Merge remote-tracking branch 'origin/v2' into search-integration

# Conflicts:
#	packages/client/test/promise.test.ts
#	packages/core/src/location-services.ts
#	packages/core/src/plugin/host.ts
#	packages/core/src/plugin/internal.ts
#	packages/core/src/plugin/supervisor.ts
#	packages/core/src/tool/websearch.ts
#	packages/core/test/config/config.test.ts
#	packages/core/test/plugin/host.ts
#	packages/core/test/tool-websearch.test.ts
#	packages/plugin/src/v2/effect/integration.ts
#	packages/plugin/src/v2/promise/integration.ts
#	packages/sdk/js/src/v2/gen/types.gen.ts
#	packages/tui/src/component/dialog-integration.tsx
#	packages/tui/src/context/data.tsx
#	packages/tui/src/routes/session/index.tsx
#	packages/tui/test/cli/tui/data.test.tsx
This commit is contained in:
Shoubhit Dash
2026-07-21 18:08:30 +05:30
1969 changed files with 71838 additions and 354322 deletions
+3 -1
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@@ -1,5 +1,7 @@
---
"@opencode-ai/client": patch
"@opencode-ai/protocol": patch
"@opencode-ai/cli": patch
---
Reuse a same-version background service when a repeated health probe succeeds instead of replacing an endpoint another client may already be using.
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.
+5
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@@ -0,0 +1,5 @@
---
"@opencode-ai/cli": patch
---
Expose a TUI plugin slot at the top of the session view.
+7
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@@ -0,0 +1,7 @@
---
"@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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@@ -0,0 +1,5 @@
---
"@opencode-ai/cli": patch
---
Expose a TUI plugin slot above the session composer.
+1
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@@ -1,2 +1,3 @@
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
+56
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@@ -121,6 +121,55 @@ 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
@@ -413,6 +462,7 @@ jobs:
needs:
- version
- build-cli
- build-node-cli
- sign-cli-windows
- build-electron
if: always() && !failure() && !cancelled()
@@ -461,6 +511,12 @@ 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:
+22
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@@ -70,6 +70,28 @@ 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
+2
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@@ -11,6 +11,7 @@ node_modules
playground
tmp
dist
dist-node
ts-dist
.turbo
.typecheck-profiles
@@ -25,6 +26,7 @@ Session.vim
a.out
target
.scripts
.cache
.direnv/
# Local dev files
+1 -1
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@@ -1,6 +1,6 @@
---
description: translate English to other languages
model: opencode/claude-opus-4-8
model: opencode/gpt-5.6-sol
---
run git diff and translate changed english doc and UI copy files to other international languages. Translate all languages in parallel to save time.
+1 -1
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@@ -4,7 +4,7 @@ import { tool } from "@opencode-ai/plugin"
const TEAM = {
tui: ["kommander", "simonklee"],
desktop_web: ["Hona", "Brendonovich"],
core: ["jlongster", "rekram1-node", "nexxeln", "kitlangton", "starptech"],
core: ["jlongster", "rekram1-node", "nexxeln", "kitlangton"],
inference: ["fwang", "MrMushrooooom", "starptech"],
windows: ["Hona"],
} as const
-2
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@@ -19,8 +19,6 @@ 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
+978 -1508
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+685
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@@ -0,0 +1,685 @@
# 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
@@ -8,6 +8,25 @@ 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",
+66 -33
View File
@@ -8,6 +8,8 @@
makeWrapper,
writableTmpDirAsHomeHook,
autoPatchelfHook,
copyDesktopItems,
makeDesktopItem,
opencode,
}:
let
@@ -27,9 +29,12 @@ stdenv.mkDerivation (finalAttrs: {
nodejs
makeWrapper
writableTmpDirAsHomeHook
] ++ lib.optionals stdenv.hostPlatform.isLinux [
]
++ lib.optionals stdenv.hostPlatform.isLinux [
autoPatchelfHook
] ++ lib.optionals stdenv.hostPlatform.isDarwin [
copyDesktopItems
]
++ lib.optionals stdenv.hostPlatform.isDarwin [
# Ad-hoc sign the .app: --config.mac.identity=null below skips signing.
darwin.autoSignDarwinBinariesHook
];
@@ -38,20 +43,37 @@ 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";
};
# 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" "'$out/opt/opencode-desktop/resources'"
done
'';
postPatch =
# NOTE: Relax Bun version check to be a warning instead of an error
''
substituteInPlace packages/script/src/index.ts \
--replace-fail 'throw new Error(`This script requires bun@''${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" "'$out/opt/opencode-desktop/resources'"
done
'';
preBuild = ''
cp -r "${electron.dist}" $HOME/.electron-dist
@@ -76,27 +98,38 @@ 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
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*} $out/opt/opencode-desktop
install -Dm644 resources/icons/32x32.png \
"$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
'';
autoPatchelfIgnoreMissingDeps = [
"libc.musl-x86_64.so.1"
+4 -4
View File
@@ -1,8 +1,8 @@
{
"nodeModules": {
"x86_64-linux": "sha256-JTtn+wXTXg+yklvIMDLcGFaYhTU6ZrCgKT9JTNEQ3gA=",
"aarch64-linux": "sha256-gXU6zyhvAZrZirkL/PlHdkHtEof/7PVSPCaE34Jnd4U=",
"aarch64-darwin": "sha256-Q0oTG3uzOlD/X2kJingLle529lKFoTpyCW2rHXOZ6iE=",
"x86_64-darwin": "sha256-LINvKHxPibTlJeNzfACQx0x+Yj5oROT6Du3I5AtqqXk="
"x86_64-linux": "sha256-qt11SKmOjq0KU542QFbs+u7YyJicn4drCcwCdg325yk=",
"aarch64-linux": "sha256-z68doReXTrWS7HeiAjc0btIjAsvzeZZ7hXAlHr0c77Q=",
"aarch64-darwin": "sha256-PILYH1Pi8XBvSkuZ+1sNnUTao5kba+m5Z8iJKx6YXPo=",
"x86_64-darwin": "sha256-KpcJzP4m0SUavu/WaSffgzOxrHq8ljdy0GOzs9p16lo="
}
}
+14 -12
View File
@@ -37,19 +37,19 @@
"packages/slack"
],
"catalog": {
"@effect/opentelemetry": "4.0.0-beta.83",
"@effect/platform-node": "4.0.0-beta.83",
"@effect/sql-sqlite-bun": "4.0.0-beta.83",
"@effect/opentelemetry": "4.0.0-beta.98",
"@effect/platform-node": "4.0.0-beta.98",
"@effect/sql-sqlite-bun": "4.0.0-beta.98",
"@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": "0.4.3",
"@opentui/keymap": "0.4.3",
"@opentui/solid": "0.4.3",
"@tanstack/solid-virtual": "3.13.28",
"@opentui/core": "0.4.5",
"@opentui/keymap": "0.4.5",
"@opentui/solid": "0.4.5",
"@tanstack/solid-virtual": "3.13.32",
"@shikijs/stream": "4.2.0",
"ulid": "3.0.1",
"@kobalte/core": "0.13.11",
@@ -69,14 +69,15 @@
"dompurify": "3.3.1",
"drizzle-kit": "1.0.0-rc.2",
"drizzle-orm": "1.0.0-rc.2",
"effect": "4.0.0-beta.83",
"effect": "4.0.0-beta.98",
"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": "17.0.1",
"marked": "17.0.6",
"marked-shiki": "1.2.1",
"remend": "1.3.0",
"@playwright/test": "1.59.1",
@@ -85,9 +86,11 @@
"@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",
@@ -161,10 +164,9 @@
"gcp-metadata@8.1.2": "patches/gcp-metadata@8.1.2.patch",
"pacote@21.5.0": "patches/pacote@21.5.0.patch",
"@ai-sdk/google@3.0.73": "patches/@ai-sdk%2Fgoogle@3.0.73.patch",
"@tanstack/solid-virtual@3.13.28": "patches/@tanstack%2Fsolid-virtual@3.13.28.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",
"@tanstack/virtual-core@3.17.0": "patches/@tanstack%2Fvirtual-core@3.17.0.patch",
"effect@4.0.0-beta.83": "patches/effect@4.0.0-beta.83.patch"
"effect@4.0.0-beta.98": "patches/effect@4.0.0-beta.98.patch",
"@tanstack/virtual-core@3.17.3": "patches/@tanstack%2Fvirtual-core@3.17.3.patch"
}
}
@@ -1,4 +1,4 @@
# LLM Package Guide
# AI Package Guide
## Effect
@@ -115,10 +115,10 @@ Keep provider facades small and explicit:
### Provider Package Entrypoints
Catalog-selected native providers use package-like export paths from `@opencode-ai/llm`. They are internal entrypoints in one npm package, not separately published provider packages. Every entrypoint implements `ProviderPackage.Definition` and exposes `model(modelID, settings)`, where settings are serializable provider configuration plus common `headers`, `body`, and `limits` overlays.
Catalog-selected native providers use package-like export paths from `@opencode-ai/ai`. They are internal entrypoints in one npm package, not separately published provider packages. Every entrypoint implements `ProviderPackage.Definition` and exposes `model(modelID, settings)`, where settings are serializable provider configuration plus common `headers`, `body`, and `limits` overlays.
```ts
import { model } from "@opencode-ai/llm/providers/openai/responses"
import { model } from "@opencode-ai/ai/providers/openai/responses"
const selected = model("gpt-5", {
apiKey,
@@ -133,7 +133,7 @@ Do not expose `Route` in provider package settings. Route composition stays an i
### Folder layout
```
packages/llm/src/
packages/ai/src/
schema/ canonical Schema model, split by concern
ids.ts branded IDs, literal types, ProviderMetadata
options.ts Generation/Provider/Http options, Limits, Model, cache policy
@@ -143,7 +143,7 @@ packages/llm/src/
index.ts barrel
llm.ts request constructors and convenience helpers
route/
index.ts @opencode-ai/llm/route advanced barrel
index.ts @opencode-ai/ai/route advanced barrel
client.ts Route.make + LLMClient.prepare/stream/generate
executor.ts RequestExecutor service + transport error mapping
protocol.ts Protocol type + Protocol.make
@@ -164,9 +164,11 @@ packages/llm/src/
bedrock-converse.ts
bedrock-event-stream.ts framing for AWS event-stream binary frames
openai-compatible-chat.ts route that reuses OpenAIChat.protocol, no canonical URL
openai-compatible-responses.ts route that reuses OpenAIResponses.protocol, no canonical URL
utils/ per-protocol helpers (auth, cache, media, tool-stream, ...)
providers/
openai-compatible.ts generic compatible helper + family model helpers
openai-compatible.ts generic Chat helper + family model helpers
openai-compatible-responses.ts generic Responses helper
openai-compatible-profile.ts family defaults (deepseek, togetherai, ...)
azure.ts / amazon-bedrock.ts / cloudflare.ts / github-copilot.ts / google.ts / xai.ts / openai.ts / anthropic.ts / openrouter.ts
tool.ts typed tool() helper
@@ -1,7 +1,7 @@
# AI Library Design
> Discussion draft. This document describes the intended replacement for the
> current private `@opencode-ai/llm` API. Names and exact TypeScript signatures
> Discussion draft. This document describes an intended clean-break redesign of
> the current private `@opencode-ai/ai` API. Names and exact TypeScript signatures
> are illustrative until implementation, but the domain boundaries and defaults
> are deliberate.
@@ -1074,7 +1074,6 @@ The redesign intentionally removes or changes these current concepts:
| Current | Proposed |
| --------------------------------------- | ----------------------------------------------------------- |
| `@opencode-ai/llm` | `@opencode-ai/ai` |
| Mandatory `LLM.request({ model, ... })` | Inline calls or model-free portable requests |
| `LLM.generate` means one turn | `LLM.generate` means complete run |
| `LLMClient.generate/stream` | `LLM.generateTurn/streamTurn` for one turn |
+360
View File
@@ -0,0 +1,360 @@
# @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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@@ -0,0 +1,108 @@
# LLM Provider Parity Status
Last reviewed: 2026-07-17
This file tracks the gap between the native `@opencode-ai/ai` package and the AI SDK provider packages that opencode still depends on for many catalog/runtime paths.
