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@@ -1,7 +1,5 @@
---
"@opencode-ai/client": patch
"@opencode-ai/protocol": patch
"@opencode-ai/cli": patch
---
Expose background-service lifecycle status, preserve one process-held owner through startup and failure, reconnect TUIs without activating replacement, and stop exact service instances gracefully.
Reuse a same-version background service when a repeated health probe succeeds instead of replacing an endpoint another client may already be using.
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@@ -1,5 +0,0 @@
---
"@opencode-ai/cli": patch
---
Expose a TUI plugin slot at the top of the session view.
-7
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@@ -1,7 +0,0 @@
---
"@opencode-ai/client": patch
"@opencode-ai/plugin": patch
"@opencode-ai/protocol": patch
---
Expose transient, read-only session generation through the HTTP API, generated clients, and V2 plugin session context.
-5
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@@ -1,5 +0,0 @@
---
"@opencode-ai/cli": patch
---
Expose a TUI plugin slot above the session composer.
-1
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@@ -1,3 +1,2 @@
packages/core/migration/**/snapshot.json linguist-generated
packages/core/src/database/migration.gen.ts linguist-generated
packages/core/src/**/*.txt text eol=lf
+1 -1
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@@ -18,4 +18,4 @@ simonklee
Slickstef11
usrnk1
vimtor
StarpTech
starptech
+4 -71
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@@ -90,18 +90,11 @@ jobs:
opencode-app-id: ${{ vars.OPENCODE_APP_ID }}
opencode-app-secret: ${{ secrets.OPENCODE_APP_SECRET }}
- name: Build legacy CLI
if: github.ref_name != 'v2'
run: ./packages/opencode/script/build.ts ${{ (github.ref_name == 'beta' && '--sourcemaps') || '' }}
env:
OPENCODE_VERSION: ${{ needs.version.outputs.version }}
OPENCODE_RELEASE: ${{ needs.version.outputs.release }}
GH_REPO: ${{ needs.version.outputs.repo }}
GH_TOKEN: ${{ steps.committer.outputs.token }}
- name: Build preview CLI
- name: Build
id: build
run: ./packages/cli/script/build.ts ${{ (github.ref_name == 'beta' && '--sourcemaps') || '' }}
run: |
./packages/opencode/script/build.ts ${{ (github.ref_name == 'beta' && '--sourcemaps') || '' }}
./packages/cli/script/build.ts ${{ (github.ref_name == 'beta' && '--sourcemaps') || '' }}
env:
OPENCODE_VERSION: ${{ needs.version.outputs.version }}
OPENCODE_RELEASE: ${{ needs.version.outputs.release }}
@@ -109,7 +102,6 @@ jobs:
GH_TOKEN: ${{ steps.committer.outputs.token }}
- uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2
if: github.ref_name != 'v2'
with:
name: opencode-cli
path: |
@@ -117,7 +109,6 @@ jobs:
packages/opencode/dist/opencode-linux*
- uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2
if: github.ref_name != 'v2'
with:
name: opencode-cli-windows
path: packages/opencode/dist/opencode-windows*
@@ -130,55 +121,6 @@ jobs:
outputs:
version: ${{ needs.version.outputs.version }}
build-node-cli:
needs: version
if: github.repository == 'anomalyco/opencode'
strategy:
fail-fast: false
matrix:
settings:
- target: linux-arm64
host: blacksmith-4vcpu-ubuntu-2404-arm
- target: linux-x64
host: blacksmith-4vcpu-ubuntu-2404
- target: darwin-arm64
host: macos-26
- target: windows-arm64
host: blacksmith-4vcpu-windows-2025
- target: windows-x64
host: blacksmith-4vcpu-windows-2025
runs-on: ${{ matrix.settings.host }}
defaults:
run:
shell: bash
steps:
- uses: actions/checkout@f43a0e5ff2bd294095638e18286ca9a3d1956744 # v3.6.0
- uses: ./.github/actions/setup-bun
with:
install-flags: --os=* --cpu=*
- uses: actions/setup-node@49933ea5288caeca8642d1e84afbd3f7d6820020 # v4.4.0
with:
node-version: "26.4.0"
- name: Build
run: bun packages/cli/script/build-node.ts --target=${{ matrix.settings.target }} --skip-install --outdir=dist/node
env:
OPENCODE_VERSION: ${{ needs.version.outputs.version }}
OPENCODE_RELEASE: ${{ needs.version.outputs.release }}
- name: Verify service lifecycle
if: matrix.settings.target != 'windows-arm64'
working-directory: packages/cli
run: bun run script/service-smoke.ts --node
- uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2
with:
name: opencode-node-cli-${{ matrix.settings.target }}
path: packages/cli/dist/node/cli-node-*
if-no-files-found: error
sign-cli-windows:
needs:
- build-cli
@@ -471,7 +413,6 @@ jobs:
needs:
- version
- build-cli
- build-node-cli
- sign-cli-windows
- build-electron
if: always() && !failure() && !cancelled()
@@ -500,13 +441,11 @@ jobs:
registry-url: "https://registry.npmjs.org"
- uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4.3.0
if: github.ref_name != 'v2'
with:
name: opencode-cli
path: packages/opencode/dist
- uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4.3.0
if: github.ref_name != 'v2'
with:
name: opencode-cli-windows
path: packages/opencode/dist
@@ -522,12 +461,6 @@ jobs:
name: opencode-preview-cli
path: packages/cli/dist
- uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4.3.0
with:
pattern: opencode-node-cli-*
path: packages/cli/dist/node
merge-multiple: true
- uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4.3.0
if: needs.version.outputs.release
with:
-27
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@@ -70,38 +70,11 @@ jobs:
env:
OPENCODE_EXPERIMENTAL_DISABLE_FILEWATCHER: ${{ runner.os == 'Windows' && 'true' || 'false' }}
- name: Verify compiled service lifecycle
if: always()
timeout-minutes: 10
working-directory: packages/cli
run: |
bun run script/build.ts --single --skip-install
bun run script/service-smoke.ts
- name: Setup Node build runtime
if: always()
uses: actions/setup-node@49933ea5288caeca8642d1e84afbd3f7d6820020 # v4.4.0
with:
node-version: "26.4.0"
- name: Verify Node build
if: always()
timeout-minutes: 15
working-directory: packages/cli
run: |
bun run script/build-node.ts --single --skip-install --outdir=dist/node
bun run script/service-smoke.ts --node
- name: Check generated client
if: runner.os == 'Linux'
working-directory: packages/client
run: bun run check:generated
- name: Check generated documentation
if: runner.os == 'Linux'
working-directory: packages/docs
run: bun run check:generated
e2e:
name: e2e (${{ matrix.settings.name }})
if: github.ref_name != 'v2' && github.head_ref != 'v2'
-2
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@@ -11,7 +11,6 @@ node_modules
playground
tmp
dist
dist-node
ts-dist
.turbo
.typecheck-profiles
@@ -26,7 +25,6 @@ Session.vim
a.out
target
.scripts
.cache
.direnv/
# Local dev files
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@@ -1,6 +1,6 @@
---
description: translate English to other languages
model: opencode/gpt-5.6-sol
model: opencode/claude-opus-4-8
---
run git diff and translate changed english doc and UI copy files to other international languages. Translate all languages in parallel to save time.
+2
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@@ -19,6 +19,8 @@ Valid types are `feat`, `fix`, `docs`, `chore`, `refactor`, and `test`. Scopes a
Examples: `fix(tui): simplify thinking toggle styling`, `docs: update contributing guide`, `chore(sdk): regenerate types`.
Never bypass Git hooks. Do not use `--no-verify` or otherwise disable, skip, or circumvent commit or push hooks. If a hook fails, fix the failure or stop and report it to the user.
## Style Guide
### General Principles
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@@ -1,685 +0,0 @@
# Service Lifecycle: Election, Restart, and Reconnect
Status: in progress
Incident: [#36688](https://github.com/anomalyco/opencode/issues/36688)
## Summary
The managed V2 service keeps its current update policy: the background updater
may install a new package, but only a freshly launched TUI activates that update
after finding an older running service. Existing TUIs never replace a service;
they only reconnect.
The restart path changes in three places:
1. A process-held OS lock, not the HTTP port or registration file, elects
exactly one server owner for its lifetime.
2. The elected process binds and registers a minimal lifecycle surface before
it initializes the application, so clients can distinguish a slow winner
from an absent server.
3. TUIs rediscover and reconnect indefinitely. Transport loss is never a
terminal error by itself.
Several clients may spawn small contenders during a restart. This is safe and
intentional: one contender acquires the lock and initializes, while every loser
exits before expensive server boot. The design does not require clients to
agree on a single initiator.
This proposal does not introduce a supervisor process, warm candidate server,
protocol negotiation, idle background restart, or general execution-recovery
framework.
## Architecture at a Glance
```text
╭───────────────────╮
│ CLI ServiceConfig │
╰─────────┬─────────╯
╭──────────────────────╮
│ CLI ServerConnection │
╰───────────┬──────────╯
╭──────────────────╰───────────────────╮
▼ ▼
╭──────────────────────────╮ ╭─────────────────────────╮
│ Client Service lifecycle │ │ CLI runPromiseWith seam │
╰─────────────┬────────────╯ ╰─────────────┬───────────╯
╰─────╮ │
▼ ▼
╭────────────────────────────╮ ╭─────────────╮
│ Background service process │ │ TUI / Solid │
╰──────────────┬─────────────╯ ╰──────┬──────╯
│ │
╰────────────◀────────────────────╯
╭───────────────────────╮
│ Server HTTP transport │
╰───────────┬───────────╯
╭──────────────────╮
│ Core application │
╰──────────────────╯
```
| Owner | Responsibility |
| ------------------------------------------------ | --------------------------------------------------------------------------------------------------- |
| `packages/client/src/effect/service.ts` | Effect-native discovery, start, and stop lifecycle operations |
| `packages/cli/src/services/service-config.ts` | CLI registration path, installed version, and daemon command |
| `packages/cli/src/services/server-connection.ts` | Resolve an endpoint and, only for the shared service, grouped reconnect and restart Effects |
| `packages/cli/src/server-process.ts` | Daemon election, registration, and server process boot |
| `packages/server/src/process.ts` | HTTP lifecycle shell and application transport |
| `packages/core` | Application behavior behind the transport |
| CLI default handler | Convert lifecycle Effects with the outer `FileSystem` context and pass grouped Promise capabilities |
| `packages/tui` Solid client context | Own event-stream reconnect, endpoint replacement, status, and user-triggered restart UI |
## Implementation Status
| Area | State |
| ------------------------- | --------------------------------------------------------------------- |
| Lifetime ownership | Implemented on this branch with a scoped OS lock |
| Contender behavior | Implemented; losers exit before the server module is imported |
| Registration repair | Implemented; the owner reasserts deleted or corrupt discovery |
| Channel isolation | Implemented with no-clobber migration for legacy preview discovery |
| Client startup waiting | Implemented; slow winners are not killed and waiting is indefinite |
| Lifecycle shell | Implemented; the owner binds and registers before application boot |
| Failed-state latching | Implemented; deterministic boot failure stays bound and actionable |
| Recovery diagnostics | Implemented; the TUI shows status instead of transport internals |
| Cross-platform validation | macOS runtime verified; Linux and Windows run in the unit-test matrix |
## Context
The V2 CLI runs a shared managed service that owns Sessions, location graphs,
plugins, permissions, and tool execution. The service updater can replace the
installed package while the current process continues running the old image.
A later TUI launch then detects the version mismatch and replaces the service.
Incident #36688 showed four failures in that replacement path:
- Multiple TUIs spawned heavyweight server contenders.
- A winner remained unobservable while it cold-booted, so another wave treated
it as absent and displaced it.
- A fresh TUI exhausted its reconnect budget and crashed with an unhandled
transport defect.
- A losing contender remained alive and consumed about 1 GB of RSS.
The `origin/v2` baseline serializes service startup with `EffectFlock`. A
contender acquires a three-second heartbeat lease, checks whether another
service became discoverable, and only the winner crosses the application-boot
boundary. This already prevents simultaneous heavy boots and makes startup
losers exit.
The lease is released immediately after registration, however, so it is not
lifetime ownership. Registration then reverts to last-writer-wins authority: a
deleted or corrupt registration can admit a second boot, a displaced server
terminates itself through its 10-second registration self-check, and a stalled
lease holder can be displaced after the three-second service staleness timeout.
`Flock` and `EffectFlock` live in `packages/core/src/util` and are also used for
config writes, MCP auth, npm installs, and repository caching. Despite the
name, the primitive is an atomic-mkdir lease with heartbeat and staleness
takeover, not an OS-held lock. It remains appropriate for bounded critical
sections, including today's startup fence, but is not lifetime service
ownership.
The current implementation also mixes three different concepts:
- **Ownership:** which process is allowed to be the managed server.
- **Discovery:** where clients can reach that process.
- **Lifecycle:** whether that process is starting, ready, stopping, or failed.
This design gives each concept one authority.
```definitions
[
{
"term": "Owner",
"definition": "The one process holding the process-held OS service lock."
},
{
"term": "Contender",
"definition": "A small serve process attempting to acquire the service lock. It must not initialize the application before winning."
},
{
"term": "Registration",
"definition": "An atomic discovery record containing the elected owner's identity and endpoint. Registration never grants ownership."
},
{
"term": "Lifecycle shell",
"definition": "The minimal HTTP surface bound by the elected process before application initialization. It serves health and retryable startup responses."
},
{
"term": "Application",
"definition": "The full server routes and global or location-scoped modules used for normal OpenCode work."
}
]
```
## Goals
- At most one process initializes and serves the managed application.
- Losing contenders exit before database, route, plugin, MCP, or location boot.
- A slow winner becomes observable before expensive initialization.
- Existing and freshly launched TUIs survive retryable service unavailability.
- Reconnect follows service state instead of displaying retry counts or raw
transport failures.
- Version-mismatch replacement remains triggered by a fresh TUI launch.
- A stale or malformed registration cannot create a second owner.
- An unresponsive owner is never killed automatically by an arbitrary TUI.
- Every spawned contender has a bounded path to ownership or exit.
## Non-goals
- Restarting automatically when a background update finds an idle window.
- Running old and candidate application servers concurrently.
- Adding a permanent steward, proxy, or supervisor process.
- Zero-downtime worker handoff or automatic rollback.
- Application protocol negotiation or automatic TUI self-restart.
- General hard-crash recovery for active Sessions.
- Defining recovery semantics for provider attempts, tools, shells, sub-agents,
permissions, questions, or background jobs.
- Automatically killing a frozen owner.
- Bounding concurrent location cold boots after clients reconnect.
- Multi-machine or clustered service placement.
## Invariants
1. **The service lock is ownership.** Exactly one process may hold the OS lock
for one installation channel and service profile.
2. **Ownership precedes boot.** A contender performs no expensive application
initialization before it acquires the lock.
3. **Ownership lasts for the process lifetime.** The owner holds an open lock
handle until the managed server exits. The OS releases it on process death
without a cleanup callback.
4. **The port is transport, not election.** The owner may select a dynamic port
after acquiring the lock.
5. **Registration is discovery, not election.** Deleting, corrupting, or
replacing registration does not invalidate a live owner's lock.
6. **Only a fresh launch enforces package version.** Existing TUIs reconnect to
the current owner without initiating version replacement.
7. **Transport loss is retryable.** It never terminates a TUI without a separate
diagnosed, non-retryable cause.
8. **Clients do not kill an unresponsive owner automatically.** Destructive
recovery requires the explicit `service restart` command.
9. **Lifecycle does not promise execution semantics.** Graceful replacement
invokes Session suspension and resumption hooks, but tool-level continuity
belongs to a separate design.
## System Model
```text
╭───────────────────────╮ ╭──────────────────────────────╮
│ Fresh or existing TUI │ │ Process-held OS service lock │
╰───────────┬───────────╯ ╰───────────────┬──────────────╯
╰─────┬ normal requests observe ───────────────────────╮ │
│ discover │ ├──╯ authorizes one owner
▼ │ ▼
╭───────────────────╮ │ ╭─────────────────╮
│ Registration file │ │ │ Lifecycle shell │
╰───────────────────╯ │ ╰────────┬────────╯
│ │
├────────────────────────╯
╭──────────────────────╮
│ OpenCode application │
╰──────────────────────╯
```
The lifecycle shell and application run in the same process. The distinction is
initialization order and responsibility, not process topology.
## Service Status
The server reports one small status value:
```typescript
type ServiceStatus =
| {
type: "starting"
}
| {
type: "ready"
}
| {
type: "stopping"
targetVersion?: string
}
| {
type: "failed"
message: string
action: string
}
```
The client adds only the discovery states needed by callers:
```typescript
type Status = { type: "missing" } | { type: "unreachable" } | { type: "unresponsive" } | ServiceStatus
```
The health response retains the existing fields for old clients and adds the
status discriminant:
```typescript
type ServiceHealth = {
healthy: true
version: string
pid: number
instanceID: string
status: ServiceStatus
}
```
`healthy: true` means the registered lifecycle shell is responding and its
identity matches registration. New clients use `status.type === "ready"` as
the application-readiness signal.
During `starting` or `stopping`, application requests are not held in memory.
They receive an immediate retryable response:
```http
HTTP/1.1 503 Service Unavailable
Retry-After: 1
Content-Type: application/json
{"code":"service_starting"}
```
`stopping` uses `service_stopping`. A failed application boot uses
`service_failed` and includes a safe diagnostic message.
A failed owner remains bound and keeps holding the service lock. Exiting on
failure would let every waiting client's `ensureRunning` loop elect a new
contender that repeats the same heavy failing boot, so staying bound turns a
deterministic boot failure into one observable `failed` state instead of a
client-driven respawn loop. Recovery still works: a fresh launch observes the
failed instance through the stop path, and explicit `service restart` replaces
it.
## Registration Contract
Registration contains only discovery identity:
```typescript
type ServiceRegistration = {
schema: 1
instanceID: string
version: string
url: string
pid: number
}
```
Authentication continues to use the existing private service credential
storage. The registration schema does not change that policy.
The owner writes registration only after the lifecycle shell has bound:
1. Bind the lifecycle shell.
2. Write a temporary registration file with mode `0600`.
3. Atomically rename it over the old registration.
4. Serve lifecycle health as `starting`.
On shutdown, the owner removes registration only if the current file still has
its `instanceID`. An old finalizer can never remove a successor's registration.
While running, the owner periodically asserts its registration. Because the
lock guarantees exactly one live owner, any registration that does not name the
owner is stale or corrupt, and the owner rewrites it. A deleted or clobbered
registration therefore heals within one assertion interval instead of leaving
clients waiting on absent discovery. This inverts today's self-check loop,
which terminates the displaced process instead of repairing discovery.
Legacy registration shapes are decoded by a compatibility adapter. The new
domain type does not make fields optional to represent old formats.
## Election
This design promotes today's startup fence into lifetime ownership.
Last-writer-wins registration is replaced by a process-held OS lock that is
acquired before any expensive boot work and held for the entire service
lifetime.
A heartbeat-and-staleness lease, including the existing `Flock` utility, is not
sufficient for service ownership: the service configures a three-second stale
timeout, after which its lock can be broken and recreated. An event-loop stall,
a suspended machine, or a debugger pause can therefore make a live owner appear
stale and allow a contender to displace it. Service ownership requires a
process-held OS lock: `flock` on Unix and an exclusively bound named pipe on
Windows. It cannot be broken because a heartbeat exceeded a timeout. Process
death releases the lock through the OS.
Neither Bun nor Node exposes `flock` directly, the existing `Flock` utility is
an mkdir-plus-heartbeat lease rather than an OS-held lock, and the common
lockfile packages are staleness-based leases as well. The platform layer uses
`bun:ffi` to call `flock` on POSIX and Node's named-pipe server support on
Windows, where Bun FFI is not available on every shipped architecture. It lives
alongside the existing utility in `packages/core/src/util`. This primitive is
the foundation of the design, so the delivery sequence spikes it first.
```text
Contender Lock Lifecycle Application
│ │ │ │
├─ try acquire ───▶ │ │
│ │ │ │
╭─ alt: lock held ────────────────────────────────────────────────╮
│ │ │ │ │ │
│ ◀─ busy ──────────┤ │ │ │
│ │ │ │ │ │
│ ├─────────╮ │ │ │ │
│ │ exit │ │ │ │ │
│ ◀─────────╯ │ │ │ │
│ │ │ │ │ │
├─ else: lock acquired ───────────────────────────────────────────┤
│ │ │ │ │ │
│ ◀─ owner ─────────┤ │ │ │
│ │ │ │ │ │
│ ├─ bind, register, starting ────────▶ │ │
│ │ │ │ │ │
│ ├─ initialize ──────────────────────────────────────────────▶ │
│ │ │ │ │ │
│╭─ alt: boot succeeds ──────────────────────────────────────────╮│
││ │ │ │ │ ││
││ │ │ ◀─ ready ───────────────┤ ││
││ │ │ │ │ ││
│├─ else: boot fails ────────────────────────────────────────────┤│
││ │ │ │ │ ││
││ │ │ ◀─ failed, stay bound ──┤ ││
││ │ │ │ │ ││
│╰───────────────────────────────────────────────────────────────╯│
│ │ │ │ │ │
╰─────────────────────────────────────────────────────────────────╯
│ │ │ │
```
Lock acquisition by a contender is nonblocking or tightly bounded. A loser
must exit before constructing application routes or importing startup-heavy
modules.
Several clients may spawn contenders concurrently. The design guarantees one
heavy winner, not one process spawn. If the winner crashes during startup, the
OS releases the lock and a later client retry starts another election.
The lock is scoped by installation channel and service profile. Local, preview,
and stable installations cannot displace one another.
## Update Activation
Background update behavior remains unchanged:
1. The running service checks for an update.
2. The updater installs the package in the background.
3. The running process continues using its existing process image.
4. No idle check or automatic restart occurs.
A fresh TUI launch activates the installed update:
1. Read registration and authenticate the responding service.
2. If its package version matches the fresh client, attach normally.
3. If the version differs, request graceful stop of that exact registered
instance using the existing authenticated stop path.
4. Re-check instance identity before every signal or escalation in that path.
5. Wait for the old process to exit and release the service lock.
6. Call `ensureRunning` until a compatible service becomes ready.
Concurrent fresh launchers may all observe the same old instance. Stopping that
exact instance must be idempotent. Once registration names a different instance,
a stale launcher stops signaling and returns to discovery.
No durable restart-transition record is introduced. The initiating fresh TUI
already knows the source and target versions and can display its update
preflight. Existing TUIs may display `Updating...` if they observed `stopping`;
otherwise `Waiting for background service...` is the honest fallback.
## Fresh Launch Versus Reconnect
Fresh launch and reconnect deliberately have different version policies:
```typescript
type ManagedConnection =
| {
type: "launch"
requiredVersion: string
}
| {
type: "reconnect"
}
```
- `launch` requires the installed package version and may activate replacement.
- `reconnect` accepts the current owner and never activates replacement.
This preserves today's permissive reconnect behavior. Explicit application
protocol negotiation and automatic TUI re-exec remain follow-ups.
## Client Reconnect
Fresh and existing TUIs use the same status loop after startup:
1. Read registration on every attempt. Do not retry a stale URL indefinitely.
2. If registration is absent, call `ensureRunning` and continue waiting.
3. If registration is unreachable, call `ensureRunning`. A live owner prevents
contenders from acquiring the lock; a dead owner does not.
4. If status is `starting` or `stopping`, wait.
5. If status is `failed`, show its actionable message.
6. If status is `ready`, rebuild HTTP and event-stream clients for the new
endpoint and perform authoritative state reconciliation.
Retry cadence is internal policy. Retry counts are telemetry, not user-facing
state. The TUI waits until the service is ready or the user exits.
Transport failures are handled at the TUI run boundary. A raw client transport
error or Effect defect must not escape to the terminal. Hard exit is reserved
for diagnosed causes such as invalid local configuration, failed authentication,
or a foreign process occupying an explicitly configured port.
The UI derives text from status:
| Status | User-facing state |
| ------------------------ | ----------------------------------- |
| No registration | `Starting background service...` |
| Registration unreachable | `Waiting for background service...` |
| `starting` | `Starting OpenCode vX...` |
| `stopping` | `Updating to vX...` |
| `failed` | Actionable failure message |
| `ready` | Normal TUI |
## Graceful Session Continuity
Version-mismatch replacement uses the existing graceful Session suspension and
resumption hooks:
1. The old server snapshots active Session IDs during graceful teardown.
2. The successor schedules those Sessions for continuation.
3. The runner reloads durable Session history before continuing.
This lifecycle design does not define what an interrupted physical provider
attempt or tool invocation means. It does not promise that external side effects
did not occur, replay the exact interrupted tool, preserve an in-memory form, or
recover process-local background work.
Those concerns require a separate execution-continuity design covering tools,
shells, sub-agents, permissions, questions, provider attempts, and hard-crash
recovery.
## Unresponsive Owner
An unreachable registration does not prove that the owner is dead. A contender
attempts the service lock:
- If the lock is free, the contender starts a replacement.
- If the lock is held, the contender exits and the client keeps waiting.
After a bounded diagnostic threshold, the client may show:
```text
The background service owns the service lock but is not responding.
Run `opencode service restart` to recover it.
```
Only explicit `service restart` may perform destructive recovery. It verifies
the complete registration and process instance before signaling, waits for
graceful exit, re-checks identity before escalation, and refuses to kill a
process it cannot positively identify.
Automatic frozen-owner recovery is deferred.
## Failure Walkthroughs
### Update with open TUIs
1. The old service installs vNext but keeps running.
2. A fresh vNext TUI finds the healthy vOld service and requests graceful stop.
3. The old service reports `stopping`, suspends active Sessions, and exits.
4. Open TUIs enter their indefinite status loops.
5. One or more clients spawn contenders.
6. One contender acquires the service lock. Losers exit before heavy boot.
7. The winner binds and registers the lifecycle shell as `starting`.
8. Clients stop spawning and wait on the observable winner.
9. The winner initializes the application and reports `ready`.
10. TUIs rebuild clients, reconcile state, and resume.
### Server crashes while ready
1. The endpoint becomes unreachable and registration may remain stale.
2. Clients call `ensureRunning`.
3. Process death has released the service lock.
4. One contender wins, replaces registration, and starts normally.
5. Detailed active-execution recovery is outside this design.
### Winner crashes during startup
1. Clients observed `starting` and remain alive.
2. Process death releases the service lock.
3. A later reconnect attempt starts another election.
4. One new contender wins; all other contenders exit.
### Registration is deleted while the owner is healthy
1. Clients may call `ensureRunning` because discovery is absent.
2. Every contender fails to acquire the owner's lock and exits.
3. No second application initializes.
4. The owner's next registration assertion republishes discovery.
### Owner is alive but unresponsive
1. Health fails, but the process still holds the service lock.
2. Contenders fail lock acquisition and exit.
3. Clients wait and eventually show explicit recovery guidance.
4. No TUI kills the owner automatically.
## TDD Verification
Implementation should proceed test-first with real subprocesses and real locks.
Mocks cannot establish process death, lock release, loser cleanup, or port
behavior.
### Election tests
| Scenario | Required result |
| ----------------------------------------------------- | ------------------------------------------------------- |
| Ten contenders start simultaneously | Exactly one crosses the application-boot boundary |
| Winner pauses after lock acquisition | No loser initializes or remains alive |
| Winner event loop pauses beyond the old stale timeout | Ownership is not displaced |
| Winner crashes before bind | Lock releases; a later attempt wins |
| Winner crashes after bind but before registration | Lock releases; a later attempt replaces stale discovery |
| Registration is deleted while owner runs | No second owner initializes |
| Registration is malformed | Lock still prevents a second owner |
| Registration names a dead PID | New contender can acquire the released lock |
| Two installation channels start | Each elects an independent owner |
| Explicit configured port is foreign-owned | Fail diagnostically; do not kill the foreign process |
The fixture records a marker immediately before application initialization. The
tests assert that only one process writes that marker and that every loser exits
within a bounded interval. The harness should also assert that a loser's peak
RSS stays an order of magnitude below an application boot, since import weight
was the observed incident cost.
### Lifecycle tests
| Scenario | Required result |
| ----------------------------------------------- | ---------------------------------------------------------------- |
| Winner owns lock but application boot is paused | Health reports `starting` |
| Application request arrives during startup | Immediate retryable `503` |
| Application becomes ready | Status changes once from `starting` to `ready` |
| Graceful replacement begins | Status reports `stopping` before disconnect |
| Application initialization fails | Actionable `failed` status; owner stays bound and holds the lock |
| Registration is deleted while owner runs | Owner republishes it within one assertion interval |
| Owner exits | Registration is removed only if it still names that owner |
### Update tests
| Scenario | Required result |
| -------------------------------------- | -------------------------------------------------------- |
| Background update installs vNext | Running vOld service does not restart |
| Fresh vNext launch finds vOld | Exact old instance stops; vNext eventually becomes ready |
| Two fresh vNext launches race | One heavy successor; both clients attach |
| Existing vOld TUI reconnects to vNext | It never requests replacement |
| Stale launcher observes a new instance | It does not signal the new instance |
### Reconnect tests
| Scenario | Required result |
| --------------------------------------------------- | -------------------------------------------------- |
| Endpoint disappears and changes port | TUI rediscovers and rebuilds clients |
| Service remains unavailable beyond old retry budget | TUI remains alive |
| Event stream reconnects | Client performs authoritative state reconciliation |
| Transport returns an unexpected defect | TUI formats it; no raw stack escapes |
| Owner remains unresponsive | TUI waits and shows explicit restart guidance |
## Delivery Sequence
1. **Spike the lock primitive.** Prove a nonblocking, process-held OS lock
under Bun on macOS, Linux, and Windows (`bun:ffi` to `flock` on POSIX and a
named pipe on Windows), including release on hard kill and behavior across
containers and network filesystems used in CI.
2. **Expand the subprocess test harness.** Begin from the baseline
two-contender test and cover ten contenders, lock release on crash, a paused
winner, deleted or corrupt registration, and bounded loser exit before
changing ownership.
3. **Contain client failure.** Make transport loss nonterminal, rediscover on
every cycle, and format unexpected failures at the TUI boundary.
4. **Promote the startup fence to process-held ownership.** Preserve the
existing pre-boot acquisition seam, replace its lease with the OS lock, hold
it until process exit, and invert the registration self-check from
self-termination to reassertion.
5. **Bind the lifecycle shell first.** Publish registration and `starting`,
return retryable `503` for application requests, then initialize the app.
The health contract change is public API: regenerate clients from
`packages/client` with `bun run generate`.
6. **Codify launch versus reconnect.** Fresh launch enforces installed version;
reconnect never activates replacement.
7. **Integrate graceful replacement.** Preserve current background-install and
fresh-launch activation behavior while invoking Session continuity hooks.
8. **Harden explicit recovery.** Verify exact process identity during explicit
`service restart`; never automatically kill an unresponsive owner.
9. **Run the full multi-process suite.** Include repeated restart cycles and
assert that no contender or child process remains afterward.
## Acceptance Criteria
- Ten concurrent restart observers produce one application initialization.
- No losing contender survives or builds a location graph.
- A 30-second application boot remains continuously observable as `starting`.
- A TUI remains alive through a service outage longer than the previous retry
budget.
- A service endpoint change does not require restarting an existing TUI.
- Background installation alone does not restart the service.
- A fresh mismatched TUI eventually attaches to the installed service version.
- Existing reconnecting TUIs never replace the current owner.
- Registration corruption cannot produce two owners.
- A deleted registration heals without restarting the owner or any client.
- An unresponsive owner is not killed without an explicit recovery command.
- Raw transport defects never escape to the terminal.
## Follow-ups
- Idle background update activation with an admission fence.
- Application protocol compatibility and automatic local TUI re-exec.
- Durable execution recovery for provider attempts and tools.
- Shell, sub-agent, permission, question, and background-job continuity.
- Automatic recovery for a positively identified frozen owner.
- Cold-boot concurrency limits and interaction-prioritized location loading.
- A steward or socket-handoff architecture if zero-downtime replacement becomes
a real requirement.
