must be valid base64", cause),
),
)
return Encoding.encodeBase64(bytes)
})
// Route by MIME. Known image/document formats lower into a typed block; anything
// else fails with a clear error instead of silently degrading to a malformed
// document block. Image MIME types not in `IMAGE_FORMATS` (e.g. `image/svg+xml`)
@@ -76,7 +66,8 @@ export const lower = Effect.fn("BedrockMedia.lower")(function* (part: MediaPart)
const mime = part.mediaType.toLowerCase()
const imageFormat = IMAGE_FORMATS[mime as keyof typeof IMAGE_FORMATS]
if (imageFormat) {
return { image: { format: imageFormat, source: { bytes: yield* mediaBase64(part) } } } satisfies ImageBlock
const media = ProviderShared.normalizeMedia(part)
return { image: { format: imageFormat, source: { bytes: media.base64 } } } satisfies ImageBlock
}
if (mime.startsWith("image/"))
return yield* ProviderShared.invalidRequest(`Bedrock Converse does not support image media type ${part.mediaType}`)
@@ -84,7 +75,8 @@ export const lower = Effect.fn("BedrockMedia.lower")(function* (part: MediaPart)
if (documentFormat) {
if (!part.filename)
return yield* ProviderShared.invalidRequest("Bedrock Converse document media requires a filename")
return documentBlock(part.filename, documentFormat, yield* mediaBase64(part))
const media = ProviderShared.normalizeMedia(part)
return documentBlock(part.filename, documentFormat, media.base64)
}
return yield* ProviderShared.invalidRequest(`Bedrock Converse does not support media type ${part.mediaType}`)
})
@@ -1,174 +0,0 @@
import { Effect, Schema, Stream } from "effect"
import {
AIError,
InvalidProviderOutputError,
CompactionPart,
CompactionResponse,
HttpOptions,
LLMRequest,
Message,
type ContentPart,
mergeJsonRecords,
} from "../../schema/index.js"
import type { CompactOperation } from "../../route/client.js"
import { Endpoint } from "../../route/endpoint.js"
import { RequestExecutor } from "../../route/executor.js"
import { HttpTransport } from "../../route/transport/index.js"
import { OpenResponses } from "../open-responses.js"
import { JsonObject, optionalNull, ProviderShared } from "../shared.js"
const Body = Schema.Struct({
model: Schema.String,
input: Schema.Array(Schema.Unknown),
instructions: optionalNull(Schema.String),
previous_response_id: optionalNull(Schema.String),
service_tier: optionalNull(Schema.String),
prompt_cache_key: optionalNull(Schema.String),
prompt_cache_retention: optionalNull(Schema.String),
prompt_cache_options: optionalNull(
Schema.Struct({ mode: Schema.optional(Schema.String), ttl: Schema.optional(Schema.String) }),
),
})
const Text = Schema.Union([OpenResponses.OpenResponsesInputText, OpenResponses.OpenResponsesOutputText])
const File = Schema.Union([
Schema.Struct({
...OpenResponses.OpenResponsesInputFile.fields,
file_url: Schema.String,
file_data: Schema.optional(Schema.Never),
}),
Schema.Struct({
...OpenResponses.OpenResponsesInputFile.fields,
file_data: Schema.String,
file_url: Schema.optional(Schema.Never),
}),
])
const MessageFields = {
type: Schema.Literal("message"),
id: Schema.optional(Schema.String),
status: Schema.optional(Schema.String),
phase: Schema.optional(OpenResponses.MessagePhase),
}
const Response = Schema.Struct({
object: Schema.Literal("response.compaction"),
output: Schema.Array(
Schema.Union([
OpenResponses.CompactionItem,
OpenResponses.OpenResponsesReasoningItem,
Schema.Struct({
...MessageFields,
role: Schema.Literal("user"),
content: Schema.Array(Schema.Union([Text, OpenResponses.OpenResponsesInputImage, File])).check(
Schema.isMinLength(1),
),
}),
Schema.Struct({
...MessageFields,
role: Schema.Literal("assistant"),
content: Schema.Array(Text).check(Schema.isMinLength(1)),
}),
]),
),
usage: Schema.optional(Schema.StructWithRest(OpenResponses.OpenResponsesUsage, [JsonObject])),
})
export const make = (adapter: OpenResponses.ProviderAdapter): CompactOperation =>
Effect.fn("ResponsesCompaction.execute")(function* (request, executor, options) {
const route = request.model.route
const native = yield* OpenResponses.lowerConversation(request, adapter)
const body = yield* ProviderShared.validateWith(Schema.decodeUnknownEffect(Body))(
mergeJsonRecords(
{
...native,
service_tier: request.providerOptions?.serviceTier,
prompt_cache_key: ProviderShared.promptCacheKey(request),
},
request.http?.body,
),
)
const url = Endpoint.render(route.endpoint, { request, body: native })
url.pathname = `${url.pathname.replace(/\/$/, "")}/compact`
const parts = yield* HttpTransport.jsonRequestParts({
request: LLMRequest.update(request, {
http: request.http === undefined ? undefined : new HttpOptions({ ...request.http, body: undefined }),
}),
body,
endpoint: Endpoint.path(url.toString()),
auth: route.auth,
encodeBody: Schema.encodeSync(Schema.fromJsonString(Body)),
})
const response = yield* executor.execute(
ProviderShared.jsonPost({ url: parts.url, body: parts.bodyText, headers: parts.headers }),
options?.http,
)
const text = yield* RequestExecutor.responseStream(response).pipe(
Stream.decodeText(),
Stream.runFold(
() => "",
(text, chunk) => text + chunk,
),
)
const invalid = (message: string, cause?: unknown) =>
new AIError({
reason: new InvalidProviderOutputError({
route: route.id,
message,
body: text,
cause,
http: RequestExecutor.responseHttp(response),
}),
})
const result = yield* Schema.decodeUnknownEffect(Schema.fromJsonString(Response))(text).pipe(
Effect.mapError((cause) => invalid("Invalid compaction response", cause)),
)
if (!result.output.some((item) => item.type === "compaction"))
return yield* invalid("Compaction response did not contain a checkpoint")
return new CompactionResponse({
replacement: result.output.map((item) => toMessage(item, request.model)),
usage: OpenResponses.mapUsage(result.usage, OpenResponses.metadataKey(request.model)),
})
})
function toMessage(item: (typeof Response.Type.output)[number], model: LLMRequest["model"]): Message {
if (item.type === "compaction")
return Message.assistant(
CompactionPart.make({ provider: model.provider, id: item.id ?? undefined, encrypted: item.encrypted_content }),
)
const key = OpenResponses.metadataKey(model)
if (item.type === "reasoning") {
const summary = item.summary.length ? item.summary : [{ text: "" }]
return Message.assistant(
summary.map((part) => ({
type: "reasoning" as const,
text: part.text,
providerMetadata: { [key]: { itemId: item.id, reasoningEncryptedContent: item.encrypted_content } },
})),
)
}
return Message.make({
role: item.role,
providerMetadata: { [key]: { itemId: item.id, type: item.type, status: item.status, phase: item.phase } },
content: item.content.map((part): ContentPart => {
if (part.type === "input_text" || part.type === "output_text") return { type: "text", text: part.text }
if (part.type === "input_image")
return {
type: "media",
data: part.image_url,
mediaType: /^data:([^;,]+)/.exec(part.image_url)?.[1] ?? "image/*",
providerMetadata: part.detail === undefined ? undefined : { [key]: { detail: part.detail } },
}
const data = part.file_url === undefined ? part.file_data : part.file_url
return {
type: "media",
data,
filename: part.filename,
mediaType: /^data:([^;,]+)/.exec(data)?.[1] ?? "application/octet-stream",
providerMetadata: part.detail === undefined ? undefined : { [key]: { detail: part.detail } },
}
}),
})
}
export * as ResponsesCompaction from "./responses-compaction.js"
@@ -3,7 +3,8 @@ import { LLMEvent, type AIError, type ToolResultPart } from "../../schema/index.
import { OpenResponses } from "../open-responses.js"
import { Lifecycle } from "./lifecycle.js"
export type Item = OpenResponses.OutputItem & {
export type Item = OpenResponses.StreamItem & {
readonly id: string
readonly status?: string
readonly action?: unknown
readonly queries?: unknown
@@ -26,8 +27,8 @@ export interface Definition {
export type Definitions = Readonly<Record<string, Definition>>
export const isItem = <Tools extends Definitions>(item: OpenResponses.OutputItem, tools: Tools): item is Item =>
item.type in tools
export const isItem = <Tools extends Definitions>(item: OpenResponses.StreamItem, tools: Tools): item is Item =>
item.type in tools && typeof item.id === "string" && item.id.length > 0
export const onDone: (
state: OpenResponses.ParserState,
+6 -17
View File
@@ -4,7 +4,6 @@ import type { LLMRequest } from "../schema/index.js"
import { OpenResponses } from "./open-responses.js"
import { JsonObject, optionalNull, ProviderShared } from "./shared.js"
import { ResponsesHostedTools } from "./utils/responses-hosted-tools.js"
import { ResponsesCompaction } from "./utils/responses-compaction.js"
const ADAPTER = "xai-responses"
const NAME = "xAI Responses"
@@ -37,22 +36,15 @@ const XAIResponsesBody = Schema.Struct({
stream: Schema.Literal(true),
})
const adapter = {
const extension = {
id: ADAPTER,
name: NAME,
restoreHostedToolItem: (item: unknown) => (Schema.is(XAIResponsesHostedToolItem)(item) ? item : undefined),
} satisfies OpenResponses.ProviderAdapter
lowerHostedToolItem: (item: unknown) => (Schema.is(XAIResponsesHostedToolItem)(item) ? item : undefined),
} satisfies OpenResponses.Extension
const decodeBody = ProviderShared.validateWith(Schema.decodeUnknownEffect(XAIResponsesBody))
const fromRequest = Effect.fn("XAIResponses.fromRequest")(function* (request: LLMRequest) {
if (request.providerOptions?.contextManagement !== undefined)
return yield* ProviderShared.unsupportedOperation({
operation: "in-band-compaction",
provider: request.model.provider,
route: request.model.route.id,
message: "xAI requires explicit compaction through LLMClient.compact; automatic context management is not supported",
})
return yield* decodeBody(yield* OpenResponses.fromRequestWithAdapter(request, adapter))
return yield* decodeBody(yield* OpenResponses.fromRequestWithExtension(request, extension))
})
const HOSTED_TOOLS = {
@@ -72,8 +64,7 @@ const HOSTED_TOOLS = {
// Grok speaks the standard Responses reasoning dialect (`reasoning_summary_text.*`,
// handled by the baseline); only its hosted tool vocabulary differs.
const step = (state: OpenResponses.ParserState, input: OpenResponses.Event) => {
const event = OpenResponses.normalize(state, input)
const step = (state: OpenResponses.ParserState, event: OpenResponses.Event) => {
if (event.type === "response.output_item.done" && event.item && ResponsesHostedTools.isItem(event.item, HOSTED_TOOLS))
return ResponsesHostedTools.onDone(state, event.item, HOSTED_TOOLS)
return OpenResponses.step(state, event)
@@ -87,12 +78,10 @@ export const protocol = Protocol.make({
},
stream: {
event: OpenResponses.protocol.stream.event,
initial: (request) => OpenResponses.initial(request, adapter),
initial: (request) => OpenResponses.initial(request, extension),
step,
terminal: OpenResponses.terminal,
},
})
export const compact = ResponsesCompaction.make(adapter)
export * as XAIResponses from "./xai-responses.js"
+1 -3
View File
@@ -1,5 +1,4 @@
import type { LanguageModel, ProviderOptions } from "./schema/index.js"
import type { CompactOperation } from "./route/client.js"
export interface Settings extends Readonly<Record<string, unknown>> {
readonly baseURL?: string
@@ -10,9 +9,8 @@ export interface Settings extends Readonly<Record<string, unknown>> {
export interface Definition<
ProviderSettings extends Settings = Settings,
Options extends ProviderOptions = ProviderOptions,
Compact extends CompactOperation | undefined = CompactOperation | undefined,
> {
readonly model: (modelID: string, settings: ProviderSettings) => LanguageModel<Options, Compact>
readonly model: (modelID: string, settings: ProviderSettings) => LanguageModel<Options>
}
export * as ProviderPackage from "./provider-package.js"
+6 -11
View File
@@ -1,7 +1,7 @@
import { Headers } from "effect/unstable/http"
import { Auth } from "../route/auth.js"
import { type AtLeastOne, type ProviderAuthOption } from "../route/auth-options.js"
import type { Route, RouteDefaultsInput, CompactOperation } from "../route/client.js"
import type { Route as RouteDef, RouteDefaultsInput } from "../route/client.js"
import type { ProviderPackage } from "../provider-package.js"
import { ProviderID, type ModelID } from "../schema/index.js"
import * as OpenAIChat from "../protocols/openai-chat.js"
@@ -102,11 +102,7 @@ const auth = (input: Config) => {
)
}
const configuredRoute = <Body, Prepared, Compact extends CompactOperation | undefined>(
route: Route<Body, Prepared, Compact>,
input: Config,
modelID: string | ModelID,
) =>
const configuredRoute = <Body, Prepared>(route: RouteDef<Body, Prepared>, input: Config, modelID: string | ModelID) =>
route.with({
auth: auth(input),
endpoint: endpoint(input, modelID),
@@ -165,11 +161,10 @@ const config = (settings: Settings): Config => {
throw new Error("Azure requires resourceName or baseURL")
}
export const responsesModel: ProviderPackage.Definition<
Settings,
OpenAIProviderOptionsInput,
CompactOperation
>["model"] = (modelID, settings) => configure(config(settings)).responses(modelID)
export const responsesModel: ProviderPackage.Definition<Settings, OpenAIProviderOptionsInput>["model"] = (
modelID,
settings,
) => configure(config(settings)).responses(modelID)
export const chatModel: ProviderPackage.Definition<Settings, OpenAIProviderOptionsInput>["model"] = (
modelID,
settings,
@@ -57,9 +57,7 @@ const route = Route.make({
}),
endpoint: Endpoint.path(({ request }) => `/${request.model.id}:streamRawPredict`),
auth: Auth.none,
transport: AnthropicMessages.transport<
Omit<AnthropicMessages.AnthropicMessagesBody, "model"> & { readonly anthropic_version: typeof VERSION }
>(),
framing: AnthropicMessages.framing,
headers: () => ({ "anthropic-version": HEADER_VERSION }),
})
-1
View File
@@ -13,7 +13,6 @@ export * as GoogleVertexChat from "./google-vertex-chat.js"
export * as GoogleVertexMessages from "./google-vertex-messages.js"
export * as GoogleVertexResponses from "./google-vertex-responses.js"
export * as Groq from "./groq.js"
export * as Mistral from "./mistral.js"
export * as OpenAI from "./openai.js"
export * as OpenAICompatible from "./openai-compatible.js"
export * as OpenAICompatibleResponses from "./openai-compatible-responses.js"
-51
View File
@@ -1,51 +0,0 @@
import type { ProviderPackage } from "../provider-package.js"
import { MistralChat } from "../protocols/mistral-chat.js"
import { AuthOptions, type ProviderAuthOption } from "../route/auth-options.js"
import type { RouteDefaultsInput } from "../route/client.js"
import { ProviderID, type ModelID } from "../schema/index.js"
export const id = ProviderID.make("mistral")
export type ProviderOptions = MistralChat.ProviderOptionsInput
export type LanguageModelOptions = Omit<RouteDefaultsInput, "providerOptions"> &
ProviderAuthOption<"optional"> & {
readonly baseURL?: string
readonly providerOptions?: ProviderOptions
}
export interface Settings extends ProviderPackage.Settings {
readonly apiKey?: string
readonly baseURL?: string
readonly providerOptions?: ProviderOptions
}
export const route = MistralChat.route
export const routes = [route]
export const configure = (input: LanguageModelOptions = {}) => {
const { apiKey: _apiKey, auth: _auth, baseURL, ...defaults } = input
const configured = route.with({
...defaults,
endpoint: { baseURL: baseURL ?? MistralChat.DEFAULT_BASE_URL },
auth: AuthOptions.bearer(input, "MISTRAL_API_KEY"),
})
return {
id,
model: (modelID: string | ModelID) => configured.model<ProviderOptions>({ id: modelID }),
configure,
}
}
export const provider = configure()
export const model: ProviderPackage.Definition<Settings, ProviderOptions>["model"] = (modelID, settings) =>
configure({
apiKey: settings.apiKey,
baseURL: settings.baseURL,
headers: settings.headers === undefined ? undefined : { ...settings.headers },
http: settings.body === undefined ? undefined : { body: { ...settings.body } },
providerOptions: settings.providerOptions,
}).model(modelID)
export * as Mistral from "./mistral.js"
@@ -1,13 +1,10 @@
import { mergeProviderOptions, type ProviderOptions } from "../schema/index.js"
import type { OpenAIServiceTier } from "../protocols/utils/openai-options.js"
import type { Options } from "../protocols/utils/open-responses-options.js"
import type { ContextManagement } from "../protocols/openai-responses.js"
export type { OpenAIResponseIncludable, OpenAIServiceTier } from "../protocols/utils/openai-options.js"
export type OpenAIOptionsInput = Omit<Options, "serviceTier"> & {
/** Advanced in-band compaction. The caller owns checkpoint persistence and recovery. */
readonly contextManagement?: ContextManagement
readonly serviceTier?: OpenAIServiceTier
readonly [key: string]: unknown
}
+3 -9
View File
@@ -1,5 +1,5 @@
import { AuthOptions, type ProviderAuthOption } from "../route/auth-options.js"
import type { Route, RouteDefaultsInput, CompactOperation } from "../route/client.js"
import type { Route, RouteDefaultsInput } from "../route/client.js"
import type { ProviderPackage } from "../provider-package.js"
import { HttpOptions, ProviderID, ToolDefinition, mergeHttpOptions, type ModelID } from "../schema/index.js"
import * as OpenAIChat from "../protocols/openai-chat.js"
@@ -73,10 +73,7 @@ const defaults = (input: Config) => {
return rest
}
const configuredRoute = <Body, Prepared, Compact extends CompactOperation | undefined>(
route: Route<Body, Prepared, Compact>,
input: Config,
) =>
const configuredRoute = <Body, Prepared>(route: Route<Body, Prepared>, input: Config) =>
route.with({
auth: auth(input),
endpoint: { baseURL: input.baseURL, query: input.queryParams },
@@ -132,10 +129,7 @@ const config = (settings: Settings): Config => {
}
}
export const model: ProviderPackage.Definition<Settings, OpenAIProviderOptionsInput, CompactOperation>["model"] = (
modelID,
settings,
) => {
export const model: ProviderPackage.Definition<Settings, OpenAIProviderOptionsInput>["model"] = (modelID, settings) => {
return configure(config(settings)).responses(modelID)
}
+3 -7
View File
@@ -1,5 +1,5 @@
import { AuthOptions, type ProviderAuthOption } from "../route/auth-options.js"
import { Route, type RouteDefaultsInput, type CompactOperation } from "../route/client.js"
import { Route, type RouteDefaultsInput } from "../route/client.js"
import { Endpoint } from "../route/endpoint.js"
import { HttpOptions, ProviderID, type ModelID } from "../schema/index.js"
import * as OpenAICompatibleProfiles from "./openai-compatible-profile.js"
@@ -13,7 +13,7 @@ import type { ProviderPackage } from "../provider-package.js"
export const id = ProviderID.make("xai")
export type XAIProviderOptionsInput = OpenAIOptionsInput & { readonly contextManagement?: never }
export type XAIProviderOptionsInput = OpenAIOptionsInput
export type LanguageModelOptions = Omit<RouteDefaultsInput, "providerOptions"> &
ProviderAuthOption<"optional"> & {
@@ -32,7 +32,6 @@ export type { XAIImageOptions } from "../protocols/xai-images.js"
const RESPONSES_WEBSOCKET_ROTATE_AFTER_MS = 24 * 60 * 1000
const responsesRoute = Route.make({
compact: XAIResponses.compact,
id: "openai-responses",
provider: id,
providerMetadataKey: "xai",
@@ -103,10 +102,7 @@ export const configure = (input: LanguageModelOptions = {}) => {
}
export const provider = configure()
export const model: ProviderPackage.Definition<Settings, XAIProviderOptionsInput, CompactOperation>["model"] = (
modelID,
settings,
) =>
export const model: ProviderPackage.Definition<Settings, XAIProviderOptionsInput>["model"] = (modelID, settings) =>
configure({
apiKey: settings.apiKey,
baseURL: settings.baseURL,
+20 -97
View File
@@ -7,13 +7,11 @@ import { HttpTransport } from "./transport/index.js"
import type { HttpMiddleware, Transport, TransportRuntime, WebSocketChannelExecutor } from "./transport/index.js"
import type { Protocol } from "./protocol.js"
import { applyCachePolicy } from "../cache-policy.js"
import { normalizeToolHistory } from "../tool-history.js"
import { sanitizeSurrogates } from "../utils/sanitize.js"
import * as ProviderShared from "../protocols/shared.js"
import type { ProtocolID, ProviderOptions } from "../schema/index.js"
import {
AIError,
CompactionResponse,
AIErrorReason,
GenerationOptions,
HttpOptions,
@@ -35,12 +33,7 @@ export interface RouteBody<Body> {
readonly from: (request: LLMRequest) => Effect.Effect<Body, AIError>
}
export interface Route<
Body,
Prepared = unknown,
Compact extends CompactOperation | undefined = CompactOperation | undefined,
> {
readonly compact: Compact
export interface Route<Body, Prepared = unknown> {
readonly id: string
readonly provider?: ProviderID
/** ProviderMetadata namespace emitted and consumed by this route. */
@@ -48,15 +41,13 @@ export interface Route<
readonly protocol: ProtocolID
readonly endpoint: Endpoint.Definition<Body>
readonly auth: Auth.Definition
/** Deployment headers resolved once for every operation, before transport authentication. */
readonly headers?: (input: { readonly request: LLMRequest }) => Record<string, string>
readonly transport: Transport<Body, Prepared, unknown>
readonly defaults: RouteDefaults
readonly body: RouteBody<Body>
readonly with: (patch: RoutePatch<Body, Prepared>) => Route<Body, Prepared, Compact>
readonly with: (patch: RoutePatch<Body, Prepared>) => Route<Body, Prepared>
readonly model: <Options extends ProviderOptions = ProviderOptions>(
input: RouteMappedLanguageModelInput,
) => LanguageModel<Options, Compact>
) => LanguageModel<Options>
readonly prepareTransport: (
body: Body,
request: LLMRequest,
@@ -74,11 +65,7 @@ export interface Route<
// Normal call sites use `OpenAIChat.route`; callers only need body types
// when preparing a request with a protocol-specific type assertion.
// oxlint-disable-next-line typescript-eslint/no-explicit-any
export type AnyRoute<Compact extends CompactOperation | undefined = CompactOperation | undefined> = Route<
any,
any,
Compact
>
export type AnyRoute = Route<any, any>
export type HttpOptionsInput = HttpOptions.Input
@@ -111,15 +98,15 @@ export interface RoutePatch<Body, Prepared> extends RouteDefaultsInput {
type RouteMappedLanguageModelInput = RouteLanguageModelInput | RouteRoutedLanguageModelInput
const makeRouteLanguageModel = <Options extends ProviderOptions, Compact extends CompactOperation | undefined>(
route: AnyRoute<Compact>,
const makeRouteLanguageModel = <Options extends ProviderOptions = ProviderOptions>(
route: AnyRoute,
mapped: RouteMappedLanguageModelInput,
) => {
const provider = route.provider ?? ("provider" in mapped ? mapped.provider : undefined)
if (!provider) throw new Error(`Route.model(${route.id}) requires a provider`)
if (!endpointBaseURL(route.endpoint))
throw new Error(`Route.model(${route.id}) requires an endpoint baseURL — configure it on the route first`)
return LanguageModel.make<Options, Compact>({
return LanguageModel.make<Options>({
...mapped,
provider,
route,
@@ -162,10 +149,6 @@ export const httpOptions = (input: HttpOptionsInput | undefined) => {
}
export interface Interface {
readonly compact: (
request: CompactionRequest,
options?: Pick<StreamOptions, "http">,
) => Effect.Effect<CompactionResponse, AIError>
readonly stream: StreamMethod
readonly generate: GenerateMethod
}
@@ -183,40 +166,24 @@ export interface GenerateMethod {
(request: LLMRequest, options?: StreamOptions): Effect.Effect<LLMResponse, AIError>
}
export type CompactOperation = (
request: LLMRequest,
executor: RequestExecutor.Interface,
options?: Pick<StreamOptions, "http">,
) => Effect.Effect<CompactionResponse, AIError>
export type CompactionRequest = LLMRequest & {
readonly model: LanguageModel<ProviderOptions, CompactOperation>
}
export const canCompact = (request: LLMRequest): request is CompactionRequest =>
request.model.route.compact !== undefined
export class Service extends Context.Service<Service, Interface>()("@opencode/LLMClient") {}
const resolveRequestOptions = (request: LLMRequest) => {
const messages = normalizeToolHistory(request.messages)
const normalized = messages === request.messages ? request : LLMRequest.update(request, { messages })
const routeDefaults = normalized.model.route.defaults
const modelDefaults = normalized.model.defaults
const generation = mergeGenerationOptions(routeDefaults.generation, modelDefaults?.generation, normalized.generation)
return LLMRequest.update(normalized, {
const routeDefaults = request.model.route.defaults
const modelDefaults = request.model.defaults
const generation = mergeGenerationOptions(routeDefaults.generation, modelDefaults?.generation, request.generation)
return LLMRequest.update(request, {
generation: generation ?? new GenerationOptions({}),
providerOptions: mergeProviderOptions(
routeDefaults.providerOptions,
modelDefaults?.providerOptions,
normalized.providerOptions,
request.providerOptions,
),
http: mergeHttpOptions(routeDefaults.http, modelDefaults?.http, normalized.http),
http: mergeHttpOptions(routeDefaults.http, modelDefaults?.http, request.http),
})
}
export interface MakeInput<Body, Frame, Event, State> {
readonly compact?: CompactOperation
/** Route id used in diagnostics and prepared request metadata. */
readonly id: string
/** Provider identity for route-owned model construction. */
@@ -238,7 +205,6 @@ export interface MakeInput<Body, Frame, Event, State> {
}
export interface MakeTransportInput<Body, Prepared, Frame, Event, State> {
readonly compact?: CompactOperation
/** Route id used in diagnostics and prepared request metadata. */
readonly id: string
/** Provider identity for route-owned model construction. */
@@ -314,14 +280,12 @@ function makeFromTransport<Body, Prepared, Frame, Event, State>(
const build = (routeInput: BuiltRouteInput): Route<Body, Prepared> => {
const route: Route<Body, Prepared> = {
compact: routeInput.compact,
id: routeInput.id,
provider: routeInput.provider === undefined ? undefined : ProviderID.make(routeInput.provider),
providerMetadataKey: routeInput.providerMetadataKey,
protocol: protocol.id,
endpoint: routeInput.endpoint,
auth: routeInput.auth ?? Auth.none,
headers: routeInput.headers,
transport: routeInput.transport,
defaults: routeInput.defaults ?? {},
body: protocol.body,
@@ -343,7 +307,7 @@ function makeFromTransport<Body, Prepared, Frame, Event, State>(
})
},
model: <Options extends ProviderOptions = ProviderOptions>(input: RouteMappedLanguageModelInput) =>
makeRouteLanguageModel<Options, CompactOperation | undefined>(route, input),
makeRouteLanguageModel<Options>(route, input),
prepareTransport: (body, request, options) =>
routeInput.transport.prepare({
body,
@@ -351,6 +315,7 @@ function makeFromTransport<Body, Prepared, Frame, Event, State>(
endpoint: routeInput.endpoint,
auth: routeInput.auth ?? Auth.none,
encodeBody,
headers: routeInput.headers,
middleware: options?.http,
webSocket: options?.webSocket,
}),
@@ -440,12 +405,6 @@ function makeFromTransport<Body, Prepared, Frame, Event, State>(
return build({ ...input, defaults: mergeRouteDefaults(undefined, input.defaults ?? {}) })
}
export function make<Body, Prepared, Frame, Event, State>(
input: MakeTransportInput<Body, Prepared, Frame, Event, State> & { readonly compact: CompactOperation },
): Route<Body, Prepared, CompactOperation>
export function make<Body, Frame, Event, State>(
input: MakeInput<Body, Frame, Event, State> & { readonly compact: CompactOperation },
): Route<Body, HttpTransport.HttpPrepared<Frame>, CompactOperation>
export function make<Body, Prepared, Frame, Event, State>(
input: MakeTransportInput<Body, Prepared, Frame, Event, State>,
): Route<Body, Prepared>
@@ -473,7 +432,6 @@ export function make<Body, Prepared, Frame, Event, State>(
if ("transport" in input) return makeFromTransport(input)
const protocol = input.protocol
return makeFromTransport({
compact: input.compact,
id: input.id,
provider: input.provider,
providerMetadataKey: input.providerMetadataKey,
@@ -486,19 +444,9 @@ export function make<Body, Prepared, Frame, Event, State>(
})
}
const prepareRequest = (request: LLMRequest) => {
const original = applyCachePolicy(resolveRequestOptions(request))
const sanitized = LLMRequest.update(original, sanitizeSurrogates({ ...LLMRequest.input(original), model: undefined }))
const tools = [...new Map(sanitized.tools.map((tool) => [tool.name, tool])).values()]
const resolved = tools.length === sanitized.tools.length ? sanitized : LLMRequest.update(sanitized, { tools })
const headers = resolved.model.route.headers?.({ request: resolved })
return headers === undefined
? resolved
: LLMRequest.update(resolved, { http: mergeHttpOptions(new HttpOptions({ headers }), resolved.http) })
}
const compile = Effect.fn("LLM.compile")(function* (request: LLMRequest, options?: StreamOptions) {
const resolved = prepareRequest(request)
const original = applyCachePolicy(resolveRequestOptions(request))
const resolved = LLMRequest.update(original, sanitizeSurrogates({ ...LLMRequest.input(original), model: undefined }))
const route = resolved.model.route
const body = yield* route.body
@@ -557,15 +505,6 @@ export function generate(request: LLMRequest, options?: StreamOptions): Effect.E
})
}
export const compact = (
request: CompactionRequest,
options?: Pick<StreamOptions, "http">,
): Effect.Effect<CompactionResponse, AIError, Service> =>
Effect.gen(function* () {
const client = yield* Service
return yield* client.compact(request, options)
})
export const streamRequest = (request: LLMRequest, options?: StreamOptions) =>
Stream.unwrap(
Effect.gen(function* () {
@@ -576,32 +515,16 @@ export const streamRequest = (request: LLMRequest, options?: StreamOptions) =>
export const layer: Layer.Layer<Service, never, RequestExecutor.Service> = Layer.effect(
Service,
Effect.gen(function* () {
const executor = yield* RequestExecutor.Service
const stream = streamRequestWith({ http: executor })
return Service.of({
stream,
generate: generateWith(stream),
compact: (request, options) =>
Effect.suspend(() => {
const operation = request.model.route.compact
if (!operation)
return ProviderShared.unsupportedOperation({
operation: "compact",
provider: request.model.provider,
route: request.model.route.id,
message: `${request.model.provider}/${request.model.route.id} does not support explicit compaction`,
})
return operation(prepareRequest(request), executor, options)
}),
const stream = streamRequestWith({
http: yield* RequestExecutor.Service,
})
return Service.of({ stream, generate: generateWith(stream) })
}),
)
export const Route = { make } as const
export const LLMClient = {
canCompact,
compact,
Service,
layer,
stream,
-16
View File
@@ -35,21 +35,6 @@ export class InvalidRequestError extends Schema.TaggedError<InvalidRequestError>
},
) {}
/**
* A caller-requested operation the selected route does not implement, such as
* explicit compaction on a route without a compact endpoint. Detected locally
* before any network I/O, so unlike transport or provider-output failures it
* never carries HTTP context from a provider round-trip.
*/
export class UnsupportedOperationError extends Schema.TaggedError<UnsupportedOperationError>(
"AI.Error.UnsupportedOperation",
)("UnsupportedOperation", {
...ReasonFields,
operation: Schema.String,
provider: Schema.optional(ProviderID),
route: Schema.optional(RouteID),
}) {}
export class NoRouteError extends Schema.TaggedError<NoRouteError>("AI.Error.NoRoute")("NoRoute", {
...ReasonFields,
route: RouteID,
@@ -122,7 +107,6 @@ export class UnknownProviderError extends Schema.TaggedError<UnknownProviderErro
export const AIErrorReason = Schema.Union([
InvalidRequestError,
UnsupportedOperationError,
NoRouteError,
AuthenticationError,
RateLimitError,
+4 -18
View File
@@ -3,7 +3,6 @@ import { LLM } from "@opencode-ai/schema/llm"
import { ContentBlockID, ToolCallID } from "./ids.js"
import {
Message,
CompactionPart,
ProviderMetadata,
ToolCallPart,
ToolOutput,
@@ -63,8 +62,6 @@ export { ProviderMetadata } from "./messages.js"
* Matches the same escape-hatch field on `LLMEvent`.
*/
export class Usage extends Schema.Class<Usage>("AI.Usage")({
/** Effective input size of the final message iteration, when reported; not billed totals. */
contextTokens: Schema.optional(Schema.Number),
inputTokens: Schema.optional(Schema.Number),
outputTokens: Schema.optional(Schema.Number),
nonCachedInputTokens: Schema.optional(Schema.Number),
@@ -75,7 +72,7 @@ export class Usage extends Schema.Class<Usage>("AI.Usage")({
providerMetadata: Schema.optional(ProviderMetadata),
}) {
/**
* Non-reasoning output tokens (including compaction summaries) — `outputTokens` minus `reasoningTokens`, clamped
* Visible output tokens — `outputTokens` minus `reasoningTokens`, clamped
* to zero. The one place subtraction happens in this contract; the clamp
* means a provider reporting `reasoningTokens > outputTokens` produces a
* harmless zero rather than a negative that crashes downstream schemas.
@@ -91,12 +88,6 @@ export class Usage extends Schema.Class<Usage>("AI.Usage")({
export type UsageInput = Usage | ConstructorParameters<typeof Usage>[0]
/** A replacement context window, not an assistant message to append to prior history. */
export class CompactionResponse extends Schema.Class<CompactionResponse>("LLM.CompactionResponse")({
replacement: Schema.Array(Message),
usage: Schema.optional(Usage),
}) {}
export const StepStart = Schema.Struct({
type: Schema.tag("step-start"),
index: Schema.Number,
@@ -250,7 +241,6 @@ export const ProviderErrorEvent = Schema.Struct({
export type ProviderErrorEvent = Schema.Schema.Type<typeof ProviderErrorEvent>
const llmEventTagged = Schema.Union([
CompactionPart,
StepStart,
TextStart,
TextDelta,
@@ -284,7 +274,6 @@ const toolCallID = (value: ToolCallID | string) => ToolCallID.make(value)
* `events.filter(LLMEvent.guards["tool-call"])`.