## Existing Status Sources
| File | What it tracks | Limitation |
| ----------------------------------- | ----------------------------------------------------------------------- | ------------------------------------------------------- |
| `packages/ai/DESIGN.md` | Future clean-break API proposal for `@opencode-ai/ai`. | Not a provider parity tracker. |
| `packages/ai/example/call-sites.md` | Route/value/provider-facade migration checklist and call-site sketches. | Architecture migration only; not AI SDK package parity. |
## Current Implementation Snapshot
| Native slice | Source | Current state | Main gaps |
| ---------------------------------- | ---------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| OpenAI Chat | `src/protocols/openai-chat.ts`, `src/providers/openai.ts` | Usable. Streams text, reasoning deltas, tool calls, usage, images, and common generation controls. | No typed structured-output / `response_format` path. Limited typed OpenAI option surface compared with SDK escape hatches. |
| OpenAI Responses HTTP | `src/protocols/openai-responses.ts`, `src/providers/openai.ts` | Usable. Supports hosted-tool event surfacing, reasoning replay metadata, GPT-5 defaults, and cache usage. | No explicit `previous_response_id` path. Typed options cover only a subset of Responses fields. Structured output is still mostly synthetic-tool based. |
| OpenAI Responses WebSocket | `src/protocols/openai-responses.ts`, `src/route/transport/websocket.ts` | Present as `OpenAI.responsesWebSocket(...)`. | Runner/catalog support explicitly must not downgrade WebSocket routes; broader runtime selection is not complete. |
| OpenAI-compatible Chat | `src/protocols/openai-compatible-chat.ts`, `src/providers/openai-compatible.ts` | Usable for generic Chat and several profiles: Baseten, Cerebras, DeepInfra, DeepSeek, Fireworks, Groq, TogetherAI. | Family quirks are mostly endpoint defaults, not full typed behavior. |
| OpenAI-compatible Responses | `src/protocols/openai-compatible-responses.ts`, `src/providers/openai-compatible-responses.ts` | Usable for deployments that implement the OpenAI Responses wire protocol. | No named family profiles or recorded deployment coverage yet. |
| Anthropic-compatible Messages | `src/protocols/anthropic-messages.ts`, `src/providers/anthropic-compatible.ts` | Usable for deployments that implement the Anthropic Messages wire protocol. Named Anthropic composes this base; MiniMax M3 has recorded text and tool-loop coverage. | No named compatible family profiles yet. |
| Anthropic Messages | `src/protocols/anthropic-messages.ts`, `src/providers/anthropic.ts` | Usable. Supports tools, thinking, cache control, images, server-hosted tool events, and usage. | Provider option surface is small. Beta/header handling, metadata, and newer Messages fields need a typed parity pass. |
| Gemini Developer API | `src/protocols/gemini.ts`, `src/providers/google.ts` | Usable for Google API key flow. Supports text, images, tools, thinking signatures, and cache usage. | This is not Vertex. Typed provider options are narrow; many Gemini request fields currently require raw `http.body` overlays. |
| Vertex Gemini | `src/protocols/gemini.ts`, `src/providers/google-vertex.ts` | Usable through API-key express mode, explicit OAuth tokens, or ADC with project/location endpoint derivation, including tuned `endpoints/...` deployments. | Core runner/catalog mapping and recorded provider coverage are missing. |
| Vertex Chat | `src/protocols/openai-chat.ts`, `src/providers/google-vertex-chat.ts` | Usable for MaaS models through OpenAI-compatible Chat Completions with explicit OAuth tokens or ADC and project/location endpoint derivation. | Core runner/catalog mapping and recorded provider coverage are missing; MaaS family-specific request parity needs review. |
| Vertex Responses | `src/protocols/openai-responses.ts`, `src/providers/google-vertex-responses.ts` | Usable for Grok models through OpenAI-compatible Responses with explicit OAuth tokens or ADC, project/location endpoint derivation, and storage disabled by default. | Core runner/catalog mapping and recorded provider coverage are missing; stateful continuation is not supported by Vertex. |
| Vertex Messages | `src/protocols/anthropic-messages.ts`, `src/providers/google-vertex-messages.ts` | Usable through explicit OAuth tokens or ADC, including global, regional, and `eu`/`us` multi-region endpoints. | Core runner/catalog mapping and recorded provider coverage are missing; Vertex-specific hosted-tool parity needs review. |
| Bedrock Converse | `src/protocols/bedrock-converse.ts`, `src/providers/amazon-bedrock.ts` | Partial but real. Supports AWS event-stream framing, SigV4 with supplied credentials, bearer auth, tools, reasoning signatures, media, cache points, and recorded tests. | Native facade does not mirror the AI SDK plugin's default AWS credential chain/profile behavior. Runner/catalog mapping is missing. Guardrails, inference profiles, region-specific model ID fixes, and model-specific request fields need a parity pass. |
| Azure OpenAI | `src/providers/azure.ts` using OpenAI Chat/Responses protocols | Partial. Supports resource/base URL setup, API key auth, API version query, Chat, and Responses selectors. | Core runner does not map `@ai-sdk/azure` to this native facade. AAD/token auth and Azure-specific endpoint variants need review. |
| Cloudflare AI Gateway / Workers AI | `src/providers/cloudflare.ts` | Present via OpenAI-compatible Chat routes. | Useful but not part of the critical AI SDK replacement set yet. Needs per-product recorded coverage before relying on it broadly. |
| OpenRouter | `src/providers/openrouter.ts` | Present with OpenRouter-specific usage/reasoning/prompt-cache options over Chat. | Responses-style OpenRouter support is absent. |
| xAI | `src/providers/xai.ts` | Present with Responses and Chat selectors. | Needs package-parity review against the AI SDK xAI provider. |
| GitHub Copilot | `src/providers/github-copilot.ts` | Present as explicit-base-URL OpenAI Chat/Responses facade. | Runtime/catalog integration remains specialized and should stay separate from public OpenAI-compatible defaults. |
## V2 Runner Status
`packages/core/src/session/runner/model.ts` currently resolves only this native subset from catalog `aisdk` metadata:
| Catalog API | Native route used today |
| --------------------------------------------------- | ---------------------------- |
| `aisdk:@ai-sdk/openai` | `OpenAIResponses.route` |
| `aisdk:@ai-sdk/anthropic` | `AnthropicMessages.route` |
| `aisdk:@ai-sdk/openai-compatible` with explicit URL | `OpenAICompatibleChat.route` |
Other `aisdk:` packages, including Google Vertex, Azure, and Bedrock, currently fall back through the AI SDK loader in the production runner. The dependency-free resolver seam rejects them with `SessionRunnerModel.UnsupportedPackageError`; they are not native route mappings yet.
## AI SDK Package Parity Matrix
| AI SDK package | Intended native target | Status | Biggest gaps |
| --------------------------------- | -------------------------------------------------------------- | ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `@ai-sdk/openai` | `OpenAI.chat`, `OpenAI.responses`, `OpenAI.responsesWebSocket` | Partial / usable | Add complete typed option coverage, structured output strategy, explicit Responses continuation support, and runner route selection between Chat/Responses/WebSocket. |
| `@ai-sdk/openai-compatible` | Generic OpenAI-compatible Chat and Responses | Partial / usable | Decide per-family namespace/profile behavior and runner API selection for providers that support Responses versus Chat only. |
| `@ai-sdk/anthropic` | `AnthropicMessages` | Partial / usable | Finish Messages API parity for headers/betas/metadata/newer fields and document hosted-tool continuation expectations. |
| `@ai-sdk/google` | Gemini Developer API | Partial / usable | Add typed options for safety, response schema/modalities, cached content, grounding/search/code execution, and non-text output modes where supported. |
| `@ai-sdk/google-vertex` | Vertex Gemini namespace/facade | Partial / usable | Add runner/catalog mapping, recorded coverage, and broader provider-option parity. |
| `@ai-sdk/google-vertex/anthropic` | Anthropic Messages over Vertex namespace/facade | Partial / usable | Add runner/catalog mapping, recorded coverage, and Vertex-specific hosted-tool parity. |
| `@ai-sdk/google-vertex/maas` | Vertex Chat | Partial / usable | Add runner/catalog mapping, recorded coverage, and MaaS family-specific request parity. |
| `@ai-sdk/google-vertex/xai` | Vertex Chat / Responses | Partial / usable | Decide Chat/Responses selection for catalog models, add runner mapping and recorded coverage, and review xAI-specific request options. |
| `@ai-sdk/azure` | Azure OpenAI Chat/Responses facade | Partial | Map runner/catalog metadata to native Azure, handle resourceName/baseURL/apiVersion variants, add AAD/token auth story, and verify Chat vs Responses deployment selection. |
| `@ai-sdk/amazon-bedrock` | Bedrock Converse | Partial | Add default AWS credential chain/profile support, region/inference-profile model ID handling, provider option parity via `additionalModelRequestFields`, guardrails/performance config, and runner/catalog mapping. |
| `@ai-sdk/amazon-bedrock/mantle` | Bedrock Mantle OpenAI-compatible Chat/Responses namespace | Missing | Decide native Mantle shape, likely separate from Converse because it uses OpenAI-compatible Chat/Responses semantics over Bedrock. Add package mapping and tests. |
## Highest-Risk Gaps
1. Runner support is narrower than the LLM package. The package has native provider facades for Google, Azure, and Bedrock, but the V2 Session runner only maps OpenAI, Anthropic, and explicit OpenAI-compatible Chat from `aisdk` catalog metadata.
2. OpenAI-compatible Responses is available as a separate package entrypoint, but the V2 runner still maps `@ai-sdk/openai-compatible` to Chat only. Catalog selection must become API-aware before Responses deployments can use it.
3. Bedrock native auth is not AI SDK parity. The AI SDK plugin uses the default AWS provider chain, profile, container credentials, and Bedrock bearer token env behavior. Native Bedrock currently expects explicit credentials or bearer auth on the facade.
4. Vertex Gemini, Vertex Chat, Vertex Responses, and Vertex Messages now have native package entrypoints, but the core runner does not map catalog metadata to them yet and recorded provider coverage is still missing.
5. Azure is only a provider facade, not a full runtime replacement. Native Azure exists, but the catalog runner does not select it, and token auth/resource variants need review.
6. Provider option typing is uneven. OpenAI, Anthropic, Gemini, Bedrock, and OpenRouter each expose a small typed subset plus raw HTTP overlays; this is useful but not equivalent to AI SDK provider option coverage.
7. Structured output is not provider-native yet. `LLM.generateObject` still uses a synthetic tool strategy, while the future design expects native structured output where reliable and tool fallback where needed.
8. Package/namespace boundaries for the current native loading set are explicit in docs and exports. Other exported provider facades are not catalog package entrypoints until they implement the contract. Vertex xAI still needs catalog API selection; the missing native boundary is Bedrock Mantle.
9. Recorded coverage is uneven. OpenAI, Anthropic, Gemini, Bedrock Converse, Cloudflare, OpenRouter, and several OpenAI-compatible Chat providers have cassettes. Azure, Vertex, and Mantle need first-class recorded scenarios before switching defaults.
## Native Namespace Shape
These are implementation/API slices, not separate npm packages.
| API slice | Package-like entrypoint | Purpose |
| ----------------------------- | ------------------------------------------------------- | ---------------------------------------------------------------------------- |
| OpenAI Chat | `@opencode-ai/ai/providers/openai/chat` | OpenAI `/chat/completions` semantics. |
| OpenAI Responses | `@opencode-ai/ai/providers/openai/responses` | OpenAI `/responses` semantics with HTTP/WebSocket selected through settings. |
| OpenAI-compatible Chat | `@opencode-ai/ai/providers/openai-compatible` | Generic OpenAI-compatible `/chat/completions`. |
| OpenAI-compatible Responses | `@opencode-ai/ai/providers/openai-compatible/responses` | Generic OpenAI-compatible `/responses`. |
| Anthropic-compatible Messages | `@opencode-ai/ai/providers/anthropic-compatible` | Generic Anthropic-compatible `/messages`. |
| Anthropic Messages | `@opencode-ai/ai/providers/anthropic` | Anthropic Messages API. |
| Gemini Developer API | `@opencode-ai/ai/providers/google` | Google AI Studio Gemini API. |
| Vertex Gemini | `@opencode-ai/ai/providers/google-vertex/gemini` | Vertex Gemini API; `providers/google-vertex` is the default alias. |
| Vertex Chat | `@opencode-ai/ai/providers/google-vertex/chat` | Vertex OpenAI-compatible Chat Completions for MaaS models. |
| Vertex Responses | `@opencode-ai/ai/providers/google-vertex/responses` | Vertex OpenAI-compatible Responses for Grok models. |
| Vertex Messages | `@opencode-ai/ai/providers/google-vertex/messages` | Vertex-hosted Anthropic Messages API. |
| Bedrock Converse | `@opencode-ai/ai/providers/amazon-bedrock` | AWS Bedrock Converse API. |
| Bedrock Mantle | Missing | AWS Bedrock Mantle OpenAI-compatible APIs. |
| Azure OpenAI Chat | `@opencode-ai/ai/providers/azure/chat` | Azure specialization of OpenAI Chat. |
| Azure OpenAI Responses | `@opencode-ai/ai/providers/azure/responses` | Azure specialization of OpenAI Responses. |
## Suggested Next Work Slices
1. Add native runner/catalog mappings for `@ai-sdk/azure`, `@ai-sdk/google`, and `@ai-sdk/amazon-bedrock` where the existing native facades are already close.
2. Add API-aware runner/catalog selection between OpenAI-compatible Chat and Responses.
3. Bring Bedrock native auth/config to AI SDK parity: region, profile, default AWS credential chain, bearer token env, endpoint override, and cross-region inference profile handling.
4. Add runner/catalog mappings and recorded scenarios for the native Vertex Gemini, Chat, Responses, and Messages entrypoints.
5. Decide Chat/Responses selection for `@ai-sdk/google-vertex/xai` catalog models.
6. Add Bedrock Mantle as a separate OpenAI-compatible Bedrock namespace after deciding whether it uses Chat, Responses, or both by model.
7. Expand typed provider options from the existing V1 lowerer knowledge in `packages/core/src/v1/config/provider-options.ts` before adding more raw overlay examples.
8. Add recorded provider tests for Azure, Vertex Gemini, Vertex Chat, Vertex Responses, Vertex Messages, Bedrock credential-chain behavior, and Mantle before making native runtime the default for those packages.
@@ -7,7 +7,7 @@ values directly.
## Conversation Summary
Kit and Aidan want provider-specific LLM behavior to move out of opencode's AI
SDK transform path and into `packages/llm` where possible. The goal is not a big
SDK transform path and into `packages/ai` where possible. The goal is not a big
generic transform layer; the goal is small composable route definitions backed by
recorded golden tests.