-19
View File
@@ -8,25 +8,6 @@ export const zoneID = "430ba34c138cfb5360826c4909f99be8"
export const awsStage = $app.stage === "production" ? "production" : "dev"
export const deployAws = $app.stage === awsStage
if ($app.stage === "production") {
new cloudflare.DnsRecord("TrustCenter", {
zoneId: zoneID,
name: "trust.opencode.ai",
type: "CNAME",
content: "3a69a5bb27875189.vercel-dns-016.com",
proxied: false,
ttl: 60,
})
new cloudflare.DnsRecord("TrustCenterVerification", {
zoneId: zoneID,
name: "opencode.ai",
type: "TXT",
content: "compai-domain-verification=org_6993a99c6200a2d642bb115d",
ttl: 60,
})
}
new cloudflare.RegionalHostname("RegionalHostname", {
hostname: domain,
regionKey: "us",
+33 -66
View File
@@ -8,8 +8,6 @@
makeWrapper,
writableTmpDirAsHomeHook,
autoPatchelfHook,
copyDesktopItems,
makeDesktopItem,
opencode,
}:
let
@@ -29,12 +27,9 @@ stdenv.mkDerivation (finalAttrs: {
nodejs
makeWrapper
writableTmpDirAsHomeHook
]
++ lib.optionals stdenv.hostPlatform.isLinux [
] ++ lib.optionals stdenv.hostPlatform.isLinux [
autoPatchelfHook
copyDesktopItems
]
++ lib.optionals stdenv.hostPlatform.isDarwin [
] ++ lib.optionals stdenv.hostPlatform.isDarwin [
# Ad-hoc sign the .app: --config.mac.identity=null below skips signing.
darwin.autoSignDarwinBinariesHook
];
@@ -43,37 +38,20 @@ stdenv.mkDerivation (finalAttrs: {
(lib.getLib stdenv.cc.cc)
];
desktopItems = lib.optional stdenv.hostPlatform.isLinux (makeDesktopItem {
name = "ai.opencode.desktop";
desktopName = "OpenCode";
exec = "opencode-desktop %U";
icon = "ai.opencode.desktop";
# Electron 41 derives X11 WM_CLASS from app.name.
startupWMClass = "OpenCode";
categories = [ "Development" ];
});
env = opencode.env // {
ELECTRON_SKIP_BINARY_DOWNLOAD = "1";
};
postPatch =
# NOTE: Relax Bun version check to be a warning instead of an error
''
substituteInPlace packages/script/src/index.ts \
--replace-fail 'throw new Error(`This script requires bun@''${expectedBunVersionRange}' \
'console.warn(`Warning: This script requires bun@''${expectedBunVersionRange}'
''
# https://github.com/electron/electron/issues/31121
# mac builds use a .app bundle which doesnt have this issue
+ lib.optionalString stdenv.isLinux ''
BASE_PATH=packages/desktop
FILES=(src/main/windows.ts)
for file in "''${FILES[@]}"; do
substituteInPlace $BASE_PATH/$file \
--replace-fail "process.resourcesPath" "'$out/opt/opencode-desktop/resources'"
done
'';
# https://github.com/electron/electron/issues/31121
# mac builds use a .app bundle which doesnt have this issue
postPatch = lib.optionalString stdenv.isLinux ''
BASE_PATH=packages/desktop
FILES=(src/main/windows.ts)
for file in "''${FILES[@]}"; do
substituteInPlace $BASE_PATH/$file \
--replace-fail "process.resourcesPath" "'$out/opt/opencode-desktop/resources'"
done
'';
preBuild = ''
cp -r "${electron.dist}" $HOME/.electron-dist
@@ -98,38 +76,27 @@ stdenv.mkDerivation (finalAttrs: {
runHook postBuild
'';
installPhase = ''
runHook preInstall
''
+ lib.optionalString stdenv.hostPlatform.isDarwin ''
mkdir -p $out/Applications
mv dist/mac*/*.app $out/Applications
makeWrapper "$out/Applications/OpenCode.app/Contents/MacOS/OpenCode" $out/bin/opencode-desktop
''
+ lib.optionalString stdenv.hostPlatform.isLinux ''
mkdir -p $out/opt/opencode-desktop
cp -r dist/linux*-unpacked/{resources,LICENSE*} $out/opt/opencode-desktop
install -Dm644 resources/icons/32x32.png \
"$out/share/icons/hicolor/32x32/apps/ai.opencode.desktop.png"
install -Dm644 resources/icons/64x64.png \
"$out/share/icons/hicolor/64x64/apps/ai.opencode.desktop.png"
install -Dm644 resources/icons/128x128.png \
"$out/share/icons/hicolor/128x128/apps/ai.opencode.desktop.png"
install -Dm644 resources/icons/128x128@2x.png \
"$out/share/icons/hicolor/256x256/apps/ai.opencode.desktop.png"
install -Dm644 resources/icons/icon.png \
"$out/share/icons/hicolor/512x512/apps/ai.opencode.desktop.png"
install -Dm644 resources/ai.opencode.desktop.metainfo.xml \
"$out/share/metainfo/ai.opencode.desktop.metainfo.xml"
makeWrapper ${lib.getExe electron} $out/bin/opencode-desktop \
--inherit-argv0 \
--set ELECTRON_FORCE_IS_PACKAGED 1 \
--add-flags $out/opt/opencode-desktop/resources/app.asar \
--add-flags "\''${NIXOS_OZONE_WL:+\''${WAYLAND_DISPLAY:+--ozone-platform-hint=auto --enable-features=WaylandWindowDecorations --enable-wayland-ime=true}}"
''
+ ''
runHook postInstall
'';
installPhase =
''
runHook preInstall
''
+ lib.optionalString stdenv.hostPlatform.isDarwin ''
mkdir -p $out/Applications
mv dist/mac*/*.app $out/Applications
makeWrapper "$out/Applications/OpenCode.app/Contents/MacOS/OpenCode" $out/bin/opencode-desktop
''
+ lib.optionalString stdenv.hostPlatform.isLinux ''
mkdir -p $out/opt/opencode-desktop
cp -r dist/linux*-unpacked/{resources,LICENSE*} $out/opt/opencode-desktop
makeWrapper ${lib.getExe electron} $out/bin/opencode-desktop \
--inherit-argv0 \
--set ELECTRON_FORCE_IS_PACKAGED 1 \
--add-flags $out/opt/opencode-desktop/resources/app.asar \
--add-flags "\''${NIXOS_OZONE_WL:+\''${WAYLAND_DISPLAY:+--ozone-platform-hint=auto --enable-features=WaylandWindowDecorations --enable-wayland-ime=true}}"
''
+ ''
runHook postInstall
'';
autoPatchelfIgnoreMissingDeps = [
"libc.musl-x86_64.so.1"
+4 -4
View File
@@ -1,8 +1,8 @@
{
"nodeModules": {
"x86_64-linux": "sha256-qt11SKmOjq0KU542QFbs+u7YyJicn4drCcwCdg325yk=",
"aarch64-linux": "sha256-z68doReXTrWS7HeiAjc0btIjAsvzeZZ7hXAlHr0c77Q=",
"aarch64-darwin": "sha256-PILYH1Pi8XBvSkuZ+1sNnUTao5kba+m5Z8iJKx6YXPo=",
"x86_64-darwin": "sha256-KpcJzP4m0SUavu/WaSffgzOxrHq8ljdy0GOzs9p16lo="
"x86_64-linux": "sha256-N4zM1zNufSg8DrDWOHWJYgVpn6vDghX/CJ0pym9ItxI=",
"aarch64-linux": "sha256-Votrb6IbVt6OS5pcAlBd3L2btkZHa62Eu3mAFzKSlGM=",
"aarch64-darwin": "sha256-Ofmy6plO4CFt/DoVdyt3Sr2rk6VJhas4zXq3DnvP/6A=",
"x86_64-darwin": "sha256-LOeqfqlPbhp1c0Gq56fvKSzve7dvcCwlooTmDMFMznw="
}
}
+9 -12
View File
@@ -15,7 +15,7 @@
"dev:www": "bun run --cwd packages/www dev",
"dev:storybook": "bun --cwd packages/storybook storybook",
"lint": "oxlint",
"lint:effect-patterns": "ast-grep scan -c script/ast-grep/sgconfig.yml packages/util/src packages/core/src packages/server/src packages/protocol/src packages/cli/src",
"lint:effect-patterns": "ast-grep scan -c script/ast-grep/sgconfig.yml packages/core/src packages/server/src packages/protocol/src packages/cli/src",
"test:lint-rules": "ast-grep test -c script/ast-grep/sgconfig.yml",
"typecheck": "bun turbo typecheck --concurrency=3",
"typecheck:profile": "bun script/profile-typecheck.ts",
@@ -37,18 +37,18 @@
"packages/slack"
],
"catalog": {
"@effect/opentelemetry": "4.0.0-beta.98",
"@effect/platform-node": "4.0.0-beta.98",
"@effect/sql-sqlite-bun": "4.0.0-beta.98",
"@effect/opentelemetry": "4.0.0-beta.83",
"@effect/platform-node": "4.0.0-beta.83",
"@effect/sql-sqlite-bun": "4.0.0-beta.83",
"@npmcli/arborist": "9.4.0",
"@types/bun": "1.3.13",
"@types/cross-spawn": "6.0.6",
"@octokit/rest": "22.0.0",
"@hono/standard-validator": "0.2.0",
"@hono/zod-validator": "0.4.2",
"@opentui/core": "0.4.5",
"@opentui/keymap": "0.4.5",
"@opentui/solid": "0.4.5",
"@opentui/core": "0.4.3",
"@opentui/keymap": "0.4.3",
"@opentui/solid": "0.4.3",
"@tanstack/solid-virtual": "3.13.32",
"@shikijs/stream": "4.2.0",
"ulid": "3.0.1",
@@ -69,13 +69,12 @@
"dompurify": "3.3.1",
"drizzle-kit": "1.0.0-rc.2",
"drizzle-orm": "1.0.0-rc.2",
"effect": "4.0.0-beta.98",
"effect": "4.0.0-beta.83",
"ai": "6.0.168",
"cross-spawn": "7.0.6",
"hono": "4.10.7",
"hono-openapi": "1.1.2",
"fuzzysort": "3.1.0",
"get-east-asian-width": "1.6.0",
"luxon": "3.6.1",
"marked": "17.0.6",
"marked-shiki": "1.2.1",
@@ -86,11 +85,9 @@
"@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": "4.2.0",
"solid-list": "0.3.0",
"string-width": "7.2.0",
"tailwindcss": "4.1.11",
"vite": "7.1.4",
"@solidjs/meta": "0.29.4",
@@ -166,7 +163,7 @@
"@ai-sdk/google@3.0.73": "patches/@ai-sdk%2Fgoogle@3.0.73.patch",
"@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",
"effect@4.0.0-beta.98": "patches/effect@4.0.0-beta.98.patch",
"effect@4.0.0-beta.83": "patches/effect@4.0.0-beta.83.patch",
"@tanstack/virtual-core@3.17.3": "patches/@tanstack%2Fvirtual-core@3.17.3.patch"
}
}
-360
View File
@@ -1,360 +0,0 @@
# @opencode-ai/ai
Schema-first AI primitives for opencode. Provider quirks live in adapters, not in calling code.
```ts
import { Effect } from "effect"
import { LLM, LLMClient } from "@opencode-ai/ai"
import { OpenAI } from "@opencode-ai/ai/providers"
const model = OpenAI.configure({ apiKey: process.env.OPENAI_API_KEY }).responses("gpt-4o-mini")
const request = LLM.request({
model,
system: "You are concise.",
prompt: "Say hello in one short sentence.",
generation: { maxTokens: 40 },
})
const program = Effect.gen(function* () {
const response = yield* LLMClient.generate(request)
console.log(response.text)
})
```
Run `LLMClient.stream(request)` instead of `generate` when you want incremental `LLMEvent`s. The event stream is provider-neutral — same shape across OpenAI Chat, OpenAI Responses, Anthropic Messages, Gemini, Bedrock Converse, and any OpenAI-compatible deployment.
## Image generation
Use `Image.generate` with an image model for direct asset generation:
```ts
import { Image, ImageInput } from "@opencode-ai/ai"
import { OpenAI } from "@opencode-ai/ai/providers"
const program = Effect.gen(function* () {
const response = yield* Image.generate({
model: OpenAI.configure({ apiKey: process.env.OPENAI_API_KEY }).image("gpt-image-2"),
prompt: "A robot tending a rooftop garden",
options: {
n: 2,
size: "1024x1024",
quality: "high", // inferred from the OpenAI image model
outputFormat: "webp",
future_option: true, // unknown native options pass through unchanged
},
})
return response.images // GeneratedImage[] with owned bytes or a provider URL
})
```
Pass ordered image inputs to the same method for editing, composition, or image-conditioned generation:
```ts
const response =
yield *
Image.generate({
model,
prompt: "Combine these product photos into one studio scene",
images: [
ImageInput.bytes(firstBytes, "image/png"),
ImageInput.url("https://example.com/second.webp"),
ImageInput.file("file_123"),
],
options,
http,
})
```
`ImageInput.fileUri(uri, mediaType)` represents provider file URIs such as Gemini Files. Raw strings are not
accepted as image inputs, avoiding ambiguity between base64, URLs, and provider IDs. Empty or omitted `images`
uses text-to-image generation; a non-empty array selects the provider's edit behavior without enforcing provider
image-count limits locally. `images` is the only common image-editing field. OpenAI uses multipart for byte/data-URL
edits and its JSON reference body for URL or file-ID edits. Its provider-specific `options.mask` accepts an
`ImageInput` for inpainting:
```ts
yield *
Image.generate({
model: OpenAI.configure({ apiKey }).image("gpt-image-2"),
prompt,
images: [ImageInput.bytes(sourceBytes, "image/png")],
options: { mask: ImageInput.bytes(maskBytes, "image/png") },
})
```
The OpenAI adapter extracts this helper value into the edit request's native `mask` field rather than passing the
tagged `ImageInput` object through as an ordinary option. On multipart requests, `http.body` can override option
fields but not structural `model`, `prompt`, `image[]`, or `mask` fields, and the transport owns the multipart
`Content-Type` boundary. For JSON requests, `http.body` remains the final raw-native overlay. Gemini does not fetch
public HTTP URLs, and hosted Z.ai image generation does not accept image inputs. These cases fail with
`InvalidRequest` before network I/O.
Provider-native image options belong to each request. Raw `http.body` fields have final precedence over them:
```ts
const model = OpenAI.configure({ apiKey }).image("gpt-image-2")
yield *
Image.generate({
model,
prompt,
options: { quality: "medium" },
http,
})
```
xAI image models use the same request API with xAI-native controls:
```ts
yield *
Image.generate({
model: XAI.configure({ apiKey }).image("any-model-id"),
prompt,
options: {
n: 2,
aspectRatio: "16:9",
resolution: "1k",
responseFormat: "b64_json",
future_option: true,
},
http,
})
```
Google's current Gemini image models use the same direct API:
```ts
import { Google } from "@opencode-ai/ai/providers"
const googleProgram = Effect.gen(function* () {
const response = yield* Image.generate({
model: Google.configure({ apiKey }).image("any-model-id"),
prompt: "A robot tending a rooftop garden",
options: {
aspectRatio: "16:9",
imageSize: "2K",
seed: 42,
thinkingLevel: "HIGH",
includeThoughts: true,
futureOption: true,
},
http,
})
return response.images
})
```
Google image options are request-scoped and inferred from the selected model. Known fields autocomplete while
future string values and arbitrary native Gemini `generationConfig` fields remain available. Native fields override
their mapped aliases, and `http.body` is the final deep overlay. The selected model ID is sent to Gemini
`generateContent` without a local allowlist.
Z.ai image models infer open Z.ai-native options from the selected model:
```ts
yield *
Image.generate({
model: ZAI.configure({ apiKey }).image("any-model-id"),
prompt,
options: {
quality: "hd",
userID: "user-123",
future_option: true,
},
http,
})
```
Z.ai does not include trustworthy MIME metadata for output URLs, so generated images use
`application/octet-stream`. Output URLs expire after 30 days; download and persist them promptly if they must
remain available.
Conversational image generation remains part of the LLM interaction. OpenAI Responses exposes it through its hosted image tool:
```ts
const program = Effect.gen(function* () {
const response = yield* LLM.generate(
LLM.request({
model: OpenAI.configure({ apiKey }).responses("gpt-5"),
prompt: "Design a solarpunk rooftop garden, then show me.",
tools: [OpenAI.imageGeneration({ quality: "high" })],
}),
)
return response.message
})
```
The hosted result is represented as a provider-executed tool call and tool result. Its image is a `file` content item with a data URI, so retaining `response.message` preserves the generated image for continuation.
## Public API
- **`LLM.request({...})`** — build a provider-neutral `LLMRequest`. Accepts ergonomic inputs (`system: string`, `prompt: string`) that normalize into the canonical Schema classes.
- **`LLM.generate` / `LLM.stream`** — re-exported from `LLMClient` for one-import use.
- **`Message.user(...)` / `Message.assistant(...)` / `Message.tool(...)`** — message constructors from the canonical schema model.
- **`Model.make(...)` / `ToolCallPart.make(...)` / `ToolResultPart.make(...)` / `ToolDefinition.make(...)`** — model and tool-related constructors from the canonical schema model.
- **`LLMClient.prepare(request)`** — compile a request through protocol body construction, validation, and HTTP preparation without sending. Useful for inspection and testing.
- **`LLMEvent.is.*`** — typed guards (`is.textDelta`, `is.toolCall`, `is.finish`, …) for filtering streams.
- **`Image.generate({...})`** — generate images through a provider-neutral image request and response model.
- **`ImageClient`** — Effect service and layer for image execution, parallel to `LLMClient`.
## 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).
### Auto placement
`"auto"` places three breakpoints — last tool definition, last system part, latest user message. The last-user-message boundary is the load-bearing detail: in a tool-use loop, a single user turn expands into many assistant/tool round-trips, all sharing that prefix. Caching at that boundary lets every intra-turn API call hit.
The math justifies the default: Anthropic's 5-minute cache write is 1.25× base, read is 0.1×, so a single reuse within 5 minutes already wins. One-shot completions below the per-model minimum-cacheable-token threshold silently no-op on the wire, so the worst case is harmless.
### Opting out
```ts
LLM.request({
model,
system,
prompt: "one-off question",
cache: "none",
})
```
### Granular policy
```ts
cache: {
tools?: boolean,
system?: boolean,
messages?: "latest-user-message" | "latest-assistant" | { tail: number },
ttlSeconds?: number, // ≥ 3600 → 1h on Anthropic/Bedrock; else 5m
}
```
### Manual hints
Inline `CacheHint` on any text / system / tool / tool-result part overrides automatic placement. The auto policy preserves manual hints; it only fills gaps.
```ts
LLM.request({
model,
system: [
{ type: "text", text: "stable system prompt", cache: { type: "ephemeral" } },
],
...
})
```
### Provider behavior table
| Protocol | `cache: "auto"` |
| ----------------------- | ------------------------------------------------------------------------- |
| Anthropic Messages | emits up to 3 `cache_control` markers (4-breakpoint cap enforced) |
| Bedrock Converse | emits up to 3 `cachePoint` blocks (4-breakpoint cap enforced) |
| OpenAI Chat / Responses | no-op (implicit caching above 1024 tokens) |
| Gemini | no-op (implicit caching on 2.5+; explicit `CachedContent` is out-of-band) |
Normalized cache usage is read back into `response.usage.cacheReadInputTokens` and `cacheWriteInputTokens` across every provider.
## Providers
Provider facades configure endpoint/auth/deployment details first, then expose model selectors that take only a model or deployment id. The selected model carries the executable route value used at runtime.
```ts
import { OpenAI, CloudflareAIGateway } from "@opencode-ai/ai/providers"
const openai = OpenAI.configure({ apiKey: process.env.OPENAI_API_KEY }).responses("gpt-4o-mini")
const gateway = CloudflareAIGateway.configure({
accountId: process.env.CLOUDFLARE_ACCOUNT_ID,
gatewayApiKey: process.env.CLOUDFLARE_API_TOKEN,
}).model("workers-ai/@cf/meta/llama-3.1-8b-instruct")
```
Included providers: OpenAI, Anthropic, Google (Gemini), Google Vertex Gemini and Anthropic, Amazon Bedrock, Azure OpenAI, Cloudflare AI Gateway, Cloudflare Workers AI, GitHub Copilot, OpenRouter, xAI, Z.ai, plus generic OpenAI-compatible Chat and Responses entrypoints and an Anthropic Messages-compatible entrypoint.
### Package-like entrypoints
Native catalog integrations load provider behavior through package-like entrypoints. These are export paths from the same `@opencode-ai/ai` npm package, not independently published packages. Each entrypoint exports the same `model(modelID, settings)` contract, and `settings` contains serializable provider configuration plus common `headers`, `body`, and `limits` overlays.
```ts
import { model } from "@opencode-ai/ai/providers/openai/responses"
const selected = model("gpt-5", {
apiKey: process.env.OPENAI_API_KEY,
transport: "websocket",
headers: { "x-application": "opencode" },
limits: { context: 200_000, output: 64_000 },
})
```
OpenAI Chat and OpenAI Responses are separate semantic entrypoints:
- `@opencode-ai/ai/providers/openai/chat`
- `@opencode-ai/ai/providers/openai/responses`
- `@opencode-ai/ai/providers/openai-compatible/responses`
- `@opencode-ai/ai/providers/anthropic-compatible`
- `@opencode-ai/ai/providers/google-vertex/gemini`
- `@opencode-ai/ai/providers/google-vertex/chat`
- `@opencode-ai/ai/providers/google-vertex/responses`
- `@opencode-ai/ai/providers/google-vertex/messages`
Responses HTTP versus WebSocket is a scoped `transport` setting on the OpenAI Responses entrypoint, not another entrypoint. Azure follows the same Chat/Responses split at `providers/azure/chat` and `providers/azure/responses`. Generic OpenAI-compatible Chat remains at `providers/openai-compatible`; compatible Responses is separate at `providers/openai-compatible/responses`. Generic Anthropic Messages-compatible providers use `providers/anthropic-compatible`, which the named Anthropic provider composes. Google Gemini and Amazon Bedrock expose their single native API through their existing provider paths.
Vertex Gemini, Vertex Chat, Vertex Responses, and Vertex Messages are separate API entrypoints. All accept `project`, `location`, and an optional `accessToken`; when no explicit token or auth override is supplied they lazily use Google Application Default Credentials. Vertex Gemini instead selects express mode when `apiKey` or `GOOGLE_VERTEX_API_KEY` is present. Vertex Chat targets MaaS models through the OpenAI-compatible Chat Completions endpoint, while Vertex Responses targets Grok models and defaults `store` to `false` as required by Vertex. `providers/google-vertex` remains the default alias for `providers/google-vertex/gemini`.
Tuned Vertex Gemini deployments use model ids shaped like `endpoints/1234567890` and require OAuth or ADC; Vertex express-mode API keys support publisher models only.
```ts
import { model } from "@opencode-ai/ai/providers/google-vertex/gemini"
model("gemini-3.5-flash", { project: "my-project", location: "global" })
```
```ts
import { model } from "@opencode-ai/ai/providers/google-vertex/chat"
model("deepseek-ai/deepseek-v3.2-maas", { project: "my-project", location: "global" })
```
```ts
import { model } from "@opencode-ai/ai/providers/google-vertex/responses"
model("xai/grok-4.20-reasoning", { project: "my-project", location: "global" })
```
```ts
import { model } from "@opencode-ai/ai/providers/google-vertex/messages"
model("claude-sonnet-4-6", { project: "my-project", location: "global" })
```
Provider facades such as `OpenAI.configure(...).responses(...)` remain the direct application API. Package-like entrypoints are the self-similar loading contract used when a catalog selects behavior by export path.
Other provider exports listed above remain direct facades until they explicitly implement the package-like contract. Exporting a provider facade does not implicitly make it a catalog-loadable provider package.
## Provider options & HTTP overlays
Three escape hatches in order of stability:
1. **`generation`** — portable knobs (`maxTokens`, `temperature`, `topP`, `topK`, penalties, seed, stop).
2. **`providerOptions: { <provider>: {...} }`** — typed-at-the-facade provider-specific knobs (OpenAI `promptCacheKey`, Anthropic `thinking`, Gemini `thinkingConfig`, OpenRouter routing).
3. **`http: { body, headers, query }`** — last-resort serializable overlays merged into the final HTTP request. Reach for this only when a stable typed path doesn't yet exist.
Route/provider defaults are overridden by request-level values for each axis.
## Routes
Adding a new model or deployment is usually 5-15 lines using `Route.make({ protocol, endpoint, auth, framing, ... })`. The route owns endpoint/auth/framing and the protocol owns body construction plus stream parsing. Transports are reusable IO templates that receive route endpoint/auth at compile time. Capability/catalog metadata lives outside this low-level package; unsupported request shapes fail during protocol lowering. See `AGENTS.md` for the architectural detail.
## Effect
This package is built on Effect. Public methods return `Effect` or `Stream`; provide `LLMClient.layer` for LLM dispatch and `ImageClient.layer` for image dispatch, then import the provider/protocol modules for the routes you use. The example at `example/tutorial.ts` is a runnable walkthrough.
