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chore: update merge branch with latest v2

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Aiden Cline 2026-07-20 20:01:59 +00:00
commit 7d07f4dfc1
516 changed files with 28957 additions and 14050 deletions
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5
.changeset/calm-sessions-header.md Normal file
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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
.changeset/clean-sessions-generate.md Normal file
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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
.changeset/fresh-composers-slot.md Normal file
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@ -0,0 +1,5 @@
---
"@opencode-ai/cli": patch
---
Expose a TUI plugin slot above the session composer.

56
.github/workflows/publish.yml vendored
View file

@ -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:

14
.github/workflows/test.yml vendored
View file

@ -78,6 +78,20 @@ jobs:
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
.gitignore vendored
View file

@ -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

127
bun.lock
View file

@ -132,27 +132,37 @@
"@opencode-ai/server": "workspace:*",
"@opencode-ai/tui": "workspace:*",
"@opentui/core": "catalog:",
"@opentui/keymap": "catalog:",
"@opentui/solid": "catalog:",
"@parcel/watcher": "2.5.1",
"effect": "catalog:",
"fuzzysort": "catalog:",
"immer": "11.1.4",
"jsonc-parser": "3.3.1",
"open": "10.1.2",
"opentui-spinner": "catalog:",
"semver": "catalog:",
"solid-js": "catalog:",
"strip-ansi": "7.1.2",
"uqr": "0.1.3",
"ws": "8.21.0",
},
"devDependencies": {
"@lydell/node-pty-darwin-arm64": "1.2.0-beta.12",
"@lydell/node-pty-darwin-x64": "1.2.0-beta.12",
"@lydell/node-pty-linux-arm64": "1.2.0-beta.12",
"@lydell/node-pty-linux-x64": "1.2.0-beta.12",
"@lydell/node-pty-win32-arm64": "1.2.0-beta.12",
"@lydell/node-pty-win32-x64": "1.2.0-beta.12",
"@opencode-ai/protocol": "workspace:*",
"@opencode-ai/script": "workspace:*",
"@parcel/watcher-darwin-arm64": "2.5.1",
"@parcel/watcher-linux-arm64-glibc": "2.5.1",
"@parcel/watcher-linux-x64-glibc": "2.5.1",
"@parcel/watcher-win32-arm64": "2.5.1",
"@parcel/watcher-win32-x64": "2.5.1",
"@tsconfig/bun": "catalog:",
"@types/bun": "catalog:",
"@types/semver": "catalog:",
"@typescript/native-preview": "catalog:",
"vite": "catalog:",
"vite-plugin-solid": "catalog:",
},
},
"packages/client": {
@ -171,7 +181,7 @@
"effect": "catalog:",
},
"peerDependencies": {
"effect": "4.0.0-beta.83",
"effect": "4.0.0-beta.98",
},
"optionalPeers": [
"effect",
@ -387,6 +397,7 @@
"mime-types": "3.0.2",
"minimatch": "10.2.5",
"npm-package-arg": "13.0.2",
"resolve.exports": "catalog:",
"semver": "^7.6.3",
"turndown": "7.2.0",
"venice-ai-sdk-provider": "2.1.1",
@ -554,7 +565,7 @@
"name": "@opencode-ai/http-recorder",
"version": "1.18.3",
"dependencies": {
"@effect/platform-node-shared": "4.0.0-beta.83",
"@effect/platform-node-shared": "4.0.0-beta.98",
},
"devDependencies": {
"@effect/platform-node": "catalog:",
@ -739,9 +750,9 @@
"typescript": "catalog:",
},
"peerDependencies": {
"@opentui/core": ">=0.4.3",
"@opentui/keymap": ">=0.4.3",
"@opentui/solid": ">=0.4.3",
"@opentui/core": ">=0.4.5",
"@opentui/keymap": ">=0.4.5",
"@opentui/solid": ">=0.4.5",
},
"optionalPeers": [
"@opentui/core",
@ -885,9 +896,13 @@
"version": "1.17.13",
"dependencies": {
"@fontsource/commit-mono": "5.2.5",
"@fontsource/noto-sans-math": "5.2.5",
"@fontsource/noto-sans-symbols": "5.2.5",
"@fontsource/noto-sans-symbols-2": "5.2.5",
"@napi-rs/canvas": "1.0.2",
"@opencode-ai/ai": "workspace:*",
"@opencode-ai/core": "workspace:*",
"@opencode-ai/plugin": "workspace:*",
"@opentui/core": "catalog:",
"effect": "catalog:",
},
@ -1011,6 +1026,7 @@
"@opencode-ai/client": "workspace:*",
"@opencode-ai/core": "workspace:*",
"@opencode-ai/plugin": "workspace:*",
"@opencode-ai/schema": "workspace:*",
"@opencode-ai/simulation": "workspace:*",
"@opencode-ai/ui": "workspace:*",
"@opentui/core": "catalog:",
@ -1021,10 +1037,12 @@
"diff": "catalog:",
"effect": "catalog:",
"fuzzysort": "catalog:",
"get-east-asian-width": "catalog:",
"open": "10.1.2",
"opentui-spinner": "catalog:",
"remeda": "catalog:",
"solid-js": "catalog:",
"string-width": "catalog:",
"strip-ansi": "7.1.2",
"uqr": "0.1.3",
},
@ -1163,12 +1181,12 @@
"@modelcontextprotocol/sdk@1.29.0": "patches/@modelcontextprotocol%2Fsdk@1.29.0.patch",
"gcp-metadata@8.1.2": "patches/gcp-metadata@8.1.2.patch",
"@standard-community/standard-openapi@0.2.9": "patches/@standard-community%2Fstandard-openapi@0.2.9.patch",
"effect@4.0.0-beta.83": "patches/effect@4.0.0-beta.83.patch",
"@npmcli/agent@4.0.2": "patches/@npmcli%2Fagent@4.0.2.patch",
"@silvia-odwyer/photon-node@0.3.4": "patches/@silvia-odwyer%2Fphoton-node@0.3.4.patch",
"solid-js@1.9.10": "patches/solid-js@1.9.10.patch",
"@ai-sdk/google@3.0.73": "patches/@ai-sdk%2Fgoogle@3.0.73.patch",
"pacote@21.5.0": "patches/pacote@21.5.0.patch",
"effect@4.0.0-beta.98": "patches/effect@4.0.0-beta.98.patch",
},
"overrides": {
"@opentui/core": "catalog:",
@ -1180,9 +1198,9 @@
"catalog": {
"@cloudflare/workers-types": "4.20251008.0",
"@corvu/drawer": "0.2.4",
"@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",
"@hono/standard-validator": "0.2.0",
"@hono/zod-validator": "0.4.2",
"@kobalte/core": "0.13.11",
@ -1190,9 +1208,9 @@
"@npmcli/arborist": "9.4.0",
"@octokit/rest": "22.0.0",
"@openauthjs/openauth": "0.0.0-20250322224806",
"@opentui/core": "0.4.3",
"@opentui/keymap": "0.4.3",
"@opentui/solid": "0.4.3",
"@opentui/core": "0.4.5",
"@opentui/keymap": "0.4.5",
"@opentui/solid": "0.4.5",
"@pierre/diffs": "1.2.10",
"@playwright/test": "1.59.1",
"@sentry/solid": "10.36.0",
@ -1218,8 +1236,9 @@
"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",
"fuzzysort": "3.1.0",
"get-east-asian-width": "1.6.0",
"hono": "4.10.7",
"hono-openapi": "1.1.2",
"luxon": "3.6.1",
@ -1228,11 +1247,13 @@
"opentui-spinner": "0.0.7",
"remeda": "2.26.0",
"remend": "1.3.0",
"resolve.exports": "2.0.3",
"semver": "7.7.4",
"shiki": "4.2.0",
"solid-js": "1.9.10",
"solid-list": "0.3.0",
"sst": "4.13.1",
"string-width": "7.2.0",
"tailwindcss": "4.1.11",
"typescript": "5.8.2",
"ulid": "3.0.1",
@ -1619,13 +1640,13 @@
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@ -1775,6 +1796,12 @@
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@ -2273,27 +2300,27 @@
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"@standard-community/standard-openapi/effect/uuid": ["uuid@14.0.0", "", { "bin": { "uuid": "dist-node/bin/uuid" } }, "sha512-Qo+uWgilfSmAhXCMav1uYFynlQO7fMFiMVZsQqZRMIXp0O7rR7qjkj+cPvBHLgBqi960QCoo/PH2/6ZtVqKvrg=="],
"@stoplight/spectral-core/minimatch/brace-expansion": ["brace-expansion@1.1.15", "", { "dependencies": { "balanced-match": "^1.0.0", "concat-map": "0.0.1" } }, "sha512-EwOCDEex4quD37XhqM3omwtMoJjr//isUZz1JopUNWms+4Z2ViyM/k1YIRePpoVNnQhENnxtFjLaxNHrT7xIUg=="],
"@storybook/addon-docs/react-dom/scheduler": ["scheduler@0.23.2", "", { "dependencies": { "loose-envify": "^1.1.0" } }, "sha512-UOShsPwz7NrMUqhR6t0hWjFduvOzbtv7toDH1/hIrfRNIDBnnBWd0CwJTGvTpngVlmwGCdP9/Zl/tVrDqcuYzQ=="],

19
package.json
View file

@ -37,18 +37,18 @@
"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",
"@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",
@ -69,12 +69,13 @@
"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": "17.0.6",
"marked-shiki": "1.2.1",
@ -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",
@ -163,7 +166,7 @@
"@ai-sdk/google@3.0.73": "patches/@ai-sdk%2Fgoogle@3.0.73.patch",
"@pierre/trees@1.0.0-beta.4": "patches/@pierre%2Ftrees@1.0.0-beta.4.patch",
"@modelcontextprotocol/sdk@1.29.0": "patches/@modelcontextprotocol%2Fsdk@1.29.0.patch",
"effect@4.0.0-beta.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"
}
}

81
packages/ai/README.md
View file

@ -1,6 +1,6 @@
# @opencode-ai/ai
Schema-first LLM core for opencode. One typed request, response, event, and tool language; provider quirks live in adapters, not in calling code.
Schema-first AI primitives for opencode. Provider quirks live in adapters, not in calling code.
```ts
import { Effect } from "effect"
@ -24,6 +24,81 @@ const program = Effect.gen(function* () {
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 } 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
})
```
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,
})
```
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.
@ -32,6 +107,8 @@ Run `LLMClient.stream(request)` instead of `generate` when you want incremental
- **`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
@ -182,7 +259,7 @@ Adding a new model or deployment is usually 5-15 lines using `Route.make({ proto
## Effect
This package is built on Effect. Public methods return `Effect` or `Stream`; provide `LLMClient.layer` for runtime dispatch and import the provider/protocol modules for the routes you use. The example at `example/tutorial.ts` is a runnable walkthrough.
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

4
packages/ai/STATUS.md
View file

@ -1,6 +1,6 @@
# LLM Provider Parity Status
Last reviewed: 2026-07-16
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.