@@ -355,11 +355,38 @@ The package-like OpenAI Responses entrypoint instead keeps transport scoped to
Responses settings while preserving the same `model(...)` contract:
```ts
import { model } from "@opencode-ai/llm/providers/openai/responses"
import { model } from "@opencode-ai/ai/providers/openai/responses"
model("gpt-4o", { apiKey, transport: "websocket" })
```
Vertex keeps Gemini, Chat, Responses, and Messages as separate package-like entrypoints,
while sharing project/location resolution and ADC authentication internally:
```ts
import { model } from "@opencode-ai/ai/providers/google-vertex/gemini"
model("gemini-3.5-flash", { project, location: "global" })
```
```ts
import { model } from "@opencode-ai/ai/providers/google-vertex/chat"
model("deepseek-ai/deepseek-v3.2-maas", { project, location: "global" })
```
```ts
import { model } from "@opencode-ai/ai/providers/google-vertex/responses"
model("xai/grok-4.20-reasoning", { project, location: "global" })
```
```ts
import { model } from "@opencode-ai/ai/providers/google-vertex/messages"
model("claude-sonnet-4-6", { project, location: "global" })
```
The client should not require a different public layer just because a selected
route uses WebSocket. Use one `LLMClient.layer` with HTTP and WebSocket runtime
capabilities available; routes that do not need WebSocket simply never touch it.
@@ -1,12 +1,12 @@
import { Config, Effect, Formatter, Layer, Schema, Stream } from "effect"
import { LLM, LLMClient, Message, ProviderID, Tool, ToolRuntime } from "@opencode-ai/llm"
import { Route, Auth, Endpoint, Framing, Protocol, RequestExecutor, WebSocketExecutor } from "@opencode-ai/llm/route"
import { OpenAI } from "@opencode-ai/llm/providers"
import { LLM, LLMClient, Message, ProviderID, Tool, ToolRuntime } from "@opencode-ai/ai"
import { Route, Auth, Endpoint, Framing, Protocol, RequestExecutor, WebSocketExecutor } from "@opencode-ai/ai/route"
import { OpenAI } from "@opencode-ai/ai/providers"
/**
* A runnable walkthrough of the LLM package use-site API.
*
* Run from `packages/llm` with an OpenAI key in the environment:
* Run from `packages/ai` with an OpenAI key in the environment:
*
* OPENAI_API_KEY=... bun example/tutorial.ts
*
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@@ -0,0 +1,37 @@
{
"$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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@@ -0,0 +1,38 @@
#!/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)
}
}
@@ -161,6 +161,18 @@ const PROVIDERS: ReadonlyArray<Provider> = [
vars: [{ name: "TOGETHER_AI_API_KEY" }],
validate: (env) => validateBearer("https://api.together.xyz/v1/models", Redacted.make(env.TOGETHER_AI_API_KEY)),
},
{
id: "minimax",
label: "MiniMax",
tier: "compatible",
note: "Anthropic-compatible Messages text/tool recorded tests",
vars: [{ name: "MINIMAX_API_KEY" }],
validate: (env) =>
HttpClientRequest.get("https://api.minimax.io/anthropic/v1/models").pipe(
HttpClientRequest.setHeader("x-api-key", Redacted.value(Redacted.make(env.MINIMAX_API_KEY))),
executeRequest,
),
},
{
id: "mistral",
label: "Mistral",
+38
View File
@@ -0,0 +1,38 @@
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
+166
View File
@@ -0,0 +1,166 @@
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
@@ -1,4 +1,5 @@
export { LLMClient } from "./route/client"
export { ImageClient } from "./image-client"
export { Auth } from "./route/auth"
export { Provider } from "./provider"
export { ProviderPackage } from "./provider-package"
@@ -10,6 +11,9 @@ export type {
Service as LLMClientService,
} from "./route/client"
export * from "./schema"
export { GeneratedImage, ImageInput, ImageInputSchema, ImageModel, ImageRequest, ImageResponse } from "./image"
export type { ImageModelOptions, ImageOptions, ImageRequestFor, ImageRequestInput, ImageRoute } from "./image"
export { Image } from "./image"
export { Tool, ToolFailure, toDefinitions } from "./tool"
export { ToolRuntime } from "./tool-runtime"
export type { DispatchResult as ToolDispatchResult, ToolSettlement } from "./tool-runtime"
@@ -18,7 +22,7 @@ export type {
AnyTool,
ExecutableTool,
ExecutableTools,
Tool as ToolShape,
Definition as ToolShape,
ToolExecute,
ToolExecuteContext,
ToolModelOutputInput,
+1
View File
@@ -0,0 +1 @@
export * from "./protocols/index"
@@ -5,6 +5,7 @@ import { Endpoint } from "../route/endpoint"
import { Framing } from "../route/framing"
import { Protocol } from "../route/protocol"
import {
LLMError,
LLMEvent,
Usage,
type CacheHint,
@@ -19,7 +20,7 @@ import {
type ToolResultPart,
} from "../schema"
import { JsonObject, optionalArray, optionalNull, ProviderShared } from "./shared"
import { isContextOverflow } from "../provider-error"
import { classifyProviderFailure } from "../provider-error"
import * as Cache from "./utils/cache"
import { Lifecycle } from "./utils/lifecycle"
import { ToolSchemaProjection } from "./utils/tool-schema"
@@ -181,7 +182,7 @@ const AnthropicBodyFields = {
thinking: Schema.optional(AnthropicThinking),
output_config: Schema.optional(AnthropicOutputConfig),
}
const AnthropicMessagesBody = Schema.Struct(AnthropicBodyFields)
export const AnthropicMessagesBody = Schema.Struct(AnthropicBodyFields)
export type AnthropicMessagesBody = Schema.Schema.Type<typeof AnthropicMessagesBody>
const AnthropicUsage = Schema.Struct({
@@ -702,7 +703,14 @@ const onContentBlockStart = (state: ParserState, event: AnthropicEvent): StepRes
providerExecuted: block.type === "server_tool_use",
}),
},
[...events, LLMEvent.toolInputStart({ id: block.id ?? String(event.index), name: block.name ?? "" })],
[
...events,
LLMEvent.toolInputStart({
id: block.id ?? String(event.index),
name: block.name ?? "",
providerExecuted: block.type === "server_tool_use" ? true : undefined,
}),
],
]
}
@@ -832,15 +840,12 @@ const providerErrorMessage = (event: AnthropicEvent): string => {
return message || type || "Anthropic Messages stream error"
}
const onError = (state: ParserState, event: AnthropicEvent): StepResult => [
state,
[
LLMEvent.providerError({
message: providerErrorMessage(event),
classification: isContextOverflow(event.error?.message ?? "") ? "context-overflow" : undefined,
}),
],
]
const onError = (event: AnthropicEvent) =>
new LLMError({
module: ADAPTER,
method: "stream",
reason: classifyProviderFailure({ message: providerErrorMessage(event), code: event.error?.type }),
})
const step = (state: ParserState, event: AnthropicEvent) => {
if (event.type === "message_start") return Effect.succeed(onMessageStart(state, event))
@@ -848,7 +853,7 @@ const step = (state: ParserState, event: AnthropicEvent) => {
if (event.type === "content_block_delta") return onContentBlockDelta(state, event)
if (event.type === "content_block_stop") return onContentBlockStop(state, event)
if (event.type === "message_delta") return Effect.succeed(onMessageDelta(state, event))
if (event.type === "error") return Effect.succeed(onError(state, event))
if (event.type === "error") return onError(event)
return Effect.succeed<StepResult>([state, NO_EVENTS])
}
@@ -3,6 +3,7 @@ import { Route } from "../route/client"
import { Endpoint } from "../route/endpoint"
import { Protocol } from "../route/protocol"
import {
LLMError,
LLMEvent,
Usage,
type CacheHint,
@@ -17,7 +18,7 @@ import {
type ToolResultPart,
} from "../schema"
import { BedrockEventStream } from "./bedrock-event-stream"
import { isContextOverflow } from "../provider-error"
import { classifyProviderFailure } from "../provider-error"
import { JsonObject, optionalArray, ProviderShared } from "./shared"
import { BedrockAuth } from "./utils/bedrock-auth"
import { BedrockCache } from "./utils/bedrock-cache"
@@ -560,7 +561,9 @@ const step = (state: ParserState, event: BedrockEvent) =>
return [
{
...state,
hasToolCalls: resultEvents.some(LLMEvent.is.toolCall) ? true : state.hasToolCalls,
hasToolCalls:
resultEvents.some((event) => LLMEvent.is.toolCall(event) || LLMEvent.is.toolInputError(event)) ||
state.hasToolCalls,
lifecycle,
tools: result.tools,
reasoningSignatures: Object.fromEntries(
@@ -586,27 +589,24 @@ const step = (state: ParserState, event: BedrockEvent) =>
return [{ ...state, pendingFinish: { reason: state.pendingFinish?.reason ?? "stop", usage } }, []] as const
}
if (event.internalServerException || event.modelStreamErrorException || event.serviceUnavailableException) {
const message =
event.internalServerException?.message ??
event.modelStreamErrorException?.message ??
event.serviceUnavailableException?.message ??
"Bedrock Converse stream error"
return [state, [LLMEvent.providerError({ message })]] as const
}
if (event.validationException || event.throttlingException) {
const message =
event.validationException?.message ?? event.throttlingException?.message ?? "Bedrock Converse error"
return [
state,
[
LLMEvent.providerError({
message,
classification: event.validationException && isContextOverflow(message) ? "context-overflow" : undefined,
}),
],
const exception = (
[
["internalServerException", event.internalServerException],
["modelStreamErrorException", event.modelStreamErrorException],
["serviceUnavailableException", event.serviceUnavailableException],
["throttlingException", event.throttlingException],
["validationException", event.validationException],
] as const
).find((entry) => entry[1] !== undefined)
if (exception) {
return yield* new LLMError({
module: ADAPTER,
method: "stream",
reason: classifyProviderFailure({
message: exception[1]?.message ?? "Bedrock Converse stream error",
code: exception[0],
}),
})
}
return [state, []] as const
@@ -1,7 +1,7 @@
import { EventStreamCodec } from "@smithy/eventstream-codec"
import { fromUtf8, toUtf8 } from "@smithy/util-utf8"
import { Effect, Stream } from "effect"
import type { Framing } from "../route/framing"
import { Framing } from "../route/framing"
import { ProviderShared } from "./shared"
// Bedrock streams responses using the AWS event stream binary protocol — each
@@ -79,7 +79,7 @@ const consumeFrames = (route: string) => (state: FrameBufferState, chunk: Uint8A
* under its `:event-type` header so the chunk schema can match the JSON
* payload directly.