*/
export const LLMEvent = Object.assign(llmEventTagged, {
compaction: CompactionPart.make,
stepStart: StepStart.make,
textStart: (input: WithID<TextStart, ContentBlockID>) => TextStart.make({ ...input, id: contentBlockID(input.id) }),
textDelta: (input: WithID<TextDelta, ContentBlockID>) => TextDelta.make({ ...input, id: contentBlockID(input.id) }),
@@ -322,7 +311,6 @@ export const LLMEvent = Object.assign(llmEventTagged, {
}),
providerError: ProviderErrorEvent.make,
is: {
compaction: llmEventTagged.guards.compaction,
stepStart: llmEventTagged.guards["step-start"],
textStart: llmEventTagged.guards["text-start"],
textDelta: llmEventTagged.guards["text-delta"],
@@ -345,10 +333,10 @@ export const LLMEvent = Object.assign(llmEventTagged, {
export type LLMEvent = Schema.Schema.Type<typeof llmEventTagged>
/** Joins deltas per fragment, letting an authoritative end value replace that fragment's accumulated deltas. */
const joinFragments = (
const joinFragments = <Delta extends { id: string; text: string }, End extends { id: string; text?: string }>(
events: ReadonlyArray<LLMEvent>,
isDelta: (event: LLMEvent) => event is LLMEvent & { id: string; text: string },
isEnd: (event: LLMEvent) => event is LLMEvent & { id: string; text?: string },
isDelta: (event: LLMEvent) => event is Extract<LLMEvent, Delta>,
isEnd: (event: LLMEvent) => event is Extract<LLMEvent, End>,
) => {
const order: string[] = []
const parts = new Map<string, string>()
@@ -575,8 +563,6 @@ const reduceToolCall = (state: ResponseState, event: ToolCall): ResponseState =>
const reduceResponseState = (state: ResponseState, event: LLMEvent): ResponseState => {
const next = appendEvent(state, event)
switch (event.type) {
case "compaction":
return appendContent(next, event)
case "text-start":
return ensureText(next, event.id, event.providerMetadata)
case "text-delta":
+14 -67
View File
@@ -7,10 +7,9 @@ import {
HttpOptions,
JsonSchema,
LanguageModelSchema,
type LanguageModel,
ProviderOptions,
} from "./options.js"
import { ProviderID } from "./ids.js"
import { isRecord } from "../utils/record.js"
export const MessageRole = Schema.Literals(["system", "user", "assistant", "tool"])
export type MessageRole = Schema.Schema.Type<typeof MessageRole>
@@ -54,10 +53,14 @@ export const MediaPart = Schema.Struct({
filename: Schema.optional(Schema.String),
cache: Schema.optional(CacheHint),
metadata: Schema.optional(Schema.Record(Schema.String, Schema.Unknown)),
providerMetadata: Schema.optional(ProviderMetadata),
}).annotate({ identifier: "LLM.Content.Media" })
export type MediaPart = Schema.Schema.Type<typeof MediaPart>
const isToolResultValue = (value: unknown): value is ToolResultValue =>
isRecord(value) &&
(value.type === "text" || value.type === "json" || value.type === "error" || value.type === "content") &&
"value" in value
const toolResultValueSchema = Schema.Union([
Schema.Struct({
type: Schema.Literal("json"),
@@ -77,7 +80,6 @@ const toolResultValueSchema = Schema.Union([
}),
]).annotate({ identifier: "LLM.ToolResult" })
export type ToolResultValue = Schema.Schema.Type<typeof toolResultValueSchema>
const isToolResultValue = Schema.is(toolResultValueSchema)
export const ToolResultValue = Object.assign(toolResultValueSchema, {
is: isToolResultValue,
@@ -188,40 +190,9 @@ export const ReasoningPart = Schema.Struct({
}).annotate({ identifier: "LLM.Content.Reasoning" })
export type ReasoningPart = Schema.Schema.Type<typeof ReasoningPart>
/** A provider-generated context checkpoint, distinct from visible assistant text. */
type CompactionContent =
| { readonly encrypted: string; readonly text?: never }
| { readonly text: string | null; readonly encrypted?: never }
const compactionPartSchema = Schema.Struct({
type: Schema.Literal("compaction"),
provider: ProviderID,
id: Schema.optional(Schema.String),
encrypted: Schema.optional(Schema.String),
/** Null means the provider failed to produce a summary; prior history must be retained. */
text: Schema.optional(Schema.NullOr(Schema.String)),
})
.pipe(
Schema.refine(
(part): part is typeof part & CompactionContent => (part.encrypted !== undefined) !== (part.text !== undefined),
{ message: "Compaction requires either encrypted content or a summary" },
),
)
.annotate({ identifier: "LLM.Content.Compaction" })
export type CompactionPart = typeof compactionPartSchema.Type
export const CompactionPart = Object.assign(compactionPartSchema, {
make: (input: Omit<CompactionPart, "type" | "encrypted" | "text"> & CompactionContent): CompactionPart =>
Schema.decodeUnknownSync(compactionPartSchema)({ type: "compaction", ...input }),
})
export const ContentPart = Schema.Union([
TextPart,
MediaPart,
ToolCallPart,
ToolResultPart,
ReasoningPart,
CompactionPart,
]).pipe(Schema.toTaggedUnion("type"))
export const ContentPart = Schema.Union([TextPart, MediaPart, ToolCallPart, ToolResultPart, ReasoningPart]).pipe(
Schema.toTaggedUnion("type"),
)
export type ContentPart = Schema.Schema.Type<typeof ContentPart>
export class Message extends Schema.Class<Message>("LLM.Message")({
@@ -229,7 +200,6 @@ export class Message extends Schema.Class<Message>("LLM.Message")({
role: MessageRole,
content: Schema.Array(ContentPart),
metadata: Schema.optional(Schema.Record(Schema.String, Schema.Unknown)),
providerMetadata: Schema.optional(ProviderMetadata),
native: Schema.optional(Schema.Record(Schema.String, Schema.Unknown)),
}) {}
@@ -307,7 +277,7 @@ export namespace ToolChoice {
}
}
const requestSchema = Schema.Struct({
export class LLMRequest extends Schema.Class<LLMRequest>("LLM.Request")({
id: Schema.optional(Schema.String),
model: LanguageModelSchema,
system: Schema.Array(SystemPart),
@@ -321,26 +291,12 @@ const requestSchema = Schema.Struct({
// Stable cache affinity for protocols that support provider-managed prompt caching.
promptCacheKey: Schema.optional(Schema.String),
metadata: Schema.optional(Schema.Record(Schema.String, Schema.Unknown)),
})
export class LLMRequest<Model extends LanguageModel = LanguageModel> extends Schema.Class<LLMRequest>("LLM.Request")(
requestSchema.fields,
) {
declare readonly model: Model
// Preserve model inference instead of inheriting the schema's erased constructor signature.
// oxlint-disable-next-line no-useless-constructor
constructor(input: LLMRequest.Input<Model>) {
super(input)
}
}
}) {}
export namespace LLMRequest {
export type Input<Model extends LanguageModel = LanguageModel> = Omit<typeof requestSchema.Type, "model"> & {
readonly model: Model
}
export type Input = ConstructorParameters<typeof LLMRequest>[0]
export const input = <Model extends LanguageModel>(request: LLMRequest<Model>): Input<Model> => ({
export const input = (request: LLMRequest): Input => ({
id: request.id,
model: request.model,
system: request.system,
@@ -355,16 +311,7 @@ export namespace LLMRequest {
metadata: request.metadata,
})
export function update<Model extends LanguageModel>(
request: LLMRequest,
patch: Partial<Input<Model>> & { readonly model: Model },
): LLMRequest<Model>
export function update<Model extends LanguageModel>(
request: LLMRequest<Model>,
patch: Partial<Omit<Input, "model">> & { readonly model?: undefined },
): LLMRequest<Model>
export function update(request: LLMRequest, patch: Partial<Input>): LLMRequest
export function update(request: LLMRequest, patch: Partial<Input>) {
export const update = (request: LLMRequest, patch: Partial<Input>) => {
if (Object.keys(patch).length === 0) return request
return new LLMRequest({
...input(request),
+11 -35
View File
@@ -1,6 +1,6 @@
import { Schema } from "effect"
import { ModelID, ProviderID } from "./ids.js"
import type { AnyRoute, CompactOperation } from "../route/client.js"
import type { AnyRoute } from "../route/client.js"
import { isRecord } from "../utils/record.js"
export const JsonSchema = Schema.Record(Schema.String, Schema.Unknown)
@@ -173,18 +173,15 @@ export namespace LanguageModelCompatibility {
input instanceof LanguageModelCompatibility ? input : new LanguageModelCompatibility(input)
}
export class LanguageModel<
Options extends ProviderOptions = ProviderOptions,
Compact extends CompactOperation | undefined = CompactOperation | undefined,
> {
export class LanguageModel<Options extends ProviderOptions = ProviderOptions> {
declare protected readonly _ProviderOptions: Options
readonly id: ModelID
readonly provider: ProviderID
readonly route: AnyRoute<Compact>
readonly route: AnyRoute
readonly defaults?: LanguageModelDefaults
readonly compatibility?: LanguageModelCompatibility
constructor(input: LanguageModel.ConstructorInput<Compact>) {
constructor(input: LanguageModel.ConstructorInput) {
this.id = input.id
this.provider = input.provider
this.route = input.route
@@ -192,11 +189,8 @@ export class LanguageModel<
this.compatibility = input.compatibility
}
static make<
Options extends ProviderOptions = ProviderOptions,
Compact extends CompactOperation | undefined = CompactOperation | undefined,
>(input: LanguageModel.Input<Compact>) {
return new LanguageModel<Options, Compact>({
static make<Options extends ProviderOptions = ProviderOptions>(input: LanguageModel.Input) {
return new LanguageModel<Options>({
id: ModelID.make(input.id),
provider: ProviderID.make(input.provider),
route: input.route,
@@ -206,9 +200,7 @@ export class LanguageModel<
})
}
static input<Options extends ProviderOptions, Compact extends CompactOperation | undefined>(
model: LanguageModel<Options, Compact>,
): LanguageModel.ConstructorInput<Compact> {
static input<Options extends ProviderOptions>(model: LanguageModel<Options>): LanguageModel.ConstructorInput {
return {
id: model.id,
provider: model.provider,
@@ -218,41 +210,25 @@ export class LanguageModel<
}
}
static update<Options extends ProviderOptions, Compact extends CompactOperation | undefined>(
model: LanguageModel<Options>,
patch: Partial<LanguageModel.Input<Compact>> & { readonly route: AnyRoute<Compact> },
): LanguageModel<Options, Compact>
static update<Options extends ProviderOptions, Compact extends CompactOperation | undefined>(
model: LanguageModel<Options, Compact>,
patch: Partial<Omit<LanguageModel.Input, "route">> & { readonly route?: undefined },
): LanguageModel<Options, Compact>
static update<Options extends ProviderOptions>(
model: LanguageModel<Options>,
patch: Partial<LanguageModel.Input>,
): LanguageModel<Options>
static update<Options extends ProviderOptions>(model: LanguageModel<Options>, patch: Partial<LanguageModel.Input>) {
if (Object.keys(patch).length === 0) return model
return LanguageModel.make<Options>({
...LanguageModel.input(model),
...patch,
route: patch.route ?? model.route,
})
}
}
export namespace LanguageModel {
export type ConstructorInput<Compact extends CompactOperation | undefined = CompactOperation | undefined> = {
export type ConstructorInput = {
readonly id: ModelID
readonly provider: ProviderID
readonly route: AnyRoute<Compact>
readonly route: AnyRoute
readonly defaults?: LanguageModelDefaults
readonly compatibility?: LanguageModelCompatibility
}
export type Input<Compact extends CompactOperation | undefined = CompactOperation | undefined> = Omit<
ConstructorInput<Compact>,
"id" | "provider" | "defaults" | "compatibility"
> & {
export type Input = Omit<ConstructorInput, "id" | "provider" | "defaults" | "compatibility"> & {
readonly id: string | ModelID
readonly provider: string | ProviderID
readonly defaults?: LanguageModelDefaults.Input
@@ -291,7 +267,7 @@ export const CachePolicyObject = Schema.Struct({
Schema.Union([
Schema.Literal("latest-user-message"),
Schema.Literal("latest-assistant"),
Schema.Struct({ tail: Schema.Natural }),
Schema.Struct({ tail: Schema.Number }),
]),
),
ttlSeconds: Schema.optional(Schema.Number),
+11 -25
View File
@@ -4,7 +4,6 @@ import { LLMClient } from "./route/client.js"
import {
LLMEvent,
LLMResponse,
CompactionResponse,
type FinishReasonDetails,
type AIError,
type LLMRequest,
@@ -13,7 +12,7 @@ import {
} from "./schema/index.js"
import { Context, Deferred, Effect, Latch, Layer, Queue, Scope, Stream } from "effect"
export type Response = readonly LLMEvent[] | Stream.Stream<LLMEvent, AIError> | CompactionResponse
export type Response = readonly LLMEvent[] | Stream.Stream<LLMEvent, AIError>
export type Gate = Readonly<{ started: Effect.Effect<void>; release: Effect.Effect<void> }>
@@ -100,6 +99,8 @@ export const failAfter = (error: AIError, ...events: readonly LLMEvent[]) =>
export const hangAfter = (...events: readonly LLMEvent[]) => Stream.concat(Stream.fromIterable(events), Stream.never)
const toStream = (response: Response) => (Stream.isStream(response) ? response : Stream.fromIterable(response))
const make = (options: LayerOptions) =>
Effect.sync(() => {
const requests: LLMRequest[] = []
@@ -112,41 +113,26 @@ const make = (options: LayerOptions) =>
requests.length >= count ? Effect.void : Deferred.await(started).pipe(Effect.andThen(wait(count))),
)
const take = (request: LLMRequest) =>
Effect.suspend(() => {
const stream: ClientInterface["stream"] = (request) =>
Stream.suspend(() => {
const count = requests.push(options.transformRequest?.(request) ?? request)
const waiting = started
started = Deferred.makeUnsafe()
const gate = activeGate
try {
const response = responses.shift() ?? (typeof fallback === "function" ? fallback(request) : fallback)
if (!response) return Effect.die(new Error(`TestLLM has no response for request ${count}`))
if (!gate) return Effect.succeed(response)
return Queue.offer(gate.started, undefined).pipe(Effect.andThen(gate.release.await), Effect.as(response))
if (!response) return Stream.die(new Error(`TestLLM has no response for request ${count}`))
const streamed = toStream(response)
if (!gate) return streamed
return Stream.unwrap(
Queue.offer(gate.started, undefined).pipe(Effect.andThen(gate.release.await), Effect.as(streamed)),
)
} finally {
// Waiters can resume synchronously; assign the reply and gate before notifying them.
Deferred.doneUnsafe(waiting, Effect.void)
}
})
const stream: ClientInterface["stream"] = (request) =>
Stream.unwrap(
take(request).pipe(
Effect.map((response) => {
if (response instanceof CompactionResponse)
return Stream.die("TestLLM generation requires an event response")
return Stream.isStream(response) ? response : Stream.fromIterable(response)
}),
),
)
const test = Test.of({
compact: (request) =>
take(request).pipe(
Effect.flatMap((response) =>
response instanceof CompactionResponse
? Effect.succeed(response)
: Effect.die("TestLLM compaction requires a CompactionResponse"),
),
),
stream,
generate: (request) =>
stream(request).pipe(
-75
View File
@@ -1,75 +0,0 @@
import { Message, ToolResultPart, type ToolCallPart } from "./schema/messages.js"
const EMPTY_TOOL_OUTPUT = "(no tool output)"
const MISSING_TOOL_RESULT = "Tool result missing"
export function normalizeToolHistory(messages: ReadonlyArray<Message>) {
const normalized: Message[] = []
const pending = new Map<string, ToolCallPart>()
const appendMissingResults = () => {
if (pending.size === 0) return
normalized.push(missingToolResults(pending.values()))
pending.clear()
}
for (const message of messages) {
if (message.role === "user" || message.role === "assistant") appendMissingResults()
if (message.role === "tool") {
const tool = normalizeToolMessage(message, pending)
if (tool) normalized.push(tool)
continue
}
normalized.push(message)
if (message.role !== "assistant") continue
for (const part of message.content) {
if (part.type === "tool-call" && part.providerExecuted !== true) pending.set(part.id, part)
}
}
return normalized.length === messages.length && normalized.every((message, index) => message === messages[index])
? messages
: normalized
}
function missingToolResults(calls: Iterable<ToolCallPart>) {
return new Message({
role: "tool",
content: [...calls].map((call) =>
ToolResultPart.make({ id: call.id, name: call.name, result: MISSING_TOOL_RESULT, resultType: "error" }),
),
})
}
function normalizeToolMessage(message: Message, pending: Map<string, ToolCallPart>): Message | undefined {
const content = message.content.map((part) => {
if (part.type !== "tool-result" || part.providerExecuted === true) return part
const call = pending.get(part.id)
if (call) pending.delete(part.id)
return normalizeToolResult(part, call?.name ?? part.name)
})
if (content.length === 0) return undefined
if (content.every((part, index) => part === message.content[index])) return message
return new Message({
id: message.id,
role: message.role,
content,
metadata: message.metadata,
providerMetadata: message.providerMetadata,
native: message.native,
})
}
function normalizeToolResult(part: ToolResultPart, name: string): ToolResultPart {
const named = part.name === name ? part : { ...part, name }
if (named.result.type === "text" && named.result.value === "")
return { ...named, result: { type: "text", value: EMPTY_TOOL_OUTPUT } }
if (named.result.type === "error" && named.result.value === "")
return { ...named, result: { type: "error", value: EMPTY_TOOL_OUTPUT } }
if (named.result.type !== "content") return named
const value = named.result.value.filter((item) => item.type !== "text" || item.text !== "")
if (value.length === 0) return { ...named, result: { type: "text", value: EMPTY_TOOL_OUTPUT } }
if (value.length === named.result.value.length) return named
return { ...named, result: { type: "content", value } }
}
-97
View File
@@ -1,97 +0,0 @@
import { expect } from "bun:test"
import { Effect } from "effect"
import { FetchHttpClient } from "effect/unstable/http"
import { LLM, LLMRequest, Message } from "../src/index.js"
import { LLMClient } from "../src/route/client.js"
import { OpenAI } from "../src/providers.js"
import { testEffect } from "./lib/effect.js"
import { runtimeLayer } from "./lib/http.js"
import { sseEvents } from "./lib/sse.js"
testEffect(runtimeLayer(FetchHttpClient.layer)).live("compaction and a tool loop work end to end over HTTP", () =>
Effect.gen(function* () {
const checkpoint = { type: "compaction", id: "cmp_local", encrypted_content: "opaque-local-state" }
const calls: string[] = []
const server = yield* Effect.acquireRelease(
Effect.sync(() =>
Bun.serve({
hostname: "127.0.0.1",
port: 0,
async fetch(request) {
const path = new URL(request.url).pathname
calls.push(path)
const body = await request.json()
expect(request.headers.get("authorization")).toBe("Bearer fixture")
if (path === "/v1/responses/compact") {
expect(body.stream).toBeUndefined()
return Response.json({
object: "response.compaction",
output: [checkpoint],
usage: { input_tokens: 100, output_tokens: 10, total_tokens: 110 },
})
}
expect(body.input[0]).toEqual(checkpoint)
expect(body.stream).toBe(true)
if (calls.length === 2)
return new Response(
sseEvents(
{
type: "response.output_item.done",
item: { type: "function_call", id: "fc_1", call_id: "call_1", name: "lookup", arguments: "{}" },
},
{ type: "response.completed", response: { id: "resp_1" } },
),
{ headers: { "content-type": "text/event-stream" } },
)
expect(body.input.at(-2)).toMatchObject({ type: "function_call", call_id: "call_1" })
expect(body.input.at(-1)).toEqual({ type: "function_call_output", call_id: "call_1", output: "42" })
const output = sseEvents(
{ type: "response.output_item.added", item: { type: "message", id: "msg_1" } },
{ type: "response.output_text.delta", item_id: "msg_1", delta: "The answer is 42." },
{
type: "response.output_item.done",
item: { type: "message", id: "msg_1", content: [{ type: "output_text", text: "The answer is 42." }] },
},
{ type: "response.completed", response: { id: "resp_2" } },
)
return new Response(
new ReadableStream({
start(controller) {
controller.enqueue(new TextEncoder().encode(output.slice(0, 37)))
controller.enqueue(new TextEncoder().encode(output.slice(37)))
controller.close()
},
}),
{ headers: { "content-type": "text/event-stream" } },
)
},
}),
),
(server) => Effect.sync(() => server.stop(true)),
)
const model = OpenAI.configure({ apiKey: "fixture", baseURL: `http://127.0.0.1:${server.port}/v1` }).responses(
"fixture",
)
const request = LLM.request({
model,
prompt: "original",
tools: [{ name: "lookup", description: "Lookup a number", inputSchema: { type: "object", properties: {} } }],
})
const compacted = yield* LLMClient.compact(request)
const messages = [...compacted.replacement, Message.user("Look up the answer")]
const first = yield* LLMClient.generate(LLMRequest.update(request, { messages }))
expect(first.toolCalls).toHaveLength(1)
const call = first.toolCalls[0]!
const last = yield* LLMClient.generate(
LLMRequest.update(request, {
messages: [
...messages,
first.message,
Message.tool({ id: call.id, name: call.name, result: "42", resultType: "text" }),
],
}),
)
expect(last.text).toBe("The answer is 42.")
expect(calls).toEqual(["/v1/responses/compact", "/v1/responses", "/v1/responses"])
}),
)
-101
View File
@@ -1,101 +0,0 @@
import { expect, test } from "bun:test"
import { Effect, Schema } from "effect"
import { CompactionPart, CompactionResponse, LLMEvent, LLMResponse, Message, ProviderID } from "../src/schema/index.js"
import { LLM, LLMClient, LLMRequest, LanguageModel } from "../src/index.js"
import { OpenAI, Anthropic } from "../src/providers.js"
import { testEffect } from "./lib/effect.js"
import { fixedResponse } from "./lib/http.js"
test("runtime capability checks follow model and route updates", () => {
const supported = OpenAI.configure({ apiKey: "test" }).responses("fixture")
const unsupported = Anthropic.configure({ apiKey: "test" }).model("fixture")
const request = LLM.request({ model: supported, prompt: "hello" })
expect(LLMClient.canCompact(request)).toBe(true)
expect(LLMClient.canCompact(LLMRequest.update(request, { messages: [] }))).toBe(true)
expect(LLMClient.canCompact(LLMRequest.update(request, { model: unsupported }))).toBe(false)
expect(
LLMClient.canCompact(LLM.request({ model: LanguageModel.update(supported, { route: unsupported.route }) })),
).toBe(false)
expect(LLMClient.canCompact(LLM.request({ model: LanguageModel.update(supported, { route: undefined }) }))).toBe(true)
})
test("explicit compaction serializes a replacement window without a messages alias", () => {
const response = new CompactionResponse({
replacement: [
Message.user("retained input"),
Message.assistant(CompactionPart.make({ provider: ProviderID.make("openai"), encrypted: "checkpoint" })),
],
})
const codec = Schema.fromJsonString(CompactionResponse)
const decoded = Schema.decodeSync(codec)(Schema.encodeSync(codec)(response))
expect(decoded.replacement).toEqual(response.replacement)
expect("messages" in decoded).toBe(false)
})
test("compaction survives event assembly and message serialization without becoming text", () => {
const part = CompactionPart.make({
provider: ProviderID.make("openai"),
id: "cmp_1",
encrypted: "opaque",
})
const response = LLMResponse.fromEvents([
LLMEvent.textStart({ id: "before" }),
LLMEvent.textDelta({ id: "before", text: "Before" }),
LLMEvent.textEnd({ id: "before" }),
part,
LLMEvent.textStart({ id: "after" }),
LLMEvent.textDelta({ id: "after", text: "After" }),
LLMEvent.textEnd({ id: "after" }),
LLMEvent.finish({ reason: { normalized: "stop" } }),
])!
expect(response.message.content.map((part) => part.type)).toEqual(["text", "compaction", "text"])
expect(response.text).toBe("BeforeAfter")
expect(response.reasoning).toBe("")
expect(response.events.filter(LLMEvent.is.compaction)).toEqual([part])
const codec = Schema.fromJsonString(Message)
expect(Schema.decodeSync(codec)(Schema.encodeSync(codec)(response.message))).toEqual(response.message)
})
test("compaction requires exactly one typed representation", () => {
const provider = ProviderID.make("anthropic")
expect(CompactionPart.make({ provider, text: null })).toEqual({ type: "compaction", provider, text: null })
const decode = Schema.decodeUnknownSync(CompactionPart)
expect(() => decode({ type: "compaction", provider })).toThrow()
expect(() => decode({ type: "compaction", provider, text: "summary", encrypted: "opaque" })).toThrow()
})
test("tagged content and event guards accept both checkpoint representations", () => {
for (const part of [
CompactionPart.make({ provider: ProviderID.make("openai"), encrypted: "opaque" }),
CompactionPart.make({ provider: ProviderID.make("anthropic"), text: "summary" }),
CompactionPart.make({ provider: ProviderID.make("anthropic"), text: null }),
]) {
expect(LLMEvent.is.compaction(part)).toBe(true)
expect(LLMEvent.guards.compaction(part)).toBe(true)
const codec = Schema.fromJsonString(Message)
const message = Message.assistant(part)
expect(Schema.decodeSync(codec)(Schema.encodeSync(codec)(message))).toEqual(message)
}
})
testEffect(fixedResponse("")).effect(
"explicit compaction on a route without a compact endpoint fails with UnsupportedOperation",
() =>
Effect.gen(function* () {
const request = LLM.request({
model: Anthropic.configure({ apiKey: "test" }).model("fixture"),
prompt: "hello",
})
expect(LLMClient.canCompact(request)).toBe(false)
const error = yield* LLMClient.compact(
request as unknown as Parameters<typeof LLMClient.compact>[0],
).pipe(Effect.flip)
expect(error.reason._tag).toBe("UnsupportedOperation")
expect(error.message).toContain("does not support explicit compaction")
if (error.reason._tag === "UnsupportedOperation") {
expect(error.reason.operation).toBe("compact")
expect(error.reason.provider).toBe("anthropic")
expect(error.reason.route).toBe("anthropic-messages")
}
}),
)
+1 -50
View File
@@ -1,7 +1,7 @@
import { describe, expect, test } from "bun:test"
import { Effect, Ref, Schema } from "effect"
import { HttpClientRequest, HttpClientResponse } from "effect/unstable/http"
import { LLM, LLMRequest, Message, ToolCallPart, ToolDefinition, mergeProviderOptions } from "../src/index.js"
import { LLM, Message, ToolCallPart, mergeProviderOptions } from "../src/index.js"
import { AnthropicMessages, OpenAIChat } from "../src/protocols.js"
import { Auth, LLMClient } from "../src/route.js"
import { compileRequest } from "../src/route/client.js"
@@ -77,55 +77,6 @@ describe("request option precedence", () => {
}),
)
it.effect("keeps the last tool definition for duplicate names", () =>
Effect.gen(function* () {
const request = LLM.request({
model: OpenAIChat.route.model({ id: "gpt-4o-mini" }),
prompt: "Use a tool.",
})
const prepared = yield* compileRequest(
LLMRequest.update(request, {
tools: [
ToolDefinition.make({ name: "lookup", description: "old", inputSchema: { type: "object" } }),
ToolDefinition.make({ name: "search", description: "search", inputSchema: { type: "object" } }),
ToolDefinition.make({ name: "lookup", description: "new", inputSchema: { type: "object" } }),
],
}),
)
expect(prepared.body.tools).toEqual([
{
type: "function",
function: { name: "lookup", description: "new", parameters: { type: "object" }, strict: false },
},
{
type: "function",
function: { name: "search", description: "search", parameters: { type: "object" }, strict: false },
},
])
}),
)
it.effect("normalizes tool history before protocol lowering", () =>
Effect.gen(function* () {
const prepared = yield* compileRequest(
LLM.request({
model: OpenAIChat.route.model({ id: "gpt-4o-mini" }),
messages: [
Message.assistant(ToolCallPart.make({ id: "call_1", name: "lookup", input: {} })),
Message.user("Continue."),
],
}),
)
expect(prepared.body.messages).toMatchObject([
{ role: "assistant", tool_calls: [{ id: "call_1", function: { name: "lookup" } }] },
{ role: "tool", tool_call_id: "call_1", content: "Tool result missing" },
{ role: "user", content: "Continue." },
])
}),
)
it.effect("applies model HTTP defaults before request HTTP overlays", () =>
LLMClient.generate(
LLM.request({
@@ -1,36 +0,0 @@
{
"version": 1,
"metadata": {
"model": "zai-glm-5-2",
"tags": [
"prefix:mistral-chat-glm",
"provider:mistral",
"protocol:mistral-chat",
"hosted-model",
"tool",
"tool-call"
],
"name": "mistral-chat-glm/streams-an-indexed-tool-call",
"recordedAt": "2026-08-30T17:38:02.921Z"
},
"interactions": [
{
"transport": "http",
"request": {
"method": "POST",
"url": "https://api.mistral.ai/v1/chat/completions",
"headers": {
"content-type": "application/json"
},
"body": "{\"model\":\"zai-glm-5-2\",\"messages\":[{\"role\":\"system\",\"content\":\"Call lookup_weather exactly once with Paris.\"},{\"role\":\"user\",\"content\":\"What is the weather?\"}],\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"lookup_weather\",\"description\":\"Look up the current weather for a city\",\"parameters\":{\"type\":\"object\",\"properties\":{\"city\":{\"type\":\"string\",\"enum\":[\"Paris\"]}},\"required\":[\"city\"],\"additionalProperties\":false},\"strict\":false}}],\"tool_choice\":{\"type\":\"function\",\"function\":{\"name\":\"lookup_weather\"}},\"stream\":true,\"max_tokens\":256,\"temperature\":0}"
},
"response": {
"status": 200,
"headers": {
"content-type": "text/event-stream; charset=utf-8"
},
"body": "data: {\"id\":\"f139bf0e4b984e51aabf6a83c237674d\",\"object\":\"chat.completion.chunk\",\"created\":1788111482,\"model\":\"zai-glm-5-2\",\"choices\":[{\"index\":0,\"delta\":{\"role\":\"assistant\",\"content\":\"\"},\"finish_reason\":null}]}\n\ndata: {\"id\":\"f139bf0e4b984e51aabf6a83c237674d\",\"object\":\"chat.completion.chunk\",\"created\":1788111482,\"model\":\"zai-glm-5-2\",\"choices\":[{\"index\":0,\"delta\":{\"role\":\"assistant\",\"index\":0,\"content\":\"\"},\"finish_reason\":null,\"logprobs\":null}]}\n\ndata: {\"id\":\"f139bf0e4b984e51aabf6a83c237674d\",\"object\":\"chat.completion.chunk\",\"created\":1788111482,\"model\":\"zai-glm-5-2\",\"choices\":[{\"index\":0,\"delta\":{\"tool_calls\":[{\"id\":\"chatcmpl-tool-8cc4d8f9f07b298a\",\"type\":\"function\",\"function\":{\"name\":\"lookup_weather\",\"arguments\":\"{\\\"city\\\": \\\"\"},\"index\":0}],\"index\":0,\"content\":\"\"},\"finish_reason\":null,\"logprobs\":null}]}\n\ndata: {\"id\":\"f139bf0e4b984e51aabf6a83c237674d\",\"object\":\"chat.completion.chunk\",\"created\":1788111482,\"model\":\"zai-glm-5-2\",\"choices\":[{\"index\":0,\"delta\":{\"tool_calls\":[{\"type\":\"function\",\"function\":{\"name\":\"\",\"arguments\":\"Paris\\\"}\"},\"index\":0}],\"index\":0,\"content\":\"\"},\"finish_reason\":null,\"logprobs\":null}]}\n\ndata: {\"id\":\"f139bf0e4b984e51aabf6a83c237674d\",\"object\":\"chat.completion.chunk\",\"created\":1788111482,\"model\":\"zai-glm-5-2\",\"choices\":[{\"index\":0,\"delta\":{\"index\":0,\"content\":\"\"},\"finish_reason\":\"stop\",\"logprobs\":null}],\"usage\":{\"prompt_tokens\":171,\"total_tokens\":182,\"completion_tokens\":11,\"prompt_tokens_details\":{\"cached_tokens\":0}}}\n\ndata: [DONE]\n\n"
}
}
]
}
@@ -1,47 +0,0 @@
{
"version": 1,
"metadata": {
"model": "mistral-small-latest",
"tags": ["prefix:mistral-chat", "provider:mistral", "protocol:mistral-chat", "tool", "tool-loop", "usage"],
"name": "mistral-chat/drives-a-tool-loop",
"recordedAt": "2026-08-30T17:18:49.552Z"
},
"interactions": [
{
"transport": "http",
"request": {
"method": "POST",
"url": "https://api.mistral.ai/v1/chat/completions",
"headers": {
"content-type": "application/json"
},
"body": "{\"model\":\"mistral-small-latest\",\"messages\":[{\"role\":\"system\",\"content\":\"Call lookup_weather exactly once with Paris.\"},{\"role\":\"user\",\"content\":\"What is the weather?\"}],\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"lookup_weather\",\"description\":\"Look up the current weather for a city\",\"parameters\":{\"type\":\"object\",\"properties\":{\"city\":{\"type\":\"string\",\"enum\":[\"Paris\"]}},\"required\":[\"city\"],\"additionalProperties\":false},\"strict\":false}}],\"tool_choice\":{\"type\":\"function\",\"function\":{\"name\":\"lookup_weather\"}},\"stream\":true,\"max_tokens\":160,\"temperature\":0,\"reasoning_effort\":\"none\"}"
},
"response": {
"status": 200,
"headers": {
"content-type": "text/event-stream; charset=utf-8"
},
"body": "data: {\"id\":\"07491e37a5ed48f9987f1583753a466b\",\"object\":\"chat.completion.chunk\",\"created\":1788110328,\"model\":\"mistral-small-latest\",\"choices\":[{\"index\":0,\"delta\":{\"role\":\"assistant\",\"content\":\"\"},\"finish_reason\":null}]}\n\ndata: {\"id\":\"07491e37a5ed48f9987f1583753a466b\",\"object\":\"chat.completion.chunk\",\"created\":1788110328,\"model\":\"mistral-small-latest\",\"choices\":[{\"index\":0,\"delta\":{\"tool_calls\":[{\"id\":\"ffJovBNqY\",\"type\":\"function\",\"function\":{\"name\":\"lookup_weather\",\"arguments\":\"{\\\"city\\\": \\\"Paris\\\"}\"},\"index\":0}]},\"finish_reason\":\"tool_calls\"}],\"usage\":{\"prompt_tokens\":110,\"total_tokens\":122,\"completion_tokens\":12,\"prompt_tokens_details\":{\"cached_tokens\":0},\"service_tier\":\"standard\"},\"p\":\"abcdefghijklm\"}\n\ndata: [DONE]\n\n"
}
},
{
"transport": "http",
"request": {
"method": "POST",
"url": "https://api.mistral.ai/v1/chat/completions",
"headers": {
"content-type": "application/json"
},
"body": "{\"model\":\"mistral-small-latest\",\"messages\":[{\"role\":\"system\",\"content\":\"Call lookup_weather exactly once with Paris.\"},{\"role\":\"user\",\"content\":\"What is the weather?\"},{\"role\":\"assistant\",\"content\":\"\",\"tool_calls\":[{\"id\":\"ffJovBNqY\",\"type\":\"function\",\"function\":{\"name\":\"lookup_weather\",\"arguments\":\"{\\\"city\\\":\\\"Paris\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"ffJovBNqY\",\"name\":\"lookup_weather\",\"content\":\"{\\\"condition\\\":\\\"sunny\\\",\\\"temperature\\\":\\\"18C\\\"}\"}],\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"lookup_weather\",\"description\":\"Look up the current weather for a city\",\"parameters\":{\"type\":\"object\",\"properties\":{\"city\":{\"type\":\"string\",\"enum\":[\"Paris\"]}},\"required\":[\"city\"],\"additionalProperties\":false},\"strict\":false}}],\"tool_choice\":\"none\",\"stream\":true,\"max_tokens\":160,\"temperature\":0,\"reasoning_effort\":\"none\"}"
},
"response": {
"status": 200,
"headers": {
"content-type": "text/event-stream; charset=utf-8"
},
"body": "data: {\"id\":\"8fcd293093b849139fc0893a48bbc7ce\",\"object\":\"chat.completion.chunk\",\"created\":1788110328,\"model\":\"mistral-small-latest\",\"choices\":[{\"index\":0,\"delta\":{\"role\":\"assistant\",\"content\":\"\"},\"finish_reason\":null}]}\n\ndata: {\"id\":\"8fcd293093b849139fc0893a48bbc7ce\",\"object\":\"chat.completion.chunk\",\"created\":1788110328,\"model\":\"mistral-small-latest\",\"choices\":[{\"index\":0,\"delta\":{\"content\":\"The\"},\"finish_reason\":null}],\"p\":\"abcdefghijklmn\"}\n\ndata: {\"id\":\"8fcd293093b849139fc0893a48bbc7ce\",\"object\":\"chat.completion.chunk\",\"created\":1788110328,\"model\":\"mistral-small-latest\",\"choices\":[{\"index\":0,\"delta\":{\"content\":\" weather in Paris is\"},\"finish_reason\":null}],\"p\":\"abcdefghijklmn\"}\n\ndata: {\"id\":\"8fcd293093b849139fc0893a48bbc7ce\",\"object\":\"chat.completion.chunk\",\"created\":1788110328,\"model\":\"mistral-small-latest\",\"choices\":[{\"index\":0,\"delta\":{\"content\":\" currently sunny with\"},\"finish_reason\":null}],\"p\":\"abcdefghijklmnopqrstu\"}\n\ndata: {\"id\":\"8fcd293093b849139fc0893a48bbc7ce\",\"object\":\"chat.completion.chunk\",\"created\":1788110328,\"model\":\"mistral-small-latest\",\"choices\":[{\"index\":0,\"delta\":{\"content\":\" a temperature of \"},\"finish_reason\":null}],\"p\":\"abcdef\"}\n\ndata: {\"id\":\"8fcd293093b849139fc0893a48bbc7ce\",\"object\":\"chat.completion.chunk\",\"created\":1788110328,\"model\":\"mistral-small-latest\",\"choices\":[{\"index\":0,\"delta\":{\"content\":\"18°C\"},\"finish_reason\":null}],\"p\":\"abcdefghijklmnopqr\"}\n\ndata: {\"id\":\"8fcd293093b849139fc0893a48bbc7ce\",\"object\":\"chat.completion.chunk\",\"created\":1788110328,\"model\":\"mistral-small-latest\",\"choices\":[{\"index\":0,\"delta\":{\"content\":\".\"},\"finish_reason\":\"stop\"}],\"usage\":{\"prompt_tokens\":57,\"total_tokens\":74,\"completion_tokens\":17,\"prompt_tokens_details\":{\"cached_tokens\":0},\"service_tier\":\"standard\"},\"p\":\"abcdefghijklmnopqrstuvwxyz\"}\n\ndata: [DONE]\n\n"
}
}
]
}
File diff suppressed because one or more lines are too long
@@ -1,29 +0,0 @@
{
"version": 1,
"metadata": {
"model": "mistral-small-latest",
"tags": ["prefix:mistral-chat", "provider:mistral", "protocol:mistral-chat", "text", "usage"],
"name": "mistral-chat/streams-text-with-usage",
"recordedAt": "2026-08-30T17:18:45.432Z"
},
"interactions": [
{
"transport": "http",
"request": {
"method": "POST",
"url": "https://api.mistral.ai/v1/chat/completions",
"headers": {
"content-type": "application/json"
},
"body": "{\"model\":\"mistral-small-latest\",\"messages\":[{\"role\":\"user\",\"content\":\"Reply with exactly one word: hello\"}],\"stream\":true,\"max_tokens\":40,\"temperature\":0,\"reasoning_effort\":\"none\"}"
},
"response": {
"status": 200,
"headers": {
"content-type": "text/event-stream; charset=utf-8"
},
"body": "data: {\"id\":\"9a4d16bdddb74e5e89c2cf9e9b91e065\",\"object\":\"chat.completion.chunk\",\"created\":1788110325,\"model\":\"mistral-small-latest\",\"choices\":[{\"index\":0,\"delta\":{\"role\":\"assistant\",\"content\":\"\"},\"finish_reason\":null}]}\n\ndata: {\"id\":\"9a4d16bdddb74e5e89c2cf9e9b91e065\",\"object\":\"chat.completion.chunk\",\"created\":1788110325,\"model\":\"mistral-small-latest\",\"choices\":[{\"index\":0,\"delta\":{\"content\":\"Hi\"},\"finish_reason\":null}],\"p\":\"abcdefghijklmnopqrs\"}\n\ndata: {\"id\":\"9a4d16bdddb74e5e89c2cf9e9b91e065\",\"object\":\"chat.completion.chunk\",\"created\":1788110325,\"model\":\"mistral-small-latest\",\"choices\":[{\"index\":0,\"delta\":{\"content\":\"\"},\"finish_reason\":\"stop\"}],\"usage\":{\"prompt_tokens\":22,\"total_tokens\":24,\"completion_tokens\":2,\"prompt_tokens_details\":{\"cached_tokens\":0},\"service_tier\":\"standard\"},\"p\":\"abcdefghijklmnopqrstuvwxyz0\"}\n\ndata: [DONE]\n\n"
}
}
]
}
@@ -1,133 +0,0 @@
import { Effect } from "effect"
import {
CompactionPart,
LanguageModel,
LLM,
LLMClient,
LLMEvent,
LLMRequest,
Message,
ProviderID,
} from "../../src/index.js"
import { OpenAI, Azure, XAI, Anthropic, OpenAICompatibleResponses } from "../../src/providers.js"
const openai = OpenAI.configure({
apiKey: "test",
providerOptions: { contextManagement: [{ type: "compaction", compactThreshold: 100000 }] },
}).responses("gpt-5.3-codex")
LLMClient.compact(LLM.request({ model: openai, prompt: "hello" }))
for (const model of [
OpenAI.configure().responses("fixture"),
Azure.configure({ resourceName: "test" }).responses("fixture"),
XAI.configure().responses("fixture"),
OpenAI.model("fixture", {}),
Azure.responsesModel("fixture", { resourceName: "test" }),
XAI.model("fixture", {}),
openai.route.with({ headers: { "x-test": "test" } }).model({ id: "fixture" }),
LanguageModel.update(openai, { defaults: { generation: { maxTokens: 100 } } }),
LanguageModel.make(LanguageModel.input(openai)),
]) {
LLMClient.compact(LLM.request({ model, prompt: "hello" }))
}
const unsupported = {
anthropic: LLM.request({ model: Anthropic.configure().model("fixture") }),
openaiChat: LLM.request({ model: OpenAI.configure().chat("fixture") }),
azureChat: LLM.request({ model: Azure.configure({ resourceName: "test" }).chat("fixture") }),
xaiChat: LLM.request({ model: XAI.configure().chat("fixture") }),
compatible: LLM.request({
model: OpenAICompatibleResponses.configure({ baseURL: "https://example.com" }).model("fixture"),
}),
}
// @ts-expect-error Anthropic has no standalone compact endpoint.