@ -20,7 +20,7 @@ This file tracks the gap between the native `@opencode-ai/ai` package and the AI
| 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. | No named compatible 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. |

2
packages/ai/package.json
View file

@ -7,7 +7,7 @@
"scripts": {
"setup:recording-env": "bun run script/setup-recording-env.ts",
"test": "bun test --timeout 30000 --only-failures",
"typecheck": "tsgo --noEmit",
"typecheck": "tsgo --noEmit && tsgo --noEmit -p tsconfig.types.json",
"build": "tsc -p tsconfig.build.json"
},
"files": [

12
packages/ai/script/setup-recording-env.ts
View file

@ -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
packages/ai/src/image-client.ts Normal file
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

131
packages/ai/src/image.ts Normal file
View file

@ -0,0 +1,131 @@
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",
})
export class ImageRequest extends Schema.Class<ImageRequest>("Image.Request")({
model: ImageModelSchema,
prompt: Schema.String,
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

4
packages/ai/src/index.ts
View file

@ -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, 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"

9
packages/ai/src/protocols/anthropic-messages.ts
View file

@ -703,7 +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,
}),
],
]
}

4
packages/ai/src/protocols/bedrock-converse.ts
View file

@ -561,7 +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(

1
packages/ai/src/protocols/index.ts
View file

@ -2,6 +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"

188
packages/ai/src/protocols/openai-chat.ts
View file

@ -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 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,
},

170
packages/ai/src/protocols/openai-images.ts Normal file
View file

@ -0,0 +1,170 @@
import { Effect, Encoding, Schema } from "effect"
import { Headers, HttpClientRequest } from "effect/unstable/http"
import { ImageModel, GeneratedImage, 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 } from "./shared"
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 type OpenAIImageString<Known extends string> = Known | (string & {})
export type OpenAIImageOptions = {
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: Record<string, unknown> | undefined) => {
if (!options) return undefined
const { 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 http = mergeHttpOptions(request.model.http, request.http)
const requestBody = mergeJsonRecords(
{ model: request.model.id, prompt: request.prompt },
nativeOptions(request.options),
http?.body,
) as OpenAIImageBody
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 OpenAI Images response")),
)
const decoded = yield* Schema.decodeUnknownEffect(OpenAIImageResponse)(payload).pipe(
Effect.mapError(() => invalidOutput("OpenAI Images returned an invalid response")),
)
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 } },
})
}),
}
return ImageModel.make<OpenAIImageOptions>({ id: input.id, provider: "openai", route, http: input.http })
}
export const OpenAIImages = {
model,
} as const

104
packages/ai/src/protocols/openai-responses.ts
View file

@ -1,4 +1,4 @@
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"
@ -25,6 +25,7 @@ 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"
@ -113,11 +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`
@ -128,7 +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),
@ -194,6 +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),
})
@ -258,21 +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 => ({
@ -420,6 +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
}
@ -485,10 +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,
@ -574,14 +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 [
@ -595,12 +653,12 @@ 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>]
@ -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
}

2
packages/ai/src/protocols/utils/lifecycle.ts
View file

@ -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
}

20
packages/ai/src/protocols/utils/openai-image.ts Normal file
View file

@ -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

72
packages/ai/src/protocols/utils/tool-stream.ts
View file

@ -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())),
}
})

184
packages/ai/src/protocols/xai-images.ts Normal file
View file

@ -0,0 +1,184 @@
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"
const ADAPTER = "xai-images"
export const DEFAULT_BASE_URL = "https://api.x.ai/v1"
export const PATH = "/images/generations"
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 requestBody = mergeJsonRecords(
{ model: request.model.id, prompt: request.prompt },
nativeOptions(request.options),
http?.body,
) as XAIImageBody
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 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

12
packages/ai/src/provider-error.ts
View file

@ -16,13 +16,17 @@ import {
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,
@ -34,11 +38,17 @@ const patterns = [
/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) =>
patterns.some((pattern) => pattern.test(message)) || /^4(00|13)\s*(status code)?\s*\(no body\)/i.test(message)
!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

50
packages/ai/src/providers/openai.ts
View file

@ -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

32
packages/ai/src/providers/openrouter.ts
View file

@ -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,

15
packages/ai/src/providers/xai.ts
View file

@ -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

4
packages/ai/src/route/auth.ts
View file

@ -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

18
packages/ai/src/schema/events.ts
View file

@ -129,6 +129,7 @@ export const ToolInputStart = Schema.Struct({
type: Schema.tag("tool-input-start"),
id: ToolCallID,
name: Schema.String,
providerExecuted: Schema.optional(Schema.Boolean),
providerMetadata: Schema.optional(ProviderMetadata),
}).annotate({ identifier: "LLM.Event.ToolInputStart" })
export type ToolInputStart = Schema.Schema.Type<typeof ToolInputStart>
@ -149,6 +150,15 @@ export const ToolInputEnd = Schema.Struct({
}).annotate({ identifier: "LLM.Event.ToolInputEnd" })
export type ToolInputEnd = Schema.Schema.Type<typeof ToolInputEnd>
/** A local tool call whose final input could not be decoded. */
export const ToolInputError = Schema.Struct({
type: Schema.tag("tool-input-error"),
id: ToolCallID,
name: Schema.String,
raw: Schema.String,
}).annotate({ identifier: "LLM.Event.ToolInputError" })
export type ToolInputError = Schema.Schema.Type<typeof ToolInputError>
export const ToolCall = Schema.Struct({
type: Schema.tag("tool-call"),
id: ToolCallID,
@ -216,6 +226,7 @@ const llmEventTagged = Schema.Union([
ToolInputStart,
ToolInputDelta,
ToolInputEnd,
ToolInputError,
ToolCall,
ToolResult,
ToolError,
@ -253,6 +264,8 @@ export const LLMEvent = Object.assign(llmEventTagged, {
toolInputDelta: (input: WithID<ToolInputDelta, ToolCallID>) =>
ToolInputDelta.make({ ...input, id: toolCallID(input.id) }),
toolInputEnd: (input: WithID<ToolInputEnd, ToolCallID>) => ToolInputEnd.make({ ...input, id: toolCallID(input.id) }),
toolInputError: (input: WithID<ToolInputError, ToolCallID>) =>
ToolInputError.make({ ...input, id: toolCallID(input.id) }),
toolCall: (input: WithID<ToolCall, ToolCallID>) => ToolCall.make({ ...input, id: toolCallID(input.id) }),
toolResult: (input: WithID<ToolResult, ToolCallID>) =>
ToolResult.make({
@ -283,6 +296,7 @@ export const LLMEvent = Object.assign(llmEventTagged, {
toolInputStart: llmEventTagged.guards["tool-input-start"],
toolInputDelta: llmEventTagged.guards["tool-input-delta"],
toolInputEnd: llmEventTagged.guards["tool-input-end"],
toolInputError: llmEventTagged.guards["tool-input-error"],
toolCall: llmEventTagged.guards["tool-call"],
toolResult: llmEventTagged.guards["tool-result"],
toolError: llmEventTagged.guards["tool-error"],
@ -548,6 +562,10 @@ const reduceResponseState = (state: ResponseState, event: LLMEvent): ResponseSta
return reduceToolInputDelta(next, event)
case "tool-input-end":
return reduceToolInputEnd(next, event)
case "tool-input-error": {
const { [event.id]: _finished, ...toolInputs } = next.toolInputs
return { ...next, toolInputs }
}
case "tool-call":
return reduceToolCall(next, event)
case "tool-result":

39
packages/ai/test/auth-options.types.ts
View file

@ -1,7 +1,6 @@
import { Config } from "effect"
import type { Auth } from "../src/route/auth"
import { Auth } from "../src/route"
import type { ModelFactory } from "../src/route/auth-options"
import { Auth as RuntimeAuth } from "../src/route/auth"
import * as OpenAIChat from "../src/protocols/openai-chat"
import * as AmazonBedrock from "../src/providers/amazon-bedrock"
import * as Anthropic from "../src/providers/anthropic"
@ -28,7 +27,7 @@ type Model = {
readonly id: string
}
declare const auth: Auth
declare const auth: Auth.Definition
declare const optionalAuthModel: ModelFactory<BaseOptions, "optional", Model>
declare const requiredAuthModel: ModelFactory<BaseOptions, "required", Model>
const configApiKey = Config.redacted("OPENAI_API_KEY")
@ -76,9 +75,9 @@ OpenAI.responses("gpt-4.1-mini")
OpenAI.configure({}).responses("gpt-4.1-mini")
OpenAI.configure({ apiKey: "sk-test" }).responses("gpt-4.1-mini")
OpenAI.configure({ apiKey: configApiKey }).responses("gpt-4.1-mini")
OpenAI.configure({ auth: RuntimeAuth.bearer("oauth-token") }).responses("gpt-4.1-mini")
OpenAI.configure({ auth: Auth.bearer("oauth-token") }).responses("gpt-4.1-mini")
OpenAI.configure({
auth: RuntimeAuth.headers({ authorization: "Bearer gateway" }),
auth: Auth.headers({ authorization: "Bearer gateway" }),
baseURL: "https://gateway.example.com/v1",
}).responses("gpt-4.1-mini")
OpenAI.configure({
@ -102,40 +101,40 @@ OpenAI.configure({ generation: { maxTokens: "many" } })
OpenAI.configure({ providerOptions: { openai: { store: "false" } } })
// @ts-expect-error auth is an override, so OpenAI rejects apiKey with auth.
OpenAI.configure({ apiKey: "sk-test", auth: RuntimeAuth.bearer("oauth-token") })
OpenAI.configure({ apiKey: "sk-test", auth: Auth.bearer("oauth-token") })
OpenAI.chat("gpt-4.1-mini")
OpenAI.configure({ apiKey: "sk-test" }).chat("gpt-4.1-mini")
OpenAI.configure({ apiKey: configApiKey }).chat("gpt-4.1-mini")
OpenAI.configure({ auth: RuntimeAuth.bearer("oauth-token") }).chat("gpt-4.1-mini")
OpenAI.configure({ auth: Auth.bearer("oauth-token") }).chat("gpt-4.1-mini")
// @ts-expect-error OpenAI chat selectors only accept model ids.
OpenAI.configure({ apiKey: "sk-test" }).chat("gpt-4.1-mini", {})
// @ts-expect-error auth is an override, so OpenAI Chat rejects apiKey with auth.
OpenAI.configure({ apiKey: "sk-test", auth: RuntimeAuth.bearer("oauth-token") })
OpenAI.configure({ apiKey: "sk-test", auth: Auth.bearer("oauth-token") })
// @ts-expect-error Azure requires at least one of `resourceName` or `baseURL`.
Azure.configure()
Azure.configure({ apiKey: "azure-key", resourceName: "resource" }).responses("deployment")
Azure.configure({ apiKey: configApiKey, resourceName: "resource" }).responses("deployment")
Azure.configure({ auth: RuntimeAuth.header("api-key", "azure-key"), resourceName: "resource" }).responses("deployment")
Azure.configure({ auth: Auth.header("api-key", "azure-key"), resourceName: "resource" }).responses("deployment")
// @ts-expect-error Azure model selectors only accept deployment ids.
Azure.configure({ apiKey: "azure-key", resourceName: "resource" }).responses("deployment", {})
// @ts-expect-error auth is an override, so Azure rejects apiKey with auth.
Azure.configure({ resourceName: "resource", apiKey: "azure-key", auth: RuntimeAuth.header("api-key", "override") })
Azure.configure({ resourceName: "resource", apiKey: "azure-key", auth: Auth.header("api-key", "override") })
Azure.configure({ apiKey: "azure-key", resourceName: "resource" }).chat("deployment")
Azure.configure({ apiKey: configApiKey, resourceName: "resource" }).chat("deployment")
Azure.configure({ auth: RuntimeAuth.header("api-key", "azure-key"), resourceName: "resource" }).chat("deployment")
Azure.configure({ auth: Auth.header("api-key", "azure-key"), resourceName: "resource" }).chat("deployment")
// @ts-expect-error Azure chat model selectors only accept deployment ids.
Azure.configure({ apiKey: "azure-key", resourceName: "resource" }).chat("deployment", {})
// @ts-expect-error auth is an override, so Azure Chat rejects apiKey with auth.
Azure.configure({ resourceName: "resource", apiKey: "azure-key", auth: RuntimeAuth.header("api-key", "override") })
Azure.configure({ resourceName: "resource", apiKey: "azure-key", auth: Auth.header("api-key", "override") })
Anthropic.configure({ apiKey: "anthropic-key" }).model("claude-haiku")
// @ts-expect-error Anthropic model selectors only accept model ids.
@ -165,7 +164,7 @@ Google.configure({ apiKey: "google-key" }).model("gemini-2.5-flash", {})
GoogleVertex.configure({ apiKey: "vertex-key" }).model("gemini-3.5-flash")
GoogleVertex.configure({ accessToken: "vertex-token", project: "project" }).model("gemini-3.5-flash")
GoogleVertex.configure({ auth: RuntimeAuth.bearer("vertex-token"), project: "project" }).model("gemini-3.5-flash")
GoogleVertex.configure({ auth: Auth.bearer("vertex-token"), project: "project" }).model("gemini-3.5-flash")
// @ts-expect-error Vertex Gemini model selectors only accept model ids.
GoogleVertex.configure({ apiKey: "vertex-key" }).model("gemini-3.5-flash", {})
// @ts-expect-error Vertex Gemini config accepts only one auth source.
@ -174,7 +173,7 @@ GoogleVertex.configure({ accessToken: "vertex-token", apiKey: "vertex-key", proj
GoogleVertex.model("gemini-3.5-flash", { accessToken: "vertex-token", apiKey: "vertex-key", project: "project" })
GoogleVertexChat.configure({ accessToken: "vertex-token", project: "project" }).model("deepseek-ai/deepseek-v3.2-maas")
GoogleVertexChat.configure({ auth: RuntimeAuth.bearer("vertex-token"), project: "project" }).model(
GoogleVertexChat.configure({ auth: Auth.bearer("vertex-token"), project: "project" }).model(
"deepseek-ai/deepseek-v3.2-maas",
)
// @ts-expect-error Vertex Chat package settings do not accept API keys.
@ -187,12 +186,12 @@ GoogleVertexChat.configure({ accessToken: "vertex-token", project: "project" }).
GoogleVertexChat.configure({
accessToken: "vertex-token",
// @ts-expect-error Vertex Chat config accepts only one auth source.
auth: RuntimeAuth.bearer("vertex-token"),
auth: Auth.bearer("vertex-token"),
project: "project",
})
GoogleVertexResponses.configure({ accessToken: "vertex-token", project: "project" }).model("xai/grok-4.20-reasoning")
GoogleVertexResponses.configure({ auth: RuntimeAuth.bearer("vertex-token"), project: "project" }).model(
GoogleVertexResponses.configure({ auth: Auth.bearer("vertex-token"), project: "project" }).model(
"xai/grok-4.20-reasoning",
)
// @ts-expect-error Vertex Responses package settings do not accept API keys.
@ -205,16 +204,14 @@ GoogleVertexResponses.configure({ accessToken: "vertex-token", project: "project
GoogleVertexResponses.configure({
accessToken: "vertex-token",
// @ts-expect-error Vertex Responses config accepts only one auth source.
auth: RuntimeAuth.bearer("vertex-token"),
auth: Auth.bearer("vertex-token"),
project: "project",
})
GoogleVertexMessages.configure({ accessToken: "vertex-token", project: "project" }).model("claude-sonnet-4-6")
// @ts-expect-error Vertex Messages package settings do not accept API keys.
GoogleVertexMessages.model("claude-sonnet-4-6", { apiKey: "vertex-key", project: "project" })
GoogleVertexMessages.configure({ auth: RuntimeAuth.bearer("vertex-token"), project: "project" }).model(
"claude-sonnet-4-6",
)
GoogleVertexMessages.configure({ auth: Auth.bearer("vertex-token"), project: "project" }).model("claude-sonnet-4-6")
GoogleVertexMessages.configure({ accessToken: "vertex-token", project: "project" }).model(
"claude-sonnet-4-6",
// @ts-expect-error Vertex Messages model selectors only accept model ids.
@ -223,7 +220,7 @@ GoogleVertexMessages.configure({ accessToken: "vertex-token", project: "project"
GoogleVertexMessages.configure({
accessToken: "vertex-token",
// @ts-expect-error Vertex Messages config accepts only one auth source.