*/
export const framing = (route: string): Framing<object> => ({
export const framing = (route: string): Framing.Definition<object> => ({
id: "aws-event-stream",
frame: (bytes) => bytes.pipe(Stream.mapAccumEffect(() => initialFrameBuffer, consumeFrames(route))),
})
+314
View File
@@ -0,0 +1,314 @@
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
@@ -2,5 +2,7 @@ export * as AnthropicMessages from "./anthropic-messages"
export * as BedrockConverse from "./bedrock-converse"
export * as Gemini from "./gemini"
export * as OpenAIChat from "./openai-chat"
export * as OpenAIImages from "./openai-images"
export * as OpenAICompatibleChat from "./openai-compatible-chat"
export * as OpenAICompatibleResponses from "./openai-compatible-responses"
export * as OpenAIResponses from "./openai-responses"
@@ -75,6 +75,9 @@ const OpenAIChatMessage = Schema.Union([
content: Schema.NullOr(Schema.String),
tool_calls: optionalArray(OpenAIChatAssistantToolCall),
reasoning_content: Schema.optional(Schema.String),
reasoning: Schema.optional(Schema.String),
reasoning_text: Schema.optional(Schema.String),
reasoning_details: optionalArray(Schema.Unknown),
}),
Schema.Struct({ role: Schema.Literal("tool"), tool_call_id: Schema.String, content: Schema.String }),
]).pipe(Schema.toTaggedUnion("role"))
@@ -145,6 +148,9 @@ type OpenAIChatToolCallDelta = Schema.Schema.Type<typeof OpenAIChatToolCallDelta
const OpenAIChatDelta = Schema.Struct({
content: optionalNull(Schema.String),
reasoning_content: optionalNull(Schema.String),
reasoning: optionalNull(Schema.String),
reasoning_text: optionalNull(Schema.String),
reasoning_details: optionalNull(Schema.Array(Schema.Unknown)),
tool_calls: optionalNull(Schema.Array(OpenAIChatToolCallDelta)),
})
@@ -160,12 +166,23 @@ export const OpenAIChatEvent = Schema.Struct({
export type OpenAIChatEvent = Schema.Schema.Type<typeof OpenAIChatEvent>
type OpenAIChatRequestMessage = LLMRequest["messages"][number]
interface ParserState {
interface PendingToolDelta {
readonly id?: string
readonly name?: string
readonly input: string
}
export interface ParserState {
readonly tools: ToolStream.State<number>
readonly pendingTools: Partial<Record<number, PendingToolDelta>>
readonly toolCallEvents: ReadonlyArray<LLMEvent>
readonly usage?: Usage
readonly finishReason?: FinishReason
readonly lifecycle: Lifecycle.State
readonly reasoningField?: "reasoning" | "reasoning_content" | "reasoning_text"
readonly reasoningDetails: Array<unknown>
readonly reasoningDetailsObserved: boolean
readonly reasoningEmitted: boolean
}
// =============================================================================
@@ -208,6 +225,20 @@ const lowerMedia = Effect.fn("OpenAIChat.lowerMedia")(function* (part: MediaPart
const openAICompatibleReasoningContent = (native: unknown) =>
isRecord(native) && typeof native.reasoning_content === "string" ? native.reasoning_content : undefined
const reasoningField = (part: ReasoningPart) => {
const field = part.providerMetadata?.openai?.reasoningField
if (field === "reasoning" || field === "reasoning_content" || field === "reasoning_text") return field
}
const reasoningDetails = (parts: ReadonlyArray<ReasoningPart>, native: unknown) => {
const observed = parts.flatMap((part) => {
const details = part.providerMetadata?.openai?.reasoningDetails
return Array.isArray(details) ? details : []
})
if (parts.some((part) => Array.isArray(part.providerMetadata?.openai?.reasoningDetails))) return observed
if (isRecord(native) && Array.isArray(native.reasoning_details)) return native.reasoning_details
}
const lowerUserMessage = Effect.fn("OpenAIChat.lowerUserMessage")(function* (message: OpenAIChatRequestMessage) {
const content: Array<Schema.Schema.Type<typeof OpenAIChatUserContent>> = []
for (const part of message.content) {
@@ -248,14 +279,29 @@ const lowerAssistantMessage = Effect.fn("OpenAIChat.lowerAssistantMessage")(func
continue
}
}
const text = reasoning.map((part) => part.text).join("")
const details = reasoningDetails(reasoning, message.native?.openaiCompatible)
const observedField = reasoning.map(reasoningField).find((value) => value !== undefined)
const nativeReasoning = openAICompatibleReasoningContent(message.native?.openaiCompatible)
const fullyStructured = reasoning.every((part) => Array.isArray(part.providerMetadata?.openai?.reasoningDetails))
const field = (() => {
if (reasoning.length === 0) return
if (observedField !== undefined) return observedField
if (nativeReasoning !== undefined) return "reasoning_content"
if (!fullyStructured) return "reasoning_content"
})()
const reasoningContent = (() => {
if (reasoning.length === 0) return nativeReasoning
if (field === "reasoning_content") return text
})()
return {
role: "assistant" as const,
content: content.length === 0 ? null : ProviderShared.joinText(content),
tool_calls: toolCalls.length === 0 ? undefined : toolCalls,
reasoning_content:
reasoning.length > 0
? reasoning.map((part) => part.text).join("")
: openAICompatibleReasoningContent(message.native?.openaiCompatible),
reasoning_content: reasoningContent,
reasoning: reasoning.length > 0 && field === "reasoning" ? text : undefined,
reasoning_text: reasoning.length > 0 && field === "reasoning_text" ? text : undefined,
reasoning_details: details,
}
})
@@ -400,6 +446,65 @@ const mapUsage = (usage: OpenAIChatEvent["usage"]): Usage | undefined => {
})
}
const reasoningDelta = (delta: Schema.Schema.Type<typeof OpenAIChatDelta> | null | undefined) => {
if (delta?.reasoning_content) return { field: "reasoning_content", text: delta.reasoning_content } as const
if (delta?.reasoning) return { field: "reasoning", text: delta.reasoning } as const
if (delta?.reasoning_text) return { field: "reasoning_text", text: delta.reasoning_text } as const
}
const detailText = (details: ReadonlyArray<unknown>) => {
const text = details.flatMap((detail) => {
if (!isRecord(detail)) return []
if (detail.type === "reasoning.text" && typeof detail.text === "string" && detail.text) return [detail.text]
if (detail.type === "reasoning.summary" && typeof detail.summary === "string" && detail.summary)
return [detail.summary]
return []
})
if (text.length > 0) return text.join("")
}
const appendReasoningDetails = (result: Array<unknown>, details: ReadonlyArray<unknown>) => {
for (const detail of details) {
const previous = result.at(-1)
if (
!isRecord(previous) ||
previous.type !== "reasoning.text" ||
!isRecord(detail) ||
detail.type !== "reasoning.text" ||
conflictingReasoningTextDetails(previous, detail)
) {
result.push(detail)
continue
}
result[result.length - 1] = {
...previous,
...Object.fromEntries(Object.entries(detail).filter((entry) => entry[1] !== undefined)),
text: `${typeof previous.text === "string" ? previous.text : ""}${typeof detail.text === "string" ? detail.text : ""}`,
signature: mergeDetailValue(previous.signature, detail.signature),
format: mergeDetailValue(previous.format, detail.format),
}
}
}
const mergeDetailValue = (previous: unknown, current: unknown) =>
previous || current || (previous !== undefined ? previous : current)
const conflictingReasoningTextDetails = (previous: Record<string, unknown>, current: Record<string, unknown>) =>
conflictingDetailValue(previous.id, current.id) ||
conflictingDetailValue(previous.index, current.index) ||
conflictingDetailValue(previous.format, current.format) ||
(Boolean(previous.signature) && Boolean(current.signature) && previous.signature !== current.signature)
const conflictingDetailValue = (previous: unknown, current: unknown) =>
previous !== undefined && previous !== null && current !== undefined && current !== null && previous !== current
const reasoningMetadata = (field: ParserState["reasoningField"], details?: ReadonlyArray<unknown>) => ({
openai: {
...(field ? { reasoningField: field } : {}),
...(details ? { reasoningDetails: details } : {}),
},
})
const step = (state: ParserState, event: OpenAIChatEvent) =>
Effect.gen(function* () {
const events: LLMEvent[] = []
@@ -409,25 +514,56 @@ const step = (state: ParserState, event: OpenAIChatEvent) =>
const delta = choice?.delta
const toolDeltas = delta?.tool_calls ?? []
let tools = state.tools
let pendingTools = state.pendingTools
let lifecycle = state.lifecycle
if (delta?.reasoning_content)
lifecycle = Lifecycle.reasoningDelta(lifecycle, events, "reasoning-0", delta.reasoning_content)
const reasoning = reasoningDelta(delta)
const reasoningField = state.reasoningField ?? (!state.lifecycle.text.has("text-0") ? reasoning?.field : undefined)
const detailDelta = Array.isArray(delta?.reasoning_details) ? delta.reasoning_details : undefined
if (detailDelta !== undefined) appendReasoningDetails(state.reasoningDetails, detailDelta)
const reasoningDetailsObserved = state.reasoningDetailsObserved || detailDelta !== undefined
const deltaMetadata = reasoningMetadata(reasoningField)
const text = detailDelta?.length ? (detailText(detailDelta) ?? reasoning?.text) : reasoning?.text
if (!state.lifecycle.text.has("text-0") && text !== undefined)
lifecycle = Lifecycle.reasoningDelta(lifecycle, events, "reasoning-0", text, deltaMetadata)
else if (
reasoningDetailsObserved &&
!lifecycle.reasoning.has("reasoning-0") &&
(Boolean(delta?.content) || toolDeltas.length > 0)
)
lifecycle = Lifecycle.reasoningStart(lifecycle, events, "reasoning-0", deltaMetadata)
const reasoningEmitted = state.reasoningEmitted || lifecycle.reasoning.has("reasoning-0")
if (delta?.content) {
lifecycle = Lifecycle.reasoningEnd(lifecycle, events, "reasoning-0")
lifecycle = Lifecycle.reasoningEnd(
lifecycle,
events,
"reasoning-0",
reasoningMetadata(reasoningField, reasoningDetailsObserved ? state.reasoningDetails : undefined),
)
lifecycle = Lifecycle.textDelta(lifecycle, events, "text-0", delta.content)
}
if (toolDeltas.length) lifecycle = Lifecycle.reasoningEnd(lifecycle, events, "reasoning-0")
for (const tool of toolDeltas) {
const current = tools[tool.index]
const pending = pendingTools[tool.index]
const id = current?.id ?? pending?.id ?? (tool.id || undefined)
const name = current?.name ?? pending?.name ?? (tool.function?.name || undefined)
const text = `${pending?.input ?? ""}${tool.function?.arguments ?? ""}`
if (!current && (!id || !name)) {
pendingTools = { ...pendingTools, [tool.index]: { id: id || undefined, name: name || undefined, input: text } }
continue
}
if (pending) {
pendingTools = { ...pendingTools }
delete pendingTools[tool.index]
}
const result = ToolStream.appendOrStart(
ADAPTER,
tools,
tool.index,
{ id: tool.id ?? undefined, name: tool.function?.name ?? undefined, text: tool.function?.arguments ?? "" },
{ id: id || undefined, name: name || undefined, text },
"OpenAI Chat tool call delta is missing id or name",
)
if (ToolStream.isError(result)) return yield* result
@@ -436,8 +572,11 @@ const step = (state: ParserState, event: OpenAIChatEvent) =>
events.push(...result.events)
}
if (finishReason !== undefined && state.finishReason === undefined && Object.keys(pendingTools).length > 0)
return yield* ProviderShared.eventError(ADAPTER, "OpenAI Chat tool call delta is missing id or name")
// Finalize accumulated tool inputs eagerly when finish_reason arrives so
// JSON parse failures fail the stream at the boundary rather than at halt.
// valid calls and malformed local calls settle independently.
const finished =
finishReason !== undefined && state.finishReason === undefined && Object.keys(tools).length > 0
? yield* ToolStream.finishAll(ADAPTER, tools)
@@ -446,10 +585,15 @@ const step = (state: ParserState, event: OpenAIChatEvent) =>
return [
{
tools: finished?.tools ?? tools,
pendingTools,
toolCallEvents: finished?.events ?? state.toolCallEvents,
usage,
finishReason,
lifecycle,
reasoningField,
reasoningDetails: state.reasoningDetails,
reasoningDetailsObserved,
reasoningEmitted,
},
events,
] as const
@@ -459,7 +603,16 @@ const finishEvents = (state: ParserState): ReadonlyArray<LLMEvent> => {
const events: LLMEvent[] = []
const hasToolCalls = state.toolCallEvents.length > 0
const reason = state.finishReason === "stop" && hasToolCalls ? "tool-calls" : state.finishReason
const lifecycle = state.toolCallEvents.length ? Lifecycle.stepStart(state.lifecycle, events) : state.lifecycle
const metadata = reasoningMetadata(
state.reasoningField,
state.reasoningDetailsObserved ? state.reasoningDetails : undefined,
)
const started =
state.reasoningDetailsObserved && !state.reasoningEmitted
? Lifecycle.reasoningStart(state.lifecycle, events, "reasoning-0", reasoningMetadata(state.reasoningField))
: state.lifecycle
const ended = Lifecycle.reasoningEnd(started, events, "reasoning-0", metadata)
const lifecycle = state.toolCallEvents.length ? Lifecycle.stepStart(ended, events) : ended
events.push(...state.toolCallEvents)
if (reason) Lifecycle.finish(lifecycle, events, { reason, usage: state.usage })
return events
@@ -482,7 +635,16 @@ export const protocol = Protocol.make({
},
stream: {
event: Protocol.jsonEvent(OpenAIChatEvent),
initial: () => ({ tools: ToolStream.empty<number>(), toolCallEvents: [], lifecycle: Lifecycle.initial() }),
initial: () => ({
tools: ToolStream.empty<number>(),
pendingTools: {},
toolCallEvents: [],
lifecycle: Lifecycle.initial(),
reasoningField: undefined,
reasoningDetails: [],
reasoningDetailsObserved: false,
reasoningEmitted: false,
}),
step,
onHalt: finishEvents,
},
@@ -0,0 +1,23 @@
import { Route, type RouteRoutedModelInput } from "../route/client"
import { Endpoint } from "../route/endpoint"
import { OpenAIResponses } from "./openai-responses"
const ADAPTER = "openai-compatible-responses"
export type OpenAICompatibleResponsesModelInput = RouteRoutedModelInput
/**
* Route for providers that expose an OpenAI Responses-compatible `/responses`
* endpoint. Provider helpers configure identity, endpoint, and auth before
* model selection while this route reuses the OpenAI Responses protocol.