LLMClient.compact(unsupported.anthropic)
// @ts-expect-error Chat does not expose Responses compaction.
LLMClient.compact(unsupported.openaiChat)
// @ts-expect-error Azure Chat does not expose Responses compaction.
LLMClient.compact(unsupported.azureChat)
// @ts-expect-error xAI Chat does not expose Responses compaction.
LLMClient.compact(unsupported.xaiChat)
// @ts-expect-error Protocol compatibility does not guarantee endpoint support.
LLMClient.compact(unsupported.compatible)
LLMClient.Service.use((client) => {
// @ts-expect-error The service enforces the same capability as the convenience function.
return client.compact(unsupported.anthropic)
})
const request = LLM.request({ model: openai, prompt: "hello" })
LLMClient.compact(LLMRequest.update(request, { messages: [Message.user("continue")] }))
LLMClient.compact(new LLMRequest(LLMRequest.input(request)))
const switched = LLMRequest.update(request, { model: Anthropic.configure().model("fixture") })
// @ts-expect-error Switching models replaces, rather than inherits, the capability.
LLMClient.compact(switched)
LLMClient.compact(LLMRequest.update(switched, { model: openai }))
LLMClient.compact(
// @ts-expect-error Replacing the route also replaces compaction capability.
LLM.request({ model: LanguageModel.update(openai, { route: Anthropic.configure().model("fixture").route }) }),
)
declare const dynamicModel: LanguageModel
declare const dynamicPatch: Partial<LLMRequest.Input>
const dynamicRequest = LLM.request({ model: dynamicModel, prompt: "hello" })
// @ts-expect-error A dynamically selected model must be narrowed first.
LLMClient.compact(dynamicRequest)
if (LLMClient.canCompact(dynamicRequest)) LLMClient.compact(dynamicRequest)
// @ts-expect-error An optional model override cannot retain the old capability statically.
LLMClient.compact(LLMRequest.update(request, dynamicPatch))
const checkpoint = CompactionPart.make({ provider: ProviderID.make("openai"), id: "cmp_1", encrypted: "opaque" })
const provider = ProviderID.make("anthropic")
CompactionPart.make({ provider, text: "summary" })
CompactionPart.make({ provider, text: null })
// @ts-expect-error A checkpoint must have a representation.
CompactionPart.make({ provider })
// @ts-expect-error Encrypted and summary representations are mutually exclusive.
CompactionPart.make({ provider, encrypted: "opaque", text: "summary" })
// @ts-expect-error A failed summary cannot also carry encrypted content.
LLMEvent.compaction({ provider, encrypted: "opaque", text: null })
// @ts-expect-error The canonical message type also enforces the invariant.
Message.assistant({ type: "compaction", provider })
if (checkpoint.encrypted !== undefined) {
checkpoint.encrypted satisfies string
checkpoint.text satisfies undefined
}
if (checkpoint.text !== undefined) {
checkpoint.text satisfies string | null
checkpoint.encrypted satisfies undefined
}
checkpoint.encrypted
// @ts-expect-error Compaction parts do not contain a generic provider payload.
checkpoint.value
LLMClient.compact(LLM.request({ model: openai, prompt: "hello" })).pipe(
Effect.map((result) => {
result.replacement satisfies ReadonlyArray<Message>
// @ts-expect-error The replacement window is named explicitly; the old field is not an alias.
result.messages
// @ts-expect-error Compaction returns replacement history, not a synthetic assistant message.
result.message
}),
)
LLM.request({
model: openai,
providerOptions: {
// @ts-expect-error A token threshold is numeric.
contextManagement: [{ type: "compaction", compactThreshold: "100000" }],
},
})
const anthropic = Anthropic.configure().model("claude-opus-4-6")
LLM.request({
model: anthropic,
providerOptions: {
contextManagement: {
edits: [{ type: "compact_20260112", pauseAfterCompaction: true, instructions: "Summarize without using tools" }],
},
},
})
LLM.request({
model: anthropic,
providerOptions: {
// @ts-expect-error A pause setting is boolean.
contextManagement: { edits: [{ type: "compact_20260112", pauseAfterCompaction: "yes" }] },
},
})
@@ -1,24 +0,0 @@
import { LLM } from "../../src/index.js"
import { Mistral } from "../../src/providers.js"
const selected = Mistral.provider.model("mistral-small-latest")
LLM.request({ model: selected, prompt: "Hello", providerOptions: { reasoningEffort: "high" } })
LLM.request({ model: selected, prompt: "Hello", providerOptions: { reasoningEffort: "future-effort" } })
LLM.request({ model: selected, prompt: "Hello", providerOptions: { promptMode: "reasoning" } })
LLM.request({ model: selected, prompt: "Hello", providerOptions: { parallelToolCalls: false } })
LLM.request({ model: selected, prompt: "Hello", providerOptions: { promptCacheKey: "session-1" } })
LLM.request({
model: selected,
prompt: "Hello",
// @ts-expect-error Mistral reasoning effort must be a string.
providerOptions: { reasoningEffort: 1 },
})
LLM.request({
model: selected,
prompt: "Hello",
// @ts-expect-error Mistral prompt mode only supports reasoning.
providerOptions: { promptMode: "standard" },
})
@@ -1,170 +0,0 @@
import { expect } from "bun:test"
import { Effect, Schema } from "effect"
import { LLM, LLMRequest, Message } from "../../src/index.js"
import { LLMClient } from "../../src/route/client.js"
import { Anthropic, GoogleVertexMessages } from "../../src/providers/index.js"
import { testEffect } from "../lib/effect.js"
import { dynamicResponse, fixedResponse } from "../lib/http.js"
import { sseEvents } from "../lib/sse.js"
for (const fixture of [
{
name: "empty iterations fall back to top-level usage",
usage: { input_tokens: 2, output_tokens: 3, cache_read_input_tokens: null, iterations: [] },
expected: { inputTokens: 2, outputTokens: 3, totalTokens: 5, contextTokens: undefined },
},
{
name: "compaction-only usage has no post-compaction context size",
usage: {
input_tokens: 0,
output_tokens: 0,
iterations: [{ type: "compaction", input_tokens: 7, cache_read_input_tokens: 3, output_tokens: 2 }],
},
expected: { inputTokens: 10, outputTokens: 2, totalTokens: 12, contextTokens: undefined },
},
{
name: "partially reported iterations preserve known totals",
usage: {
iterations: [
{ type: "compaction", input_tokens: 7, cache_creation_input_tokens: 2 },
{ type: "message", output_tokens: 3 },
],
},
expected: { inputTokens: 9, outputTokens: 3, totalTokens: 12, contextTokens: undefined },
},
{
name: "missing counters remain unknown rather than zero",
usage: { iterations: [{ type: "message" }] },
expected: { inputTokens: undefined, outputTokens: undefined, totalTokens: undefined, contextTokens: undefined },
},
]) {
testEffect(
fixedResponse(
sseEvents(
{ type: "message_start", message: { usage: fixture.usage } },
{ type: "message_delta", delta: { stop_reason: "end_turn" } },
{ type: "message_stop" },
),
),
).effect(fixture.name, () =>
Effect.gen(function* () {
const result = yield* LLMClient.generate(
LLM.request({
model: Anthropic.configure({ apiKey: "test" }).model("claude-opus-4-6"),
prompt: "hello",
}),
)
expect(result.usage).toMatchObject(fixture.expected)
}),
)
}
for (const model of [
Anthropic.configure({ apiKey: "test" }).model("claude-opus-4-6"),
GoogleVertexMessages.configure({ accessToken: "test", project: "test" }).model("claude-opus-4-6"),
]) {
for (const summary of ["Summary of the conversation", null]) {
const block = { type: "compaction", content: summary }
testEffect(
dynamicResponse(({ request, text, respond }) =>
Effect.sync(() => {
const body = JSON.parse(text)
expect(request.headers["anthropic-beta"]).toBe("existing-beta,compact-2026-01-12")
if (body.messages.length === 1) {
expect(body.context_management.edits).toEqual([
{
type: "compact_20260112",
trigger: { type: "input_tokens", value: 50000 },
pause_after_compaction: true,
},
])
}
if (body.messages.length > 1) {
expect(body.messages[1].content).toEqual([block])
expect(body.context_management).toBeUndefined()
}
return respond(
sseEvents(
{ type: "message_start", message: { usage: { input_tokens: 50000, output_tokens: 0 } } },
{ type: "content_block_start", index: 0, content_block: { type: "compaction", content: null } },
{ type: "content_block_delta", index: 0, delta: { type: "compaction_delta", content: summary } },
{ type: "content_block_stop", index: 0 },
{
type: "message_delta",
delta: { stop_reason: "compaction" },
usage: {
input_tokens: 1000,
output_tokens: 5,
iterations: [
{ type: "compaction", input_tokens: 50000, output_tokens: 1000, cache_read_input_tokens: 10 },
{ type: "message", input_tokens: 1000, output_tokens: 5 },
],
},
},
{ type: "message_stop" },
),
{ headers: { "content-type": "text/event-stream" } },
)
}),
),
).effect(
`${model.provider} replays ${summary === null ? "failed" : "successful"} compaction with billing and context usage`,
() =>
Effect.gen(function* () {
const request = LLM.request({
model,
prompt: "hello",
http: { headers: { "anthropic-beta": "existing-beta" } },
providerOptions: {
contextManagement: {
edits: [
{
type: "compact_20260112",
trigger: { type: "input_tokens", value: 50000 },
pauseAfterCompaction: true,
},
],
},
},
})
const first = yield* LLMClient.generate(request)
expect(first.finishReason.raw).toBe("compaction")
expect(first.message.content).toEqual([{ type: "compaction", provider: model.provider, text: summary }])
expect(first.text).toBe("")
expect(first.usage?.inputTokens).toBe(51010)
expect(first.usage?.outputTokens).toBe(1005)
expect(first.usage?.totalTokens).toBe(52015)
expect(first.usage?.contextTokens).toBe(1000)
const codec = Schema.fromJsonString(Message)
const message = Schema.decodeSync(codec)(Schema.encodeSync(codec)(first.message))
yield* LLMClient.generate(
LLMRequest.update(request, {
providerOptions: {},
messages: [...request.messages, message, Message.user("continue")],
}),
)
}),
)
}
}
for (const events of [
[{ type: "content_block_start", index: 0, content_block: { type: "compaction", content: 42 } }],
[{ type: "content_block_delta", index: 0, delta: { type: "compaction_delta", content: "no start" } }],
[
{ type: "content_block_start", index: 0, content_block: { type: "compaction", content: null } },
{ type: "message_stop" },
],
]) {
testEffect(fixedResponse(sseEvents(...events))).effect(
`rejects malformed compaction lifecycle: ${JSON.stringify(events)}`,
() =>
Effect.gen(function* () {
const error = yield* LLMClient.generate(
LLM.request({ model: Anthropic.configure({ apiKey: "test" }).model("claude-opus-4-6"), prompt: "hello" }),
).pipe(Effect.flip)
expect(error.reason._tag).toBe("InvalidProviderOutput")
expect(error.reason.http?.status).toBe(200)
}),
)
}
@@ -74,48 +74,6 @@ describe("Anthropic Messages route", () => {
}),
)
it.effect("omits empty system text while preserving whitespace", () =>
Effect.gen(function* () {
const empty = yield* compileRequest(LLMRequest.update(request, { system: [{ type: "text", text: "" }] }))
const whitespace = yield* compileRequest(LLMRequest.update(request, { system: [{ type: "text", text: " " }] }))
expect(empty.body.system).toBeUndefined()
expect(whitespace.body.system).toEqual([{ type: "text", text: " " }])
}),
)
it.effect("filters whitespace-only text and removes empty messages", () =>
Effect.gen(function* () {
const prepared = yield* compileRequest(
LLM.request({
model,
messages: [
Message.user(" \n\t"),
Message.user([]),
Message.user([
{ type: "text", text: "" },
{ type: "text", text: " Keep this spacing. " },
{ type: "text", text: " \n\t" },
]),
Message.assistant(" \n\t"),
Message.assistant([]),
Message.assistant([{ type: "reasoning", text: "" }]),
Message.assistant([
{ type: "text", text: "" },
{ type: "reasoning", text: "", providerMetadata: { anthropic: { signature: "sig_1" } } },
]),
],
cache: "none",
}),
)
expect(prepared.body.messages).toEqual([
{ role: "user", content: [{ type: "text", text: " Keep this spacing. " }] },
{ role: "assistant", content: [{ type: "thinking", thinking: "", signature: "sig_1" }] },
])
}),
)
it.effect("lowers adaptive thinking settings with effort", () =>
Effect.gen(function* () {
const prepared = yield* compileRequest(
@@ -945,54 +903,6 @@ describe("Anthropic Messages route", () => {
}),
)
it.effect("preserves a reasoning signature when message_stop closes the block", () =>
Effect.gen(function* () {
const compatible = Route.make({
id: "custom-anthropic-messages",
provider: "custom-anthropic",
protocol: AnthropicMessages.protocol,
endpoint: Endpoint.path("/messages", { baseURL: "https://compatible.test/v1" }),
auth: Auth.header("x-api-key", "test"),
framing: AnthropicMessages.framing,
}).model({ id: "custom-model" })
const body = sseEvents(
{ type: "message_start", message: { usage: { input_tokens: 5 } } },
{
type: "content_block_start",
index: 0,
content_block: { type: "thinking", thinking: "", signature: "" },
},
{ type: "content_block_delta", index: 0, delta: { type: "thinking_delta", thinking: "Reasoning." } },
{ type: "content_block_delta", index: 0, delta: { type: "signature_delta", signature: "sig_1" } },
{ type: "message_delta", delta: { stop_reason: "end_turn" }, usage: { output_tokens: 2 } },
{ type: "message_stop" },
)
const response = yield* LLMClient.generate(LLM.request({ model: compatible, prompt: "Think." })).pipe(
Effect.provide(fixedResponse(body)),
)
const reasoningEnds = response.events.filter((event) => event.type === "reasoning-end")
expect(reasoningEnds).toHaveLength(1)
expect(reasoningEnds[0]).toMatchObject({
providerMetadata: { "custom-anthropic": { signature: "sig_1" } },
})
expect(response.message.content).toEqual([
{
type: "reasoning",
text: "Reasoning.",
providerMetadata: { "custom-anthropic": { signature: "sig_1" } },
},
])
const prepared = yield* compileRequest(
LLM.request({ model: compatible, messages: [response.message], cache: "none" }),
)
expect(prepared.body.messages).toEqual([
{ role: "assistant", content: [{ type: "thinking", thinking: "Reasoning.", signature: "sig_1" }] },
])
}),
)
it.effect("parses text, reasoning, and usage stream fixtures", () =>
Effect.gen(function* () {
const body = sseEvents(
@@ -1026,7 +936,6 @@ describe("Anthropic Messages route", () => {
expect(response.events.find((event) => event.type === "reasoning-end")).toMatchObject({
providerMetadata: { anthropic: { signature: "sig_1" } },
})
expect(response.events.filter((event) => event.type === "reasoning-end")).toHaveLength(1)
expect(response.events.find((event) => event.type === "reasoning-delta" && event.text === "")).toBeUndefined()
expect(response.message.content).toEqual([
{ type: "text", text: "Hello!" },
@@ -1,12 +1,11 @@
import { EventStreamCodec } from "@smithy/eventstream-codec"
import { fromUtf8, toUtf8 } from "@smithy/util-utf8"
import { describe, expect } from "bun:test"
import { Effect, Encoding, Ref, Stream } from "effect"
import { Effect } from "effect"
import {
CacheHint,
GenerationOptions,
LLM,
LLMEvent,
LLMRequest,
Message,
ToolCallPart,
@@ -84,17 +83,6 @@ const eventStreamBody = (...payloads: ReadonlyArray<readonly [string, object]>)
const fixedBytes = (bytes: Uint8Array) =>
fixedResponse(bytes.slice().buffer, { headers: { "content-type": "application/vnd.amazon.eventstream" } })
const fixedByteChunks = (...chunks: ReadonlyArray<Uint8Array>) =>
fixedResponse(
new ReadableStream<Uint8Array>({
start(controller) {
chunks.forEach((chunk) => controller.enqueue(chunk))
controller.close()
},
}),
{ headers: { "content-type": "application/vnd.amazon.eventstream" } },
)
const model = AmazonBedrock.configure({
baseURL: "https://bedrock-runtime.test",
apiKey: "test-bearer",
@@ -125,50 +113,6 @@ describe("Bedrock Converse route", () => {
}),
)
it.effect("omits empty initial system blocks", () =>
Effect.gen(function* () {
const empty = yield* compileRequest(LLM.request({ model, system: "", prompt: "hello" }))
const cachedEmpty = yield* compileRequest(
LLM.request({
model,
system: [{ type: "text", text: "", cache: new CacheHint({ type: "ephemeral" }) }],
prompt: "hello",
cache: "none",
}),
)
expect(empty.body.system).toBeUndefined()
expect(cachedEmpty.body.system).toBeUndefined()
}),
)
it.effect("omits empty system blocks while preserving order and cache hints", () =>
Effect.gen(function* () {
const cache = new CacheHint({ type: "ephemeral" })
const prepared = yield* compileRequest(
LLM.request({
model,
system: [
{ type: "text", text: "", cache },
{ type: "text", text: "First." },
{ type: "text", text: " " },
{ type: "text", text: "" },
{ type: "text", text: "Second.", cache },
],
prompt: "hello",
cache: "none",
}),
)
expect(prepared.body.system).toEqual([
{ text: "First." },
{ text: " " },
{ text: "Second." },
{ cachePoint: { type: "default" } },
])
}),
)
it.effect("passes topK through additionalModelRequestFields as top_k", () =>
Effect.gen(function* () {
const prepared = yield* compileRequest(
@@ -311,79 +255,6 @@ describe("Bedrock Converse route", () => {
}),
)
it.effect("removes empty keys recursively from outbound tool inputs without mutating history", () =>
Effect.gen(function* () {
const input = {
path: "file.ts",
edits: [
{ oldText: "a", newText: "b", "": "" },
null,
true,
7,
"text",
["kept", { "": false, nested: { "": null, value: "ok" } }],
],
nested: { "": "drop", empty: {}, onlyEmpty: { "": 1 } },
" ": "preserve whitespace key",
"": "drop",
}
const original = structuredClone(input)
const call = ToolCallPart.make({ id: "tool_1", name: "edit", input })
const prepared = yield* compileRequest(
LLM.request({ model, messages: [Message.assistant([call])], cache: "none" }),
)
expect(prepared.body.messages).toEqual([
{
role: "assistant",
content: [
{
toolUse: {
toolUseId: "tool_1",
name: "edit",
input: {
path: "file.ts",
edits: [{ oldText: "a", newText: "b" }, null, true, 7, "text", ["kept", { nested: { value: "ok" } }]],
nested: { empty: {}, onlyEmpty: {} },
" ": "preserve whitespace key",
},
},
},
],
},
])
expect(input).toEqual(original)
expect(call.input).toBe(input)
}),
)
it.effect("keeps empty tool inputs and empties inputs containing only empty keys", () =>
Effect.gen(function* () {
const prepared = yield* compileRequest(
LLM.request({
model,
messages: [
Message.assistant([
ToolCallPart.make({ id: "tool_empty_key", name: "first", input: { "": { value: true } } }),
ToolCallPart.make({ id: "tool_empty_object", name: "second", input: {} }),
]),
],
cache: "none",
}),
)
expect(prepared.body.messages).toEqual([
{
role: "assistant",
content: [
{ toolUse: { toolUseId: "tool_empty_key", name: "first", input: {} } },
{ toolUse: { toolUseId: "tool_empty_object", name: "second", input: {} } },
],
},
])
}),
)
it.effect("merges parallel tool results into one user message", () =>
Effect.gen(function* () {
const prepared = yield* compileRequest(
@@ -514,77 +385,19 @@ describe("Bedrock Converse route", () => {
}),
)
it.effect("rejects truncated event-stream frames after message stop", () =>
it.effect("maps truncation and malformed output stop reasons", () =>
Effect.gen(function* () {
const partialFrames = [
eventFrame("metadata", { usage: { inputTokens: 5, outputTokens: 2, totalTokens: 7 } }).subarray(0, 3),
exceptionFrame("modelStreamErrorException", { originalMessage: "Upstream model failed" }).subarray(0, -1),
]
const reasons = [
["model_context_window_exceeded", "length"],
["malformed_model_output", "error"],
["malformed_tool_use", "error"],
] as const
for (const partial of partialFrames) {
const error = yield* LLMClient.generate(baseRequest).pipe(
Effect.provide(fixedBytes(concat([eventFrame("messageStop", { stopReason: "end_turn" }), partial]))),
Effect.flip,
for (const [raw, normalized] of reasons) {
const response = yield* LLMClient.generate(baseRequest).pipe(
Effect.provide(fixedBytes(eventStreamBody(["messageStop", { stopReason: raw }]))),
)
expect(error).toMatchObject({
reason: { _tag: "InvalidProviderOutput", classification: "incomplete-stream" },
message: `Incomplete Bedrock Converse event-stream frame: ${partial.length} buffered bytes remain at end of stream`,
})
expect(error.reason.body).toBe(Encoding.encodeBase64(partial))
}
}),
)
it.effect("decodes frames split across transport chunks through exact-boundary EOF", () =>
Effect.gen(function* () {
const body = eventStreamBody(
["messageStart", { role: "assistant" }],
["contentBlockDelta", { contentBlockIndex: 0, delta: { text: "Hello" } }],
["messageStop", { stopReason: "end_turn" }],
)
const response = yield* LLMClient.generate(baseRequest).pipe(
Effect.provide(fixedByteChunks(body.subarray(0, 2), body.subarray(2, 17), body.subarray(17))),
)
expect(response.text).toBe("Hello")
expect(response.finishReason).toEqual({ normalized: "stop", raw: "end_turn" })
}),
)
it.effect("maps model context window exhaustion to length", () =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(baseRequest).pipe(
Effect.provide(fixedBytes(eventStreamBody(["messageStop", { stopReason: "model_context_window_exceeded" }]))),
)
expect(response.finishReason).toEqual({
normalized: "length",
raw: "model_context_window_exceeded",
})
}),
)
it.effect("fails malformed output stop reasons", () =>
Effect.gen(function* () {
for (const reason of ["malformed_model_output", "malformed_tool_use"] as const) {
const events = yield* Ref.make<ReadonlyArray<LLMEvent>>([])
const error = yield* LLMClient.stream(baseRequest).pipe(
Stream.tap((event) => Ref.update(events, (current) => [...current, event])),
Stream.runDrain,
Effect.provide(fixedBytes(eventStreamBody(["messageStop", { stopReason: reason }]))),
Effect.flip,
)
expect(error).toMatchObject({
reason: { _tag: "InvalidProviderOutput" },
message: `Bedrock Converse stopped with ${reason}`,
})
expect(JSON.parse(error.reason.body ?? "")).toMatchObject({
headers: { ":event-type": { value: "messageStop" } },
body: JSON.stringify({ stopReason: reason }),
})
expect((yield* Ref.get(events)).some((event) => event.type === "finish")).toBeFalse()
expect(response.finishReason).toEqual({ normalized, raw })
}
}),
)
@@ -631,45 +444,10 @@ describe("Bedrock Converse route", () => {
)
const response = yield* LLMClient.generate(baseRequest).pipe(Effect.provide(fixedBytes(body)))
expect(response.events.filter((event) => event.type === "finish")).toHaveLength(1)
expect(response.usage).toMatchObject({ inputTokens: 5, outputTokens: 2, totalTokens: 7 })
}),
)
it.effect("retains metadata usage that arrives before messageStop", () =>
Effect.gen(function* () {
const body = eventStreamBody(
["metadata", { usage: { inputTokens: 5, outputTokens: 2, totalTokens: 7 } }],
["messageStop", { stopReason: "end_turn" }],
)
const response = yield* LLMClient.generate(baseRequest).pipe(Effect.provide(fixedBytes(body)))
expect(response.events.filter((event) => event.type === "finish")).toHaveLength(1)
expect(response.finishReason).toEqual({ normalized: "stop", raw: "end_turn" })
expect(response.usage).toMatchObject({ inputTokens: 5, outputTokens: 2, totalTokens: 7 })
}),
)
it.effect("rejects metadata-only streams as incomplete with HTTP context", () =>
Effect.gen(function* () {
const error = yield* LLMClient.generate(baseRequest).pipe(
Effect.provide(
fixedBytes(eventStreamBody(["metadata", { usage: { inputTokens: 5, outputTokens: 2, totalTokens: 7 } }])),
),
Effect.flip,
)
expect(error.reason).toMatchObject({
_tag: "InvalidProviderOutput",
classification: "incomplete-stream",
http: {
status: 200,
headers: { "content-type": "application/vnd.amazon.eventstream" },
},
})
}),
)
it.effect("assembles streamed tool call input", () =>
Effect.gen(function* () {
const body = eventStreamBody(
@@ -1075,7 +853,7 @@ describe("Bedrock Converse route", () => {
Effect.gen(function* () {
// Bedrock represents redactedContent blobs as base64 strings on its JSON
// wire. The provider owns the payload and requires byte-exact replay.
const redactedData = "AQID"
const redactedData = "cmVkYWN0ZWQtdGhpbmtpbmc="
const response = yield* LLMClient.generate(
LLMRequest.update(baseRequest, {
tools: [ToolDefinition.make({ name: "lookup", description: "Lookup data", inputSchema: { type: "object" } })],
@@ -1085,8 +863,10 @@ describe("Bedrock Converse route", () => {
fixedBytes(
eventStreamBody(
["messageStart", { role: "assistant" }],
["contentBlockDelta", { contentBlockIndex: 0, delta: { reasoningContent: { redactedContent: "AQ==" } } }],
["contentBlockDelta", { contentBlockIndex: 0, delta: { reasoningContent: { redactedContent: "AgM=" } } }],
[
"contentBlockDelta",
{ contentBlockIndex: 0, delta: { reasoningContent: { redactedContent: redactedData } } },
],
["contentBlockStop", { contentBlockIndex: 0 }],
[
"contentBlockStart",
@@ -1102,17 +882,12 @@ describe("Bedrock Converse route", () => {
),
),
)
expect(response.events.filter((event) => event.type === "reasoning-delta" && event.text === "").at(-1)).toEqual({
expect(response.events.find((event) => event.type === "reasoning-delta" && event.text === "")).toEqual({
type: "reasoning-delta",
id: "reasoning-0",
text: "",
providerMetadata: { bedrock: { redactedData } },
})
expect(response.events.find((event) => event.type === "reasoning-end")).toEqual({
type: "reasoning-end",
id: "reasoning-0",
providerMetadata: { bedrock: { redactedData } },
})
const prepared = yield* compileRequest(
LLM.request({
model,
@@ -1143,73 +918,6 @@ describe("Bedrock Converse route", () => {
}),
)
it.effect("keeps redacted reasoning accumulation separate by content block index", () =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(baseRequest).pipe(
Effect.provide(
fixedBytes(
eventStreamBody(
["messageStart", { role: "assistant" }],
["contentBlockDelta", { contentBlockIndex: 2, delta: { reasoningContent: { redactedContent: "AQ==" } } }],
["contentBlockDelta", { contentBlockIndex: 2, delta: { reasoningContent: { redactedContent: "Ag==" } } }],
["contentBlockStop", { contentBlockIndex: 2 }],
["contentBlockDelta", { contentBlockIndex: 7, delta: { reasoningContent: { redactedContent: "Aw==" } } }],
["contentBlockDelta", { contentBlockIndex: 7, delta: { reasoningContent: { redactedContent: "BA==" } } }],
["contentBlockStop", { contentBlockIndex: 7 }],
["messageStop", { stopReason: "end_turn" }],
),
),
),
)
expect(response.message.content).toEqual([
{ type: "reasoning", text: "", providerMetadata: { bedrock: { redactedData: "AQI=" } } },
{ type: "reasoning", text: "", providerMetadata: { bedrock: { redactedData: "AwQ=" } } },
])
}),
)
it.effect("preserves split redacted reasoning when contentBlockStop is missing", () =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(baseRequest).pipe(
Effect.provide(
fixedBytes(
eventStreamBody(
["messageStart", { role: "assistant" }],
["contentBlockDelta", { contentBlockIndex: 0, delta: { reasoningContent: { redactedContent: "AQ==" } } }],
["contentBlockDelta", { contentBlockIndex: 0, delta: { reasoningContent: { redactedContent: "AgM=" } } }],
["messageStop", { stopReason: "end_turn" }],
),
),
),
)
expect(response.message.content).toEqual([
{ type: "reasoning", text: "", providerMetadata: { bedrock: { redactedData: "AQID" } } },
])
}),
)
it.effect("rejects invalid redacted reasoning base64 with the triggering event", () =>
Effect.gen(function* () {
const payload = { contentBlockIndex: 0, delta: { reasoningContent: { redactedContent: "%%==" } } }
const error = yield* LLMClient.generate(baseRequest).pipe(
Effect.provide(fixedBytes(eventStreamBody(["contentBlockDelta", payload]))),
Effect.flip,
)
expect(error).toMatchObject({
reason: { _tag: "InvalidProviderOutput" },
message: "Bedrock Converse reasoningContent.redactedContent contains invalid base64 data",
})
expect(JSON.parse(error.reason.body ?? "")).toMatchObject({
headers: { ":event-type": { value: "contentBlockDelta" } },
body: JSON.stringify(payload),
})
expect(error.reason.cause).toBeInstanceOf(Error)
}),
)
it.effect("ignores unknown normal stream events", () =>
Effect.gen(function* () {
const body = concat([
@@ -1464,20 +1172,6 @@ describe("Bedrock Converse route", () => {
}),
)
it.effect("rejects image media that is not valid base64", () =>
Effect.gen(function* () {
const error = yield* compileRequest(
LLM.request({
model,
messages: [Message.user({ type: "media", mediaType: "image/png", data: "https://example.test/image.png" })],
}),
).pipe(Effect.flip)
expect(error).toMatchObject({ reason: { _tag: "InvalidRequest" } })
expect(error.message).toContain("Bedrock Converse media data must be valid base64")
}),
)
it.effect("lowers document media into Bedrock document blocks with format and name", () =>
Effect.gen(function* () {
const prepared = yield* compileRequest(
@@ -1601,37 +1295,6 @@ describe("Bedrock Converse route", () => {
}),
)
it.effect("rejects remote media URLs in tool results", () =>
Effect.gen(function* () {
const error = yield* compileRequest(
LLM.request({
model,
messages: [
Message.assistant([ToolCallPart.make({ id: "call_1", name: "read", input: {} })]),
Message.tool({
id: "call_1",
name: "read",
result: {
type: "content",
value: [
{
type: "file",
uri: "https://example.test/report.pdf",
mime: "application/pdf",
name: "report.pdf",
},
],
},
}),
],
}),
).pipe(Effect.flip)
expect(error).toMatchObject({ reason: { _tag: "InvalidRequest" } })
expect(error.message).toContain("Bedrock Converse media data must be valid base64")
}),
)
it.effect("rejects unsupported image media types", () =>
Effect.gen(function* () {
const error = yield* compileRequest(
@@ -1,50 +0,0 @@
import { expect } from "bun:test"
import { Effect, Stream } from "effect"
import { LLM, LLMRequest, Message } from "../../src/index.js"
import { LLMClient, WebSocketTransport } from "../../src/route.js"
import { OpenAI } from "../../src/providers.js"
import { testEffect } from "../lib/effect.js"
import { fixedResponse } from "../lib/http.js"
testEffect(fixedResponse("unexpected HTTP fallback")).effect(
"WebSocket responses preserve compaction options and replay state",
() =>
Effect.gen(function* () {
const checkpoint = { type: "compaction", id: "cmp_ws", encrypted_content: "opaque" }
const sent: unknown[] = []
const webSocket = WebSocketTransport.makeDirect({
open: () =>
Effect.succeed({
sendText: (message) =>
Effect.sync(() => {
const body = JSON.parse(message)
expect(body.context_management).toEqual([{ type: "compaction", compact_threshold: 100000 }])
expect(body.stream).toBeUndefined()
if (sent.length) expect(body.input[1]).toEqual(checkpoint)
sent.push(body)
}),
messages: Stream.fromIterable(
[
{ type: "response.created", response: { id: "resp_ws" } },
{ type: "response.output_item.done", item: checkpoint },
{ type: "response.completed", response: { id: "resp_ws", output: [checkpoint] } },
].map((event) => JSON.stringify(event)),
),
close: Effect.void,
}),
})
const request = LLM.request({
model: OpenAI.configure({ apiKey: "test" }).responses("gpt-5.3-codex"),
prompt: "hello",
providerOptions: { contextManagement: [{ type: "compaction", compactThreshold: 100000 }] },
})
const first = yield* LLMClient.generate(request, { webSocket })
expect(first.message.content).toHaveLength(1)
expect(first.message.content[0]?.type).toBe("compaction")
yield* LLMClient.generate(
LLMRequest.update(request, { messages: [...request.messages, first.message, Message.user("continue")] }),
{ webSocket },
)
expect(sent).toHaveLength(2)
}),
)
@@ -1,91 +0,0 @@
import { expect } from "bun:test"
import { Effect } from "effect"
import { LLM, LLMRequest, Message } from "../../src/index.js"
import { LLMClient } from "../../src/route/client.js"
import { OpenAI, XAI, Anthropic } from "../../src/providers.js"
import { recordedTests } from "../recorded-test.js"
const history = [
Message.user("Remember the project codename COPPER-ORBIT-42."),
Message.assistant(
"The project codename is COPPER-ORBIT-42. " + "We reviewed the implementation and tests. ".repeat(1000),
),
]
for (const provider of [
{
id: "openai",
key: "OPENAI_API_KEY",
model: OpenAI.configure({ apiKey: process.env.OPENAI_API_KEY ?? "fixture" }).responses("gpt-5.3-codex"),
},
{
id: "xai",
key: "XAI_API_KEY",
model: XAI.configure({ apiKey: process.env.XAI_API_KEY ?? "fixture" }).responses("grok-4.6"),
},
]) {
recordedTests({ prefix: `${provider.id}-compaction`, provider: provider.id, requires: [provider.key] }).effect(
"compacts and continues with the provider checkpoint",
() =>
Effect.gen(function* () {
const request = LLM.request({ model: provider.model, messages: history, generation: { maxTokens: 1024 } })
const compacted = yield* LLMClient.compact(request)
const result = yield* LLMClient.generate(
LLMRequest.update(request, {
messages: [
...compacted.replacement,
Message.user("What is the project codename? Reply only with the codename."),
],
}),
)
expect(result.text).toContain("COPPER-ORBIT-42")
}),
120000,
)
}
recordedTests({
prefix: "anthropic-compaction",
provider: "anthropic",
requires: ["ANTHROPIC_API_KEY"],
options: { redact: { allowRequestHeaders: ["anthropic-version", "anthropic-beta"] } },
}).effect(
"automatically compacts and continues after a pause",
() =>
Effect.gen(function* () {
const model = Anthropic.configure({ apiKey: process.env.ANTHROPIC_API_KEY ?? "fixture" }).model(
"claude-sonnet-4-6",
)
const request = LLM.request({
model,
messages: [
Message.user(
"Remember the project codename COPPER-ORBIT-42. " +
"The implementation and tests were reviewed. ".repeat(10000),
),
],
generation: { maxTokens: 4096 },
providerOptions: {
contextManagement: {
edits: [
{ type: "compact_20260112", trigger: { type: "input_tokens", value: 50000 }, pauseAfterCompaction: true },
],
},
},
})
const first = yield* LLMClient.generate(request)
expect(first.finishReason.raw).toBe("compaction")
expect(first.message.content.some((part) => part.type === "compaction")).toBe(true)
const result = yield* LLMClient.generate(
LLMRequest.update(request, {
messages: [
...request.messages,
first.message,
Message.user("What is the project codename? Reply only with the codename."),
],
}),
)
expect(result.text).toContain("COPPER-ORBIT-42")
}),
120000,
)
@@ -1,139 +0,0 @@
import { expect } from "bun:test"
import { Effect, Schema } from "effect"
import { LLM, LLMRequest, Message } from "../../src/index.js"
import { LLMClient } from "../../src/route/client.js"
import { OpenAI, Azure, XAI } from "../../src/providers/index.js"
import { testEffect } from "../lib/effect.js"
import { dynamicResponse, fixedResponse } from "../lib/http.js"
import { sseEvents } from "../lib/sse.js"
const checkpoint = { type: "compaction", id: "cmp_1", encrypted_content: "opaque" }
const response = sseEvents(
{ type: "response.output_item.done", item: checkpoint },
{
type: "response.completed",
response: { id: "resp_1", output: [checkpoint], usage: { input_tokens: 10, output_tokens: 2, total_tokens: 12 } },
},
)
for (const model of [
OpenAI.configure({ apiKey: "test" }).responses("gpt-5.3-codex"),
Azure.configure({ apiKey: "test", resourceName: "test" }).responses("deployment"),
]) {
testEffect(
dynamicResponse(({ text, respond }) =>
Effect.sync(() => {
const body = JSON.parse(text)
expect(body.context_management).toEqual([{ type: "compaction", compact_threshold: 100000 }])
expect(body.store).toBe(false)
if (body.input.length > 1) expect(body.input[1]).toEqual(checkpoint)
return respond(response, { headers: { "content-type": "text/event-stream" } })
}),
),
).effect(`${model.provider} compaction survives generation, serialization, and a second request`, () =>
Effect.gen(function* () {
const request = LLM.request({
model,
prompt: "hello",
providerOptions: { contextManagement: [{ type: "compaction", compactThreshold: 100000 }] },
})
const first = yield* LLMClient.generate(request)
expect(first.message.content).toHaveLength(1)
expect(first.message.content[0]?.type).toBe("compaction")
expect(first.text).toBe("")
const codec = Schema.fromJsonString(Message)
const message = Schema.decodeSync(codec)(Schema.encodeSync(codec)(first.message))
yield* LLMClient.generate(
LLMRequest.update(request, { messages: [...request.messages, message, Message.user("continue")] }),
)
const rejected = yield* LLMClient.generate(
LLMRequest.update(request, {
model: XAI.configure({ apiKey: "test" }).responses("grok-4.6"),
providerOptions: {},
messages: [message],
}),
).pipe(Effect.flip)
expect(rejected.reason._tag).toBe("InvalidRequest")
}),
)
}
testEffect(fixedResponse(sseEvents({ type: "response.completed", response: { output: [checkpoint] } }))).effect(
"recovers compaction from the terminal output when item completion is absent",
() =>
Effect.gen(function* () {
const result = yield* LLMClient.generate(
LLM.request({ model: OpenAI.configure({ apiKey: "test" }).responses("gpt-5.3-codex"), prompt: "hello" }),
)
expect(result.message.content).toHaveLength(1)
expect(result.message.content[0]?.type).toBe("compaction")
}),
)
testEffect(
fixedResponse(sseEvents({ type: "response.output_item.done", item: { type: "compaction", id: "cmp_bad" } })),
).effect("rejects incomplete compaction payloads without publishing a checkpoint", () =>
Effect.gen(function* () {
const error = yield* LLMClient.generate(
LLM.request({ model: OpenAI.configure({ apiKey: "test" }).responses("gpt-5.3-codex"), prompt: "hello" }),
).pipe(Effect.flip)
expect(error.reason._tag).toBe("InvalidProviderOutput")
expect(error.reason.body).toContain("cmp_bad")
}),
)
const textItem = {
type: "message",
id: "msg_after",
role: "assistant",
content: [{ type: "output_text", text: "After checkpoint" }],
}
for (const completed of [false, true]) {
testEffect(
fixedResponse(
sseEvents(
{ type: "response.output_item.added", output_index: 0, item: { type: "compaction", id: checkpoint.id } },
...(completed ? [{ type: "response.output_item.done", output_index: 0, item: checkpoint }] : []),
{ type: "response.output_item.added", output_index: 1, item: textItem },
{ type: "response.output_text.delta", output_index: 1, item_id: textItem.id, delta: "After checkpoint" },
{ type: "response.output_item.done", output_index: 1, item: textItem },
{ type: "response.completed", response: { id: "resp_1", output: [checkpoint, textItem] } },
),
),
).effect(completed ? "keeps streamed checkpoints before later text" : "rejects order-unsafe terminal recovery", () =>
Effect.gen(function* () {
const request = LLM.request({ model: OpenAI.configure({ apiKey: "test" }).responses("fixture"), prompt: "hello" })
if (completed) {
const response = yield* LLMClient.generate(request)
expect(response.message.content.map((part) => part.type)).toEqual(["compaction", "text"])
return
}
const error = yield* LLMClient.generate(request).pipe(Effect.flip)
expect(error.reason._tag).toBe("InvalidProviderOutput")
expect(error.message).toContain("Cannot recover a compaction checkpoint")
expect(error.reason.body).toContain("response.completed")
expect(error.reason.http?.status).toBe(200)
}),
)
}
testEffect(
fixedResponse(
sseEvents({
type: "response.completed",
response: { output: [{ type: "compaction", encrypted_content: "opaque" }] },
}),
),
).effect("mints an id for terminal checkpoints that omit one", () =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(
LLM.request({ model: OpenAI.configure({ apiKey: "test" }).responses("fixture"), prompt: "hello" }),
)
const part = response.message.content[0]
expect(part?.type).toBe("compaction")
if (part?.type !== "compaction") return
expect(part.id).toMatch(/^cmp_[0-9a-f]{32}$/)
expect(part.encrypted).toBe("opaque")
}),
)
@@ -1,30 +0,0 @@
import { expect } from "bun:test"
import { Effect } from "effect"
import { LLM, Message } from "../../src/index.js"
import { OpenAI } from "../../src/providers.js"
import { OpenResponses } from "../../src/protocols/open-responses.js"
import { it } from "../lib/effect.js"
it.effect("conversation lowering excludes generation settings and tool definitions", () =>
Effect.gen(function* () {
const body = yield* OpenResponses.lowerConversation(
LLM.request({
model: OpenAI.configure({ apiKey: "test" }).responses("fixture"),
system: "Keep the context",
messages: [Message.user("hello"), Message.assistant("hi")],
generation: { maxTokens: 100, temperature: 0.5 },
providerOptions: { store: false },