## See also
- `AGENTS.md` — architecture, route construction, contributor guide
- `STATUS.md` — native provider parity status and AI SDK migration gaps
- `example/tutorial.ts` — runnable end-to-end walkthrough
- `test/provider/*.test.ts` — fixture-first protocol tests; `*.recorded.test.ts` files cover live cassettes
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{
"$schema": "https://json.schemastore.org/package.json",
"version": "1.17.20",
"name": "@opencode-ai/ai",
"type": "module",
"license": "MIT",
"scripts": {
"setup:recording-env": "bun run script/setup-recording-env.ts",
"test": "bun test --timeout 30000 --only-failures",
"typecheck": "tsgo --noEmit && tsgo --noEmit -p tsconfig.types.json",
"build": "tsc -p tsconfig.build.json"
},
"files": [
"dist"
],
"exports": {
".": "./src/index.ts",
"./*": "./src/*.ts"
},
"devDependencies": {
"@clack/prompts": "1.0.0-alpha.1",
"@effect/platform-node": "catalog:",
"@opencode-ai/http-recorder": "workspace:*",
"@tsconfig/bun": "catalog:",
"@types/bun": "catalog:",
"@typescript/native-preview": "catalog:",
"typescript": "catalog:"
},
"dependencies": {
"@smithy/eventstream-codec": "4.2.14",
"@smithy/util-utf8": "4.2.2",
"@opencode-ai/schema": "workspace:*",
"aws4fetch": "1.0.20",
"effect": "catalog:",
"google-auth-library": "10.5.0"
}
}
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#!/usr/bin/env bun
import { Script } from "@opencode-ai/script"
import { $ } from "bun"
import { fileURLToPath } from "url"
const dir = fileURLToPath(new URL("..", import.meta.url))
process.chdir(dir)
async function published(name: string, version: string) {
return (await $`npm view ${name}@${version} version`.nothrow()).exitCode === 0
}
await $`bun run build`
const originalText = await Bun.file("package.json").text()
const pkg = JSON.parse(originalText) as {
name: string
version: string
exports: Record<string, string>
}
if (await published(pkg.name, pkg.version)) {
console.log(`already published ${pkg.name}@${pkg.version}`)
} else {
for (const [key, value] of Object.entries(pkg.exports)) {
const file = value.replace("./src/", "./dist/").replace(".ts", "")
// @ts-ignore
pkg.exports[key] = {
import: file + ".js",
types: file + ".d.ts",
}
}
await Bun.write("package.json", JSON.stringify(pkg, null, 2))
try {
await $`bun pm pack`
await $`npm publish *.tgz --tag ${Script.channel} --access public`
} finally {
await Bun.write("package.json", originalText)
}
}
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import { Context, Effect, Layer } from "effect"
import { RequestExecutor } from "./route/executor"
import type { ImageOptions, ImageRequest, ImageRequestFor, ImageResponse } from "./image"
import type { LLMError } from "./schema"
export type Execute = RequestExecutor.Interface["execute"]
export interface Interface {
readonly generate: <Options extends ImageOptions>(
request: ImageRequestFor<Options>,
) => Effect.Effect<ImageResponse, LLMError>
}
export class Service extends Context.Service<Service, Interface>()("@opencode/ImageClient") {}
export const generate = <Options extends ImageOptions>(
request: ImageRequestFor<Options>,
): Effect.Effect<ImageResponse, LLMError> =>
Effect.gen(function* () {
const client = yield* Service
return yield* client.generate(request)
}) as Effect.Effect<ImageResponse, LLMError>
export const layer: Layer.Layer<Service, never, RequestExecutor.Service> = Layer.effect(
Service,
Effect.gen(function* () {
const executor = yield* RequestExecutor.Service
return Service.of({
generate: (request) => request.model.route.generate(request, executor.execute),
})
}),
)
export const ImageClient = {
Service,
layer,
generate,
} as const
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import { Effect, Schema } from "effect"
import { HttpOptions, InvalidRequestReason, LLMError, ModelID, ProviderID, ProviderMetadata, Usage } from "./schema"
import { ImageClient, Service, type Execute as ImageExecute } from "./image-client"
export interface ImageRoute<Options extends ImageOptions = ImageOptions> {
readonly id: string
readonly generate: (
request: ImageRequestFor<Options>,
execute: ImageExecute,
) => Effect.Effect<ImageResponse, LLMError>
}
export type ImageOptions = Record<string, unknown>
export class ImageModel<Options extends ImageOptions = ImageOptions> {
declare protected readonly _Options: (options: Options) => Options
readonly id: ModelID
readonly provider: ProviderID
readonly route: ImageRoute<Options>
readonly http?: HttpOptions
constructor(input: ImageModel.Input<Options>) {
this.id = input.id
this.provider = input.provider
this.route = input.route
this.http = input.http
}
static make<Options extends ImageOptions = ImageOptions>(input: ImageModel.MakeInput<Options>) {
return new ImageModel<Options>({
id: ModelID.make(input.id),
provider: ProviderID.make(input.provider),
route: input.route,
http: input.http,
})
}
}
export namespace ImageModel {
export interface Input<Options extends ImageOptions = ImageOptions> {
readonly id: ModelID
readonly provider: ProviderID
readonly route: ImageRoute<Options>
readonly http?: HttpOptions
}
export interface MakeInput<Options extends ImageOptions = ImageOptions>
extends Omit<Input<Options>, "id" | "provider"> {
readonly id: string | ModelID
readonly provider: string | ProviderID
}
}
export const ImageModelSchema = Schema.declare((value): value is ImageModel => value instanceof ImageModel, {
expected: "Image.Model",
})
const ImageBytesInput = Schema.Struct({
type: Schema.Literal("bytes"),
data: Schema.Uint8Array,
mediaType: Schema.String,
})
const ImageUrlInput = Schema.Struct({
type: Schema.Literal("url"),
url: Schema.String,
})
const ImageFileIDInput = Schema.Struct({
type: Schema.Literal("file-id"),
id: Schema.String,
})
const ImageFileURIInput = Schema.Struct({
type: Schema.Literal("file-uri"),
uri: Schema.String,
mediaType: Schema.String,
})
export const ImageInputSchema = Schema.Union([
ImageBytesInput,
ImageUrlInput,
ImageFileIDInput,
ImageFileURIInput,
]).pipe(Schema.toTaggedUnion("type"))
export type ImageInput = Schema.Schema.Type<typeof ImageInputSchema>
export const ImageInput = {
bytes: (data: Uint8Array, mediaType: string): ImageInput => ({ type: "bytes", data, mediaType }),
url: (url: string): ImageInput => ({ type: "url", url }),
file: (id: string): ImageInput => ({ type: "file-id", id }),
fileUri: (uri: string, mediaType: string): ImageInput => ({ type: "file-uri", uri, mediaType }),
} as const
export class ImageRequest extends Schema.Class<ImageRequest>("Image.Request")({
model: ImageModelSchema,
prompt: Schema.String,
images: Schema.optional(Schema.Array(ImageInputSchema)),
options: Schema.optional(Schema.Record(Schema.String, Schema.Unknown)),
http: Schema.optional(HttpOptions),
}) {
declare protected readonly _ImageRequest: void
}
export type ImageRequestFor<Options extends ImageOptions = ImageOptions> = Omit<ImageRequest, "model" | "options"> & {
readonly model: ImageModel<Options>
readonly options?: Options
}
export type ImageModelOptions<Model> = Model extends ImageModel<infer Options> ? Options : never
export type ImageRequestInput<Model extends object = ImageModel> = Omit<
ConstructorParameters<typeof ImageRequest>[0],
"model" | "options" | "http"
> & {
readonly model: Model
readonly options?: NoInfer<ImageModelOptions<Model>>
readonly http?: HttpOptions.Input
} & (Model extends ImageModel<ImageModelOptions<Model>> ? unknown : never)
export class GeneratedImage extends Schema.Class<GeneratedImage>("Image.Generated")({
mediaType: Schema.String,
data: Schema.Union([Schema.String, Schema.Uint8Array]),
providerMetadata: Schema.optional(ProviderMetadata),
}) {}
export class ImageResponse extends Schema.Class<ImageResponse>("Image.Response")({
images: Schema.Array(GeneratedImage),
usage: Schema.optional(Usage),
providerMetadata: Schema.optional(ProviderMetadata),
}) {
get image() {
return this.images[0]
}
}
export function request<const Model extends object>(
input: ImageRequestInput<Model>,
): ImageRequestFor<ImageModelOptions<Model>>
export function request(input: ImageRequest): ImageRequest
export function request(input: ImageRequest | ImageRequestInput) {
if (input instanceof ImageRequest) return input
return new ImageRequest({
...input,
model: input.model as unknown as ImageModel,
http: input.http === undefined ? undefined : HttpOptions.make(input.http),
})
}
export function generate<const Model extends object>(
input: ImageRequestInput<Model>,
): Effect.Effect<ImageResponse, LLMError, Service>
export function generate(input: ImageRequest): Effect.Effect<ImageResponse, LLMError, Service>
export function generate(input: ImageRequest | ImageRequestInput) {
return Effect.try({
try: () => (input instanceof ImageRequest ? input : request(input)),
catch: (error) =>
new LLMError({
module: "Image",
method: "generate",
reason: new InvalidRequestReason({ message: error instanceof Error ? error.message : String(error) }),
}),
}).pipe(Effect.flatMap((request) => ImageClient.generate(request as unknown as ImageRequestFor<ImageOptions>)))
}
export const Image = {
request,
generate,
} as const
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export * from "./protocols/index"
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import { Effect, Encoding, Schema } from "effect"
import { Headers, HttpClientRequest } from "effect/unstable/http"
import {
GeneratedImage,
ImageModel,
ImageResponse,
type ImageInput,
type ImageRequestFor,
type ImageRoute,
} from "../image"
import { Auth, type Definition as AuthDefinition } from "../route/auth"
import {
InvalidProviderOutputReason,
LLMError,
Usage,
mergeHttpOptions,
mergeJsonRecords,
type HttpOptions,
type ProviderMetadata,
} from "../schema"
import { ProviderShared } from "./shared"
import { ImageInputs } from "./utils/image-input"
const ADAPTER = "google-images"
export const DEFAULT_BASE_URL = "https://generativelanguage.googleapis.com/v1beta"
export type GoogleImageString<Known extends string> = Known | (string & {})
export type GoogleImageOptions = {
readonly aspectRatio?: GoogleImageString<
"1:1" | "2:3" | "3:2" | "3:4" | "4:3" | "4:5" | "5:4" | "9:16" | "16:9" | "21:9"
>
readonly imageSize?: GoogleImageString<"1K" | "2K" | "4K">
readonly seed?: number
readonly thinkingLevel?: GoogleImageString<"MINIMAL" | "LOW" | "MEDIUM" | "HIGH">
readonly includeThoughts?: boolean
} & Record<string, unknown>
export type GoogleImageBody = Record<string, unknown> & {
readonly contents: ReadonlyArray<{
readonly role: "user"
readonly parts: ReadonlyArray<Record<string, unknown>>
}>
readonly generationConfig: Record<string, unknown>
}
const GoogleUsage = Schema.StructWithRest(
Schema.Struct({
cachedContentTokenCount: Schema.optional(Schema.Number),
thoughtsTokenCount: Schema.optional(Schema.Number),
promptTokenCount: Schema.optional(Schema.Number),
candidatesTokenCount: Schema.optional(Schema.Number),
totalTokenCount: Schema.optional(Schema.Number),
promptTokensDetails: Schema.optional(Schema.Unknown),
candidatesTokensDetails: Schema.optional(Schema.Unknown),
}),
[Schema.Record(Schema.String, Schema.Unknown)],
)
const GoogleImageResponse = Schema.Struct({
candidates: Schema.optional(
Schema.Array(
Schema.Struct({
index: Schema.optional(Schema.Number),
content: Schema.optional(
Schema.Struct({
parts: Schema.Array(
Schema.Struct({
text: Schema.optional(Schema.String),
thought: Schema.optional(Schema.Boolean),
thoughtSignature: Schema.optional(Schema.String),
inlineData: Schema.optional(
Schema.Struct({
mimeType: Schema.String,
data: Schema.String,
}),
),
}),
),
}),
),
finishReason: Schema.optional(Schema.String),
finishMessage: Schema.optional(Schema.String),
safetyRatings: Schema.optional(Schema.Unknown),
citationMetadata: Schema.optional(Schema.Unknown),
groundingMetadata: Schema.optional(Schema.Unknown),
}),
),
),
usageMetadata: Schema.optional(GoogleUsage),
modelVersion: Schema.optional(Schema.String),
responseId: Schema.optional(Schema.String),
promptFeedback: Schema.optional(Schema.Unknown),
})
export interface ModelInput {
readonly id: string
readonly auth: AuthDefinition
readonly baseURL?: string
readonly headers?: Record<string, string>
readonly http?: HttpOptions
}
const nativeOptions = (options: GoogleImageOptions | undefined) => {
const { aspectRatio, imageSize, seed, thinkingLevel, includeThoughts, ...native } = options ?? {}
const image = {
aspectRatio,
imageSize,
}
const thinkingConfig = {
thinkingLevel,
includeThoughts,
}
return (
mergeJsonRecords(
{
responseModalities: ["IMAGE"],
imageConfig: Object.values(image).some((value) => value !== undefined) ? image : undefined,
seed,
thinkingConfig: Object.values(thinkingConfig).some((value) => value !== undefined) ? thinkingConfig : undefined,
},
native,
) ?? { responseModalities: ["IMAGE"] }
)
}
const invalidOutput = (message: string, providerMetadata?: ProviderMetadata) =>
new LLMError({
module: ADAPTER,
method: "generate",
reason: new InvalidProviderOutputReason({ message, route: ADAPTER, providerMetadata }),
})
const applyQuery = (url: string, query: Record<string, string> | undefined) => {
if (!query) return url
const next = new URL(url)
Object.entries(query).forEach(([key, value]) => next.searchParams.set(key, value))
return next.toString()
}
export const model = (input: ModelInput) => {
const route: ImageRoute<GoogleImageOptions> = {
id: ADAPTER,
generate: Effect.fn("GoogleImages.generate")(function* (request: ImageRequestFor<GoogleImageOptions>, execute) {
const imageParts = yield* Effect.forEach(request.images ?? [], googleImagePart)
const http = mergeHttpOptions(request.model.http, request.http)
const requestBody = mergeJsonRecords(
{
contents: [{ role: "user", parts: [{ text: request.prompt }, ...imageParts] }],
generationConfig: nativeOptions(request.options),
},
http?.body,
) as GoogleImageBody
const text = ProviderShared.encodeJson(requestBody)
const url = applyQuery(
`${(input.baseURL ?? DEFAULT_BASE_URL).replace(/\/$/, "")}/models/${request.model.id}:generateContent`,
http?.query,
)
const headers = yield* Auth.toEffect(input.auth)({
request,
method: "POST",
url,
body: text,
headers: Headers.fromInput({ ...input.headers, ...http?.headers }),
})
const response = yield* execute(
HttpClientRequest.post(url).pipe(
HttpClientRequest.setHeaders(headers),
HttpClientRequest.bodyText(text, "application/json"),
),
)
const payload = yield* response.json.pipe(
Effect.mapError(() => invalidOutput("Failed to read the Google Images response")),
)
const decoded = yield* Schema.decodeUnknownEffect(GoogleImageResponse)(payload).pipe(
Effect.mapError(() => invalidOutput("Google Images returned an invalid response")),
)
const candidates = decoded.candidates ?? []
const candidateMetadata = candidates.map((candidate, candidateIndex) => ({
index: candidate.index ?? candidateIndex,
finishReason: candidate.finishReason,
finishMessage: candidate.finishMessage,
safetyRatings: candidate.safetyRatings,
citationMetadata: candidate.citationMetadata,
groundingMetadata: candidate.groundingMetadata,
parts: (candidate.content?.parts ?? []).map((part) =>
part.inlineData === undefined
? {
type: "text",
text: part.text,
thought: part.thought,
thoughtSignature: part.thoughtSignature,
}
: {
type: "inlineData",
mediaType: part.inlineData.mimeType,
thought: part.thought,
thoughtSignature: part.thoughtSignature,
},
),
}))
const encoded = candidates.flatMap((candidate, candidateIndex) =>
(candidate.content?.parts ?? []).flatMap((part, partIndex) =>
part.inlineData === undefined || part.thought === true
? []
: [{ candidate, candidateIndex, partIndex, inlineData: part.inlineData }],
),
)
const images = yield* Effect.forEach(encoded, (item) =>
Effect.fromResult(Encoding.decodeBase64(item.inlineData.data)).pipe(
Effect.mapError(() =>
invalidOutput(
`Google Images candidate ${item.candidateIndex} part ${item.partIndex} contains invalid base64 data`,
),
),
Effect.map(
(data) =>
new GeneratedImage({
mediaType: item.inlineData.mimeType,
data,
providerMetadata: {
google: {
candidateIndex: item.candidate.index ?? item.candidateIndex,
partIndex: item.partIndex,
finishReason: item.candidate.finishReason,
safetyRatings: item.candidate.safetyRatings,
citationMetadata: item.candidate.citationMetadata,
groundingMetadata: item.candidate.groundingMetadata,
thoughtSignature: item.candidate.content?.parts[item.partIndex]?.thoughtSignature,
},
},
}),
),
),
)
if (images.length === 0) {
const finishReasons = candidates.flatMap((candidate) =>
candidate.finishReason === undefined ? [] : [candidate.finishReason],
)
return yield* invalidOutput(
`Google Images returned no final images${
finishReasons.length === 0 ? "" : ` (finish reasons: ${finishReasons.join(", ")})`
}; inspect reason.providerMetadata.google for prompt feedback and candidate details`,
{
google: {
promptFeedback: decoded.promptFeedback,
candidates: candidateMetadata,
},
},
)
}
const usage = decoded.usageMetadata
const outputTokens =
usage?.candidatesTokenCount === undefined
? undefined
: usage.candidatesTokenCount + (usage.thoughtsTokenCount ?? 0)
return new ImageResponse({
images,
usage:
usage === undefined
? undefined
: new Usage({
inputTokens: usage.promptTokenCount,
outputTokens,
nonCachedInputTokens: ProviderShared.subtractTokens(
usage.promptTokenCount,
usage.cachedContentTokenCount,
),
cacheReadInputTokens: usage.cachedContentTokenCount,
reasoningTokens: usage.thoughtsTokenCount,
totalTokens: ProviderShared.totalTokens(usage.promptTokenCount, outputTokens, usage.totalTokenCount),
providerMetadata: { google: usage },
}),
providerMetadata: {
google: {
modelVersion: decoded.modelVersion,
responseId: decoded.responseId,
promptFeedback: decoded.promptFeedback,
candidates: candidateMetadata,
},
},
})
}),
}
return ImageModel.make<GoogleImageOptions>({ id: input.id, provider: "google", route, http: input.http })
}
const googleImagePart = (image: ImageInput): Effect.Effect<Record<string, unknown>, LLMError> => {
if (image.type === "bytes")
return Effect.succeed({ inlineData: { mimeType: image.mediaType, data: Encoding.encodeBase64(image.data) } })
if (image.type === "file-uri") return Effect.succeed({ fileData: { mimeType: image.mediaType, fileUri: image.uri } })
if (image.type === "url")
return ImageInputs.decodeDataUrl(image.url, ADAPTER).pipe(
Effect.flatMap((decoded) => {
if (decoded === undefined)
return Effect.fail(
ImageInputs.invalid(
ADAPTER,
"Google generateContent does not fetch public image URLs; use bytes, a data URL, or a Gemini file URI",
),
)
return Effect.succeed({
inlineData: { mimeType: decoded.mediaType, data: Encoding.encodeBase64(decoded.data) },
})
}),
)
return Effect.fail(
ImageInputs.invalid(ADAPTER, "Google generateContent requires Gemini file URIs rather than provider file IDs"),
)
}
export const GoogleImages = {
model,
} as const
-270
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@@ -1,270 +0,0 @@
import { Effect, Encoding, Schema } from "effect"
import { Headers, HttpClientRequest, HttpClientResponse } from "effect/unstable/http"
import {
ImageModel,
GeneratedImage,
ImageResponse,
type ImageInput,
type ImageRequestFor,
type ImageRoute,
} from "../image"
import { Auth, type Definition as AuthDefinition } from "../route/auth"
import {
InvalidProviderOutputReason,
LLMError,
Usage,
mergeHttpOptions,
mergeJsonRecords,
type HttpOptions,
} from "../schema"
import { ProviderShared } from "./shared"
import { ImageInputs } from "./utils/image-input"
import { OpenAIImage } from "./utils/openai-image"
const ADAPTER = "openai-images"
export const DEFAULT_BASE_URL = "https://api.openai.com/v1"
export const PATH = "/images/generations"
export const EDIT_PATH = "/images/edits"
export type OpenAIImageString<Known extends string> = Known | (string & {})
export type OpenAIImageOptions = {
readonly mask?: ImageInput
readonly n?: number
readonly size?: OpenAIImageString<
"auto" | "256x256" | "512x512" | "1024x1024" | "1536x1024" | "1024x1536" | "1792x1024" | "1024x1792"
>
readonly quality?: OpenAIImageString<"auto" | "low" | "medium" | "high" | "standard" | "hd">
readonly background?: OpenAIImageString<"auto" | "opaque" | "transparent">
readonly moderation?: OpenAIImageString<"auto" | "low">
readonly outputFormat?: OpenAIImageString<"png" | "jpeg" | "webp">
readonly outputCompression?: number
} & Record<string, unknown>
export type OpenAIImageBody = Record<string, unknown> & {
readonly model: string
readonly prompt: string
}
const OpenAIImageResponse = Schema.Struct({
data: Schema.Array(
Schema.Struct({
b64_json: Schema.optional(Schema.String),
url: Schema.optional(Schema.String),
revised_prompt: Schema.optional(Schema.String),
}),
),
output_format: Schema.optional(Schema.String),
usage: Schema.optional(
Schema.Struct({
input_tokens: Schema.optional(Schema.Number),
output_tokens: Schema.optional(Schema.Number),
total_tokens: Schema.optional(Schema.Number),
input_tokens_details: Schema.optional(Schema.Record(Schema.String, Schema.Unknown)),
output_tokens_details: Schema.optional(Schema.Record(Schema.String, Schema.Unknown)),
}),
),
})
export interface ModelInput {
readonly id: string
readonly auth: AuthDefinition
readonly baseURL?: string
readonly headers?: Record<string, string>
readonly http?: HttpOptions
}
const nativeOptions = (options: OpenAIImageOptions | undefined) => {
if (!options) return undefined
const { mask: _, outputFormat, outputCompression, ...native } = options
return {
output_format: outputFormat,
output_compression: outputCompression,
...native,
}
}
const invalidOutput = (message: string) =>
new LLMError({
module: ADAPTER,
method: "generate",
reason: new InvalidProviderOutputReason({ message, route: ADAPTER }),
})
const applyQuery = (url: string, query: Record<string, string> | undefined) => {
if (!query) return url
const next = new URL(url)
Object.entries(query).forEach(([key, value]) => next.searchParams.set(key, value))
return next.toString()
}
export const model = (input: ModelInput) => {
const route: ImageRoute<OpenAIImageOptions> = {
id: ADAPTER,
generate: Effect.fn("OpenAIImages.generate")(function* (request: ImageRequestFor<OpenAIImageOptions>, execute) {
const mask = request.options?.mask
if (mask !== undefined && (request.images?.length ?? 0) === 0)
return yield* ImageInputs.invalid(ADAPTER, "An OpenAI image mask requires at least one input image")
const http = mergeHttpOptions(request.model.http, request.http)
const sourceImages = request.images ?? []
const multipartImages = yield* Effect.forEach(sourceImages, (image) => {
if (image.type === "bytes") return Effect.succeed({ data: image.data, mediaType: image.mediaType })
if (image.type === "url") return ImageInputs.decodeDataUrl(image.url, ADAPTER)
return Effect.succeed(undefined)
})
const multipartMask =
mask === undefined
? undefined
: mask.type === "bytes"
? { data: mask.data, mediaType: mask.mediaType }
: mask.type === "url"
? yield* ImageInputs.decodeDataUrl(mask.url, ADAPTER)
: undefined
const useMultipart =
sourceImages.length > 0 &&
multipartImages.every((image) => image !== undefined) &&
(mask === undefined || multipartMask !== undefined)
const path = sourceImages.length === 0 ? PATH : EDIT_PATH
const url = applyQuery(`${(input.baseURL ?? DEFAULT_BASE_URL).replace(/\/$/, "")}${path}`, http?.query)
if (useMultipart) {
const form = new FormData()
form.append("model", request.model.id)
form.append("prompt", request.prompt)
Object.entries(mergeJsonRecords(nativeOptions(request.options), http?.body) ?? {}).forEach(([key, value]) => {
if (["model", "prompt", "image", "image[]", "images", "mask"].includes(key)) return
form.append(key, typeof value === "string" ? value : ProviderShared.encodeJson(value))
})
multipartImages.forEach((image, index) => {
if (image === undefined) return
form.append("image[]", imageBlob(image.data, image.mediaType), `image-${index}`)
})
if (multipartMask !== undefined)
form.append("mask", imageBlob(multipartMask.data, multipartMask.mediaType), "mask")
const headers = yield* Auth.toEffect(input.auth)({
request,
method: "POST",
url,
body: "[multipart/form-data]",
headers: Headers.remove(Headers.fromInput({ ...input.headers, ...http?.headers }), "content-type"),
})
const response = yield* execute(
HttpClientRequest.post(url).pipe(HttpClientRequest.setHeaders(headers), HttpClientRequest.bodyFormData(form)),
)
return yield* parseResponse(response, request.options, http?.body)
}
const references = sourceImages.map((image) => {
if (image.type === "bytes") return { image_url: ImageInputs.dataUrl(image) }
if (image.type === "url") return { image_url: image.url }
if (image.type === "file-id") return { file_id: image.id }
return undefined
})
if (references.some((image) => image === undefined))
return yield* ImageInputs.invalid(ADAPTER, "OpenAI Images accepts image URLs, data URLs, bytes, and file IDs")
const maskReference =
mask === undefined
? undefined
: mask.type === "bytes"
? { image_url: ImageInputs.dataUrl(mask) }
: mask.type === "url"
? { image_url: mask.url }
: mask.type === "file-id"
? { file_id: mask.id }
: undefined
if (mask !== undefined && maskReference === undefined)
return yield* ImageInputs.invalid(ADAPTER, "OpenAI Images accepts masks as URLs, data URLs, bytes, or file IDs")
const requestBody = mergeJsonRecords(
{
model: request.model.id,
prompt: request.prompt,
images: references.length === 0 ? undefined : references,
mask: maskReference,
},
nativeOptions(request.options),
http?.body,
) as OpenAIImageBody
const text = ProviderShared.encodeJson(requestBody)
const headers = yield* Auth.toEffect(input.auth)({
request,
method: "POST",
url,
body: text,
headers: Headers.fromInput({ ...input.headers, ...http?.headers }),
})
const response = yield* execute(
HttpClientRequest.post(url).pipe(
HttpClientRequest.setHeaders(headers),
HttpClientRequest.bodyText(text, "application/json"),
),
)
return yield* parseResponse(response, request.options, http?.body)
}),
}
return ImageModel.make<OpenAIImageOptions>({ id: input.id, provider: "openai", route, http: input.http })
}
const parseResponse = Effect.fn("OpenAIImages.parseResponse")(function* (
response: HttpClientResponse.HttpClientResponse,
options: OpenAIImageOptions | undefined,
overlay: Record<string, unknown> | undefined,
) {
const payload = yield* response.json.pipe(
Effect.mapError(() => invalidOutput("Failed to read the OpenAI Images response")),
)
const decoded = yield* Schema.decodeUnknownEffect(OpenAIImageResponse)(payload).pipe(
Effect.mapError(() => invalidOutput("OpenAI Images returned an invalid response")),
)
const requestBody = mergeJsonRecords(nativeOptions(options), overlay)
const format =
decoded.output_format ?? (typeof requestBody?.output_format === "string" ? requestBody.output_format : "png")
const images = yield* Effect.forEach(decoded.data, (item, index) => {
if (item.b64_json)
return Effect.fromResult(Encoding.decodeBase64(item.b64_json)).pipe(
Effect.mapError(() => invalidOutput(`OpenAI Images result ${index} contains invalid base64 data`)),
Effect.map(
(data) =>
new GeneratedImage({
mediaType: `image/${format}`,
data,
providerMetadata:
item.revised_prompt === undefined ? undefined : { openai: { revisedPrompt: item.revised_prompt } },
}),
),
)
if (item.url)
return Effect.succeed(
new GeneratedImage({
mediaType: `image/${format}`,
data: item.url,
providerMetadata:
item.revised_prompt === undefined ? undefined : { openai: { revisedPrompt: item.revised_prompt } },
}),
)
return Effect.fail(invalidOutput(`OpenAI Images result ${index} has neither image data nor a URL`))
})
if (images.length === 0) return yield* invalidOutput("OpenAI Images returned no images")
return new ImageResponse({
images,
usage:
decoded.usage === undefined
? undefined
: new Usage({
inputTokens: decoded.usage.input_tokens,
outputTokens: decoded.usage.output_tokens,
totalTokens: decoded.usage.total_tokens,
providerMetadata: { openai: decoded.usage },
}),
providerMetadata: { openai: { outputFormat: format } },
})
})
const imageBlob = (data: Uint8Array, mediaType: string) => {
const buffer = new ArrayBuffer(data.byteLength)
new Uint8Array(buffer).set(data)
return new Blob([buffer], { type: mediaType })
}
export const OpenAIImages = {
model,
} as const
@@ -1,34 +0,0 @@
import { Effect, Encoding } from "effect"
import type { ImageInput } from "../../image"
import { InvalidRequestReason, LLMError } from "../../schema"
const invalid = (module: string, message: string) =>
new LLMError({
module,
method: "generate",
reason: new InvalidRequestReason({ message }),
})
export const dataUrl = (input: Extract<ImageInput, { readonly type: "bytes" }>) =>
`data:${input.mediaType};base64,${Encoding.encodeBase64(input.data)}`
export const decodeDataUrl = (
url: string,
module: string,
): Effect.Effect<{ readonly mediaType: string; readonly data: Uint8Array } | undefined, LLMError> => {
if (!url.startsWith("data:")) return Effect.succeed(undefined)
const match = /^data:([^;,]+);base64,(.*)$/s.exec(url)
if (!match) return Effect.fail(invalid(module, "Image data URLs must contain a MIME type and base64 data"))
return Effect.fromResult(Encoding.decodeBase64(match[2])).pipe(
Effect.mapError(() => invalid(module, "Image data URL contains invalid base64 data")),
Effect.map((data) => ({ mediaType: match[1], data })),
)
}
export const invalidImageInput = invalid
export const ImageInputs = {
dataUrl,
decodeDataUrl,
invalid: invalidImageInput,
} as const
@@ -1,20 +0,0 @@
import { Schema } from "effect"
const dimensions = (value: string) => {
const match = /^(\d+)x(\d+)$/.exec(value)
if (!match) return undefined
return { width: Number(match[1]), height: Number(match[2]) }
}
export const Size = Schema.String.check(
Schema.makeFilter((value) => {
if (value === "auto") return undefined
const parsed = dimensions(value)
if (!parsed) return "image size must be `auto` or `{width}x{height}`"
return parsed.width > 0 && parsed.height > 0 ? undefined : "image dimensions must be positive integers"
}),
)
export const OpenAIImage = {
Size,
} as const
-202
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@@ -1,202 +0,0 @@
import { Effect, Encoding, Schema } from "effect"
import { Headers, HttpClientRequest } from "effect/unstable/http"
import { GeneratedImage, ImageModel, ImageResponse, type ImageRequestFor, type ImageRoute } from "../image"
import { Auth, type Definition as AuthDefinition } from "../route/auth"
import {
InvalidProviderOutputReason,
LLMError,
Usage,
mergeHttpOptions,
mergeJsonRecords,
type HttpOptions,
} from "../schema"
import { ProviderShared, optionalNull } from "./shared"
import { ImageInputs } from "./utils/image-input"
const ADAPTER = "xai-images"
export const DEFAULT_BASE_URL = "https://api.x.ai/v1"
export const PATH = "/images/generations"
export const EDIT_PATH = "/images/edits"
export type XAIImageString<Known extends string> = Known | (string & {})
export type XAIImageOptions = {
readonly n?: number
readonly aspectRatio?: XAIImageString<
| "1:1"
| "3:4"
| "4:3"
| "9:16"
| "16:9"
| "2:3"
| "3:2"
| "9:19.5"
| "19.5:9"
| "9:20"
| "20:9"
| "1:2"
| "2:1"
| "auto"
>
readonly aspect_ratio?: XAIImageString<
| "1:1"
| "3:4"
| "4:3"
| "9:16"
| "16:9"
| "2:3"
| "3:2"
| "9:19.5"
| "19.5:9"
| "9:20"
| "20:9"
| "1:2"
| "2:1"
| "auto"
>
readonly resolution?: XAIImageString<"1k" | "2k">
readonly responseFormat?: XAIImageString<"url" | "b64_json">
readonly response_format?: XAIImageString<"url" | "b64_json">
} & Record<string, unknown>
type XAIImageBody = Record<string, unknown> & {
readonly model: string
readonly prompt: string
}
const XAIImageResponse = Schema.Struct({
data: Schema.Array(
Schema.Struct({
b64_json: optionalNull(Schema.String),
url: optionalNull(Schema.String),
revised_prompt: optionalNull(Schema.String),
mime_type: optionalNull(Schema.String),
}),
),
usage: Schema.optional(Schema.Unknown),
})
export interface ModelInput {
readonly id: string
readonly auth: AuthDefinition
readonly baseURL?: string
readonly headers?: Record<string, string>
readonly http?: HttpOptions
}
const nativeOptions = (options: XAIImageOptions | undefined) => {
if (!options) return undefined
const { aspectRatio, responseFormat, ...native } = options
return {
aspect_ratio: aspectRatio,
response_format: responseFormat,
...native,
}
}
const invalidOutput = (message: string) =>
new LLMError({
module: ADAPTER,
method: "generate",
reason: new InvalidProviderOutputReason({ message, route: ADAPTER }),
})
const applyQuery = (url: string, query: Record<string, string> | undefined) => {
if (!query) return url
const next = new URL(url)
Object.entries(query).forEach(([key, value]) => next.searchParams.set(key, value))
return next.toString()
}
export const model = (input: ModelInput) => {
const route: ImageRoute<XAIImageOptions> = {
id: ADAPTER,
generate: Effect.fn("XAIImages.generate")(function* (request: ImageRequestFor<XAIImageOptions>, execute) {
const http = mergeHttpOptions(request.model.http, request.http)
const imageReferences = (request.images ?? []).map((image) => {
if (image.type === "bytes") return { url: ImageInputs.dataUrl(image), type: "image_url" as const }
if (image.type === "url") return { url: image.url, type: "image_url" as const }
if (image.type === "file-id") return { file_id: image.id }
return undefined
})
if (imageReferences.some((image) => image === undefined))
return yield* ImageInputs.invalid(ADAPTER, "xAI Images accepts image URLs, data URLs, bytes, and file IDs")
const requestBody = mergeJsonRecords(
{
model: request.model.id,
prompt: request.prompt,
image: imageReferences.length === 1 ? imageReferences[0] : undefined,
images: imageReferences.length > 1 ? imageReferences : undefined,
},
nativeOptions(request.options),
http?.body,
) as XAIImageBody
const text = ProviderShared.encodeJson(requestBody)
const url = applyQuery(
`${(input.baseURL ?? DEFAULT_BASE_URL).replace(/\/$/, "")}${imageReferences.length === 0 ? PATH : EDIT_PATH}`,
http?.query,
)
const headers = yield* Auth.toEffect(input.auth)({
request,
method: "POST",
url,
body: text,
headers: Headers.fromInput({ ...input.headers, ...http?.headers }),
})
const response = yield* execute(
HttpClientRequest.post(url).pipe(
HttpClientRequest.setHeaders(headers),
HttpClientRequest.bodyText(text, "application/json"),
),
)
const payload = yield* response.json.pipe(
Effect.mapError(() => invalidOutput("Failed to read the xAI Images response")),
)
const decoded = yield* Schema.decodeUnknownEffect(XAIImageResponse)(payload).pipe(
Effect.mapError(() => invalidOutput("xAI Images returned an invalid response")),
)
const images = yield* Effect.forEach(decoded.data, (item, index) => {
const mediaType = item.mime_type ?? "application/octet-stream"
if (item.b64_json)
return Effect.fromResult(Encoding.decodeBase64(item.b64_json)).pipe(
Effect.mapError(() => invalidOutput(`xAI Images result ${index} contains invalid base64 data`)),
Effect.map(
(data) =>
new GeneratedImage({
mediaType,
data,
providerMetadata:
item.revised_prompt === undefined || item.revised_prompt === null
? undefined
: { xai: { revisedPrompt: item.revised_prompt } },
}),
),
)
if (item.url)
return Effect.succeed(
new GeneratedImage({
mediaType,
data: item.url,
providerMetadata:
item.revised_prompt === undefined || item.revised_prompt === null
? undefined
: { xai: { revisedPrompt: item.revised_prompt } },
}),
)
return Effect.fail(invalidOutput(`xAI Images result ${index} has neither image data nor a URL`))
})
if (images.length === 0) return yield* invalidOutput("xAI Images returned no images")
const usage = ProviderShared.isRecord(decoded.usage) ? decoded.usage : undefined
return new ImageResponse({
images,
usage: usage === undefined ? undefined : new Usage({ providerMetadata: { xai: usage } }),
providerMetadata: usage === undefined ? undefined : { xai: { usage } },
})
}),
}
return ImageModel.make<XAIImageOptions>({ id: input.id, provider: "xai", route, http: input.http })
}
export const XAIImages = {
model,
} as const
-132
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@@ -1,132 +0,0 @@
import { Effect, Schema } from "effect"
import { Headers, HttpClientRequest } from "effect/unstable/http"
import { GeneratedImage, ImageModel, ImageResponse, type ImageRequestFor, type ImageRoute } from "../image"
import { Auth, type Definition as AuthDefinition } from "../route/auth"
import { InvalidProviderOutputReason, LLMError, mergeHttpOptions, mergeJsonRecords, type HttpOptions } from "../schema"
import { ProviderShared } from "./shared"
import { ImageInputs } from "./utils/image-input"
const ADAPTER = "zai-images"
export const DEFAULT_BASE_URL = "https://api.z.ai/api/paas/v4"
export const PATH = "/images/generations"
export type ZAIImageString<Known extends string> = Known | (string & {})
export type ZAIImageOptions = {
readonly size?: ZAIImageString<
"1024x1024" | "768x1344" | "864x1152" | "1344x768" | "1152x864" | "1440x720" | "720x1440"
>
readonly quality?: ZAIImageString<"hd" | "standard">
readonly userID?: string
} & Record<string, unknown>
type ZAIImageBody = Record<string, unknown> & {
readonly model: string
readonly prompt: string
}
const ZAIImageResponse = Schema.Struct({
created: Schema.optional(Schema.Int),
id: Schema.optional(Schema.String),
request_id: Schema.optional(Schema.String),
data: Schema.Array(Schema.Struct({ url: Schema.String })),
content_filter: Schema.optional(
Schema.Array(
Schema.Struct({
role: Schema.optional(Schema.String),
level: Schema.optional(Schema.Number),
}),
),
),
})
export interface ModelInput {
readonly id: string
readonly auth: AuthDefinition
readonly baseURL?: string
readonly headers?: Record<string, string>
readonly http?: HttpOptions
}
const nativeOptions = (options: ZAIImageOptions | undefined) => {
if (!options) return undefined
const { userID, ...native } = options
return {
user_id: userID,
...native,
}
}
const invalidOutput = (message: string) =>
new LLMError({
module: ADAPTER,
method: "generate",
reason: new InvalidProviderOutputReason({ message, route: ADAPTER }),
})
const applyQuery = (url: string, query: Record<string, string> | undefined) => {
if (!query) return url
const next = new URL(url)
Object.entries(query).forEach(([key, value]) => next.searchParams.set(key, value))
return next.toString()
}
export const model = (input: ModelInput) => {
const route: ImageRoute<ZAIImageOptions> = {
id: ADAPTER,
generate: Effect.fn("ZAIImages.generate")(function* (request: ImageRequestFor<ZAIImageOptions>, execute) {
if ((request.images?.length ?? 0) > 0)
return yield* ImageInputs.invalid(ADAPTER, "Z.ai hosted image generation does not support image inputs")
const http = mergeHttpOptions(request.model.http, request.http)
const requestBody = mergeJsonRecords(
{ model: request.model.id, prompt: request.prompt },
nativeOptions(request.options),
http?.body,
) as ZAIImageBody
const text = ProviderShared.encodeJson(requestBody)
const url = applyQuery(`${(input.baseURL ?? DEFAULT_BASE_URL).replace(/\/$/, "")}${PATH}`, http?.query)
const headers = yield* Auth.toEffect(input.auth)({
request,
method: "POST",
url,
body: text,
headers: Headers.fromInput({ ...input.headers, ...http?.headers }),
})
const response = yield* execute(
HttpClientRequest.post(url).pipe(
HttpClientRequest.setHeaders(headers),
HttpClientRequest.bodyText(text, "application/json"),
),
)
const payload = yield* response.json.pipe(
Effect.mapError(() => invalidOutput("Failed to read the Z.ai Images response")),
)
const decoded = yield* Schema.decodeUnknownEffect(ZAIImageResponse)(payload).pipe(
Effect.mapError(() => invalidOutput("Z.ai Images returned an invalid response")),
)
if (decoded.data.length === 0) return yield* invalidOutput("Z.ai Images returned no images")
return new ImageResponse({
images: decoded.data.map(
(item) =>
new GeneratedImage({
mediaType: "application/octet-stream",
data: item.url,
}),
),
providerMetadata: {
zai: {
created: decoded.created,
id: decoded.id,
requestID: decoded.request_id,
contentFilter: decoded.content_filter,
},
},
})
}),
}
return ImageModel.make<ZAIImageOptions>({ id: input.id, provider: "zai", route, http: input.http })
}
export const ZAIImages = {
model,
} as const
-1
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@@ -1 +0,0 @@
export * from "./providers/index"
@@ -1,67 +0,0 @@
import type { ProviderPackage } from "../provider-package"
import { AnthropicMessages } from "../protocols/anthropic-messages"
import { Auth } from "../route/auth"
import type { ProviderAuthOption } from "../route/auth-options"
import type { RouteDefaultsInput } from "../route/client"
import { ProviderID, type ModelID } from "../schema"
export const id = ProviderID.make("anthropic-compatible")
export type Config = RouteDefaultsInput &
ProviderAuthOption<"optional"> & {
readonly provider?: string
readonly baseURL: string
}
export type Settings = ProviderPackage.Settings &
(
| { readonly apiKey?: string; readonly authToken?: never }
| { readonly apiKey?: never; readonly authToken?: string }
) & {
readonly baseURL: string
readonly provider?: string
}
export const routes = [AnthropicMessages.route]
const auth = (input: ProviderAuthOption<"optional">) => {
if ("auth" in input && input.auth) return input.auth
return Auth.optional("apiKey" in input ? input.apiKey : undefined, "apiKey").pipe(Auth.header("x-api-key"))
}
export const configure = (input: Config) => {
if (!input.baseURL) throw new Error("Anthropic-compatible providers require a baseURL")
const provider = input.provider ?? "anthropic-compatible"
const { provider: _, baseURL, apiKey: _apiKey, auth: _auth, ...rest } = input
const route = AnthropicMessages.route.with({
...rest,
provider,
endpoint: { baseURL },
auth: auth(input),
})
return {
id: ProviderID.make(provider),
model: (modelID: string | ModelID) => route.model({ id: modelID }),
configure,
}
}
export const provider = {
id,
configure,
}
export const model: ProviderPackage.Definition<Settings>["model"] = (modelID, settings) => {
if (settings.apiKey !== undefined && settings.authToken !== undefined)
throw new Error("Anthropic-compatible apiKey cannot be combined with authToken")
return configure({
...(settings.authToken === undefined ? { apiKey: settings.apiKey } : { auth: Auth.bearer(settings.authToken) }),
baseURL: settings.baseURL,
headers: settings.headers === undefined ? undefined : { ...settings.headers },
http: settings.body === undefined ? undefined : { body: { ...settings.body } },
limits: settings.limits,
provider: settings.provider,
}).model(modelID)
}
export * as AnthropicCompatible from "./anthropic-compatible"
@@ -1,81 +0,0 @@
import type { ProviderPackage } from "../provider-package"
import { OpenAICompatibleChat } from "../protocols/openai-compatible-chat"
import type { RouteDefaultsInput } from "../route/client"
import { ProviderID, type ModelID, type ProviderOptions } from "../schema"
import { GoogleVertexShared } from "./google-vertex-shared"
export const id = ProviderID.make("google-vertex")
export type Config = RouteDefaultsInput &
GoogleVertexShared.OAuthOptions & {
readonly baseURL?: string
readonly location?: string
readonly project?: string
}
export interface Settings extends ProviderPackage.Settings {
readonly accessToken?: string
readonly apiKey?: never
readonly baseURL?: string
readonly location?: string
readonly project?: string
readonly providerOptions?: ProviderOptions
}
const route = OpenAICompatibleChat.route.with({
id: "google-vertex-chat",
provider: id,
})
export const routes = [route]
const configuredRoute = (input: Config) => {
if ("apiKey" in input && input.apiKey !== undefined) throw new Error("Google Vertex Chat does not support API keys")
const {
accessToken: _accessToken,
auth: _auth,
baseURL,
location: inputLocation,
project: inputProject,
...rest
} = input
const location = GoogleVertexShared.location(inputLocation, "global")
const project = GoogleVertexShared.project(inputProject)
return route.with({
...rest,
endpoint: {
baseURL:
baseURL ??