*/
export const route = Route.make({
id: ADAPTER,
providerMetadataKey: "openai",
protocol: OpenAIResponses.protocol,
endpoint: Endpoint.path(OpenAIResponses.PATH),
transport: OpenAIResponses.httpTransport,
defaults: { providerOptions: { openai: { store: false } } },
})
export * as OpenAICompatibleResponses from "./openai-compatible-responses"
+270
View File
@@ -0,0 +1,270 @@
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
@@ -1,10 +1,11 @@
import { Effect, Schema } from "effect"
import { Effect, Encoding, Schema } from "effect"
import { Route } from "../route/client"
import { Auth } from "../route/auth"
import { Endpoint } from "../route/endpoint"
import { HttpTransport, WebSocketTransport } from "../route/transport"
import { Protocol } from "../route/protocol"
import {
LLMError,
LLMEvent,
Usage,
type FinishReason,
@@ -19,11 +20,12 @@ import {
type ToolResultPart,
} from "../schema"
import { JsonObject, optionalArray, optionalNull, ProviderShared } from "./shared"
import { isContextOverflow } from "../provider-error"
import { classifyProviderFailure } from "../provider-error"
import { OpenAIOptions } from "./utils/openai-options"
import { Lifecycle } from "./utils/lifecycle"
import { ToolSchemaProjection } from "./utils/tool-schema"
import { ToolStream } from "./utils/tool-stream"
import { OpenAIImage } from "./utils/openai-image"
const ADAPTER = "openai-responses"
export const DEFAULT_BASE_URL = "https://api.openai.com/v1"
@@ -112,11 +114,24 @@ const OpenAIResponsesTool = Schema.Struct({
parameters: JsonObject,
strict: Schema.optional(Schema.Boolean),
})
type OpenAIResponsesTool = Schema.Schema.Type<typeof OpenAIResponsesTool>
const OpenAIResponsesImageGenerationTool = Schema.Struct({
type: Schema.tag("image_generation"),
action: Schema.optional(Schema.Literals(["auto", "generate", "edit"])),
background: Schema.optional(Schema.Literals(["auto", "opaque", "transparent"])),
input_fidelity: Schema.optional(Schema.Literals(["low", "high"])),
output_compression: Schema.optional(Schema.Int.check(Schema.isBetween({ minimum: 0, maximum: 100 }))),
output_format: Schema.optional(Schema.Literals(["png", "jpeg", "webp"])),
partial_images: Schema.optional(Schema.Int.check(Schema.isGreaterThanOrEqualTo(0))),
quality: Schema.optional(Schema.Literals(["auto", "low", "medium", "high"])),
size: Schema.optional(OpenAIImage.Size),
})
const OpenAIResponsesTools = Schema.Union([OpenAIResponsesTool, OpenAIResponsesImageGenerationTool])
type OpenAIResponsesTool = Schema.Schema.Type<typeof OpenAIResponsesTools>
const OpenAIResponsesToolChoice = Schema.Union([
Schema.Literals(["auto", "none", "required"]),
Schema.Struct({ type: Schema.tag("function"), name: Schema.String }),
Schema.Struct({ type: Schema.tag("image_generation") }),
])
// Fields shared between the HTTP body and the WebSocket `response.create`
@@ -127,7 +142,7 @@ const OpenAIResponsesCoreFields = {
model: Schema.String,
input: Schema.Array(OpenAIResponsesInputItem),
instructions: Schema.optional(Schema.String),
tools: optionalArray(OpenAIResponsesTool),
tools: optionalArray(OpenAIResponsesTools),
tool_choice: Schema.optional(OpenAIResponsesToolChoice),
store: Schema.optional(Schema.Boolean),
service_tier: Schema.optional(OpenAIOptions.OpenAIServiceTier),
@@ -193,6 +208,8 @@ const OpenAIResponsesStreamItem = Schema.Struct({
outputs: Schema.optional(Schema.Unknown),
server_label: Schema.optional(Schema.String),
output: Schema.optional(Schema.Unknown),
result: Schema.optional(Schema.String),
output_format: Schema.optional(Schema.Literals(["png", "jpeg", "webp"])),
error: Schema.optional(Schema.Unknown),
encrypted_content: optionalNull(Schema.String),
})
@@ -257,21 +274,41 @@ const invalid = ProviderShared.invalidRequest
// =============================================================================
// Request Lowering
// =============================================================================
const lowerTool = (tool: ToolDefinition, inputSchema: JsonSchema): OpenAIResponsesTool => ({
type: "function",
name: tool.name,
description: tool.description,
parameters: ToolSchemaProjection.openAI(inputSchema),
// TODO: Read this from OpenAI-specific tool options so direct LLM callers can opt into strict schemas.
strict: false,
const nativeImageToolInput = (tool: ToolDefinition) => {
const native = tool.native?.openai
return ProviderShared.isRecord(native) && native.type === "image_generation" ? native : undefined
}
const nativeImageTool = (tool: ToolDefinition) => {
const native = nativeImageToolInput(tool)
return Schema.is(OpenAIResponsesImageGenerationTool)(native) ? native : undefined
}
const lowerTool = Effect.fn("OpenAIResponses.lowerTool")(function* (tool: ToolDefinition, inputSchema: JsonSchema) {
const native = nativeImageToolInput(tool)
if (native !== undefined) {
if (Schema.is(OpenAIResponsesImageGenerationTool)(native)) return native
return yield* invalid("OpenAI Responses image generation tool options are invalid")
}
return {
type: "function" as const,
name: tool.name,
description: tool.description,
parameters: ToolSchemaProjection.openAI(inputSchema),
// TODO: Read this from OpenAI-specific tool options so direct LLM callers can opt into strict schemas.
strict: false,
}
})
const lowerToolChoice = (toolChoice: NonNullable<LLMRequest["toolChoice"]>) =>
const lowerToolChoice = (toolChoice: NonNullable<LLMRequest["toolChoice"]>, tools: ReadonlyArray<ToolDefinition>) =>
ProviderShared.matchToolChoice("OpenAI Responses", toolChoice, {
auto: () => "auto" as const,
none: () => "none" as const,
required: () => "required" as const,
tool: (name) => ({ type: "function" as const, name }),
tool: (name) =>
tools.some((tool) => tool.name === name && nativeImageTool(tool) !== undefined)
? ({ type: "image_generation" } as const)
: { type: "function" as const, name },
})
const lowerToolCall = (part: ToolCallPart): OpenAIResponsesInputItem => ({
@@ -419,6 +456,13 @@ const lowerMessages = Effect.fn("OpenAIResponses.lowerMessages")(function* (requ
const itemID = hostedToolItemID(part)
if (store !== false && itemID && !hostedToolReferences.has(itemID))
input.push({ type: "item_reference", id: itemID })
if (store === false && part.name === "image_generation" && part.result.type === "content") {
const content: ReadonlyArray<ToolContent> = part.result.value
input.push({
role: "user",
content: yield* Effect.forEach(content, lowerToolResultContentItem),
})
}
if (itemID) hostedToolReferences.add(itemID)
continue
}
@@ -484,10 +528,10 @@ const fromRequest = Effect.fn("OpenAIResponses.fromRequest")(function* (request:
tools:
request.tools.length === 0
? undefined
: request.tools.map((tool) =>
: yield* Effect.forEach(request.tools, (tool) =>
lowerTool(tool, ToolSchemaProjection.modelCompatibility(tool.inputSchema, toolSchemaCompatibility)),
),
tool_choice: request.toolChoice ? yield* lowerToolChoice(request.toolChoice) : undefined,
tool_choice: request.toolChoice ? yield* lowerToolChoice(request.toolChoice, request.tools) : undefined,
stream: true as const,
max_output_tokens: generation?.maxTokens,
temperature: generation?.temperature,
@@ -573,14 +617,29 @@ const isReasoningItem = (
// Round-trip the full item as the structured result so consumers can extract
// outputs / sources / status without re-decoding.
const hostedToolResult = (item: OpenAIResponsesStreamItem) => {
const hostedToolResult = Effect.fn("OpenAIResponses.hostedToolResult")(function* (item: OpenAIResponsesStreamItem) {
const isError = typeof item.error !== "undefined" && item.error !== null
if (item.type === "image_generation_call" && item.result) {
yield* Effect.fromResult(Encoding.decodeBase64(item.result)).pipe(
Effect.mapError(() => ProviderShared.eventError(ADAPTER, "OpenAI Responses returned invalid image base64")),
)
return {
type: "content" as const,
value: [
{
type: "file" as const,
uri: `data:image/${item.output_format ?? "png"};base64,${item.result}`,
mime: `image/${item.output_format ?? "png"}`,
},
],
}
}
return isError ? { type: "error" as const, value: item.error } : { type: "json" as const, value: item }
}
})
const hostedToolEvents = (
const hostedToolEvents = Effect.fn("OpenAIResponses.hostedToolEvents")(function* (
item: OpenAIResponsesStreamItem & { type: HostedToolType; id: string },
): ReadonlyArray<LLMEvent> => {
) {
const tool = HOSTED_TOOLS[item.type]
const providerMetadata = openaiMetadata({ itemId: item.id })
return [
@@ -594,21 +653,20 @@ const hostedToolEvents = (
LLMEvent.toolResult({
id: item.id,
name: tool.name,
result: hostedToolResult(item),
result: yield* hostedToolResult(item),
providerExecuted: true,
providerMetadata,
}),
]
}
})
type StepResult = readonly [ParserState, ReadonlyArray<LLMEvent>]
const NO_EVENTS: StepResult["1"] = []
// `response.completed` / `response.incomplete` are clean finishes that emit a
// `finish` event; `response.failed` is a hard failure that emits a
// `provider-error`. All three end the stream — kept in one set so `step` and
// the protocol's `terminal` predicate stay in sync.
// `finish` event; `response.failed` is a hard failure. All three end the stream,
// so keep this set aligned with `step` and the protocol's terminal predicate.
const TERMINAL_TYPES = new Set(["response.completed", "response.incomplete", "response.failed"])
const onOutputTextDelta = (state: ParserState, event: OpenAIResponsesEvent): StepResult => {
@@ -835,7 +893,9 @@ const onOutputItemDone = Effect.fn("OpenAIResponses.onOutputItemDone")(function*
{
...state,
lifecycle,
hasFunctionCall: resultEvents.some(LLMEvent.is.toolCall) ? true : state.hasFunctionCall,
hasFunctionCall:
resultEvents.some((event) => LLMEvent.is.toolCall(event) || LLMEvent.is.toolInputError(event)) ||
state.hasFunctionCall,
tools: result.tools,
},
events,
@@ -845,7 +905,7 @@ const onOutputItemDone = Effect.fn("OpenAIResponses.onOutputItemDone")(function*
if (isHostedToolItem(item)) {
const events: LLMEvent[] = []
const lifecycle = Lifecycle.stepStart(state.lifecycle, events)
events.push(...hostedToolEvents(item))
events.push(...(yield* hostedToolEvents(item)))
return [{ ...state, lifecycle }, events] satisfies StepResult
}
@@ -910,22 +970,13 @@ const providerErrorMessage = (event: OpenAIResponsesEvent, fallback: string): st
const providerError = (event: OpenAIResponsesEvent, fallback: string) => {
const code = event.code || event.error?.code || event.response?.error?.code || undefined
const message = providerErrorMessage(event, fallback)
return LLMEvent.providerError({
message,
classification: code === "context_length_exceeded" || isContextOverflow(message) ? "context-overflow" : undefined,
return new LLMError({
module: ADAPTER,
method: "stream",
reason: classifyProviderFailure({ message, code }),
})
}
const onResponseFailed = (state: ParserState, event: OpenAIResponsesEvent): StepResult => [
state,
[providerError(event, "OpenAI Responses response failed")],
]
const onError = (state: ParserState, event: OpenAIResponsesEvent): StepResult => [
state,
[providerError(event, "OpenAI Responses stream error")],
]
const step = (state: ParserState, event: OpenAIResponsesEvent) => {
if (event.type === "response.output_text.delta") return Effect.succeed(onOutputTextDelta(state, event))
if (event.type === "response.output_text.done") return Effect.succeed(onOutputTextDone(state, event))
@@ -950,8 +1001,8 @@ const step = (state: ParserState, event: OpenAIResponsesEvent) => {
if (event.type === "response.output_item.done") return onOutputItemDone(state, event)
if (event.type === "response.completed" || event.type === "response.incomplete")
return Effect.succeed(onResponseFinish(state, event))
if (event.type === "response.failed") return Effect.succeed(onResponseFailed(state, event))
if (event.type === "error") return Effect.succeed(onError(state, event))
if (event.type === "response.failed") return providerError(event, "OpenAI Responses response failed")
if (event.type === "error") return providerError(event, "OpenAI Responses stream error")
return Effect.succeed<StepResult>([state, NO_EVENTS])
}
@@ -0,0 +1,34 @@
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,(.*)$/s.exec(url)
if (!match) return Effect.fail(invalid(module, "Image data URLs must contain a MIME type and base64 data"))
return Effect.fromResult(Encoding.decodeBase64(match[2])).pipe(
Effect.mapError(() => invalid(module, "Image data URL contains invalid base64 data")),
Effect.map((data) => ({ mediaType: match[1], data })),
)
}
export const invalidImageInput = invalid
export const ImageInputs = {
dataUrl,
decodeDataUrl,
invalid: invalidImageInput,
} as const
@@ -44,7 +44,7 @@ export const reasoningDelta = (
providerMetadata?: ProviderMetadata,
): State => {
const started = reasoningStart(state, events, id, providerMetadata)
events.push(LLMEvent.reasoningDelta({ id, text }))
events.push(LLMEvent.reasoningDelta({ id, text, providerMetadata }))
return started
}
@@ -0,0 +1,20 @@
import { Schema } from "effect"
const dimensions = (value: string) => {
const match = /^(\d+)x(\d+)$/.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
@@ -1,5 +1,5 @@
import { Effect } from "effect"
import { LLMError, LLMEvent, type ProviderMetadata, type ToolCall } from "../../schema"
import { LLMError, LLMEvent, type ProviderMetadata, type ToolCall, type ToolInputError } from "../../schema"
import { eventError, parseToolInput, type ToolAccumulator } from "../shared"
type StreamKey = string | number
@@ -53,6 +53,7 @@ const inputStart = (tool: PendingTool) =>
LLMEvent.toolInputStart({
id: tool.id,
name: tool.name,
providerExecuted: tool.providerExecuted ? true : undefined,
providerMetadata: tool.providerMetadata,
})
@@ -63,19 +64,36 @@ const inputDelta = (tool: PendingTool, text: string) =>
text,
})
const toolCall = (route: string, tool: PendingTool, inputOverride?: string) =>
parseToolInput(route, tool.name, inputOverride ?? tool.input).pipe(
Effect.map(
(input): ToolCall =>
LLMEvent.toolCall({
id: tool.id,
name: tool.name,
input,
providerExecuted: tool.providerExecuted ? true : undefined,
providerMetadata: tool.providerMetadata,
}),
const toolCall = (route: string, tool: PendingTool, inputOverride?: string) => {
const raw = inputOverride ?? tool.input
return parseToolInput(route, tool.name, raw).pipe(
Effect.map((input): ToolCall | ToolInputError =>
LLMEvent.toolCall({
id: tool.id,
name: tool.name,
input,
providerExecuted: tool.providerExecuted ? true : undefined,
providerMetadata: tool.providerMetadata,
}),
),
Effect.catch((error) =>
tool.providerExecuted
? Effect.fail(error)
: Effect.succeed(
LLMEvent.toolInputError({
id: tool.id,
name: tool.name,
raw,
}),
),
),
)
}
const finishEvents = (tool: PendingTool, event: ToolCall | ToolInputError): ReadonlyArray<LLMEvent> =>
event.type === "tool-input-error"
? [event]
: [LLMEvent.toolInputEnd({ id: tool.id, name: tool.name, providerMetadata: tool.providerMetadata }), event]
/** Store the updated tool and produce the optional public delta event. */
const appendTool = <K extends StreamKey>(
@@ -122,8 +140,8 @@ export const appendOrStart = <K extends StreamKey>(
missingToolMessage: string,
): AppendOutcome<K> | LLMError => {
const current = tools[key]
const id = delta.id ?? current?.id
const name = delta.name ?? current?.name
const id = current?.id ?? delta.id
const name = current?.name ?? delta.name
if (!id || !name) return eventError(route, missingToolMessage)
const tool = {
@@ -158,8 +176,9 @@ export const appendExisting = <K extends StreamKey>(
/**
* Finalize one pending tool call: parse the accumulated raw JSON, remove it
* from state, and return the optional public `tool-call` event. Missing keys are
* a no-op because some providers emit stop events for non-tool content blocks.