tools: [{ name: "unsupported", description: "Generation only", inputSchema: {}, native: { unsupported: {} } }],
}),
{ id: "open-responses", name: "Open Responses" },
)
expect(body).toEqual({
model: "fixture",
instructions: "Keep the context",
input: [
{ role: "user", content: [{ type: "input_text", text: "hello" }] },
{ type: "message", role: "assistant", content: [{ type: "output_text", text: "hi" }] },
],
})
}),
)
@@ -62,62 +62,6 @@ describe("provider error retention", () => {
)
}
it.effect("classifies a message-less Gemini 429 and retains its event and HTTP context", () =>
Effect.gen(function* () {
const body = JSON.stringify({
error: { code: 429, status: "RESOURCE_EXHAUSTED", details: { opaque: [1, 2] } },
trace: { opaque: "outer" },
})
const error = yield* LLMClient.generate(
LLM.request({ model: Google.configure(options).model("gemini"), prompt: "hello" }),
).pipe(
Effect.provide(
fixedResponse(sseEvents(body), {
headers: { "content-type": "text/event-stream", "x-provider-trace": "trace-1" },
}),
),
Effect.flip,
)
expect(error.message).toBe("RESOURCE_EXHAUSTED")
expect(error.reason._tag).toBe("RateLimit")
expect(error.reason.body).toBe(body)
expect(error.reason.http).toMatchObject({ status: 200, headers: { "x-provider-trace": "trace-1" } })
expect(error.reason.http?.url).toStartWith("https://provider.test/")
}),
)
it.effect("rejects a malformed non-record Gemini error", () =>
Effect.gen(function* () {
const body = JSON.stringify({ error: "RESOURCE_EXHAUSTED", trace: { opaque: "outer" } })
const error = yield* LLMClient.generate(
LLM.request({ model: Google.configure(options).model("gemini"), prompt: "hello" }),
).pipe(Effect.provide(fixedResponse(sseEvents(body))), Effect.flip)
expect(error.reason._tag).toBe("InvalidProviderOutput")
expect(error.message).toContain("Invalid google/gemini stream event")
expect(error.reason.body).toBe(body)
expect(error.reason.http?.status).toBe(200)
}),
)
it.effect("rejects and retains an explicit null Gemini error", () =>
Effect.gen(function* () {
const body = JSON.stringify({ error: null, trace: { opaque: "outer" } })
const error = yield* LLMClient.generate(
LLM.request({ model: Google.configure(options).model("gemini"), prompt: "hello" }),
).pipe(
Effect.provide(fixedResponse(sseEvents(body), { headers: { "x-provider-trace": "trace-null" } })),
Effect.flip,
)
expect(error.reason._tag).toBe("InvalidProviderOutput")
expect(error.reason.body).toBe(body)
expect(error.reason.http).toMatchObject({ status: 200, headers: { "x-provider-trace": "trace-null" } })
expect(error.reason.http?.url).toStartWith("https://provider.test/")
}),
)
it.effect("retains malformed provider frames and the original decode cause", () =>
Effect.gen(function* () {
const body = '{"type":"error","error":{"message":42,"opaque":{"nested":true}},"trace":"outer"}'
@@ -1,456 +0,0 @@
import { expect } from "bun:test"
import { Effect, Schema } from "effect"
import { LLM, LLMRequest, Message } from "../../src/index.js"
import { LLMClient, Route } from "../../src/route/client.js"
import { Auth } from "../../src/route/auth.js"
import { Endpoint } from "../../src/route/endpoint.js"
import { OpenAIResponses } from "../../src/protocols/openai-responses.js"
import { OpenAI, Azure, XAI, Anthropic, OpenAICompatibleResponses } from "../../src/providers/index.js"
import { testEffect } from "../lib/effect.js"
import { dynamicResponse, fixedResponse } from "../lib/http.js"
import { sseEvents } from "../lib/sse.js"
const checkpoint = { type: "compaction", id: "cmp_1", encrypted_content: "opaque" }
const retained = {
type: "message",
role: "user",
id: "msg_1",
status: "completed",
content: [{ type: "input_text", text: "retained" }],
}
const output = [retained, checkpoint]
testEffect(
dynamicResponse(({ request, text, respond }) =>
Effect.sync(() => {
expect(request.headers["x-deployment"]).toBe("fixture")
expect(request.headers["x-override"]).toBe("request")
expect(request.headers["x-default"]).toBe("configured")
expect(request.headers.authorization).toBe("Bearer test")
expect(new URL(request.url).searchParams.get("api-version")).toBe("fixture")
expect(new URL(request.url).searchParams.get("trace")).toBe("request")
if (new URL(request.url).pathname.endsWith("/compact")) {
expect(JSON.parse(text)).toEqual({
model: "overlaid",
input: [{ role: "user", content: [{ type: "input_text", text: "hello" }] }],
instructions: "request instructions",
previous_response_id: "resp_previous",
})
return respond(JSON.stringify({ object: "response.compaction", output }))
}
return respond(sseEvents({ type: "response.completed", response: { id: "resp_1" } }))
}),
),
).effect("generation and compaction share deployment headers, defaults, auth, query, and middleware", () =>
Effect.gen(function* () {
const headers: string[] = []
const middleware: string[] = []
const route = Route.make({
id: "compaction-headers",
provider: "openai",
protocol: OpenAIResponses.protocol,
compact: OpenAIResponses.route.compact,
transport: OpenAIResponses.httpTransport,
endpoint: Endpoint.path(({ body }) => `/${body.model}/responses`, {
baseURL: "https://example.com",
query: { "api-version": "fixture" },
}),
auth: Auth.bearer("test"),
headers: ({ request }) => {
expect(request.providerOptions?.store).toBe(false)
headers.push(String(request.model.id))
return { "x-deployment": "fixture", "x-override": "route" }
},
defaults: {
headers: { "x-default": "configured", "x-override": "configured" },
providerOptions: { store: false },
http: { body: { instructions: "default instructions" } },
},
})
const request = LLM.request({
model: route.model({ id: "fixture" }),
prompt: "hello",
system: "system instructions",
http: {
headers: { "x-override": "request" },
query: { trace: "request" },
body: {
model: "overlaid",
instructions: "request instructions",
previous_response_id: "resp_previous",
store: false,
stream: true,
},
},
})
const options: Parameters<typeof LLMClient.compact>[1] = {
http: (request, next) => {
middleware.push(new URL(request.url).pathname)
return next(request)
},
}
yield* LLMClient.generate(request, options)
yield* LLMClient.compact(request, options)
expect(headers).toEqual(["fixture", "fixture"])
expect(middleware).toEqual(["/fixture/responses", "/fixture/responses/compact"])
}),
)
for (const model of [
OpenAI.configure({ apiKey: "test" }).responses("fixture"),
Azure.configure({ apiKey: "test", resourceName: "test" }).responses("fixture"),
XAI.configure({ apiKey: "test" }).responses("fixture"),
]) {
const item = {
type: model.provider === "xai" ? "x_search_call" : "computer_call",
id: "hosted_1",
status: "completed",
}
testEffect(
dynamicResponse(({ request, text, respond }) =>
Effect.sync(() => {
expect(new URL(request.url).pathname).toEndWith("/responses/compact")
expect(JSON.parse(text)).toEqual({ model: "fixture", input: [item], instructions: "Keep the context" })
return respond(JSON.stringify({ object: "response.compaction", output: [checkpoint] }))
}),
),
).effect(`${model.provider} compacts provider-specific history without lowering generation settings`, () =>
Effect.gen(function* () {
const request = LLM.request({
model,
system: "Keep the context",
messages: [
Message.assistant({
type: "tool-result",
id: item.id,
name: item.type,
result: { type: "json", value: item },
providerExecuted: true,
providerMetadata: { [model.route.providerMetadataKey ?? model.provider]: { itemId: item.id } },
}),
],
})
for (const [candidate, tag] of [
[
LLMRequest.update(request, {
tools: [
{ name: "unsupported", description: "Generation only", inputSchema: {}, native: { unsupported: {} } },
],
}),
"InvalidRequest",
],
[
LLMRequest.update(request, { providerOptions: { contextManagement: "invalid-generation-option" } }),
model.provider === "xai" ? "UnsupportedOperation" : "InvalidRequest",
],
] as const) {
const error = yield* LLMClient.generate(candidate).pipe(Effect.flip)
expect(error.reason._tag).toBe(tag)
const response = yield* LLMClient.compact(candidate)
expect(response.replacement[0]?.content[0]?.type).toBe("compaction")
}
}),
)
}
const retainedItems = [
retained,
{
type: "message",
id: "msg_assistant",
role: "assistant",
status: "completed",
phase: "commentary",
content: [
{ type: "output_text", text: "First" },
{ type: "output_text", text: "Second" },
],
},
{
type: "reasoning",
id: "rs_1",
summary: [
{ type: "summary_text", text: "Thinking" },
{ type: "summary_text", text: "More thinking" },
],
encrypted_content: "reasoning-state",
},
{ type: "reasoning", id: "rs_2", summary: [], encrypted_content: "hidden-reasoning" },
{
type: "message",
id: "msg_media",
role: "user",
content: [
{ type: "input_image", image_url: "https://example.com/image.png" },
{ type: "input_file", filename: "report.pdf", file_data: "data:application/pdf;base64,cGRm", detail: "high" },
{ type: "input_file", filename: "other.pdf", file_url: "https://example.com/report.pdf", detail: "low" },
],
},
checkpoint,
]
for (const model of [
OpenAI.configure({ apiKey: "test" }).responses("gpt-5.3-codex"),
...[undefined, "custom"].map((providerMetadataKey) =>
Route.make({
id: providerMetadataKey ?? "default-metadata",
provider: "openai",
providerMetadataKey,
protocol: OpenAIResponses.protocol,
compact: OpenAIResponses.route.compact,
endpoint: OpenAIResponses.route.endpoint,
transport: OpenAIResponses.httpTransport,
}).model({ id: "fixture" }),
),
]) {
testEffect(
dynamicResponse(({ request, text, respond }) =>
Effect.sync(() => {
if (new URL(request.url).pathname.endsWith("/compact"))
return respond(JSON.stringify({ object: "response.compaction", output: retainedItems }))
expect(JSON.parse(text).input).toEqual(retainedItems)
return respond(sseEvents({ type: "response.completed", response: { id: "resp_1" } }), {
headers: { "content-type": "text/event-stream" },
})
}),
),
).effect(`${model.route.id} retains messages, reasoning, and media through typed conversation parts`, () =>
Effect.gen(function* () {
const request = LLM.request({
model,
prompt: "hello",
})
const compacted = yield* LLMClient.compact(request)
expect(compacted.replacement.map((message) => message.role)).toEqual([
"user",
"assistant",
"assistant",
"assistant",
"user",
"assistant",
])
expect(compacted.replacement[1]?.content).toEqual([
{ type: "text", text: "First" },
{ type: "text", text: "Second" },
])
expect(compacted.replacement[2]?.content.map((part) => part.type)).toEqual(["reasoning", "reasoning"])
expect(compacted.replacement[4]?.content.map((part) => part.type)).toEqual(["media", "media", "media"])
const codec = Schema.fromJsonString(Schema.Array(Message))
const messages = Schema.decodeSync(codec)(Schema.encodeSync(codec)(compacted.replacement))
yield* LLMClient.generate(LLMRequest.update(request, { messages }))
}),
)
}
for (const overlay of [undefined, { service_tier: "priority", prompt_cache_key: "overridden" }]) {
testEffect(
dynamicResponse(({ text, respond }) =>
Effect.sync(() => {
expect(JSON.parse(text)).toEqual({
model: "fixture",
input: [{ role: "user", content: [{ type: "input_text", text: "hello" }] }],
service_tier: overlay?.service_tier ?? "flex",
prompt_cache_key: overlay?.prompt_cache_key ?? "affinity",
prompt_cache_retention: "24h",
prompt_cache_options: { mode: "explicit", ttl: "30m" },
})
return respond(JSON.stringify({ object: "response.compaction", output: [checkpoint] }))
}),
),
).effect(`compact preserves supported request controls${overlay ? " with HTTP overrides" : ""}`, () =>
LLMClient.compact(
LLM.request({
model: OpenAI.configure({ apiKey: "test" }).responses("fixture"),
prompt: "hello",
promptCacheKey: "affinity",
providerOptions: { serviceTier: "flex" },
generation: { maxTokens: 100 },
http: {
body: {
stream: true,
store: false,
prompt_cache_retention: "24h",
prompt_cache_options: { mode: "explicit", ttl: "30m" },
...overlay,
},
},
}),
),
)
}
for (const item of [
{ type: "unknown_provider_item", data: "do not hide in a compaction part" },
{
type: "message",
role: "user",
content: [{ type: "input_image", image_url: "https://example.com/image.png", detail: 42 }],
},
{ type: "message", role: "user", content: [] },
{
type: "message",
role: "assistant",
content: [{ type: "input_image", image_url: "https://example.com/image.png" }],
},
{ type: "message", role: "user", content: [{ type: "input_file", filename: "missing.pdf" }] },
{
type: "message",
role: "user",
content: [{ type: "input_file", filename: "bad.pdf", file_url: "https://example.com/report.pdf", detail: 42 }],
},
{
type: "message",
role: "user",
content: [
{
type: "input_file",
filename: "both.pdf",
file_url: "https://example.com/report.pdf",
file_data: "data:application/pdf;base64,cGRm",
},
],
},
]) {
testEffect(fixedResponse(JSON.stringify({ object: "response.compaction", output: [item, checkpoint] }))).effect(
`rejects unsupported compact output: ${JSON.stringify(item)}`,
() =>
Effect.gen(function* () {
const error = yield* LLMClient.compact(
LLM.request({ model: OpenAI.configure({ apiKey: "test" }).responses("gpt-5.3-codex"), prompt: "hello" }),
).pipe(Effect.flip)
expect(error.reason._tag).toBe("InvalidProviderOutput")
expect(error.reason.body).toContain(JSON.stringify(item))
expect(error.reason.http?.status).toBe(200)
}),
)
}
for (const model of [
OpenAI.configure({ apiKey: "test" }).responses("fixture"),
Azure.configure({ apiKey: "test", resourceName: "test" }).responses("fixture"),
XAI.configure({ apiKey: "test" }).responses("fixture"),
]) {
const images = [undefined, "low", "high", "auto"].map((detail) => ({
type: "input_image",
image_url: "https://example.com/image.png",
...(detail === undefined ? {} : { detail }),
}))
testEffect(
dynamicResponse(({ request, text, respond }) =>
Effect.sync(() => {
if (new URL(request.url).pathname.endsWith("/compact"))
return respond(
JSON.stringify({
object: "response.compaction",
output: [{ type: "message", role: "user", content: images }, checkpoint],
}),
)
expect(JSON.parse(text).input[0].content).toEqual(images)
return respond(sseEvents({ type: "response.completed", response: { id: "resp_1" } }))
}),
),
).effect(`${model.provider} preserves retained image detail through serialization and replay`, () =>
Effect.gen(function* () {
const request = LLM.request({ model, prompt: "hello" })
const compacted = yield* LLMClient.compact(request)
const codec = Schema.fromJsonString(Schema.Array(Message))
const messages = Schema.decodeSync(codec)(Schema.encodeSync(codec)(compacted.replacement))
yield* LLMClient.generate(LLMRequest.update(request, { messages }))
}),
)
}
testEffect(fixedResponse("must not execute")).effect("xAI rejects automatic compaction options", () =>
Effect.gen(function* () {
const request = LLMRequest.update(
LLM.request({ model: XAI.configure({ apiKey: "test" }).responses("grok-4.6"), prompt: "hello" }),
{ providerOptions: { contextManagement: [{ type: "compaction" }] } },
)
const error = yield* LLMClient.generate(request).pipe(Effect.flip)
expect(error.reason._tag).toBe("UnsupportedOperation")
expect(error.message).toContain("LLMClient.compact")
if (error.reason._tag === "UnsupportedOperation") expect(error.reason.operation).toBe("in-band-compaction")
}),
)
for (const model of [
OpenAI.configure({ apiKey: "test" }).responses("gpt-5.3-codex"),
Azure.configure({ apiKey: "test", resourceName: "test" }).responses("deployment"),
XAI.configure({ apiKey: "test" }).responses("grok-4.6"),
]) {
testEffect(
dynamicResponse(({ request, text, respond }) =>
Effect.sync(() => {
const body = JSON.parse(text)
expect(request.method).toBe("POST")
expect(request.headers[model.provider === "azure" ? "api-key" : "authorization"]).toBe(
model.provider === "azure" ? "test" : "Bearer test",
)
if (new URL(request.url).pathname.endsWith("/responses/compact")) {
expect(body).toEqual({
model: model.id,
input: [{ role: "user", content: [{ type: "input_text", text: "original" }] }],
instructions: "system",
})
return respond(
JSON.stringify({
object: "response.compaction",
output,
usage: { input_tokens: 1000, output_tokens: 10, total_tokens: 1010 },
}),
{ headers: { "content-type": "application/json" } },
)
}
expect(new URL(request.url).pathname.endsWith("/responses")).toBe(true)
expect(body.input).toEqual([...output, { role: "user", content: [{ type: "input_text", text: "continue" }] }])
return respond(sseEvents({ type: "response.completed", response: { id: "resp_1", output: [] } }), {
headers: { "content-type": "text/event-stream" },
})
}),
),
).effect(`${model.provider} explicitly compacts and replays the entire canonical window`, () =>
Effect.gen(function* () {
const request = LLM.request({ model, prompt: "original", system: "system", http: { body: { store: false } } })
const compacted = yield* LLMClient.compact(request)
expect(compacted.usage?.totalTokens).toBe(1010)
expect(compacted.replacement.map((message) => message.role)).toEqual(["user", "assistant"])
expect(compacted.replacement[0]?.content).toEqual([{ type: "text", text: "retained" }])
expect(compacted.replacement[1]?.content).toEqual([
{ type: "compaction", provider: model.provider, id: "cmp_1", encrypted: "opaque" },
])
const codec = Schema.fromJsonString(Schema.Array(Message))
const messages = Schema.decodeSync(codec)(Schema.encodeSync(codec)(compacted.replacement))
yield* LLMClient.generate(LLMRequest.update(request, { messages: [...messages, Message.user("continue")] }))
}),
)
}
for (const model of [
Anthropic.configure({ apiKey: "test" }).model("claude-opus-4-6"),
OpenAICompatibleResponses.configure({ apiKey: "test", baseURL: "https://compatible.example/v1" }).model("model"),
]) {
testEffect(fixedResponse("must not execute")).effect(
`${model.route.id} does not inherit an unsupported compact endpoint`,
() =>
Effect.gen(function* () {
// @ts-expect-error Untyped callers must still receive the runtime capability error.
const error = yield* LLMClient.compact(LLM.request({ model, prompt: "hello" })).pipe(Effect.flip)
expect(error.reason._tag).toBe("UnsupportedOperation")
expect(error.message).toContain("does not support explicit compaction")
if (error.reason._tag === "UnsupportedOperation") expect(error.reason.operation).toBe("compact")
}),
)
}
testEffect(
fixedResponse(JSON.stringify({ object: "response.compaction", output: [retained], debug: "original payload" })),
).effect("invalid explicit compaction preserves the original response and HTTP context", () =>
Effect.gen(function* () {
const error = yield* LLMClient.compact(
LLM.request({ model: OpenAI.configure({ apiKey: "test" }).responses("gpt-5.3-codex"), prompt: "hello" }),
).pipe(Effect.flip)
expect(error.reason._tag).toBe("InvalidProviderOutput")
expect(error.reason.body).toContain("original payload")
expect(error.reason.http?.status).toBe(200)
}),
)
-138
View File
@@ -906,94 +906,6 @@ describe("Gemini route", () => {
}),
)
it.effect("assigns unique ids to separated reasoning blocks", () =>
Effect.gen(function* () {
const body = sseEvents(
{
candidates: [
{
content: {
role: "model",
parts: [{ text: "A", thought: true, thoughtSignature: "reasoning_sig_a" }],
},
},
],
},
{
candidates: [
{
content: { role: "model", parts: [{ text: "X", thoughtSignature: "text_sig_x" }] },
},
],
},
{
candidates: [
{
content: {
role: "model",
parts: [{ text: "B", thought: true, thoughtSignature: "reasoning_sig_b" }],
},
},
],
},
{
candidates: [
{
content: { role: "model", parts: [{ text: "Y", thoughtSignature: "text_sig_y" }] },
finishReason: "STOP",
},
],
},
)
const response = yield* LLMClient.generate(request).pipe(Effect.provide(fixedResponse(body)))
const starts = response.events.filter((event) => event.type === "reasoning-start")
const deltas = response.events.filter((event) => event.type === "reasoning-delta")
const ends = response.events.filter((event) => event.type === "reasoning-end")
expect(starts.map((event) => event.id)).toEqual(["reasoning-0", "reasoning-1"])
expect(starts[0]?.id).not.toBe(starts[1]?.id)
expect(deltas.map((event) => ({ id: event.id, text: event.text }))).toEqual([
{ id: "reasoning-0", text: "A" },
{ id: "reasoning-1", text: "B" },
])
expect(ends.map((event) => event.id)).toEqual(["reasoning-0", "reasoning-1"])
expect(response.events.filter((event) => event.type === "text-start").map((event) => event.id)).toEqual([
"text-0",
"text-1",
])
expect(response.events.filter((event) => event.type === "text-delta").map((event) => event.id)).toEqual([
"text-0",
"text-1",
])
expect(response.events.filter((event) => event.type === "text-end").map((event) => event.id)).toEqual([
"text-0",
"text-1",
])
expect(response.message.content).toEqual([
{
type: "reasoning",
text: "A",
providerMetadata: { google: { thoughtSignature: "reasoning_sig_a" } },
},
{
type: "text",
text: "X",
providerMetadata: { google: { thoughtSignature: "text_sig_x" } },
},
{
type: "reasoning",
text: "B",
providerMetadata: { google: { thoughtSignature: "reasoning_sig_b" } },
},
{
type: "text",
text: "Y",
providerMetadata: { google: { thoughtSignature: "text_sig_y" } },
},
])
}),
)
it.effect("ignores unknown response parts", () =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(request).pipe(
@@ -1368,56 +1280,6 @@ describe("Gemini route", () => {
}),
)
it.effect("separates text blocks around streamed tool calls", () =>
Effect.gen(function* () {
const body = sseEvents({
candidates: [
{
content: {
role: "model",
parts: [
{ text: "before" },
{ functionCall: { id: "call_1", name: "lookup", args: { query: "weather" } } },
{ text: "after" },
],
},
finishReason: "STOP",
},
],
})
const response = yield* LLMClient.generate(
LLMRequest.update(request, {
tools: [ToolDefinition.make({ name: "lookup", description: "Lookup data", inputSchema: { type: "object" } })],
}),
).pipe(Effect.provide(fixedResponse(body)))
expect(response.events.slice(1, 8)).toEqual([
{ type: "text-start", id: "text-0" },
{ type: "text-delta", id: "text-0", text: "before" },
{ type: "text-end", id: "text-0" },
{
type: "tool-call",
id: "call_1",
name: "lookup",
input: { query: "weather" },
providerExecuted: undefined,
providerMetadata: undefined,
},
{ type: "text-start", id: "text-1" },
{ type: "text-delta", id: "text-1", text: "after" },
{ type: "text-end", id: "text-1" },
])
const textStarts = response.events.filter((event) => event.type === "text-start")
expect(textStarts[0]?.id).not.toBe(textStarts[1]?.id)
expect(response.message.content).toEqual([
{ type: "text", text: "before" },
{ type: "tool-call", id: "call_1", name: "lookup", input: { query: "weather" } },
{ type: "text", text: "after" },
])
expect(response.finishReason).toEqual({ normalized: "tool-calls", raw: "STOP" })
}),
)
it.effect("defaults omitted function call args to an empty object", () =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(
@@ -1,60 +0,0 @@
import { expect } from "bun:test"
import { Effect, Schema } from "effect"
import { LLM, Message } from "../../src/index.js"
import { OpenAI, Azure, XAI } from "../../src/providers.js"
import { compileRequest } from "../../src/route/client.js"
import { it } from "../lib/effect.js"
for (const model of [
OpenAI.configure({ apiKey: "test" }).responses("fixture"),
Azure.configure({ apiKey: "test", resourceName: "test" }).responses("fixture"),
XAI.configure({ apiKey: "test" }).responses("fixture"),
]) {
it.effect(`${model.provider} preserves image detail through message serialization and lowering`, () =>
Effect.gen(function* () {
const details = [undefined, "low", "high", "auto"]
const message = Message.user(
details.map((detail) => ({
type: "media",
mediaType: "image/png",
data: "https://example.com/image.png",
providerMetadata:
detail === undefined ? undefined : { [model.route.providerMetadataKey ?? model.provider]: { detail } },
})),
)
const codec = Schema.fromJsonString(Message)
const prepared = yield* compileRequest(
LLM.request({
model,
messages: [Schema.decodeSync(codec)(Schema.encodeSync(codec)(message))],
}),
)
expect(prepared.body.input[0].content).toEqual(
details.map((detail) => ({
type: "input_image",
image_url: "https://example.com/image.png",
detail,
})),
)
}),
)
}
it.effect("rejects malformed image detail instead of silently discarding it", () =>
Effect.gen(function* () {
const error = yield* compileRequest(
LLM.request({
model: OpenAI.configure({ apiKey: "test" }).responses("fixture"),
messages: [
Message.user({
type: "media",
mediaType: "image/png",
data: "https://example.com/image.png",
providerMetadata: { openai: { detail: 42 } },
}),
],
}),
).pipe(Effect.flip)
expect(error.reason._tag).toBe("InvalidRequest")
}),
)
@@ -1,694 +0,0 @@
import { describe, expect, test } from "bun:test"
import { ConfigProvider, Effect } from "effect"
import { HttpClientRequest } from "effect/unstable/http"
import { LLM, LLMEvent, Message, ToolDefinition } from "../../src/index.js"
import { Mistral } from "../../src/providers/index.js"
import { MistralChat } from "../../src/protocols/index.js"
import { LLMClient } from "../../src/route.js"
import { compileRequest } from "../../src/route/client.js"
import { it } from "../lib/effect.js"
import { dynamicResponse, fixedResponse } from "../lib/http.js"
import { sseEvents } from "../lib/sse.js"
const model = Mistral.configure({ apiKey: "fixture" }).model("mistral-large-latest")
const request = LLM.request({ model, prompt: "Hello" })
const chunk = (delta: object, finishReason: string | null = null, usage?: object) => ({
choices: [{ delta, finish_reason: finishReason }],
usage,
})
describe("Mistral Chat", () => {
test("exposes native provider and protocol identities", async () => {
const entrypoint = await import("@opencode-ai/ai/providers/mistral")
expect(Mistral.id).toBe("mistral")
expect(MistralChat.protocol.id).toBe("mistral-chat")
expect(Mistral.route).toMatchObject({
id: "mistral-chat",
provider: "mistral",
providerMetadataKey: "mistral",
protocol: "mistral-chat",
})
expect(Mistral.route.endpoint).toMatchObject({
baseURL: "https://api.mistral.ai/v1",
path: "/chat/completions",
})
expect(entrypoint.model).toBeFunction()
})
it.effect("lowers native messages, media, tool choice, options, and replay IDs", () =>
Effect.gen(function* () {
const prepared = yield* compileRequest(
LLM.request({
model,
system: "Initial",
messages: [
Message.system("Updated"),
Message.user([
{ type: "text", text: "Inspect" },
{ type: "media", mediaType: "image/png", data: "aW1hZ2U=" },
{ type: "media", mediaType: "application/pdf", data: "cGRm" },
]),
Message.assistant([
{ type: "reasoning", text: "Think" },
{ type: "text", text: "Calling" },
{ type: "tool-call", id: "call.same-prefix-1", name: "lookup", input: { city: "Paris" } },
{ type: "tool-call", id: "call.same-prefix-2", name: "other", input: {} },
]),
Message.tool({ id: "call.same-prefix-1", name: "lookup", result: { ok: true } }),
],
tools: [
ToolDefinition.make({ name: "lookup", description: "Look up a city", inputSchema: { type: "object" } }),
ToolDefinition.make({ name: "other", description: "Other operation", inputSchema: { type: "object" } }),
],
toolChoice: "lookup",
promptCacheKey: "session-1",
generation: {
maxTokens: 64,
seed: 7,
temperature: 0.2,
topP: 0.8,
frequencyPenalty: 0.1,
presencePenalty: 0.3,
stop: ["done"],
},
providerOptions: {
safePrompt: true,
documentImageLimit: 3,
documentPageLimit: 8,
parallelToolCalls: false,
reasoningEffort: "high",
},
}),
)
expect(prepared.body).toMatchObject({
model: "mistral-large-latest",
tools: [{ function: { name: "lookup", strict: false } }, { function: { name: "other", strict: false } }],
tool_choice: { type: "function", function: { name: "lookup" } },
stream: true,
max_tokens: 64,
random_seed: 7,
temperature: 0.2,
top_p: 0.8,
frequency_penalty: 0.1,
presence_penalty: 0.3,
stop: ["done"],
prompt_cache_key: "session-1",
safe_prompt: true,
document_image_limit: 3,
document_page_limit: 8,
parallel_tool_calls: false,
reasoning_effort: "high",
})
expect(prepared.body.messages.slice(0, 4)).toMatchObject([
{ role: "system", content: "Initial" },
{ role: "user", content: "<system-update>\nUpdated\n</system-update>" },
{
role: "user",
content: [
{ type: "text", text: "Inspect" },
{ type: "image_url", image_url: "data:image/png;base64,aW1hZ2U=" },
{ type: "document_url", document_url: "data:application/pdf;base64,cGRm" },
],
},
{
role: "assistant",
content: "ThinkCalling",
},
])
const assistant = prepared.body.messages[3]
const toolResult = prepared.body.messages[4]
expect(assistant?.role).toBe("assistant")
expect(toolResult?.role).toBe("tool")
if (assistant?.role !== "assistant" || toolResult?.role !== "tool") return
const ids = assistant.tool_calls?.map((tool) => tool.id) ?? []
expect(ids).toHaveLength(2)
expect(ids[0]).toMatch(/^[A-Za-z0-9]{9}$/)
expect(ids[1]).toMatch(/^[A-Za-z0-9]{9}$/)
expect(ids[0]).not.toBe(ids[1])
expect(toolResult.tool_call_id).toBe(ids[0])
expect(toolResult.name).toBe("lookup")
}),
)
it.effect("preserves valid replay IDs", () =>
Effect.gen(function* () {
const prepared = yield* compileRequest(
LLM.request({
model,
messages: [
Message.assistant({ type: "tool-call", id: "Ab12Cd34E", name: "lookup", input: {} }),
Message.tool({ id: "Ab12Cd34E", name: "lookup", result: "ok" }),
],
}),
)
expect(prepared.body.messages).toMatchObject([
{ tool_calls: [{ id: "Ab12Cd34E" }] },
{ tool_call_id: "Ab12Cd34E" },
])
}),
)
it.effect("applies trailing prefix, cache, and reasoning options without changing earlier assistants", () =>
Effect.gen(function* () {
const prepared = yield* compileRequest(
LLM.request({
model,
promptCacheKey: "common-key",
messages: [Message.assistant("Earlier"), Message.user("Continue"), Message.assistant("Prefix")],
providerOptions: { promptCacheKey: "native-key", promptMode: "reasoning" },
}),
)
expect(prepared.body.prompt_cache_key).toBe("native-key")
expect(prepared.body.prompt_mode).toBe("reasoning")
expect(prepared.body.messages).toEqual([
{ role: "assistant", content: "Earlier" },
{ role: "user", content: "Continue" },
{ role: "assistant", content: "Prefix", prefix: true },
])
const uncached = yield* compileRequest(
LLM.request({
model,
prompt: "Hello",
promptCacheKey: "common-key",
cache: "none",
providerOptions: { promptCacheKey: "native-key" },
}),
)
expect(uncached.body.prompt_cache_key).toBeUndefined()
const longKey = "cache-key-".repeat(10)
const unbounded = yield* compileRequest(
LLM.request({
model,
prompt: "Hello",
promptCacheKey: longKey,
}),
)
expect(unbounded.body.prompt_cache_key).toBe(longKey)
const conflict = yield* compileRequest(
LLM.request({
model,
prompt: "Hello",
providerOptions: { reasoningEffort: "high", promptMode: "reasoning" },
}),
).pipe(Effect.flip)
expect(conflict.message).toContain("mutually exclusive")
}),
)
it.effect("omits empty assistant history unless it carries a tool call", () =>
Effect.gen(function* () {
const prepared = yield* compileRequest(
LLM.request({
model,
messages: [
Message.assistant(" \n "),
Message.assistant({ type: "reasoning", text: "\t" }),
Message.assistant({ type: "tool-call", id: "Ab12Cd34E", name: "lookup", input: {} }),
],
}),
)
expect(prepared.body.messages).toEqual([
{
role: "assistant",
content: "",
tool_calls: [{ id: "Ab12Cd34E", type: "function", function: { name: "lookup", arguments: "{}" } }],
},
])
}),
)
it.effect("preserves remote media URLs and structured tool-result media", () =>
Effect.gen(function* () {
const prepared = yield* compileRequest(
LLM.request({
model,
messages: [
Message.user({
type: "media",
mediaType: "image/png",
data: "https://assets.example.test/input.png",
}),
Message.tool({
id: "Ab12Cd34E",
name: "inspect",
resultType: "content",
result: [
{ type: "text", text: "Result" },
{ type: "file", mime: "image/jpeg", uri: "https://assets.example.test/output.jpg" },
{ type: "file", mime: "application/pdf", uri: "cGRm" },
],
}),
],
}),
)
expect(prepared.body.messages).toEqual([
{
role: "user",
content: [{ type: "image_url", image_url: "https://assets.example.test/input.png" }],
},
{
role: "tool",
tool_call_id: "Ab12Cd34E",
name: "inspect",
content: [
{ type: "text", text: "Result" },
{ type: "image_url", image_url: "https://assets.example.test/output.jpg" },
{ type: "document_url", document_url: "data:application/pdf;base64,cGRm" },
],
},
])
}),
)
it.effect("concatenates text-only user and tool content without separators", () =>
Effect.gen(function* () {
const prepared = yield* compileRequest(
LLM.request({
model,
messages: [
Message.user([
{ type: "text", text: "first" },
{ type: "text", text: "second" },
]),
Message.tool({
id: "Ab12Cd34E",
name: "lookup",
resultType: "content",
result: [
{ type: "text", text: "third" },
{ type: "text", text: "fourth" },
],
}),
],
}),
)
expect(prepared.body.messages).toEqual([
{ role: "user", content: "firstsecond" },
{ role: "tool", tool_call_id: "Ab12Cd34E", name: "lookup", content: "thirdfourth" },
])
}),
)
it.effect("streams ordered thinking and text and replays native thinking metadata", () =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(request).pipe(
Effect.provide(
fixedResponse(
sseEvents(
chunk({ content: [{ type: "thinking", thinking: [], marker: "empty" }] }),
chunk({ content: [{ type: "thinking", thinking: [{ type: "text", text: "Consider" }] }] }),
chunk({ content: [{ type: "text", text: "Answer" }] }),
chunk({}, "stop"),
),
),
),
)
expect(response.reasoning).toBe("Consider")
expect(response.text).toBe("Answer")
expect(response.message.content).toEqual([
{
type: "reasoning",
text: "Consider",
providerMetadata: {
mistral: {
thinking: {
type: "thinking",
thinking: [{ type: "text", text: "Consider" }],
marker: "empty",
},
},
},
},
{ type: "text", text: "Answer" },
])
const replay = yield* compileRequest(LLM.request({ model, messages: [response.message] }))
expect(replay.body.messages).toEqual([
{
role: "assistant",
content: [
{
type: "thinking",
thinking: [{ type: "text", text: "Consider" }],
marker: "empty",
},
{ type: "text", text: "Answer" },
],
prefix: true,
},
])
}),
)
it.effect("replays metadata-only native thinking", () =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(request).pipe(
Effect.provide(
fixedResponse(
sseEvents(chunk({ content: [{ type: "thinking", thinking: [], marker: "opaque" }] }), chunk({}, "stop")),
),
),
)
expect(response.message.content).toEqual([
{
type: "reasoning",
text: "",
providerMetadata: {
mistral: { thinking: { type: "thinking", thinking: [], marker: "opaque" } },
},
},
])
const replay = yield* compileRequest(LLM.request({ model, messages: [response.message] }))
expect(replay.body.messages).toEqual([
{
role: "assistant",
content: [{ type: "thinking", thinking: [], marker: "opaque" }],
prefix: true,
},
])
}),
)
it.effect("merges indexed argument fragments with missing continuation identity", () =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(request).pipe(
Effect.provide(
fixedResponse(
sseEvents(
chunk({
tool_calls: [{ index: 0, id: "Ab12Cd34E", function: { name: "lookup", arguments: '{"city":' } }],
}),
chunk({ tool_calls: [{ index: 0, function: { name: "", arguments: '"Paris"}' } }] }),
chunk({}, "tool_calls"),
),
),
),
)
expect(response.message.content).toContainEqual({
type: "tool-call",
id: "Ab12Cd34E",
name: "lookup",
input: { city: "Paris" },
})
expect(
response.events.filter(
(event) =>
LLMEvent.is.toolInputStart(event) ||
LLMEvent.is.toolInputDelta(event) ||
LLMEvent.is.toolInputEnd(event) ||
LLMEvent.is.toolCall(event),
),
).toEqual([
{ type: "tool-input-start", id: "Ab12Cd34E", name: "lookup", providerMetadata: undefined },
{
type: "tool-input-delta",
id: "Ab12Cd34E",
name: "lookup",
text: '{"city":',
input: {},
},
{
type: "tool-input-delta",
id: "Ab12Cd34E",
name: "lookup",
text: '"Paris"}',
input: { city: "Paris" },
},
{ type: "tool-input-end", id: "Ab12Cd34E", name: "lookup", providerMetadata: undefined },
{
type: "tool-call",
id: "Ab12Cd34E",
name: "lookup",
input: { city: "Paris" },
providerExecuted: undefined,
providerMetadata: undefined,
},
])
expect(response.events.filter(LLMEvent.is.toolCall)).toHaveLength(1)
}),
)
it.effect("normalizes stop to tool calls when a hosted model emits indexed tool fragments", () =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(request).pipe(
Effect.provide(
fixedResponse(
sseEvents(
chunk({
tool_calls: [
{
index: 0,
id: "chatcmpl-tool-8cc4d8f9f07b298a",
function: { name: "lookup", arguments: '{"city":"' },
},
],
}),
chunk({ tool_calls: [{ index: 0, function: { name: "", arguments: 'Paris"}' } }] }),
chunk({}, "stop"),
),
),
),
)
expect(response.finishReason).toEqual({ normalized: "tool-calls", raw: "stop" })
expect(response.toolCalls).toMatchObject([{ name: "lookup", input: { city: "Paris" } }])
expect(response.events.filter(LLMEvent.is.toolCall)).toHaveLength(1)
}),
)
it.effect("generates a stable ID when the first indexed fragment has null identity", () =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(request).pipe(
Effect.provide(
fixedResponse(
sseEvents(
chunk({
tool_calls: [{ index: 0, id: null, function: { name: "lookup", arguments: { city: "Paris" } } }],
}),
chunk({}, "tool_calls"),
),
),
),
)
const call = response.message.content.find((part) => part.type === "tool-call")
expect(call?.id).toMatch(/^[A-Za-z0-9]{9}$/)
expect(call).toMatchObject({ name: "lookup", input: { city: "Paris" } })
}),
)
it.effect("generates distinct IDs for parallel null and literal-null identities", () =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(request).pipe(
Effect.provide(
fixedResponse(
sseEvents(
chunk({
tool_calls: [
{ index: 0, id: null, function: { name: "first", arguments: {} } },
{ index: 1, id: "null", function: { name: "second", arguments: {} } },
],
}),
chunk({}, "tool_calls"),
),
),
),
)
const calls = response.message.content.filter((part) => part.type === "tool-call")
expect(calls).toHaveLength(2)
expect(calls[0]?.id).toMatch(/^[A-Za-z0-9]{9}$/)
expect(calls[1]?.id).toMatch(/^[A-Za-z0-9]{9}$/)
expect(calls[0]?.id).not.toBe(calls[1]?.id)
}),
)
it.effect("keeps parallel indexed calls independent", () =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(request).pipe(
Effect.provide(
fixedResponse(
sseEvents(
chunk({
tool_calls: [
{ index: 0, id: "Ab12Cd34E", function: { name: "first", arguments: '{"n":' } },
{ index: 1, id: "Fg56Hi78J", function: { name: "second", arguments: '{"n":' } },
],
}),
chunk({
tool_calls: [
{ index: 0, function: { arguments: "1}" } },
{ index: 1, function: { arguments: "2}" } },
],
}),
chunk({}, "tool_calls"),
),
),
),
)
expect(response.message.content.filter((part) => part.type === "tool-call")).toEqual([
{ type: "tool-call", id: "Ab12Cd34E", name: "first", input: { n: 1 } },
{ type: "tool-call", id: "Fg56Hi78J", name: "second", input: { n: 2 } },
])
}),
)
it.effect("correlates parallel identity-less fragments by batch position", () =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(request).pipe(
Effect.provide(
fixedResponse(
sseEvents(
chunk({
tool_calls: [
{ function: { name: "first", arguments: '{"n":' } },
{ function: { name: "second", arguments: '{"n":' } },
],
}),
chunk({
tool_calls: [{ function: { arguments: "1}" } }, { function: { arguments: "2}" } }],
}),
chunk({}, "tool_calls"),
),
),
),
)
expect(response.message.content.filter((part) => part.type === "tool-call")).toMatchObject([
{ name: "first", input: { n: 1 } },
{ name: "second", input: { n: 2 } },
])
}),
)
it.effect("maps usage variants and clamps cache reads", () =>
Effect.gen(function* () {
for (const usage of [
{ prompt_tokens: 5, completion_tokens: 2, total_tokens: 7, num_cached_tokens: 9 },
{ prompt_tokens: 5, completion_tokens: 2, prompt_token_details: { cached_tokens: 2 } },
{ prompt_tokens: 5, completion_tokens: 2, prompt_tokens_details: { cached_tokens: 3 } },
]) {
const response = yield* LLMClient.generate(request).pipe(
Effect.provide(fixedResponse(sseEvents(chunk({}, "stop", usage)))),
)
expect(response.usage).toMatchObject({
inputTokens: 5,
outputTokens: 2,
totalTokens: 7,
})
expect(response.usage?.cacheReadInputTokens).toBe(
Math.min(
5,
usage.num_cached_tokens ??