auth: RuntimeAuth.bearer("vertex-token"),
auth: Auth.bearer("vertex-token"),
project: "project",
})

40
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{
"version": 1,
"metadata": {
"provider": "minimax",
"protocol": "anthropic-messages",
"route": "anthropic-messages",
"transport": "http",
"model": "MiniMax-M3",
"tags": [
"prefix:anthropic-compatible-messages",
"provider:minimax",
"protocol:anthropic-messages",
"text",
"golden"
],
"name": "anthropic-compatible-messages/minimax-m3-anthropic-compatible-text",
"recordedAt": "2026-07-18T03:42:22.893Z"
},
"interactions": [
{
"transport": "http",
"request": {
"method": "POST",
"url": "https://api.minimax.io/anthropic/v1/messages",
"headers": {
"anthropic-version": "2023-06-01",
"content-type": "application/json"
},
"body": "{\"model\":\"MiniMax-M3\",\"system\":[{\"type\":\"text\",\"text\":\"You are concise.\"}],\"messages\":[{\"role\":\"user\",\"content\":[{\"type\":\"text\",\"text\":\"Reply exactly with: Hello!\"}]}],\"stream\":true,\"max_tokens\":40,\"temperature\":0}"
},
"response": {
"status": 200,
"headers": {
"content-type": "text/event-stream; charset=utf-8"
},
"body": "event: message_start\ndata: {\"type\":\"message_start\",\"message\":{\"id\":\"1a0b363d0882af316faebcec4d4855a8\",\"type\":\"message\",\"role\":\"assistant\",\"content\":[],\"model\":\"MiniMax-M3\",\"stop_reason\":null,\"stop_sequence\":null,\"usage\":{\"input_tokens\":53,\"output_tokens\":0,\"cache_creation_input_tokens\":0,\"cache_read_input_tokens\":114,\"service_tier\":\"standard\"},\"service_tier\":\"standard\"}}\n\nevent: ping\ndata: {\"type\":\"ping\"}\n\nevent: content_block_start\ndata: {\"type\":\"content_block_start\",\"index\":0,\"content_block\":{\"type\":\"text\",\"text\":\"\"}}\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\"Hello\"}}\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\"!\"}}\n\nevent: content_block_stop\ndata: {\"type\":\"content_block_stop\",\"index\":0}\n\nevent: message_delta\ndata: {\"type\":\"message_delta\",\"delta\":{\"stop_reason\":\"end_turn\"},\"usage\":{\"input_tokens\":53,\"output_tokens\":2,\"cache_read_input_tokens\":114,\"service_tier\":\"standard\"}}\n\nevent: message_stop\ndata: {\"type\":\"message_stop\"}\n\n"
}
}
]
}

41
packages/ai/test/fixtures/recordings/anthropic-compatible-messages/minimax-m3-anthropic-compatible-tool-call.json vendored Normal file
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@ -0,0 +1,41 @@
{
"version": 1,
"metadata": {
"provider": "minimax",
"protocol": "anthropic-messages",
"route": "anthropic-messages",
"transport": "http",
"model": "MiniMax-M3",
"tags": [
"prefix:anthropic-compatible-messages",
"provider:minimax",
"protocol:anthropic-messages",
"tool",
"tool-call",
"golden"
],
"name": "anthropic-compatible-messages/minimax-m3-anthropic-compatible-tool-call",
"recordedAt": "2026-07-18T03:42:23.876Z"
},
"interactions": [
{
"transport": "http",
"request": {
"method": "POST",
"url": "https://api.minimax.io/anthropic/v1/messages",
"headers": {
"anthropic-version": "2023-06-01",
"content-type": "application/json"
},
"body": "{\"model\":\"MiniMax-M3\",\"system\":[{\"type\":\"text\",\"text\":\"Call tools exactly as requested.\"}],\"messages\":[{\"role\":\"user\",\"content\":[{\"type\":\"text\",\"text\":\"Call get_weather with city exactly Paris.\"}]}],\"tools\":[{\"name\":\"get_weather\",\"description\":\"Get current weather for a city.\",\"input_schema\":{\"type\":\"object\",\"properties\":{\"city\":{\"type\":\"string\"}},\"required\":[\"city\"],\"additionalProperties\":false}}],\"tool_choice\":{\"type\":\"tool\",\"name\":\"get_weather\"},\"stream\":true,\"max_tokens\":80,\"temperature\":0}"
},
"response": {
"status": 200,
"headers": {
"content-type": "text/event-stream; charset=utf-8"
},
"body": "event: message_start\ndata: {\"type\":\"message_start\",\"message\":{\"id\":\"6731ecc323233459d1792df9a733dd98\",\"type\":\"message\",\"role\":\"assistant\",\"content\":[],\"model\":\"MiniMax-M3\",\"stop_reason\":null,\"stop_sequence\":null,\"usage\":{\"input_tokens\":0,\"output_tokens\":0,\"cache_creation_input_tokens\":0,\"cache_read_input_tokens\":404,\"service_tier\":\"standard\"},\"service_tier\":\"standard\"}}\n\nevent: ping\ndata: {\"type\":\"ping\"}\n\nevent: content_block_start\ndata: {\"type\":\"content_block_start\",\"index\":0,\"content_block\":{\"type\":\"tool_use\",\"id\":\"call_function_vkxtif4epmvm_1\",\"name\":\"get_weather\",\"input\":{}}}\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"input_json_delta\",\"partial_json\":\"{\\\"city\\\": \\\"Paris\"}}\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"input_json_delta\",\"partial_json\":\"\\\"}\"}}\n\nevent: content_block_stop\ndata: {\"type\":\"content_block_stop\",\"index\":0}\n\nevent: message_delta\ndata: {\"type\":\"message_delta\",\"delta\":{\"stop_reason\":\"tool_use\"},\"usage\":{\"input_tokens\":290,\"output_tokens\":27,\"cache_read_input_tokens\":114,\"service_tier\":\"standard\"}}\n\nevent: message_stop\ndata: {\"type\":\"message_stop\"}\n\n"
}
}
]
}

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{
"version": 1,
"metadata": {
"provider": "minimax",
"protocol": "anthropic-messages",
"route": "anthropic-messages",
"transport": "http",
"model": "MiniMax-M3",
"tags": [
"prefix:anthropic-compatible-messages",
"provider:minimax",
"protocol:anthropic-messages",
"tool",
"tool-loop",
"golden"
],
"name": "anthropic-compatible-messages/minimax-m3-anthropic-compatible-tool-loop",
"recordedAt": "2026-07-18T03:42:25.248Z"
},
"interactions": [
{
"transport": "http",
"request": {
"method": "POST",
"url": "https://api.minimax.io/anthropic/v1/messages",
"headers": {
"anthropic-version": "2023-06-01",
"content-type": "application/json"
},
"body": "{\"model\":\"MiniMax-M3\",\"system\":[{\"type\":\"text\",\"text\":\"Use the get_weather tool exactly once. After the tool result, reply exactly: Paris is sunny.\"}],\"messages\":[{\"role\":\"user\",\"content\":[{\"type\":\"text\",\"text\":\"What is the weather in Paris?\"}]}],\"tools\":[{\"name\":\"get_weather\",\"description\":\"Get current weather for a city.\",\"input_schema\":{\"type\":\"object\",\"properties\":{\"city\":{\"type\":\"string\"}},\"required\":[\"city\"],\"additionalProperties\":false}}],\"stream\":true,\"max_tokens\":80,\"temperature\":0}"
},
"response": {
"status": 200,
"headers": {
"content-type": "text/event-stream; charset=utf-8"
},
"body": "event: message_start\ndata: {\"type\":\"message_start\",\"message\":{\"id\":\"3807fa12f9ecb9357df511e099da6da0\",\"type\":\"message\",\"role\":\"assistant\",\"content\":[],\"model\":\"MiniMax-M3\",\"stop_reason\":null,\"stop_sequence\":null,\"usage\":{\"input_tokens\":0,\"output_tokens\":0,\"cache_creation_input_tokens\":0,\"cache_read_input_tokens\":417,\"service_tier\":\"standard\"},\"service_tier\":\"standard\"}}\n\nevent: ping\ndata: {\"type\":\"ping\"}\n\nevent: content_block_start\ndata: {\"type\":\"content_block_start\",\"index\":0,\"content_block\":{\"type\":\"tool_use\",\"id\":\"call_function_yr64rwmre4gr_1\",\"name\":\"get_weather\",\"input\":{}}}\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"input_json_delta\",\"partial_json\":\"{\\\"city\\\": \\\"Paris\"}}\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"input_json_delta\",\"partial_json\":\"\\\"}\"}}\n\nevent: content_block_stop\ndata: {\"type\":\"content_block_stop\",\"index\":0}\n\nevent: message_delta\ndata: {\"type\":\"message_delta\",\"delta\":{\"stop_reason\":\"tool_use\"},\"usage\":{\"input_tokens\":303,\"output_tokens\":27,\"cache_read_input_tokens\":114,\"service_tier\":\"standard\"}}\n\nevent: message_stop\ndata: {\"type\":\"message_stop\"}\n\n"
}
},
{
"transport": "http",
"request": {
"method": "POST",
"url": "https://api.minimax.io/anthropic/v1/messages",
"headers": {
"anthropic-version": "2023-06-01",
"content-type": "application/json"
},
"body": "{\"model\":\"MiniMax-M3\",\"system\":[{\"type\":\"text\",\"text\":\"Use the get_weather tool exactly once. After the tool result, reply exactly: Paris is sunny.\"}],\"messages\":[{\"role\":\"user\",\"content\":[{\"type\":\"text\",\"text\":\"What is the weather in Paris?\"}]},{\"role\":\"assistant\",\"content\":[{\"type\":\"tool_use\",\"id\":\"call_function_yr64rwmre4gr_1\",\"name\":\"get_weather\",\"input\":{\"city\":\"Paris\"}}]},{\"role\":\"user\",\"content\":[{\"type\":\"tool_result\",\"tool_use_id\":\"call_function_yr64rwmre4gr_1\",\"content\":\"{\\\"temperature\\\":22,\\\"condition\\\":\\\"sunny\\\"}\"}]}],\"tools\":[{\"name\":\"get_weather\",\"description\":\"Get current weather for a city.\",\"input_schema\":{\"type\":\"object\",\"properties\":{\"city\":{\"type\":\"string\"}},\"required\":[\"city\"],\"additionalProperties\":false}}],\"stream\":true,\"max_tokens\":80,\"temperature\":0}"
},
"response": {
"status": 200,
"headers": {
"content-type": "text/event-stream; charset=utf-8"
},
"body": "event: message_start\ndata: {\"type\":\"message_start\",\"message\":{\"id\":\"92f8a1e86f29946eb2699d40a088fc08\",\"type\":\"message\",\"role\":\"assistant\",\"content\":[],\"model\":\"MiniMax-M3\",\"stop_reason\":null,\"stop_sequence\":null,\"usage\":{\"input_tokens\":41,\"output_tokens\":0,\"cache_creation_input_tokens\":0,\"cache_read_input_tokens\":430,\"service_tier\":\"standard\"},\"service_tier\":\"standard\"}}\n\nevent: ping\ndata: {\"type\":\"ping\"}\n\nevent: content_block_start\ndata: {\"type\":\"content_block_start\",\"index\":0,\"content_block\":{\"type\":\"text\",\"text\":\"\"}}\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\"Paris\"}}\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\" is sunny.\"}}\n\nevent: content_block_stop\ndata: {\"type\":\"content_block_stop\",\"index\":0}\n\nevent: message_delta\ndata: {\"type\":\"message_delta\",\"delta\":{\"stop_reason\":\"end_turn\"},\"usage\":{\"input_tokens\":41,\"output_tokens\":4,\"cache_read_input_tokens\":430,\"service_tier\":\"standard\"}}\n\nevent: message_stop\ndata: {\"type\":\"message_stop\"}\n\n"
}
}
]
}

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{
"version": 1,
"metadata": {
"model": "anthropic/claude-sonnet-4.6",
"tags": [
"prefix:openai-compatible-chat",
"provider:openrouter",
"protocol:openai-chat",
"reasoning"
],
"name": "openrouter-reasoning",
"recordedAt": "2026-07-18T11:28:39.267Z"
},
"interactions": [
{
"transport": "http",
"request": {
"method": "POST",
"url": "https://openrouter.ai/api/v1/chat/completions",
"headers": {
"content-type": "application/json"
},
"body": "{\"model\":\"anthropic/claude-sonnet-4.6\",\"messages\":[{\"role\":\"system\",\"content\":\"Think through the arithmetic, then reply with only the final integer.\"},{\"role\":\"user\",\"content\":\"What is 173 multiplied by 219?\"}],\"stream\":true,\"stream_options\":{\"include_usage\":true},\"max_tokens\":1536,\"temperature\":0,\"reasoning\":{\"max_tokens\":1024}}"
},
"response": {
"status": 200,
"headers": {
"content-type": "text/event-stream"
},
"body": ": OPENROUTER PROCESSING\n\n: OPENROUTER PROCESSING\n\ndata: {\"id\":\"gen-1784374117-AXXPsQRoclZeQGx2uHeK\",\"object\":\"chat.completion.chunk\",\"created\":1784374117,\"model\":\"anthropic/claude-sonnet-4.6\",\"provider\":\"Anthropic\",\"choices\":[{\"index\":0,\"delta\":{\"content\":\"\",\"role\":\"assistant\",\"reasoning\":\"173\",\"reasoning_details\":[{\"type\":\"reasoning.text\",\"text\":\"173\",\"format\":\"anthropic-claude-v1\",\"index\":0}]},\"finish_reason\":null,\"native_finish_reason\":null}]}\n\n: OPENROUTER PROCESSING\n\ndata: {\"id\":\"gen-1784374117-AXXPsQRoclZeQGx2uHeK\",\"object\":\"chat.completion.chunk\",\"created\":1784374117,\"model\":\"anthropic/claude-sonnet-4.6\",\"provider\":\"Anthropic\",\"choices\":[{\"index\":0,\"delta\":{\"content\":\"\",\"role\":\"assistant\",\"reasoning\":\" × 219\\n\\n173 × 200 = 34,600\\n173 × 19 = 173 × 20 - 173 = 3,460 - 173 = 3,287\",\"reasoning_details\":[{\"type\":\"reasoning.text\",\"text\":\" × 219\\n\\n173 × 200 = 34,600\\n173 × 19 = 173 × 20 - 173 = 3,460 - 173 = 3,287\",\"format\":\"anthropic-claude-v1\",\"index\":0}]},\"finish_reason\":null,\"native_finish_reason\":null}]}\n\ndata: {\"id\":\"gen-1784374117-AXXPsQRoclZeQGx2uHeK\",\"object\":\"chat.completion.chunk\",\"created\":1784374117,\"model\":\"anthropic/claude-sonnet-4.6\",\"provider\":\"Anthropic\",\"choices\":[{\"index\":0,\"delta\":{\"content\":\"\",\"role\":\"assistant\",\"reasoning\":\"\\n\\n34,600 + 3,287 = 37,887\",\"reasoning_details\":[{\"type\":\"reasoning.text\",\"text\":\"\\n\\n34,600 + 3,287 = 37,887\",\"format\":\"anthropic-claude-v1\",\"index\":0}]},\"finish_reason\":null,\"native_finish_reason\":null}]}\n\ndata: {\"id\":\"gen-1784374117-AXXPsQRoclZeQGx2uHeK\",\"object\":\"chat.completion.chunk\",\"created\":1784374117,\"model\":\"anthropic/claude-sonnet-4.6\",\"provider\":\"Anthropic\",\"choices\":[{\"index\":0,\"delta\":{\"content\":\"\",\"role\":\"assistant\",\"reasoning_details\":[{\"type\":\"reasoning.text\",\"signature\":\"EtgCCosBCA8YAipA0W4viH3kgBs43Cl5ewwVBPXTQElvzfbA2TLF4iSbKy9ZZDCSDjjAlF3Bs4ELEnP3vrrTuTioC6OB380lXQdyIDIRY2xhdWRlLXNvbm5ldC00LTY4AEIIdGhpbmtpbmdaJDRjMGYwNDZmLTI1ZmQtNDVmYi1iZmIzLWEwOGE4ZTI0OWNhNxIMMiUlJC3x/5p5PuTwGgwlc8eipZyoM94BHwMiMO45uQx/ymeOjbugi7RDVPZ4jZXSIiEbVi2CD7zPjAK5fFQoVGP1HD55v9CER823JCp6Dg5Xb7Lrk6NUd1XN2KTKrttK7mATE+IBrDTFmor/1cNeg+9gjIbxM/jn/6L5HPmh3/esEVu24Q0IGLZVoE7cTgGgxsrceKMD71Jp2XQgIWD8ltsPfWw3gSc4p+z18UuPN6LuR0mHHENTnClHrAPnOrxbDIl4ZwZgMX8YAQ==\",\"format\":\"anthropic-claude-v1\",\"index\":0}]},\"finish_reason\":null,\"native_finish_reason\":null}]}\n\ndata: {\"id\":\"gen-1784374117-AXXPsQRoclZeQGx2uHeK\",\"object\":\"chat.completion.chunk\",\"created\":1784374117,\"model\":\"anthropic/claude-sonnet-4.6\",\"provider\":\"Anthropic\",\"choices\":[{\"index\":0,\"delta\":{\"content\":\"37887\",\"role\":\"assistant\"},\"finish_reason\":null,\"native_finish_reason\":null}]}\n\ndata: {\"id\":\"gen-1784374117-AXXPsQRoclZeQGx2uHeK\",\"object\":\"chat.completion.chunk\",\"created\":1784374117,\"model\":\"anthropic/claude-sonnet-4.6\",\"provider\":\"Anthropic\",\"choices\":[{\"index\":0,\"delta\":{\"content\":\"\",\"role\":\"assistant\",\"reasoning\":null},\"finish_reason\":\"stop\",\"native_finish_reason\":\"end_turn\"}]}\n\ndata: {\"id\":\"gen-1784374117-AXXPsQRoclZeQGx2uHeK\",\"object\":\"chat.completion.chunk\",\"created\":1784374117,\"model\":\"anthropic/claude-sonnet-4.6\",\"provider\":\"Anthropic\",\"service_tier\":\"default\",\"choices\":[{\"index\":0,\"delta\":{\"content\":\"\",\"role\":\"assistant\"},\"finish_reason\":\"stop\",\"native_finish_reason\":\"end_turn\"}],\"usage\":{\"prompt_tokens\":61,\"completion_tokens\":80,\"total_tokens\":141,\"cost\":0.001383,\"is_byok\":false,\"prompt_tokens_details\":{\"cached_tokens\":0,\"cache_write_tokens\":0,\"audio_tokens\":0,\"video_tokens\":0},\"cache_creation\":{\"ephemeral_5m_input_tokens\":0,\"ephemeral_1h_input_tokens\":0},\"cost_details\":{\"upstream_inference_cost\":0.001383,\"upstream_inference_prompt_cost\":0.000183,\"upstream_inference_completions_cost\":0.0012},\"completion_tokens_details\":{\"reasoning_tokens\":29,\"image_tokens\":0,\"audio_tokens\":0}}}\n\ndata: [DONE]\n\n"