* from state, and return either a call or a non-executable local input error.
* Missing keys are a no-op because some providers emit stop events for
* non-tool content blocks.
*/
export const finish = <K extends StreamKey>(route: string, tools: State<K>, key: K) =>
Effect.gen(function* () {
@@ -167,10 +186,7 @@ export const finish = <K extends StreamKey>(route: string, tools: State<K>, key:
if (!tool) return { tools }
return {
tools: withoutTool(tools, key),
events: [
LLMEvent.toolInputEnd({ id: tool.id, name: tool.name, providerMetadata: tool.providerMetadata }),
yield* toolCall(route, tool),
],
events: finishEvents(tool, yield* toolCall(route, tool)),
}
})
@@ -185,17 +201,14 @@ export const finishWithInput = <K extends StreamKey>(route: string, tools: State
if (!tool) return { tools }
return {
tools: withoutTool(tools, key),
events: [
LLMEvent.toolInputEnd({ id: tool.id, name: tool.name, providerMetadata: tool.providerMetadata }),
yield* toolCall(route, tool, input),
],
events: finishEvents(tool, yield* toolCall(route, tool, input)),
}
})
/**
* Finalize every pending tool call at once. OpenAI Chat has this shape: it does
* not emit per-tool stop events, so all accumulated calls finish when the choice
* receives a terminal `finish_reason`.
* not emit per-tool stop events, so all accumulated calls finish independently
* when the choice receives a terminal `finish_reason`.
*/
export const finishAll = <K extends StreamKey>(route: string, tools: State<K>) =>
Effect.gen(function* () {
@@ -205,12 +218,7 @@ export const finishAll = <K extends StreamKey>(route: string, tools: State<K>) =
return {
tools: empty<K>(),
events: yield* Effect.forEach(pending, (tool) =>
toolCall(route, tool).pipe(
Effect.map((call) => [
LLMEvent.toolInputEnd({ id: tool.id, name: tool.name, providerMetadata: tool.providerMetadata }),
call,
]),
),
toolCall(route, tool).pipe(Effect.map((event) => finishEvents(tool, event))),
).pipe(Effect.map((events) => events.flat())),
}
})
+202
View File
@@ -0,0 +1,202 @@
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
View File
@@ -0,0 +1,132 @@
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
+165
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@@ -0,0 +1,165 @@
import { Option, Schema } from "effect"
import {
AuthenticationReason,
ContentPolicyReason,
InvalidRequestReason,
LLMError,
ProviderErrorEvent,
ProviderInternalReason,
QuotaExceededReason,
RateLimitReason,
UnknownProviderReason,
type HttpContext,
type HttpRateLimitDetails,
type ProviderMetadata,
} from "./schema"
const patterns = [
/prompt is too long/i,
/request_too_large/i,
/input is too long for requested model/i,
/exceeds the context window/i,
/exceeds (?:the )?(?:model'?s )?maximum context length(?: of [\d,]+ tokens?|\s*\([\d,]+\))/i,
/input token count.*exceeds the maximum/i,
/tokens in request more than max tokens allowed/i,
/maximum prompt length is \d+/i,
/reduce the length of the messages/i,
/maximum context length is \d+ tokens/i,
/exceeds (?:the )?maximum allowed input length of [\d,]+ tokens?/i,
/input \(\d+ tokens\) is longer than the model'?s context length \(\d+ tokens\)/i,
/exceeds the limit of \d+/i,
/exceeds the available context size/i,
/greater than the context length/i,
/context window exceeds limit/i,
/exceeded model token limit/i,
/context[_ ]length[_ ]exceeded/i,
/request entity too large/i,
/context length is only \d+ tokens/i,
/input length.*exceeds.*context length/i,
/prompt too long; exceeded (?:max )?context length/i,
/too large for model with \d+ maximum context length/i,
/prompt has [\d,]+ tokens?, but the configured context size is [\d,]+ tokens?/i,
/model_context_window_exceeded/i,
/too many tokens/i,
/token limit exceeded/i,
]
const exclusions = [/^(throttling error|service unavailable):/i, /rate limit/i, /too many requests/i]
export const isContextOverflow = (message: string) =>
!exclusions.some((pattern) => pattern.test(message)) &&
(patterns.some((pattern) => pattern.test(message)) || /^4(00|13)\s*(status code)?\s*\(no body\)/i.test(message))
export const isContextOverflowFailure = (failure: unknown) =>
failure instanceof LLMError
? failure.reason._tag === "InvalidRequest" && failure.reason.classification === "context-overflow"
: Schema.is(ProviderErrorEvent)(failure) && failure.classification === "context-overflow"
const decodeJson = Schema.decodeUnknownOption(Schema.UnknownFromJsonString)
const QUOTA_CODES = new Set(["insufficient_quota", "usage_not_included", "billing_error"])
const SERVER_CODES = new Set([
"api_error",
"internal_error",
"internalserverexception",
"modelstreamerrorexception",
"overloaded_error",
"server_error",
"server_is_overloaded",
"serviceunavailableexception",
])
const INVALID_REQUEST_CODES = new Set(["invalid_prompt", "invalid_request_error", "validationexception"])
const RATE_LIMIT_TEXT = /rate increased too quickly|rate[-_\s]?limit|too[_\s]?many[_\s]?requests/i
const QUOTA_TEXT = /insufficient[-_\s]?quota|quota[-_\s]?exceeded/i
const CONTENT_POLICY_TEXT = /content[-_\s]?policy|content_filter|safety/i
export interface ProviderFailure {
readonly message: string
readonly status?: number | undefined
readonly code?: string | undefined
readonly retryAfterMs?: number | undefined
readonly rateLimit?: HttpRateLimitDetails | undefined
readonly http?: HttpContext | undefined
readonly providerMetadata?: ProviderMetadata | undefined
}
// Keep HTTP failures and provider-reported stream failures on one typed path so
// session retry policy never needs provider-specific string matching.
export function classifyProviderFailure(input: ProviderFailure): LLMError["reason"] {
const body = input.http?.body ?? ""
const codes = [input.code, ...providerCodes(body), ...providerCodes(input.message)]
.filter((code): code is string => code !== undefined)
.map((code) => code.toLowerCase())
const text = body || input.message
const common = { message: input.message, providerMetadata: input.providerMetadata, http: input.http }
const clientScoped = input.status === undefined || (input.status >= 400 && input.status < 500)
if (
clientScoped &&
(codes.includes("context_length_exceeded") ||
codes.includes("model_context_window_exceeded") ||
isContextOverflow(text))
)
return new InvalidRequestReason({ ...common, classification: "context-overflow" })
if (CONTENT_POLICY_TEXT.test(text)) return new ContentPolicyReason(common)
if (codes.some((code) => QUOTA_CODES.has(code)) || (input.status === 429 && QUOTA_TEXT.test(text)))
return new QuotaExceededReason(common)
if (input.status === 401) return new AuthenticationReason({ ...common, kind: "invalid" })
if (input.status === 403) return new AuthenticationReason({ ...common, kind: "insufficient-permissions" })
if (codes.includes("authentication_error")) return new AuthenticationReason({ ...common, kind: "invalid" })
if (codes.includes("permission_error"))
return new AuthenticationReason({ ...common, kind: "insufficient-permissions" })
if (
codes.some((code) => code.includes("rate_limit") || code === "too_many_requests" || code === "throttlingexception")
)
return new RateLimitReason({
...common,
retryAfterMs: input.retryAfterMs,
rateLimit: input.rateLimit,
})
if (RATE_LIMIT_TEXT.test(text))
return new RateLimitReason({
...common,
retryAfterMs: input.retryAfterMs,
rateLimit: input.rateLimit,
})
if (codes.some((code) => SERVER_CODES.has(code) || code.includes("exhausted") || code.includes("unavailable")))
return new ProviderInternalReason({
...common,
status: input.status,
retryAfterMs: input.retryAfterMs,
})
if (input.status === 429) {
return new RateLimitReason({
...common,
retryAfterMs: input.retryAfterMs,
rateLimit: input.rateLimit,
})
}
if (input.status !== undefined && input.status >= 500)
return new ProviderInternalReason({
...common,
status: input.status,
retryAfterMs: input.retryAfterMs,
})
if (codes.some((code) => INVALID_REQUEST_CODES.has(code))) return new InvalidRequestReason(common)
if (
input.status === 400 ||
input.status === 404 ||
input.status === 409 ||
input.status === 413 ||
input.status === 422
)
return new InvalidRequestReason(common)
return new UnknownProviderReason({ ...common, status: input.status })
}
function providerCodes(value: string) {
const decoded = Option.getOrUndefined(decodeJson(value))
if (!isRecord(decoded)) return []
const error = isRecord(decoded.error) ? decoded.error : undefined
return [decoded.code, error?.code, error?.type].filter((value): value is string => typeof value === "string")
}
function isRecord(value: unknown): value is Record<string, unknown> {
return typeof value === "object" && value !== null
}
+1
View File
@@ -0,0 +1 @@
export * from "./providers/index"
@@ -0,0 +1,67 @@
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"
@@ -3,7 +3,8 @@ import { Auth } from "../route/auth"
import type { ProviderAuthOption } from "../route/auth-options"
import type { ProviderPackage } from "../provider-package"
import { ProviderID, type ModelID } from "../schema"
import * as AnthropicMessages from "../protocols/anthropic-messages"
import { AnthropicMessages } from "../protocols/anthropic-messages"
import { AnthropicCompatible } from "./anthropic-compatible"
export const id = ProviderID.make("anthropic")
@@ -11,11 +12,13 @@ export const routes = [AnthropicMessages.route]
export type Config = RouteDefaultsInput & ProviderAuthOption<"optional"> & { readonly baseURL?: string }
export interface Settings extends ProviderPackage.Settings {
readonly apiKey?: string
readonly authToken?: string
readonly baseURL?: string
}
export type Settings = ProviderPackage.Settings &
(
| { readonly apiKey?: string; readonly authToken?: never }
| { readonly apiKey?: never; readonly authToken?: string }
) & {
readonly baseURL?: string
}
const auth = (options: ProviderAuthOption<"optional">) => {
if ("auth" in options && options.auth) return options.auth
@@ -24,26 +27,30 @@ const auth = (options: ProviderAuthOption<"optional">) => {
.pipe(Auth.header("x-api-key"))
}
const configuredRoute = (input: Config) => {
const { apiKey: _, auth: _auth, baseURL, ...rest } = input
return AnthropicMessages.route.with({ ...rest, endpoint: { baseURL }, auth: auth(input) })
}
export const configure = (input: Config = {}) => {
const route = configuredRoute(input)
const { apiKey: _, auth: _auth, baseURL, ...rest } = input
const compatible = AnthropicCompatible.configure({
...rest,
auth: auth(input),
baseURL: baseURL ?? AnthropicMessages.DEFAULT_BASE_URL,
provider: id,
})
return {
id,
model: (modelID: string | ModelID) => route.model({ id: modelID }),
model: (modelID: string | ModelID) => compatible.model(modelID),
configure,
}
}
export const provider = configure()
export const model: ProviderPackage.Definition<Settings>["model"] = (modelID, settings) =>
configure({
export const model: ProviderPackage.Definition<Settings>["model"] = (modelID, settings) => {
if (settings.apiKey !== undefined && settings.authToken !== undefined)
throw new Error("Anthropic 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,
}).model(modelID)
}
@@ -0,0 +1,81 @@
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)
}
@@ -0,0 +1,111 @@
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)
}
@@ -0,0 +1,82 @@
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)
}
@@ -0,0 +1,77 @@
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"
@@ -0,0 +1,98 @@
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)
}
@@ -0,0 +1,2 @@
export { model } from "../google-vertex-chat"