`https://aiplatform.googleapis.com/v1/projects/${GoogleVertexShared.requireProject(project)}/locations/${location}/endpoints/openapi`,
},
auth: GoogleVertexShared.oauth(input, project),
})
}
export const configure = (input: Config = {}) => {
const route = configuredRoute(input)
return {
id,
model: (modelID: string | ModelID) => route.model({ id: modelID }),
configure,
}
}
export const provider = {
id,
configure,
}
export const model: ProviderPackage.Definition<Settings>["model"] = (modelID, settings) => {
if (settings.apiKey !== undefined) throw new Error("Google Vertex Chat does not support API keys")
return configure({
accessToken: settings.accessToken,
baseURL: settings.baseURL,
headers: settings.headers === undefined ? undefined : { ...settings.headers },
http: settings.body === undefined ? undefined : { body: { ...settings.body } },
limits: settings.limits,
location: settings.location,
project: settings.project,
providerOptions: settings.providerOptions,
}).model(modelID)
}
@@ -1,111 +0,0 @@
import { Effect, Schema, Struct } from "effect"
import type { ProviderPackage } from "../provider-package"
import { AnthropicMessages } from "../protocols/anthropic-messages"
import { Auth } from "../route/auth"
import { Route, type RouteDefaultsInput } from "../route/client"
import { Endpoint } from "../route/endpoint"
import { Framing } from "../route/framing"
import { Protocol } from "../route/protocol"
import { ProviderID, type ModelID, type ProviderOptions } from "../schema"
import { GoogleVertexShared } from "./google-vertex-shared"
const VERSION = "vertex-2023-10-16" as const
// models.dev uses this provider id even though the API contract is Anthropic Messages.
export const id = ProviderID.make("google-vertex-anthropic")
export type Config = RouteDefaultsInput &
GoogleVertexShared.OAuthOptions & {
readonly baseURL?: string
readonly location?: string
readonly project?: string
}
export interface Settings extends ProviderPackage.Settings {
readonly accessToken?: string
readonly apiKey?: never
readonly baseURL?: string
readonly location?: string
readonly project?: string
readonly providerOptions?: ProviderOptions
}
const route = Route.make({
id: "google-vertex-messages",
provider: id,
providerMetadataKey: "anthropic",
protocol: Protocol.make({
id: AnthropicMessages.protocol.id,
body: {
schema: Schema.Struct({
...Struct.omit(AnthropicMessages.AnthropicMessagesBody.fields, ["model"]),
anthropic_version: Schema.Literal(VERSION),
}),
from: (request) =>
AnthropicMessages.protocol.body.from(request).pipe(
Effect.map((body) => ({
...Struct.omit(body, ["model"]),
anthropic_version: VERSION,
})),
),
},
stream: AnthropicMessages.protocol.stream,
}),
endpoint: Endpoint.path(({ request }) => `/${request.model.id}:streamRawPredict`),
auth: Auth.none,
framing: Framing.sse,
})
export const routes = [route]
const configuredRoute = (input: Config) => {
if ("apiKey" in input && input.apiKey !== undefined)
throw new Error("Google Vertex Messages does not support API keys")
const {
accessToken: _accessToken,
auth: _auth,
baseURL,
location: inputLocation,
project: inputProject,
...rest
} = input
const location = GoogleVertexShared.location(inputLocation, "global")
const project = GoogleVertexShared.project(inputProject)
return route.with({
...rest,
endpoint: {
baseURL:
baseURL ??
`https://${GoogleVertexShared.host(location)}/v1/projects/${GoogleVertexShared.requireProject(project)}/locations/${location}/publishers/anthropic/models`,
},
auth: GoogleVertexShared.oauth(input, project),
})
}
export const configure = (input: Config = {}) => {
const route = configuredRoute(input)
return {
id,
model: (modelID: string | ModelID) => route.model({ id: modelID }),
configure,
}
}
export const provider = {
id,
configure,
}
export const model: ProviderPackage.Definition<Settings>["model"] = (modelID, settings) => {
if (settings.apiKey !== undefined) throw new Error("Google Vertex Messages does not support API keys")
return configure({
accessToken: settings.accessToken,
baseURL: settings.baseURL,
headers: settings.headers === undefined ? undefined : { ...settings.headers },
http: settings.body === undefined ? undefined : { body: { ...settings.body } },
limits: settings.limits,
location: settings.location,
project: settings.project,
providerOptions: settings.providerOptions,
}).model(modelID)
}
@@ -1,82 +0,0 @@
import type { ProviderPackage } from "../provider-package"
import { OpenAICompatibleResponses } from "../protocols/openai-compatible-responses"
import type { RouteDefaultsInput } from "../route/client"
import { ProviderID, type ModelID, type ProviderOptions } from "../schema"
import { GoogleVertexShared } from "./google-vertex-shared"
export const id = ProviderID.make("google-vertex")
export type Config = RouteDefaultsInput &
GoogleVertexShared.OAuthOptions & {
readonly baseURL?: string
readonly location?: string
readonly project?: string
}
export interface Settings extends ProviderPackage.Settings {
readonly accessToken?: string
readonly apiKey?: never
readonly baseURL?: string
readonly location?: string
readonly project?: string
readonly providerOptions?: ProviderOptions
}
const route = OpenAICompatibleResponses.route.with({
id: "google-vertex-responses",
provider: id,
})
export const routes = [route]
const configuredRoute = (input: Config) => {
if ("apiKey" in input && input.apiKey !== undefined)
throw new Error("Google Vertex Responses does not support API keys")
const {
accessToken: _accessToken,
auth: _auth,
baseURL,
location: inputLocation,
project: inputProject,
...rest
} = input
const location = GoogleVertexShared.location(inputLocation, "global")
const project = GoogleVertexShared.project(inputProject)
return route.with({
...rest,
endpoint: {
baseURL:
baseURL ??
`https://aiplatform.googleapis.com/v1/projects/${GoogleVertexShared.requireProject(project)}/locations/${location}/endpoints/openapi`,
},
auth: GoogleVertexShared.oauth(input, project),
})
}
export const configure = (input: Config = {}) => {
const route = configuredRoute(input)
return {
id,
model: (modelID: string | ModelID) => route.model({ id: modelID }),
configure,
}
}
export const provider = {
id,
configure,
}
export const model: ProviderPackage.Definition<Settings>["model"] = (modelID, settings) => {
if (settings.apiKey !== undefined) throw new Error("Google Vertex Responses does not support API keys")
return configure({
accessToken: settings.accessToken,
baseURL: settings.baseURL,
headers: settings.headers === undefined ? undefined : { ...settings.headers },
http: settings.body === undefined ? undefined : { body: { ...settings.body } },
limits: settings.limits,
location: settings.location,
project: settings.project,
providerOptions: settings.providerOptions,
}).model(modelID)
}
@@ -1,77 +0,0 @@
import type { AnyAuthClient } from "google-auth-library"
import { Effect, Redacted } from "effect"
import { Auth, MissingCredentialError } from "../route/auth"
const SCOPE = "https://www.googleapis.com/auth/cloud-platform"
export type OAuthOptions =
| { readonly accessToken?: string; readonly auth?: never }
| { readonly accessToken?: never; readonly auth?: Auth.Definition }
export type ApiKeyOptions =
| (OAuthOptions & { readonly apiKey?: never })
| { readonly accessToken?: never; readonly apiKey?: string; readonly auth?: never }
export const project = (value?: string) =>
value ??
process.env.GOOGLE_VERTEX_PROJECT ??
process.env.GOOGLE_CLOUD_PROJECT ??
process.env.GCP_PROJECT ??
process.env.GCLOUD_PROJECT
export const location = (value: string | undefined, fallback: string) =>
value ??
process.env.GOOGLE_VERTEX_LOCATION ??
process.env.GOOGLE_CLOUD_LOCATION ??
process.env.VERTEX_LOCATION ??
fallback
export const host = (location: string) => {
if (location === "global") return "aiplatform.googleapis.com"
// Jurisdictional multi-regions use Regional Endpoint Platform domains.
if (location === "eu" || location === "us") return `aiplatform.${location}.rep.googleapis.com`
return `${location}-aiplatform.googleapis.com`
}
export const requireProject = (value: string | undefined) => {
if (value) return value
throw new Error("Google Vertex requires a project when baseURL is not configured")
}
export const apiKey = (input: ApiKeyOptions) => {
if (input.apiKey !== undefined && (input.accessToken !== undefined || input.auth !== undefined))
throw new Error("Google Vertex apiKey cannot be combined with accessToken or auth")
if (input.accessToken !== undefined || input.auth !== undefined) return undefined
return input.apiKey ?? process.env.GOOGLE_VERTEX_API_KEY
}
const adc = (project?: string) => {
let client: Promise<AnyAuthClient> | undefined
const loadClient = () => {
if (client) return client
client = import("google-auth-library").then(({ GoogleAuth }) =>
new GoogleAuth({ projectId: project, scopes: [SCOPE] }).getClient(),
)
return client
}
return Auth.effect(
Effect.tryPromise({
try: async () => {
const token = await (await loadClient()).getAccessToken()
if (!token.token) throw new Error("Google ADC returned an empty access token")
return Redacted.make(token.token)
},
catch: () => new MissingCredentialError("Google Application Default Credentials"),
}),
).bearer()
}
export const oauth = (input: OAuthOptions, project?: string) => {
if (input.accessToken !== undefined && input.auth !== undefined)
throw new Error("Google Vertex accessToken cannot be combined with auth")
if (input.auth) return input.auth
if (input.accessToken !== undefined) return Auth.bearer(input.accessToken)
return adc(project)
}
export * as GoogleVertexShared from "./google-vertex-shared"
@@ -1,98 +0,0 @@
import type { ProviderPackage } from "../provider-package"
import { Gemini } from "../protocols/gemini"
import { Auth } from "../route/auth"
import { Route, type RouteDefaultsInput } from "../route/client"
import { Endpoint } from "../route/endpoint"
import { Framing } from "../route/framing"
import { ProviderID, type ModelID, type ProviderOptions } from "../schema"
import { GoogleVertexShared } from "./google-vertex-shared"
export const id = ProviderID.make("google-vertex")
export type Config = RouteDefaultsInput &
GoogleVertexShared.ApiKeyOptions & {
readonly baseURL?: string
readonly location?: string
readonly project?: string
}
export type Settings = ProviderPackage.Settings &
(
| { readonly accessToken?: string; readonly apiKey?: never }
| { readonly accessToken?: never; readonly apiKey?: string }
) & {
readonly baseURL?: string
readonly location?: string
readonly project?: string
readonly providerOptions?: ProviderOptions
}
const route = Route.make({
id: "google-vertex-gemini",
provider: id,
providerMetadataKey: "google",
protocol: Gemini.protocol,
endpoint: Endpoint.path(({ request }) => {
const model = String(request.model.id)
return `/${model.startsWith("endpoints/") ? model : `models/${model}`}:streamGenerateContent?alt=sse`
}),
auth: Auth.none,
framing: Framing.sse,
})
export const routes = [route]
const configuredRoute = (input: Config, modelID: string | ModelID) => {
const {
accessToken: _accessToken,
apiKey: _apiKey,
auth: _auth,
baseURL,
location: inputLocation,
project: inputProject,
...rest
} = input
const apiKey = GoogleVertexShared.apiKey(input)
const endpointModel = String(modelID).startsWith("endpoints/")
if (apiKey !== undefined && endpointModel)
throw new Error("Google Vertex tuned models do not support Express Mode API keys")
const location = GoogleVertexShared.location(inputLocation, "us-central1")
const project = GoogleVertexShared.project(inputProject)
const endpoint =
baseURL ??
(apiKey
? "https://aiplatform.googleapis.com/v1/publishers/google"
: `https://${GoogleVertexShared.host(location)}/v1beta1/projects/${GoogleVertexShared.requireProject(project)}/locations/${location}${endpointModel ? "" : "/publishers/google"}`)
return route.with({
...rest,
endpoint: { baseURL: endpoint },
auth: apiKey === undefined ? GoogleVertexShared.oauth(input, project) : Auth.header("x-goog-api-key", apiKey),
})
}
export const configure = (input: Config = {}) => {
return {
id,
model: (modelID: string | ModelID) => configuredRoute(input, modelID).model({ id: modelID }),
configure,
}
}
export const provider = {
id,
configure,
}
export const model: ProviderPackage.Definition<Settings>["model"] = (modelID, settings) => {
if (settings.apiKey !== undefined && settings.accessToken !== undefined)
throw new Error("Google Vertex apiKey cannot be combined with accessToken or auth")
return configure({
...(settings.apiKey === undefined ? { accessToken: settings.accessToken } : { apiKey: settings.apiKey }),
baseURL: settings.baseURL,
headers: settings.headers === undefined ? undefined : { ...settings.headers },
http: settings.body === undefined ? undefined : { body: { ...settings.body } },
limits: settings.limits,
location: settings.location,
project: settings.project,
providerOptions: settings.providerOptions,
}).model(modelID)
}
@@ -1,2 +0,0 @@
export { model } from "../google-vertex-chat"
export type { Settings } from "../google-vertex-chat"
@@ -1,2 +0,0 @@
export { model } from "../google-vertex"
export type { Settings } from "../google-vertex"
@@ -1,2 +0,0 @@
export { model } from "../google-vertex-messages"
export type { Settings } from "../google-vertex-messages"
@@ -1,2 +0,0 @@
export { model } from "../google-vertex-responses"
export type { Settings } from "../google-vertex-responses"
@@ -1 +0,0 @@
export * from "../openai-compatible-responses"
-35
View File
@@ -1,35 +0,0 @@
import { ZAIImages } from "../protocols/zai-images"
import { AuthOptions, type ProviderAuthOption } from "../route/auth-options"
import { HttpOptions, ProviderID, type ModelID } from "../schema"
export const id = ProviderID.make("zai")
export type Config = ProviderAuthOption<"optional"> & {
readonly baseURL?: string
readonly headers?: Record<string, string>
readonly http?: HttpOptions.Input
}
export type { ZAIImageOptions } from "../protocols/zai-images"
const auth = (options: ProviderAuthOption<"optional">) => AuthOptions.bearer(options, "ZAI_API_KEY")
export const configure = (input: Config = {}) => {
const image = (modelID: string | ModelID) =>
ZAIImages.model({
id: modelID,
auth: auth(input),
baseURL: input.baseURL,
headers: input.headers,
http: input.http === undefined ? undefined : HttpOptions.make(input.http),
})
return {
id,
image,
configure,
}
}
export const provider = configure()
export const image = provider.image
-1
View File
@@ -1 +0,0 @@
export * from "./route/index"
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Before

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@@ -1,40 +0,0 @@
{
"version": 1,
"metadata": {
"provider": "minimax",
"protocol": "anthropic-messages",
"route": "anthropic-messages",
"transport": "http",
"model": "MiniMax-M3",
"tags": [
"prefix:anthropic-compatible-messages",
"provider:minimax",
"protocol:anthropic-messages",
"text",
"golden"
],
"name": "anthropic-compatible-messages/minimax-m3-anthropic-compatible-text",
"recordedAt": "2026-07-18T03:42:22.893Z"
},
"interactions": [
{
"transport": "http",
"request": {
"method": "POST",
"url": "https://api.minimax.io/anthropic/v1/messages",
"headers": {
"anthropic-version": "2023-06-01",
"content-type": "application/json"
},
"body": "{\"model\":\"MiniMax-M3\",\"system\":[{\"type\":\"text\",\"text\":\"You are concise.\"}],\"messages\":[{\"role\":\"user\",\"content\":[{\"type\":\"text\",\"text\":\"Reply exactly with: Hello!\"}]}],\"stream\":true,\"max_tokens\":40,\"temperature\":0}"
},
"response": {
"status": 200,
"headers": {
"content-type": "text/event-stream; charset=utf-8"
},
"body": "event: message_start\ndata: {\"type\":\"message_start\",\"message\":{\"id\":\"1a0b363d0882af316faebcec4d4855a8\",\"type\":\"message\",\"role\":\"assistant\",\"content\":[],\"model\":\"MiniMax-M3\",\"stop_reason\":null,\"stop_sequence\":null,\"usage\":{\"input_tokens\":53,\"output_tokens\":0,\"cache_creation_input_tokens\":0,\"cache_read_input_tokens\":114,\"service_tier\":\"standard\"},\"service_tier\":\"standard\"}}\n\nevent: ping\ndata: {\"type\":\"ping\"}\n\nevent: content_block_start\ndata: {\"type\":\"content_block_start\",\"index\":0,\"content_block\":{\"type\":\"text\",\"text\":\"\"}}\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\"Hello\"}}\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\"!\"}}\n\nevent: content_block_stop\ndata: {\"type\":\"content_block_stop\",\"index\":0}\n\nevent: message_delta\ndata: {\"type\":\"message_delta\",\"delta\":{\"stop_reason\":\"end_turn\"},\"usage\":{\"input_tokens\":53,\"output_tokens\":2,\"cache_read_input_tokens\":114,\"service_tier\":\"standard\"}}\n\nevent: message_stop\ndata: {\"type\":\"message_stop\"}\n\n"
}
}
]
}
@@ -1,41 +0,0 @@
{
"version": 1,
"metadata": {
"provider": "minimax",
"protocol": "anthropic-messages",
"route": "anthropic-messages",
"transport": "http",
"model": "MiniMax-M3",
"tags": [
"prefix:anthropic-compatible-messages",
"provider:minimax",
"protocol:anthropic-messages",
"tool",
"tool-call",
"golden"
],
"name": "anthropic-compatible-messages/minimax-m3-anthropic-compatible-tool-call",
"recordedAt": "2026-07-18T03:42:23.876Z"
},
"interactions": [
{
"transport": "http",
"request": {
"method": "POST",
"url": "https://api.minimax.io/anthropic/v1/messages",
"headers": {
"anthropic-version": "2023-06-01",
"content-type": "application/json"
},
"body": "{\"model\":\"MiniMax-M3\",\"system\":[{\"type\":\"text\",\"text\":\"Call tools exactly as requested.\"}],\"messages\":[{\"role\":\"user\",\"content\":[{\"type\":\"text\",\"text\":\"Call get_weather with city exactly Paris.\"}]}],\"tools\":[{\"name\":\"get_weather\",\"description\":\"Get current weather for a city.\",\"input_schema\":{\"type\":\"object\",\"properties\":{\"city\":{\"type\":\"string\"}},\"required\":[\"city\"],\"additionalProperties\":false}}],\"tool_choice\":{\"type\":\"tool\",\"name\":\"get_weather\"},\"stream\":true,\"max_tokens\":80,\"temperature\":0}"
},
"response": {
"status": 200,
"headers": {
"content-type": "text/event-stream; charset=utf-8"
},
"body": "event: message_start\ndata: {\"type\":\"message_start\",\"message\":{\"id\":\"6731ecc323233459d1792df9a733dd98\",\"type\":\"message\",\"role\":\"assistant\",\"content\":[],\"model\":\"MiniMax-M3\",\"stop_reason\":null,\"stop_sequence\":null,\"usage\":{\"input_tokens\":0,\"output_tokens\":0,\"cache_creation_input_tokens\":0,\"cache_read_input_tokens\":404,\"service_tier\":\"standard\"},\"service_tier\":\"standard\"}}\n\nevent: ping\ndata: {\"type\":\"ping\"}\n\nevent: content_block_start\ndata: {\"type\":\"content_block_start\",\"index\":0,\"content_block\":{\"type\":\"tool_use\",\"id\":\"call_function_vkxtif4epmvm_1\",\"name\":\"get_weather\",\"input\":{}}}\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"input_json_delta\",\"partial_json\":\"{\\\"city\\\": \\\"Paris\"}}\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"input_json_delta\",\"partial_json\":\"\\\"}\"}}\n\nevent: content_block_stop\ndata: {\"type\":\"content_block_stop\",\"index\":0}\n\nevent: message_delta\ndata: {\"type\":\"message_delta\",\"delta\":{\"stop_reason\":\"tool_use\"},\"usage\":{\"input_tokens\":290,\"output_tokens\":27,\"cache_read_input_tokens\":114,\"service_tier\":\"standard\"}}\n\nevent: message_stop\ndata: {\"type\":\"message_stop\"}\n\n"
}
}
]
}
@@ -1,60 +0,0 @@
{
"version": 1,
"metadata": {
"provider": "minimax",
"protocol": "anthropic-messages",
"route": "anthropic-messages",
"transport": "http",
"model": "MiniMax-M3",
"tags": [
"prefix:anthropic-compatible-messages",
"provider:minimax",
"protocol:anthropic-messages",
"tool",
"tool-loop",
"golden"
],
"name": "anthropic-compatible-messages/minimax-m3-anthropic-compatible-tool-loop",
"recordedAt": "2026-07-18T03:42:25.248Z"
},
"interactions": [
{
"transport": "http",
"request": {
"method": "POST",
"url": "https://api.minimax.io/anthropic/v1/messages",
"headers": {
"anthropic-version": "2023-06-01",
"content-type": "application/json"
},
"body": "{\"model\":\"MiniMax-M3\",\"system\":[{\"type\":\"text\",\"text\":\"Use the get_weather tool exactly once. After the tool result, reply exactly: Paris is sunny.\"}],\"messages\":[{\"role\":\"user\",\"content\":[{\"type\":\"text\",\"text\":\"What is the weather in Paris?\"}]}],\"tools\":[{\"name\":\"get_weather\",\"description\":\"Get current weather for a city.\",\"input_schema\":{\"type\":\"object\",\"properties\":{\"city\":{\"type\":\"string\"}},\"required\":[\"city\"],\"additionalProperties\":false}}],\"stream\":true,\"max_tokens\":80,\"temperature\":0}"
},
"response": {
"status": 200,
"headers": {
"content-type": "text/event-stream; charset=utf-8"
},
"body": "event: message_start\ndata: {\"type\":\"message_start\",\"message\":{\"id\":\"3807fa12f9ecb9357df511e099da6da0\",\"type\":\"message\",\"role\":\"assistant\",\"content\":[],\"model\":\"MiniMax-M3\",\"stop_reason\":null,\"stop_sequence\":null,\"usage\":{\"input_tokens\":0,\"output_tokens\":0,\"cache_creation_input_tokens\":0,\"cache_read_input_tokens\":417,\"service_tier\":\"standard\"},\"service_tier\":\"standard\"}}\n\nevent: ping\ndata: {\"type\":\"ping\"}\n\nevent: content_block_start\ndata: {\"type\":\"content_block_start\",\"index\":0,\"content_block\":{\"type\":\"tool_use\",\"id\":\"call_function_yr64rwmre4gr_1\",\"name\":\"get_weather\",\"input\":{}}}\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"input_json_delta\",\"partial_json\":\"{\\\"city\\\": \\\"Paris\"}}\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"input_json_delta\",\"partial_json\":\"\\\"}\"}}\n\nevent: content_block_stop\ndata: {\"type\":\"content_block_stop\",\"index\":0}\n\nevent: message_delta\ndata: {\"type\":\"message_delta\",\"delta\":{\"stop_reason\":\"tool_use\"},\"usage\":{\"input_tokens\":303,\"output_tokens\":27,\"cache_read_input_tokens\":114,\"service_tier\":\"standard\"}}\n\nevent: message_stop\ndata: {\"type\":\"message_stop\"}\n\n"
}
},
{
"transport": "http",
"request": {
"method": "POST",
"url": "https://api.minimax.io/anthropic/v1/messages",
"headers": {
"anthropic-version": "2023-06-01",
"content-type": "application/json"
},
"body": "{\"model\":\"MiniMax-M3\",\"system\":[{\"type\":\"text\",\"text\":\"Use the get_weather tool exactly once. After the tool result, reply exactly: Paris is sunny.\"}],\"messages\":[{\"role\":\"user\",\"content\":[{\"type\":\"text\",\"text\":\"What is the weather in Paris?\"}]},{\"role\":\"assistant\",\"content\":[{\"type\":\"tool_use\",\"id\":\"call_function_yr64rwmre4gr_1\",\"name\":\"get_weather\",\"input\":{\"city\":\"Paris\"}}]},{\"role\":\"user\",\"content\":[{\"type\":\"tool_result\",\"tool_use_id\":\"call_function_yr64rwmre4gr_1\",\"content\":\"{\\\"temperature\\\":22,\\\"condition\\\":\\\"sunny\\\"}\"}]}],\"tools\":[{\"name\":\"get_weather\",\"description\":\"Get current weather for a city.\",\"input_schema\":{\"type\":\"object\",\"properties\":{\"city\":{\"type\":\"string\"}},\"required\":[\"city\"],\"additionalProperties\":false}}],\"stream\":true,\"max_tokens\":80,\"temperature\":0}"
},
"response": {
"status": 200,
"headers": {
"content-type": "text/event-stream; charset=utf-8"
},
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}
}
]
}
@@ -1,35 +0,0 @@
{
"version": 1,
"metadata": {
"tags": [
"prefix:pdf",
"pdf",
"provider:amazon-bedrock",
"protocol:bedrock-converse",
"user-input"
],
"name": "pdf/bedrock-user-input",
"recordedAt": "2026-07-22T18:15:48.408Z"
},
"interactions": [
{
"transport": "http",
"request": {
"method": "POST",
"url": "https://bedrock-runtime.us-east-1.amazonaws.com/model/us.anthropic.claude-haiku-4-5-20251001-v1%3A0/converse-stream",
"headers": {
"content-type": "application/json"
},
"body": "{\"modelId\":\"us.anthropic.claude-haiku-4-5-20251001-v1:0\",\"messages\":[{\"role\":\"user\",\"content\":[{\"document\":{\"format\":\"pdf\",\"name\":\"verification\",\"source\":{\"bytes\":\"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\"}}},{\"text\":\"Return only the verification code from the PDF.\"}]}],\"inferenceConfig\":{\"maxTokens\":40,\"temperature\":0}}"
},
"response": {
"status": 200,
"headers": {
"content-type": "application/vnd.amazon.eventstream"
},
"body": "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",
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}
}
]
}
@@ -1,53 +0,0 @@
{
"version": 1,
"metadata": {
"tags": [
"prefix:pdf",
"pdf",
"provider:google",
"protocol:gemini",
"tool",
"tool-result"
],
"name": "pdf/gemini-tool-result",
"recordedAt": "2026-07-22T18:21:59.606Z"
},
"interactions": [
{
"transport": "http",
"request": {
"method": "POST",
"url": "https://generativelanguage.googleapis.com/v1beta/models/gemini-3.5-flash:streamGenerateContent?alt=sse",
"headers": {
"content-type": "application/json"
},
"body": "{\"contents\":[{\"role\":\"user\",\"parts\":[{\"text\":\"Use read_pdf with path verification.pdf and return the verification code.\"}]}],\"systemInstruction\":{\"parts\":[{\"text\":\"Call read_pdf exactly once with path verification.pdf, then reply only with the verification code from its PDF.\"}]},\"tools\":[{\"functionDeclarations\":[{\"name\":\"read_pdf\",\"description\":\"Read the attached PDF.\",\"parameters\":{\"required\":[\"path\"],\"type\":\"object\",\"properties\":{\"path\":{\"type\":\"string\"}}}}]}],\"generationConfig\":{\"maxOutputTokens\":256,\"temperature\":0}}"
},
"response": {
"status": 200,
"headers": {
"content-type": "text/event-stream"
},
"body": "data: {\"candidates\": [{\"content\": {\"parts\": [{\"functionCall\": {\"name\": \"read_pdf\",\"args\": {\"path\": \"verification.pdf\"},\"id\": \"58shgmez\"},\"thoughtSignature\": \"EqkCCqYCARFNMg/JrCTv5i3zYENFBVpZNFL3pbzJmi5Eu387ncF703xFMB4pwyaP7a1gi49EqBhCI2hWOpesU5nZQOLAhGgExKGa2GM+HzpEB5g62r0NFblm/BGkVZaImTuHR7bytfRC5jHQlHKo4OS27OLUVjvkMkBIYsvjhDErY7niERbXJVpyxTVqUf1GgZMSu8kC9/5WDlMs9xVKNT/6KMW4PhhSR9nXg4KZUa+bC03/ydhsWWgBa5aLCgvTq7WPj217xIsmUkSiRedIffPsUSNjYdMHUvWi8bOlvM1veEEP6GIfv5h9gXXzjnHbEHfQxV8PZuBAyY7iM6nqyfkJNdkZ1HdB7DXMBsMsRN6SgrIrFoXX2WaGrkoEI5tdZx1t/gdwF1jEVT6k\"}],\"role\": \"model\"},\"index\": 0}],\"usageMetadata\": {\"promptTokenCount\": 81,\"candidatesTokenCount\": 18,\"totalTokenCount\": 151,\"promptTokensDetails\": [{\"modality\": \"TEXT\",\"tokenCount\": 81}],\"thoughtsTokenCount\": 52,\"serviceTier\": \"standard\"},\"modelVersion\": \"gemini-3.5-flash\",\"responseId\": \"RAphaui3OaSHz7IPy8Kb4Ak\"}\r\n\r\ndata: {\"candidates\": [{\"content\": {\"parts\": [{\"text\": \"\"}],\"role\": \"model\"},\"finishReason\": \"STOP\",\"index\": 0}],\"usageMetadata\": {\"promptTokenCount\": 81,\"candidatesTokenCount\": 18,\"totalTokenCount\": 151,\"promptTokensDetails\": [{\"modality\": \"TEXT\",\"tokenCount\": 81}],\"thoughtsTokenCount\": 52,\"serviceTier\": \"standard\"},\"modelVersion\": \"gemini-3.5-flash\",\"responseId\": \"RAphaui3OaSHz7IPy8Kb4Ak\"}\r\n\r\n"
}
},
{
"transport": "http",
"request": {
"method": "POST",
"url": "https://generativelanguage.googleapis.com/v1beta/models/gemini-3.5-flash:streamGenerateContent?alt=sse",
"headers": {
"content-type": "application/json"
},
"body": "{\"contents\":[{\"role\":\"user\",\"parts\":[{\"text\":\"Use read_pdf with path verification.pdf and return the verification code.\"}]},{\"role\":\"model\",\"parts\":[{\"functionCall\":{\"id\":\"58shgmez\",\"name\":\"read_pdf\",\"args\":{\"path\":\"verification.pdf\"}},\"thoughtSignature\":\"EqkCCqYCARFNMg/JrCTv5i3zYENFBVpZNFL3pbzJmi5Eu387ncF703xFMB4pwyaP7a1gi49EqBhCI2hWOpesU5nZQOLAhGgExKGa2GM+HzpEB5g62r0NFblm/BGkVZaImTuHR7bytfRC5jHQlHKo4OS27OLUVjvkMkBIYsvjhDErY7niERbXJVpyxTVqUf1GgZMSu8kC9/5WDlMs9xVKNT/6KMW4PhhSR9nXg4KZUa+bC03/ydhsWWgBa5aLCgvTq7WPj217xIsmUkSiRedIffPsUSNjYdMHUvWi8bOlvM1veEEP6GIfv5h9gXXzjnHbEHfQxV8PZuBAyY7iM6nqyfkJNdkZ1HdB7DXMBsMsRN6SgrIrFoXX2WaGrkoEI5tdZx1t/gdwF1jEVT6k\"}]},{\"role\":\"user\",\"parts\":[{\"functionResponse\":{\"id\":\"58shgmez\",\"name\":\"read_pdf\",\"response\":{\"name\":\"read_pdf\",\"content\":\"PDF read successfully\"},\"parts\":[{\"inlineData\":{\"mimeType\":\"application/pdf\",\"data\":\"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\"}}]}}]}],\"systemInstruction\":{\"parts\":[{\"text\":\"Call read_pdf exactly once with path verification.pdf, then reply only with the verification code from its PDF.\"}]},\"tools\":[{\"functionDeclarations\":[{\"name\":\"read_pdf\",\"description\":\"Read the attached PDF.\",\"parameters\":{\"required\":[\"path\"],\"type\":\"object\",\"properties\":{\"path\":{\"type\":\"string\"}}}}]}],\"generationConfig\":{\"maxOutputTokens\":256,\"temperature\":0}}"