}
}
]
}

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import { describe, expect } from "bun:test"
import { Effect, Layer } from "effect"
import { HttpClientRequest } from "effect/unstable/http"
import { Image, ImageClient } from "../src"
import { OpenAI } from "../src/providers"
import { it } from "./lib/effect"
import { dynamicResponse } from "./lib/http"
describe("Image", () => {
it.effect("generates images through the OpenAI Images API", () =>
Effect.gen(function* () {
const response = yield* Image.generate({
model: OpenAI.configure({
apiKey: "test",
baseURL: "https://api.openai.test/v1",
queryParams: { "api-version": "v1" },
http: { body: { deployment: "test" }, headers: { "x-default": "yes" } },
}).image("gpt-image-2"),
prompt: "A robot tending a rooftop garden",
options: {
n: 2,
size: "2048x2048",
quality: "future-quality",
outputFormat: "jpeg",
output_format: "avif",
outputCompression: 30,
output_compression: 40,
background: "opaque",
native_default: true,
future_option: true,
},
http: {
body: { output_format: "webp", output_compression: 50, future_option: "http", request_metadata: "value" },
headers: { "x-request": "yes" },
query: { trace: "1" },
},
})
expect(response.images).toHaveLength(2)
expect(response.image?.mediaType).toBe("image/webp")
expect(response.image?.data).toEqual(Uint8Array.from([1, 2, 3]))
expect(response.image?.providerMetadata).toEqual({ openai: { revisedPrompt: "A precise robot" } })
expect(response.usage?.totalTokens).toBe(12)
}).pipe(
Effect.provide(
ImageClient.layer.pipe(
Layer.provide(
dynamicResponse((input) =>
Effect.gen(function* () {
const request = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
expect(request.url).toBe("https://api.openai.test/v1/images/generations?api-version=v1&trace=1")
expect(request.headers.get("authorization")).toBe("Bearer test")
expect(request.headers.get("x-default")).toBe("yes")
expect(request.headers.get("x-request")).toBe("yes")
expect(JSON.parse(input.text)).toEqual({
model: "gpt-image-2",
prompt: "A robot tending a rooftop garden",
n: 2,
size: "2048x2048",
quality: "future-quality",
background: "opaque",
output_format: "webp",
output_compression: 50,
native_default: true,
future_option: "http",
deployment: "test",
request_metadata: "value",
})
return input.respond(
JSON.stringify({
data: [{ b64_json: "AQID", revised_prompt: "A precise robot" }, { b64_json: "BAUG" }],
output_format: "webp",
usage: { input_tokens: 4, output_tokens: 8, total_tokens: 12 },
}),
{ headers: { "content-type": "application/json" } },
)
}),
),
),
),
),
),
)
it.effect("preserves native snake_case and unknown request options", () =>
Image.generate({
model: OpenAI.configure({
apiKey: "test",
baseURL: "https://api.openai.test/v1",
}).image("future-image-model"),
prompt: "A lighthouse in fog",
options: {
outputFormat: "jpeg",
output_format: "avif",
outputCompression: 30,
output_compression: 40,
provider_future_option: { enabled: true },
},
}).pipe(
Effect.tap((response) =>
Effect.sync(() => {
expect(response.image?.mediaType).toBe("image/avif")
}),
),
Effect.provide(
ImageClient.layer.pipe(
Layer.provide(
dynamicResponse((input) => {
expect(JSON.parse(input.text)).toEqual({
model: "future-image-model",
prompt: "A lighthouse in fog",
output_format: "avif",
output_compression: 40,
provider_future_option: { enabled: true },
})
return Effect.succeed(
input.respond(JSON.stringify({ data: [{ b64_json: "AQID" }] }), {
headers: { "content-type": "application/json" },
}),
)
}),
),
),
),
),
)
})

95
packages/ai/test/image.types.ts Normal file
View file

@ -0,0 +1,95 @@
import {
Image,
ImageModel,
type ImageModelOptions,
type ImageOptions,
type ImageRequestFor,
type ImageRoute,
} from "../src"
import { OpenAI, XAI } from "../src/providers"
type GoogleLikeOptions = {
readonly aspectRatio?: "1:1" | "16:9"
readonly imageSize?: "1K" | "2K"
} & Record<string, unknown>
declare const route: ImageRoute<GoogleLikeOptions>
const google = ImageModel.make<GoogleLikeOptions>({ id: "gemini-image", provider: "google", route })
// @ts-expect-error Extracted model options retain known provider fields.
const invalidGoogleOptions: ImageModelOptions<typeof google> = { aspectRatio: "wide" }
void invalidGoogleOptions
Image.generate({
model: google,
prompt: "A lighthouse",
options: { aspectRatio: "16:9", imageSize: "2K", futureOption: true },
})
const openai = OpenAI.image("gpt-image-2")
// @ts-expect-error Image generation options are request-scoped, not provider configuration.
OpenAI.configure({ image: { options: { quality: "medium" } } })
const futureOpenAIOptions: ImageModelOptions<typeof openai> = { quality: "future-quality" }
void futureOpenAIOptions
Image.generate({
model: openai,
prompt: "A lighthouse",
options: { quality: "hd", outputFormat: "webp", size: "2048x2048", future_option: true },
})
Image.generate({ model: openai, prompt: "A lighthouse", options: { quality: "future-quality", size: "256x256" } })
Image.generate({ model: openai, prompt: "A lighthouse", options: { size: "1792x1024" } })
Image.generate({ model: openai, prompt: "A lighthouse", options: { native_future_option: true } })
// @ts-expect-error Known OpenAI string options retain their value kind.
Image.generate({ model: openai, prompt: "A lighthouse", options: { quality: 1 } })
// @ts-expect-error Known OpenAI numeric options retain their value kind.
Image.generate({ model: openai, prompt: "A lighthouse", options: { outputCompression: "80" } })
OpenAI.imageGeneration({ action: "future-action", quality: "future-quality", size: "2048x2048" })
// @ts-expect-error Hosted image generation numeric options retain their value kind.
OpenAI.imageGeneration({ partialImages: "2" })
// @ts-expect-error Known Google-like options are inferred from the selected model.
Image.generate({ model: google, prompt: "A lighthouse", options: { aspectRatio: "wide" } })
const xai = XAI.configure({ apiKey: "test" }).image("any-model-id")
// @ts-expect-error Image generation options are request-scoped, not provider configuration.
XAI.configure({ image: { options: { resolution: "1k" } } })
Image.generate({
model: xai,
prompt: "A lighthouse",
options: {
n: 2,
aspectRatio: "future-ratio",
resolution: "future-resolution",
responseFormat: "future-format",
future_option: true,
},
})
Image.generate({
model: xai,
prompt: "A lighthouse",
options: { aspect_ratio: "16:9", response_format: "b64_json", native_future_option: true },
})
// @ts-expect-error Known xAI numeric options retain their value kind.
Image.generate({ model: xai, prompt: "A lighthouse", options: { n: "2" } })
// @ts-expect-error Known xAI string options retain their value kind.
Image.generate({ model: xai, prompt: "A lighthouse", options: { resolution: 2 } })
declare const generic: ImageModel<ImageOptions>
Image.generate({ model: generic, prompt: "A lighthouse", options: { arbitrary: true } })
const request = Image.request({
model: google,
prompt: "A lighthouse",
options: { aspectRatio: "1:1", futureOption: true },
})
const typedRequest: ImageRequestFor<GoogleLikeOptions> = request
void typedRequest
// @ts-expect-error Image requests no longer expose a common count option.
Image.generate({ model: openai, prompt: "A lighthouse", count: 2 })
// @ts-expect-error Image requests no longer expose a common size option.
Image.generate({ model: openai, prompt: "A lighthouse", size: { width: 1024, height: 1024 } })
// @ts-expect-error Image requests no longer expose a common aspectRatio option.
Image.generate({ model: openai, prompt: "A lighthouse", aspectRatio: "16:9" })
// @ts-expect-error Image requests no longer expose a common seed option.
Image.generate({ model: openai, prompt: "A lighthouse", seed: 1 })
// @ts-expect-error Image requests do not expose metadata.
Image.generate({ model: openai, prompt: "A lighthouse", metadata: { trace: true } })

26
packages/ai/test/provider-error.test.ts
View file

@ -3,8 +3,30 @@ import { isContextOverflow } from "../src"
import { classifyProviderFailure } from "../src/provider-error"
describe("provider error classification", () => {
test("classifies Z.AI GLM token limit messages as context overflow", () => {
expect(isContextOverflow("tokens in request more than max tokens allowed")).toBe(true)
test("classifies provider token limit messages as context overflow", () => {
const messages = [
"tokens in request more than max tokens allowed",
'{"error":{"type":"request_too_large","message":"Request exceeds the maximum size"}}',
"Requested token count exceeds the model's maximum context length of 131072 tokens.",
"Input length (265330) exceeds model's maximum context length (262144).",
"Input length 131393 exceeds the maximum allowed input length of 131040 tokens.",
"The input (516368 tokens) is longer than the model's context length (262144 tokens).",
"Prompt has 5,958,968 tokens, but the configured context size is 256,000 tokens",
"Too many tokens",
"Token limit exceeded",
]
expect(messages.every(isContextOverflow)).toBe(true)
})
test("does not classify rate limits as context overflow", () => {
const messages = [
"Throttling error: Too many tokens, please wait before trying again.",
"Rate limit exceeded, please retry after 30 seconds.",
"Too many requests. Please slow down.",
]
expect(messages.some(isContextOverflow)).toBe(false)
})
test("classifies V1 plain-text rate limit fallbacks", () => {

24
packages/ai/test/provider/anthropic-messages.test.ts
View file

@ -484,6 +484,30 @@ describe("Anthropic Messages route", () => {
}),
)
it.effect("keeps malformed server tool input terminal", () =>
Effect.gen(function* () {
const body = sseEvents(
{ type: "message_start", message: { usage: { input_tokens: 5 } } },
{
type: "content_block_start",
index: 0,
content_block: { type: "server_tool_use", id: "call_1", name: "web_search" },
},
{
type: "content_block_delta",
index: 0,
delta: { type: "input_json_delta", partial_json: '{"query":"partial' },
},
{ type: "content_block_stop", index: 0 },
)
const error = yield* LLMClient.generate(request).pipe(Effect.provide(fixedResponse(body)), Effect.flip)
expect(error).toBeInstanceOf(LLMError)
expect(error.message).toContain("Invalid JSON input for anthropic-messages tool call web_search")
}),
)
it.effect("fails with a typed provider error for stream error frames", () =>
Effect.gen(function* () {
const error = yield* LLMClient.generate(request).pipe(

26
packages/ai/test/provider/bedrock-converse.test.ts
View file

@ -303,6 +303,32 @@ describe("Bedrock Converse route", () => {
}),
)
it.effect("emits malformed tool input as an unexecuted tool error", () =>
Effect.gen(function* () {
const body = eventStreamBody(
["messageStart", { role: "assistant" }],
[
"contentBlockStart",
{
contentBlockIndex: 0,
start: { toolUse: { toolUseId: "tool_1", name: "lookup" } },
},
],
["contentBlockDelta", { contentBlockIndex: 0, delta: { toolUse: { input: '{"query":"partial' } } }],
["contentBlockStop", { contentBlockIndex: 0 }],
["messageStop", { stopReason: "end_turn" }],
)
const response = yield* LLMClient.generate(baseRequest).pipe(Effect.provide(fixedBytes(body)))
expect(response.events.find((event) => event.type === "tool-input-error")).toMatchObject({
id: "tool_1",
name: "lookup",
raw: '{"query":"partial',
})
expect(response.finishReason).toBe("tool-calls")
}),
)
it.effect("decodes reasoning deltas", () =>
Effect.gen(function* () {
const body = eventStreamBody(

55
packages/ai/test/provider/cloudflare.test.ts
View file

@ -1,7 +1,7 @@
import { describe, expect } from "bun:test"
import { ConfigProvider, Effect, Schema } from "effect"
import { HttpClientRequest } from "effect/unstable/http"
import { LLM } from "../../src"
import { LLM, LLMEvent } from "../../src"
import { CloudflareAIGateway, CloudflareWorkersAI } from "../../src/providers/cloudflare"
import { LLMClient } from "../../src/route"
import { it } from "../lib/effect"
@ -83,6 +83,59 @@ describe("Cloudflare", () => {
}),
)
it.effect("preserves reasoning details for AI Gateway continuation", () =>
Effect.gen(function* () {
const model = CloudflareAIGateway.configure({
accountId: "test-account",
gatewayId: "test-gateway",
apiKey: "test-token",
}).model("anthropic/claude-sonnet-4.6")
const details = [
{ type: "reasoning.text", text: "Think", format: "anthropic-claude-v1", index: 0 },
{ type: "reasoning.text", text: "ing", format: "anthropic-claude-v1", index: 0 },
{ type: "reasoning.text", signature: "signed", format: "anthropic-claude-v1", index: 0 },
]
const merged = [
{
type: "reasoning.text",
text: "Thinking",
signature: "signed",
format: "anthropic-claude-v1",
index: 0,
},
]
const response = yield* LLM.generate(LLM.request({ model, prompt: "Say hello." })).pipe(
Effect.provide(
dynamicResponse((input) =>
Effect.succeed(
input.respond(
sseEvents(
deltaChunk({ reasoning: "Think", reasoning_details: [details[0]] }),
deltaChunk({ reasoning: "ing", reasoning_details: [details[1]] }),
deltaChunk({ reasoning_details: [details[2]] }),
deltaChunk({ content: "Hello" }),
deltaChunk({}, "stop"),
),
{ headers: { "content-type": "text/event-stream" } },
),
),
),
),
)
expect(response.reasoning).toBe("Thinking")
expect(response.events.filter(LLMEvent.is.reasoningDelta)).toHaveLength(2)
expect(response.message.content.find((part) => part.type === "reasoning")?.providerMetadata).toEqual({
openai: { reasoningField: "reasoning", reasoningDetails: merged },
})
const replay = yield* LLMClient.prepare(LLM.request({ model, messages: [response.message] }))
expect(replay.body.messages).toEqual([
{ role: "assistant", content: "Hello", reasoning: "Thinking", reasoning_details: merged },
])
}),
)
it.effect("defaults AI Gateway id to default when omitted or blank", () =>
Effect.gen(function* () {
expect(

15
packages/ai/test/provider/golden.recorded.test.ts
View file

@ -1,4 +1,5 @@
import * as Anthropic from "../../src/providers/anthropic"
import * as AnthropicCompatible from "../../src/providers/anthropic-compatible"
import { CloudflareAIGateway, CloudflareWorkersAI } from "../../src/providers/cloudflare"
import * as Google from "../../src/providers/google"
import * as OpenAI from "../../src/providers/openai"
@ -17,6 +18,11 @@ const anthropic = Anthropic.configure({
})
const anthropicHaiku = anthropic.model("claude-haiku-4-5-20251001")
const anthropicOpus = anthropic.model("claude-opus-4-7")
const minimax = AnthropicCompatible.configure({
apiKey: process.env.MINIMAX_API_KEY ?? "fixture",
baseURL: "https://api.minimax.io/anthropic/v1",
provider: "minimax",
}).model("MiniMax-M3")
const google = Google.configure({ apiKey: process.env.GOOGLE_GENERATIVE_AI_API_KEY ?? "fixture" })
const gemini = google.model("gemini-2.5-flash")