export type { Settings } from "../google-vertex-chat"
@@ -0,0 +1,2 @@
export { model } from "../google-vertex"
export type { Settings } from "../google-vertex"
@@ -0,0 +1,2 @@
export { model } from "../google-vertex-messages"
export type { Settings } from "../google-vertex-messages"
@@ -0,0 +1,2 @@
export { model } from "../google-vertex-responses"
export type { Settings } from "../google-vertex-responses"
@@ -2,14 +2,20 @@ import type { RouteDefaultsInput } from "../route/client"
import { Auth } from "../route/auth"
import type { ProviderAuthOption } from "../route/auth-options"
import type { ProviderPackage } from "../provider-package"
import { ProviderID, type ModelID, type ProviderOptions } from "../schema"
import * as Gemini from "../protocols/gemini"
import { HttpOptions, ProviderID, mergeHttpOptions, type ModelID, type ProviderOptions } from "../schema"
import { Gemini } from "../protocols/gemini"
import { GoogleImages } from "../protocols/google-images"
export type { GoogleImageOptions } from "../protocols/google-images"
export const id = ProviderID.make("google")
export const routes = [Gemini.route]
export type Config = RouteDefaultsInput & ProviderAuthOption<"optional"> & { readonly baseURL?: string }
export type Config = RouteDefaultsInput &
ProviderAuthOption<"optional"> & {
readonly baseURL?: string
}
export interface Settings extends ProviderPackage.Settings {
readonly apiKey?: string
@@ -31,9 +37,18 @@ const configuredRoute = (input: Config) => {
export const configure = (input: Config = {}) => {
const route = configuredRoute(input)
const image = (modelID: string | ModelID) =>
GoogleImages.model({
id: modelID,
auth: auth(input),
baseURL: input.baseURL,
headers: input.headers,
http: mergeHttpOptions(input.http === undefined ? undefined : HttpOptions.make(input.http)),
})
return {
id,
model: (modelID: string | ModelID) => route.model({ id: modelID }),
image,
configure,
}
}
@@ -48,3 +63,5 @@ export const model: ProviderPackage.Definition<Settings>["model"] = (modelID, se
limits: settings.limits,
providerOptions: settings.providerOptions,
}).model(modelID)
export const image = provider.image
@@ -1,11 +1,18 @@
export * as Anthropic from "./anthropic"
export * as AnthropicCompatible from "./anthropic-compatible"
export * as AmazonBedrock from "./amazon-bedrock"
export * as Azure from "./azure"
export * as Cloudflare from "./cloudflare"
export { CloudflareAIGateway, CloudflareWorkersAI } from "./cloudflare"
export * as GitHubCopilot from "./github-copilot"
export * as Google from "./google"
export * as GoogleVertex from "./google-vertex"
export * as GoogleVertexChat from "./google-vertex-chat"
export * as GoogleVertexMessages from "./google-vertex-messages"
export * as GoogleVertexResponses from "./google-vertex-responses"
export * as OpenAI from "./openai"
export * as OpenAICompatible from "./openai-compatible"
export * as OpenAICompatibleResponses from "./openai-compatible-responses"
export * as OpenRouter from "./openrouter"
export * as XAI from "./xai"
export * as ZAI from "./zai"
@@ -0,0 +1,55 @@
import type { ProviderPackage } from "../provider-package"
import { OpenAICompatibleResponses } from "../protocols/openai-compatible-responses"
import { AuthOptions, type ProviderAuthOption } from "../route/auth-options"
import type { RouteDefaultsInput } from "../route/client"
import { ProviderID, type ModelID } from "../schema"
import type { OpenAIProviderOptionsInput } from "./openai-options"
export const id = ProviderID.make("openai-compatible")
export type Config = RouteDefaultsInput &
ProviderAuthOption<"optional"> & {
readonly provider?: string
readonly baseURL: string
}
export interface Settings extends ProviderPackage.Settings {
readonly apiKey?: string
readonly baseURL: string
readonly provider?: string
readonly providerOptions?: OpenAIProviderOptionsInput
}
export const routes = [OpenAICompatibleResponses.route]
export const configure = (input: Config) => {
const provider = input.provider ?? "openai-compatible"
const { provider: _, baseURL, apiKey: _apiKey, auth: _auth, ...rest } = input
const route = OpenAICompatibleResponses.route.with({
...rest,
provider,
endpoint: { baseURL },
auth: AuthOptions.bearer(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) =>
configure({
apiKey: settings.apiKey,
baseURL: settings.baseURL,
headers: settings.headers === undefined ? undefined : { ...settings.headers },
http: settings.body === undefined ? undefined : { body: { ...settings.body } },
limits: settings.limits,
provider: settings.provider,
providerOptions: settings.providerOptions,
}).model(modelID)
@@ -0,0 +1 @@
export * from "../openai-compatible-responses"
@@ -1,12 +1,14 @@
import { AuthOptions, type ProviderAuthOption } from "../route/auth-options"
import type { Route, RouteDefaultsInput } from "../route/client"
import type { ProviderPackage } from "../provider-package"
import { ProviderID, type ModelID } from "../schema"
import { HttpOptions, ProviderID, ToolDefinition, mergeHttpOptions, type ModelID } from "../schema"
import * as OpenAIChat from "../protocols/openai-chat"
import * as OpenAIResponses from "../protocols/openai-responses"
import { withOpenAIOptions, type OpenAIProviderOptionsInput } from "./openai-options"
import { OpenAIImages, type OpenAIImageString } from "../protocols/openai-images"
export type { OpenAIOptionsInput, OpenAIResponseIncludable } from "./openai-options"
export type { OpenAIImageOptions } from "../protocols/openai-images"
export const id = ProviderID.make("openai")
@@ -22,6 +24,39 @@ export type Config = RouteDefaultsInput &
readonly providerOptions?: OpenAIProviderOptionsInput
}
export interface ImageGenerationOptions {
readonly action?: OpenAIImageString<"auto" | "generate" | "edit">
readonly background?: OpenAIImageString<"auto" | "opaque" | "transparent">
readonly inputFidelity?: OpenAIImageString<"low" | "high">
readonly outputCompression?: number
readonly outputFormat?: OpenAIImageString<"png" | "jpeg" | "webp">
readonly partialImages?: number
readonly quality?: OpenAIImageString<"auto" | "low" | "medium" | "high" | "standard" | "hd">
readonly size?: OpenAIImageString<
"auto" | "256x256" | "512x512" | "1024x1024" | "1536x1024" | "1024x1536" | "1792x1024" | "1024x1792"
>
}
export const imageGeneration = (options: ImageGenerationOptions = {}) =>
ToolDefinition.make({
name: "image_generation",
description: "Generate or edit an image using OpenAI's hosted image generation tool.",
inputSchema: { type: "object", properties: {}, additionalProperties: false },
native: {
openai: {
type: "image_generation",
action: options.action,
background: options.background,
input_fidelity: options.inputFidelity,
output_compression: options.outputCompression,
output_format: options.outputFormat,
partial_images: options.partialImages,
quality: options.quality,
size: options.size,
},
},
})
export interface Settings extends ProviderPackage.Settings {
readonly apiKey?: string
readonly baseURL?: string
@@ -55,6 +90,17 @@ export const configure = (input: Config = {}) => {
const responsesWebSocket = (id: string | ModelID) =>
responsesWebSocketRoute.with(withOpenAIOptions(id, modelDefaults, { textVerbosity: true })).model({ id })
const chat = (id: string | ModelID) => chatRoute.with(withOpenAIOptions(id, modelDefaults)).model({ id })
const image = (modelID: string | ModelID) =>
OpenAIImages.model({
id: modelID,
auth: auth(input),
baseURL: input.baseURL,
headers: input.headers,
http: mergeHttpOptions(
input.http === undefined ? undefined : HttpOptions.make(input.http),
input.queryParams === undefined ? undefined : new HttpOptions({ query: input.queryParams }),
),
})
return {
id,
@@ -62,6 +108,7 @@ export const configure = (input: Config = {}) => {
responses,
responsesWebSocket,
chat,
image,
configure,
}
}
@@ -97,3 +144,4 @@ export const chatModel: ProviderPackage.Definition<Settings>["model"] = (modelID
export const responses = provider.responses
export const responsesWebSocket = provider.responsesWebSocket
export const chat = provider.chat
export const image = provider.image
@@ -41,13 +41,31 @@ export const protocol = Protocol.make({
schema: OpenRouterBody,
from: (request) =>
OpenAIChat.protocol.body.from(request).pipe(
Effect.map(
(body) =>
({
...body,
...bodyOptions(request.providerOptions?.openrouter),
}) as OpenRouterBody,
),
Effect.map((body) => {
const sourceAssistants = request.messages.filter((message) => message.role === "assistant")
let assistantIndex = 0
const messages = body.messages.map((message) => {
if (message.role !== "assistant") return message
const source = sourceAssistants[assistantIndex++]
const reasoning = source?.content
.filter((part) => part.type === "reasoning")
.map((part) => part.text)
.join("")
const reasoningDetails = Array.isArray(message.reasoning_details) ? message.reasoning_details : undefined
return {
...message,
reasoning_content: undefined,
reasoning_text: undefined,
reasoning: reasoning && reasoningDetails && reasoningDetails.length > 0 ? reasoning : undefined,
reasoning_details: reasoningDetails,
}
})
return {
...body,
messages,
...bodyOptions(request.providerOptions?.openrouter),
} as OpenRouterBody
}),
),
},
stream: OpenAIChat.protocol.stream,
@@ -1,9 +1,10 @@
import { AuthOptions, type ProviderAuthOption } from "../route/auth-options"
import type { RouteDefaultsInput } from "../route/client"
import { ProviderID, type ModelID } from "../schema"
import { HttpOptions, ProviderID, type ModelID } from "../schema"
import * as OpenAICompatibleProfiles from "./openai-compatible-profile"
import * as OpenAICompatibleChat from "../protocols/openai-compatible-chat"
import * as OpenAIResponses from "../protocols/openai-responses"
import { XAIImages } from "../protocols/xai-images"
export const id = ProviderID.make("xai")
@@ -12,6 +13,8 @@ export type ModelOptions = RouteDefaultsInput &
readonly baseURL?: string
}
export type { XAIImageOptions } from "../protocols/xai-images"
export const routes = [OpenAIResponses.route, OpenAICompatibleChat.route]
const auth = (options: ProviderAuthOption<"optional">) => AuthOptions.bearer(options, "XAI_API_KEY")
@@ -41,11 +44,20 @@ export const configure = (input: ModelOptions = {}) => {
const chatRoute = configuredChatRoute(input)
const responses = (modelID: string | ModelID) => responsesRoute.model({ id: modelID })
const chat = (modelID: string | ModelID) => chatRoute.model({ id: modelID })
const image = (modelID: string | ModelID) =>
XAIImages.model({
id: modelID,
auth: auth(input),
baseURL: input.baseURL ?? OpenAICompatibleProfiles.profiles.xai.baseURL,
headers: input.headers,
http: input.http === undefined ? undefined : HttpOptions.make(input.http),
})
return {
id,
model: responses,
responses,
chat,
image,
configure,
}
}
@@ -54,3 +66,4 @@ export const provider = configure()
export const model = provider.model
export const responses = provider.responses
export const chat = provider.chat
export const image = provider.image
+35
View File
@@ -0,0 +1,35 @@
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
@@ -0,0 +1 @@
export * from "./route/index"
@@ -4,7 +4,7 @@ import { Auth } from "./auth"
export type ApiKeyMode = "optional" | "required"
export type AuthOverride = {
readonly auth: Auth
readonly auth: Auth.Definition
readonly apiKey?: never
}
@@ -44,7 +44,10 @@ export type AtLeastOne<T> = {
* override, otherwise resolve `apiKey` (option > config var) and apply it as
* a bearer token.