usage.prompt_token_details?.cached_tokens ??
usage.prompt_tokens_details?.cached_tokens ??
0,
),
)
}
}),
)
it.effect("maps finish reasons and does not finalize truncated tool calls", () =>
Effect.gen(function* () {
for (const [raw, normalized] of [
["stop", "stop"],
["model_length", "length"],
["tool_calls", "tool-calls"],
["error", "error"],
["future_reason", "unknown"],
] as const) {
const response = yield* LLMClient.generate(request).pipe(
Effect.provide(fixedResponse(sseEvents(chunk({}, raw)))),
)
expect(response.finishReason).toEqual({ normalized, raw })
}
const truncated = yield* LLMClient.generate(request).pipe(
Effect.provide(
fixedResponse(
sseEvents(
chunk({
tool_calls: [{ index: 0, id: "Ab12Cd34E", function: { name: "lookup", arguments: '{"city":' } }],
}),
chunk({}, "length"),
),
),
),
)
expect(truncated.finishReason).toEqual({ normalized: "length", raw: "length" })
expect(truncated.events.some(LLMEvent.is.toolCall)).toBe(false)
expect(truncated.events.some(LLMEvent.is.toolInputEnd)).toBe(false)
}),
)
it.effect("ignores non-text output parts and rejects invalid stream endings", () =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(request).pipe(
Effect.provide(
fixedResponse(
sseEvents(
chunk({ content: null }),
chunk({
content: [
{ type: "reference", reference_ids: [1] },
{ type: "image_url", image_url: "https://example.test/image.png" },
{ type: "text", text: "Answer" },
],
}),
chunk({}, "stop"),
),
),
),
)
expect(response.text).toBe("Answer")
const missingFinish = yield* LLMClient.generate(request).pipe(
Effect.provide(fixedResponse(sseEvents(chunk({ content: "partial" })))),
Effect.flip,
)
expect(missingFinish.message).toContain("without finish_reason")
const lateContent = yield* LLMClient.generate(request).pipe(
Effect.provide(
fixedResponse(sseEvents(chunk({}, "stop"), chunk({ content: [{ type: "text", text: "late" }] }))),
),
Effect.flip,
)
expect(lateContent.message).toContain("content after the finish reason")
}),
)
it.effect("uses environment bearer auth and custom package settings", () =>
LLMClient.generate(
LLM.request({
model: Mistral.model("fixture-model", {
baseURL: "https://mistral.test/v1",
headers: { "x-app": "test" },
body: { service_tier: "priority" },
providerOptions: { safePrompt: true },
}),
prompt: "Hello",
}),
).pipe(
Effect.provide(
dynamicResponse((input) =>
Effect.gen(function* () {
const web = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
expect(web.url).toBe("https://mistral.test/v1/chat/completions")
expect(web.headers.get("authorization")).toBe("Bearer secret")
expect(web.headers.get("x-app")).toBe("test")
expect(input.text).toContain('"service_tier":"priority"')
return input.respond(sseEvents(chunk({}, "stop")), { headers: { "content-type": "text/event-stream" } })
}),
),
),
Effect.provide(ConfigProvider.layer(ConfigProvider.fromEnv({ env: { MISTRAL_API_KEY: "secret" } }))),
),
)
})
@@ -1,159 +0,0 @@
import { configure } from "@opencode-ai/ai/providers/mistral"
import { describe, expect } from "bun:test"
import { Effect } from "effect"
import { LLM, LLMEvent, LLMRequest, Message, ToolChoice, ToolDefinition } from "../../src/index.js"
import { LLMClient } from "../../src/route.js"
import { compileRequest } from "../../src/route/client.js"
import { recordedTests } from "../recorded-test.js"
const apiKey = process.env.MISTRAL_API_KEY ?? "fixture"
const recorded = recordedTests({
prefix: "mistral-chat",
provider: "mistral",
protocol: "mistral-chat",
requires: ["MISTRAL_API_KEY"],
})
const glmRecorded = recordedTests({
prefix: "mistral-chat-glm",
provider: "mistral",
protocol: "mistral-chat",
requires: ["MISTRAL_API_KEY"],
})
const weather = ToolDefinition.make({
name: "lookup_weather",
description: "Look up the current weather for a city",
inputSchema: {
type: "object",
properties: { city: { type: "string", enum: ["Paris"] } },
required: ["city"],
additionalProperties: false,
},
})
describe("Mistral recorded", () => {
recorded.effect.with(
"streams text with usage",
{ tags: ["text", "usage"], metadata: { model: "mistral-small-latest" } },
() =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(
LLM.request({
model: configure({ apiKey, providerOptions: { reasoningEffort: "none" } }).model("mistral-small-latest"),
prompt: "Reply with exactly one word: hello",
generation: { maxTokens: 40, temperature: 0 },
}),
)
expect(response.text.trim()).toMatch(/^(?:hello|hi)[!.]?$/i)
expect(response.finishReason.normalized).toBe("stop")
expect(response.usage?.inputTokens).toBeGreaterThan(0)
expect(response.usage?.outputTokens).toBeGreaterThan(0)
}),
60_000,
)
recorded.effect.with(
"replays native reasoning",
{ tags: ["reasoning", "replay", "usage"], metadata: { model: "mistral-small-latest" } },
() =>
Effect.gen(function* () {
const model = configure({ apiKey, providerOptions: { reasoningEffort: "high" } }).model("mistral-small-latest")
const firstRequest = LLM.request({
model,
prompt: "Calculate 17 multiplied by 23. Think briefly, then reply with only the integer.",
generation: { maxTokens: 512, temperature: 0 },
})
const first = yield* LLMClient.generate(firstRequest)
expect(first.text.trim()).toBe("391")
expect(first.reasoning.length).toBeGreaterThan(0)
expect(first.events.some(LLMEvent.is.reasoningDelta)).toBe(true)
const followUp = LLMRequest.update(firstRequest, {
messages: [...firstRequest.messages, first.message, Message.user("Reply with exactly: Done.")],
generation: { maxTokens: 256, temperature: 0 },
})
const replay = yield* compileRequest(followUp)
expect(replay.body.messages).toContainEqual(
expect.objectContaining({
role: "assistant",
content: expect.arrayContaining([expect.objectContaining({ type: "thinking" })]),
}),
)
const second = yield* LLMClient.generate(followUp)
expect(second.text.trim()).toMatch(/Done\.?$/)
expect(second.finishReason.normalized).toBe("stop")
}),
60_000,
)
recorded.effect.with(
"drives a tool loop",
{ tags: ["tool", "tool-loop", "usage"], metadata: { model: "mistral-small-latest" } },
() =>
Effect.gen(function* () {
const model = configure({ apiKey, providerOptions: { reasoningEffort: "none" } }).model("mistral-small-latest")
const firstRequest = LLM.request({
model,
system: "Call lookup_weather exactly once with Paris.",
prompt: "What is the weather?",
tools: [weather],
toolChoice: weather,
generation: { maxTokens: 160, temperature: 0 },
})
const first = yield* LLMClient.generate(firstRequest)
expect(first.finishReason.normalized).toBe("tool-calls")
expect(first.toolCalls).toMatchObject([{ name: "lookup_weather", input: { city: "Paris" } }])
expect(first.events.filter(LLMEvent.is.toolCall)).toHaveLength(1)
const call = first.toolCalls[0]
if (!call) throw new Error("Mistral did not return a tool call")
const followUp = LLMRequest.update(firstRequest, {
toolChoice: ToolChoice.make("none"),
messages: [
...firstRequest.messages,
first.message,
Message.tool({ id: call.id, name: call.name, result: { condition: "sunny", temperature: "18C" } }),
],
generation: { maxTokens: 160, temperature: 0 },
})
const second = yield* LLMClient.generate(followUp)
expect(second.finishReason.normalized).toBe("stop")
expect(second.toolCalls).toHaveLength(0)
expect(second.text.toLowerCase()).toContain("sunny")
}),
60_000,
)
})
describe("Mistral hosted GLM recorded", () => {
glmRecorded.effect.with(
"streams an indexed tool call",
{ tags: ["hosted-model", "tool", "tool-call"], metadata: { model: "zai-glm-5-2" } },
() =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(
LLM.request({
model: configure({ apiKey }).model("zai-glm-5-2"),
system: "Call lookup_weather exactly once with Paris.",
prompt: "What is the weather?",
tools: [weather],
toolChoice: weather,
generation: { maxTokens: 256, temperature: 0 },
}),
)
expect(response.finishReason.normalized).toBe("tool-calls")
expect(response.toolCalls).toMatchObject([{ name: "lookup_weather", input: { city: "Paris" } }])
expect(response.events.filter(LLMEvent.is.toolInputStart)).toHaveLength(1)
expect(response.events.filter(LLMEvent.is.toolInputDelta).length).toBeGreaterThan(0)
expect(response.events.filter(LLMEvent.is.toolInputEnd)).toHaveLength(1)
expect(response.events.filter(LLMEvent.is.toolCall)).toHaveLength(1)
}),
60_000,
)
})
@@ -216,171 +216,91 @@ describe("Open Responses basic-item lifecycles", () => {
])
}),
)
it.effect("preserves non-empty done-only message content", () =>
it.effect("allows a message to be registered again without inheriting its previous phase", () =>
Effect.gen(function* () {
const text = {
type: "message",
id: "msg_text",
content: [{ type: "output_text", text: "Done-only text." }],
}
const refusal = {
type: "message",
id: "msg_refusal",
content: [{ type: "refusal", refusal: "Done-only refusal." }],
}
const events = yield* collect(
{ type: "response.output_item.done", item: text },
{
type: "response.output_item.done",
item: { type: "message", id: "msg_empty", content: [{ type: "output_text", text: "" }] },
},
{ type: "response.output_item.done", item: refusal },
{ type: "response.output_item.added", item: { type: "message", id: "msg_1", phase: "commentary" } },
{ type: "response.output_text.delta", item_id: "msg_1", delta: "First" },
{ type: "response.output_item.done", item: { type: "message", id: "msg_1" } },
{ type: "response.output_item.added", item: { type: "message", id: "msg_1" } },
{ type: "response.output_text.delta", item_id: "msg_1", delta: "Second" },
{ type: "response.output_item.done", item: { type: "message", id: "msg_1" } },
completed,
)
expect(events.filter((event) => event.type.startsWith("text-"))).toEqual([
{
type: "text-start",
id: "msg_text",
providerMetadata: { "openai-compatible": { itemId: "msg_text" } },
},
expect(events.filter(LLMEvent.is.textEnd)).toEqual([
{
type: "text-end",
id: "msg_text",
text: "Done-only text.",
providerMetadata: { "openai-compatible": { itemId: "msg_text" } },
},
{
type: "text-start",
id: "msg_refusal",
providerMetadata: { "openai-compatible": { itemId: "msg_refusal" } },
},
{
type: "text-end",
id: "msg_refusal",
text: "Done-only refusal.",
providerMetadata: { "openai-compatible": { itemId: "msg_refusal" } },
id: "msg_1",
providerMetadata: { "openai-compatible": { itemId: "msg_1", phase: "commentary" } },
},
{ type: "text-end", id: "msg_1", providerMetadata: { "openai-compatible": { itemId: "msg_1" } } },
])
expect(events.filter(LLMEvent.is.textDelta).map((event) => event.text)).toEqual(["First", "Second"])
}),
)
// Captured from Bedrock Mantle (openai.gpt-oss-120b): the terminal function_call
// items rename `id` to `item_id` and carry a stray `output_index`.
it.effect("recovers a terminal function_call id from its output slot", () =>
Effect.gen(function* () {
const terminal = {
type: "function_call",
item_id: "fc_828bee50dee1d029",
call_id: "call_bc1eb4b42e70ee53",
name: "get_weather",
arguments: '{\n "city": "Paris"\n}',
output_index: 1,
status: "completed",
}
const events = yield* collect(
{
type: "response.output_item.added",
output_index: 0,
item: { type: "reasoning", id: "msg_879a68b589198b4c" },
},
{ type: "response.output_item.done", output_index: 0, item: { type: "reasoning", id: "msg_879a68b589198b4c" } },
{
type: "response.output_item.added",
output_index: 1,
item: {
type: "function_call",
id: "fc_828bee50dee1d029",
call_id: "call_bc1eb4b42e70ee53",
name: "get_weather",
arguments: "",
status: "in_progress",
;[undefined, "fc_1"].forEach((id) => {
it.effect(`opens and closes a done-only tool ${id === undefined ? "without" : "with"} an item id`, () =>
Effect.gen(function* () {
const item = {
type: "function_call",
...(id === undefined ? {} : { id }),
call_id: "call_1",
name: "lookup",
arguments: '{"query":"weather"}',
}
const events = yield* collect(
{ type: "response.output_item.done", item },
{ type: "response.output_item.done", item: { ...item, id: "fc_1" } },
{ type: "response.output_item.added", item },
completed,
)
const providerMetadata = id === undefined ? undefined : { "openai-compatible": { itemId: id } }
expect(events.filter((event) => event.type.startsWith("tool-"))).toEqual([
{ type: "tool-input-start", id: "call_1", name: "lookup", providerMetadata },
{ type: "tool-input-end", id: "call_1", name: "lookup", providerMetadata },
{ type: "tool-call", id: "call_1", name: "lookup", input: { query: "weather" }, providerMetadata },
])
expect(events.filter(LLMEvent.is.finish)).toEqual([
{
type: "finish",
reason: { normalized: "tool-calls", raw: undefined },
providerMetadata: { "openai-compatible": { responseId: "resp_1", serviceTier: undefined } },
},
},
{
type: "response.function_call_arguments.delta",
output_index: 1,
item_id: "fc_828bee50dee1d029",
delta: '{\n "city": "Paris"\n}',
},
{
type: "response.function_call_arguments.done",
output_index: 1,
item_id: "fc_828bee50dee1d029",
arguments: '{\n "city": "Paris"\n}',
},
{ type: "response.output_item.done", output_index: 1, item: terminal },
{
type: "response.completed",
response: { id: "resp_1", output: [{ type: "reasoning", id: "msg_879a68b589198b4c" }, terminal] },
},
)
const providerMetadata = { "openai-compatible": { itemId: "fc_828bee50dee1d029" } }
expect(events.filter((event) => event.type.startsWith("tool-"))).toEqual([
{ type: "tool-input-start", id: "call_bc1eb4b42e70ee53", name: "get_weather", providerMetadata },
{
type: "tool-input-delta",
id: "call_bc1eb4b42e70ee53",
name: "get_weather",
text: '{\n "city": "Paris"\n}',
input: { city: "Paris" },
},
{ type: "tool-input-end", id: "call_bc1eb4b42e70ee53", name: "get_weather", providerMetadata },
{
type: "tool-call",
id: "call_bc1eb4b42e70ee53",
name: "get_weather",
input: { city: "Paris" },
providerMetadata,
},
])
}),
)
])
}),
)
it.effect("mints an id for a done-only tool that never had one", () =>
Effect.gen(function* () {
const events = yield* collect(
{
type: "response.output_item.done",
output_index: 0,
item: { type: "function_call", call_id: "call_1", name: "lookup", arguments: '{"query":"weather"}' },
},
completed,
)
const call = events.find(LLMEvent.is.toolCall)
expect(call).toMatchObject({ id: "call_1", name: "lookup", input: { query: "weather" } })
expect(call?.providerMetadata?.["openai-compatible"]).toMatchObject({
itemId: expect.stringMatching(/^fc_[0-9a-f]{32}$/),
})
}),
)
it.effect("opens and closes a done-only tool", () =>
Effect.gen(function* () {
const item = {
type: "function_call",
id: "fc_1",
call_id: "call_1",
name: "lookup",
arguments: '{"query":"weather"}',
}
const events = yield* collect({ type: "response.output_item.done", item }, completed)
const providerMetadata = { "openai-compatible": { itemId: "fc_1" } }
expect(events.filter((event) => event.type.startsWith("tool-"))).toEqual([
{ type: "tool-input-start", id: "call_1", name: "lookup", providerMetadata },
{ type: "tool-input-end", id: "call_1", name: "lookup", providerMetadata },
{ type: "tool-call", id: "call_1", name: "lookup", input: { query: "weather" }, providerMetadata },
])
expect(events.filter(LLMEvent.is.finish)).toEqual([
{
type: "finish",
reason: { normalized: "tool-calls", raw: undefined },
providerMetadata: { "openai-compatible": { responseId: "resp_1", serviceTier: undefined } },
},
])
}),
)
it.effect(`deduplicates a pending call whose item id is ${id === undefined ? "introduced" : "omitted"} later`, () =>
Effect.gen(function* () {
const item = { type: "function_call", call_id: "call_1", name: "lookup" }
const first = { ...item, ...(id === undefined ? {} : { id }) }
const duplicate = { ...item, ...(id === undefined ? { id: "fc_1" } : {}) }
const events = yield* collect(
{ type: "response.output_item.added", item: first },
{ type: "response.function_call_arguments.delta", item_id: id ?? "call_1", delta: '{"query":"weather"}' },
{ type: "response.output_item.added", item: duplicate },
{ type: "response.output_item.done", item: duplicate },
{ type: "response.output_item.done", item: first },
{ type: "response.output_item.added", item: duplicate },
completed,
)
// Identity metadata comes from the first admission, not the duplicate.
const providerMetadata = id === undefined ? undefined : { "openai-compatible": { itemId: id } }
expect(events.filter((event) => event.type.startsWith("tool-"))).toEqual([
{ type: "tool-input-start", id: "call_1", name: "lookup", providerMetadata },
{
type: "tool-input-delta",
id: "call_1",
name: "lookup",
text: '{"query":"weather"}',
input: { query: "weather" },
},
{ type: "tool-input-end", id: "call_1", name: "lookup", providerMetadata },
{ type: "tool-call", id: "call_1", name: "lookup", input: { query: "weather" }, providerMetadata },
])
}),
)
})
it.effect("recovers pending calls without reconciling terminal reasoning", () =>
Effect.gen(function* () {
@@ -425,6 +345,21 @@ describe("Open Responses basic-item lifecycles", () => {
}),
)
it.effect("preserves call identity and pending order when an item id is reused", () =>
Effect.gen(function* () {
const first = { type: "function_call", id: "fc_1", call_id: "call_1", name: "lookup", arguments: "{}" }
const events = yield* collect(
{ type: "response.output_item.added", item: first },
{ type: "response.output_item.added", item: { ...first, id: "fc_2", call_id: "call_2" } },
{ type: "response.output_item.done", item: first },
{ type: "response.output_item.added", item: { ...first, call_id: "call_3" } },
{ type: "response.output_item.done", item: first },
completed,
)
expect(events.filter(LLMEvent.is.toolCall).map((event) => event.id)).toEqual(["call_1", "call_2", "call_3"])
}),
)
it.effect("keeps text and reasoning identities separate even with empty item ids", () =>
Effect.gen(function* () {
const events = yield* collect(
@@ -474,15 +409,14 @@ describe("Open Responses basic-item lifecycles", () => {
{ type: "response.output_text.delta", item_id: "msg_1", delta: "Answer" },
{
type: "response.output_item.added",
item: { type: "function_call", id: "fc_1", call_id: "call_1", name: "lookup", arguments: "{}" },
item: { type: "function_call", call_id: "call_1", name: "lookup", arguments: "{}" },
},
completed,
)
// Generic terminal closure does not repeat the message's phase metadata.
const providerMetadata = { "openai-compatible": { itemId: "fc_1" } }
expect(events.slice(4, -2)).toEqual([
{ type: "tool-input-end", id: "call_1", name: "lookup", providerMetadata },
{ type: "tool-call", id: "call_1", name: "lookup", input: {}, providerMetadata },
{ type: "tool-input-end", id: "call_1", name: "lookup" },
{ type: "tool-call", id: "call_1", name: "lookup", input: {} },
{ type: "text-end", id: "msg_1" },
])
}),
@@ -96,27 +96,6 @@ describe("Open Responses-compatible route", () => {
}),
)
it.effect("omits user messages with no content", () =>
Effect.gen(function* () {
const model = configure({
apiKey: "test-key",
baseURL: "https://responses.example.test/v1",
provider: "example",
}).model("example-model")
const prepared = yield* compileRequest(
LLM.request({
model,
messages: [Message.user("Before."), Message.user([]), Message.user("After.")],
}),
)
expect(prepared.body.input).toEqual([
{ role: "user", content: [{ type: "input_text", text: "Before." }] },
{ role: "user", content: [{ type: "input_text", text: "After." }] },
])
}),
)
it.effect("uses data URLs for embedded PDF messages and tool results", () =>
Effect.gen(function* () {
const model = configure({
@@ -586,21 +565,23 @@ describe("Open Responses-compatible route", () => {
Effect.gen(function* () {
yield* Effect.forEach(["response.output_item.added", "response.output_item.done"], (type) =>
Effect.forEach(fixtures, (fixture) =>
Effect.forEach([null, 0, false, {}, []], (id) =>
Effect.gen(function* () {
const error = yield* LLMClient.generate(request).pipe(
Effect.provide(
fixedResponse(
sseEvents(
{ type, item: { ...fixture.item, id } },
{ type: "response.completed", response: { id: "resp_1" } },
Effect.forEach(
fixture.item.type === "message" ? [undefined, null, 0, false, {}, []] : [null, 0, false, {}, []],
(id) =>
Effect.gen(function* () {
const error = yield* LLMClient.generate(request).pipe(
Effect.provide(
fixedResponse(
sseEvents(
{ type, item: { ...fixture.item, id } },
{ type: "response.completed", response: { id: "resp_1" } },
),
),
),
),
Effect.flip,
)
expect(error.reason._tag).toBe("InvalidProviderOutput")
}),
Effect.flip,
)
expect(error.reason._tag).toBe("InvalidProviderOutput")
}),
),
),
)
@@ -608,6 +589,43 @@ describe("Open Responses-compatible route", () => {
)
})
it.effect("streams function calls without optional item ids through the shared baseline", () =>
Effect.gen(function* () {
const model = configure({
apiKey: "test-key",
baseURL: "https://responses.example.test/v1",
provider: "example",
}).model("example-model")
const item = { type: "function_call", call_id: "call_1", name: "lookup", arguments: "" }
const response = yield* LLMClient.generate(LLM.request({ model, prompt: "Look it up." })).pipe(
Effect.provide(
fixedResponse(
sseEvents(
{ type: "response.output_item.added", output_index: 1, item },
{
type: "response.function_call_arguments.delta",
output_index: 1,
item_id: "opaque_item",
delta: '{"query":"shared"}',
},
{
type: "response.output_item.done",
output_index: 1,
item: { ...item, arguments: '{"query":"complete"}' },
},
{ type: "response.completed", response: { id: "resp_1" } },
),
),
),
)
expect(response.events.filter(LLMEvent.is.toolCall)).toEqual([
expect.objectContaining({ id: "call_1", name: "lookup", input: { query: "complete" } }),
])
expect(response.events.find(LLMEvent.is.toolCall)?.providerMetadata).toBeUndefined()
}),
)
it.effect("finalizes pending function calls from completed response output", () =>
Effect.gen(function* () {
const model = configure({
@@ -469,7 +469,7 @@ describe("OpenAI Responses route", () => {
}),
)
it.effect("continues a streamed tool call with only the new tool output", () =>
it.effect("continues an item-id-less tool call with only the new tool output", () =>
Effect.gen(function* () {
const firstRequest = {
type: "response.create",
@@ -485,7 +485,6 @@ describe("OpenAI Responses route", () => {
type: "response.output_item.done",
item: {
type: "function_call",
id: "fc_1",
status: "completed",
call_id: "call_1",
name: "weather",
@@ -2130,6 +2129,47 @@ describe("OpenAI Responses route", () => {
}),
)
it.effect("routes item-id-less function arguments by output index and prefers item completion", () =>
Effect.gen(function* () {
const item = { type: "function_call", call_id: "call_1", name: "lookup", arguments: "" }
const response = yield* LLMClient.generate(request).pipe(
Effect.provide(
fixedResponse(
sseEvents(
{ type: "response.output_item.added", output_index: 2, item },
{
type: "response.function_call_arguments.delta",
output_index: 2,
item_id: "opaque_delta",
delta: '{"query":"streamed"}',
},
{
type: "response.function_call_arguments.done",
output_index: 2,
item_id: "opaque_done",
arguments: '{"query":"arguments-done"}',
},
{
type: "response.output_item.done",
output_index: 2,
item: { ...item, arguments: '{"query":"output-item-done"}' },
},
{ type: "response.completed", response: { id: "resp_1" } },
),
),
),
)
expect(response.events.filter((event) => event.type === "tool-input-delta")).toMatchObject([
{ id: "call_1", text: '{"query":"streamed"}' },
])
expect(response.events.filter(LLMEvent.is.toolCall)).toEqual([
expect.objectContaining({ id: "call_1", name: "lookup", input: { query: "output-item-done" } }),
])
expect(response.events.find(LLMEvent.is.toolCall)?.providerMetadata).toBeUndefined()
}),
)
it.effect("routes reasoning summary events by output index", () =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(request).pipe(
@@ -2349,7 +2389,7 @@ describe("OpenAI Responses route", () => {
{
type: "response.output_item.added",
output_index: 0,
item: { type: "function_call", id: "fc_1", call_id: "call_1", name: "lookup", arguments: "" },
item: { type: "function_call", call_id: "call_1", name: "lookup", arguments: "" },
},
event,
{ type: "response.completed", response: { id: "resp_1" } },
@@ -2850,7 +2890,7 @@ describe("OpenAI Responses route", () => {
}),
)
it.effect("ignores duplicate item start events", () =>
it.effect("ignores duplicate item boundary events", () =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(request).pipe(
Effect.provide(
@@ -2881,6 +2921,25 @@ describe("OpenAI Responses route", () => {
arguments: '{"query":"weather"}',
},
},
{
type: "response.output_item.done",
item: {
type: "function_call",
id: "fc_1",
call_id: "call_1",
name: "lookup",
arguments: '{"query":"weather"}',
},
},
// Duplicates that drop the item id still resolve the same call.