const xai = XAI.configure({ apiKey: process.env.XAI_API_KEY ?? "fixture" })
@ -108,6 +114,15 @@ describeRecordedGoldenScenarios([
{ id: "image-tool-result", temperature: false, maxTokens: 40 },
],
},
{
name: "MiniMax M3 Anthropic-compatible",
prefix: "anthropic-compatible-messages",
protocol: "anthropic-messages",
model: minimax,
requires: ["MINIMAX_API_KEY"],
options: { redact: { allowRequestHeaders: ["anthropic-version"] } },
scenarios: ["text", "tool-call", "tool-loop"],
},
{
name: "Gemini 2.5 Flash",
prefix: "gemini",

142
packages/ai/test/provider/openai-chat-reasoning.recorded.test.ts Normal file
View file

@ -0,0 +1,142 @@
import { describe, expect } from "bun:test"
import { Effect } from "effect"
import { LLM, LLMEvent, LLMResponse } from "../../src"
import { OpenAIChat } from "../../src/protocols/openai-chat"
import * as OpenAICompatible from "../../src/providers/openai-compatible"
import * as OpenRouter from "../../src/providers/openrouter"
import { LLMClient } from "../../src/route"
import { recordedTests } from "../recorded-test"
import { expectWeatherToolLoop, goldenWeatherToolLoopRequest, runWeatherToolLoop } from "../recorded-scenarios"
const cases = [
{
name: "OpenRouter",
model: OpenRouter.configure({
apiKey: process.env.OPENROUTER_API_KEY ?? "fixture",
providerOptions: { openrouter: { reasoning: { max_tokens: 1024 } } },
}).model("anthropic/claude-sonnet-4.6"),
requires: ["OPENROUTER_API_KEY"],
cassette: "openrouter-reasoning",
structured: true,
},
{
name: "Vercel AI Gateway",
model: OpenAICompatible.configure({
provider: "vercel-ai-gateway",
baseURL: "https://ai-gateway.vercel.sh/v1",
apiKey: process.env.AI_GATEWAY_API_KEY ?? "fixture",
http: { body: { reasoning: { enabled: true, max_tokens: 1024 } } },
}).model("anthropic/claude-sonnet-4.6"),
requires: ["AI_GATEWAY_API_KEY"],
cassette: "vercel-ai-gateway-reasoning",
structured: true,
},
] as const
for (const item of cases) {
const recorded = recordedTests({
prefix: "openai-compatible-chat",
provider: item.model.provider,
protocol: "openai-chat",
requires: item.requires,
tags: ["reasoning"],
metadata: { model: item.model.id },
})
describe(`${item.name} reasoning recorded`, () => {
recorded.effect.with(
"streams scalar reasoning",
{ cassette: item.cassette },
() =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(
LLM.request({
model: item.model,
system: "Think through the arithmetic, then reply with only the final integer.",
prompt: "What is 173 multiplied by 219?",
generation: { maxTokens: 1536, temperature: 0 },
}),
)
expect(response.text.replaceAll(",", "").trim()).toBe("37887")
expect(response.reasoning.length).toBeGreaterThan(0)
expect(response.events.some(LLMEvent.is.reasoningDelta)).toBe(true)
const metadata = response.message.content.find((part) => part.type === "reasoning")?.providerMetadata
expect(metadata?.openai?.reasoningField).toBe(item.structured ? "reasoning" : "reasoning_content")
expect(Array.isArray(metadata?.openai?.reasoningDetails)).toBe(item.structured)
if (!item.structured) return
const details = metadata?.openai?.reasoningDetails
if (!Array.isArray(details)) return
expect(
details.some(
(detail) =>
typeof detail === "object" &&
detail !== null &&
"signature" in detail &&
typeof detail.signature === "string" &&
detail.signature.length > 0,
),
).toBe(true)
const replay = yield* LLMClient.prepare<OpenAIChat.OpenAIChatBody>(
LLM.request({ model: item.model, messages: [response.message] }),
)
expect(replay.body.messages).toMatchObject([
{ role: "assistant", content: response.text, reasoning: response.reasoning },
])
const replayDetails =
replay.body.messages[0]?.role === "assistant" ? replay.body.messages[0].reasoning_details : undefined
expect(Array.isArray(replayDetails)).toBe(true)
if (!Array.isArray(replayDetails)) return
expect(replayDetails).toEqual(details)
expect(replayDetails).toHaveLength(1)
expect(replayDetails[0]).toMatchObject({
type: "reasoning.text",
text: response.reasoning,
signature: expect.any(String),
})
}),
30_000,
)
recorded.effect.with(
"continues signed reasoning through a tool loop",
{ cassette: `${item.cassette}-tool-loop`, tags: ["continuation", "tool", "tool-loop"] },
() =>
Effect.gen(function* () {
const events = yield* runWeatherToolLoop(
goldenWeatherToolLoopRequest({
id: `${item.cassette}-tool-loop`,
model: item.model,
maxTokens: 1536,
temperature: false,
}),
)
expectWeatherToolLoop(events)
expect(
LLMResponse.text({
events: events.slice(events.findIndex(LLMEvent.is.stepFinish) + 1),
}).trim(),
).toMatch(/^Paris is sunny\.?$/)
const details = events
.filter(LLMEvent.is.reasoningEnd)
.map((event) => event.providerMetadata?.openai?.reasoningDetails)
.find(Array.isArray)
expect(Array.isArray(details)).toBe(item.structured)
if (!item.structured || !Array.isArray(details)) return
expect(
details.some(
(detail) =>
typeof detail === "object" &&
detail !== null &&
"signature" in detail &&
typeof detail.signature === "string" &&
detail.signature.length > 0,
),
).toBe(true)
}),
60_000,
)
})
}

468
packages/ai/test/provider/openai-chat.test.ts
View file

@ -540,28 +540,401 @@ describe("OpenAI Chat route", () => {
}),
)
it.effect("parses OpenAI-compatible reasoning content deltas", () =>
it.effect("parses and replays OpenAI-compatible reasoning fields", () =>
Effect.gen(function* () {
const body = sseEvents(
{ choices: [{ delta: { reasoning_content: "thinking" } }] },
{ choices: [{ delta: { content: "Hello" } }] },
{ choices: [{ delta: {}, finish_reason: "stop" }] },
const fields = ["reasoning_content", "reasoning", "reasoning_text"] as const
for (const field of fields) {
const response = yield* LLMClient.generate(request).pipe(
Effect.provide(
fixedResponse(
sseEvents(
{ choices: [{ delta: { [field]: "thinking" } }] },
{ choices: [{ delta: { content: "Hello" } }] },
{ choices: [{ delta: {}, finish_reason: "stop" }] },
),
),
),
)
expect(response.reasoning).toBe("thinking")
expect(response.text).toBe("Hello")
expect(response.message.content.find((part) => part.type === "reasoning")?.providerMetadata).toEqual({
openai: { reasoningField: field },
})
const replay = yield* LLMClient.prepare<OpenAIChat.OpenAIChatBody>(
LLM.request({ model, messages: [response.message] }),
)
expect(replay.body.messages).toEqual([{ role: "assistant", content: "Hello", [field]: "thinking" }])
}
}),
)
it.effect("preserves and replays reasoning details alongside scalar reasoning", () =>
Effect.gen(function* () {
const details = [
{ type: "reasoning.text", text: "thinking", format: "anthropic-claude-v1", index: 0 },
{ type: "reasoning.encrypted", data: "opaque", format: "anthropic-claude-v1", index: 1 },
]
const response = yield* LLMClient.generate(
LLM.updateRequest(request, {
tools: [{ name: "lookup", description: "Lookup data", inputSchema: { type: "object" } }],
}),
).pipe(
Effect.provide(
fixedResponse(
sseEvents(
{ choices: [{ delta: { reasoning: "thinking", reasoning_details: [details[0]] } }] },
{ choices: [{ delta: { reasoning_details: [details[1]] } }] },
{
choices: [
{
delta: {
tool_calls: [
{ index: 0, id: "call_1", function: { name: "lookup", arguments: '{"query":"weather"}' } },
],
},
finish_reason: "tool_calls",
},
],
},
),
),
),
)
const response = yield* LLMClient.generate(request).pipe(Effect.provide(fixedResponse(body)))
expect(response.reasoning).toBe("thinking")
expect(response.message.content.find((part) => part.type === "reasoning")?.providerMetadata).toEqual({
openai: { reasoningField: "reasoning", reasoningDetails: details },
})
const replay = yield* LLMClient.prepare<OpenAIChat.OpenAIChatBody>(
LLM.request({ model, messages: [response.message] }),
)
expect(replay.body.messages).toEqual([
{
role: "assistant",
content: null,
reasoning: "thinking",
reasoning_details: details,
tool_calls: [
{
id: "call_1",
type: "function",
function: { name: "lookup", arguments: '{"query":"weather"}' },
},
],
},
])
}),
)
it.effect("uses reasoning details as display fallback without inventing a scalar replay field", () =>
Effect.gen(function* () {
const details = [
{ type: "reasoning.summary", summary: "thinking", format: "openai-responses-v1", index: 0 },
{ type: "reasoning.encrypted", data: "opaque", format: "openai-responses-v1", index: 1 },
]
const response = yield* LLMClient.generate(request).pipe(
Effect.provide(
fixedResponse(
sseEvents(
{ choices: [{ delta: { reasoning_details: [details[0]] } }] },
{ choices: [{ delta: { reasoning_details: [details[1]] } }] },
{ choices: [{ delta: { content: "Hello" } }] },
{ choices: [{ delta: {}, finish_reason: "stop" }] },
),
),
),
)
expect(response.reasoning).toBe("thinking")
expect(response.text).toBe("Hello")
expect(response.events).toMatchObject([
{ type: "step-start", index: 0 },
{ type: "reasoning-start", id: "reasoning-0" },
{ type: "reasoning-delta", id: "reasoning-0", text: "thinking" },
{ type: "reasoning-end", id: "reasoning-0" },
{ type: "text-start", id: "text-0" },
{ type: "text-delta", id: "text-0", text: "Hello" },
{ type: "text-end", id: "text-0" },
{ type: "step-finish", index: 0, reason: "stop" },
{ type: "finish", reason: "stop" },
expect(response.message.content.find((part) => part.type === "reasoning")?.providerMetadata).toEqual({
openai: { reasoningDetails: details },
})
const replay = yield* LLMClient.prepare<OpenAIChat.OpenAIChatBody>(
LLM.request({ model, messages: [response.message] }),
)
expect(replay.body.messages).toEqual([{ role: "assistant", content: "Hello", reasoning_details: details }])
}),
)
it.effect("preserves unknown reasoning details while using scalar display text", () =>
Effect.gen(function* () {
const details = [{ type: "reasoning.future", format: "provider-v2", state: { opaque: true } }]
const response = yield* LLMClient.generate(request).pipe(
Effect.provide(
fixedResponse(
sseEvents(
{ choices: [{ delta: { reasoning: "thinking", reasoning_details: details } }] },
{ choices: [{ delta: { content: "Hello" } }] },
{ choices: [{ delta: {}, finish_reason: "stop" }] },
),
),
),
)
expect(response.reasoning).toBe("thinking")
expect(response.message.content.find((part) => part.type === "reasoning")?.providerMetadata).toEqual({
openai: { reasoningField: "reasoning", reasoningDetails: details },
})
const replay = yield* LLMClient.prepare<OpenAIChat.OpenAIChatBody>(
LLM.request({ model, messages: [response.message] }),
)
expect(replay.body.messages).toEqual([
{ role: "assistant", content: "Hello", reasoning: "thinking", reasoning_details: details },
])
}),
)
it.effect("uses scalar display text for signature-only reasoning details", () =>
Effect.gen(function* () {
const details = [{ type: "reasoning.text", signature: "signed", format: "provider-v2", index: 0 }]
const response = yield* LLMClient.generate(request).pipe(
Effect.provide(
fixedResponse(
sseEvents(
{ choices: [{ delta: { reasoning: "thinking", reasoning_details: details } }] },
{ choices: [{ delta: { content: "Hello" } }] },
{ choices: [{ delta: {}, finish_reason: "stop" }] },
),
),
),
)
expect(response.reasoning).toBe("thinking")
expect(response.message.content.find((part) => part.type === "reasoning")?.providerMetadata).toEqual({
openai: { reasoningField: "reasoning", reasoningDetails: details },
})
}),
)
it.effect("ignores scalar reasoning after content starts", () =>
Effect.gen(function* () {
const details = [{ type: "reasoning.text", text: "detail", format: "unknown", index: 0 }]
const response = yield* LLMClient.generate(request).pipe(
Effect.provide(
fixedResponse(
sseEvents(
{ choices: [{ delta: { reasoning_details: details } }] },
{ choices: [{ delta: { content: "Hello" } }] },
{ choices: [{ delta: { reasoning: "scalar" } }] },
{ choices: [{ delta: {}, finish_reason: "stop" }] },
),
),
),
)
expect(response.reasoning).toBe("detail")
expect(response.events.filter(LLMEvent.is.reasoningStart)).toHaveLength(1)
expect(response.events.filter(LLMEvent.is.reasoningEnd)).toHaveLength(1)
expect(response.message.content.find((part) => part.type === "reasoning")?.providerMetadata).toEqual({
openai: { reasoningDetails: details },
})
}),
)
it.effect("preserves an explicitly empty reasoning details array", () =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(request).pipe(
Effect.provide(
fixedResponse(
sseEvents(
{ choices: [{ delta: { reasoning_details: [] } }] },
{ choices: [{ delta: { content: "Hello" } }] },
{ choices: [{ delta: {}, finish_reason: "stop" }] },
),
),
),
)
expect(response.reasoning).toBe("")
expect(response.message.content.find((part) => part.type === "reasoning")?.providerMetadata).toEqual({
openai: { reasoningDetails: [] },
})
const replay = yield* LLMClient.prepare<OpenAIChat.OpenAIChatBody>(
LLM.request({ model, messages: [response.message] }),
)
expect(replay.body.messages).toEqual([{ role: "assistant", content: "Hello", reasoning_details: [] }])
}),
)
it.effect("attaches signature-only details that arrive after content", () =>
Effect.gen(function* () {
const details = [
{ type: "reasoning.text", text: "thinking", format: "anthropic-claude-v1", index: 0 },
{ type: "reasoning.text", signature: "signed", format: "anthropic-claude-v1", index: 0 },
]
const merged = [
{
type: "reasoning.text",
text: "thinking",
signature: "signed",
format: "anthropic-claude-v1",
index: 0,
},
]
const response = yield* LLMClient.generate(request).pipe(
Effect.provide(
fixedResponse(
sseEvents(
{ choices: [{ delta: { reasoning: "thinking", reasoning_details: [details[0]] } }] },
{ choices: [{ delta: { content: "Hello" } }] },
{ choices: [{ delta: { reasoning_details: [details[1]] } }] },
{ choices: [{ delta: {}, finish_reason: "stop" }] },
),
),
),
)
expect(response.reasoning).toBe("thinking")
expect(response.message.content.filter((part) => part.type === "reasoning")).toHaveLength(1)
expect(response.message.content.find((part) => part.type === "reasoning")?.providerMetadata).toEqual({
openai: { reasoningField: "reasoning", reasoningDetails: merged },
})
expect(response.events.filter(LLMEvent.is.reasoningStart)).toHaveLength(1)
expect(response.events.filter(LLMEvent.is.reasoningDelta)).toHaveLength(1)
expect(response.events.filter(LLMEvent.is.reasoningEnd)).toHaveLength(1)
expect(response.events.filter(LLMEvent.is.reasoningEnd).at(-1)?.providerMetadata).toEqual({
openai: { reasoningField: "reasoning", reasoningDetails: merged },
})
expect(response.events.findIndex(LLMEvent.is.reasoningEnd)).toBeLessThan(
response.events.findIndex(LLMEvent.is.textStart),
)
const replay = yield* LLMClient.prepare<OpenAIChat.OpenAIChatBody>(
LLM.request({ model, messages: [response.message] }),
)
expect(replay.body.messages).toEqual([
{ role: "assistant", content: "Hello", reasoning: "thinking", reasoning_details: merged },
])
}),
)
it.effect("preserves metadata-only reasoning when the stream ends", () =>
Effect.gen(function* () {
const details = [{ type: "reasoning.encrypted", data: "opaque", format: "openai-responses-v1", index: 0 }]