*/
export const bearer = (options: ProviderAuthOption<"optional">, envVar: string | ReadonlyArray<string>): Auth => {
export const bearer = (
options: ProviderAuthOption<"optional">,
envVar: string | ReadonlyArray<string>,
): Auth.Definition => {
if ("auth" in options && options.auth) return options.auth
return (Array.isArray(envVar) ? envVar : [envVar])
.reduce(
@@ -1,6 +1,6 @@
import { Config, Effect, Redacted } from "effect"
import { Headers } from "effect/unstable/http"
import { AuthenticationReason, InvalidRequestReason, LLMError, type LLMRequest } from "../schema"
import { AuthenticationReason, InvalidRequestReason, LLMError, type HttpOptions } from "../schema"
export class MissingCredentialError extends Error {
readonly _tag = "MissingCredentialError"
@@ -15,7 +15,7 @@ export type AuthError = CredentialError | LLMError
type Secret = string | Redacted.Redacted | Config.Config<string | Redacted.Redacted>
export interface AuthInput {
readonly request: LLMRequest
readonly request: { readonly http?: HttpOptions }
readonly method: "POST" | "GET"
readonly url: string
readonly body: string
@@ -25,19 +25,19 @@ export interface AuthInput {
export interface Credential {
readonly load: Effect.Effect<Redacted.Redacted, CredentialError>
readonly orElse: (that: Credential) => Credential
readonly bearer: () => Auth
readonly header: (name: string) => Auth
readonly bearer: () => Definition
readonly header: (name: string) => Definition
readonly pipe: <A>(f: (self: Credential) => A) => A
}
export interface Auth {
export interface Definition {
readonly apply: (input: AuthInput) => Effect.Effect<Headers.Headers, AuthError>
readonly andThen: (that: Auth) => Auth
readonly orElse: (that: Auth) => Auth
readonly pipe: <A>(f: (self: Auth) => A) => A
readonly andThen: (that: Definition) => Definition
readonly orElse: (that: Definition) => Definition
readonly pipe: <A>(f: (self: Definition) => A) => A
}
export const isAuth = (input: unknown): input is Auth =>
export const isAuth = (input: unknown): input is Definition =>
typeof input === "object" && input !== null && "apply" in input && typeof input.apply === "function"
const credential = (load: Effect.Effect<Redacted.Redacted, CredentialError>): Credential => {
@@ -51,8 +51,8 @@ const credential = (load: Effect.Effect<Redacted.Redacted, CredentialError>): Cr
return self
}
const auth = (apply: Auth["apply"]): Auth => {
const self: Auth = {
const auth = (apply: Definition["apply"]): Definition => {
const self: Definition = {
apply,
andThen: (that) =>
auth((input) => apply(input).pipe(Effect.flatMap((headers) => that.apply({ ...input, headers })))),
@@ -109,15 +109,15 @@ const credentialInput = (source: Secret | Credential) =>
? credentialFromSecret(source, "value")
: source
export function bearer(source: Secret | Credential): Auth
export function bearer(source: Secret | Credential): Definition
export function bearer(source: Secret | Credential) {
return credentialInput(source).bearer()
}
export const apiKey = bearer
export function header(name: string): (source: Secret | Credential) => Auth
export function header(name: string, source: Secret | Credential): Auth
export function header(name: string): (source: Secret | Credential) => Definition
export function header(name: string, source: Secret | Credential): Definition
export function header(name: string, source?: Secret | Credential) {
if (source === undefined) {
return (next: Secret | Credential) => credentialInput(next).header(name)
@@ -125,8 +125,8 @@ export function header(name: string, source?: Secret | Credential) {
return credentialInput(source).header(name)
}
export function bearerHeader(name: string): (source: Secret | Credential) => Auth
export function bearerHeader(name: string, source: Secret | Credential): Auth
export function bearerHeader(name: string): (source: Secret | Credential) => Definition
export function bearerHeader(name: string, source: Secret | Credential): Definition
export function bearerHeader(name: string, source?: Secret | Credential) {
const render = (input: Secret | Credential) =>
fromCredential(credentialInput(input), (secret) => ({ [name]: `Bearer ${secret}` }))
@@ -149,7 +149,7 @@ const toLLMError = (error: AuthError): LLMError => {
}
export const toEffect =
(input: Auth) =>
(input: Definition) =>
(authInput: AuthInput): Effect.Effect<Headers.Headers, LLMError> =>
input.apply(authInput).pipe(Effect.mapError(toLLMError))
@@ -1,16 +1,16 @@
import { Cause, Context, Effect, Layer, Schema, Stream } from "effect"
import * as Option from "effect/Option"
import { Auth, type Auth as AuthDef } from "./auth"
import { Auth } from "./auth"
import { Endpoint, type EndpointPatch } from "./endpoint"
import { RequestExecutor } from "./executor"
import type { Framing } from "./framing"
import { Framing } from "./framing"
import { HttpTransport } from "./transport"
import type { Transport, TransportRuntime } from "./transport"
import { WebSocketExecutor } from "./transport"
import type { Protocol } from "./protocol"
import { applyCachePolicy } from "../cache-policy"
import * as ProviderShared from "../protocols/shared"
import type { LLMError, LLMEvent, PreparedRequestOf, ProtocolID, ProviderOptions } from "../schema"
import type { LLMError, PreparedRequestOf, ProtocolID, ProviderOptions } from "../schema"
import {
GenerationOptions,
HttpOptions,
@@ -19,6 +19,7 @@ import {
Model,
ModelLimits,
LLMError as LLMErrorClass,
LLMEvent,
PreparedRequest,
ProviderID,
mergeGenerationOptions,
@@ -39,8 +40,8 @@ export interface Route<Body, Prepared = unknown> {
/** ProviderMetadata namespace emitted and consumed by this route. */
readonly providerMetadataKey?: string
readonly protocol: ProtocolID
readonly endpoint: Endpoint<Body>
readonly auth: AuthDef
readonly endpoint: Endpoint.Definition<Body>
readonly auth: Auth.Definition
readonly transport: Transport<Body, Prepared, unknown>
readonly defaults: RouteDefaults
readonly body: RouteBody<Body>
@@ -85,7 +86,7 @@ export interface RouteDefaultsInput {
export interface RoutePatch<Body, Prepared> extends RouteDefaultsInput {
readonly id?: string
readonly provider?: string | ProviderID
readonly auth?: AuthDef
readonly auth?: Auth.Definition
readonly transport?: Transport<Body, Prepared, unknown>
readonly endpoint?: EndpointPatch<Body>
}
@@ -121,7 +122,7 @@ const mergeRouteDefaults = (base: RouteDefaults | undefined, patch: RouteDefault
}
}
const endpointBaseURL = <Body>(endpoint: Endpoint<Body>) =>
const endpointBaseURL = <Body>(endpoint: Endpoint.Definition<Body>) =>
typeof endpoint.baseURL === "string" ? endpoint.baseURL : undefined
const mergeHeaders = (...items: ReadonlyArray<Record<string, string> | undefined>) => {
@@ -191,11 +192,11 @@ export interface MakeInput<Body, Frame, Event, State> {
/** Semantic API contract — owns body construction, body schema, and parsing. */
readonly protocol: Protocol<Body, Frame, Event, State>
/** Where the request is sent. */
readonly endpoint: Endpoint<Body>
readonly endpoint: Endpoint.Definition<Body>
/** Per-request transport auth. Provider facades override this via `route.with(...)`. */
readonly auth?: AuthDef
readonly auth?: Auth.Definition
/** Stream framing — bytes -> frames before `protocol.stream.event` decoding. */
readonly framing: Framing<Frame>
readonly framing: Framing.Definition<Frame>
/** Static / per-request headers added before `auth` runs. */
readonly headers?: (input: { readonly request: LLMRequest }) => Record<string, string>
/** Route/request defaults used when compiling requests for this route. */
@@ -212,9 +213,9 @@ export interface MakeTransportInput<Body, Prepared, Frame, Event, State> {
/** Semantic API contract — owns body construction, body schema, and parsing. */
readonly protocol: Protocol<Body, Frame, Event, State>
/** Where the request is sent. */
readonly endpoint: Endpoint<Body>
readonly endpoint: Endpoint.Definition<Body>
/** Per-request transport auth. Provider facades override this via `route.with(...)`. */
readonly auth?: AuthDef
readonly auth?: Auth.Definition
/** Static / per-request headers added before `auth` runs. */
readonly headers?: (input: { readonly request: LLMRequest }) => Record<string, string>
/** Runnable transport route. */
@@ -229,6 +230,28 @@ const streamError = (route: string, message: string, cause: Cause.Cause<unknown>
return ProviderShared.eventError(route, message, Cause.pretty(cause))
}
const requireTerminalEvent = (route: string) => (events: Stream.Stream<LLMEvent, LLMError>) =>
Stream.suspend(() => {
let terminal = false
return events.pipe(
Stream.mapEffect((event) => {
if (terminal)
return Effect.fail(
ProviderShared.eventError(route, `Provider emitted ${event.type} after the terminal event`),
)
if (LLMEvent.is.finish(event) || LLMEvent.is.providerError(event)) terminal = true
return Effect.succeed(event)
}),
Stream.onEnd(
Effect.suspend(() =>
terminal
? Effect.void
: Effect.fail(ProviderShared.eventError(route, "Provider stream ended without a terminal finish event")),
),
),
)
})
function makeFromTransport<Body, Prepared, Frame, Event, State>(
input: MakeTransportInput<Body, Prepared, Frame, Event, State>,
): Route<Body, Prepared> {
@@ -298,6 +321,7 @@ function makeFromTransport<Body, Prepared, Frame, Event, State>(
protocol.stream.onHalt ? { onHalt: protocol.stream.onHalt } : undefined,
),
Stream.catchCause((cause) => Stream.fail(streamError(route, `Failed to read ${route} stream`, cause))),
requireTerminalEvent(route),
)
},
} satisfies Route<Body, Prepared>
@@ -19,21 +19,24 @@ export type EndpointPart<Body> = string | ((input: EndpointInput<Body>) => strin
* URL embeds the model id, region, or another body field (e.g. Bedrock,
* Gemini).
*/
export interface Endpoint<Body> {
export interface Definition<Body> {
readonly baseURL?: string
readonly path: EndpointPart<Body>
readonly query?: Record<string, string>
}
export type EndpointPatch<Body> = Partial<Endpoint<Body>>
export type EndpointPatch<Body> = Partial<Definition<Body>>
/** Construct an `Endpoint` from a path string or path function. */
export const path = <Body>(value: EndpointPart<Body>, options: Omit<Endpoint<Body>, "path"> = {}): Endpoint<Body> => ({
export const path = <Body>(
value: EndpointPart<Body>,
options: Omit<Definition<Body>, "path"> = {},
): Definition<Body> => ({
...options,
path: value,
})
export const merge = <Body>(base: Endpoint<Body>, patch: EndpointPatch<Body>): Endpoint<Body> => ({
export const merge = <Body>(base: Definition<Body>, patch: EndpointPatch<Body>): Definition<Body> => ({
...base,
...patch,
baseURL: patch.baseURL ?? base.baseURL,
@@ -44,7 +47,7 @@ export const merge = <Body>(base: Endpoint<Body>, patch: EndpointPatch<Body>): E
const renderPart = <Body>(part: EndpointPart<Body>, input: EndpointInput<Body>) =>
typeof part === "function" ? part(input) : part
export const render = <Body>(endpoint: Endpoint<Body>, input: EndpointInput<Body>) => {
export const render = <Body>(endpoint: Definition<Body>, input: EndpointInput<Body>) => {
const url = new URL(`${ProviderShared.trimBaseUrl(endpoint.baseURL ?? "")}${renderPart(endpoint.path, input)}`)
for (const [key, value] of Object.entries(endpoint.query ?? {})) url.searchParams.set(key, value)
return url
@@ -8,21 +8,14 @@ import {
HttpClientResponse,
} from "effect/unstable/http"
import {
AuthenticationReason,
ContentPolicyReason,
HttpContext,
HttpRateLimitDetails,
HttpRequestDetails,
HttpResponseDetails,
InvalidRequestReason,
LLMError,
ProviderInternalReason,
QuotaExceededReason,
RateLimitReason,
TransportReason,
UnknownProviderReason,
} from "../schema"
import { isContextOverflow } from "../provider-error"
import { classifyProviderFailure } from "../provider-error"
export interface Interface {
readonly execute: (
@@ -85,8 +78,6 @@ const requestId = (headers: Record<string, string>) => {
)
}
const providerInternalStatus = (status: number) => status === 429 || status === 503 || status === 504 || status === 529
const retryAfterMs = (headers: Record<string, string>) => {
const millis = Number(headers["retry-after-ms"])
if (Number.isFinite(millis)) return Math.max(0, millis)
@@ -219,58 +210,6 @@ const responseHttp = (input: {
rateLimit: input.rateLimit,
})
const statusReason = (input: {
readonly status: number
readonly message: string
readonly retryAfterMs?: number | undefined
readonly rateLimit?: HttpRateLimitDetails | undefined
readonly http: HttpContext
}) => {
const body = input.http.body ?? ""
if (/content[-_\s]?policy|content_filter|safety/i.test(body)) {
return new ContentPolicyReason({ message: input.message, http: input.http })
}
if (input.status === 401) {
return new AuthenticationReason({ message: input.message, kind: "invalid", http: input.http })
}
if (input.status === 403) {
return new AuthenticationReason({ message: input.message, kind: "insufficient-permissions", http: input.http })
}
if (input.status === 429) {
if (/insufficient[-_\s]?quota|quota[-_\s]?exceeded/i.test(body)) {
return new QuotaExceededReason({ message: input.message, http: input.http })
}
return new RateLimitReason({
message: input.message,
retryAfterMs: input.retryAfterMs,
rateLimit: input.rateLimit,
http: input.http,
})
}
if (
input.status === 400 ||
input.status === 404 ||
input.status === 409 ||
input.status === 413 ||
input.status === 422
) {
return new InvalidRequestReason({
message: input.message,
classification: isContextOverflow(body) ? "context-overflow" : undefined,
http: input.http,
})
}
if (input.status >= 500 || providerInternalStatus(input.status)) {
return new ProviderInternalReason({
message: input.message,
status: input.status,
retryAfterMs: input.retryAfterMs,
http: input.http,
})
}
return new UnknownProviderReason({ message: input.message, status: input.status, http: input.http })
}
const statusError =
(request: HttpClientRequest.HttpClientRequest, redactedNames: ReadonlyArray<string | RegExp>) =>
(response: HttpClientResponse.HttpClientResponse) =>
@@ -284,7 +223,7 @@ const statusError =
return yield* new LLMError({
module: "RequestExecutor",
method: "execute",
reason: statusReason({
reason: classifyProviderFailure({
status: response.status,
message: providerMessage(response.status, details),
retryAfterMs: retryAfter,

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