},
"response": {
"status": 200,
"headers": {
"content-type": "text/event-stream"
},
"body": "data: {\"candidates\": [{\"content\": {\"parts\": [{\"text\": \"ORCHID-7391\"}],\"role\": \"model\"},\"index\": 0}],\"usageMetadata\": {\"promptTokenCount\": 123,\"candidatesTokenCount\": 8,\"totalTokenCount\": 184,\"promptTokensDetails\": [{\"modality\": \"TEXT\",\"tokenCount\": 123}],\"thoughtsTokenCount\": 53,\"serviceTier\": \"standard\"},\"modelVersion\": \"gemini-3.5-flash\",\"responseId\": \"RgphaoL6CMjQz7IPjOnEmQI\"}\r\n\r\ndata: {\"candidates\": [{\"content\": {\"parts\": [{\"text\": \"\",\"thoughtSignature\": \"EqECCp4CARFNMg9obBl8O6iU9lawUIWiE+1vztZm9NtaT9FuyJz343hd9ruz+xPco4Q1DY1GF81ZiSI2ElBkt8Wfwsqtix9LNGSMvbZhhk/ZnB54t05M/Dft1kujcMvEdZUWUI/jWaJ349tO1bKVH9MacG5+gl0n4y8DwyQZSV3xIcet547drSkcA/TM03RB+yj1/dcLHsvUjmv9EnO897vZgO2Dk4tbZ2NyCtOeQ3JKVhUTLg2pjkGk+POCNiOdESWiUzxdQKw9LiV6nnzi071tXNiMeVimq6d7xAzRVNapI2uXynvn9Uk3eyn85purOFa8cKriK9oD6vcyGMqgd9+gu2m3to0IHqd7o+2YSr1m5qV1xT1R2/WRQEtb1b1AuOAU6w==\"}],\"role\": \"model\"},\"finishReason\": \"STOP\",\"index\": 0}],\"usageMetadata\": {\"promptTokenCount\": 1277,\"candidatesTokenCount\": 8,\"totalTokenCount\": 1338,\"promptTokensDetails\": [{\"modality\": \"IMAGE\",\"tokenCount\": 1102},{\"modality\": \"TEXT\",\"tokenCount\": 175}],\"thoughtsTokenCount\": 53,\"serviceTier\": \"standard\"},\"modelVersion\": \"gemini-3.5-flash\",\"responseId\": \"RgphaoL6CMjQz7IPjOnEmQI\"}\r\n\r\n"
}
}
]
}
@@ -1,34 +0,0 @@
{
"version": 1,
"metadata": {
"tags": [
"prefix:pdf",
"pdf",
"provider:google",
"protocol:gemini",
"user-input"
],
"name": "pdf/gemini-user-input",
"recordedAt": "2026-07-22T18:20:55.140Z"
},
"interactions": [
{
"transport": "http",
"request": {
"method": "POST",
"url": "https://generativelanguage.googleapis.com/v1beta/models/gemini-3.5-flash:streamGenerateContent?alt=sse",
"headers": {
"content-type": "application/json"
},
"body": "{\"contents\":[{\"role\":\"user\",\"parts\":[{\"inlineData\":{\"mimeType\":\"application/pdf\",\"data\":\"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\"}},{\"text\":\"Return only the verification code from the PDF.\"}]}],\"generationConfig\":{\"maxOutputTokens\":256,\"temperature\":0}}"
},
"response": {
"status": 200,
"headers": {
"content-type": "text/event-stream"
},
"body": "data: {\"candidates\": [{\"content\": {\"parts\": [{\"text\": \"ORCH\"}],\"role\": \"model\"},\"index\": 0}],\"usageMetadata\": {\"promptTokenCount\": 10,\"candidatesTokenCount\": 2,\"totalTokenCount\": 127,\"promptTokensDetails\": [{\"modality\": \"TEXT\",\"tokenCount\": 10}],\"thoughtsTokenCount\": 115,\"serviceTier\": \"standard\"},\"modelVersion\": \"gemini-3.5-flash\",\"responseId\": \"BQpharW2KaPgz7IP6uSLiAw\"}\r\n\r\ndata: {\"candidates\": [{\"content\": {\"parts\": [{\"text\": \"ID-7391\"}],\"role\": \"model\"},\"index\": 0}],\"usageMetadata\": {\"promptTokenCount\": 10,\"candidatesTokenCount\": 8,\"totalTokenCount\": 133,\"promptTokensDetails\": [{\"modality\": \"TEXT\",\"tokenCount\": 10}],\"thoughtsTokenCount\": 115,\"serviceTier\": \"standard\"},\"modelVersion\": \"gemini-3.5-flash\",\"responseId\": \"BQpharW2KaPgz7IP6uSLiAw\"}\r\n\r\ndata: {\"candidates\": [{\"content\": {\"parts\": [{\"text\": \"\",\"thoughtSignature\": \"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\"}],\"role\": \"model\"},\"finishReason\": \"STOP\",\"index\": 0}],\"usageMetadata\": {\"promptTokenCount\": 530,\"candidatesTokenCount\": 8,\"totalTokenCount\": 653,\"promptTokensDetails\": [{\"modality\": \"IMAGE\",\"tokenCount\": 520},{\"modality\": \"TEXT\",\"tokenCount\": 10}],\"thoughtsTokenCount\": 115,\"serviceTier\": \"standard\"},\"modelVersion\": \"gemini-3.5-flash\",\"responseId\": \"BQpharW2KaPgz7IP6uSLiAw\"}\r\n\r\n"
}
}
]
}
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@@ -1,28 +0,0 @@
{
"version": 1,
"metadata": {
"tags": ["prefix:zai-images", "provider:zai", "protocol:zai-images"],
"name": "zai-images/generates-an-image",
"recordedAt": "2026-07-19T16:03:55.761Z"
},
"interactions": [
{
"transport": "http",
"request": {
"method": "POST",
"url": "https://api.z.ai/api/paas/v4/images/generations",
"headers": {
"content-type": "application/json"
},
"body": "{\"model\":\"cogview-4-250304\",\"prompt\":\"A simple flat red circle centered on a plain white background.\",\"size\":\"1024x1024\",\"quality\":\"standard\",\"user_id\":\"opencode-image-test\"}"
},
"response": {
"status": 200,
"headers": {
"content-type": "application/json; charset=UTF-8"
},
"body": "{\"created\":1784477028,\"data\":[{\"url\":\"https://mfile.z.ai/1784477035500-43574eab2b6e402da9063d6ac22dfefb.png?ufileattname=202607200003482062c3bba9b04f7d_watermark.png\"}],\"id\":\"202607200003482062c3bba9b04f7d\",\"request_id\":\"202607200003482062c3bba9b04f7d\"}"
}
}
]
}
-578
View File
@@ -1,578 +0,0 @@
import { describe, expect } from "bun:test"
import { Effect, Layer } from "effect"
import { HttpClientRequest } from "effect/unstable/http"
import { Image, ImageClient, ImageInput } from "../src"
import { Google, OpenAI, XAI, ZAI } from "../src/providers"
import { it } from "./lib/effect"
import { dynamicResponse } from "./lib/http"
describe("Image", () => {
it.effect("generates images through the OpenAI Images API", () =>
Effect.gen(function* () {
const response = yield* Image.generate({
model: OpenAI.configure({
apiKey: "test",
baseURL: "https://api.openai.test/v1",
queryParams: { "api-version": "v1" },
http: { body: { deployment: "test" }, headers: { "x-default": "yes" } },
}).image("gpt-image-2"),
prompt: "A robot tending a rooftop garden",
options: {
n: 2,
size: "2048x2048",
quality: "future-quality",
outputFormat: "jpeg",
output_format: "avif",
outputCompression: 30,
output_compression: 40,
background: "opaque",
native_default: true,
future_option: true,
},
http: {
body: { output_format: "webp", output_compression: 50, future_option: "http", request_metadata: "value" },
headers: { "x-request": "yes" },
query: { trace: "1" },
},
})
expect(response.images).toHaveLength(2)
expect(response.image?.mediaType).toBe("image/webp")
expect(response.image?.data).toEqual(Uint8Array.from([1, 2, 3]))
expect(response.image?.providerMetadata).toEqual({ openai: { revisedPrompt: "A precise robot" } })
expect(response.usage?.totalTokens).toBe(12)
}).pipe(
Effect.provide(
ImageClient.layer.pipe(
Layer.provide(
dynamicResponse((input) =>
Effect.gen(function* () {
const request = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
expect(request.url).toBe("https://api.openai.test/v1/images/generations?api-version=v1&trace=1")
expect(request.headers.get("authorization")).toBe("Bearer test")
expect(request.headers.get("x-default")).toBe("yes")
expect(request.headers.get("x-request")).toBe("yes")
expect(JSON.parse(input.text)).toEqual({
model: "gpt-image-2",
prompt: "A robot tending a rooftop garden",
n: 2,
size: "2048x2048",
quality: "future-quality",
background: "opaque",
output_format: "webp",
output_compression: 50,
native_default: true,
future_option: "http",
deployment: "test",
request_metadata: "value",
})
return input.respond(
JSON.stringify({
data: [{ b64_json: "AQID", revised_prompt: "A precise robot" }, { b64_json: "BAUG" }],
output_format: "webp",
usage: { input_tokens: 4, output_tokens: 8, total_tokens: 12 },
}),
{ headers: { "content-type": "application/json" } },
)
}),
),
),
),
),
),
)
it.effect("preserves native snake_case and unknown request options", () =>
Image.generate({
model: OpenAI.configure({
apiKey: "test",
baseURL: "https://api.openai.test/v1",
}).image("future-image-model"),
prompt: "A lighthouse in fog",
options: {
outputFormat: "jpeg",
output_format: "avif",
outputCompression: 30,
output_compression: 40,
provider_future_option: { enabled: true },
},
}).pipe(
Effect.tap((response) =>
Effect.sync(() => {
expect(response.image?.mediaType).toBe("image/avif")
}),
),
Effect.provide(
ImageClient.layer.pipe(
Layer.provide(
dynamicResponse((input) => {
expect(JSON.parse(input.text)).toEqual({
model: "future-image-model",
prompt: "A lighthouse in fog",
output_format: "avif",
output_compression: 40,
provider_future_option: { enabled: true },
})
return Effect.succeed(
input.respond(JSON.stringify({ data: [{ b64_json: "AQID" }] }), {
headers: { "content-type": "application/json" },
}),
)
}),
),
),
),
),
)
it.effect("routes OpenAI byte inputs and masks through multipart edits", () =>
Image.generate({
model: OpenAI.configure({ apiKey: "test", baseURL: "https://api.openai.test/v1" }).image("future-model"),
prompt: "Combine these images",
images: [
ImageInput.bytes(Uint8Array.from([1, 2, 3]), "image/png"),
ImageInput.url("data:image/jpeg;base64,BAUG"),
],
options: {
mask: ImageInput.bytes(Uint8Array.from([7, 8, 9]), "image/png"),
quality: "high",
future_option: true,
},
http: {
body: { quality: "low", model: "corrupt", prompt: "corrupt", image: "corrupt", "image[]": "corrupt" },
headers: { "content-type": "application/json" },
},
}).pipe(
Effect.provide(
ImageClient.layer.pipe(
Layer.provide(
dynamicResponse((input) =>
Effect.gen(function* () {
const request = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
expect(request.url).toBe("https://api.openai.test/v1/images/edits")
expect(request.headers.get("content-type")).toStartWith("multipart/form-data; boundary=")
expect(input.text).toContain('name="model"\r\n\r\nfuture-model')
expect(input.text).toContain('name="prompt"\r\n\r\nCombine these images')
expect(input.text.match(/name="image\[\]"/g)).toHaveLength(2)
expect(input.text).toContain('name="mask"')
expect(input.text).toContain('name="quality"\r\n\r\nlow')
expect(input.text).not.toContain("corrupt")
return input.respond(JSON.stringify({ data: [{ b64_json: "AQID" }] }), {
headers: { "content-type": "application/json" },
})
}),
),
),
),
),
),
)
it.effect("routes OpenAI URL and file inputs through JSON edits", () =>
Image.generate({
model: OpenAI.configure({ apiKey: "test", baseURL: "https://api.openai.test/v1" }).image("future-model"),
prompt: "Combine these images",
images: [ImageInput.url("https://example.test/source.png"), ImageInput.file("file_123")],
options: { mask: ImageInput.file("file_mask") },
http: { body: { future_option: true } },
}).pipe(
Effect.provide(
ImageClient.layer.pipe(
Layer.provide(
dynamicResponse((input) => {
expect(JSON.parse(input.text)).toEqual({
model: "future-model",
prompt: "Combine these images",
images: [{ image_url: "https://example.test/source.png" }, { file_id: "file_123" }],
mask: { file_id: "file_mask" },
future_option: true,
})
return Effect.succeed(
input.respond(JSON.stringify({ data: [{ b64_json: "AQID" }] }), {
headers: { "content-type": "application/json" },
}),
)
}),
),
),
),
),
)
it.effect("routes ordered xAI image inputs through JSON edits", () =>
Image.generate({
model: XAI.configure({ apiKey: "test", baseURL: "https://api.xai.test/v1" }).image("future-model"),
prompt: "Combine these images",
images: [
ImageInput.bytes(Uint8Array.from([1, 2, 3]), "image/png"),
ImageInput.url("https://example.test/source.jpg"),
ImageInput.file("file_123"),
],
}).pipe(
Effect.provide(
ImageClient.layer.pipe(
Layer.provide(
dynamicResponse((input) => {
expect(JSON.parse(input.text)).toEqual({
model: "future-model",
prompt: "Combine these images",
images: [
{ url: "data:image/png;base64,AQID", type: "image_url" },
{ url: "https://example.test/source.jpg", type: "image_url" },
{ file_id: "file_123" },
],
})
return Effect.succeed(
input.respond(JSON.stringify({ data: [{ b64_json: "AQID", mime_type: "image/png" }] }), {
headers: { "content-type": "application/json" },
}),
)
}),
),
),
),
),
)
it.effect("uses xAI's singular image field for one input", () =>
Image.generate({
model: XAI.configure({ apiKey: "test", baseURL: "https://api.xai.test/v1" }).image("future-model"),
prompt: "Edit this image",
images: [ImageInput.file("file_123")],
}).pipe(
Effect.provide(
ImageClient.layer.pipe(
Layer.provide(
dynamicResponse((input) => {
expect(JSON.parse(input.text)).toEqual({
model: "future-model",
prompt: "Edit this image",
image: { file_id: "file_123" },
})
return Effect.succeed(
input.respond(JSON.stringify({ data: [{ b64_json: "AQID", mime_type: "image/png" }] }), {
headers: { "content-type": "application/json" },
}),
)
}),
),
),
),
),
)
it.effect("lowers ordered Google image inputs into generateContent parts", () =>
Image.generate({
model: Google.configure({ apiKey: "test", baseURL: "https://google.test/v1beta" }).image("future-model"),
prompt: "Combine these images",
images: [
ImageInput.bytes(Uint8Array.from([1, 2, 3]), "image/png"),
ImageInput.url("data:image/jpeg;base64,BAUG"),
ImageInput.fileUri("https://generativelanguage.googleapis.com/v1beta/files/123", "image/webp"),
],
}).pipe(
Effect.provide(
ImageClient.layer.pipe(
Layer.provide(
dynamicResponse((input) => {
expect(JSON.parse(input.text).contents[0].parts).toEqual([
{ text: "Combine these images" },
{ inlineData: { mimeType: "image/png", data: "AQID" } },
{ inlineData: { mimeType: "image/jpeg", data: "BAUG" } },
{
fileData: {
mimeType: "image/webp",
fileUri: "https://generativelanguage.googleapis.com/v1beta/files/123",
},
},
])
return Effect.succeed(
input.respond(
JSON.stringify({
candidates: [{ content: { parts: [{ inlineData: { mimeType: "image/png", data: "AQID" } }] } }],
}),
{ headers: { "content-type": "application/json" } },
),
)
}),
),
),
),
),
)
it.effect("rejects unsupported provider inputs before sending", () =>
Effect.gen(function* () {
const cases = [
Image.generate({
model: Google.configure({ apiKey: "test" }).image("model"),
prompt: "edit",
images: [ImageInput.url("https://example.test/image.png")],
}),
Image.generate({
model: ZAI.configure({ apiKey: "test" }).image("model"),
prompt: "edit",
images: [ImageInput.bytes(Uint8Array.from([1]), "image/png")],
}),
]
yield* Effect.forEach(cases, (program) =>
program.pipe(
Effect.flip,
Effect.tap((error) => Effect.sync(() => expect(error.reason._tag).toBe("InvalidRequest"))),
),
)
}).pipe(
Effect.provide(
ImageClient.layer.pipe(
Layer.provide(dynamicResponse(() => Effect.die("unsupported input reached the network"))),
),
),
),
)
it.effect("generates images through the Google generateContent API", () =>
Effect.gen(function* () {
const response = yield* Image.generate({
model: Google.configure({
apiKey: "test",
baseURL: "https://generativelanguage.test/v1beta/",
headers: { "x-default": "yes" },
http: { body: { labels: { deployment: "test" } }, query: { api: "v1" } },
}).image("any-model-id"),
prompt: "A robot tending a rooftop garden",
options: {
aspectRatio: "16:9",
imageSize: "2K",
seed: 42,
thinkingLevel: "HIGH",
includeThoughts: true,
futureOption: true,
imageConfig: { aspectRatio: "4:3", nativeImageOption: true },
thinkingConfig: { thinkingLevel: "LOW", nativeThinkingOption: true },
},
http: {
body: {
safetySettings: [],
generationConfig: {
imageConfig: { aspectRatio: "3:2", httpImageOption: true },
thinkingConfig: { includeThoughts: false, httpThinkingOption: true },
futureOption: "http",
httpOption: true,
},
},
headers: { "x-request": "yes" },
query: { trace: "1" },
},
})
expect(response.images).toHaveLength(3)
expect(response.images.map((image) => image.data)).toEqual([
Uint8Array.from([1, 2, 3]),
Uint8Array.from([4, 5, 6]),
Uint8Array.from([7, 8, 9]),
])
expect(response.images.map((image) => image.mediaType)).toEqual(["image/png", "image/jpeg", "image/webp"])
expect(response.images[0].providerMetadata).toMatchObject({ google: { thoughtSignature: "signature-1" } })
expect(response.images[1].providerMetadata).toMatchObject({
google: { candidateIndex: 0, partIndex: 3, finishReason: "STOP" },
})
expect(response.images[2].providerMetadata).toMatchObject({ google: { candidateIndex: 7, partIndex: 0 } })
expect(response.usage?.inputTokens).toBe(5)
expect(response.usage?.outputTokens).toBe(10)
expect(response.usage?.reasoningTokens).toBe(3)
expect(response.usage?.providerMetadata).toMatchObject({ google: { serviceTier: "STANDARD" } })
expect(response.providerMetadata).toEqual({
google: {
modelVersion: "gemini-3.1-flash-image",
responseId: "response-1",
promptFeedback: undefined,
candidates: [
{
index: 0,
finishReason: "STOP",
finishMessage: undefined,
safetyRatings: [{ category: "safe" }],
citationMetadata: undefined,
groundingMetadata: undefined,
parts: [
{
type: "inlineData",
mediaType: "image/png",
thought: undefined,
thoughtSignature: "signature-1",
},
{ type: "text", text: "planning", thought: true, thoughtSignature: "text-signature" },
{
type: "inlineData",
mediaType: "image/png",
thought: true,
thoughtSignature: "draft-signature",
},
{
type: "inlineData",
mediaType: "image/jpeg",
thought: undefined,
thoughtSignature: undefined,
},
],
},
{
index: 7,
finishReason: undefined,
finishMessage: undefined,
safetyRatings: undefined,
citationMetadata: undefined,
groundingMetadata: undefined,
parts: [
{
type: "inlineData",
mediaType: "image/webp",
thought: undefined,
thoughtSignature: undefined,
},
],
},
],
},
})
}).pipe(
Effect.provide(
ImageClient.layer.pipe(
Layer.provide(
dynamicResponse((input) =>
Effect.gen(function* () {
const request = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
expect(request.url).toBe(
"https://generativelanguage.test/v1beta/models/any-model-id:generateContent?api=v1&trace=1",
)
expect(request.headers.get("x-goog-api-key")).toBe("test")
expect(request.headers.get("x-default")).toBe("yes")
expect(request.headers.get("x-request")).toBe("yes")
expect(JSON.parse(input.text)).toEqual({
contents: [{ role: "user", parts: [{ text: "A robot tending a rooftop garden" }] }],
generationConfig: {
responseModalities: ["IMAGE"],
imageConfig: {
aspectRatio: "3:2",
imageSize: "2K",
nativeImageOption: true,
httpImageOption: true,
},
seed: 42,
thinkingConfig: {
thinkingLevel: "LOW",
includeThoughts: false,
nativeThinkingOption: true,
httpThinkingOption: true,
},
futureOption: "http",
httpOption: true,
},
labels: { deployment: "test" },
safetySettings: [],
})
return input.respond(
JSON.stringify({
candidates: [
{
content: {
parts: [
{
inlineData: { mimeType: "image/png", data: "AQID" },
thoughtSignature: "signature-1",
},
{ text: "planning", thought: true, thoughtSignature: "text-signature" },
{
inlineData: { mimeType: "image/png", data: "CgsM" },
thought: true,
thoughtSignature: "draft-signature",
},
{ inlineData: { mimeType: "image/jpeg", data: "BAUG" } },
],
},
finishReason: "STOP",
safetyRatings: [{ category: "safe" }],
},
{
index: 7,
content: { parts: [{ inlineData: { mimeType: "image/webp", data: "BwgJ" } }] },
},
],
usageMetadata: {
promptTokenCount: 5,
candidatesTokenCount: 7,
thoughtsTokenCount: 3,
totalTokenCount: 15,
serviceTier: "STANDARD",
},
modelVersion: "gemini-3.1-flash-image",
responseId: "response-1",
}),
{ headers: { "content-type": "application/json" } },
)
}),
),
),
),
),
),
)
it.effect("includes Google diagnostics when no final image is returned", () =>
Image.generate({
model: Google.configure({ apiKey: "test", baseURL: "https://generativelanguage.test/v1beta" }).image(
"gemini-3.1-flash-image",
),
prompt: "A robot tending a rooftop garden",
}).pipe(
Effect.flip,
Effect.tap((error) =>
Effect.sync(() => {
expect(error.reason._tag).toBe("InvalidProviderOutput")
if (error.reason._tag !== "InvalidProviderOutput") return
expect(error.reason.message).toContain("finish reasons: IMAGE_SAFETY")
expect(error.reason.providerMetadata).toEqual({
google: {
promptFeedback: { blockReason: "SAFETY" },
candidates: [
{
index: 0,
finishReason: "IMAGE_SAFETY",
finishMessage: "The generated image was blocked by safety filters.",
safetyRatings: [{ category: "HARM_CATEGORY_DANGEROUS_CONTENT", blocked: true }],
citationMetadata: undefined,
groundingMetadata: undefined,
parts: [{ type: "text", text: "blocked", thought: false, thoughtSignature: undefined }],
},
],
},
})
}),
),
Effect.provide(
ImageClient.layer.pipe(
Layer.provide(
dynamicResponse((input) =>
Effect.succeed(
input.respond(
JSON.stringify({
candidates: [
{
content: { parts: [{ text: "blocked", thought: false }] },
finishReason: "IMAGE_SAFETY",
finishMessage: "The generated image was blocked by safety filters.",
safetyRatings: [{ category: "HARM_CATEGORY_DANGEROUS_CONTENT", blocked: true }],
},
],
promptFeedback: { blockReason: "SAFETY" },
}),
{ headers: { "content-type": "application/json" } },
),
),
),
),
),
),
),
)
})
-161
View File
@@ -1,161 +0,0 @@
import {
Image,
ImageInput,
ImageModel,
type ImageModelOptions,
type ImageOptions,
type ImageRequestFor,
type ImageRoute,
} from "../src"
import { Google, OpenAI, XAI, ZAI } from "../src/providers"
type GoogleLikeOptions = {
readonly aspectRatio?: "1:1" | "16:9"
readonly imageSize?: "1K" | "2K"
} & Record<string, unknown>
declare const route: ImageRoute<GoogleLikeOptions>
const google = ImageModel.make<GoogleLikeOptions>({ id: "gemini-image", provider: "google", route })
// @ts-expect-error Extracted model options retain known provider fields.
const invalidGoogleOptions: ImageModelOptions<typeof google> = { aspectRatio: "wide" }
void invalidGoogleOptions
Image.generate({
model: google,
prompt: "A lighthouse",
images: [
ImageInput.bytes(Uint8Array.from([1, 2, 3]), "image/png"),
ImageInput.url("data:image/jpeg;base64,AQID"),
ImageInput.fileUri("https://generativelanguage.googleapis.com/v1beta/files/example", "image/webp"),
],
options: { aspectRatio: "16:9", imageSize: "2K", futureOption: true },
})
const googleProvider = Google.configure({ apiKey: "test" }).image("any-model-id")
Image.generate({
model: googleProvider,
prompt: "A lighthouse",
options: {
aspectRatio: "16:9",
imageSize: "2K",
seed: 42,
thinkingLevel: "HIGH",
includeThoughts: true,
futureOption: true,
},
})
Image.generate({
model: googleProvider,
prompt: "A lighthouse",
options: { aspectRatio: "future-ratio", imageSize: "8K", thinkingLevel: "FUTURE" },
})
// @ts-expect-error Image generation options are request-scoped, not provider configuration.
Google.configure({ image: { providerOptions: { imageSize: "2K" } } })
// @ts-expect-error Known Google string options retain their value kind.
Image.generate({ model: googleProvider, prompt: "A lighthouse", options: { imageSize: 2 } })
// @ts-expect-error Known Google numeric options retain their value kind.
Image.generate({ model: googleProvider, prompt: "A lighthouse", options: { seed: "42" } })
// @ts-expect-error Known Google boolean options retain their value kind.
Image.generate({ model: googleProvider, prompt: "A lighthouse", options: { includeThoughts: "yes" } })
const openai = OpenAI.image("gpt-image-2")
// @ts-expect-error Image generation options are request-scoped, not provider configuration.
OpenAI.configure({ image: { options: { quality: "medium" } } })
const futureOpenAIOptions: ImageModelOptions<typeof openai> = { quality: "future-quality" }
void futureOpenAIOptions
Image.generate({
model: openai,
prompt: "A lighthouse",
images: [ImageInput.url("https://example.com/source.png"), ImageInput.file("file_123")],
options: {
mask: ImageInput.bytes(Uint8Array.from([1]), "image/png"),
quality: "hd",
outputFormat: "webp",
size: "2048x2048",
future_option: true,
},
})
Image.generate({ model: openai, prompt: "A lighthouse", options: { quality: "future-quality", size: "256x256" } })
Image.generate({ model: openai, prompt: "A lighthouse", options: { size: "1792x1024" } })
Image.generate({ model: openai, prompt: "A lighthouse", options: { native_future_option: true } })
// @ts-expect-error Known OpenAI string options retain their value kind.
Image.generate({ model: openai, prompt: "A lighthouse", options: { quality: 1 } })
// @ts-expect-error Known OpenAI numeric options retain their value kind.
Image.generate({ model: openai, prompt: "A lighthouse", options: { outputCompression: "80" } })
OpenAI.imageGeneration({ action: "future-action", quality: "future-quality", size: "2048x2048" })
// @ts-expect-error Hosted image generation numeric options retain their value kind.
OpenAI.imageGeneration({ partialImages: "2" })
// @ts-expect-error Known Google-like options are inferred from the selected model.
Image.generate({ model: google, prompt: "A lighthouse", options: { aspectRatio: "wide" } })
const xai = XAI.configure({ apiKey: "test" }).image("any-model-id")
// @ts-expect-error Image generation options are request-scoped, not provider configuration.
XAI.configure({ image: { options: { resolution: "1k" } } })
Image.generate({
model: xai,
prompt: "A lighthouse",
images: [ImageInput.url("data:image/png;base64,AQID"), ImageInput.file("file_123")],
options: {
n: 2,
aspectRatio: "future-ratio",
resolution: "future-resolution",
responseFormat: "future-format",
future_option: true,
},
})
Image.generate({
model: xai,
prompt: "A lighthouse",
options: { aspect_ratio: "16:9", response_format: "b64_json", native_future_option: true },
})
// @ts-expect-error Known xAI numeric options retain their value kind.
Image.generate({ model: xai, prompt: "A lighthouse", options: { n: "2" } })
// @ts-expect-error Known xAI string options retain their value kind.
Image.generate({ model: xai, prompt: "A lighthouse", options: { resolution: 2 } })
const zai = ZAI.configure({ apiKey: "test" }).image("any-model-id")
// @ts-expect-error Image generation options are request-scoped, not provider configuration.
ZAI.configure({ image: { options: { quality: "hd" } } })
Image.generate({
model: zai,
prompt: "A lighthouse",
options: { quality: "future-quality", userID: "user-123", future_option: true },
})
Image.generate({ model: zai, prompt: "A lighthouse", options: { user_id: "raw-user" } })
// @ts-expect-error Known Z.ai string options retain their value kind.
Image.generate({ model: zai, prompt: "A lighthouse", options: { quality: 1 } })
// @ts-expect-error Known Z.ai user IDs retain their value kind.
Image.generate({ model: zai, prompt: "A lighthouse", options: { userID: 1 } })
declare const generic: ImageModel<ImageOptions>
Image.generate({ model: generic, prompt: "A lighthouse", options: { arbitrary: true } })
const explicitImageInput: ImageInput = ImageInput.url("https://example.com/image.png")
void explicitImageInput
// @ts-expect-error Raw strings are ambiguous and are not image inputs.
Image.generate({ model: openai, prompt: "A lighthouse", images: ["AQID"] })
// @ts-expect-error Byte image inputs require an explicit MIME type.
Image.generate({ model: openai, prompt: "A lighthouse", images: [{ type: "bytes", data: new Uint8Array() }] })
// @ts-expect-error File URIs require an explicit MIME type for Gemini fileData.
Image.generate({ model: google, prompt: "A lighthouse", images: [{ type: "file-uri", uri: "files/123" }] })
const request = Image.request({
model: google,
prompt: "A lighthouse",
options: { aspectRatio: "1:1", futureOption: true },
})
const typedRequest: ImageRequestFor<GoogleLikeOptions> = request
void typedRequest
// @ts-expect-error Image requests no longer expose a common count option.
Image.generate({ model: openai, prompt: "A lighthouse", count: 2 })
// @ts-expect-error Image requests no longer expose a common size option.
Image.generate({ model: openai, prompt: "A lighthouse", size: { width: 1024, height: 1024 } })
// @ts-expect-error Image requests no longer expose a common aspectRatio option.
Image.generate({ model: openai, prompt: "A lighthouse", aspectRatio: "16:9" })
// @ts-expect-error Image requests no longer expose a common seed option.
Image.generate({ model: openai, prompt: "A lighthouse", seed: 1 })
// @ts-expect-error Image requests do not expose metadata.
Image.generate({ model: openai, prompt: "A lighthouse", metadata: { trace: true } })
// @ts-expect-error Masks are provider options, not a common image request field.