const response = yield* LLMClient.generate(request).pipe(
Effect.provide(
fixedResponse(
sseEvents(
{ choices: [{ delta: { reasoning_details: details } }] },
{ choices: [{ delta: {}, finish_reason: "stop" }] },
),
),
),
)
expect(response.message.content).toEqual([
{ type: "reasoning", text: "", providerMetadata: { openai: { reasoningDetails: details } } },
])
expect(response.events.filter(LLMEvent.is.reasoningStart)).toHaveLength(1)
expect(response.events.filter(LLMEvent.is.reasoningEnd)).toHaveLength(1)
const replay = yield* LLMClient.prepare<OpenAIChat.OpenAIChatBody>(
LLM.request({ model, messages: [response.message] }),
)
expect(replay.body.messages).toEqual([{ role: "assistant", content: null, reasoning_details: details }])
}),
)
it.effect("flushes details-only display reasoning when the stream ends", () =>
Effect.gen(function* () {
const details = [{ type: "reasoning.summary", summary: "summary", format: "openai-responses-v1", index: 0 }]
const response = yield* LLMClient.generate(request).pipe(
Effect.provide(
fixedResponse(
sseEvents(
{ choices: [{ delta: { reasoning_details: details } }] },
{ choices: [{ delta: {}, finish_reason: "stop" }] },
),
),
),
)
expect(response.reasoning).toBe("summary")
expect(response.message.content).toEqual([
{ type: "reasoning", text: "summary", providerMetadata: { openai: { reasoningDetails: details } } },
])
}),
)
it.effect("replays details from multiple reasoning parts in order", () =>
Effect.gen(function* () {
const first = { type: "reasoning.text", text: "first", signature: "signed-0", index: 0 }
const second = { type: "reasoning.text", text: "second", signature: "signed-1", index: 1 }
const replay = yield* LLMClient.prepare<OpenAIChat.OpenAIChatBody>(
LLM.request({
model,
messages: [
Message.assistant([
{
type: "reasoning",
text: "first",
providerMetadata: { openai: { reasoningDetails: [first] } },
},
{
type: "reasoning",
text: "second",
providerMetadata: { openai: { reasoningField: "reasoning", reasoningDetails: [second] } },
},
]),
],
}),
)
expect(replay.body.messages).toEqual([
{ role: "assistant", content: null, reasoning: "firstsecond", reasoning_details: [first, second] },
])
}),
)
it.effect("retains scalar replay for mixed structured reasoning parts", () =>
Effect.gen(function* () {
const detail = { type: "reasoning.encrypted", data: "opaque", index: 0 }
const replay = yield* LLMClient.prepare<OpenAIChat.OpenAIChatBody>(
LLM.request({
model,
messages: [
Message.assistant([
{
type: "reasoning",
text: "A",
providerMetadata: { openai: { reasoningDetails: [detail] } },
},
{ type: "reasoning", text: "B" },
]),
],
}),
)
expect(replay.body.messages).toEqual([
{ role: "assistant", content: null, reasoning_content: "AB", reasoning_details: [detail] },
])
}),
)
it.effect("replays native scalar reasoning alongside native details", () =>
Effect.gen(function* () {
const details = [{ type: "reasoning.encrypted", data: "opaque", index: 0 }]
const replay = yield* LLMClient.prepare<OpenAIChat.OpenAIChatBody>(
LLM.request({
model,
messages: [
Message.make({
role: "assistant",
content: [{ type: "reasoning", text: "thinking" }],
native: { openaiCompatible: { reasoning_content: "thinking", reasoning_details: details } },
}),
],
}),
)
expect(replay.body.messages).toEqual([
{ role: "assistant", content: null, reasoning_content: "thinking", reasoning_details: details },
])
}),
)
@ -602,6 +975,67 @@ describe("OpenAI Chat route", () => {
}),
)
it.effect("ignores empty identity fields on later tool call deltas", () =>
Effect.gen(function* () {
const body = sseEvents(
deltaChunk({
tool_calls: [{ index: 0, id: "call_1", function: { name: "lookup", arguments: "{" } }],
}),
deltaChunk({
tool_calls: [{ index: 0, id: "", function: { name: "", arguments: '\"query\":\"weather\"}' } }],
}),
deltaChunk({}, "tool_calls"),
)
const response = yield* LLMClient.generate(
LLM.updateRequest(request, {
tools: [{ name: "lookup", description: "Lookup data", inputSchema: { type: "object" } }],
}),
).pipe(Effect.provide(fixedResponse(body)))
expect(response.toolCalls).toMatchObject([{ id: "call_1", name: "lookup", input: { query: "weather" } }])
}),
)
it.effect("buffers tool call deltas until the function name arrives", () =>
Effect.gen(function* () {
const body = sseEvents(
deltaChunk({
tool_calls: [{ index: 0, id: "call_1", function: { arguments: "{" } }],
}),
deltaChunk({
tool_calls: [{ index: 0, function: { name: "lookup", arguments: '\"query\":' } }],
}),
deltaChunk({ tool_calls: [{ index: 0, function: { arguments: '\"weather\"}' } }] }),
deltaChunk({}, "tool_calls"),
)
const response = yield* LLMClient.generate(
LLM.updateRequest(request, {
tools: [{ name: "lookup", description: "Lookup data", inputSchema: { type: "object" } }],
}),
).pipe(Effect.provide(fixedResponse(body)))
expect(response.toolCalls).toMatchObject([{ id: "call_1", name: "lookup", input: { query: "weather" } }])
}),
)
it.effect("fails when a buffered tool call never receives a function name", () =>
Effect.gen(function* () {
const body = sseEvents(
deltaChunk({
tool_calls: [{ index: 0, id: "call_1", function: { arguments: "{}" } }],
}),
deltaChunk({}, "tool_calls"),
)
const error = yield* LLMClient.generate(
LLM.updateRequest(request, {
tools: [{ name: "lookup", description: "Lookup data", inputSchema: { type: "object" } }],
}),
).pipe(Effect.provide(fixedResponse(body)), Effect.flip)
expect(error.message).toContain("OpenAI Chat tool call delta is missing id or name")
}),
)
it.effect("fails a streamed tool call when the provider ends without a finish reason", () =>
Effect.gen(function* () {
const body = sseEvents(

33
packages/ai/test/provider/openai-images.recorded.test.ts Normal file
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@ -0,0 +1,33 @@
import { describe, expect } from "bun:test"
import { Effect } from "effect"
import { Image } from "../../src"
import { OpenAI } from "../../src/providers"
import { recordedTests } from "../recorded-test"
const model = OpenAI.configure({
apiKey: process.env.OPENAI_API_KEY ?? "fixture",
}).image("gpt-image-1-mini")
const recorded = recordedTests({
prefix: "openai-images",
provider: "openai",
protocol: "openai-images",
requires: ["OPENAI_API_KEY"],
})
describe("OpenAI Images recorded", () => {
recorded.effect("generates an image", () =>
Effect.gen(function* () {
const response = yield* Image.generate({
model,
prompt: "A simple flat black circle centered on a plain white background.",
options: { quality: "low", outputFormat: "jpeg", outputCompression: 10, size: "1024x1024" },
})
expect(response.images).toHaveLength(1)
expect(response.image?.mediaType).toBe("image/jpeg")
expect(response.image?.data).toBeInstanceOf(Uint8Array)
expect(response.image?.data.length).toBeGreaterThan(0)
}),
)
})

66
packages/ai/test/provider/openai-responses-images.recorded.test.ts Normal file
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@ -0,0 +1,66 @@
import { describe, expect } from "bun:test"
import { Effect } from "effect"
import { LLM, LLMEvent, Message } from "../../src"
import { OpenAI } from "../../src/providers"
import { recordedTests } from "../recorded-test"
const openai = OpenAI.configure({
apiKey: process.env.OPENAI_API_KEY ?? "fixture",
})
const recorded = recordedTests({
prefix: "openai-responses-images",
provider: "openai",
protocol: "openai-responses",
requires: ["OPENAI_API_KEY"],
})
describe("OpenAI Responses image generation recorded", () => {
recorded.effect("generates and edits an image with the hosted tool", () =>
Effect.gen(function* () {
const initial = Message.user("Generate a simple flat black triangle centered on a plain white background.")
const tools = [
OpenAI.imageGeneration({
action: "auto",
quality: "low",
size: "1024x1024",
outputFormat: "jpeg",
outputCompression: 10,
partialImages: 0,
}),
]
const response = yield* LLM.generate(
LLM.request({
model: openai.responses("gpt-5-mini"),
messages: [initial],
tools,
toolChoice: "image_generation",
}),
)
const result = response.events.find(LLMEvent.is.toolResult)
expect(result).toBeDefined()
expect(result?.providerExecuted).toBe(true)
expect(result?.result.type).toBe("content")
if (result?.result.type !== "content") return
expect(result.result.value).toHaveLength(1)
expect(result.result.value[0]?.type).toBe("file")
if (result.result.value[0]?.type !== "file") return
expect(result.result.value[0].mime).toBe("image/jpeg")
expect(result.result.value[0].uri.startsWith("data:image/jpeg;base64,")).toBe(true)
const edited = yield* LLM.generate(
LLM.request({
model: openai.responses("gpt-5-mini"),
messages: [initial, response.message, Message.user("Now make the triangle blue.")],
tools,
toolChoice: "image_generation",
}),
)
const editedResult = edited.events.find(LLMEvent.is.toolResult)
expect(editedResult?.result.type).toBe("content")
if (editedResult?.result.type !== "content") return
expect(editedResult.result.value[0]?.type).toBe("file")
}),
)
})

193
packages/ai/test/provider/openai-responses.test.ts
View file

@ -1,7 +1,7 @@
import { describe, expect } from "bun:test"
import { ConfigProvider, Effect, Layer, Stream } from "effect"
import { Headers, HttpClientRequest } from "effect/unstable/http"
import { LLM, LLMError, Message, Model, ToolCallPart, Usage } from "../../src"
import { LLM, LLMError, LLMEvent, Message, Model, ToolCallPart, ToolResultPart, Usage } from "../../src"
import { Auth, LLMClient, RequestExecutor, WebSocketExecutor } from "../../src/route"
import * as Azure from "../../src/providers/azure"
import * as OpenAI from "../../src/providers/openai"
@ -58,6 +58,39 @@ describe("OpenAI Responses route", () => {
}),
)
it.effect("lowers the hosted OpenAI image generation tool", () =>
Effect.gen(function* () {
const prepared = yield* LLMClient.prepare<OpenAIResponses.OpenAIResponsesBody>(
LLM.request({
model,
prompt: "Show me a rooftop garden.",
tools: [OpenAI.imageGeneration({ action: "generate", quality: "high", size: "1024x1024" })],
toolChoice: "image_generation",
}),
)
expect(prepared.body.tools).toEqual([
{ type: "image_generation", action: "generate", quality: "high", size: "1024x1024" },
])
expect(prepared.body.tool_choice).toEqual({ type: "image_generation" })
}),
)
it.effect("rejects invalid hosted image generation options locally", () =>
Effect.gen(function* () {
const error = yield* LLMClient.prepare(
LLM.request({
model,
prompt: "Show me a rooftop garden.",
tools: [OpenAI.imageGeneration({ outputCompression: -1, partialImages: 4, size: "bogus" })],
}),
).pipe(Effect.flip)
expect(error.reason._tag).toBe("InvalidRequest")
expect(error.message).toContain("image generation tool options are invalid")
}),
)
it.effect("lowers semantic service tier options", () =>
Effect.gen(function* () {
const input = LLM.updateRequest(request, { providerOptions: { openai: { serviceTier: "priority" } } })
@ -1103,6 +1136,48 @@ describe("OpenAI Responses route", () => {
}),
)
it.effect("continues stateless hosted image generation with the generated image", () =>
Effect.gen(function* () {
const imageTool = OpenAI.imageGeneration({ action: "edit" })
const prepared = yield* LLMClient.prepare<OpenAIResponses.OpenAIResponsesBody>(
LLM.request({
model,
messages: [
Message.user("Generate a black triangle."),
Message.assistant([
ToolCallPart.make({
id: "ig_1",
name: "image_generation",
input: {},
providerExecuted: true,
providerMetadata: { openai: { itemId: "ig_1" } },
}),
ToolResultPart.make({
id: "ig_1",
name: "image_generation",
result: {
type: "content",
value: [{ type: "file", uri: "data:image/png;base64,AQID", mime: "image/png" }],
},
providerExecuted: true,
providerMetadata: { openai: { itemId: "ig_1" } },
}),
]),
Message.user("Make it blue."),
],
tools: [imageTool],
}),
)
expect(prepared.body.store).toBe(false)
expect(prepared.body.input).toEqual([
{ role: "user", content: [{ type: "input_text", text: "Generate a black triangle." }] },
{ role: "user", content: [{ type: "input_image", image_url: "data:image/png;base64,AQID" }] },
{ role: "user", content: [{ type: "input_text", text: "Make it blue." }] },
])
}),
)
it.effect("joins streamed summary blocks into one continuation reasoning item", () =>
Effect.gen(function* () {
const prepared = yield* LLMClient.prepare<OpenAIResponses.OpenAIResponsesBody>(
@ -1259,6 +1334,69 @@ describe("OpenAI Responses route", () => {
}),
)
it.effect("emits malformed final function arguments as an unexecuted tool error", () =>
Effect.gen(function* () {
const body = sseEvents(
{
type: "response.output_item.added",
item: { type: "function_call", id: "item_1", call_id: "call_1", name: "lookup", arguments: "" },
},
{ type: "response.function_call_arguments.delta", item_id: "item_1", delta: '{"query":"streamed"}' },
{
type: "response.output_item.done",
item: {
type: "function_call",
id: "item_1",
call_id: "call_1",
name: "lookup",
arguments: '{"query":"partial',
},
},
{ type: "response.completed", response: { usage: { input_tokens: 5, output_tokens: 1 } } },
)
const response = yield* LLMClient.generate(
LLM.updateRequest(request, {
tools: [{ name: "lookup", description: "Lookup data", inputSchema: { type: "object" } }],
}),
).pipe(Effect.provide(fixedResponse(body)))
expect(response.events.find(LLMEvent.is.toolInputError)).toEqual({
type: "tool-input-error",
id: "call_1",
name: "lookup",
raw: '{"query":"partial',
})
expect(response.finishReason).toBe("tool-calls")
expect(response.events.some(LLMEvent.is.toolCall)).toBeFalse()
}),
)
it.effect("settles malformed function arguments when output_item.added is absent", () =>
Effect.gen(function* () {
const body = sseEvents(
{
type: "response.output_item.done",
item: {
type: "function_call",
id: "item_1",
call_id: "call_1",
name: "lookup",
arguments: '{"query":"partial',
},
},
{ type: "response.completed", response: { usage: { input_tokens: 5, output_tokens: 1 } } },
)
const response = yield* LLMClient.generate(request).pipe(Effect.provide(fixedResponse(body)))
expect(response.events.find(LLMEvent.is.toolInputError)).toMatchObject({
id: "call_1",
name: "lookup",
raw: '{"query":"partial',
})
expect(response.finishReason).toBe("tool-calls")
}),
)
it.effect("decodes web_search_call as provider-executed tool-call + tool-result", () =>
Effect.gen(function* () {
const item = {
@ -1298,6 +1436,59 @@ describe("OpenAI Responses route", () => {
}),
)
it.effect("decodes image generation output as image content", () =>
Effect.gen(function* () {
const item = {
type: "image_generation_call",
id: "ig_1",
status: "completed",
result: "AQID",
}
const response = yield* LLMClient.generate(request).pipe(
Effect.provide(
fixedResponse(
sseEvents(
{ type: "response.output_item.done", item },
{ type: "response.completed", response: { usage: { input_tokens: 5, output_tokens: 1 } } },
),
),
),
)
expect(response.events.find(LLMEvent.is.toolResult)).toMatchObject({
id: "ig_1",
name: "image_generation",
providerExecuted: true,
result: {
type: "content",