{
type: "response.output_item.done",
item: { type: "function_call", call_id: "call_1", name: "lookup", arguments: '{"query":"weather"}' },
},
{
type: "response.output_item.added",
item: { type: "function_call", call_id: "call_1", name: "lookup", arguments: "" },
},
{ type: "response.completed", response: { id: "resp_1" } },
),
),
@@ -3734,6 +3793,43 @@ describe("OpenAI Responses route", () => {
}),
)
it.effect("finalizes and replays a completed function call without an optional item id", () =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(request).pipe(
Effect.provide(
fixedResponse(
sseEvents(
{
type: "response.output_item.done",
item: { type: "function_call", call_id: "call_1", name: "lookup", arguments: '{"query":"weather"}' },
},
{ type: "response.completed", response: { id: "resp_1" } },
),
),
),
)
expect(response.events.filter(LLMEvent.is.toolCall)).toEqual([
expect.objectContaining({ id: "call_1", name: "lookup", input: { query: "weather" } }),
])
expect(response.events.find(LLMEvent.is.toolCall)?.providerMetadata).toBeUndefined()
const prepared = yield* compileRequest(
LLM.request({
model,
messages: [
response.message,
Message.tool({ id: "call_1", name: "lookup", resultType: "json", result: { forecast: "sunny" } }),
],
}),
)
expect(prepared.body.input).toEqual([
{ type: "function_call", call_id: "call_1", name: "lookup", arguments: '{"query":"weather"}' },
{ type: "function_call_output", call_id: "call_1", output: '{"forecast":"sunny"}' },
])
}),
)
it.effect("emits only missing function arguments from the arguments done event", () =>
Effect.gen(function* () {
const body = sseEvents(
@@ -3958,6 +4054,37 @@ describe("OpenAI Responses route", () => {
}),
)
it.effect("reconciles an item-id-less pending function call from completed response output", () =>
Effect.gen(function* () {
const item = { type: "function_call", call_id: "call_1", name: "lookup", arguments: "" }
const response = yield* LLMClient.generate(request).pipe(
Effect.provide(
fixedResponse(
sseEvents(
{ type: "response.output_item.added", output_index: 0, item },
{
type: "response.function_call_arguments.delta",
output_index: 0,
item_id: "opaque_delta",
delta: '{"query":"partial',
},
{
type: "response.completed",
response: { id: "resp_1", output: [{ ...item, arguments: '{"query":"complete"}' }] },
},
),
),
),
)
expect(response.events.filter(LLMEvent.is.toolCall)).toEqual([
expect.objectContaining({ id: "call_1", name: "lookup", input: { query: "complete" } }),
])
expect(response.events.find(LLMEvent.is.toolCall)?.providerMetadata).toBeUndefined()
expect(response.events.filter(LLMEvent.is.toolInputEnd)).toHaveLength(1)
}),
)
it.effect("lets completed response output override arguments done", () =>
Effect.gen(function* () {
const body = sseEvents(
-40
View File
@@ -21,10 +21,8 @@ import {
QuotaExceededError,
RateLimitError,
RouteID,
ToolResultValue,
TransportError,
UnknownProviderError,
UnsupportedOperationError,
Usage,
} from "../src/schema/index.js"
import { ProviderShared } from "../src/protocols/shared.js"
@@ -85,37 +83,6 @@ describe("llm schema", () => {
})
})
describe("ToolResultValue", () => {
test("uses the canonical schema guard", () => {
const cases: ReadonlyArray<{ readonly value: unknown; readonly expected: boolean }> = [
{ value: { type: "json", value: { ok: true } }, expected: true },
{ value: { type: "text", value: "done" }, expected: true },
{ value: { type: "error", value: "failed" }, expected: true },
{ value: { type: "content", value: [{ type: "text", text: "done" }] }, expected: true },
{ value: { type: "content", value: [{ type: "text" }] }, expected: false },
{ value: { type: "content", value: "done" }, expected: false },
{ value: { type: "json" }, expected: false },
{ value: { type: "unknown", value: "done" }, expected: false },
]
for (const item of cases) {
expect(Schema.is(ToolResultValue)(item.value)).toBe(item.expected)
expect(ToolResultValue.is(item.value)).toBe(item.expected)
}
})
test("accepts canonical results with extra fields", () => {
expect(ToolResultValue.is({ type: "json", value: { ok: true }, metadata: { source: "tool" } })).toBe(true)
expect(
ToolResultValue.is({
type: "content",
value: [{ type: "file", uri: "https://example.test/result.txt", mime: "text/plain", checksum: "abc" }],
metadata: { source: "tool" },
}),
).toBe(true)
})
})
describe("AI.Usage", () => {
test("subtractTokens clamps non-sensical breakdowns to zero", () => {
// Defense against a provider reporting cached_tokens > prompt_tokens or
@@ -277,12 +244,6 @@ test("AI errors serialize diagnostics only on their typed reason", () => {
test("AI error reasons are tagged Errors with required messages", () => {
const reasons = [
new InvalidRequestError({ message: "Invalid request" }),
new UnsupportedOperationError({
message: "Unsupported operation",
operation: "compact",
provider: model.provider,
route: "fake-route",
}),
new NoRouteError({
message: "No route",
route: RouteID.make("missing"),
@@ -300,7 +261,6 @@ test("AI error reasons are tagged Errors with required messages", () => {
]
expect(reasons.map((reason) => reason._tag)).toEqual([
"InvalidRequest",
"UnsupportedOperation",
"NoRoute",
"Authentication",
"RateLimit",
+1 -62
View File
@@ -1,18 +1,5 @@
import { describe, expect } from "bun:test"
import {
AIError,
CompactionPart,
CompactionResponse,
LanguageModel,
LLM,
LLMClient,
LLMEvent,
LLMRequest,
Message,
ProviderID,
RateLimitError,
} from "../src/index.js"
import { OpenAI } from "../src/providers.js"
import { AIError, LanguageModel, LLM, LLMClient, LLMEvent, LLMRequest, RateLimitError } from "../src/index.js"
import { OpenAIChat } from "../src/protocols/openai-chat.js"
import { TestLLM } from "../src/testing.js"
import { Effect, Fiber, Latch, Stream } from "effect"
@@ -79,54 +66,6 @@ describe("TestLLM legacy client", () => {
})
describe("TestLLM first-class client", () => {
it.effect("rejects response fixtures for the wrong operation", () =>
Effect.gen(function* () {
const client = yield* TestLLM.Test
const request = LLM.request({ model: OpenAI.configure({ apiKey: "test" }).responses("fixture"), prompt: "hello" })
yield* client.push(TestLLM.stop(), new CompactionResponse({ replacement: [] }))
expect(yield* client.compact(request).pipe(Effect.catchDefect(Effect.succeed))).toBe(
"TestLLM compaction requires a CompactionResponse",
)
expect(yield* client.generate(request).pipe(Effect.catchDefect(Effect.succeed))).toBe(
"TestLLM generation requires an event response",
)
}),
)
it.effect("scripts replacement windows with the same lazy recording, gates, and fallback controls", () =>
Effect.gen(function* () {
const client = yield* TestLLM.Test
const request = LLM.request({ model: OpenAI.configure({ apiKey: "test" }).responses("fixture"), prompt: "hello" })
const compacted = new CompactionResponse({
replacement: [
Message.user("retained input"),
Message.assistant(CompactionPart.make({ provider: ProviderID.make("openai"), encrypted: "checkpoint" })),
Message.user("retained tail"),
],
})
yield* client.push(compacted, TestLLM.text("continued", "answer"))
const operation = LLMClient.compact(request)
expect(yield* client.requests()).toEqual([])
const gate = yield* client.gate()
const fiber = yield* operation.pipe(Effect.forkChild({ startImmediately: true }))
yield* gate.started
yield* client.wait(1)
expect(fiber.pollUnsafe()).toBeUndefined()
yield* gate.release
expect(yield* Fiber.join(fiber)).toBe(compacted)
const next = LLMRequest.update(request, { messages: compacted.replacement })
expect((yield* LLMClient.generate(next)).text).toBe("continued")
yield* client.serve((observed) => {
expect(observed).toBe(next)
return compacted
})
expect(yield* LLMClient.compact(next)).toBe(compacted)
yield* client.always(compacted)
expect(yield* LLMClient.compact(next)).toBe(compacted)
expect(yield* client.requests()).toEqual([request, next, next, next])
}),
)
it.effect("provides the same object under normal and test tags with snapshot observations", () =>
Effect.gen(function* () {
const llm = yield* TestLLM.Test
-77
View File
@@ -1,77 +0,0 @@
import { describe, expect, test } from "bun:test"
import { Message, ToolCallPart, ToolResultPart } from "../src/schema/messages.js"
import { normalizeToolHistory } from "../src/tool-history.js"
const toolCall = (id: string, name = id) => ToolCallPart.make({ id, name, input: {} })
const toolResult = (id: string, value: unknown, name = id, resultType?: "text" | "content" | "error") =>
Message.tool(ToolResultPart.make({ id, name, result: value, resultType }))
describe("tool history normalization", () => {
test("fills missing local results before the next step", () => {
const normalized = normalizeToolHistory([
Message.assistant([toolCall("first"), toolCall("second")]),
toolResult("first", "done", "wrong", "text"),
Message.user("Continue."),
Message.assistant(toolCall("trailing")),
])
expect(normalized.map((message) => message.role)).toEqual([
"assistant",
"tool",
"tool",
"user",
"assistant",
])
expect(normalized[1]?.content[0]).toMatchObject({ type: "tool-result", id: "first", name: "first" })
expect(normalized[2]?.content).toEqual([
{ type: "tool-result", id: "second", name: "second", result: { type: "error", value: "Tool result missing" } },
])
expect(normalized[4]?.content).toEqual([toolCall("trailing")])
})
test("normalizes empty results without changing whitespace or media", () => {
const media = { type: "file" as const, uri: "data:image/png;base64,AQID", mime: "image/png" }
const normalized = normalizeToolHistory([
Message.assistant([
toolCall("text"),
toolCall("content"),
toolCall("error"),
toolCall("mixed"),
toolCall("whitespace"),
]),
toolResult("text", "", "text", "text"),
toolResult("content", [], "content", "content"),
toolResult("error", "", "error", "error"),
toolResult("mixed", [{ type: "text", text: "" }, media], "mixed", "content"),
toolResult("whitespace", " ", "whitespace", "text"),
])
expect(normalized.slice(1).map((message) => message.content[0])).toEqual([
{ type: "tool-result", id: "text", name: "text", result: { type: "text", value: "(no tool output)" } },
{ type: "tool-result", id: "content", name: "content", result: { type: "text", value: "(no tool output)" } },
{ type: "tool-result", id: "error", name: "error", result: { type: "error", value: "(no tool output)" } },
{ type: "tool-result", id: "mixed", name: "mixed", result: { type: "content", value: [media] } },
{ type: "tool-result", id: "whitespace", name: "whitespace", result: { type: "text", value: " " } },
])
})
test("leaves unmatched and provider-executed history unchanged", () => {
const hostedCall = ToolCallPart.make({
id: "hosted",
name: "web_search",
input: {},
providerExecuted: true,
})
const hostedResult = ToolResultPart.make({
id: "hosted",
name: "web_search",
result: "",
resultType: "text",
providerExecuted: true,
})
const hosted = Message.assistant([hostedCall, hostedResult])
const orphan = toolResult("orphan", "ignored", "orphan", "text")
expect(normalizeToolHistory([orphan, hosted])).toEqual([orphan, hosted])
})
})
-27
View File
@@ -442,33 +442,6 @@ describe("LLMClient tools", () => {
}),
)
it.effect("projects malformed tagged dynamic output as opaque JSON", () =>
Effect.gen(function* () {
const malformed = { type: "content", value: [{ type: "text" }] }
const dynamic = Tool.make({
description: "Return caller-defined JSON.",
jsonSchema: { type: "object", properties: {} },
execute: () => Effect.succeed(malformed),
})
const dispatched = yield* ToolRuntime.dispatch(
{ dynamic },
LLMEvent.toolCall({ id: "call_1", name: "dynamic", input: {} }),
)
expect(dispatched.result).toEqual({ type: "json", value: malformed })
expect(dispatched.output).toEqual({ structured: malformed, content: [] })
expect(dispatched.events).toEqual([
LLMEvent.toolResult({
id: "call_1",
name: "dynamic",
result: { type: "json", value: malformed },
output: { structured: malformed, content: [] },
}),
])
}),
)
it.effect("executes tool calls for one step without looping by default", () =>
Effect.gen(function* () {
const layer = scriptedResponses([
@@ -1,62 +1,5 @@
import { expect, story } from "../../storybook/playwright/story"
story("raises the docked composer only in dark mode", async ({ mount, page }) => {
const component = await mount("opencode-composer-flow--empty-draft")
const composer = component.locator('[data-component="composer"]')
await page.locator("html").evaluate((root) => root.setAttribute("data-color-scheme", "light"))
await expect(composer).toHaveCSS("background-color", "rgb(255, 255, 255)")
await page.locator("html").evaluate((root) => root.setAttribute("data-color-scheme", "dark"))
await expect(composer).toHaveCSS("background-color", "rgb(36, 36, 36)")
})
story("centers add menu shortcuts in a consistent column", async ({ mount, page }) => {
const component = await mount("opencode-composer-flow--empty-draft")
await component.locator('[data-action="composer-attach"]').click()
const shortcuts = page.locator('[role="menu"] [data-slot="menu-v2-item-shortcut"]')
await expect(shortcuts).toHaveCount(4)
const boxes = await shortcuts.evaluateAll((items) =>
items.map((item) => {
const box = item.getBoundingClientRect()
return { width: box.width, center: box.left + box.width / 2 }
}),
)
expect(new Set(boxes.map((box) => box.width)).size).toBe(1)
expect(new Set(boxes.map((box) => box.center)).size).toBe(1)
})
for (const draft of ["empty-draft", "multiline-draft", "mixed-attachments"]) {
story(`select all stays inside the composer with ${draft}`, async ({ mount, page }) => {
const component = await mount(`opencode-composer-flow--${draft}`)
const input = component.getByRole("textbox", { name: "Prompt", exact: true })
const text = await input.textContent()
for (let count = 0; count < 2; count++) {
await input.press("ControlOrMeta+a")
expect(
await input.evaluate((editor) => {
const selection = window.getSelection()
return {
text: selection?.toString(),
inside: editor.contains(selection?.anchorNode ?? null) && editor.contains(selection?.focusNode ?? null),
}
}),
).toEqual({ text, inside: true })
}
await page.keyboard.type("Replacement draft")
await expect(input).toHaveText("Replacement draft")
await expect(component.getByRole("status")).toHaveText("Ready")
if (draft === "mixed-attachments") {
await expect(component.getByAltText("layout.png")).toBeVisible()
await expect(component.getByText("Keep the normal flow flat", { exact: true })).toBeVisible()
}
})
}
story("renders a draft once and supports editing, caret restoration, and failure recovery", async ({ mount, page }) => {
await page.addInitScript(() => {
const replace = Element.prototype.replaceChildren
@@ -1,13 +0,0 @@
import { expect, story } from "../../storybook/playwright/story"
story("keeps the comment options button pressed while its menu is open", async ({ mount, page }) => {
const component = await mount("ui-line-comment--display")
const trigger = component.locator('[data-slot="line-comment-v2-overflow"]')
const rest = await trigger.evaluate((element) => getComputedStyle(element).backgroundColor)
await trigger.click()
await expect(page.getByRole("menu")).toBeVisible()
await expect(trigger).toHaveAttribute("data-expanded", "")
await expect(trigger).not.toHaveCSS("background-color", rest)
})
@@ -8,30 +8,6 @@ story.beforeEach(async ({ mount }) => {
await expect(component.getByRole("textbox", { name: "Prompt", exact: true })).toBeVisible()
})
story("spaces the first mobile message without changing desktop spacing", async ({ page }) => {
await page.setViewportSize({ width: 390, height: 844 })
await page.evaluate(async (fixture) => {
const { mountTimelineVirtualizer } = await import(fixture)
mountTimelineVirtualizer({ count: 1, rowHeight: 60, immediate: true })
}, fixture)
const root = page.getByTestId("timeline-virtualizer-fixture")
await root.getByRole("button", { name: "Complete Markdown", exact: true }).click()
const content = root.locator("[data-timeline-virtual-content]")
await expect(content).toHaveCSS("visibility", "visible")
const gap = () =>
root.locator('[data-timeline-key="user-message:message-0"]').evaluate((element) => {
const viewport = element.closest("[data-scrollable]")!
return element.getBoundingClientRect().top - viewport.getBoundingClientRect().top
})
await expect.poll(gap).toBe(16)
await root.evaluate((element) => element.setAttribute("dir", "rtl"))
await expect.poll(gap).toBe(16)
await page.setViewportSize({ width: 1280, height: 900 })
await expect.poll(gap).toBe(0)
await page.setViewportSize({ width: 390, height: 844 })
await expect.poll(gap).toBe(16)
})
story("bounds the cheap suffix and reveals only ready measured rows", async ({ page }) => {
await page.evaluate(async (fixture) => {
const { mountTimelineVirtualizer } = await import(fixture)
+3 -5
View File
@@ -85,16 +85,14 @@ Each tab scenario reports one sample, including its raw observations. Use Playwr
The tab-switch workload uses two equally long sessions: 200 user/assistant exchanges (400 messages) per tab. Every answer includes headings, emphasis, links, a blockquote, task and nested lists, an eight-row table, and four highlighted code fences (TSX, JSON, SQL, Bash), alongside the stress fixture's reasoning and tools. The mock API deliberately returns all 400 messages in one response so every scenario measures a long loaded history, not a short paginated tail. The viewport is fixed at 1440 x 900. Results include the fixture version, Markdown and serialized-message byte counts, and message-request count. These numbers are not directly comparable to the earlier 12-exchange source / 72-exchange destination fixture.
Cold means the destination transcript has never loaded or rendered in that fresh browser context. Its measured switch includes one fixture message fetch. Warm means its complex answer was rendered and ready before switching away and back, and asserts no message fetch during the measured switch. Neither includes app startup or the source session's Markdown engine initialization. These cold results are not comparable to older prefetched cold-render results. Setup waits for mounted Markdown to finish and for the review-pane width transition to complete. Service workers are blocked to exclude the web build's background asset precache from this renderer benchmark. Screenshots are attached after measurement for the first repetition; Playwright video and trace recording are disabled for this workload, while opt-in Chrome profiling remains available. For a baseline distribution, use `--repeat-each=20 --retries=0`, keep profiling disabled, and report the median and p95 of `firstCorrectObservedMs` separately from the three-observation `stableObservedMs`.
Cold means the destination transcript has never rendered in that fresh browser context. Warm means its complex answer was rendered and ready before switching away and back. Both use the app's normal restored-tab data prefetch, which completes before measurement; neither includes app startup, the source session's Markdown engine initialization, or a cold backend fetch. The suite asserts no message fetch during either measured switch. Setup waits for mounted Markdown to finish and for the review-pane width transition to complete. Service workers are blocked to exclude the web build's background asset precache from this renderer benchmark. Screenshots are attached after measurement for the first repetition; Playwright video and trace recording are disabled for this workload, while opt-in Chrome profiling remains available. For a baseline distribution, use `--repeat-each=20 --retries=0`, keep profiling disabled, and report the median and p95 of `firstCorrectObservedMs` separately from the three-observation `stableObservedMs`.
```sh
bunx playwright test --config e2e/performance/playwright.config.ts \
timeline/session-tab-switch-benchmark.spec.ts --repeat-each=20 --retries=0
```
**The tab-switch fixture returns full history, not normal pagination.** Measure cold API navigation, Home-row opening, and restored-but-unvisited tabs separately with normal pagination. Do not combine these entry paths or compare different transports and machine-load periods as one experiment.
`inactive-tab-prefetch-benchmark.spec.ts` restores eight tabs with normal 20-message pages (44 parts and 139,257 response bytes per page). It gates heavy responses independently until every tab's attention callback has run, then measures selection with ready answer Markdown and bottom anchoring. A separate case closes an inactive tab before releasing the responses. The fixture reports speculative transcript/inbox reads, request concurrency, response bytes, and activation latency. Set `OPENCODE_PERFORMANCE_MEMORY=1` only in separate retention runs; those force GC before selection and must not be mixed into clean timing results. The scope is the production browser renderer, not total desktop memory. Live background events and eviction of previously visited transcripts are separate workloads.
**The tab-switch fixture is not an end-to-end cold-data benchmark.** It prefetches destination messages and returns full history. Measure cold API navigation, Home-row opening, and prefetched-but-unvisited tabs separately with normal pagination. Do not combine these entry paths or compare different transports and machine-load periods as one experiment.
Keep one-off reports, recorded results, and traces outside git, in the ignored `e2e/performance/results/` directory or an external artifact directory. Preserve raw observations locally and publish anonymized summaries and charts in the PR description, not as committed experiment files.
@@ -106,7 +104,7 @@ bun run bench:tabs
This runs only the tab-switch benchmark against the production build with 20 serial repetitions and no retries. It prints the median (mean of the two middle values for even sample counts) and nearest-rank p95 for `firstCorrectObservedMs` and `stableObservedMs` per scenario. Only records whose benchmark and Playwright statuses are passed and whose two metrics are finite enter the summary. Test and record statuses, missing records, and excluded samples are reported separately.
For fresh entry paths, run `bun run bench:entry` from `packages/app`. It uses the same production, serial-repetition, and reporting defaults. The cases open an empty draft from the actual Home button, create a draft with the titlebar plus from an active session, and open a cold paginated session from Home. Draft readiness requires a focused editable composer, the expected model, project control, and new tab; typing and absence of backend mutations are checked afterward. Session readiness requires the latest group, ready answer Markdown, and bottom anchoring. These cases are separate from cached tab remounts.
For fresh entry paths, run `bun run bench:entry` from `packages/app`. It uses the same production, serial-repetition, and reporting defaults. The cases open an empty draft from the actual Home button, create a draft with the titlebar plus from an active session, and open a cold paginated session from Home. Draft readiness requires a focused editable composer, the expected model, project control, and new tab; typing and absence of backend mutations are checked afterward. Session readiness requires the latest group, ready answer Markdown, and bottom anchoring. These cases are separate from prefetched tab remounts.
For milestone charts, rerun frozen builds with one workload and counterbalanced serial order. Do not connect historical medians from different transports, preparation, or machine-load periods. Show samples or ranges, name the checkpoints accurately, and distinguish experimental build snapshots from Git commits.
@@ -1,37 +0,0 @@
# Composer History Hydration
Manual benchmark for an empty destination composer. Runs the production
`ComposerEditor`, `createComposerEditor`, `createComposerHistory`, persistence
codec, and browser IndexedDB draft store. It does not run the surrounding app
shell or native desktop IPC.
Workload: 100 normal prompts with realistic review instructions/code and 100
shell commands. Separate cases have no images, 50 unique screenshots, or 50
references to 5 screenshots. The fixture generates valid 1440 x 900 PNG code
screenshots before timing and reports their exact byte sizes. Each isolated
browser context measures a cold URL-cache mount followed by a warm remount.
The database was just seeded; this does not simulate a cold disk cache.
`historyReadyMs` measures the mount action until both production history stores
are populated. This is history availability, not time to first editable input
(input can be usable before history finishes). The benchmark then verifies
ArrowUp recall and a decoded screenshot in the real editor. `recallObservedMs`
includes Playwright action/assertion overhead and is reported separately.
`mountRecallObservedMs` includes the mount, readiness checks, keyboard action,
and correct text/image completion; it also includes Playwright overhead.
IndexedDB reads and blob sizes are mechanism metrics, not desktop IPC bytes or
process memory. No timing threshold is enforced.
From `packages/app`, set `OPENCODE_HISTORY_BUILD` and
`OPENCODE_HISTORY_OUTPUT` to artifact directories outside Git, then run:
```sh
bun x vite build --config e2e/performance/composer-history/vite.config.ts
bun x playwright test --config e2e/performance/composer-history/playwright.config.ts --repeat-each=20
```
The preview server owns port 4783 and is stopped by Playwright. Preserve each
build and its revision/hash for comparisons. `BENCHMARK` JSON lines contain all
raw samples. Optional Chrome traces use the existing
`OPENCODE_PERFORMANCE_TRACE_DIR` setting; keep trace runs separate from clean
timing. Screenshots are captured after timing on the first repeat only.
@@ -1,48 +0,0 @@
import { benchmark, expect } from "../benchmark"
benchmark.use({ traceScope: "page" })
for (const shape of ["text", "unique", "repeated"]) {
benchmark(`composer global history: ${shape}, cold and warm mounts`, async ({ page, report }, testInfo) => {
const errors: string[] = []
page.on("pageerror", (error) => errors.push(error.message))
await page.goto(`/?shape=${shape}`)
const button = page.getByRole("button", { name: "Mount empty composer", exact: true })
const input = page.getByRole("textbox", { name: "Prompt", exact: true })
const samples = []
for (const cache of ["cold", "warm"]) {
await expect(button).toBeEnabled()
const mountStarted = performance.now()
await button.click()
await expect(page.getByTestId("history-ready")).toHaveText("ready")
await expect(input).toBeEditable()
await expect(input).toBeEmpty()
const result = JSON.parse((await page.getByTestId("history-result").textContent())!)
expect(result.documents).toBe(2)
expect(result.historyReadyMs).toBeGreaterThan(0)
const start = performance.now()
await input.press("ArrowUp")
await expect(input).toContainText("Review the retry policy in src/network/request-0.ts.")
const images = page.getByRole("img", { name: "request-0.png", exact: true })
await expect(images).toHaveCount(shape === "text" ? 0 : 1)
if (shape !== "text")
await expect
.poll(() => images.evaluate((image: HTMLImageElement) => image.complete && image.naturalWidth === 1440))
.toBe(true)
samples.push({
cache,
...result,
recallObservedMs: performance.now() - start,
mountRecallObservedMs: performance.now() - mountStarted,
})
}
expect(errors).toEqual([])
report(
{ samples },
{
browser: page.context().browser()!.version(),
scope: "production composer editor/history, browser IndexedDB; no native IPC",
},
)
if (testInfo.repeatEachIndex === 0) await page.screenshot({ path: testInfo.outputPath(`${shape}.png`) })
})
}
@@ -1,174 +0,0 @@
/// <reference types="vite/client" />
import { createEffect, Show } from "solid-js"
import { createStore } from "solid-js/store"
import { render } from "solid-js/web"
import { PlatformProvider, type Platform } from "@/runtime/platform/platform"
import { createBrowserDraftStore } from "@/runtime/persistence/drafts"
import { createComposerHistory } from "@/composer/history/store"
import { ComposerEditor } from "@/composer/editor/editor"
import { createComposerEditor } from "@/composer/editor/interaction"
import type { ComposerPersistedState } from "@/composer/types"
import "@/index.css"
const shape = new URLSearchParams(location.search).get("shape") ?? "text"
const normal = Array.from({ length: 100 }, (_, index) => {
const content =
`Review the retry policy in src/network/request-${index}.ts. Preserve cancellation and the existing error messages.\n\n` +
`The request should stop after three attempts. Add coverage for a 429 response, a connection reset, and a successful retry. Verify that only idempotent requests are retried.\n\n` +
`Report ${index}:\n\`\`\`ts\nexport async function request(input: Request) {\n const response = await fetch(input)\n if (!response.ok) throw new Error(response.statusText)\n return response.json()\n}\n\`\`\``
return {
prompt: [
{ type: "text", content, start: 0, end: content.length },
...(shape !== "text" && index % 2 === 0
? [
{
type: "image",
id: `attachment-${index}`,
filename: `request-${index}.png`,
mime: "image/png",
blob: { id: `screenshot-${shape === "repeated" ? index % 10 : index}` },
},
]
: []),
],
comments: [],
}
})
const shell = Array.from({ length: 100 }, (_, index) => {
const content = `bun test src/network/request-${index}.test.ts --timeout 30000`
return { prompt: [{ type: "text", content, start: 0, end: content.length }], comments: [] }
})
// Seed only this Playwright context, before opening the production draft store.
const request = indexedDB.open("opencode-drafts", 1)
request.onupgradeneeded = () => {
request.result.createObjectStore("documents")
request.result.createObjectStore("blobs")
}
const db = await new Promise<IDBDatabase>((resolve, reject) => {
request.onsuccess = () => resolve(request.result)
request.onerror = () => reject(request.error)
})
const ids = [...new Set(normal.flatMap((entry) => entry.prompt.flatMap((part) => (part.blob ? [part.blob.id] : []))))]
const screenshots: { id: string; blob: Blob }[] = []
for (const id of ids) {
const canvas = document.createElement("canvas")
canvas.width = 1440
canvas.height = 900
const context = canvas.getContext("2d")!
context.fillStyle = "#15191f"
context.fillRect(0, 0, canvas.width, canvas.height)
context.font = "16px monospace"
context.fillStyle = "#b8c8d8"
context.fillText(`request.ts - ${id}`, 30, 35)
for (let line = 0; line < 38; line++) {
context.fillStyle = line % 3 ? "#a8c7ba" : "#d4a882"
context.fillText(
`${String(line + 1).padStart(3)} const response${line} = await fetch('/api/request/${id}/${line}', { signal, headers });`,
30,
70 + line * 20,
)
}
const blob = await new Promise<Blob>((resolve) => canvas.toBlob((blob) => resolve(blob!), "image/png"))
screenshots.push({ id, blob })
}
const transaction = db.transaction(["documents", "blobs"], "readwrite")
transaction.objectStore("documents").put(JSON.stringify({ entries: normal }), "opencode.global.dat:prompt-history")
transaction.objectStore("documents").put(JSON.stringify({ entries: shell }), "opencode.global.dat:prompt-history-shell")
screenshots.forEach(({ id, blob }) => transaction.objectStore("blobs").put(blob, id))
await new Promise<void>((resolve, reject) => {
transaction.oncomplete = () => resolve()
transaction.onerror = () => reject(transaction.error)
})
db.close()
const metrics = { reads: 0, blobBytes: 0, documents: 0 }
const originalGet = IDBObjectStore.prototype.get
IDBObjectStore.prototype.get = function (key) {
const request = originalGet.call(this, key)
if (this.name === "documents") metrics.documents++
if (this.name === "blobs") {
metrics.reads++
request.addEventListener("success", () => {
metrics.blobBytes += request.result?.size ?? 0
})
}
return request
}
const platform: Platform = {
platform: "web",
draftStore: createBrowserDraftStore(),
openExternal() {},
restart: async () => {},
notify: async () => {},
}
const [state, setState] = createStore({ mount: 0, ready: false, result: "" })
const workload = {
shape,
normalEntries: normal.length,
shellEntries: shell.length,
imageReferences: shape === "text" ? 0 : 50,
uniqueImages: ids.length,
storedImageBytes: screenshots.reduce((sum, item) => sum + item.blob.size, 0),
documentBytes: [normal, shell].reduce(
(sum, entries) => sum + new TextEncoder().encode(JSON.stringify({ entries })).length,
0,
),
screenshotDimensions: [1440, 900],
}
let started = 0
function mount() {
metrics.reads = 0
metrics.blobBytes = 0
metrics.documents = 0
setState({ ready: false, result: "" })
started = performance.now()
setState("mount", state.mount + 1)
}
function Destination() {
// Same history creation and editor mapping as createComposerModel. Destination draft is empty.
const history = createComposerHistory()
const store = createStore<ComposerPersistedState>({
prompt: [{ type: "text", content: "", start: 0, end: 0 }],
cursor: 0,
context: { items: [] },
})
const controller = createComposerEditor({
store,
commands: () => [],
context: () => [],
searchContextFiles: () => [],
history: {
entries: (mode) => history.entries(mode).map((entry) => ({ prompt: entry.prompt, metadata: entry.comments })),
add: (prompt, mode) => history.add(prompt, mode, []),
},
view: {
placeholder: () => "Empty destination composer",
submit: { stopping: () => false, onSubmit() {}, onStop() {} },
},
})
createEffect(() => {
if (history.entries("normal").length !== 100 || history.entries("shell").length !== 100) return
setState({
ready: true,
result: JSON.stringify({ historyReadyMs: performance.now() - started, ...metrics, ...workload }),
})
})
return <ComposerEditor controller={controller} />
}
render(
() => (
<PlatformProvider value={platform}>
<main style={{ padding: "40px", width: "900px" }}>
<h1>Composer global history: {shape}</h1>
<button onClick={mount}>Mount empty composer</button>
<output data-testid="history-ready">{state.ready ? "ready" : "idle"}</output>
<pre data-testid="history-result">{state.result}</pre>
<Show when={state.mount} keyed>
{(_mount) => <Destination />}
</Show>
</main>
</PlatformProvider>
),
document.getElementById("root")!,
)
@@ -1,11 +0,0 @@
<!doctype html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<title>Composer history benchmark</title>
</head>
<body>
<div id="root"></div>
<script type="module" src="./fixture.tsx"></script>
</body>
</html>
@@ -1,20 +0,0 @@
import { defineConfig } from "@playwright/test"
import { fileURLToPath } from "node:url"
export default defineConfig({
testDir: ".",
testMatch: "composer-history.bench.ts",
workers: 1,
retries: 0,
timeout: 60_000,
reporter: "line",
outputDir: process.env.OPENCODE_HISTORY_OUTPUT,
use: { baseURL: "http://127.0.0.1:4783", viewport: { width: 1440, height: 900 }, trace: "off", video: "off" },
webServer: {
cwd: fileURLToPath(new URL("../../../", import.meta.url)),
command:
"bun x vite preview --config e2e/performance/composer-history/vite.config.ts --host 127.0.0.1 --port 4783 --strictPort",
url: "http://127.0.0.1:4783",
reuseExistingServer: false,
},
})
@@ -1,10 +0,0 @@
import { defineConfig } from "vite"
import { fileURLToPath } from "node:url"
import app from "../../../vite"
export default defineConfig({
root: fileURLToPath(new URL(".", import.meta.url)),
publicDir: fileURLToPath(new URL("../../../public", import.meta.url)),
plugins: [app],
build: { target: "esnext", outDir: process.env.OPENCODE_HISTORY_BUILD, emptyOutDir: true },
})
@@ -1,44 +0,0 @@
# Timeline Preload Lifetime
This manual benchmark uses the production app, restored session tabs, the real
`MessageTimeline` preload, and the real Markdown worker. Only API data and result
delivery timing are fixture-owned. It does not connect to a running OpenCode
service or send prompts.
From `packages/app`, set absolute `MARKDOWN_APP_BUILD_DIR` and
`MARKDOWN_RESULTS_DIR` artifact paths, then run:
```sh
bun run build
# Copy dist into MARKDOWN_APP_BUILD_DIR before editing production source.
bun --bun x playwright test --config e2e/performance/markdown/playwright.config.ts --repeat-each 20
```
Each isolated sample restores two sessions with one user message and one completed
assistant text part each. The cold target has a realistic recovery review with
either two TypeScript fences (typical) or 36 fences (large). The source has a short
completed answer. Target data is prefetched before selection, but its Markdown is
not parsed until the target is selected.
The app's service-worker generator reads `dist`, so use the normal build output
and freeze a copy, rather than overriding Vite's build output directory.
The real worker result is held after admission. The test selects the original
session again and releases the held result only after the abandoned timeline row
detaches and the selected answer reports production Markdown readiness. This
exercises both the timeline preload and the nested Markdown consumer, including
the case where either one would otherwise keep a shared parse alive.
Destination readiness and post-disposal result settlement are separate metrics.
The latter is a MessageChannel task after the result's promise microtasks drain.
The DOMParser probe counts actual DOMPurify input containing the abandoned answer
after disposal, in characters. CDP reports renderer task/script time and JS heap.
These are not worker CPU, Electron process RAM, or ungated tab-switch measurements.
In particular, this gate releases the result after destination readiness and must
not be used to claim a destination-readiness gain from skipping sanitization.
`MARKDOWN_ASSERT_DISPOSAL=1` enables the no-obsolete-sanitization assertion. Use
`MARKDOWN_RETAINED=1` only in separate post-GC runs. The repository trace collector
is available through `OPENCODE_PERFORMANCE_TRACE_DIR`, and `MARKDOWN_SCREENSHOT`
captures final output after timing. Run serially, preserve frozen builds, and keep
all results outside Git.