Image.generate({ model: openai, prompt: "A lighthouse", mask: ImageInput.url("https://example.com/mask.png") })
-29
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@@ -1,29 +0,0 @@
export const dimensions = (data: Uint8Array) => {
if (data[0] === 0x89 && data[1] === 0x50 && data[2] === 0x4e && data[3] === 0x47)
return {
width: readUint32(data, 16),
height: readUint32(data, 20),
}
if (data[0] === 0xff && data[1] === 0xd8) {
for (let offset = 2; offset + 8 < data.length; ) {
if (data[offset] !== 0xff) {
offset++
continue
}
const marker = data[offset + 1]
if (
marker !== undefined &&
[0xc0, 0xc1, 0xc2, 0xc3, 0xc5, 0xc6, 0xc7, 0xc9, 0xca, 0xcb, 0xcd, 0xce, 0xcf].includes(marker)
)
return {
width: (data[offset + 7] << 8) | data[offset + 8],
height: (data[offset + 5] << 8) | data[offset + 6],
}
offset += 2 + ((data[offset + 2] << 8) | data[offset + 3])
}
}
throw new Error("Unsupported image fixture format")
}
const readUint32 = (data: Uint8Array, offset: number) =>
((data[offset] << 24) | (data[offset + 1] << 16) | (data[offset + 2] << 8) | data[offset + 3]) >>> 0
-296
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@@ -1,296 +0,0 @@
import { describe, expect, test } from "bun:test"
import { model } from "@opencode-ai/ai/providers/openai"
describe("provider package entrypoints", () => {
test("semantic API aliases expose the same contract", async () => {
const modules = await Promise.all([
import("@opencode-ai/ai/providers/openai"),
import("@opencode-ai/ai/providers/openai/responses"),
import("@opencode-ai/ai/providers/openai/chat"),
import("@opencode-ai/ai/providers/anthropic"),
import("@opencode-ai/ai/providers/anthropic-compatible"),
import("@opencode-ai/ai/providers/openai-compatible"),
import("@opencode-ai/ai/providers/openai-compatible/responses"),
import("@opencode-ai/ai/providers/amazon-bedrock"),
import("@opencode-ai/ai/providers/azure"),
import("@opencode-ai/ai/providers/azure/responses"),
import("@opencode-ai/ai/providers/azure/chat"),
import("@opencode-ai/ai/providers/google"),
import("@opencode-ai/ai/providers/google-vertex"),
import("@opencode-ai/ai/providers/google-vertex/gemini"),
import("@opencode-ai/ai/providers/google-vertex/chat"),
import("@opencode-ai/ai/providers/google-vertex/responses"),
import("@opencode-ai/ai/providers/google-vertex/messages"),
])
for (const module of modules) expect(module.model).toBeFunction()
expect(modules[0].model).toBe(modules[1].model)
expect(modules[8].model).toBe(modules[9].model)
expect(modules[12].model).toBe(modules[13].model)
})
test("maps package settings onto the executable model", () => {
const selected = model("gpt-5", {
apiKey: "fixture",
baseURL: "https://api.openai.test/v1",
headers: { "x-application": "opencode" },
body: { service_tier: "priority" },
limits: { context: 200_000, output: 64_000 },
unrelatedInheritedSetting: true,
})
expect(selected.route.id).toBe("openai-responses")
expect(selected.route.defaults.headers).toEqual({ "x-application": "opencode" })
expect(selected.route.defaults.http?.body).toEqual({ service_tier: "priority" })
expect(selected.route.defaults.limits).toEqual({ context: 200_000, output: 64_000 })
})
test("selects transport without changing the semantic API", () => {
expect(model("gpt-5", { apiKey: "fixture" }).route.id).toBe("openai-responses")
expect(model("gpt-5", { apiKey: "fixture", transport: "websocket" }).route.id).toBe("openai-responses-websocket")
})
test("maps OpenAI-compatible Responses settings onto the executable model", async () => {
const OpenAICompatibleResponses = await import("@opencode-ai/ai/providers/openai-compatible/responses")
const selected = OpenAICompatibleResponses.model("custom-model", {
apiKey: "fixture",
baseURL: "https://responses.example.test/v1",
provider: "example",
headers: { "x-application": "opencode" },
body: { service_tier: "priority" },
limits: { context: 200_000, output: 64_000 },
providerOptions: { openai: { reasoningEffort: "low", store: true } },
})
expect(String(selected.provider)).toBe("example")
expect(selected.route.id).toBe("openai-compatible-responses")
expect(selected.route.endpoint).toMatchObject({
baseURL: "https://responses.example.test/v1",
path: "/responses",
})
expect(selected.route.defaults.headers).toEqual({ "x-application": "opencode" })
expect(selected.route.defaults.http?.body).toEqual({ service_tier: "priority" })
expect(selected.route.defaults.limits).toEqual({ context: 200_000, output: 64_000 })
expect(selected.route.defaults.providerOptions).toEqual({
openai: { reasoningEffort: "low", store: true },
})
})
test("maps Anthropic-compatible settings onto the executable model", async () => {
const AnthropicCompatible = await import("@opencode-ai/ai/providers/anthropic-compatible")
const selected = AnthropicCompatible.model("compatible-model", {
apiKey: "fixture",
baseURL: "https://messages.example.test/v1",
provider: "example",
headers: { "x-application": "opencode" },
body: { metadata: { user_id: "user_1" } },
limits: { context: 200_000, output: 64_000 },
})
expect(String(selected.provider)).toBe("example")
expect(selected.route.id).toBe("anthropic-messages")
expect(selected.route.endpoint).toMatchObject({
baseURL: "https://messages.example.test/v1",
path: "/messages",
})
expect(selected.route.defaults.headers).toEqual({ "x-application": "opencode" })
expect(selected.route.defaults.http?.body).toEqual({ metadata: { user_id: "user_1" } })
expect(selected.route.defaults.limits).toEqual({ context: 200_000, output: 64_000 })
})
test("requires an Anthropic-compatible base URL at runtime", async () => {
const AnthropicCompatible = await import("@opencode-ai/ai/providers/anthropic-compatible")
expect(() =>
Reflect.apply(AnthropicCompatible.model, undefined, ["compatible-model", { apiKey: "fixture" }]),
).toThrow("Anthropic-compatible providers require a baseURL")
})
test("rejects conflicting Anthropic-compatible auth settings at runtime", async () => {
const Anthropic = await import("@opencode-ai/ai/providers/anthropic")
const AnthropicCompatible = await import("@opencode-ai/ai/providers/anthropic-compatible")
expect(() =>
Reflect.apply(AnthropicCompatible.model, undefined, [
"compatible-model",
{
apiKey: "fixture",
authToken: "token",
baseURL: "https://messages.example.test/v1",
},
]),
).toThrow("Anthropic-compatible apiKey cannot be combined with authToken")
expect(() =>
Reflect.apply(Anthropic.model, undefined, ["claude-sonnet-4-6", { apiKey: "fixture", authToken: "token" }]),
).toThrow("Anthropic apiKey cannot be combined with authToken")
})
test("maps legacy OpenAI organization and project settings to headers", () => {
const selected = model("gpt-5", {
apiKey: "fixture",
organization: "org_123",
project: "proj_123",
})
expect(selected.route.defaults.headers).toMatchObject({
"OpenAI-Organization": "org_123",
"OpenAI-Project": "proj_123",
})
})
test("selects Azure API entrypoints with the same model contract", async () => {
const Azure = await import("@opencode-ai/ai/providers/azure")
const AzureChat = await import("@opencode-ai/ai/providers/azure/chat")
const AzureResponses = await import("@opencode-ai/ai/providers/azure/responses")
const settings = {
apiKey: "fixture",
resourceName: "opencode-test",
headers: { "x-application": "opencode" },
body: { service_tier: "priority" },
limits: { context: 200_000, output: 64_000 },
}
const responses = AzureResponses.model("deployment", settings)
const chat = AzureChat.model("deployment", settings)
expect(Azure.model("deployment", settings).route.id).toBe("azure-openai-responses")
expect(responses.route.id).toBe("azure-openai-responses")
expect(responses.route.endpoint.baseURL).toBe("https://opencode-test.openai.azure.com/openai/v1")
expect(responses.route.defaults.headers).toEqual({ "x-application": "opencode" })
expect(responses.route.defaults.http?.body).toEqual({ service_tier: "priority" })
expect(responses.route.defaults.limits).toEqual({ context: 200_000, output: 64_000 })
expect(chat.route.id).toBe("azure-openai-chat")
})
test("maps Google package settings onto the Gemini model", async () => {
const Google = await import("@opencode-ai/ai/providers/google")
const selected = Google.model("gemini-2.5-flash", {
apiKey: "fixture",
baseURL: "https://generativelanguage.test/v1beta",
headers: { "x-application": "opencode" },
body: { safetySettings: [] },
limits: { context: 1_000_000, output: 65_536 },
providerOptions: { gemini: { thinkingConfig: { thinkingBudget: 1_024 } } },
})
expect(selected.route.id).toBe("gemini")
expect(selected.route.endpoint.baseURL).toBe("https://generativelanguage.test/v1beta")
expect(selected.route.defaults.headers).toEqual({ "x-application": "opencode" })
expect(selected.route.defaults.http?.body).toEqual({ safetySettings: [] })
expect(selected.route.defaults.limits).toEqual({ context: 1_000_000, output: 65_536 })
expect(selected.route.defaults.providerOptions).toEqual({
gemini: { thinkingConfig: { thinkingBudget: 1_024 } },
})
})
test("selects Vertex entrypoints with the same model contract", async () => {
const GoogleVertex = await import("@opencode-ai/ai/providers/google-vertex")
const GoogleVertexGemini = await import("@opencode-ai/ai/providers/google-vertex/gemini")
const GoogleVertexChat = await import("@opencode-ai/ai/providers/google-vertex/chat")
const GoogleVertexResponses = await import("@opencode-ai/ai/providers/google-vertex/responses")
const GoogleVertexMessages = await import("@opencode-ai/ai/providers/google-vertex/messages")
const gemini = GoogleVertex.model("gemini-3.5-flash", {
apiKey: "fixture",
headers: { "x-application": "opencode" },
body: { safetySettings: [] },
limits: { context: 1_000_000, output: 65_536 },
})
const messages = GoogleVertexMessages.model("claude-sonnet-4-6", {
accessToken: "fixture",
location: "global",
project: "vertex-project",
})
const chat = GoogleVertexChat.model("deepseek-ai/deepseek-v3.2-maas", {
accessToken: "fixture",
location: "global",
project: "vertex-project",
})
const responses = GoogleVertexResponses.model("xai/grok-4.20-reasoning", {
accessToken: "fixture",
location: "global",
project: "vertex-project",
})
expect(GoogleVertexGemini.model).toBe(GoogleVertex.model)
expect(gemini.route.id).toBe("google-vertex-gemini")
expect(gemini.route.protocol).toBe("gemini")
expect(gemini.route.endpoint.baseURL).toBe("https://aiplatform.googleapis.com/v1/publishers/google")
expect(gemini.route.defaults.headers).toEqual({ "x-application": "opencode" })
expect(gemini.route.defaults.http?.body).toEqual({ safetySettings: [] })
expect(gemini.route.defaults.limits).toEqual({ context: 1_000_000, output: 65_536 })
expect(
GoogleVertex.model("gemini-3.5-flash", {
accessToken: "fixture",
location: "eu",
project: "vertex-project",
}).route.endpoint.baseURL,
).toBe("https://aiplatform.eu.rep.googleapis.com/v1beta1/projects/vertex-project/locations/eu/publishers/google")
expect(messages.route.id).toBe("google-vertex-messages")
expect(messages.route.protocol).toBe("anthropic-messages")
expect(messages.route.endpoint.baseURL).toBe(
"https://aiplatform.googleapis.com/v1/projects/vertex-project/locations/global/publishers/anthropic/models",
)
expect(chat.route.id).toBe("google-vertex-chat")
expect(chat.route.protocol).toBe("openai-chat")
expect(chat.route.endpoint).toMatchObject({
baseURL: "https://aiplatform.googleapis.com/v1/projects/vertex-project/locations/global/endpoints/openapi",
path: "/chat/completions",
})
expect(responses.route.id).toBe("google-vertex-responses")
expect(responses.route.protocol).toBe("openai-responses")
expect(responses.route.endpoint).toMatchObject({
baseURL: "https://aiplatform.googleapis.com/v1/projects/vertex-project/locations/global/endpoints/openapi",
path: "/responses",
})
expect(responses.route.defaults.providerOptions).toEqual({ openai: { store: false } })
})
test("rejects conflicting Vertex auth settings at runtime", async () => {
const GoogleVertex = await import("@opencode-ai/ai/providers/google-vertex")
const GoogleVertexChat = await import("@opencode-ai/ai/providers/google-vertex/chat")
const GoogleVertexMessages = await import("@opencode-ai/ai/providers/google-vertex/messages")
const GoogleVertexResponses = await import("@opencode-ai/ai/providers/google-vertex/responses")
const Providers = await import("@opencode-ai/ai/providers")
expect(() =>
Reflect.apply(GoogleVertex.model, undefined, [
"gemini-3.5-flash",
{ accessToken: "token", apiKey: "fixture", project: "vertex-project" },
]),
).toThrow("Google Vertex apiKey cannot be combined with accessToken or auth")
const configured = Reflect.apply(GoogleVertex.configure, undefined, [
{ accessToken: "token", auth: {}, project: "vertex-project" },
])
expect(() => configured.model("gemini-3.5-flash")).toThrow("Google Vertex accessToken cannot be combined with auth")
expect(() =>
Reflect.apply(GoogleVertexMessages.model, undefined, [
"claude-sonnet-4-6",
{ apiKey: "fixture", project: "vertex-project" },
]),
).toThrow("Google Vertex Messages does not support API keys")
expect(() =>
Reflect.apply(Providers.GoogleVertexMessages.configure, undefined, [
{ apiKey: "fixture", project: "vertex-project" },
]),
).toThrow("Google Vertex Messages does not support API keys")
expect(() =>
Reflect.apply(GoogleVertexChat.model, undefined, [
"deepseek-ai/deepseek-v3.2-maas",
{ apiKey: "fixture", project: "vertex-project" },
]),
).toThrow("Google Vertex Chat does not support API keys")
expect(() =>
Reflect.apply(Providers.GoogleVertexChat.configure, undefined, [
{ apiKey: "fixture", project: "vertex-project" },
]),
).toThrow("Google Vertex Chat does not support API keys")
expect(() =>
Reflect.apply(GoogleVertexResponses.model, undefined, [
"xai/grok-4.20-reasoning",
{ apiKey: "fixture", project: "vertex-project" },
]),
).toThrow("Google Vertex Responses does not support API keys")
expect(() =>
Reflect.apply(Providers.GoogleVertexResponses.configure, undefined, [
{ apiKey: "fixture", project: "vertex-project" },
]),
).toThrow("Google Vertex Responses does not support API keys")
})
})
@@ -1,56 +0,0 @@
import { describe, expect } from "bun:test"
import { Effect } from "effect"
import { Image, ImageInput } from "../../src"
import { Google } from "../../src/providers"
import { dimensions } from "../lib/image"
import { recordedTests } from "../recorded-test"
const model = Google.configure({
apiKey: process.env.GOOGLE_GENERATIVE_AI_API_KEY ?? "fixture",
}).image("gemini-3.1-flash-image")
const recorded = recordedTests({
prefix: "google-images",
provider: "google",
protocol: "google-images",
requires: ["GOOGLE_GENERATIVE_AI_API_KEY"],
})
describe("Google Images recorded", () => {
recorded.effect("generates an image", () =>
Effect.gen(function* () {
const response = yield* Image.generate({
model,
prompt: "A simple flat blue circle centered on a plain white background.",
options: { aspectRatio: "1:1" },
})
expect(response.images).toHaveLength(1)
expect(response.image?.mediaType).toMatch(/^image\//)
expect(response.image?.data).toBeInstanceOf(Uint8Array)
expect(response.image?.data.length).toBeGreaterThan(0)
}),
)
recorded.effect("edits an image", () =>
Effect.gen(function* () {
const response = yield* Image.generate({
model,
prompt:
"Transform this minimal source into a bright orange sun icon with eight rounded rays on a pale blue background.",
images: [
ImageInput.bytes(
yield* Effect.promise(() => Bun.file("test/fixtures/images/edit-source.jpg").bytes()),
"image/jpeg",
),
],
options: { aspectRatio: "1:1" },
})
expect(response.image?.mediaType).toBe("image/jpeg")
expect(response.image?.data).toBeInstanceOf(Uint8Array)
if (!(response.image?.data instanceof Uint8Array)) throw new Error("Expected owned Google image bytes")
expect(dimensions(response.image.data)).toEqual({ width: 1024, height: 1024 })
}),
)
})
@@ -1,246 +0,0 @@
import { describe, expect } from "bun:test"
import { Effect } from "effect"
import { HttpClientRequest } from "effect/unstable/http"
import { LLM } from "../../src"
import { GoogleVertex, GoogleVertexChat, GoogleVertexMessages, GoogleVertexResponses } from "../../src/providers"
import { LLMClient } from "../../src/route"
import { it } from "../lib/effect"
import { dynamicResponse } from "../lib/http"
import { deltaChunk, finishChunk } from "../lib/openai-chunks"
import { sseEvents } from "../lib/sse"
describe("Google Vertex providers", () => {
it.effect("sends Gemini requests to the global Vertex endpoint", () =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(
LLM.request({
model: GoogleVertex.configure({
accessToken: "vertex-token",
location: "global",
project: "vertex-project",
}).model("gemini-3.5-flash"),
prompt: "Say hello.",
}),
).pipe(
Effect.provide(
dynamicResponse((input) =>
Effect.gen(function* () {
const request = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
expect(request.url).toBe(
"https://aiplatform.googleapis.com/v1beta1/projects/vertex-project/locations/global/publishers/google/models/gemini-3.5-flash:streamGenerateContent?alt=sse",
)
expect(request.headers.get("authorization")).toBe("Bearer vertex-token")
expect(yield* Effect.promise(() => request.json())).toMatchObject({
contents: [{ role: "user", parts: [{ text: "Say hello." }] }],
})
return input.respond(
sseEvents({
candidates: [
{
content: { role: "model", parts: [{ text: "Hello." }] },
finishReason: "STOP",
},
],
}),
{ headers: { "content-type": "text/event-stream" } },
)
}),
),
),
)
expect(response.text).toBe("Hello.")
}),
)
it.effect("projects Anthropic Messages onto the Vertex raw-predict API", () =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(
LLM.request({
model: GoogleVertexMessages.configure({
accessToken: "vertex-token",
location: "eu",
project: "vertex-project",
}).model("claude-sonnet-4-6"),
prompt: "Say hello.",
}),
).pipe(
Effect.provide(
dynamicResponse((input) =>
Effect.gen(function* () {
const request = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
expect(request.url).toBe(
"https://aiplatform.eu.rep.googleapis.com/v1/projects/vertex-project/locations/eu/publishers/anthropic/models/claude-sonnet-4-6:streamRawPredict",
)
expect(request.headers.get("authorization")).toBe("Bearer vertex-token")
expect(request.headers.get("anthropic-version")).toBeNull()
const body = yield* Effect.promise(() => request.json())
expect(body).toMatchObject({
anthropic_version: "vertex-2023-10-16",
messages: [{ role: "user", content: [{ type: "text", text: "Say hello." }] }],
stream: true,
})
expect(body).not.toHaveProperty("model")
return input.respond(
sseEvents(
{ type: "content_block_start", index: 0, content_block: { type: "text", text: "" } },
{ type: "content_block_delta", index: 0, delta: { type: "text_delta", text: "Hello." } },
{ type: "content_block_stop", index: 0 },
{ type: "message_delta", delta: { stop_reason: "end_turn" }, usage: { output_tokens: 2 } },
{ type: "message_stop" },
),
{ headers: { "content-type": "text/event-stream" } },
)
}),
),
),
)
expect(response.text).toBe("Hello.")
}),
)
it.effect("sends MaaS requests through Vertex Chat Completions", () =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(
LLM.request({
model: GoogleVertexChat.configure({
accessToken: "vertex-token",
location: "global",
project: "vertex-project",
}).model("deepseek-ai/deepseek-v3.2-maas"),
prompt: "Say hello.",
}),
).pipe(
Effect.provide(
dynamicResponse((input) =>
Effect.gen(function* () {
const request = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
expect(request.url).toBe(
"https://aiplatform.googleapis.com/v1/projects/vertex-project/locations/global/endpoints/openapi/chat/completions",
)
expect(request.headers.get("authorization")).toBe("Bearer vertex-token")
expect(yield* Effect.promise(() => request.json())).toMatchObject({
model: "deepseek-ai/deepseek-v3.2-maas",
messages: [{ role: "user", content: "Say hello." }],
stream: true,
stream_options: { include_usage: true },
})
return input.respond(sseEvents(deltaChunk({ content: "Hello." }), finishChunk("stop")), {
headers: { "content-type": "text/event-stream" },
})
}),
),
),
)
expect(response.text).toBe("Hello.")
}),
)
it.effect("sends Grok requests through Vertex Responses", () =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(
LLM.request({
model: GoogleVertexResponses.configure({
accessToken: "vertex-token",
location: "global",
project: "vertex-project",
}).model("xai/grok-4.20-reasoning"),
prompt: "Say hello.",
}),
).pipe(
Effect.provide(
dynamicResponse((input) =>
Effect.gen(function* () {
const request = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
expect(request.url).toBe(
"https://aiplatform.googleapis.com/v1/projects/vertex-project/locations/global/endpoints/openapi/responses",
)
expect(request.headers.get("authorization")).toBe("Bearer vertex-token")
expect(yield* Effect.promise(() => request.json())).toMatchObject({
model: "xai/grok-4.20-reasoning",
input: [{ role: "user", content: [{ type: "input_text", text: "Say hello." }] }],
store: false,
stream: true,
})
return input.respond(
sseEvents(
{ type: "response.output_text.delta", item_id: "msg_1", delta: "Hello." },
{ type: "response.completed", response: { id: "resp_1" } },
),
{ headers: { "content-type": "text/event-stream" } },
)
}),
),
),
)
expect(response.text).toBe("Hello.")
}),
)
it.effect("protects the Vertex Messages API version from body overlays", () =>
Effect.gen(function* () {
const error = yield* LLMClient.prepare(
LLM.request({
model: GoogleVertexMessages.configure({
accessToken: "vertex-token",
http: { body: { anthropic_version: "wrong" } },
project: "vertex-project",
}).model("claude-sonnet-4-6"),
prompt: "Say hello.",
}),
).pipe(Effect.flip)
expect(error.message).toContain("http.body cannot overlay protocol-owned field(s): anthropic_version")
}),
)
it.effect("routes tuned Gemini models through their deployed endpoint", () =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(
LLM.request({
model: GoogleVertex.configure({
accessToken: "vertex-token",
location: "us-central1",
project: "vertex-project",
}).model("endpoints/1234567890"),
prompt: "Say hello.",
}),
).pipe(
Effect.provide(
dynamicResponse((input) =>
Effect.gen(function* () {
const request = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
expect(request.url).toBe(
"https://us-central1-aiplatform.googleapis.com/v1beta1/projects/vertex-project/locations/us-central1/endpoints/1234567890:streamGenerateContent?alt=sse",
)
return input.respond(
sseEvents({
candidates: [
{
content: { role: "model", parts: [{ text: "Hello." }] },
finishReason: "STOP",
},
],
}),
{ headers: { "content-type": "text/event-stream" } },
)
}),
),
),
)
expect(response.text).toBe("Hello.")
}),
)
it.effect("rejects tuned Gemini models in express mode", () =>
Effect.sync(() => {
expect(() => GoogleVertex.configure({ apiKey: "fixture" }).model("endpoints/1234567890")).toThrow(
"Google Vertex tuned models do not support Express Mode API keys",
)
}),
)
})
@@ -1,148 +0,0 @@
import { describe, expect } from "bun:test"
import { Effect } from "effect"
import { LLM, LLMEvent, LLMResponse, Model } from "../../src"
import { OpenAIChat } from "../../src/protocols/openai-chat"
import * as OpenAICompatible from "../../src/providers/openai-compatible"
import * as OpenRouter from "../../src/providers/openrouter"
import { LLMClient } from "../../src/route"
import { recordedTests } from "../recorded-test"
import { expectWeatherToolLoop, goldenWeatherToolLoopRequest, runWeatherToolLoop } from "../recorded-scenarios"
const cases = [
{
name: "OpenRouter",
model: Model.update(
OpenRouter.configure({
apiKey: process.env.OPENROUTER_API_KEY ?? "fixture",
providerOptions: { openrouter: { reasoning: { max_tokens: 1024 } } },
}).model("anthropic/claude-sonnet-4.6"),
{ compatibility: { reasoningField: "reasoning" } },
),
requires: ["OPENROUTER_API_KEY"],
cassette: "openrouter-reasoning",
structured: true,
},
{
name: "Vercel AI Gateway",
model: Model.update(
OpenAICompatible.configure({
provider: "vercel-ai-gateway",
baseURL: "https://ai-gateway.vercel.sh/v1",
apiKey: process.env.AI_GATEWAY_API_KEY ?? "fixture",
http: { body: { reasoning: { enabled: true, max_tokens: 1024 } } },
}).model("anthropic/claude-sonnet-4.6"),
{ compatibility: { reasoningField: "reasoning" } },
),
requires: ["AI_GATEWAY_API_KEY"],
cassette: "vercel-ai-gateway-reasoning",
structured: true,
},
] as const
for (const item of cases) {
const recorded = recordedTests({
prefix: "openai-compatible-chat",
provider: item.model.provider,
protocol: "openai-chat",
requires: item.requires,
tags: ["reasoning"],
metadata: { model: item.model.id },
})
describe(`${item.name} reasoning recorded`, () => {
recorded.effect.with(
"streams scalar reasoning",
{ cassette: item.cassette },
() =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(
LLM.request({
model: item.model,
system: "Think through the arithmetic, then reply with only the final integer.",
prompt: "What is 173 multiplied by 219?",
generation: { maxTokens: 1536, temperature: 0 },
}),
)
expect(response.text.replaceAll(",", "").trim()).toBe("37887")
expect(response.reasoning.length).toBeGreaterThan(0)
expect(response.events.some(LLMEvent.is.reasoningDelta)).toBe(true)
const metadata = response.message.content.find((part) => part.type === "reasoning")?.providerMetadata
expect(metadata?.openai?.reasoningField).toBe(item.structured ? "reasoning" : "reasoning_content")
expect(Array.isArray(metadata?.openai?.reasoningDetails)).toBe(item.structured)
if (!item.structured) return
const details = metadata?.openai?.reasoningDetails
if (!Array.isArray(details)) return
expect(
details.some(
(detail) =>
typeof detail === "object" &&
detail !== null &&
"signature" in detail &&
typeof detail.signature === "string" &&
detail.signature.length > 0,
),
).toBe(true)
const replay = yield* LLMClient.prepare<OpenAIChat.OpenAIChatBody>(
LLM.request({ model: item.model, messages: [response.message] }),
)
expect(replay.body.messages).toMatchObject([
{ role: "assistant", content: response.text, reasoning: response.reasoning },
])
const replayDetails =
replay.body.messages[0]?.role === "assistant" ? replay.body.messages[0].reasoning_details : undefined
expect(Array.isArray(replayDetails)).toBe(true)
if (!Array.isArray(replayDetails)) return
expect(replayDetails).toEqual(details)
expect(replayDetails).toHaveLength(1)
expect(replayDetails[0]).toMatchObject({
type: "reasoning.text",
text: response.reasoning,
signature: expect.any(String),
})
}),
30_000,
)
recorded.effect.with(
"continues signed reasoning through a tool loop",
{ cassette: `${item.cassette}-tool-loop`, tags: ["continuation", "tool", "tool-loop"] },
() =>
Effect.gen(function* () {
const events = yield* runWeatherToolLoop(
goldenWeatherToolLoopRequest({
id: `${item.cassette}-tool-loop`,
model: item.model,
maxTokens: 1536,
temperature: false,
}),
)
expectWeatherToolLoop(events)
expect(
LLMResponse.text({
events: events.slice(events.findIndex(LLMEvent.is.stepFinish) + 1),
}).trim(),
).toMatch(/^Paris is sunny\.?$/)
const details = events
.filter(LLMEvent.is.reasoningEnd)
.map((event) => event.providerMetadata?.openai?.reasoningDetails)
.find(Array.isArray)
expect(Array.isArray(details)).toBe(item.structured)
if (!item.structured || !Array.isArray(details)) return
expect(
details.some(
(detail) =>
typeof detail === "object" &&
detail !== null &&
"signature" in detail &&
typeof detail.signature === "string" &&
detail.signature.length > 0,
),
).toBe(true)
}),
60_000,
)
})
}
@@ -1,62 +0,0 @@
import { describe, expect } from "bun:test"
import { Effect } from "effect"
import { Image, ImageInput } from "../../src"
import { OpenAI } from "../../src/providers"
import { dimensions } from "../lib/image"
import { recordedTests } from "../recorded-test"
const model = OpenAI.configure({
apiKey: process.env.OPENAI_API_KEY ?? "fixture",
}).image("gpt-image-1-mini")
const recorded = recordedTests({
prefix: "openai-images",
provider: "openai",
protocol: "openai-images",
requires: ["OPENAI_API_KEY"],
})
describe("OpenAI Images recorded", () => {
recorded.effect("generates an image", () =>
Effect.gen(function* () {
const response = yield* Image.generate({
model,
prompt: "A simple flat black circle centered on a plain white background.",
options: { quality: "low", outputFormat: "jpeg", outputCompression: 10, size: "1024x1024" },
})
expect(response.images).toHaveLength(1)
expect(response.image?.mediaType).toBe("image/jpeg")
expect(response.image?.data).toBeInstanceOf(Uint8Array)
expect(response.image?.data.length).toBeGreaterThan(0)
}),
)
recorded.effect.with(
"edits an image",
{
options: {
match: (incoming, recorded) => incoming.method === recorded.method && incoming.url === recorded.url,
},
},
() =>
Effect.gen(function* () {
const response = yield* Image.generate({
model,
prompt: "Keep the simple shape and change it from black to bright green.",
images: [
ImageInput.bytes(
yield* Effect.promise(() => Bun.file("test/fixtures/images/edit-source.jpg").bytes()),
"image/jpeg",
),
],
options: { quality: "low", outputFormat: "jpeg", outputCompression: 10, size: "1024x1024" },
})
expect(response.image?.mediaType).toBe("image/jpeg")
expect(response.image?.data).toBeInstanceOf(Uint8Array)
if (!(response.image?.data instanceof Uint8Array)) throw new Error("Expected owned OpenAI image bytes")
expect(dimensions(response.image.data)).toEqual({ width: 1024, height: 1024 })
}),
)
})
@@ -1,66 +0,0 @@
import { describe, expect } from "bun:test"
import { Effect } from "effect"
import { LLM, LLMEvent, Message } from "../../src"
import { OpenAI } from "../../src/providers"
import { recordedTests } from "../recorded-test"
const openai = OpenAI.configure({
apiKey: process.env.OPENAI_API_KEY ?? "fixture",
})
const recorded = recordedTests({
prefix: "openai-responses-images",
provider: "openai",
protocol: "openai-responses",
requires: ["OPENAI_API_KEY"],
})
describe("OpenAI Responses image generation recorded", () => {
recorded.effect("generates and edits an image with the hosted tool", () =>
Effect.gen(function* () {
const initial = Message.user("Generate a simple flat black triangle centered on a plain white background.")