value: [{ type: "file", uri: "data:image/png;base64,AQID", mime: "image/png" }],
},
})
}),
)
it.effect("rejects malformed image generation base64", () =>
Effect.gen(function* () {
const error = yield* LLMClient.generate(request).pipe(
Effect.provide(
fixedResponse(
sseEvents(
{
type: "response.output_item.done",
item: { type: "image_generation_call", id: "ig_bad", status: "completed", result: "%%%" },
},
{ type: "response.completed", response: {} },
),
),
),
Effect.flip,
)
expect(error.reason._tag).toBe("InvalidProviderOutput")
expect(error.message).toContain("invalid image base64")
}),
)
it.effect("decodes code_interpreter_call as provider-executed events with code input", () =>
Effect.gen(function* () {
const item = {

100
packages/ai/test/provider/openrouter.test.ts
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@ -1,6 +1,6 @@
import { describe, expect } from "bun:test"
import { Effect } from "effect"
import { LLM } from "../../src"
import { LLM, Message } from "../../src"
import { LLMClient } from "../../src/route"
import * as OpenRouter from "../../src/providers/openrouter"
import { it } from "../lib/effect"
@ -53,4 +53,102 @@ describe("OpenRouter", () => {
})
}),
)
it.effect("preserves manually supplied reasoning details", () =>
Effect.gen(function* () {
const details = [
{ type: "reasoning.text", text: "Think", format: "anthropic-claude-v1", index: 0 },
{ type: "reasoning.text", text: "ing", format: "anthropic-claude-v1", index: 0 },
{ type: "reasoning.text", signature: "signed", format: "anthropic-claude-v1", index: 0 },
{ type: "reasoning.encrypted", data: "opaque", format: "openai-responses-v1", index: 1 },
]
const prepared = yield* LLMClient.prepare<OpenRouter.OpenRouterBody>(
LLM.request({
model: OpenRouter.configure({ apiKey: "test-key" }).model("anthropic/claude-sonnet-4.6"),
messages: [
Message.assistant([
{
type: "reasoning",
text: "Thinking",
providerMetadata: { openai: { reasoningField: "reasoning", reasoningDetails: details } },
},
]),
],
}),
)
expect(prepared.body.messages).toEqual([
{
role: "assistant",
content: null,
reasoning: "Thinking",
reasoning_details: details,
},
])
}),
)
it.effect("preserves opaque and duplicate continuation details", () =>
Effect.gen(function* () {
const details = [
{ type: "reasoning.future", format: "provider-v2", state: { opaque: true } },
{ type: "reasoning.encrypted", id: "state", data: "opaque" },
{ type: "reasoning.encrypted", id: "state", data: "opaque" },
]
const prepared = yield* LLMClient.prepare<OpenRouter.OpenRouterBody>(
LLM.request({
model: OpenRouter.configure({ apiKey: "test-key" }).model("anthropic/claude-sonnet-4.6"),
messages: [
Message.assistant({
type: "reasoning",
text: "Thinking",
providerMetadata: { openai: { reasoningField: "reasoning", reasoningDetails: details } },
}),
],
}),
)
expect(prepared.body.messages).toEqual([
{ role: "assistant", content: null, reasoning: "Thinking", reasoning_details: details },
])
}),
)
it.effect("does not merge distinct adjacent reasoning text blocks", () =>
Effect.gen(function* () {
const details = [
{ type: "reasoning.text", id: "first", index: 0, text: "A", opaque: "first" },
{ type: "reasoning.text", id: "second", index: 1, text: "B", opaque: "second" },
]
const prepared = yield* LLMClient.prepare<OpenRouter.OpenRouterBody>(
LLM.request({
model: OpenRouter.configure({ apiKey: "test-key" }).model("anthropic/claude-sonnet-4.6"),
messages: [
Message.assistant({
type: "reasoning",
text: "AB",
providerMetadata: { openai: { reasoningField: "reasoning", reasoningDetails: details } },
}),
],
}),
)
expect(prepared.body.messages).toEqual([
{ role: "assistant", content: null, reasoning: "AB", reasoning_details: details },
])
}),
)
it.effect("omits scalar reasoning without continuation details", () =>
Effect.gen(function* () {
const prepared = yield* LLMClient.prepare<OpenRouter.OpenRouterBody>(
LLM.request({
model: OpenRouter.configure({ apiKey: "test-key" }).model("anthropic/claude-sonnet-4.6"),
messages: [Message.assistant({ type: "reasoning", text: "Thinking" })],
}),
)
expect(prepared.body.messages).toEqual([{ role: "assistant", content: null }])
}),
)
})

33
packages/ai/test/provider/xai-images.recorded.test.ts Normal file
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@ -0,0 +1,33 @@
import { describe, expect } from "bun:test"
import { Effect } from "effect"
import { Image } from "../../src"
import { XAI } from "../../src/providers"
import { recordedTests } from "../recorded-test"
const model = XAI.configure({
apiKey: process.env.XAI_API_KEY ?? "fixture",
}).image("grok-imagine-image")
const recorded = recordedTests({
prefix: "xai-images",
provider: "xai",
protocol: "xai-images",
requires: ["XAI_API_KEY"],
})
describe("xAI Images recorded", () => {
recorded.effect("generates an image", () =>
Effect.gen(function* () {
const response = yield* Image.generate({
model,
prompt: "A simple flat black diamond centered on a plain white background.",
options: { aspectRatio: "1:1", resolution: "1k", responseFormat: "b64_json" },
})
expect(response.images).toHaveLength(1)
expect(response.image?.mediaType.startsWith("image/")).toBe(true)
expect(response.image?.data).toBeInstanceOf(Uint8Array)
expect(response.image?.data.length).toBeGreaterThan(0)
}),
)
})

109
packages/ai/test/provider/xai-images.test.ts Normal file
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@ -0,0 +1,109 @@
import { describe, expect } from "bun:test"
import { Effect, Layer } from "effect"
import { Headers, HttpClientRequest } from "effect/unstable/http"
import { Image, ImageClient } from "../../src"
import { XAI } from "../../src/providers"
import { Auth } from "../../src/route"
import { it } from "../lib/effect"
import { dynamicResponse } from "../lib/http"
describe("xAI Images", () => {
it.effect("generates through the OpenAI-compatible Images API", () =>
Effect.gen(function* () {
const response = yield* Image.generate({
model: XAI.configure({
apiKey: "test",
baseURL: "https://api.xai.test/v1",
http: { body: { configured: true }, headers: { "x-default": "yes" } },
}).image("grok-imagine-image"),
prompt: "A robot tending a rooftop garden",
options: {
n: 2,
aspectRatio: "16:9",
aspect_ratio: "4:3",
resolution: "1k",
responseFormat: "url",
response_format: "b64_json",
future_option: true,
},
http: {
body: { resolution: "2k", future_option: "http" },
headers: { "x-request": "yes" },
query: { trace: "1" },
},
})
expect(response.images).toHaveLength(2)
expect(response.image?.mediaType).toBe("image/jpeg")
expect(response.image?.data).toEqual(Uint8Array.from([1, 2, 3]))
expect(response.images[1]?.mediaType).toBe("application/octet-stream")
expect(response.images[1]?.data).toBe("https://api.xai.test/image.jpg")
expect(response.usage?.providerMetadata).toEqual({ xai: { num_images: 2 } })
expect(response.providerMetadata).toEqual({ xai: { usage: { num_images: 2 } } })
}).pipe(
Effect.provide(
ImageClient.layer.pipe(
Layer.provide(
dynamicResponse((input) =>
Effect.gen(function* () {
const request = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
expect(request.url).toBe("https://api.xai.test/v1/images/generations?trace=1")
expect(request.headers.get("authorization")).toBe("Bearer test")
expect(request.headers.get("x-default")).toBe("yes")
expect(request.headers.get("x-request")).toBe("yes")
expect(JSON.parse(input.text)).toEqual({
model: "grok-imagine-image",
prompt: "A robot tending a rooftop garden",
n: 2,
aspect_ratio: "4:3",
resolution: "2k",
response_format: "b64_json",
future_option: "http",
configured: true,
})
return input.respond(
JSON.stringify({
data: [
{ b64_json: "AQID", url: null, mime_type: "image/jpeg" },
{ b64_json: null, url: "https://api.xai.test/image.jpg", mime_type: null },
],
usage: { num_images: 2 },
}),
{ headers: { "content-type": "application/json" } },
)
}),
),
),
),
),
),
)
it.effect("supports request-level custom auth", () =>
Image.generate({
model: XAI.configure({
baseURL: "https://api.xai.test/v1",
auth: Auth.custom((input) =>
Effect.succeed(Headers.set(input.headers, "x-custom-auth", new URL(input.url).hostname)),
),
}).image("grok-imagine-image"),
prompt: "A robot tending a rooftop garden",
}).pipe(
Effect.provide(
ImageClient.layer.pipe(
Layer.provide(
dynamicResponse((input) =>
Effect.gen(function* () {
const request = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
expect(request.headers.get("x-custom-auth")).toBe("api.xai.test")
return input.respond(JSON.stringify({ data: [{ b64_json: "AQID", mime_type: "image/png" }] }), {
headers: { "content-type": "application/json" },
})
}),
),
),
),
),
),
)
})

25
packages/ai/test/recorded-scenarios.ts
View file

@ -120,29 +120,8 @@ export const runWeatherToolLoop = (request: LLMRequest) =>
throw new Error("Weather tool loop exceeded 10 steps")
})
const assistantContent = (events: ReadonlyArray<LLMEvent>) => {
const content: ContentPart[] = []
for (const event of events) {
if (event.type === "text-delta" || event.type === "reasoning-delta") {
const type = event.type === "text-delta" ? "text" : "reasoning"
const last = content.at(-1)
if (last?.type === type) {
content[content.length - 1] = { ...last, text: `${last.text}${event.text}` }
} else {
content.push({ type, text: event.text })
}
continue
}
if (event.type === "text-end" || event.type === "reasoning-end") {
const type = event.type === "text-end" ? "text" : "reasoning"
const last = content.at(-1)
if (last?.type === type) content[content.length - 1] = { ...last, providerMetadata: event.providerMetadata }
continue
}
if (event.type === "tool-call") content.push(event)
}
return content
}
const assistantContent = (events: ReadonlyArray<LLMEvent>) =>
events.reduce(LLMResponse.reduce, LLMResponse.empty()).message.content
export const expectFinish = (
events: ReadonlyArray<LLMEvent>,

10
packages/ai/test/recorded-test.ts
View file

@ -3,6 +3,8 @@ import { Layer } from "effect"
import * as path from "node:path"
import { fileURLToPath } from "node:url"
import { LLMClient, RequestExecutor, WebSocketExecutor } from "../src/route"
import { ImageClient } from "../src/image-client"
import type { Service as ImageClientService } from "../src/image-client"
import type { Service as LLMClientService } from "../src/route/client"
import type { Service as RequestExecutorService } from "../src/route/executor"
import type { Service as WebSocketExecutorService } from "../src/route/transport/websocket"
@ -15,7 +17,7 @@ import {
const __dirname = path.dirname(fileURLToPath(import.meta.url))
const FIXTURES_DIR = path.resolve(__dirname, "fixtures", "recordings")
type RecordedEnv = RequestExecutorService | WebSocketExecutorService | LLMClientService
type RecordedEnv = RequestExecutorService | WebSocketExecutorService | LLMClientService | ImageClientService
type RecordedTestsOptions = RecordedGroupOptions & {
readonly options?: HttpRecorder.RecorderOptions
@ -81,6 +83,10 @@ export const recordedTests = (options: RecordedTestsOptions) =>
),
)
const deps = Layer.mergeAll(requestExecutor, WebSocketExecutor.layer)
return Layer.mergeAll(deps, LLMClient.layer.pipe(Layer.provide(deps)))
return Layer.mergeAll(
deps,
LLMClient.layer.pipe(Layer.provide(deps)),
ImageClient.layer.pipe(Layer.provide(deps)),
)
},
})

15
packages/ai/test/response.test.ts
View file

@ -95,4 +95,19 @@ describe("LLMResponse reducer", () => {
{ type: "tool-call", id: "call_1", name: "lookup", input: { query: "weather" } },
])
})
test("clears malformed tool input without appending an executable call", () => {
const state = reduce([
LLMEvent.toolInputStart({ id: "call_1", name: "lookup" }),
LLMEvent.toolInputDelta({ id: "call_1", name: "lookup", text: '{"query":"partial' }),
LLMEvent.toolInputError({
id: "call_1",
name: "lookup",
raw: '{"query":"partial',
}),
])
expect(state.toolInputs).toEqual({})
expect(state.message.content).toEqual([])
})
})

94
packages/ai/test/tool-stream.test.ts
View file

@ -36,6 +36,33 @@ describe("ToolStream", () => {
}),
)
it.effect("keeps accumulated identity when later deltas contain empty strings", () =>
Effect.gen(function* () {
const first = ToolStream.appendOrStart(
ADAPTER,
ToolStream.empty<number>(),
0,
{ id: "call_1", name: "lookup", text: '{"query"' },
"missing tool",
)
if (ToolStream.isError(first)) return yield* first
const second = ToolStream.appendOrStart(
ADAPTER,
first.tools,
0,
{ id: "", name: "", text: ':"weather"}' },
"missing tool",
)
if (ToolStream.isError(second)) return yield* second
const finished = yield* ToolStream.finish(ADAPTER, second.tools, 0)
expect(finished.events).toEqual([
{ type: "tool-input-end", id: "call_1", name: "lookup" },
{ type: "tool-call", id: "call_1", name: "lookup", input: { query: "weather" } },
])
}),
)
it.effect("fails appendExisting when the provider skipped the tool start", () =>
Effect.gen(function* () {
const error = ToolStream.appendExisting(ADAPTER, ToolStream.empty<number>(), 0, "{}", "missing tool")
@ -64,6 +91,73 @@ describe("ToolStream", () => {
}),
)
it.effect("finalizes malformed local input as a non-executable tool error", () =>
Effect.gen(function* () {
const tools = ToolStream.start(ToolStream.empty<string>(), "item_1", {
id: "call_1",
name: "lookup",
input: '{"query":"partial',
})
const finished = yield* ToolStream.finish(ADAPTER, tools, "item_1")
expect(finished).toEqual({
tools: {},
events: [
{
type: "tool-input-error",
id: "call_1",
name: "lookup",
raw: '{"query":"partial',
},
],
})
}),
)
it.effect("preserves valid siblings when one parallel input is malformed", () =>
Effect.gen(function* () {
const valid = ToolStream.start(ToolStream.empty<number>(), 0, {
id: "call_valid",
name: "lookup",
input: '{"query":"weather"}',
})
const tools = ToolStream.start(valid, 1, {
id: "call_invalid",
name: "lookup",
input: '{"query":"partial',
})
const finished = yield* ToolStream.finishAll(ADAPTER, tools)
expect(finished).toEqual({
tools: {},
events: [
{ type: "tool-input-end", id: "call_valid", name: "lookup" },
{ type: "tool-call", id: "call_valid", name: "lookup", input: { query: "weather" } },
{
type: "tool-input-error",
id: "call_invalid",
name: "lookup",
raw: '{"query":"partial',
},
],
})
}),
)
it.effect("keeps malformed provider-executed input terminal", () =>
Effect.gen(function* () {
const tools = ToolStream.start(ToolStream.empty<string>(), "item_1", {
id: "call_1",
name: "web_search",
input: '{"query":"partial',
providerExecuted: true,
})
const result = yield* Effect.exit(ToolStream.finish(ADAPTER, tools, "item_1"))
expect(result._tag).toBe("Failure")
}),
)
it.effect("preserves providerExecuted and clears all tools", () =>
Effect.gen(function* () {
const first: ToolStream.State<number> = ToolStream.start(ToolStream.empty<number>(), 0, {

9
packages/ai/tsconfig.types.json Normal file
View file

@ -0,0 +1,9 @@
{
"$schema": "https://json.schemastore.org/tsconfig",
"extends": "./tsconfig.json",
"compilerOptions": {
"noEmit": true,
"rootDir": "."