@@ -1,125 +0,0 @@
import type { SessionMessageInfo } from "@opencode-ai/client/promise"
import { benchmark, expect } from "../benchmark"
import { mockOpenCodeServer } from "../../utils/mock-server"
import { fixture } from "../timeline/session-timeline-stress.fixture"
import { installStressSessionTabs, installTimelineSettings, stressSessionHref } from "../timeline/timeline-test-helpers"
import { completedAnswer } from "../../../../session-ui/performance/markdown-lifetime/answer"
import { installMarkdownGate } from "./probe"
for (const size of ["typical", "large"]) {
benchmark(`timeline preload disposal: ${size}`, async ({ page, report }) => {
const answer = completedAnswer(size === "typical" ? 2 : 36)
const errors: string[] = []
page.on("pageerror", (error) => errors.push(error.message))
const messages: Record<string, SessionMessageInfo[]> = Object.fromEntries(
[fixture.sourceID, fixture.targetID].map((id) => [
id,
[
{
id: `msg_1_${id}_user`,
type: "user",
time: { created: 1700000000000 },
text: "Review the recovery boundary.",
},
{
id: `msg_2_${id}_assistant`,
type: "assistant",
time: { created: 1700000001000, completed: 1700000008000 },
model: { id: "claude-opus-4-6", providerID: "opencode" },
agent: "build",
cost: 0.01,
tokens: { input: 100, output: 200, reasoning: 0, cache: { read: 0, write: 0 } },
finish: "stop",
content: [
{
type: "text",
text:
id === fixture.targetID
? answer
: "## Current destination\n\nThe selected session is ready.\n\n```typescript\nconst current = { ready: true }\n```",
},
],
},
] satisfies SessionMessageInfo[],
]),
)
await mockOpenCodeServer(page, {
sessions: fixture.sessions.filter((session) => session.id !== fixture.childID),
provider: fixture.provider,
directory: fixture.directory,
project: fixture.project,
pageMessages: (id) => ({ items: messages[id] ?? [] }),
})
await installTimelineSettings(page)
await installStressSessionTabs(page)
const targetPart = `msg_2_${fixture.targetID}_assistant:text:0`
const sourcePart = `msg_2_${fixture.sourceID}_assistant:text:0`
await installMarkdownGate(page, { answer, targetPart, sourcePart, href: stressSessionHref(fixture.sourceID) })
const prefetched = page.waitForResponse((response) =>
new URL(response.url()).pathname.endsWith(`/session/${fixture.targetID}/message`),
)
await page.goto(stressSessionHref(fixture.sourceID))
await prefetched
const source = page.locator(`[data-timeline-part-id="${sourcePart}"] [data-component="markdown"]`)
await expect(source).toHaveAttribute("data-markdown-ready", "")
await page.locator(`[data-slot="titlebar-tabs"] a[href="${stressSessionHref(fixture.targetID)}"]`).click()
await page.waitForFunction(() => Reflect.get(window, "markdownGate").held)
await expect(page.locator(`[data-timeline-part-id="${targetPart}"]`)).toBeAttached()
const cdp = await page.context().newCDPSession(page)
await cdp.send("Performance.enable")
const before = await cdp.send("Performance.getMetrics")
await page.evaluate(() => Reflect.get(window, "markdownGate").arm())
await page.locator(`[data-slot="titlebar-tabs"] a[href="${stressSessionHref(fixture.sourceID)}"]`).click()
await expect(source).toHaveAttribute("data-markdown-ready", "")
await expect(source.getByRole("heading", { name: "Current destination" })).toBeVisible()
await expect(page.locator(`[data-timeline-part-id="${targetPart}"]`)).toHaveCount(0)
await page.waitForFunction(() => Reflect.get(window, "markdownGate").settled > 0)
const after = await cdp.send("Performance.getMetrics")
const stats = await page.evaluate(() => {
const value = Reflect.get(window, "markdownGate")
return {
admitted: value.admitted,
responses: value.responses,
started: value.started,
ready: value.ready,
released: value.released,
settled: value.settled,
sanitizeCalls: value.sanitizeCalls,
sanitizeChars: value.sanitizeChars,
}
})
expect(stats.admitted).toBe(1)
expect(stats.responses).toBe(1)
expect(stats.ready).toBeGreaterThan(stats.started)
expect(stats.settled).toBeGreaterThan(stats.released)
expect(errors).toEqual([])
if (process.env.MARKDOWN_ASSERT_DISPOSAL === "1") expect(stats.sanitizeCalls).toBe(0)
const value = (data: typeof after, name: string) => data.metrics.find((item) => item.name === name)!.value
const retained = process.env.MARKDOWN_RETAINED === "1"
if (retained) await cdp.send("HeapProfiler.collectGarbage")
report(
{
...stats,
destinationReadyMs: stats.ready - stats.started,
releasedSettledMs: stats.settled - stats.released,
taskMs: (value(after, "TaskDuration") - value(before, "TaskDuration")) * 1000,
scriptMs: (value(after, "ScriptDuration") - value(before, "ScriptDuration")) * 1000,
usedHeapBytes: (await cdp.send("Runtime.getHeapUsage")).usedSize,
},
{
size,
retained,
answerBytes: Buffer.byteLength(answer),
messagesPerSession: 2,
partsPerAnswer: 1,
fences: size === "typical" ? 2 : 36,
browser: page.context().browser()!.version(),
transport: "playwright-route",
build: process.env.MARKDOWN_APP_BUILD_DIR,
},
)
if (process.env.MARKDOWN_SCREENSHOT)
await page.screenshot({ path: `${process.env.MARKDOWN_SCREENSHOT}/timeline-${size}.png` })
await cdp.detach()
})
}
@@ -1,24 +0,0 @@
import { defineConfig } from "@playwright/test"
import { fileURLToPath } from "node:url"
process.env.PLAYWRIGHT_PORT = "6199"
process.env.PLAYWRIGHT_SERVER_PORT = "6199"
process.env.PLAYWRIGHT_SERVER_HOST = "127.0.0.1"
export default defineConfig({
testDir: ".",
testMatch: "*.bench.ts",
outputDir: process.env.MARKDOWN_RESULTS_DIR,
workers: 1,
retries: 0,
timeout: 60_000,
expect: { timeout: 15_000 },
reporter: [["line"]],
use: { baseURL: "http://127.0.0.1:6199", viewport: { width: 1280, height: 900 }, serviceWorkers: "block" },
webServer: {
cwd: fileURLToPath(new URL("../../..", import.meta.url)),
command: `bun run serve -- --host 127.0.0.1 --port 6199 --strictPort --outDir "${process.env.MARKDOWN_APP_BUILD_DIR}"`,
url: "http://127.0.0.1:6199",
reuseExistingServer: false,
},
})
@@ -1,93 +0,0 @@
import type { Page } from "@playwright/test"
import type {
MarkdownWorkerRequest,
MarkdownWorkerResponse,
} from "../../../../session-ui/src/components/markdown-worker-protocol"
export async function installMarkdownGate(
page: Page,
input: { answer: string; sourcePart: string; targetPart: string; href: string },
) {
await page.addInitScript(({ answer, sourcePart, targetPart, href }) => {
const stats = {
admitted: 0,
responses: 0,
held: false,
started: 0,
ready: 0,
released: 0,
settled: 0,
sanitizeCalls: 0,
sanitizeChars: 0,
arm: () => {
armed = true
},
}
let armed = false
let id: number | undefined
let release: (() => void) | undefined
const descriptor = Object.getOwnPropertyDescriptor(Worker.prototype, "onmessage")!
const post = Worker.prototype.postMessage
Object.defineProperty(Worker.prototype, "onmessage", {
configurable: true,
get: descriptor.get,
set(callback: (event: MessageEvent<MarkdownWorkerResponse>) => void) {
descriptor.set!.call(this, (event: MessageEvent<MarkdownWorkerResponse>) => {
if (event.data.type === "parse" && event.data.id === id) {
stats.responses++
stats.held = true
release = () => callback.call(this, event)
return
}
callback.call(this, event)
})
},
})
Worker.prototype.postMessage = function (request: MarkdownWorkerRequest) {
if (request.type === "parse" && request.text === answer) {
id = request.id
stats.admitted++
}
post.call(this, request)
}
const parse = DOMParser.prototype.parseFromString
DOMParser.prototype.parseFromString = function (text, type) {
if (stats.released && String(text).includes("Recovery implementation review")) {
stats.sanitizeCalls++
stats.sanitizeChars += String(text).length
}
return parse.call(this, text, type)
}
document.addEventListener(
"mousedown",
(event) => {
if (!armed || stats.started) return
const target = event.target instanceof Element ? event.target.closest("a") : undefined
if (target?.getAttribute("href") !== href) return
stats.started = performance.now()
},
true,
)
// The app can retain the outgoing view until the destination is ready. Release
// only after its actual row detaches, rather than assuming click means dispose.
new MutationObserver(() => {
if (!stats.started || stats.released) return
const current = document.querySelector(`[data-timeline-part-id="${sourcePart}"] [data-markdown-ready]`)
if (!current) return
stats.ready ||= performance.now()
if (document.querySelector(`[data-timeline-part-id="${targetPart}"]`)) return
stats.released = performance.now()
performance.mark("markdown-timeline-disposed")
release!()
release = undefined
const channel = new MessageChannel()
channel.port1.onmessage = () => {
stats.settled = performance.now()
channel.port1.close()
channel.port2.close()
}
channel.port2.postMessage(null)
}).observe(document, { childList: true, subtree: true, attributes: true })
Object.defineProperty(window, "markdownGate", { value: stats })
}, input)
}
@@ -1,41 +0,0 @@
# Patch Group Benchmark
This manual benchmark mounts the production `CurrentFileToolGroup` and `File`
components with completed edit results. A separate case mounts `ToolDisplay`
with a patch result. It uses four real Core tool source files, with deterministic
identifier renames, rather than repeated filler. It does not connect to a server.
From `packages/app`, set `PATCH_BUILD_DIR` and `PATCH_RESULTS_DIR` to external
artifact directories, then run:
```sh
bun x vite build --config e2e/performance/patch-groups/vite.config.ts
bun x playwright test --config e2e/performance/patch-groups/playwright.config.ts --repeat-each=20
```
Run under the shared exclusive gate when collecting measurements on a shared
machine. The Playwright-owned static server uses `PATCH_PORT` (default 4317),
refuses to reuse an existing server, and shuts down after the run.
Each fresh browser context measures a cold collapsed mount, a warm remount,
and opening `edit.ts` through its real accordion. Mount timing covers synchronous
component construction through layout. Expansion timing starts at the click and
ends at the production file renderer's `onRendered` callback. Assertions check
the exact file count, collapsed state, and completed file rendering. Results
include payload bytes, source bytes, file/tool counts, and supporting warm
`patchFileGroups` timings with and without reading views. No timing thresholds
are enforced. This is a browser component workload, not a full desktop memory test.
Freeze the build before changing production code. Use the same fixture, browser,
viewport, sample count, and completion checks for both revisions.
`PATCH_REVISION=<git-sha>` loads the grouping module and tool renderer from that
revision at build time without changing the worktree. This is useful when fixing
the harness after freezing a baseline. All other production sources must match
between revisions; this switch only covers those two measured modules.
For a separate diagnostic build, set `PATCH_COUNTERS=1`. Its build-only transform
counts grouping, normalization, reconstruction, and line-diff calls with User
Timing marks. Do not mix instrumented results with clean timings. Set
`OPENCODE_PERFORMANCE_TRACE_DIR` for the existing Chrome trace collector, and
`PATCH_SCREENSHOTS=1` for collapsed/expanded screenshots after measurement.
@@ -1,143 +0,0 @@
/// <reference types="vite/client" />
import { render } from "solid-js/web"
import { Show } from "solid-js"
import { createStore } from "solid-js/store"
import { ThemeProvider } from "@opencode-ai/ui/theme"
import { CurrentSessionProviders } from "../../../../session-ui/src/storybook/current-session-story"
import { emptySessionDocument } from "../../../../session-ui/src/storybook/current-session-fixtures"
import { CurrentFileToolGroup, ToolDisplay } from "../../../../session-ui/src/tools/tool-renderer"
import { patchFileGroups } from "../../../../session-ui/src/components/apply-patch-file"
import type { SessionMessageAssistantTool } from "@opencode-ai/client/promise"
import { createTwoFilesPatch, diffLines } from "diff"
import edit from "../../../../core/src/tool/plugin/edit.ts?raw"
import patch from "../../../../core/src/tool/plugin/patch.ts?raw"
import read from "../../../../core/src/tool/plugin/read.ts?raw"
import shell from "../../../../core/src/tool/plugin/shell.ts?raw"
import "../../../src/index.css"
const scenario = new URLSearchParams(location.search).get("scenario") ?? "complete"
const sources = [edit, patch, read, shell].map((text) => text.replaceAll("\r\n", "\n"))
const names = ["edit", "patch", "read", "shell"]
const changed = (text: string) => text.replaceAll(/\bcontext\b/g, "invocation")
const entry = (index: number, before: string, after: string) => ({
file: `src/tool/plugin/${names[index]}.ts`,
patch: createTwoFilesPatch(names[index], names[index], before, after, "", "", {
context: scenario === "partial" ? 3 : Infinity,
}),
...diffLines(before, after).reduce(
(counts, item) => ({
additions: counts.additions + (item.added ? item.count : 0),
deletions: counts.deletions + (item.removed ? item.count : 0),
}),
{ additions: 0, deletions: 0 },
),
status: "modified" as const,
})
const files =
scenario === "multi"
? sources.map((text, index) => entry(index, text, changed(text)))
: [
entry(0, sources[0], changed(sources[0])),
...(scenario === "chained"
? [entry(0, changed(sources[0]), changed(sources[0]).replaceAll(/\binput\b/g, "parameters"))]
: []),
]
const tools: SessionMessageAssistantTool[] = files.map((file, index) => ({
id: `fixture-edit-${index}`,
type: "tool",
name: "edit",
state: {
status: "completed",
input: { path: file.file, oldString: "context", newString: "invocation", replaceAll: true },
metadata: { files: [file] },
content: [{ type: "text", text: `Edited ${file.file}` }],
},
time: { created: 1, ran: 2, completed: 3 },
}))
declare global {
interface Window {
patchBenchmark: {
payloadBytes: number
sourceBytes: number
files: number
tools: number
grouping: (expanded: boolean) => { ms: number; groups: number; views: number }
}
}
}
window.patchBenchmark = {
payloadBytes: new TextEncoder().encode(JSON.stringify(tools)).length,
sourceBytes: new TextEncoder().encode(sources.slice(0, scenario === "multi" ? 4 : 1).join("")).length,
files: new Set(files.map((file) => file.file)).size,
tools: tools.length,
grouping(expanded) {
const start = performance.now()
const groups = patchFileGroups(files)
const views = expanded ? groups.reduce((count, file) => count + file.views.length, 0) : 0
return { ms: performance.now() - start, groups: groups.length, views }
},
}
function Fixture() {
const [state, setState] = createStore({ mounted: false, duration: 0, rendered: 0 })
let start = 0
return (
<ThemeProvider>
<section style={{ margin: "24px auto", "max-width": "960px" }}>
<button
onClick={() => {
start = performance.now()
setState("mounted", true)
document.querySelector("[data-component=apply-patch-tool]")!.getBoundingClientRect()
setState("duration", performance.now() - start)
}}
>
Mount tools
</button>
<button
onClick={() => {
setState({ mounted: false, rendered: 0 })
}}
>
Unmount tools
</button>
<output data-testid="mount-ms">{state.duration}</output>
<output data-testid="rendered">{state.rendered}</output>
<div
on:click={{
capture: true,
handleEvent() {
start = performance.now()
},
}}
>
<Show when={state.mounted}>
<CurrentSessionProviders document={emptySessionDocument}>
<Show
when={scenario === "direct"}
fallback={
<CurrentFileToolGroup
tools={tools}
onSizeChange={() => setState("rendered", performance.now() - start)}
/>
}
>
<ToolDisplay
id="fixture-patch"
tool="patch"
input={{}}
metadata={{ files }}
status="completed"
onContentRendered={() => setState("rendered", performance.now() - start)}
/>
</Show>
</CurrentSessionProviders>
</Show>
</div>
</section>
</ThemeProvider>
)
}
render(() => <Fixture />, document.getElementById("root")!)
@@ -1,11 +0,0 @@
<!doctype html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<title>Patch groups benchmark</title>
</head>
<body>
<main id="root"></main>
<script type="module" src="./fixture.tsx"></script>
</body>
</html>
@@ -1,66 +0,0 @@
import { benchmark, expect } from "../benchmark"
for (const scenario of ["complete", "partial", "chained", "multi", "direct"]) {
benchmark(`patch groups ${scenario}`, async ({ page, report }, info) => {
await page.goto(`/?scenario=${scenario}`)
await expect(page.getByRole("button", { name: "Mount tools", exact: true })).toBeEnabled()
await page.evaluate(() => document.fonts.ready)
expect(await page.evaluate(() => document.fonts.check('13px "Inter"'))).toBe(true)
const shape = await page.evaluate(() => {
const { grouping, ...shape } = window.patchBenchmark
performance.clearMarks()
return shape
})
const mount = async () => {
await page.getByRole("button", { name: "Mount tools", exact: true }).click()
await expect(page.locator('[data-slot="apply-patch-filename"]')).toHaveCount(shape.files)
await expect(page.locator('[data-component="file"]')).toHaveCount(0)
return Number(await page.getByTestId("mount-ms").textContent())
}
const cold = await mount()
const counters = await page.evaluate(() =>
Object.fromEntries(
["patchFileGroups", "normalize", "completePatchContents", "diffLines"].map((name) => [
name,
performance.getEntriesByName(`patch-counter:${name}`).length,
]),
),
)
await page.getByRole("button", { name: "Unmount tools", exact: true }).click()
await expect(page.locator('[data-component="apply-patch-tool"]')).toHaveCount(0)
await page.evaluate(() => performance.clearMarks())
const warm = await mount()
const warmCounters = await page.evaluate(() =>
Object.fromEntries(
["patchFileGroups", "normalize", "completePatchContents", "diffLines"].map((name) => [
name,
performance.getEntriesByName(`patch-counter:${name}`).length,
]),
),
)
const file = page.locator('[data-scope="apply-patch"] button').filter({ hasText: "edit.ts" })
await expect(file).toHaveAttribute("aria-expanded", "false")
await file.click()
await expect(file).toHaveAttribute("aria-expanded", "true")
await expect(page.getByTestId("rendered")).not.toHaveText("0")
await expect(page.locator('[data-component="file"]')).toBeVisible()
const expansion = Number(await page.getByTestId("rendered").textContent())
const grouping = await page.evaluate(() => ({
collapsed: window.patchBenchmark.grouping(false),
expanded: window.patchBenchmark.grouping(true),
}))
expect(grouping.collapsed.groups).toBe(shape.files)
report(
{ cold, warm, expansion, grouping, counters, warmCounters },
{ scenario, ...shape, scope: "production tool components" },
)
if (process.env.PATCH_SCREENSHOTS === "1") {
await page.screenshot({ path: info.outputPath(`${scenario}-expanded.png`) })
await file.click()
await expect(file).toHaveAttribute("aria-expanded", "false")
await page
.locator('[data-component="apply-patch-tool"]')
.screenshot({ path: info.outputPath(`${scenario}-collapsed.png`) })
}
})
}
@@ -1,18 +0,0 @@
import { defineConfig } from "@playwright/test"
const baseURL = `http://127.0.0.1:${process.env.PATCH_PORT ?? 4317}`
export default defineConfig({
testDir: ".",
testMatch: "*.bench.ts",
workers: 1,
retries: 0,
timeout: 60_000,
outputDir: process.env.PATCH_RESULTS_DIR,
reporter: "line",
use: { baseURL, viewport: { width: 1366, height: 768 }, colorScheme: "light" },
webServer: {
command: "bun serve.ts",
url: baseURL,
reuseExistingServer: false,
},
})
@@ -1,13 +0,0 @@
import path from "node:path"
const directory = process.env.PATCH_BUILD_DIR
if (!directory) throw new Error("PATCH_BUILD_DIR is required")
Bun.serve({
hostname: "127.0.0.1",
port: Number(process.env.PATCH_PORT ?? 4317),
async fetch(request) {
const pathname = new URL(request.url).pathname
const file = Bun.file(path.join(directory, pathname === "/" ? "index.html" : pathname))
return (await file.exists()) ? new Response(file) : new Response("Not found", { status: 404 })
},
})
@@ -1,51 +0,0 @@
import { defineConfig } from "vite"
import solid from "vite-plugin-solid"
import tailwindcss from "@tailwindcss/vite"
import { fileURLToPath } from "node:url"
import { execFileSync } from "node:child_process"
import path from "node:path"
export default defineConfig({
root: fileURLToPath(new URL(".", import.meta.url)),
publicDir: fileURLToPath(new URL("../../../public", import.meta.url)),
plugins: [
solid(),
tailwindcss(),
{
name: "patch-group-counters",
enforce: "pre",
load(id) {
if (!process.env.PATCH_REVISION) return
const root = fileURLToPath(new URL("../../../../..", import.meta.url))
const file = path.relative(root, id).replaceAll("\\", "/")
if (
![
"packages/session-ui/src/components/apply-patch-file.ts",
"packages/session-ui/src/tools/tool-renderer.tsx",
].includes(file)
)
return
return execFileSync("git", ["show", `${process.env.PATCH_REVISION}:${file}`], { cwd: root, encoding: "utf8" })
},
transform(code, id) {
if (process.env.PATCH_COUNTERS !== "1") return
const functions = id.replaceAll("\\", "/").endsWith("/apply-patch-file.ts")
? ["patchFileGroups"]
: id.replaceAll("\\", "/").endsWith("/session-diff.ts")
? ["normalize", "completePatchContents"]
: id.replaceAll("\\", "/").endsWith("/diff/line.js")
? ["diffLines"]
: []
for (const name of functions) {
const pattern = new RegExp(`(export function ${name}\\([^)]*\\)[^{]*\\{)`)
if (!pattern.test(code)) throw new Error(`Missing instrumented function ${name} in ${id}`)
code = code.replace(pattern, `$1 performance.mark("patch-counter:${name}");`)
}
return functions.length ? { code, map: null } : undefined
},
},
],
resolve: { dedupe: ["solid-js", "@solidjs/meta"] },
worker: { format: "es" },
build: { outDir: process.env.PATCH_BUILD_DIR, emptyOutDir: true, sourcemap: true },
})
@@ -1,44 +0,0 @@
# Native Terminal Benchmark
Manual Windows benchmark. Run only in an isolated development worktree. It does
not connect to an OpenCode service, user profile, or database.
Build from `packages/app` with
`bun x vite build --config e2e/performance/terminals/vite.config.ts`, then freeze
`dist` outside the repository. Set `PLAYWRIGHT_BUILD=1`, `PLAYWRIGHT_BASE_URL` to
an unused loopback URL, `TERMINAL_BUILD` to the frozen build,
`TERMINAL_ARTIFACTS` to an existing external directory, and `TERMINAL_RESULTS`
to an external result directory. Run:
```sh
bun x playwright test --config e2e/performance/terminals/playwright.config.ts --repeat-each=20
```
The runner owns its preview server and each test owns a PowerShell ConPTY process.
Session metadata is deterministic. Native output is forwarded through Playwright's
WebSocket fixture into the real production `Terminal`, writer, Ghostty WASM/canvas,
and serializer. No output is dropped, paused, or delayed. This is native terminal
plus production renderer evidence, not the production PTY backend or Electron IPC.
The workload is 12,000 colored build/test log lines with file paths, durations, and
result descriptions. Cases separate visible output, the same output while hidden,
and closing the session tab after filling the configured scrollback. Ghostty
converts the app's 10,000-line setting to bytes at its initial 80-column width;
resizing reduces the effective row capacity. The report records actual retained
rows and the first retained fixture record rather than assuming 10,000 rows. Completion
requires the final marker in Ghostty and completion of its write callbacks, not
just WebSocket delivery. Teardown requires Home readiness and the final serialized
snapshot. Input, focus, resizing, and native process survival are checked.
`probe.ts` is included only by this benchmark build. It observes actual writes,
renderer calls, and serialization. Chrome `TaskDuration` measures renderer task
time, not total process CPU or RAM. For attribution, set
`OPENCODE_PERFORMANCE_TRACE_DIR`; keep traced runs separate from clean timing.
`TERMINAL_DRAW_PROBE=1` separately counts actual canvas draws to verify hidden
rendering; do not mix these instrumented samples with clean timing.
Use `TERMINAL_REVISION` and `TERMINAL_BUNDLE` to identify frozen artifacts.
`TERMINAL_SCREENSHOTS` captures the visible result after timing.
The benchmark has no machine-dependent performance thresholds. Keep raw logs,
snapshots, traces, and screenshots outside Git. Run heavy work through the
coordinator's exclusive gate when participating in a shared performance wave.
@@ -1,20 +0,0 @@
import { defineConfig } from "@playwright/test"
import config from "../../../playwright.config"
export default defineConfig({
...config,
testDir: ".",
testIgnore: [],
testMatch: "terminal-benchmark.spec.ts",
workers: 1,
retries: 0,
timeout: 120_000,
outputDir: process.env.TERMINAL_RESULTS,
reporter: [["line"]],
webServer: {
command: `bun x vite preview --host 127.0.0.1 --port ${new URL(process.env.PLAYWRIGHT_BASE_URL!).port} --strictPort --outDir ${process.env.TERMINAL_BUILD}`,
url: process.env.PLAYWRIGHT_BASE_URL,
reuseExistingServer: false,
},
use: { ...config.use, viewport: { width: 1440, height: 900 }, trace: "off", video: "off", serviceWorkers: "block" },
})
@@ -1,74 +0,0 @@
import { Terminal } from "ghostty-web"
import { SerializeAddon } from "../../../src/session/terminal/serialize"
export type TerminalProbe = {
term?: Terminal
writes: number
pending: number
bytes: number
renders: number
hiddenRenders: number
draws: number
hiddenDraws: number
serialized: { ms: number; bytes: number; value: string }[]
}
declare global {
interface Window {
terminalProbe: TerminalProbe
}
}
const probe: TerminalProbe = {
writes: 0,
pending: 0,
bytes: 0,
renders: 0,
hiddenRenders: 0,
draws: 0,
hiddenDraws: 0,
serialized: [],
}
window.terminalProbe = probe
const open = Terminal.prototype.open
Terminal.prototype.open = function (element) {
probe.term = this
open.call(this, element)
// Ghostty does not expose render events. This benchmark-only wrapper observes its
// actual renderer; it does not alter scheduling, parsing, or drawing.
const renderer = (this as unknown as { renderer: { render: (...args: unknown[]) => void } }).renderer
const render = renderer.render
let hidden = false
renderer.render = function (...args) {
probe.renders++
hidden = !element.checkVisibility()
if (hidden) probe.hiddenRenders++
return render.apply(this, args)
}
if (new URL(location.href).searchParams.has("terminalDrawProbe")) {
const context = element.querySelector("canvas")!.getContext("2d")!
const draw = context.drawImage
context.drawImage = function (...args: unknown[]) {
probe.draws++
if (hidden) probe.hiddenDraws++
Reflect.apply(draw, this, args)
}
}
}
const write = Terminal.prototype.write
Terminal.prototype.write = function (data, done) {
probe.writes++
probe.pending++
probe.bytes += typeof data === "string" ? new TextEncoder().encode(data).byteLength : data.byteLength
return write.call(this, data, () => {
probe.pending--
done?.()
})
}
const serialize = SerializeAddon.prototype.serialize
SerializeAddon.prototype.serialize = function (options) {
const start = performance.now()
const value = serialize.call(this, options)
probe.serialized.push({ ms: performance.now() - start, bytes: new TextEncoder().encode(value).byteLength, value })
return value
}
@@ -1,13 +0,0 @@
param([Parameter(Mandatory = $true)][string]$Fixture)
$ErrorActionPreference = 'Stop'
[Console]::WriteLine('TERMINAL_FIXTURE_READY')
while ($null -ne ($command = [Console]::ReadLine())) {
if ($command -eq 'exit') { exit 0 }
if ($command -eq 'run') {
foreach ($line in [System.IO.File]::ReadLines($Fixture)) {
[Console]::WriteLine($line)
}
[Console]::WriteLine('TERMINAL_WORKLOAD_DONE')
}
if ($command -eq 'ping') { [Console]::WriteLine('TERMINAL_PROCESS_ALIVE') }
}
@@ -1,352 +0,0 @@
import { createRequire } from "node:module"
import { mkdtemp, writeFile, rm } from "node:fs/promises"
import { tmpdir } from "node:os"
import path from "node:path"
import { fileURLToPath } from "node:url"
import type { Page } from "@playwright/test"
import { benchmark, benchmarkDiagnostics, expect } from "../benchmark"
import { mockOpenCodeServer } from "../../utils/mock-server"
import { expectSessionTitle } from "../../utils/waits"
import type {} from "./probe"
// Use the same installed native PTY package as Core, with a fixture-owned process.
const native = createRequire(new URL("../../../../core/package.json", import.meta.url))("@lydell/node-pty") as {
spawn: (
file: string,
args: string[],
options: { cols: number; rows: number; cwd: string },
) => {
pid: number
write: (data: string) => void
resize: (cols: number, rows: number) => void
kill: () => void
onData: (handler: (data: string) => void) => { dispose: () => void }
onExit: (handler: () => void) => { dispose: () => void }
}
}
const sessionID = "ses_terminal_benchmark"
const ptyID = "pty_terminal_benchmark"
const title = "Terminal build output"
const server = process.env.PLAYWRIGHT_BASE_URL!
const href = `/server/${Buffer.from(server).toString("base64url")}/session/${sessionID}`
const lines = Array.from({ length: 12_000 }, (_, i) => {
const unit = ["session/history", "session/runner", "project/discovery", "tool/shell", "provider/stream"][i % 5]
return `\x1b[32mPASS\x1b[0m packages/core/test/${unit}-${String(i).padStart(5, "0")}.test.ts \x1b[2m[${10 + (i % 237)}ms]\x1b[0m validates ordered output and durable recovery`
}).join("\r\n")
benchmark.use({ traceScope: "interaction", viewport: { width: 1440, height: 900 } })
for (const scenario of ["visible-output", "hidden-output", "full-scrollback-teardown"] as const) {
benchmark(scenario, async ({ page, report }, info) => {
const dir = await mkdtemp(path.join(process.env.TERMINAL_ARTIFACTS ?? tmpdir(), "terminal-fixture-"))
await writeFile(path.join(dir, "build.log"), lines)
const pty = native.spawn(
"pwsh.exe",
[
"-NoLogo",
"-NoProfile",
"-NonInteractive",
"-File",
fileURLToPath(new URL("./shell.ps1", import.meta.url)),
"-Fixture",
path.join(dir, "build.log"),
],
{ cols: 120, rows: 24, cwd: dir },
)
const exited = new Promise<void>((resolve) => pty.onExit(resolve))
let output = ""
let connected = 0
let closed = 0
let send: ((data: string) => void) | undefined
const listener = pty.onData((data) => {
output += data
send?.(data)
})
const sizes: { cols: number; rows: number }[] = []
const removals: string[] = []
try {
if (process.env.TERMINAL_DRAW_PROBE) {
await page.addInitScript(() => {
const fill = CanvasRenderingContext2D.prototype.fillText
CanvasRenderingContext2D.prototype.fillText = function (...args: Parameters<typeof fill>) {
if (this.canvas instanceof HTMLCanvasElement && this.canvas.closest('[data-component="terminal"]')) {
window.terminalProbe.draws++
if (!this.canvas.checkVisibility()) window.terminalProbe.hiddenDraws++
}
Reflect.apply(fill, this, args)
}
})
}
const location = { directory: dir, project: { id: "proj_terminal_benchmark", directory: dir } }
const data = {
id: ptyID,
title: "Terminal 1",
command: "pwsh.exe",
args: [],
cwd: dir,
status: "running",
pid: pty.pid,
}
await mockOpenCodeServer(page, {
directory: dir,
project: {
id: location.project.id,
worktree: dir,
vcs: "git",
name: "terminal-benchmark",
time: { created: 1, updated: 1 },
sandboxes: [],
},
provider: {
all: [
{
id: "opencode",
name: "OpenCode",
models: { test: { id: "test", name: "Test", limit: { context: 200_000 } } },
},
],
connected: ["opencode"],
default: { providerID: "opencode", modelID: "test" },
},
sessions: [
{
id: sessionID,
slug: sessionID,
projectID: location.project.id,
directory: dir,
title,
version: "dev",
time: { created: 1700000000000, updated: 1700000000000 },
},
],
pageMessages: () => ({ items: [] }),
})
await page.route("**/api/pty**", async (route) => {
if (route.request().method() === "DELETE") removals.push(route.request().url())
const body = route.request().postDataJSON()
if (body?.size) {
sizes.push(body.size)
pty.resize(body.size.cols, body.size.rows)
}
return route.fulfill({
status: 200,
contentType: "application/json",
body: JSON.stringify({
location,
data: route.request().url().includes("connect-token") ? { ticket: "fixture", expires_in: 60 } : data,
}),
})
})
await page.routeWebSocket(new RegExp(`/api/pty/${ptyID}/connect`), (socket) => {
connected++
send = (data) => socket.send(data)
socket.send(output.slice(Number(new URL(socket.url()).searchParams.get("cursor") ?? 0)))
socket.onMessage((data) => pty.write(String(data)))
socket.onClose(() => {
closed++
send = undefined
})
})
await page.addInitScript(
({ server, sessionID }) => {
localStorage.setItem("settings.v3", JSON.stringify({ general: { terminalPlacement: "bottom" } }))
localStorage.setItem(
"opencode.window.browser.dat:tabs",
JSON.stringify([{ type: "session", server, sessionId: sessionID }]),
)
},
{ server, sessionID },
)
await page.goto(
`${href}${process.env.TERMINAL_DRAW_PROBE && scenario !== "full-scrollback-teardown" ? "?terminalDrawProbe" : ""}`,
)
await expectSessionTitle(page, title)
await page.keyboard.press("Control+Backquote")
await waitForText(page, "TERMINAL_FIXTURE_READY")
const terminal = page.locator('[data-component="terminal"]')
await expect(terminal).toBeVisible()
await page.evaluate(() => document.fonts.ready.then(() => undefined))
await expect
.poll(async () => {
const size = await page.evaluate(() => ({
cols: window.terminalProbe.term!.cols,
rows: window.terminalProbe.term!.rows,
}))
return sizes.at(-1)?.cols === size.cols && sizes.at(-1)?.rows === size.rows
})
.toBe(true)
if (scenario === "hidden-output") {
await page.keyboard.press("Control+Backquote")
await expect(terminal).toBeHidden()
}
const cdp = await page.context().newCDPSession(page)
await cdp.send("Performance.enable")
const before = await cdp.send("Performance.getMetrics")
const start = await page.evaluate(() => {
window.terminalProbe.renders = 0
window.terminalProbe.hiddenRenders = 0
window.terminalProbe.draws = 0
window.terminalProbe.hiddenDraws = 0
return performance.now()
})
await benchmarkDiagnostics(page).startTrace()
// The producer is not throttled. The visible and hidden cases receive the same bytes.
pty.write("run\r")
await waitForText(page, "TERMINAL_WORKLOAD_DONE")
const produced = await page.evaluate(
(start) => ({
ms: performance.now() - start,
renders: window.terminalProbe.renders,
hiddenRenders: window.terminalProbe.hiddenRenders,
draws: window.terminalProbe.draws,
hiddenDraws: window.terminalProbe.hiddenDraws,
bytes: window.terminalProbe.bytes,
scrollback: window.terminalProbe.term!.getScrollbackLength(),
cols: window.terminalProbe.term!.cols,
rows: window.terminalProbe.term!.rows,
firstRecord: Number(
window.terminalProbe
.term!.buffer.normal.getLine(0)
?.translateToString(true)
.match(/-(\d{5})\.test\.ts/)?.[1],
),
}),
start,
)
const after = await cdp.send("Performance.getMetrics")
const cpuMs =
(after.metrics.find((x) => x.name === "TaskDuration")!.value -
before.metrics.find((x) => x.name === "TaskDuration")!.value) *
1000
let interaction: Record<string, unknown> = {}
if (scenario === "hidden-output") {
const start = await page.evaluate(() => performance.now())
await page.keyboard.press("Control+Backquote")
await expect(terminal).toBeVisible()
await waitForText(page, "TERMINAL_WORKLOAD_DONE")
interaction = { returnMs: await page.evaluate((start) => performance.now() - start, start) }
}
if (scenario === "full-scrollback-teardown") {
// Ghostty converts the configured line limit to bytes at the initial
// 80-column size. Resizing changes the effective retained row count.
expect(produced.scrollback).toBeGreaterThan(0)
expect(produced.firstRecord).toBeGreaterThan(0)
expect(produced.firstRecord).toBeLessThan(11_999)
const close = page.locator(`[data-titlebar-tab-slot]:has(a[href="${href}"]) [data-component="icon-button-v2"]`)
await expect(close).toBeVisible()
const cpuBefore = await cdp.send("Performance.getMetrics")
const start = await page.evaluate(() => performance.now())
await close.click()
await expect(page).toHaveURL("/")
await expect(page.locator('[data-component="home-session-search"]')).toBeVisible()
await expect(page.locator('[data-component="home-session-search"] input')).toBeEditable()
await expect.poll(() => page.evaluate(() => window.terminalProbe.serialized.length)).toBe(1)
interaction = await page.evaluate(
(start) => ({
homeReadyMs: performance.now() - start,
serializeMs: window.terminalProbe.serialized[0].ms,
serializedBytes: window.terminalProbe.serialized[0].bytes,
}),
start,
)
const cpuAfter = await cdp.send("Performance.getMetrics")
interaction.teardownCpuMs =
(cpuAfter.metrics.find((x) => x.name === "TaskDuration")!.value -
cpuBefore.metrics.find((x) => x.name === "TaskDuration")!.value) *
1000
const snapshot = await page.evaluate(() => window.terminalProbe.serialized[0].value)
expect(Array.from(snapshot.matchAll(/-(\d{5})\.test\.ts/g), (match) => Number(match[1]))).toEqual(
Array.from({ length: 12_000 - produced.firstRecord }, (_, index) => produced.firstRecord + index),
)
expect(snapshot).toContain("TERMINAL_WORKLOAD_DONE")
await writeFile(
path.join(
process.env.TERMINAL_ARTIFACTS ?? tmpdir(),
`${process.env.TERMINAL_BUNDLE}-${info.repeatEachIndex}.ansi`,
),
snapshot,
)
await expect(terminal).toHaveCount(0)
expect(closed).toBe(1)
// UI teardown must not terminate the native process.
pty.write("ping\r")
await expect.poll(() => output.includes("TERMINAL_PROCESS_ALIVE")).toBe(true)
}
await benchmarkDiagnostics(page).stop()
expect(connected).toBe(1)
expect(removals).toEqual([])
expect(sizes.length).toBeGreaterThan(0)
report(
{ ...produced, cpuMs, ...interaction },
{
revision: process.env.TERMINAL_REVISION,
bundle: process.env.TERMINAL_BUNDLE,
fixtureBytes: Buffer.byteLength(lines),
fixtureLines: 12_000,
transport: "Windows ConPTY -> Playwright WebSocket fixture -> production Terminal/writer/Ghostty",
scope: "Chromium renderer; not Electron total RAM or production backend IPC",
},
)
if (scenario !== "full-scrollback-teardown") {
// Validate input, focus, and resize after both visible and hidden output.
await terminal.click()
await expect(terminal.locator("textarea")).toBeFocused()
await page.keyboard.type("ping")
await page.keyboard.press("Enter")
await waitForText(page, "TERMINAL_PROCESS_ALIVE")
const columns = await page.evaluate(() => window.terminalProbe.term!.cols)
await page.setViewportSize({ width: 1100, height: 800 })
await expect.poll(() => page.evaluate(() => window.terminalProbe.term!.cols)).not.toBe(columns)
await expect
.poll(async () => sizes.at(-1)?.cols === (await page.evaluate(() => window.terminalProbe.term!.cols)))
.toBe(true)
expect(closed).toBe(0)
}
if (process.env.TERMINAL_SCREENSHOTS && scenario !== "full-scrollback-teardown") {
await page.screenshot({
path: path.join(process.env.TERMINAL_SCREENSHOTS, `${scenario}-${info.repeatEachIndex}.png`),
})
}
} finally {
listener.dispose()
try {
await benchmarkDiagnostics(page).stop()
// Stop fixture request handlers before killing their native resource. The
// app debounces PTY resize requests independently of the canvas resize.
await page.unrouteAll({ behavior: "wait" })
await page.close()
} finally {
pty.kill()
await exited
await writeFile(
path.join(
process.env.TERMINAL_ARTIFACTS ?? tmpdir(),
`${process.env.TERMINAL_BUNDLE}-${scenario}-${info.repeatEachIndex}.native.log`,
),
output,
)
await rm(dir, { recursive: true, force: true })
}
}
})
}
async function waitForText(page: Page, text: string) {
await expect
.poll(() =>
page.evaluate((text) => {
const probe = window.terminalProbe
const term = probe?.term
if (!term || probe.pending !== 0) return false
const buffer = term.buffer.active
return Array.from(
{ length: term.rows },
(_, i) => buffer.getLine(buffer.length - term.rows + i)?.translateToString(true) ?? "",
)
.join("\n")
.includes(text)
}, text),
)
.toBe(true)
}
@@ -1,17 +0,0 @@
import { mergeConfig } from "vite"
import config from "../../../vite.config"
// The probe is included only in this manual benchmark build, never in the app build.
export default mergeConfig(config, {
plugins: [
{
name: "terminal-benchmark-probe",
transformIndexHtml: {
order: "pre",
handler: () => [
{ tag: "script", attrs: { type: "module", src: "/e2e/performance/terminals/probe.ts" }, injectTo: "head" },
],
},
},
],
})
@@ -11,7 +11,6 @@ import type {
} from "@opencode-ai/client/promise"
import { EventManifest } from "@opencode-ai/schema/event-manifest"
import { SessionMessage } from "@opencode-ai/schema/session-message"
import type { TimelineDetail } from "@opencode-ai/session-ui/timeline/detail"
import { expect, type Page } from "@playwright/test"
import { Schema } from "effect"
import { mockOpenCodeServer } from "../../utils/mock-server"
@@ -123,7 +122,7 @@ export async function setupTimeline(
messages?: TimelineMessage[]
sessionMessages?: SessionMessageInfo[]
sessionStatus?: Record<string, SessionStatus>
settings?: Record<string, boolean | TimelineDetail>
settings?: Record<string, boolean>
sessions?: Session[]
cpuRate?: number
viewport?: { width: number; height: number }
@@ -1,197 +0,0 @@
import { base64Encode } from "@opencode-ai/util/encode"
import { benchmark, benchmarkDiagnostics, expect } from "../benchmark"
import { mockOpenCodeServer } from "../../utils/mock-server"
import { expectSessionTitle } from "../../utils/waits"
import { fixture } from "./session-timeline-stress.fixture"
const sessionID = "ses_composer_write_batch"
const title = "Composer persistence workload"
const addition = " Keep the existing error handling and add coverage."