const tools = [
OpenAI.imageGeneration({
action: "auto",
quality: "low",
size: "1024x1024",
outputFormat: "jpeg",
outputCompression: 10,
partialImages: 0,
}),
]
const response = yield* LLM.generate(
LLM.request({
model: openai.responses("gpt-5-mini"),
messages: [initial],
tools,
toolChoice: "image_generation",
}),
)
const result = response.events.find(LLMEvent.is.toolResult)
expect(result).toBeDefined()
expect(result?.providerExecuted).toBe(true)
expect(result?.result.type).toBe("content")
if (result?.result.type !== "content") return
expect(result.result.value).toHaveLength(1)
expect(result.result.value[0]?.type).toBe("file")
if (result.result.value[0]?.type !== "file") return
expect(result.result.value[0].mime).toBe("image/jpeg")
expect(result.result.value[0].uri.startsWith("data:image/jpeg;base64,")).toBe(true)
const edited = yield* LLM.generate(
LLM.request({
model: openai.responses("gpt-5-mini"),
messages: [initial, response.message, Message.user("Now make the triangle blue.")],
tools,
toolChoice: "image_generation",
}),
)
const editedResult = edited.events.find(LLMEvent.is.toolResult)
expect(editedResult?.result.type).toBe("content")
if (editedResult?.result.type !== "content") return
expect(editedResult.result.value[0]?.type).toBe("file")
}),
)
})
@@ -1,154 +0,0 @@
import { describe, expect } from "bun:test"
import { Effect } from "effect"
import { LLM, Message } from "../../src"
import { LLMClient } from "../../src/route"
import * as OpenRouter from "../../src/providers/openrouter"
import { it } from "../lib/effect"
describe("OpenRouter", () => {
it.effect("prepares OpenRouter models through the OpenAI-compatible Chat route", () =>
Effect.gen(function* () {
const model = OpenRouter.configure({ apiKey: "test-key" }).model("openai/gpt-4o-mini")
expect(model).toMatchObject({
id: "openai/gpt-4o-mini",
provider: "openrouter",
route: { id: "openrouter" },
})
expect(model.route.endpoint.baseURL).toBe("https://openrouter.ai/api/v1")
const prepared = yield* LLMClient.prepare(LLM.request({ model, prompt: "Say hello." }))
expect(prepared.route).toBe("openrouter")
expect(prepared.body).toMatchObject({
model: "openai/gpt-4o-mini",
messages: [{ role: "user", content: "Say hello." }],
stream: true,
})
}),
)
it.effect("applies OpenRouter payload options from the model helper", () =>
Effect.gen(function* () {
const prepared = yield* LLMClient.prepare(
LLM.request({
model: OpenRouter.configure({
apiKey: "test-key",
providerOptions: {
openrouter: {
usage: true,
reasoning: { effort: "high" },
promptCacheKey: "session_123",
},
},
}).model("anthropic/claude-3.7-sonnet:thinking"),
prompt: "Think briefly.",
}),
)
expect(prepared.body).toMatchObject({
usage: { include: true },
reasoning: { effort: "high" },
prompt_cache_key: "session_123",
})
}),
)
it.effect("preserves manually supplied reasoning details", () =>
Effect.gen(function* () {
const details = [
{ type: "reasoning.text", text: "Think", format: "anthropic-claude-v1", index: 0 },
{ type: "reasoning.text", text: "ing", format: "anthropic-claude-v1", index: 0 },
{ type: "reasoning.text", signature: "signed", format: "anthropic-claude-v1", index: 0 },
{ type: "reasoning.encrypted", data: "opaque", format: "openai-responses-v1", index: 1 },
]
const prepared = yield* LLMClient.prepare<OpenRouter.OpenRouterBody>(
LLM.request({
model: OpenRouter.configure({ apiKey: "test-key" }).model("anthropic/claude-sonnet-4.6"),
messages: [
Message.assistant([
{
type: "reasoning",
text: "Thinking",
providerMetadata: { openai: { reasoningField: "reasoning", reasoningDetails: details } },
},
]),
],
}),
)
expect(prepared.body.messages).toEqual([
{
role: "assistant",
content: null,
reasoning: "Thinking",
reasoning_details: details,
},
])
}),
)
it.effect("preserves opaque and duplicate continuation details", () =>
Effect.gen(function* () {
const details = [
{ type: "reasoning.future", format: "provider-v2", state: { opaque: true } },
{ type: "reasoning.encrypted", id: "state", data: "opaque" },
{ type: "reasoning.encrypted", id: "state", data: "opaque" },
]
const prepared = yield* LLMClient.prepare<OpenRouter.OpenRouterBody>(
LLM.request({
model: OpenRouter.configure({ apiKey: "test-key" }).model("anthropic/claude-sonnet-4.6"),
messages: [
Message.assistant({
type: "reasoning",
text: "Thinking",
providerMetadata: { openai: { reasoningField: "reasoning", reasoningDetails: details } },
}),
],
}),
)
expect(prepared.body.messages).toEqual([
{ role: "assistant", content: null, reasoning: "Thinking", reasoning_details: details },
])
}),
)
it.effect("does not merge distinct adjacent reasoning text blocks", () =>
Effect.gen(function* () {
const details = [
{ type: "reasoning.text", id: "first", index: 0, text: "A", opaque: "first" },
{ type: "reasoning.text", id: "second", index: 1, text: "B", opaque: "second" },
]
const prepared = yield* LLMClient.prepare<OpenRouter.OpenRouterBody>(
LLM.request({
model: OpenRouter.configure({ apiKey: "test-key" }).model("anthropic/claude-sonnet-4.6"),
messages: [
Message.assistant({
type: "reasoning",
text: "AB",
providerMetadata: { openai: { reasoningField: "reasoning", reasoningDetails: details } },
}),
],
}),
)
expect(prepared.body.messages).toEqual([
{ role: "assistant", content: null, reasoning: "AB", reasoning_details: details },
])
}),
)
it.effect("omits scalar reasoning without continuation details", () =>
Effect.gen(function* () {
const prepared = yield* LLMClient.prepare<OpenRouter.OpenRouterBody>(
LLM.request({
model: OpenRouter.configure({ apiKey: "test-key" }).model("anthropic/claude-sonnet-4.6"),
messages: [Message.assistant({ type: "reasoning", text: "Thinking" })],
}),
)
expect(prepared.body.messages).toEqual([{ role: "assistant", content: null }])
}),
)
})
@@ -1,207 +0,0 @@
import { describe, expect } from "bun:test"
import { Effect, Schema, Stream } from "effect"
import { LLM, LLMResponse, Message, ToolDefinition, type Model } from "../../src"
import { AmazonBedrock, Anthropic, Google, OpenAI, XAI } from "../../src/providers"
import { LLMClient } from "../../src/route"
import { Tool } from "../../src/tool"
import { runTools } from "../lib/tool-runtime"
import { recordedTests } from "../recorded-test"
const CODE = "ORCHID-7391"
const PDF =
"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"
const openai = OpenAI.configure({ apiKey: process.env.OPENAI_API_KEY ?? "fixture" })
const anthropic = Anthropic.configure({ apiKey: process.env.ANTHROPIC_API_KEY ?? "fixture" })
const google = Google.configure({ apiKey: process.env.GOOGLE_API_KEY ?? "fixture" })
const xai = XAI.configure({ apiKey: process.env.XAI_API_KEY ?? "fixture" })
const bedrock = AmazonBedrock.configure({
apiKey: process.env.AWS_BEDROCK_API_KEY ?? "fixture",
region: process.env.AWS_REGION ?? "us-east-1",
})
const targets: ReadonlyArray<{
readonly id: string
readonly name: string
readonly provider: string
readonly protocol: string
readonly requires: string
readonly filename: string
readonly maxTokens: number
readonly model: Model
}> = [
{
id: "openai",
name: "OpenAI Responses gpt-4o-mini",
provider: "openai",
protocol: "openai-responses",
requires: "OPENAI_API_KEY",
filename: "verification.pdf",
maxTokens: 40,
model: openai.responses("gpt-4o-mini"),
},
{
id: "anthropic",
name: "Anthropic Haiku 4.5",
provider: "anthropic",
protocol: "anthropic-messages",
requires: "ANTHROPIC_API_KEY",
filename: "verification.pdf",
maxTokens: 40,
model: anthropic.model("claude-haiku-4-5-20251001"),
},
{
id: "gemini",
name: "Gemini 3.5 Flash",
provider: "google",
protocol: "gemini",
requires: "GOOGLE_API_KEY",
filename: "verification.pdf",
maxTokens: 256,
model: google.model("gemini-3.5-flash"),
},
{
id: "xai",
name: "xAI Grok 4.5",
provider: "xai",
protocol: "openai-responses",
requires: "XAI_API_KEY",
filename: "verification.pdf",
maxTokens: 40,
model: xai.responses("grok-4.5"),
},
{
id: "bedrock",
name: "Bedrock Claude Haiku 4.5",
provider: "amazon-bedrock",
protocol: "bedrock-converse",
requires: "AWS_BEDROCK_API_KEY",
filename: "verification",
maxTokens: 40,
model: bedrock.model("us.anthropic.claude-haiku-4-5-20251001-v1:0"),
},
]
const recorded = recordedTests({ prefix: "pdf", tags: ["pdf"] })
const prompt = "Return only the verification code from the PDF."
const readPdf = ToolDefinition.make({
name: "read_pdf",
description: "Read the attached PDF.",
inputSchema: { type: "object", properties: {}, additionalProperties: false },
})
const readPdfRuntime = Tool.make({
description: readPdf.description,
parameters: Schema.Struct({ path: Schema.String }),
success: Schema.String,
execute: () => Effect.succeed("PDF read successfully"),
toModelOutput: () => [
{ type: "text", text: "PDF read successfully" },
{
type: "file",
uri: `data:application/pdf;base64,${PDF}`,
mime: "application/pdf",
name: "verification.pdf",
},
],
})
const expectCode = (response: LLMResponse) => {
expect(response.finishReason).toBe("stop")
expect(response.text.toUpperCase()).toContain(CODE)
}
describe("PDF recorded", () => {
for (const target of targets) {
recorded.effect.with(
`reads a user PDF with ${target.name}`,
{
id: `${target.id}-user-input`,
provider: target.provider,
protocol: target.protocol,
requires: [target.requires],
tags: ["user-input"],
},
Effect.gen(function* () {
expectCode(
yield* LLMClient.generate(
LLM.request({
id: `recorded_pdf_${target.id}_user_input`,
model: target.model,
cache: "none",
generation: { maxTokens: target.maxTokens, temperature: 0 },
messages: [
Message.user([
{ type: "media", mediaType: "application/pdf", data: PDF, filename: target.filename },
{ type: "text", text: prompt },
]),
],
}),
),
)
}),
)
recorded.effect.with(
`reads a PDF tool result with ${target.name}`,
{
id: `${target.id}-tool-result`,
provider: target.provider,
protocol: target.protocol,
requires: [target.requires],
tags: ["tool", "tool-result"],
},
Effect.gen(function* () {
if (target.id === "gemini") {
const events = Array.from(
yield* runTools({
request: LLM.request({
id: "recorded_pdf_gemini_tool_result",
model: target.model,
system:
"Call read_pdf exactly once with path verification.pdf, then reply only with the verification code from its PDF.",
prompt: "Use read_pdf with path verification.pdf and return the verification code.",
cache: "none",
generation: { maxTokens: target.maxTokens, temperature: 0 },
}),
tools: { read_pdf: readPdfRuntime },
}).pipe(Stream.runCollect),
)
expect(events.at(-1)).toMatchObject({ type: "finish", reason: "stop" })
expect(LLMResponse.text({ events }).toUpperCase()).toContain(CODE)
return
}
expectCode(
yield* LLMClient.generate(
LLM.request({
id: `recorded_pdf_${target.id}_tool_result`,
model: target.model,
system: "Read the PDF returned by the tool and follow the user's response format exactly.",
cache: "none",
generation: { maxTokens: target.maxTokens, temperature: 0 },
messages: [
Message.user(prompt),
Message.assistant([{ type: "tool-call", id: "call_pdf_1", name: readPdf.name, input: {} }]),
Message.tool({
id: "call_pdf_1",
name: readPdf.name,
resultType: "content",
result: [
{ type: "text", text: "PDF read successfully" },
{
type: "file",
uri: `data:application/pdf;base64,${PDF}`,
mime: "application/pdf",
name: target.filename,
},
],
}),
],
tools: [readPdf],
}),
),
)
}),
)
}
})
@@ -1,55 +0,0 @@
import { describe, expect } from "bun:test"
import { Effect } from "effect"
import { Image, ImageInput } from "../../src"
import { XAI } from "../../src/providers"
import { dimensions } from "../lib/image"
import { recordedTests } from "../recorded-test"
const model = XAI.configure({
apiKey: process.env.XAI_API_KEY ?? "fixture",
}).image("grok-imagine-image")
const recorded = recordedTests({
prefix: "xai-images",
provider: "xai",
protocol: "xai-images",
requires: ["XAI_API_KEY"],
})
describe("xAI Images recorded", () => {
recorded.effect("generates an image", () =>
Effect.gen(function* () {
const response = yield* Image.generate({
model,
prompt: "A simple flat black diamond centered on a plain white background.",
options: { aspectRatio: "1:1", resolution: "1k", responseFormat: "b64_json" },
})
expect(response.images).toHaveLength(1)
expect(response.image?.mediaType.startsWith("image/")).toBe(true)
expect(response.image?.data).toBeInstanceOf(Uint8Array)
expect(response.image?.data.length).toBeGreaterThan(0)
}),
)
recorded.effect("edits an image", () =>
Effect.gen(function* () {
const response = yield* Image.generate({
model,
prompt: "Keep the simple shape and change it from black to bright purple.",
images: [
ImageInput.bytes(
yield* Effect.promise(() => Bun.file("test/fixtures/images/edit-source.jpg").bytes()),
"image/jpeg",
),
],
options: { aspectRatio: "1:1", resolution: "1k", responseFormat: "b64_json" },
})
expect(response.image?.mediaType).toMatch(/^image\/(jpeg|png)$/)
expect(response.image?.data).toBeInstanceOf(Uint8Array)
if (!(response.image?.data instanceof Uint8Array)) throw new Error("Expected owned xAI image bytes")
expect(dimensions(response.image.data)).toEqual({ width: 1024, height: 1024 })
}),
)
})
@@ -1,109 +0,0 @@
import { describe, expect } from "bun:test"
import { Effect, Layer } from "effect"
import { Headers, HttpClientRequest } from "effect/unstable/http"
import { Image, ImageClient } from "../../src"
import { XAI } from "../../src/providers"
import { Auth } from "../../src/route"
import { it } from "../lib/effect"
import { dynamicResponse } from "../lib/http"
describe("xAI Images", () => {
it.effect("generates through the OpenAI-compatible Images API", () =>
Effect.gen(function* () {
const response = yield* Image.generate({
model: XAI.configure({
apiKey: "test",
baseURL: "https://api.xai.test/v1",
http: { body: { configured: true }, headers: { "x-default": "yes" } },
}).image("grok-imagine-image"),
prompt: "A robot tending a rooftop garden",
options: {
n: 2,
aspectRatio: "16:9",
aspect_ratio: "4:3",
resolution: "1k",
responseFormat: "url",
response_format: "b64_json",
future_option: true,
},
http: {
body: { resolution: "2k", future_option: "http" },
headers: { "x-request": "yes" },
query: { trace: "1" },
},
})
expect(response.images).toHaveLength(2)
expect(response.image?.mediaType).toBe("image/jpeg")
expect(response.image?.data).toEqual(Uint8Array.from([1, 2, 3]))
expect(response.images[1]?.mediaType).toBe("application/octet-stream")
expect(response.images[1]?.data).toBe("https://api.xai.test/image.jpg")
expect(response.usage?.providerMetadata).toEqual({ xai: { num_images: 2 } })
expect(response.providerMetadata).toEqual({ xai: { usage: { num_images: 2 } } })
}).pipe(
Effect.provide(
ImageClient.layer.pipe(
Layer.provide(
dynamicResponse((input) =>
Effect.gen(function* () {
const request = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
expect(request.url).toBe("https://api.xai.test/v1/images/generations?trace=1")
expect(request.headers.get("authorization")).toBe("Bearer test")
expect(request.headers.get("x-default")).toBe("yes")
expect(request.headers.get("x-request")).toBe("yes")
expect(JSON.parse(input.text)).toEqual({
model: "grok-imagine-image",
prompt: "A robot tending a rooftop garden",
n: 2,
aspect_ratio: "4:3",
resolution: "2k",
response_format: "b64_json",
future_option: "http",
configured: true,
})
return input.respond(
JSON.stringify({
data: [
{ b64_json: "AQID", url: null, mime_type: "image/jpeg" },
{ b64_json: null, url: "https://api.xai.test/image.jpg", mime_type: null },
],
usage: { num_images: 2 },
}),
{ headers: { "content-type": "application/json" } },
)
}),
),
),
),
),
),
)
it.effect("supports request-level custom auth", () =>
Image.generate({
model: XAI.configure({
baseURL: "https://api.xai.test/v1",
auth: Auth.custom((input) =>
Effect.succeed(Headers.set(input.headers, "x-custom-auth", new URL(input.url).hostname)),
),
}).image("grok-imagine-image"),
prompt: "A robot tending a rooftop garden",
}).pipe(
Effect.provide(
ImageClient.layer.pipe(
Layer.provide(
dynamicResponse((input) =>
Effect.gen(function* () {
const request = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
expect(request.headers.get("x-custom-auth")).toBe("api.xai.test")
return input.respond(JSON.stringify({ data: [{ b64_json: "AQID", mime_type: "image/png" }] }), {
headers: { "content-type": "application/json" },
})
}),
),
),
),
),
),
)
})
@@ -1,32 +0,0 @@
import { describe, expect } from "bun:test"
import { Effect } from "effect"
import { Image } from "../../src"
import { ZAI } from "../../src/providers"
import { recordedTests } from "../recorded-test"
const model = ZAI.configure({ apiKey: process.env.ZAI_API_KEY ?? "fixture" }).image("cogview-4-250304")
const recorded = recordedTests({
prefix: "zai-images",
provider: "zai",
protocol: "zai-images",
requires: ["ZAI_API_KEY"],
})
describe("Z.ai Images recorded", () => {
recorded.effect("generates an image", () =>
Effect.gen(function* () {
const response = yield* Image.generate({
model,
prompt: "A simple flat red circle centered on a plain white background.",
options: { size: "1024x1024", quality: "standard", userID: "opencode-image-test" },
})
expect(response.images).toHaveLength(1)
expect(response.image?.mediaType).toBe("application/octet-stream")
expect(response.image?.data).toBeString()
expect(response.image?.data).toStartWith("https://")
expect(response.providerMetadata?.zai).toBeDefined()
}),
)
})
@@ -1,130 +0,0 @@
import { describe, expect } from "bun:test"
import { Effect, Layer } from "effect"
import { HttpClientRequest } from "effect/unstable/http"
import { Image, ImageClient } from "../../src"
import { ZAI } from "../../src/providers"
import { it } from "../lib/effect"
import { dynamicResponse, fixedResponse } from "../lib/http"
describe("Z.ai Images", () => {
it.effect("generates through the Z.ai Images API", () =>
Effect.gen(function* () {
const response = yield* Image.generate({
model: ZAI.configure({
apiKey: "test",
baseURL: "https://api.z.ai.test/api/paas/v4",
headers: { "x-default": "yes" },
http: { body: { configured: true, quality: "configured" }, query: { trace: "default" } },
}).image("glm-image"),
prompt: "A red circle on a white background",
options: {
quality: "hd",
userID: "alias-user",
user_id: "raw-user",
future_option: true,
},
http: {
headers: { "x-request": "yes" },
query: { trace: "request" },
body: { quality: "final", user_id: "final-user" },
},
})
expect(response.images).toHaveLength(1)
expect(response.image?.mediaType).toBe("application/octet-stream")
expect(response.image?.data).toBe("https://cdn.z.ai/generated.png")
expect(response.providerMetadata).toEqual({
zai: {
created: 1_760_335_349,
id: "generation-1",
requestID: "request-1",
contentFilter: [{ role: "future-role", level: 4.5 }],
},
})
}).pipe(
Effect.provide(
ImageClient.layer.pipe(
Layer.provide(
dynamicResponse((input) =>
Effect.gen(function* () {
const request = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
expect(request.url).toBe("https://api.z.ai.test/api/paas/v4/images/generations?trace=request")
expect(request.headers.get("authorization")).toBe("Bearer test")
expect(request.headers.get("x-default")).toBe("yes")
expect(request.headers.get("x-request")).toBe("yes")
expect(JSON.parse(input.text)).toEqual({
model: "glm-image",
prompt: "A red circle on a white background",
quality: "final",
user_id: "final-user",
future_option: true,
configured: true,
})
return input.respond(
JSON.stringify({
created: 1_760_335_349,
id: "generation-1",
request_id: "request-1",
data: [{ url: "https://cdn.z.ai/generated.png" }],
content_filter: [{ role: "future-role", level: 4.5 }],
}),
{ headers: { "content-type": "application/json" } },
)
}),
),
),
),
),
),
)
it.effect("lets raw native options override aliases", () =>
Image.generate({
model: ZAI.configure({ apiKey: "test" }).image("model"),
prompt: "test",
options: { quality: "future-quality", userID: "x", user_id: "raw-user" },
}).pipe(
Effect.provide(
ImageClient.layer.pipe(
Layer.provide(
dynamicResponse((input) => {
expect(JSON.parse(input.text)).toMatchObject({ quality: "future-quality", user_id: "raw-user" })
return Effect.succeed(
input.respond(JSON.stringify({ data: [{ url: "https://example.test/image.jpg" }] }), {
headers: { "content-type": "application/json" },
}),
)
}),
),
),
),
),
)
it.effect("rejects invalid response structures", () =>
Effect.gen(function* () {
const model = ZAI.configure({ apiKey: "test" }).image("model")
const payloads = [
{},
{ data: [] },
{ data: [{ b64_json: "image" }] },
{ data: [{ url: 1 }] },
{ data: [{ url: "https://example.test/image.jpg" }], content_filter: [{ role: 1, level: "high" }] },
]
yield* Effect.forEach(payloads, (payload) =>
Image.generate({ model, prompt: "test" }).pipe(
Effect.provide(
ImageClient.layer.pipe(
Layer.provide(
fixedResponse(JSON.stringify(payload), { headers: { "content-type": "application/json" } }),
),
),
),
Effect.flip,
Effect.tap((error) => Effect.sync(() => expect(error.reason._tag).toBe("InvalidProviderOutput"))),
),
)
}),
)
})
-8
View File
@@ -1,8 +0,0 @@
{
"$schema": "https://json.schemastore.org/tsconfig",
"extends": "./tsconfig.json",
"compilerOptions": {
"allowImportingTsExtensions": false,
"noEmit": false
}
}
-12
View File
@@ -1,12 +0,0 @@
{
"$schema": "https://json.schemastore.org/tsconfig.json",
"extends": "@tsconfig/bun/tsconfig.json",
"compilerOptions": {
"rootDir": "src",
"outDir": "dist",
"declaration": true,
"lib": ["ESNext", "DOM", "DOM.Iterable"],
"noUncheckedIndexedAccess": false
},
"include": ["src"]
}
-9
View File
@@ -1,9 +0,0 @@
{
"$schema": "https://json.schemastore.org/tsconfig",
"extends": "./tsconfig.json",
"compilerOptions": {
"noEmit": true,
"rootDir": "."
},
"include": ["test/**/*.types.ts"]
}
@@ -136,9 +136,6 @@ export async function setupTimeline(
},
}),
)
if (settings.newLayoutDesigns === false) {
localStorage.setItem("app-version.v1", JSON.stringify({ version: "1.17.20" }))
}
}, input.settings ?? {})
if (input.locale) {
await page.addInitScript((locale) => {
@@ -24,7 +24,6 @@ test("redirects a draft to the legacy new-session route", async ({ page }) => {
await page.addInitScript(
({ directory, draftID, server }) => {
localStorage.setItem("settings.v3", JSON.stringify({ general: { newLayoutDesigns: false } }))
localStorage.setItem("app-version.v1", JSON.stringify({ version: "1.17.20" }))
localStorage.setItem(
"opencode.window.browser.dat:tabs",
JSON.stringify([{ type: "draft", draftID, server, directory }]),
@@ -1,50 +0,0 @@
import { expect, test } from "@playwright/test"
import { base64Encode } from "@opencode-ai/core/util/encode"
import { mockOpenCodeServer } from "../utils/mock-server"
import { expectAppVisible } from "../utils/waits"
const directory = "C:/OpenCode/PromptInputV2Editing"
const projectID = "proj_prompt_input_v2_editing"
const sessionID = "ses_prompt_input_v2_editing"
test("preserves the draft when a populated command menu triggers a built-in", async ({ page }) => {
await mockOpenCodeServer(page, {
directory,
project: {
id: projectID,
worktree: directory,
vcs: "git",
name: "prompt-input-v2-editing",
time: { created: 1700000000000, updated: 1700000000000 },
sandboxes: [],
},
provider: { all: [], connected: [], default: {} },
sessions: [
{
id: sessionID,
slug: "prompt-input-v2-editing",
projectID,
directory,
title: "Prompt input V2 editing",
version: "dev",
time: { created: 1700000000000, updated: 1700000000000 },
},
],
pageMessages: () => ({ items: [] }),
})
await page.addInitScript(() => {
localStorage.setItem("settings.v3", JSON.stringify({ general: { newLayoutDesigns: true } }))
})
await page.goto(`/${base64Encode(directory)}/session/${sessionID}`)
const composer = page.locator('[data-component="prompt-input-v2"]')
const input = composer.locator('[data-component="prompt-input"]')
await expectAppVisible(composer)
await input.fill("keep me")
await composer.getByRole("button", { name: "Add images and files" }).click()
await page.getByRole("menuitem", { name: "Commands" }).click()
await page.locator('[data-suggestion-id="model.choose"]').click()
await expect(input).toHaveText("keep me")
})
@@ -54,15 +54,18 @@ test("shows the V2 thinking level control while relevant", async ({ page }) => {
})
await page.goto(`/${base64Encode(directory)}/session/${sessionID}`)
const composer = page.locator('[data-component="prompt-input-v2"]')
const composer = page.locator('[data-component="session-composer"]')
const input = composer.locator('[data-component="prompt-input"]')
const control = composer.getByRole("button", { name: "Choose model variant" })
const control = composer.locator('[data-component="prompt-variant-control"]')
await expectAppVisible(composer)
await idleComposer(page)
await expect(control).toBeHidden()
await composer.hover()
await expect(control).toBeVisible()
await control.click()
await control.locator('[data-action="prompt-model-variant"]').click()
const high = page.getByRole("menuitemradio", { name: "high" })
await expect(high).toBeVisible()
await page.mouse.move(0, 0)
@@ -1,270 +0,0 @@
import { base64Encode } from "@opencode-ai/core/util/encode"
import { expect, test, type Page, type Route } from "@playwright/test"
import { installSseTransport } from "../utils/sse-transport"
const serverA = "http://127.0.0.1:4096"
const serverB = "http://127.0.0.1:4097"
const directoryA = "C:/server-a"
const directoryB = "/home/server-b"
const sessionA = session("ses_server_a", directoryA, "Server A session")
const childSessionA = { ...session("ses_server_a_child", directoryA, "Server A child session"), parentID: sessionA.id }
const sessionB = session("ses_server_b", directoryB, "Server B session")
test("session settings use the remote server context", async ({ page }) => {
const permissionRequests: string[] = []
await mockServers(page, permissionRequests)
await configureServers(page)
await page.goto(`/server/${base64Encode(serverB)}/session/${sessionB.id}`)
await expect(page.getByText(sessionB.title).first()).toBeVisible()
await page.keyboard.press(process.platform === "darwin" ? "Meta+," : "Control+,")
const dialog = page.locator(".settings-v2-dialog")
const autoAccept = dialog.locator('[data-action="settings-auto-accept-permissions"]')
const input = autoAccept.getByRole("switch")
await expect(autoAccept).toBeVisible()
await expect(input).toBeEnabled()
permissionRequests.length = 0
await autoAccept.locator('[data-slot="switch-control"]').click()
await expect(input).toBeChecked()
await expect
.poll(() =>
permissionRequests.some((request) => {
const url = new URL(request)
return url.origin === serverB && url.searchParams.get("directory") === directoryB
}),
)
.toBe(true)
expect(permissionRequests.every((request) => new URL(request).origin === serverB)).toBe(true)
await dialog.getByRole("tab", { name: "Models" }).click()
await expect(dialog.getByRole("switch", { name: "Server B Model" })).toBeEnabled()
await expect(dialog.getByRole("switch", { name: "Server A Model" })).toHaveCount(0)
})
test("auto-accept responds for an unfocused server session", async ({ page }) => {
const permissionRequests: string[] = []
const permissionResponses: PermissionResponse[] = []
const transport = await installSseTransport<{ directory: string; payload: Record<string, unknown> }>(page, {
server: serverA,
retry: 20,
})
await mockServers(page, permissionRequests, permissionResponses)
await configureServers(page, [
{ type: "session", server: serverA, sessionId: sessionA.id },
{ type: "session", server: serverB, sessionId: sessionB.id },
])
const hrefB = `/server/${base64Encode(serverB)}/session/${sessionB.id}`
await page.goto(`/server/${base64Encode(serverA)}/session/${sessionA.id}`)
await expect(page.getByText(sessionA.title).first()).toBeVisible()
await page.keyboard.press(process.platform === "darwin" ? "Meta+," : "Control+,")
const autoAccept = page.locator(".settings-v2-dialog").locator('[data-action="settings-auto-accept-permissions"]')
await autoAccept.locator('[data-slot="switch-control"]').click()
await expect(autoAccept.getByRole("switch")).toBeChecked()
await expect
.poll(() =>
permissionRequests.some((request) => {
const url = new URL(request)
return url.origin === serverA && url.searchParams.get("directory") === directoryA
}),
)
.toBe(true)
await page.keyboard.press("Escape")
await page.locator(`[data-titlebar-tab-slot]:has(a[href="${hrefB}"])`).click()
await expect(page).toHaveURL(new RegExp(`${hrefB.replace(/[.*+?^${}()|[\]\\]/g, "\\$&")}$`))
await expect(page.getByText(sessionB.title).first()).toBeVisible()
await transport.waitForConnection()
await transport.send({
directory: directoryA,
payload: {
id: "event-permission-background-a",
type: "permission.asked",
properties: {
id: "permission-background-a",
sessionID: sessionA.id,
permission: "bash",
patterns: ["git status"],
metadata: {},
always: [],
},
},
})
await expect
.poll(() => permissionResponses)
.toEqual([
{
origin: serverA,
directory: directoryA,
sessionID: sessionA.id,
permissionID: "permission-background-a",
body: { response: "once" },
},
])
await transport.send({
directory: directoryA,
payload: {
id: "event-permission-background-a-child",
type: "permission.asked",
properties: {
id: "permission-background-a-child",
sessionID: childSessionA.id,
permission: "bash",
patterns: ["git diff"],
metadata: {},
always: [],
},
},
})
await expect
.poll(() => permissionResponses)
.toEqual([
{
origin: serverA,
directory: directoryA,
sessionID: sessionA.id,
permissionID: "permission-background-a",
body: { response: "once" },
},
{
origin: serverA,
directory: directoryA,
sessionID: childSessionA.id,
permissionID: "permission-background-a-child",
body: { response: "once" },
},
])
})
type PermissionResponse = {
origin: string
directory?: string
sessionID: string
permissionID: string
body: unknown
}
async function configureServers(page: Page, tabs: { type: "session"; server: string; sessionId: string }[] = []) {
await page.addInitScript(
({ serverB, tabs }) => {
localStorage.setItem("settings.v3", JSON.stringify({ general: { newLayoutDesigns: true } }))
localStorage.setItem("opencode.global.dat:server", JSON.stringify({ list: [serverB] }))
localStorage.setItem("opencode.window.browser.dat:tabs", JSON.stringify(tabs))
},
{ serverB, tabs },
)
}
async function mockServers(page: Page, permissionRequests: string[], permissionResponses: PermissionResponse[] = []) {
await page.route("**/*", async (route) => {
const url = new URL(route.request().url())
if (url.origin !== serverA && url.origin !== serverB) return route.fallback()
const remote = url.origin === serverB
const directory = remote ? directoryB : directoryA
const sessions = remote ? [sessionB] : [sessionA, childSessionA]
const requestDirectory = url.searchParams.get("directory")
const response = url.pathname.match(/^\/session\/([^/]+)\/permissions\/([^/]+)$/)
if (route.request().method() === "POST" && response) {
permissionResponses.push({
origin: url.origin,
directory: requestDirectory ?? undefined,
sessionID: response[1]!,
permissionID: response[2]!,
body: route.request().postDataJSON(),
})
return json(route, true)
}
if (requestDirectory && requestDirectory !== directory) return json(route, { name: "InvalidDirectory" }, 500)
if (url.pathname === "/global/event" || url.pathname === "/event") return sse(route)
if (url.pathname === "/global/health") return json(route, { healthy: true })
if (url.pathname === "/session/status") return json(route, {})
if (url.pathname === "/session") return json(route, sessions)
const current = sessions.find((session) => url.pathname === `/session/${session.id}`)
if (current) return json(route, current)
if (/^\/session\/[^/]+$/.test(url.pathname)) return json(route, { name: "NotFoundError" }, 404)
if (/^\/session\/[^/]+\/message$/.test(url.pathname)) return json(route, [])
if (/^\/session\/[^/]+\/(children|todo|diff)$/.test(url.pathname)) return json(route, [])
if (url.pathname === "/permission") {
permissionRequests.push(url.toString())
return json(route, [])
}
if (["/skill", "/command", "/lsp", "/formatter", "/question", "/vcs/diff", "/pty/shells"].includes(url.pathname))
return json(route, [])
if (["/global/config", "/config", "/provider/auth", "/mcp"].includes(url.pathname)) return json(route, {})
if (url.pathname === "/provider") return json(route, provider(remote ? "server-b" : "server-a"))
if (url.pathname === "/agent") return json(route, [{ name: "build", mode: "primary" }])
if (url.pathname === "/project" || url.pathname === "/project/current") {
const project = {
id: remote ? sessionB.projectID : "project-server-a",
worktree: directory,
vcs: "git",
time: { created: 1, updated: 1 },
sandboxes: [],
}
return json(route, url.pathname === "/project" ? [project] : project)
}
if (url.pathname === "/path")
return json(route, {
state: directory,
config: directory,
worktree: directory,
directory,
home: directory,
})
if (url.pathname === "/vcs") return json(route, { branch: "main", default_branch: "main" })
return json(route, {})
})
}
function session(id: string, directory: string, title: string) {
return {
id,
slug: id,
projectID: `project-${id}`,
directory,
title,
version: "dev",
time: { created: 1, updated: 1 },
}
}
function provider(id: string) {
const name = id === "server-b" ? "Server B" : "Server A"
return {
all: [
{
id,
name: `${name} Provider`,
models: {
[id]: {
id,
name: `${name} Model`,
family: id,
release_date: "2026-01-01",
limit: { context: 200_000 },
},
},
},
],
connected: [id],
default: { providerID: id, modelID: id },
}
}
function json(route: Route, body: unknown, status = 200) {
return route.fulfill({
status,
contentType: "application/json",
headers: { "access-control-allow-origin": "*" },
body: JSON.stringify(body),
})
}
function sse(route: Route) {
return route.fulfill({ status: 200, contentType: "text/event-stream", body: ": ok\n\n" })
}
@@ -17,14 +17,12 @@ import { mockOpenCodeServer } from "../utils/mock-server"
import { installSseTransport } from "../utils/sse-transport"
import { expectSessionTitle } from "../utils/waits"
const initialPageSize = 20
const historyPageSize = 200
const assistants = Array.from({ length: initialPageSize + 1 }, (_, index) =>
const assistants = Array.from({ length: 14 }, (_, index) =>
assistantMessage([textPart(`prt_history_root_${index}`, `Assistant response ${index}`)], {
id: `msg_${String(index + 1001).padStart(4, "0")}_history_root_assistant`,
parentID: userID,
created: 1700000001000 + index * 1_000,
completed: index < initialPageSize,
completed: index < 13,
}),
)
const messages = [userMessage(), ...assistants]
@@ -48,7 +46,7 @@ const scenarios = [
test.use({ viewport: { width: 646, height: 1385 } })
for (const scenario of scenarios) {
test(`keeps visible timeline content visible through ${scenario.name}`, async ({ page }) => {
test(`keeps the latest user turn visible through ${scenario.name}`, async ({ page }) => {
const requests: { before?: string; phase: "start" | "end" }[] = []
const pages: { before?: string; limit: number }[] = []
const roots: { sessionID: string; messageID: string }[] = []
@@ -103,51 +101,36 @@ for (const scenario of scenarios) {
}
},
})
await page.addInitScript(() => {
const visibleParts = () => {
const virtual = document.querySelector<HTMLElement>("[data-timeline-virtual-content]")
const viewport = virtual?.closest<HTMLElement>(".scroll-view__viewport")
const view = viewport?.getBoundingClientRect()
if (!viewport || !view) return []
return [...viewport.querySelectorAll<HTMLElement>("[data-timeline-part-id]")]
.filter((part) => {
const rect = part.getBoundingClientRect()
return rect.width > 0 && rect.height > 0 && rect.bottom > view.top && rect.top < view.bottom
})
.flatMap((part) => (part.dataset.timelinePartId ? [part.dataset.timelinePartId] : []))
}
const state = {
armed: false,
hidden: false,
visibleParts: [] as string[],
samples: 0,
stop: false,
arm() {
state.visibleParts = visibleParts()
state.armed = true
},
}
;(window as Window & { __historyRootProbe?: typeof state }).__historyRootProbe = state
const sample = () => {
if (state.armed) {
const virtual = document.querySelector<HTMLElement>("[data-timeline-virtual-content]")
const viewport = virtual?.closest<HTMLElement>(".scroll-view__viewport")
const view = viewport?.getBoundingClientRect()
const visible = (partID: string) => {
const part = viewport?.querySelector<HTMLElement>(`[data-timeline-part-id="${CSS.escape(partID)}"]`)
const rect = part?.getBoundingClientRect()
return (
!!rect && !!view && rect.width > 0 && rect.height > 0 && rect.bottom > view.top && rect.top < view.bottom
)
await page.addInitScript(
({ userPartID, lastPartID }) => {
const state = { armed: false, hidden: false, samples: 0, stop: false }
;(window as Window & { __historyRootProbe?: typeof state }).__historyRootProbe = state
const sample = () => {
if (state.armed) {
const virtual = document.querySelector<HTMLElement>("[data-timeline-virtual-content]")
const viewport = virtual?.closest<HTMLElement>(".scroll-view__viewport")
const view = viewport?.getBoundingClientRect()
const visible = (partID: string) => {
const part = viewport?.querySelector<HTMLElement>(`[data-timeline-part-id="${partID}"]`)
const rect = part?.getBoundingClientRect()
return (
!!rect &&
!!view &&
rect.width > 0 &&
rect.height > 0 &&
rect.bottom > view.top &&
rect.top < view.bottom
)
}
if (!virtual || !visible(userPartID) || !visible(lastPartID)) state.hidden = true
state.samples++
}
if (!virtual || state.visibleParts.length === 0 || state.visibleParts.some((partID) => !visible(partID)))
state.hidden = true
state.samples++
if (!state.stop) requestAnimationFrame(() => setTimeout(sample, 0))
}
if (!state.stop) requestAnimationFrame(() => setTimeout(sample, 0))
}
requestAnimationFrame(() => setTimeout(sample, 0))
})
requestAnimationFrame(() => setTimeout(sample, 0))
},
{ userPartID, lastPartID },
)
await page.goto(`/${base64Encode(directory)}/session/${sessionID}`)
await transport.waitForConnection()
@@ -160,28 +143,23 @@ for (const scenario of scenarios) {
"messages:start:latest",
"messages:end:latest",
`message:${userID}`,
`messages:start:${messages.at(-initialPageSize)!.info.id}`,
`messages:start:${messages.at(-2)!.info.id}`,
])
await expect(page.locator('[data-timeline-part-id^="prt_history_root_"]')).toHaveCount(initialPageSize)
await page.evaluate(() => {
;(
window as Window & {
__historyRootProbe?: { arm(): void }
__historyRootProbe?: { armed: boolean }
}
).__historyRootProbe!.arm()
).__historyRootProbe!.armed = true
})
await waitForProbeSamples(page, 0)
expect(await visibleContentHidden(page)).toBe(false)
expect(await historyRootHidden(page)).toBe(false)
const beforeHistory = await probeSamples(page)
history.resolve()
await expect(page.locator('[data-timeline-part-id^="prt_history_root_"]')).toHaveCount(assistants.length)
await expect.poll(() => requests.filter((request) => request.phase === "end").length).toBe(2)
await expect(page.locator('[data-timeline-part-id^="prt_history_root_"]')).toHaveCount(14)
await expect(page.getByRole("button", { name: "Stop" })).toBeVisible()
await waitForProbeSamples(page, beforeHistory)
expect(pages).toEqual([
{ before: undefined, limit: initialPageSize },
{ before: messages.at(-initialPageSize)!.info.id, limit: historyPageSize },
])
expect(pages[0]).toEqual({ before: undefined, limit: 2 })
expect(roots).toEqual([{ sessionID, messageID: userID }])
const message = messageUpdated(scenario.info)
@@ -235,7 +213,7 @@ async function waitForProbeSamples(page: Page, after: number) {
)
}
function visibleContentHidden(page: Page) {
function historyRootHidden(page: Page) {
return page.evaluate(
() => (window as Window & { __historyRootProbe?: { hidden: boolean } }).__historyRootProbe!.hidden,
)
@@ -32,23 +32,6 @@ for (const expanded of [false, true]) {
})
}
test("shows and expands a running shell command without shimmering it", async ({ page }) => {
const id = "prt_shell_running_command"
const command = "sleep 10 && echo done"
await setupTimeline(page, {
messages: [userMessage(), assistantMessage([shell(id, "running", "still running", command)], { completed: false })],
settings: { shellToolPartsExpanded: false },
})
const tool = page.locator(`[data-timeline-part-id="${id}"]`)
await expect(tool.locator('[data-component="text-shimmer"]')).toHaveAttribute("data-active", "true")
await expect(tool.locator('[data-component="shell-submessage"]')).toHaveText(command)
await expect(tool.locator('[data-component="shell-submessage"] [data-component="text-shimmer"]')).toHaveCount(0)
await tool.locator('[data-slot="collapsible-trigger"]').click()
await expect(tool.locator('[data-slot="collapsible-trigger"]')).toHaveAttribute("aria-expanded", "true")
await expect(tool.locator('[data-slot="bash-pre"]')).toContainText("still running")
})
test("transitions thinking and hidden reasoning through busy to idle", async ({ page }) => {
const reasoningID = "prt_reasoning_hidden"
const assistant = assistantMessage([reasoningPart(reasoningID, "## Inspecting stability")], { completed: false })
@@ -1,5 +1,5 @@
import { base64Encode } from "@opencode-ai/core/util/encode"
import { expect, test, type Page } from "@playwright/test"
import { expect, test } from "@playwright/test"
import { mockOpenCodeServer } from "../utils/mock-server"
import { expectSessionTitle } from "../utils/waits"
@@ -7,11 +7,10 @@ const directory = "C:/OpenCode/TerminalComposerFocus"
const projectID = "proj_terminal_composer_focus"
const sessionID = "ses_terminal_composer_focus"
const ptyID = "pty_terminal_composer_focus"
const newPtyID = "pty_terminal_composer_focus_new"
test.use({ viewport: { width: 1440, height: 900 } })
test.beforeEach(async ({ page }) => {
test("routes typing to the composer unless the open terminal is focused", async ({ page }) => {
await mockOpenCodeServer(page, {
directory,
project: {
@@ -68,9 +67,7 @@ test.beforeEach(async ({ page }) => {
await page.addInitScript(() => {
localStorage.setItem("settings.v3", JSON.stringify({ general: { newLayoutDesigns: true } }))
})
})
test("routes typing to the composer unless the open terminal is focused", async ({ page }) => {
await page.goto(`/${base64Encode(directory)}/session/${sessionID}`)
await expectSessionTitle(page, "Terminal composer focus")
@@ -90,120 +87,3 @@ test("routes typing to the composer unless the open terminal is focused", async
await expect(composer).toBeFocused()
await expect(composer).toHaveText("a")
})
test("keeps composer focus when a cached terminal finishes mounting", async ({ page }) => {
const ghostty = Promise.withResolvers<void>()
const release = Promise.withResolvers<void>()
const created = { count: 0 }
await page.route("**/pty", (route) => {
created.count += 1
return route.fulfill({
status: 200,
contentType: "application/json",
body: JSON.stringify({ id: ptyID, title: "Terminal 1" }),
})
})
await page.route(/ghostty-web/, async (route) => {
ghostty.resolve()
await release.promise
await route.continue()
})
await seedCachedTerminal(page)
await page.goto(`/${base64Encode(directory)}/session/${sessionID}`, { waitUntil: "commit" })
await expectSessionTitle(page, "Terminal composer focus")
const composer = page.locator('[data-component="prompt-input"]')
const terminal = page.locator('[data-component="terminal"]')
await expect(terminal).toBeVisible()
expect(created.count).toBe(0)
await ghostty.promise
await composer.click()
await expect(composer).toBeFocused()
release.resolve()
await expect(terminal.locator("textarea")).toHaveCount(1)
await page.waitForTimeout(300)
await expect(composer).toBeFocused()
})
test("keeps newer composer focus while an explicit terminal open finishes", async ({ page }) => {
const ghostty = Promise.withResolvers<void>()
const release = Promise.withResolvers<void>()
await page.route(/ghostty-web/, async (route) => {
ghostty.resolve()
await release.promise
await route.continue()
})
await page.goto(`/${base64Encode(directory)}/session/${sessionID}`)
await expectSessionTitle(page, "Terminal composer focus")
const composer = page.locator('[data-component="prompt-input"]')
const terminal = page.locator('[data-component="terminal"]')
await page.keyboard.press("Control+Backquote")
await expect(terminal).toBeVisible()
await ghostty.promise
await composer.click()
await expect(composer).toBeFocused()
release.resolve()
await expect(terminal.locator("textarea")).toHaveCount(1)
await page.waitForTimeout(50)
await expect(composer).toBeFocused()
})
test("focuses a terminal created from the new-terminal button", async ({ page }) => {
const created = { count: 0 }
await page.route("**/pty", (route) => {
created.count += 1
const next = created.count === 1 ? { id: ptyID, title: "Terminal 1" } : { id: newPtyID, title: "Terminal 2" }
return route.fulfill({
status: 200,
contentType: "application/json",
body: JSON.stringify(next),
})
})
await page.route(`**/pty/${newPtyID}`, (route) =>
route.fulfill({ status: 200, contentType: "application/json", body: "{}" }),
)
await page.route(`**/pty/${newPtyID}/connect-token*`, (route) =>
route.fulfill({
status: 200,
contentType: "application/json",
headers: { "access-control-allow-origin": "*" },
body: JSON.stringify({ ticket: "e2e-ticket" }),
}),
)
await page.routeWebSocket(new RegExp(`/pty/${newPtyID}/connect`), () => undefined)
await page.goto(`/${base64Encode(directory)}/session/${sessionID}`)
await expectSessionTitle(page, "Terminal composer focus")
const composer = page.locator('[data-component="prompt-input"]')
const terminal = page.locator('[data-component="terminal"]')
await page.keyboard.press("Control+Backquote")
await expect(terminal.locator("textarea")).toHaveCount(1)
await composer.click()
await expect(composer).toBeFocused()
await page.getByRole("button", { name: "New terminal" }).click()
await expect(page.getByRole("tab", { name: "Terminal 2" })).toHaveAttribute("aria-selected", "true")
await expect.poll(() => terminal.evaluate((element) => element.contains(document.activeElement))).toBe(true)
})
function seedCachedTerminal(page: Page) {
return page.addInitScript(
({ terminalKey, ptyID }) => {
localStorage.setItem("opencode.global.dat:layout", JSON.stringify({ terminal: { height: 320, opened: true } }))
localStorage.setItem(
terminalKey,
JSON.stringify({
active: ptyID,
all: [{ id: ptyID, title: "Terminal 1", titleNumber: 1 }],
}),
)
},
{ terminalKey: `${base64Encode(directory)}/terminal.v1`, ptyID },
)
}
@@ -1,34 +0,0 @@
<!doctype html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Timeline Suspense Reproduction</title>
<style>
* {
box-sizing: border-box;
}
html,
body,
#root {
margin: 0;
min-height: 100%;
}
body {
background: #171717;
color: #f5f5f5;
font-family: ui-monospace, SFMono-Regular, Consolas, monospace;
}
#root {
padding: 24px;
}
</style>
</head>
<body>
<main id="root"></main>
<script type="module" src="/main.tsx"></script>
</body>
</html>
@@ -1,315 +0,0 @@
import { createResource, createSignal, For, onMount, Suspense } from "solid-js"
import { render } from "solid-js/web"
import { createVirtualizer, observeElementOffset, observeElementRect } from "@tanstack/solid-virtual"
import { observeElementOffsetReconnectAware } from "../../../src/pages/session/timeline/observe-element-offset"
const rowCount = 2_000
const rowHeight = 40
const parameters = new URLSearchParams(location.search)
const resourceMode = parameters.get("resource") === "guard" ? "guard" : "baseline"
const reconnectMode = parameters.get("reconnect") === "candidate" ? "candidate" : "baseline"
type MutationEvent = {
kind: "removed" | "added"
callbackTime: number
callbackFrame: number
routeConnectedInCallback: boolean
nativeOffsetInCallback: number
}
type Snapshot = {
mode: {
resource: "baseline" | "guard"
reconnect: "baseline" | "candidate"
}
operation: {
sequence: number
phase: string
time: number
frame: number
}
resourceState: string
routeConnected: boolean
viewportConnected: boolean
viewportOwnedByRoute: boolean
sameRoute: boolean
sameViewport: boolean
sameSurface: boolean
sameMountedRows: boolean
nativeOffset: number
coreOffset: number
rangeStart: number
rangeEnd: number
indexes: number[]
domIndexes: number[]
logicalSurfaceHeight: number
renderedSurfaceHeight: number
viewportClientHeight: number
viewportScrollHeight: number
visibleRows: number
minimumRowTop: number
domScrollEvents: number
lastScrollTrusted: boolean
coreOffsetCallbackCalls: number
offsetCallbackSources: "observer"[]
rectObserverCallbacks: number
ignoredDetachedZeroRects: number
syntheticScrollDispatches: number
mutationEvents: MutationEvent[]
}
declare global {
interface Window {
timelineSuspense: {
prepare: () => Promise<Snapshot>
trigger: () => void
resolve: () => void
frames: (count?: number) => Promise<void>
snapshot: () => Snapshot
}
}
}
function App() {
const [refresh, setRefresh] = createSignal(false)
let resolveResource: (() => void) | undefined
const [resource] = createResource(
refresh,
(version) =>
new Promise<string>((resolve) => {
resolveResource = () => resolve(`settled-${version}`)
}),
{ initialValue: "settled" },
)
function Route() {
let route: HTMLElement | undefined
let viewport: HTMLDivElement | undefined
let surface: HTMLDivElement | undefined
let initialRoute: HTMLElement | undefined
let initialViewport: HTMLDivElement | undefined
let initialSurface: HTMLDivElement | undefined
let initialRows: HTMLElement[] = []
let phase = "mounting"
let browserFrame = 0
let snapshotSequence = 0
let domScrollEvents = 0
let lastScrollTrusted = false
let coreOffsetCallbackCalls = 0
let rectObserverCallbacks = 0
let ignoredDetachedZeroRects = 0
const offsetCallbackSources: "observer"[] = []
const mutationEvents: MutationEvent[] = []
const virtualizer = createVirtualizer<HTMLDivElement, HTMLDivElement>({
count: rowCount,
getScrollElement: () => viewport ?? null,
estimateSize: () => rowHeight,
initialRect: { width: 900, height: 600 },
overscan: 2,
observeElementRect: (instance, callback) =>
observeElementRect(instance, (rect) => {
rectObserverCallbacks++
// A fixed 600px viewport has no usable geometry while detached. Keep the last connected rect.
if (!instance.scrollElement?.isConnected && rect.height === 0) {
ignoredDetachedZeroRects++
return
}
callback(rect)
}),
observeElementOffset: (instance, callback) => {
const deliver = (offset: number, isScrolling: boolean) => {
coreOffsetCallbackCalls++
offsetCallbackSources.push("observer")
callback(offset, isScrolling)
}
if (reconnectMode === "candidate") return observeElementOffsetReconnectAware(instance, deliver)
return observeElementOffset(instance, deliver)
},
})
const frames = async (count = 2) => {
for (let index = 0; index < count; index++) {
await new Promise<void>((resolve) => requestAnimationFrame(() => resolve()))
}
}
const mountedRows = () => [...(surface?.querySelectorAll<HTMLElement>("[data-row-index]") ?? [])]
const snapshot = (): Snapshot => {
const rows = mountedRows()
const view = viewport?.getBoundingClientRect()
const visibleRows =
viewport?.isConnected && view
? rows.filter((row) => {
const rect = row.getBoundingClientRect()
return rect.bottom > view.top && rect.top < view.bottom
}).length
: 0
return {
mode: { resource: resourceMode, reconnect: reconnectMode },
operation: {
sequence: ++snapshotSequence,
phase,
time: performance.now(),
frame: browserFrame,
},
resourceState: resource.state,
routeConnected: route?.isConnected ?? false,
viewportConnected: viewport?.isConnected ?? false,
viewportOwnedByRoute: !!route && !!viewport && route.contains(viewport),
sameRoute: route === initialRoute,
sameViewport: viewport === initialViewport,
sameSurface: surface === initialSurface,
sameMountedRows:
initialRows.length > 0 &&
initialRows.length === rows.length &&
initialRows.every((row, index) => row === rows[index]),
nativeOffset: viewport?.scrollTop ?? -1,
coreOffset: virtualizer.scrollOffset ?? -1,
rangeStart: virtualizer.range?.startIndex ?? -1,
rangeEnd: virtualizer.range?.endIndex ?? -1,
indexes: virtualizer.getVirtualItems().map((item) => item.index),
domIndexes: rows.map((row) => Number(row.dataset.rowIndex)),
logicalSurfaceHeight: Number.parseFloat(surface?.style.height ?? "-1"),
renderedSurfaceHeight: surface?.getBoundingClientRect().height ?? -1,
viewportClientHeight: viewport?.clientHeight ?? -1,
viewportScrollHeight: viewport?.scrollHeight ?? -1,
visibleRows,
minimumRowTop:
rows.length && view ? Math.min(...rows.map((row) => row.getBoundingClientRect().top - view.top)) : -1,
domScrollEvents,
lastScrollTrusted,
coreOffsetCallbackCalls,
offsetCallbackSources: [...offsetCallbackSources],
rectObserverCallbacks,
ignoredDetachedZeroRects,
syntheticScrollDispatches: 0,
mutationEvents: mutationEvents.map((event) => ({ ...event })),
}
}
onMount(() => {
if (!route || !viewport || !surface) throw new Error("Timeline fixture did not mount")
const routeRoot = route.parentElement
if (!routeRoot) throw new Error("Timeline route root did not mount")
initialRoute = route
initialViewport = viewport
initialSurface = surface
viewport.addEventListener("scroll", (event) => {
domScrollEvents++
lastScrollTrusted = event.isTrusted
})
const countFrames = () => {
browserFrame++
requestAnimationFrame(countFrames)
}
requestAnimationFrame(countFrames)
new MutationObserver((records) => {
const callbackTime = performance.now()
records.forEach((record) => {
;([...(record.removedNodes ?? [])] as Node[]).forEach((node) => {
if (node !== route) return
phase = "detached"
mutationEvents.push({
kind: "removed",
callbackTime,
callbackFrame: browserFrame,
routeConnectedInCallback: route.isConnected,
nativeOffsetInCallback: viewport.scrollTop,
})
})
;([...(record.addedNodes ?? [])] as Node[]).forEach((node) => {
if (node !== route) return
phase = "reinserted"
mutationEvents.push({
kind: "added",
callbackTime,
callbackFrame: browserFrame,
routeConnectedInCallback: route.isConnected,
nativeOffsetInCallback: viewport.scrollTop,
})
})
})
}).observe(routeRoot, { childList: true })
window.timelineSuspense = {
prepare: async () => {
phase = "preparing"
await frames(2)
viewport.scrollTop = viewport.scrollHeight
await frames(3)
await new Promise((resolve) => setTimeout(resolve, 200))
await frames(2)
initialRows = mountedRows()
phase = "prepared"
return snapshot()
},
trigger: () => {
phase = "triggering"
setRefresh(true)
},
resolve: () => {
if (!resolveResource) throw new Error("Resource is not pending")
phase = "resolving"
resolveResource()
},
frames,
snapshot,
}
})
return (
<section ref={route} data-route style={{ width: "900px", margin: "0 auto" }}>
<span aria-hidden="true" style={{ display: "none" }}>
{resourceMode === "guard" && resource.state === "refreshing" ? resource.latest : resource()}
</span>
<div
ref={viewport}
data-viewport
style={{
height: "600px",
overflow: "auto",
"overflow-anchor": "none",
position: "relative",
background: "#202020",
outline: "1px solid #3f3f46",
}}
>
<div
ref={surface}
data-surface
style={{ height: `${virtualizer.getTotalSize()}px`, position: "relative", "overflow-anchor": "none" }}
>
<For each={virtualizer.getVirtualItems()}>
{(item) => (
<div
data-row-index={item.index}
style={{
position: "absolute",
top: "0",
left: "0",
width: "100%",
height: `${item.size}px`,
transform: `translateY(${item.start}px)`,
padding: "10px 14px",
border: "0 solid #333",
"border-bottom-width": "1px",
}}
>
logical row {item.index}
</div>
)}
</For>
</div>
</div>
</section>
)
}
return (
<main>
<Suspense>
<Route />
</Suspense>
</main>
)
}
render(() => <App />, document.getElementById("root")!)
@@ -1,34 +0,0 @@
import { defineConfig, devices } from "@playwright/test"
const port = Number(process.env.PLAYWRIGHT_TIMELINE_SUSPENSE_PORT ?? 4317)
export default defineConfig({
testDir: ".",
testMatch: "timeline-suspense.repro.ts",
outputDir: "../../test-results/timeline-suspense",
fullyParallel: false,
workers: 1,
retries: 0,
reporter: "line",
timeout: 30_000,
expect: {
timeout: 10_000,
},
webServer: {
command: `bunx vite --config vite.config.ts --host 127.0.0.1 --port ${port} --strictPort`,
cwd: import.meta.dirname,
url: `http://127.0.0.1:${port}`,
reuseExistingServer: false,
},
use: {
baseURL: `http://127.0.0.1:${port}`,
trace: "retain-on-failure",
screenshot: "only-on-failure",
},
projects: [
{
name: "chromium",
use: { ...devices["Desktop Chrome"] },
},
],
})
@@ -1,179 +0,0 @@
import { expect, test, type Page } from "@playwright/test"
test.beforeEach(async ({ page }) => {
page.on("pageerror", (error) => console.error(error))
await page.goto("/")
await expect.poll(() => page.evaluate(() => !!window.timelineSuspense)).toBe(true)
})
test("desired: preserves visible timeline continuity across descendant resource suspension", async ({ page }) => {
await page.goto("/?reconnect=candidate")
await expect.poll(() => page.evaluate(() => window.timelineSuspense.snapshot().mode.reconnect)).toBe("candidate")
const before = await prepare(page)
await triggerBaselineSuspension(page)
const pending = await page.evaluate(() => window.timelineSuspense.snapshot())
expect(pending.nativeOffset).toBe(0)
expect(pending.coreOffset).toBe(before.coreOffset)
expect(pending.indexes).toEqual(before.indexes)
expect(pending.sameRoute).toBe(true)
expect(pending.sameViewport).toBe(true)
expect(pending.sameSurface).toBe(true)
expect(pending.sameMountedRows).toBe(true)
await resolveSuspension(page)
await expect.poll(() => page.evaluate(() => window.timelineSuspense.snapshot().coreOffset)).toBe(0)
await page.waitForTimeout(250)
await page.evaluate(() => window.timelineSuspense.frames(2))
const after = await page.evaluate(() => window.timelineSuspense.snapshot())
expect(after.sameRoute).toBe(true)
expect(after.sameViewport).toBe(true)
expect(after.sameSurface).toBe(true)
expect(after.nativeOffset).toBe(0)
expect(after.coreOffset).toBe(0)
expect(after.rangeStart).toBeLessThan(10)
expect(after.visibleRows, diagnostic({ before, pending, after })).toBeGreaterThan(0)
expect(after.domScrollEvents).toBe(before.domScrollEvents)
expect(after.coreOffsetCallbackCalls).toBe(before.coreOffsetCallbackCalls + 1)
expect(after.offsetCallbackSources.at(-1)).toBe("observer")
expect(after.syntheticScrollDispatches).toBe(0)
})
test("forensic: proves detached same-node viewport leaves TanStack's bottom range blank until a real scroll", async ({
page,
}) => {
const before = await prepare(page)
const beforeRows = before.domIndexes
expect(before.mode).toEqual({ resource: "baseline", reconnect: "baseline" })
expect(before.logicalSurfaceHeight).toBe(80_000)
expect(before.renderedSurfaceHeight).toBe(80_000)
expect(before.viewportClientHeight).toBe(600)
expect(before.viewportScrollHeight).toBe(80_000)
expect(before.rangeStart).toBeGreaterThan(1_900)
expect(before.nativeOffset).toBe(before.coreOffset)
expect(before.visibleRows).toBeGreaterThan(0)
await triggerBaselineSuspension(page)
const pending = await page.evaluate(() => window.timelineSuspense.snapshot())
expect(pending.resourceState).toBe("refreshing")
expect(pending.routeConnected).toBe(false)
expect(pending.viewportConnected).toBe(false)
expect(pending.viewportOwnedByRoute).toBe(true)
expect(pending.nativeOffset).toBe(0)
expect(pending.coreOffset).toBe(before.coreOffset)
expect(pending.rangeStart).toBe(before.rangeStart)
expect(pending.rangeEnd).toBe(before.rangeEnd)
expect(pending.indexes).toEqual(before.indexes)
expect(pending.domIndexes).toEqual(beforeRows)
expect(pending.sameMountedRows).toBe(true)
expect(pending.domScrollEvents).toBe(before.domScrollEvents)
expect(pending.coreOffsetCallbackCalls).toBe(before.coreOffsetCallbackCalls)
expect(pending.ignoredDetachedZeroRects).toBeGreaterThan(before.ignoredDetachedZeroRects)
expect(pending.mutationEvents).toHaveLength(1)
expect(pending.mutationEvents[0]).toMatchObject({
kind: "removed",
routeConnectedInCallback: false,
nativeOffsetInCallback: 0,
})
expect(pending.mutationEvents[0]!.callbackTime).toBeLessThanOrEqual(pending.operation.time)
expect(pending.mutationEvents[0]!.callbackFrame).toBeLessThanOrEqual(pending.operation.frame)
const after = await resolveSuspension(page)
expect(after.resourceState).toBe("ready")
expect(after.routeConnected).toBe(true)
expect(after.viewportConnected).toBe(true)
expect(after.viewportOwnedByRoute).toBe(true)
expect(after.sameRoute).toBe(true)
expect(after.sameViewport).toBe(true)
expect(after.sameSurface).toBe(true)
expect(after.sameMountedRows).toBe(true)
expect(after.nativeOffset).toBe(0)
expect(after.coreOffset).toBe(before.coreOffset)
expect(after.rangeStart).toBe(before.rangeStart)
expect(after.rangeEnd).toBe(before.rangeEnd)
expect(after.indexes).toEqual(before.indexes)
expect(after.domIndexes).toEqual(beforeRows)
expect(after.domScrollEvents).toBe(before.domScrollEvents)
expect(after.coreOffsetCallbackCalls).toBe(before.coreOffsetCallbackCalls)
expect(after.mutationEvents).toHaveLength(2)
expect(after.mutationEvents[1]).toMatchObject({
kind: "added",
routeConnectedInCallback: true,
nativeOffsetInCallback: 0,
})
expect(after.mutationEvents[1]!.callbackTime).toBeLessThanOrEqual(after.operation.time)
expect(after.mutationEvents[1]!.callbackFrame).toBeLessThanOrEqual(after.operation.frame)
expect(after.visibleRows).toBe(0)
expect(after.minimumRowTop).toBeGreaterThan(50_000)
expect(after.syntheticScrollDispatches).toBe(0)
await page.locator("[data-viewport]").hover()
await page.mouse.wheel(0, 80)
await expect
.poll(() =>
page.evaluate(() => {
const value = window.timelineSuspense.snapshot()
return value.nativeOffset > 0 && value.coreOffset === value.nativeOffset
}),
)
.toBe(true)
await page.evaluate(() => window.timelineSuspense.frames(2))
const recovered = await page.evaluate(() => window.timelineSuspense.snapshot())
expect(recovered.domScrollEvents).toBeGreaterThan(after.domScrollEvents)
expect(recovered.coreOffsetCallbackCalls).toBeGreaterThan(after.coreOffsetCallbackCalls)
expect(recovered.offsetCallbackSources.at(-1)).toBe("observer")
expect(recovered.lastScrollTrusted).toBe(true)
expect(recovered.rangeStart).toBeLessThan(10)
expect(recovered.visibleRows).toBeGreaterThan(0)
})
test("matrix: fixture-only settled-resource guard keeps the route connected", async ({ page }) => {
await page.goto("/?resource=guard")
await expect.poll(() => page.evaluate(() => window.timelineSuspense.snapshot().mode.resource)).toBe("guard")
const before = await prepare(page)
await page.evaluate(() => window.timelineSuspense.trigger())
await expect.poll(() => page.evaluate(() => window.timelineSuspense.snapshot().resourceState)).toBe("refreshing")
await page.evaluate(() => window.timelineSuspense.frames(3))
const pending = await page.evaluate(() => window.timelineSuspense.snapshot())
expect(pending.routeConnected).toBe(true)
expect(pending.mutationEvents).toEqual([])
expect(pending.nativeOffset).toBe(before.nativeOffset)
expect(pending.coreOffset).toBe(before.coreOffset)
expect(pending.visibleRows).toBeGreaterThan(0)
const after = await resolveSuspension(page)
expect(after.routeConnected).toBe(true)
expect(after.nativeOffset).toBe(before.nativeOffset)
expect(after.coreOffset).toBe(before.coreOffset)
expect(after.visibleRows).toBeGreaterThan(0)
})
async function prepare(page: Page) {
const before = await page.evaluate(() => window.timelineSuspense.prepare())
expect(before.routeConnected).toBe(true)
expect(before.viewportConnected).toBe(true)
expect(before.viewportOwnedByRoute).toBe(true)
expect(before.sameMountedRows).toBe(true)
expect(before.rangeStart).toBeGreaterThan(1_900)
expect(before.nativeOffset).toBe(before.coreOffset)
return before
}
async function triggerBaselineSuspension(page: Page) {
await page.evaluate(() => window.timelineSuspense.trigger())
await expect.poll(() => page.evaluate(() => window.timelineSuspense.snapshot().resourceState)).toBe("refreshing")
await expect.poll(() => page.evaluate(() => window.timelineSuspense.snapshot().routeConnected)).toBe(false)
await page.evaluate(() => window.timelineSuspense.frames(3))
}
async function resolveSuspension(page: Page) {
await page.evaluate(() => window.timelineSuspense.resolve())
await expect.poll(() => page.evaluate(() => window.timelineSuspense.snapshot().resourceState)).toBe("ready")
await expect.poll(() => page.evaluate(() => window.timelineSuspense.snapshot().routeConnected)).toBe(true)
await page.evaluate(() => window.timelineSuspense.frames(3))
return page.evaluate(() => window.timelineSuspense.snapshot())
}
function diagnostic(value: unknown) {
return JSON.stringify(value, null, 2)
}

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