const text =
Array.from(
{ length: 180 },
(_, index) =>
`Review requirement ${index + 1}: preserve request ordering in src/queue/worker-${index % 12}.ts. ` +
`A failed request must retain its payload, report its cause, and remain safe to retry.\n` +
`Expected: await queue.flush(); expect(await repository.read(id)).toEqual(accepted);\n`,
).join("") + "Implementation notes:"
const items = Array.from({ length: 8 }, (_, index) => ({
type: "file",
path: `src/queue/worker-${index}.ts`,
selection: { startLine: 10, startChar: 0, endLine: 24, endChar: 0 },
commentID: `composer-write-batch-${index}`,
comment: `Check retry path ${index}: keep the original request identity and error cause.`,
preview: Array.from(
{ length: 24 },
(_, line) => ` const request${line} = await repository.loadPending("queue-${index}");`,
).join("\n"),
}))
const document = {
prompt: [{ type: "text", content: text, start: 0, end: text.length }],
cursor: text.length,
mode: "normal",
context: { items },
}
type Probe = { active: boolean; encodes: number; bytes: number; inputs: number; keyups: number }
type ProbeWindow = typeof window & { composerWriteBatch: Probe }
benchmark.use({
viewport: { width: 1440, height: 900 },
video: "off",
trace: "off",
serviceWorkers: "block",
traceScope: "interaction",
})
for (const scenario of ["typing", "cursor-movement", "cursor-noop", "submit-cleanup"] as const) {
benchmark(`composer-write-batch: ${scenario}`, async ({ page, report }, testInfo) => {
const submitted: Record<string, unknown>[] = []
await mockOpenCodeServer(page, {
directory: fixture.directory,
project: fixture.project,
provider: fixture.provider,
sessions: [{ ...fixture.sessions[0], id: sessionID, title }],
pageMessages: () => ({ items: [] }),
onPrompt: (input) => submitted.push(input.body),
})
await page.addInitScript(
({ key, value, counts }) => {
localStorage.setItem(key, JSON.stringify(value))
const probe: Probe = { active: false, encodes: 0, bytes: 0, inputs: 0, keyups: 0 }
;(window as ProbeWindow).composerWriteBatch = probe
// The draft adapter parses each schema-encoded composer document once before
// its asynchronous blob walk. Count at this boundary, not at the IDB write
// (which already discards superseded writes). This fixture is ASCII only.
if (counts) {
const parse = JSON.parse
JSON.parse = (value, reviver) => {
if (probe.active && typeof value === "string" && value.startsWith('{"prompt":[')) {
probe.encodes++
probe.bytes += value.length
}
return parse(value, reviver)
}
}
window.addEventListener("input", (event) => {
if (
probe.active &&
event.target instanceof Element &&
event.target.matches('[data-component="composer-editor"]')
)
probe.inputs++
})
window.addEventListener("keyup", (event) => {
if (
probe.active &&
event.target instanceof Element &&
event.target.matches('[data-component="composer-editor"]')
)
probe.keyups++
})
},
{
key: `${base64Encode(fixture.directory)}/prompt/${sessionID}.v2`,
value: document,
counts: process.env.OPENCODE_PERSISTENCE_COUNTS === "1",
},
)
const server = `http://${process.env.PLAYWRIGHT_SERVER_HOST ?? "127.0.0.1"}:${process.env.PLAYWRIGHT_SERVER_PORT ?? "4096"}`
await page.goto(`/server/${base64Encode(server)}/session/${sessionID}`)
await expectSessionTitle(page, title)
const editor = page.getByRole("textbox", { name: "Prompt", exact: true })
await expect(editor).toBeEditable()
await expect(editor).toHaveText(text)
await editor.focus()
await editor.press("ControlOrMeta+End")
await page.evaluate(() => window.document.fonts.ready)
const stored = async () =>
page.evaluate(async (sessionID) => {
const db = await new Promise<IDBDatabase>((resolve, reject) => {
const request = indexedDB.open("opencode-drafts", 1)
request.onsuccess = () => resolve(request.result)
request.onerror = () => reject(request.error)
})
try {
const transaction = db.transaction("documents")
const keys = transaction.objectStore("documents").getAllKeys()
const values = transaction.objectStore("documents").getAll()
await new Promise<void>((resolve, reject) => {
transaction.oncomplete = () => resolve()
transaction.onerror = () => reject(transaction.error)
})
const index = keys.result.findIndex((key) => String(key).endsWith(`session:${sessionID}:prompt`))
// Parse after disabling the count so the observation is not part of it.
const probe = (window as ProbeWindow).composerWriteBatch
const active = probe.active
probe.active = false
const value = index < 0 ? undefined : JSON.parse(values.result[index])
probe.active = active
return value as { prompt: { content: string }[]; cursor: number; context: { items: unknown[] } } | undefined
} finally {
db.close()
}
}, sessionID)
await expect.poll(async () => (await stored())?.cursor).toBe(text.length)
expect((await stored())?.context.items).toHaveLength(items.length)
const cdp = await page.context().newCDPSession(page)
await cdp.send("Performance.enable")
await benchmarkDiagnostics(page).startTrace()
const before = await cdp.send("Performance.getMetrics")
await page.evaluate(() => {
;(window as ProbeWindow).composerWriteBatch.active = true
performance.mark("composer-write-batch-start")
})
const start = performance.now()
if (scenario === "typing") await editor.pressSequentially(addition)
if (scenario === "cursor-movement") await editor.press("ArrowLeft")
if (scenario === "cursor-noop") await editor.press("ArrowRight")
if (scenario === "submit-cleanup") await editor.press("Enter")
const expectedText = scenario === "typing" ? text + addition : scenario === "submit-cleanup" ? "" : text
const expectedCursor =
scenario === "typing"
? text.length + addition.length
: scenario === "submit-cleanup"
? 0
: text.length - Number(scenario === "cursor-movement")
await expect(editor).toHaveText(expectedText)
await expect.poll(async () => (await stored())?.cursor).toBe(expectedCursor)
const elapsedMs = performance.now() - start
const after = await cdp.send("Performance.getMetrics")
const probe = await page.evaluate(() => {
performance.mark("composer-write-batch-end")
const probe = (window as ProbeWindow).composerWriteBatch
probe.active = false
return probe
})
expect((await stored())?.prompt.map((part) => part.content).join("")).toBe(expectedText)
if (scenario === "submit-cleanup") {
await expect.poll(() => submitted.length).toBe(1)
expect(submitted[0].text).toContain(text)
expect((await stored())?.context.items).toHaveLength(0)
}
expect(probe.keyups).toBe(scenario === "typing" ? addition.length : 1)
expect(probe.inputs).toBe(scenario === "typing" ? addition.length : 0)
const metric = (name: string) =>
1000 *
((after.metrics.find((x) => x.name === name)?.value ?? 0) -
(before.metrics.find((x) => x.name === name)?.value ?? 0))
report(
{ elapsedMs, taskMs: metric("TaskDuration"), scriptMs: metric("ScriptDuration"), ...probe },
{
scenario,
promptBytes: Buffer.byteLength(text),
contextItems: items.length,
persistedBytes: Buffer.byteLength(JSON.stringify(document)),
typedCharacters: scenario === "typing" ? addition.length : 0,
counts: process.env.OPENCODE_PERSISTENCE_COUNTS === "1",
browser: page.context().browser()!.version(),
build: process.env.OPENCODE_PERSISTENCE_BUILD,
transport: "playwright-route",
completion: "editor text and committed IDB cursor",
},
)
await benchmarkDiagnostics(page).stop()
await cdp.detach()
if (testInfo.repeatEachIndex === 0) await page.screenshot({ path: testInfo.outputPath(`${scenario}.png`) })
})
}
@@ -1,315 +0,0 @@
import type { CDPSession, Page } from "@playwright/test"
import { benchmark, expect } from "../benchmark"
import { mockOpenCodeServer } from "../../utils/mock-server"
import { APP_READY_TIMEOUT } from "../../utils/waits"
import { fixture as stress } from "./session-timeline-stress.fixture"
import { createHomeIndexFixture, type HomeIndexFixture } from "./home-session-index.fixture"
// Home fetches the root-session index on mount. These cases hold the visible
// output constant (newest 64 rows, same order) while the index size grows, so
// bytes, main-thread work, time to actionable rows, and retained heap can be
// attributed to index handling rather than to what the user sees.
const sizes = (process.env.HOME_INDEX_SIZES ?? "500,5000,10000").split(",").map(Number)
const churnSize = Number(process.env.HOME_INDEX_CHURN_SIZE ?? 10_000)
const updates = Number(process.env.HOME_INDEX_UPDATES ?? 20)
// Forced GC changes timing; retention runs stay separate from clean timing runs.
const memory = process.env.OPENCODE_PERFORMANCE_MEMORY === "1"
const rowContainer = '[data-component="home-session-row-container"]'
const row = '[data-component="home-session-row"]'
type Probe = {
expected: number
rows?: number
frame?: number
pending: Record<string, string>
titles: Record<string, number>
}
type ProbeWindow = Window & {
__homeIndexProbe?: Probe
__mockServerStream?: { push: (payloads: unknown[]) => void }
}
// Interaction-scoped tracing keeps the page-lifetime Chrome trace off unless a
// scenario starts one; service workers stay out of the renderer measurement.
benchmark.use({
viewport: { width: 1440, height: 900 },
video: "off",
trace: "off",
serviceWorkers: "block",
traceScope: "interaction",
})
benchmark.describe("performance: home session index", () => {
for (const count of sizes) {
benchmark(`loads home with ${count} root sessions`, async ({ page, report }, testInfo) => {
benchmark.setTimeout(180_000)
const fixture = createHomeIndexFixture({ count, now: Date.now() })
const network = await setup(page, fixture)
const cdp = await page.context().newCDPSession(page)
await cdp.send("Performance.enable")
await page.goto("/")
const rows = page.locator(row)
await expect(rows).toHaveCount(fixture.expected.visible, { timeout: APP_READY_TIMEOUT })
const first = page.locator(rowContainer).filter({ hasText: fixture.expected.newestTitle })
await expect(first).toHaveAttribute("data-session-id", fixture.expected.newestID)
await expect(first.locator(row)).toBeEnabled()
// Row order is part of the held-constant output: the DOM must list the
// newest session first.
await expect(page.locator(rowContainer).nth(0)).toHaveAttribute("data-session-id", fixture.expected.newestID)
const probe = await readProbe(page)
const metrics = await performanceMetrics(cdp)
const retained = memory ? await retainedHeap(cdp) : undefined
await network.settle()
if (testInfo.repeatEachIndex === 0) {
const path = testInfo.outputPath(`home-${count}.png`)
await page.screenshot({ path })
await testInfo.attach(`home-${count}`, { path, contentType: "image/png" })
}
report(
{
listRequests: network.list.requests,
listBytes: network.list.bytes,
rowsMs: probe.rows,
frameMs: probe.frame,
listEndMs: probe.listEnd,
processMs: probe.rows - probe.listEnd,
// ThreadTime is main-thread CPU time; ScriptDuration only covers
// Blink-invoked callbacks, so promise continuations are missing from it.
threadMs: metrics.ThreadTime * 1000,
scriptMs: metrics.ScriptDuration * 1000,
taskMs: metrics.TaskDuration * 1000,
layoutMs: metrics.LayoutDuration * 1000,
styleMs: metrics.RecalcStyleDuration * 1000,
heapUsedMB: metrics.JSHeapUsedSize / 1_048_576,
heapTotalMB: metrics.JSHeapTotalSize / 1_048_576,
nodes: metrics.Nodes,
...(retained ? { retainedHeapMB: retained.usedSize / 1_048_576, retainedNodes: retained.nodes } : {}),
},
{
sessions: count,
directories: fixture.directories.length,
fixtureVersion: fixture.version,
fixtureListBytes: fixture.listBytes,
visibleRows: fixture.expected.visible,
gc: memory ? "explicit" : "none",
scope: "renderer main isolate; not total desktop RAM",
},
)
expect(probe.rows).toBeGreaterThan(0)
await cdp.detach()
})
}
benchmark(
`applies ${updates} background session updates on home with ${churnSize} root sessions`,
async ({ page, report }) => {
benchmark.setTimeout(180_000)
const fixture = createHomeIndexFixture({ count: churnSize, now: Date.now() })
const target = fixture.sessions[fixture.sessions.length - 1]
const network = await setup(page, fixture)
const cdp = await page.context().newCDPSession(page)
await cdp.send("Performance.enable")
// Home prefetches the two newest sessions, which makes them locally known
// and therefore part of every later index merge, like an open session.
const prefetch = page.waitForResponse(
(response) => response.request().method() === "GET" && response.url().includes(`/api/session/${target.id}`),
)
await page.goto("/")
await expect(page.locator(row)).toHaveCount(fixture.expected.visible, { timeout: APP_READY_TIMEOUT })
await prefetch
const titleLocator = page.locator(
`${rowContainer}[data-session-id="${target.id}"] [data-component="home-session-title"]`,
)
await expect(titleLocator).toHaveText(fixture.expected.newestTitle)
const before = await performanceMetrics(cdp)
const samples: number[] = []
for (let index = 1; index <= updates; index++) {
const title = `${fixture.expected.newestTitle} · update ${index}`
// The completed run bumps the session's updated time and title on the
// server; the client re-reads the session and re-merges the index.
target.title = title
target.time.updated += 1000
target.time.idle = target.time.updated
const pushed = await page.evaluate(
({ id, title, event }) => {
const host = window as ProbeWindow
if (!host.__homeIndexProbe || !host.__mockServerStream) throw new Error("Missing Home index probe")
host.__homeIndexProbe.pending[id] = title
host.__mockServerStream.push([event])
return performance.now()
},
{
id: target.id,
title,
event: {
id: `evt_home_update_${index}`,
created: Date.now(),
type: "session.execution.succeeded",
data: { sessionID: target.id },
},
},
)
await expect(titleLocator).toHaveText(title)
const seen = await page.evaluate(({ title }) => (window as ProbeWindow).__homeIndexProbe?.titles[title], {
title,
})
if (seen === undefined) throw new Error(`Probe did not observe title: ${title}`)
samples.push(seen - pushed)
}
const after = await performanceMetrics(cdp)
await network.settle()
const sorted = samples.toSorted((a, b) => a - b)
report(
{
updates,
updateMs: sorted,
updateMedianMs: median(sorted),
updateP95Ms: sorted[Math.min(sorted.length - 1, Math.ceil(sorted.length * 0.95) - 1)],
threadMs: (after.ThreadTime - before.ThreadTime) * 1000,
scriptMs: (after.ScriptDuration - before.ScriptDuration) * 1000,
taskMs: (after.TaskDuration - before.TaskDuration) * 1000,
layoutMs: (after.LayoutDuration - before.LayoutDuration) * 1000,
sessionReads: network.get.requests,
},
{
sessions: churnSize,
directories: fixture.directories.length,
fixtureVersion: fixture.version,
event: "session.execution.succeeded",
scope: "renderer main isolate; latency from event push to row title update",
},
)
expect(samples).toHaveLength(updates)
await cdp.detach()
},
)
})
async function setup(page: Page, fixture: HomeIndexFixture) {
const primary = fixture.directories[0]
await mockOpenCodeServer(page, {
directory: primary.directory,
project: {
id: primary.projectID,
worktree: primary.directory,
vcs: "git",
name: primary.name,
time: { created: fixture.now - 400 * 86_400_000, updated: fixture.now },
sandboxes: [],
},
sessions: fixture.sessions,
pageMessages: () => ({ items: [] }),
provider: stress.provider,
})
await page.addInitScript(
({ projects }) => {
localStorage.setItem(
"opencode.global.dat:server",
JSON.stringify({
projects: { local: projects.map((worktree, index) => ({ worktree, expanded: index === 0 })) },
lastProject: { local: projects[0] },
}),
)
},
{ projects: fixture.directories.filter((entry) => entry.project).map((entry) => entry.directory) },
)
await page.addInitScript(
({ expected }) => {
const host = window as ProbeWindow
const probe: Probe = { expected, pending: {}, titles: {} }
host.__homeIndexProbe = probe
const observer = new MutationObserver(() => {
if (probe.rows === undefined) {
const count = document.querySelectorAll('[data-component="home-session-row"]').length
if (count >= probe.expected) {
probe.rows = performance.now()
requestAnimationFrame((time) => {
probe.frame = time
})
}
}
for (const [id, title] of Object.entries(probe.pending)) {
const element = document.querySelector(
`[data-component="home-session-row-container"][data-session-id="${id}"] [data-component="home-session-title"]`,
)
if (element?.textContent !== title) continue
probe.titles[title] = performance.now()
delete probe.pending[id]
}
})
// Init scripts run before <html> exists; the document node itself is always observable.
observer.observe(document, { childList: true, subtree: true, characterData: true })
},
{ expected: fixture.expected.visible },
)
const list = { requests: 0, bytes: 0 }
const get = { requests: 0, bytes: 0 }
const pending: Promise<void>[] = []
page.on("response", (response) => {
const request = response.request()
if (request.method() !== "GET") return
const url = new URL(response.url())
const isList = url.pathname === "/api/session"
const isGet = /^\/api\/session\/[^/]+$/.test(url.pathname)
if (!isList && !isGet) return
const bucket = isList ? list : get
bucket.requests += 1
pending.push(
response
.body()
.then((body) => {
bucket.bytes += body.byteLength
})
.catch(() => {}),
)
})
return {
list,
get,
settle: () => Promise.all(pending).then(() => {}),
}
}
async function readProbe(page: Page) {
const probe = await page.evaluate(() => {
const host = window as ProbeWindow
if (!host.__homeIndexProbe) throw new Error("Missing Home index probe")
// Resource timing marks when the last index page finished arriving, so
// rows - listEnd isolates parse, merge, and render from transfer and boot.
const listEnd = Math.max(
0,
...performance
.getEntriesByType("resource")
.filter((entry) => new URL(entry.name).pathname === "/api/session")
.map((entry) => (entry as PerformanceResourceTiming).responseEnd),
)
return { rows: host.__homeIndexProbe.rows, frame: host.__homeIndexProbe.frame, listEnd }
})
if (probe.rows === undefined) throw new Error("Probe did not observe the expected Home rows")
return { rows: probe.rows, frame: probe.frame, listEnd: probe.listEnd }
}
async function performanceMetrics(cdp: CDPSession) {
const result = await cdp.send("Performance.getMetrics")
return Object.fromEntries(result.metrics.map((metric) => [metric.name, metric.value])) as Record<string, number>
}
async function retainedHeap(cdp: CDPSession) {
// GC is an explicit retained-heap measurement, not an application optimization or readiness wait.
await cdp.send("HeapProfiler.collectGarbage")
const heap = await cdp.send("Runtime.getHeapUsage")
const dom = await cdp.send("Memory.getDOMCounters")
return { usedSize: heap.usedSize, nodes: dom.nodes }
}
function median(sorted: number[]) {
if (sorted.length === 0) return undefined
const middle = Math.floor(sorted.length / 2)
return sorted.length % 2 === 0 ? (sorted[middle - 1] + sorted[middle]) / 2 : sorted[middle]
}
@@ -1,234 +0,0 @@
import { currentSession } from "../../utils/mock-server"
export const HOME_INDEX_FIXTURE_VERSION = 1
// Home shows the newest 64 root sessions; the fixture asserts that many rows.
export const HOME_INDEX_VISIBLE_LIMIT = 64
export type HomeIndexSession = {
id: string
projectID: string
title?: string
agent: string
model: { id: string; providerID: string; variant: string }
cost: number
tokens: { input: number; output: number; reasoning: number; cache: { read: number; write: number } }
outcome: "succeeded" | "failed" | "interrupted"
time: { created: number; updated: number; idle: number; viewed?: number }
location: { directory: string }
}
export type HomeIndexDirectory = {
directory: string
name: string
projectID: string
// Local project entries appear in the Home project list; the rest model
// sessions whose project was removed from the sidebar.
project: boolean
}
const repos = [
"opencode",
"storefront-api",
"billing-worker",
"design-system",
"mobile-app",
"infra-terraform",
"docs-site",
"analytics-pipeline",
"auth-service",
"legacy-admin",
"notebooks",
"dotfiles",
]
const verbs = [
"Fix",
"Investigate",
"Refactor",
"Add",
"Remove",
"Debug",
"Migrate",
"Implement",
"Review",
"Optimize",
"Document",
"Rename",
"Extract",
"Wire up",
"Stabilize",
]
const objects = [
"flaky retry in the session runner",
"memory growth in the Home index",
"i18n keys for the settings dialog",
"the review pane remount on tab switch",
"SQLite migration for session inbox",
"OAuth callback handling",
"terminal scrollback serialization",
"Playwright visual stability probes",
"the composer paste path",
"provider catalog normalization",
"the worktree preparation flow",
"CI cache keys for bun install",
"Markdown highlighting for large fences",
"the permission auto-approver",
"cursor pagination for /api/session",
"the desktop titlebar on Windows",
"the file tree lazy loading",
"event replay ordering",
"RTL layout in the sidebar",
"unread badges for background sessions",
]
const contexts = [
"",
"",
"",
" (#{n})",
" in packages/app",
" in packages/core",
" for v2",
" before release",
" — follow-up",
" · src/{file}.ts",
" after the Electron upgrade",
" with tests",
]
const files = ["controller", "index", "records", "store", "runtime", "layout", "timeline", "composer", "data", "sync"]
const agents = ["build", "build", "build", "build", "plan", "general"]
const models = [
{ id: "claude-opus-4-6", providerID: "anthropic", variant: "default" },
{ id: "claude-sonnet-4-6", providerID: "anthropic", variant: "default" },
{ id: "gpt-5.3-codex", providerID: "openai", variant: "high" },
{ id: "gemini-3-pro", providerID: "google", variant: "default" },
]
const DAY = 24 * 60 * 60 * 1000
export function createHomeIndexFixture(input: { count: number; now: number; directories?: number }) {
const random = mulberry32(0x5eed_0000 + input.count)
const directoryCount = Math.min(input.directories ?? 12, repos.length)
const directories: HomeIndexDirectory[] = repos.slice(0, directoryCount).map((name, index) => ({
directory: `/Users/dev/repos/${name}`,
name,
projectID: `prj_${hex(random, 16)}`,
project: index < Math.max(1, Math.round(directoryCount * 0.66)),
}))
// Zipf-like spread: a few repositories hold most of the history.
const weights = directories.map((_, index) => 1 / Math.pow(index + 1, 0.9))
const total = weights.reduce((sum, weight) => sum + weight, 0)
const cumulative = weights.map((_, index) => weights.slice(0, index + 1).reduce((sum, w) => sum + w, 0) / total)
// Newest first; adding the index after sorting keeps offsets strictly
// increasing so no two sessions share an updated time.
const offsets = Array.from({ length: input.count }, () => {
const bucket = random()
// 5% today, 5% yesterday, the rest skewed toward recent months over 18 months.
if (bucket < 0.05) return Math.floor(random() * DAY * 0.9)
if (bucket < 0.1) return DAY + Math.floor(random() * DAY * 0.9)
return 2 * DAY + Math.floor(Math.pow(random(), 2) * 538 * DAY)
})
.sort((a, b) => a - b)
.map((offset, index) => offset + index)
const newestFirst: HomeIndexSession[] = offsets.map((offset, index) => {
const pick = random()
const directory = directories[cumulative.findIndex((edge) => pick <= edge)] ?? directories[0]
const updated = input.now - offset
const duration = 5 * 60_000 + Math.floor(random() * 6 * 60 * 60_000)
const tokens = {
input: 5_000 + Math.floor(random() * 400_000),
output: 500 + Math.floor(random() * 60_000),
reasoning: random() < 0.6 ? Math.floor(random() * 20_000) : 0,
cache: { read: Math.floor(random() * 900_000), write: Math.floor(random() * 120_000) },
}
const outcome = random() < 0.9 ? "succeeded" : random() < 0.6 ? "failed" : "interrupted"
return {
id: `ses_${base62(random, 26)}`,
projectID: directory.projectID,
...(random() < 0.97 ? { title: title(random, index) } : {}),
agent: agents[Math.floor(random() * agents.length)],
model: models[Math.floor(random() * models.length)],
// USD at $3/M input, $15/M output, $0.30/M cache read, $3.75/M cache write.
cost:
Math.round(
(tokens.input * 3 + tokens.output * 15 + tokens.cache.read * 0.3 + tokens.cache.write * 3.75) / 100,
) / 10_000,
tokens,
outcome,
time: {
created: updated - duration,
updated,
idle: updated - Math.floor(random() * 2_000),
...(random() < 0.8 ? { viewed: updated } : {}),
},
location: { directory: directory.directory },
}
})
// The mock lists sessions in array order and reverses for `desc`, so keep
// the fixture ascending by updated time like the server's index order.
const sessions = newestFirst.toReversed()
const newest = newestFirst[0]
const encoded = JSON.stringify({ data: sessions.map((session) => currentSession(session)), cursor: {} })
return {
version: HOME_INDEX_FIXTURE_VERSION,
count: input.count,
now: input.now,
directories,
sessions,
// Bytes the mock serves for the complete index when it fits one page.
listBytes: Buffer.byteLength(encoded),
expected: {
visible: Math.min(HOME_INDEX_VISIBLE_LIMIT, input.count),
newestID: newest.id,
// The mock labels untitled sessions with their ID.
newestTitle: newest.title ?? newest.id,
perDirectory: Object.fromEntries(
[...Map.groupBy(sessions, (session) => session.location.directory)].map(([directory, items]) => [
directory,
items.length,
]),
),
},
}
}
export type HomeIndexFixture = ReturnType<typeof createHomeIndexFixture>
function title(random: () => number, index: number) {
const verb = verbs[Math.floor(random() * verbs.length)]
const object = objects[Math.floor(random() * objects.length)]
const context = contexts[Math.floor(random() * contexts.length)]
.replace("{n}", String(1000 + Math.floor(random() * 45_000)))
.replace("{file}", files[Math.floor(random() * files.length)])
// Keep titles unique so row identity checks cannot match a sibling.
return `${verb} ${object}${context} [${index.toString(36)}]`
}
function mulberry32(seed: number) {
let state = seed >>> 0
return () => {
state = (state + 0x6d2b79f5) >>> 0
let t = state
t = Math.imul(t ^ (t >>> 15), t | 1)
t ^= t + Math.imul(t ^ (t >>> 7), t | 61)
return ((t ^ (t >>> 14)) >>> 0) / 4294967296
}
}
const alphabet = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
function base62(random: () => number, length: number) {
return Array.from({ length }, () => alphabet[Math.floor(random() * alphabet.length)]).join("")
}
function hex(random: () => number, length: number) {
return Array.from({ length }, () => Math.floor(random() * 16).toString(16)).join("")
}
@@ -1,166 +0,0 @@
import { benchmark, expect } from "../benchmark"
import { mockOpenCodeServer } from "../../utils/mock-server"
import { expectSessionTitle } from "../../utils/waits"
import { fixture } from "./session-timeline-stress.fixture"
import { messages } from "./session-tab-switch.fixture"
import { installStressSessionTabs, stressSessionHref } from "./timeline-test-helpers"
import { measureSessionSwitch, waitForStableTimeline } from "./session-tab-switch-probe"
const sessions = Array.from({ length: 8 }, (_, index) => ({
...fixture.sessions[0],
id: `ses_prefetch_${index}`,
title: `Renderer review ${index}`,
}))
// A normal first page, not the full-history response used by the tab-switch benchmark.
const pages = Object.fromEntries(
sessions.map((session) => [
session.id,
messages[fixture.targetID].slice(-20).map((message) => ({ ...message, id: `${message.id}_${session.id}` })),
]),
)
const workload = {
sessions: sessions.length,
messagesPerPage: 20,
payloadBytes: Object.fromEntries(
sessions.map((session) => [
session.id,
Buffer.byteLength(JSON.stringify({ data: pages[session.id].toReversed(), cursor: {} })),
]),
),
partsPerPage: pages[sessions[0].id].reduce(
(count, message) => count + (message.type === "assistant" ? message.content.length : 1),
0,
),
events: 0,
}
type ProbeWindow = Window & { __prefetchBodies?: Record<string, number> }
benchmark.use({ viewport: { width: 1440, height: 900 }, video: "off", trace: "off", serviceWorkers: "block" })
for (const close of [false, true]) {
benchmark(
`inactive tab prefetch: ${close ? "close before response" : "activate after restore"}`,
async ({ page, report }, testInfo) => {
const gate = Promise.withResolvers<void>()
const reads: string[] = []
const inboxReads: string[] = []
const pending = new Set<string>()
const concurrency = { peak: 0 }
const mutations: string[] = []
const errors: string[] = []
page.on("pageerror", (error) => errors.push(error.message))
page.on("response", (response) => {
if (new URL(response.url()).pathname.startsWith("/api/") && !response.ok())
errors.push(`HTTP ${response.status()}: ${response.url()}`)
})
page.on("request", (request) => {
const path = new URL(request.url()).pathname
if (request.method() === "DELETE" || /\/(interrupt|prompt)$/.test(path)) mutations.push(request.url())
const inbox = path.match(/^\/api\/session\/([^/]+)\/inbox$/)
if (request.method() === "GET" && inbox) inboxReads.push(inbox[1])
})
await page.addInitScript(() => {
const host = window as ProbeWindow
host.__prefetchBodies = {}
const text = Response.prototype.text
Response.prototype.text = async function () {
const body = await text.call(this)
if (this.url) {
const path = new URL(this.url).pathname
host.__prefetchBodies![path] = (host.__prefetchBodies![path] ?? 0) + 1
}
return body
}
})
await mockOpenCodeServer(page, {
...fixture,
sessions,
pageMessages: (id) => ({ items: pages[id] ?? [] }),
beforeMessagesResponse: ({ sessionID }) => (sessionID === sessions[0].id ? Promise.resolve() : gate.promise),
onMessages: ({ sessionID, phase }) => {
if (phase === "end") return void pending.delete(sessionID)
reads.push(sessionID)
pending.add(sessionID)
concurrency.peak = Math.max(concurrency.peak, pending.size)
},
})
await installStressSessionTabs(page, { sessionIDs: sessions.map((session) => session.id) })
const cdp = await page.context().newCDPSession(page)
await cdp.send("Performance.enable")
await page.goto(stressSessionHref(sessions[0].id))
await expectSessionTitle(page, sessions[0].title)
await waitForStableTimeline(page, pages[sessions[0].id].at(-2)!.id)
// Every inactive tab's scheduled attention request must finish. This gates on the
// same production callback as prefetch, without a sleep or waiting for a removed read.
await page.waitForFunction(
(ids) => ids.every((id) => (window as ProbeWindow).__prefetchBodies![`/api/session/${id}/form`] > 0),
sessions.slice(1).map((session) => session.id),
)
const speculativeReads = reads.filter((id) => id !== sessions[0].id)
const speculativeInboxReads = inboxReads.filter((id) => id !== sessions[0].id).length
const closed = sessions.at(-1)!
if (close) {
const tab = page
.locator("[data-titlebar-tab-slot]")
.filter({ has: page.locator(`a[href="${stressSessionHref(closed.id)}"]`) })
await tab.getByRole("button", { name: "Close tab", exact: true }).click()
await expect(tab).toHaveCount(0)
}
gate.resolve()
await page.waitForFunction(
(ids) => ids.every((id) => (window as ProbeWindow).__prefetchBodies![`/api/session/${id}/message`] > 0),
reads,
)
await expectSessionTitle(page, sessions[0].title)
const heap =
process.env.OPENCODE_PERFORMANCE_MEMORY === "1"
? await cdp.send("HeapProfiler.collectGarbage").then(() => cdp.send("Runtime.getHeapUsage"))
: undefined
const task =
(await cdp.send("Performance.getMetrics")).metrics.find((metric) => metric.name === "TaskDuration")!.value *
1000
const before = reads.length
const target = sessions[1]
const result = await measureSessionSwitch(page, {
destinationIDs: pages[target.id].map((message) => message.id),
sourceIDs: pages[sessions[0].id].map((message) => message.id),
lastID: pages[target.id].at(-2)!.id,
requiredPartID: `${pages[target.id].at(-1)!.id}:text:0`,
href: stressSessionHref(target.id),
switch: async () => {
await page.locator(`[data-slot="titlebar-tabs"] a[href="${stressSessionHref(target.id)}"]`).click()
await expectSessionTitle(page, target.title)
},
})
await expect(
page.locator(`[data-timeline-part-id="${pages[target.id].at(-1)!.id}:text:0"] [data-component="markdown"]`),
).toHaveAttribute("data-markdown-ready", "")
expect(result.firstCorrectObservedMs).not.toBeNull()
expect(mutations).toEqual([])
expect(errors).toEqual([])
report(
{
speculativeReads: speculativeReads.length,
speculativeInboxReads,
speculativePayloadBytes: speculativeReads.reduce((bytes, id) => bytes + workload.payloadBytes[id], 0),
peakMessageRequests: concurrency.peak,
closedSessionReads: close ? reads.filter((id) => id === closed.id).length : undefined,
activationReads: reads.length - before,
startupTaskMs: task,
retainedHeap: heap,
...result,
},
{
...workload,
close,
gc: heap ? "forced retention; timing diagnostic only" : "natural; clean timing",
transport: "playwright-route",
browser: page.context().browser()!.version(),
scope: "production app renderer; not total desktop RAM",
},
)
if (testInfo.repeatEachIndex === 0) await page.screenshot({ path: testInfo.outputPath("destination.png") })
await cdp.detach()
},
)
}
@@ -1,15 +0,0 @@
import config from "../playwright.config"
export default {
...config,
testDir: ".",
testMatch: "timeline-projection-benchmark.spec.ts",
outputDir: process.env.PROJECTION_OUTPUT,
webServer: {
...config.webServer,
command: `bun run serve -- --host 127.0.0.1 --port ${process.env.PLAYWRIGHT_PORT ?? 3000} --strictPort --outDir ${process.env.PROJECTION_BUNDLE ?? "dist"}`,
url: `http://127.0.0.1:${process.env.PLAYWRIGHT_PORT ?? 3000}`,
reuseExistingServer: false,
},
use: { ...config.use, video: "off" as const, trace: "off" as const },
}
@@ -1,43 +0,0 @@
import { defineConfig, mergeConfig } from "vite"
import config from "../../../vite.config"
// Benchmark-only instrumentation. Normal production builds contain no probes.
export default mergeConfig(
config,
defineConfig({
plugins: [
{
name: "timeline-projection-measurement",
enforce: "pre",
transform(source, id) {
if (!id.replaceAll("\\", "/").endsWith("/session-ui/src/timeline/projection.ts")) return
const start = " type Turn = {"
const end = "\n export function constructMessageRows("
if (!source.includes(start) || !source.includes(end)) throw new Error("Projection probe boundary changed")
return source
.replace(
start,
`
const probe = globalThis.__timelineProjectionProbe
const started = probe ? performance.now() : 0
try {
${start}`,
)
.replace(
end,
`
finally {
if (probe) {
probe.calls += 1
probe.entries += messages.length
probe.ms += performance.now() - started
}
}
}
${end}`,
)
},
},
],
}),
)
@@ -48,27 +48,20 @@ benchmark("samples cached session repaint after the click", async ({ page, repor
expect(result.samples.length).toBeGreaterThan(0)
})
benchmark("loads only the selected restored tab's transcript", async ({ page, report }) => {
const loaded = new Set<string>()
benchmark("prefetches every open session tab", async ({ page, report }) => {
const prefetched = new Set<string>()
await mockStressTimeline(page, {
onMessages: (input) => {
if (!input.before && input.phase === "start") loaded.add(input.sessionID)
if (!input.before && input.phase === "start") prefetched.add(input.sessionID)
},
})
await installStressSessionTabs(page, {
sessionIDs: [fixture.sourceID, fixture.targetID, fixture.childID],
})
await installTimelineSettings(page)
const attention = Promise.all(
[fixture.targetID, fixture.childID].map((id) =>
page.waitForResponse((response) => new URL(response.url()).pathname === `/api/session/${id}/form`),
),
)
await page.goto(stressSessionHref(fixture.sourceID))
await expectSessionTitle(page, fixture.expected.sourceTitle)
await attention
await waitForStableTimeline(page, fixture.expected.sourceMessageIDs.at(-1)!)
expect([...loaded]).toEqual([fixture.sourceID])
report({ loaded: [...loaded] })
await expect.poll(() => prefetched.has(fixture.childID)).toBe(true)
report({ prefetched: [...prefetched] })
})
@@ -49,7 +49,7 @@ scenarios.forEach((scenario) => {
expect(result.firstCorrectObservedMs).not.toBeNull()
expect(result.stableObservedMs).not.toBeNull()
expect(requests).toHaveLength(requestsBefore + (scenario.cache === "cold" ? 1 : 0))
expect(requests).toHaveLength(requestsBefore)
await expectReadyTimeline(page, fixture.targetID)
report(
{
@@ -65,7 +65,7 @@ scenarios.forEach((scenario) => {
browserVersion: page.context().browser()!.version(),
serviceWorkers: "blocked",
reviewFiles: scenario.review === "closed" ? 0 : reviewDiffs.length,
data: scenario.cache === "cold" ? "on-demand" : "cached",
data: "prefetched",
transport: process.env.OPENCODE_PERFORMANCE_HTTP_FIXTURE === "1" ? "http" : "playwright-route",
inputEvent: "mousedown",
requireReadyAnswer: true,
@@ -97,11 +97,16 @@ async function prepareSessionTabs(page: Page) {
})
await installTimelineSettings(page)
await installStressSessionTabs(page)
// Restored tabs prefetch their data even when their transcript has never rendered.
const prefetch = page.waitForResponse((response) =>
new URL(response.url()).pathname.endsWith(`/session/${fixture.targetID}/message`),
)
await page.goto(stressSessionHref(fixture.sourceID))
expect(await (await prefetch).finished()).toBeNull()
await expectSessionTitle(page, fixture.expected.sourceTitle)
await expectReadyTimeline(page, fixture.sourceID)
await expect(page.locator(`[data-timeline-part-id="${expected[fixture.targetID].answerID}"]`)).toHaveCount(0)
expect(requests).toEqual([fixture.sourceID])
expect(requests.toSorted()).toEqual([fixture.sourceID, fixture.targetID].toSorted())
return requests
}

Some files were not shown because too many files have changed in this diff Show More