},
"include": ["test/**/*.types.ts"]
}

11
packages/cli/bin/opencode2.cjs
View file

@ -31,11 +31,13 @@ function run(target) {
const envPath = process.env.OPENCODE_BIN_PATH
const scriptDir = path.dirname(fs.realpathSync(__filename))
const cached = path.join(scriptDir, ".opencode2")
const command = path.basename(__filename).replace(/\.cjs$/, "")
const nodeBuild = command === "opencode2-node"
const cached = path.join(scriptDir, `.${command}`)
const platform = { darwin: "darwin", linux: "linux", win32: "windows" }[os.platform()] || os.platform()
const arch = { x64: "x64", arm64: "arm64", arm: "arm" }[os.arch()] || os.arch()
const base = "@opencode-ai/cli-" + platform + "-" + arch
const binary = platform === "windows" ? "opencode2.exe" : "opencode2"
const base = `@opencode-ai/cli${nodeBuild ? "-node" : ""}-` + platform + "-" + arch
const binary = platform === "windows" ? `${command}.exe` : command
function supportsAvx2() {
if (arch !== "x64") return false
@ -77,6 +79,7 @@ function supportsAvx2() {
}
const names = (() => {
if (nodeBuild) return [base]
const baseline = arch === "x64" && !supportsAvx2()
if (platform === "linux") {
const musl = (() => {
@ -121,7 +124,7 @@ function findBinary(startDir) {
const resolved = envPath || (fs.existsSync(cached) ? cached : findBinary(scriptDir))
if (!resolved) {
console.error(
"It seems that your package manager failed to install the right opencode2 CLI package. Try manually installing " +
`It seems that your package manager failed to install the right ${command} CLI package. Try manually installing ` +
names.map((name) => `"${name}"`).join(" or ") +
" package",
)

34
packages/cli/package.json
View file

@ -12,20 +12,12 @@
],
"exports": {
"./daemon": "./src/daemon.ts",
"./mini": "./src/mini/index.ts",
"./mini/footer.command": "./src/mini/footer.command.tsx",
"./mini/footer.menu": "./src/mini/footer.menu.tsx",
"./mini/footer.permission": "./src/mini/footer.permission.tsx",
"./mini/footer.prompt": "./src/mini/footer.prompt.tsx",
"./mini/footer.question": "./src/mini/footer.question.tsx",
"./mini/footer.subagent": "./src/mini/footer.subagent.tsx",
"./mini/footer.view": "./src/mini/footer.view.tsx",
"./mini/scrollback.writer": "./src/mini/scrollback.writer.tsx",
"./mini/*": "./src/mini/*.ts",
"./run": "./src/run/index.ts",
"./server-process": "./src/server-process.ts"
},
"scripts": {
"build": "bun run script/build.ts",
"build:node": "bun run script/build-node.ts",
"dev": "bun run src/index.ts",
"test": "bun test --timeout 30000 --only-failures",
"typecheck": "tsgo --noEmit"
@ -39,19 +31,16 @@
"@opencode-ai/server": "workspace:*",
"@opencode-ai/tui": "workspace:*",
"@opentui/core": "catalog:",
"@opentui/keymap": "catalog:",
"@opentui/solid": "catalog:",
"@parcel/watcher": "2.5.1",
"effect": "catalog:",
"fuzzysort": "catalog:",
"immer": "11.1.4",
"jsonc-parser": "3.3.1",
"open": "10.1.2",
"opentui-spinner": "catalog:",
"semver": "catalog:",
"solid-js": "catalog:",
"strip-ansi": "7.1.2",
"uqr": "0.1.3"
"uqr": "0.1.3",
"ws": "8.21.0"
},
"devDependencies": {
"@opencode-ai/script": "workspace:*",
@ -59,6 +48,19 @@
"@tsconfig/bun": "catalog:",
"@types/bun": "catalog:",
"@types/semver": "catalog:",
"@typescript/native-preview": "catalog:"
"@typescript/native-preview": "catalog:",
"@lydell/node-pty-darwin-arm64": "1.2.0-beta.12",
"@lydell/node-pty-darwin-x64": "1.2.0-beta.12",
"@lydell/node-pty-linux-arm64": "1.2.0-beta.12",
"@lydell/node-pty-linux-x64": "1.2.0-beta.12",
"@lydell/node-pty-win32-arm64": "1.2.0-beta.12",
"@lydell/node-pty-win32-x64": "1.2.0-beta.12",
"@parcel/watcher-darwin-arm64": "2.5.1",
"@parcel/watcher-linux-arm64-glibc": "2.5.1",
"@parcel/watcher-linux-x64-glibc": "2.5.1",
"@parcel/watcher-win32-arm64": "2.5.1",
"@parcel/watcher-win32-x64": "2.5.1",
"vite": "catalog:",
"vite-plugin-solid": "catalog:"
}
}

203
packages/cli/script/build-node.ts Normal file
View file

@ -0,0 +1,203 @@
#!/usr/bin/env bun
import { spawnSync } from "node:child_process"
import { createHash } from "node:crypto"
import { chmod, copyFile, mkdir, mkdtemp, realpath, rename, rm, stat, writeFile } from "node:fs/promises"
import os from "node:os"
import path from "node:path"
import { build } from "vite"
import { Script } from "@opencode-ai/script"
import pkg from "../package.json"
import { modelsData } from "./generate"
import { collectNodeAssets, copyNodeAssets, hashNodeAssets, seaAssetMap } from "./node-assets"
import { mainConfig } from "../vite.node.config"
import { nodeExecArgv, nodeTarget, type NodeTarget } from "../src/node/target"
const NODE_VERSION = "26.4.0"
const dir = path.resolve(import.meta.dirname, "..")
const outdir = path.resolve(
dir,
process.argv.find((arg) => arg.startsWith("--outdir="))?.slice("--outdir=".length) ?? "dist",
)
if (outdir === dir) throw new Error("--outdir must not be the package directory")
if (outdir === path.join(dir, "dist-node")) {
throw new Error("--outdir must not be dist-node because it contains temporary files")
}
const bundleOnly = process.argv.includes("--bundle-only")
const single = process.argv.includes("--single")
const skipInstall = process.argv.includes("--skip-install")
const requested = process.argv.find((arg) => arg.startsWith("--target="))?.slice("--target=".length)
const allTargets = [
nodeTarget("linux", "arm64"),
nodeTarget("linux", "x64"),
nodeTarget("darwin", "arm64"),
nodeTarget("win32", "arm64"),
nodeTarget("win32", "x64"),
]
const targets = requested
? allTargets.filter((target) => targetName(target) === requested)
: single || bundleOnly
? [nodeTarget(process.platform, process.arch)]
: allTargets
if (targets.length === 0) {
if (requested === "darwin-x64") throw new Error("Node 26.4 SEA does not support macOS x64")
throw new Error(`Unknown Node target: ${requested}`)
}
if (!bundleOnly && targets.some((target) => target.platform === "darwin" && target.arch === "x64")) {
throw new Error("Node 26.4 SEA does not support macOS x64")
}
process.chdir(dir)
if (!skipInstall) run(process.execPath, ["install", "--os=*", "--cpu=*"])
if (!bundleOnly) await rm(outdir, { recursive: true, force: true })
const builder =
!bundleOnly || targets.some((target) => target.platform === process.platform && target.arch === process.arch)
? await resolveHostNode()
: undefined
for (const target of targets) {
console.log(`building cli-node-${targetName(target)}`)
const assets = await collectNodeAssets(target)
await rm("dist-node", { recursive: true, force: true })
const assetHash = await hashNodeAssets(assets)
const input = { version: Script.version, channel: Script.channel, models: modelsData, assetHash, target }
await build(mainConfig(input))
await copyNodeAssets(assets)
const host = target.platform === process.platform && target.arch === process.arch
if (host) {
if (!builder) throw new Error("Node SEA builder is unavailable")
run(builder, [...nodeExecArgv, "dist-node/opencode.mjs", "--version"])
run(builder, [...nodeExecArgv, "dist-node/opencode.mjs", "--help"])
}
if (bundleOnly) continue
const name = `cli-node-${targetName(target)}`
const binary = target.platform === "win32" ? "opencode2-node.exe" : "opencode2-node"
const output = path.join(outdir, name, "bin", binary)
if (!builder) throw new Error("Node SEA builder is unavailable")
await mkdir(path.dirname(output), { recursive: true })
const config = {
main: "dist-node/opencode.mjs",
mainFormat: "module",
executable: await resolveTargetNode(target, builder),
output: path.relative(dir, output),
disableExperimentalSEAWarning: true,
useSnapshot: false,
useCodeCache: false,
execArgv: nodeExecArgv,
execArgvExtension: "none",
assets: await seaAssetMap(),
}
await writeFile("dist-node/sea.json", `${JSON.stringify(config, null, 2)}\n`)
run(builder, ["--build-sea", "dist-node/sea.json"])
if (target.platform !== "win32") await chmod(output, 0o755)
if (target.platform === "darwin" && process.platform === "darwin") run("codesign", ["--sign", "-", output])
if (target.platform === "darwin" && process.platform !== "darwin") {
console.warn(`${output} must be signed on macOS before it can run`)
}
await writeFile(
path.join(outdir, name, "package.json"),
`${JSON.stringify(
{
name: `@opencode-ai/${name}`,
version: Script.version,
license: pkg.license,
repository: { type: "git", url: "git+https://github.com/anomalyco/opencode.git" },
os: [target.platform],
cpu: [target.arch],
},
null,
2,
)}\n`,
)
if (host) await smoke(output)
}
async function resolveHostNode() {
const candidates = [process.env.NODE_BIN, "node"].filter((item): item is string => Boolean(item))
for (const candidate of candidates) {
const result = spawnSync(
candidate,
["-p", "JSON.stringify({version:process.versions.node,path:process.execPath})"],
{
encoding: "utf8",
},
)
if (result.status !== 0) continue
const info = JSON.parse(result.stdout) as { version: string; path: string }
if (info.version === NODE_VERSION) return realpath(info.path)
}
return resolveTargetNode(nodeTarget(process.platform, process.arch))
}
async function resolveTargetNode(target: NodeTarget, host?: string) {
if (host && target.platform === process.platform && target.arch === process.arch) return host
const cache = path.resolve(dir, ".cache", "node")
const platform = target.platform === "win32" ? "win" : target.platform
const archiveName = `node-v${NODE_VERSION}-${platform}-${target.arch}`
const targetDirectory = path.join(cache, archiveName)
const executable = path.join(targetDirectory, target.platform === "win32" ? "node.exe" : "bin/node")
if (
(await stat(executable).then(
() => true,
() => false,
)) &&
(await stat(path.join(targetDirectory, ".verified")).then(
() => true,
() => false,
))
)
return realpath(executable)
await mkdir(cache, { recursive: true })
const extension = target.platform === "win32" ? "zip" : "tar.gz"
const filename = `${archiveName}.${extension}`
const archive = path.join(cache, filename)
const base = `https://nodejs.org/dist/v${NODE_VERSION}`
const [response, sums] = await Promise.all([fetch(`${base}/${filename}`), fetch(`${base}/SHASUMS256.txt`)])
if (!response.ok) throw new Error(`Failed to download Node ${NODE_VERSION}: ${response.status}`)
if (!sums.ok) throw new Error(`Failed to download Node ${NODE_VERSION} checksums: ${sums.status}`)
const data = new Uint8Array(await response.arrayBuffer())
const expected = (await sums.text())
.split("\n")
.find((line) => line.endsWith(` ${filename}`))
?.split(/\s+/)[0]
if (!expected) throw new Error(`Missing checksum for ${filename}`)
if (createHash("sha256").update(data).digest("hex") !== expected) throw new Error(`Checksum mismatch for ${filename}`)
await writeFile(archive, data)
const temporary = path.join(cache, `${archiveName}.${process.pid}.tmp`)
await rm(temporary, { recursive: true, force: true })
await mkdir(temporary)
if (target.platform !== "win32") run("tar", ["-xzf", archive, "-C", temporary])
if (target.platform === "win32" && process.platform === "win32") {
run(path.join(process.env.SystemRoot ?? "C:\\Windows", "System32", "tar.exe"), ["-xf", archive, "-C", temporary])
}
if (target.platform === "win32" && process.platform !== "win32") run("unzip", ["-q", archive, "-d", temporary])
await rm(targetDirectory, { recursive: true, force: true })
await rename(path.join(temporary, archiveName), targetDirectory)
await writeFile(path.join(targetDirectory, ".verified"), `${expected}\n`)
await rm(temporary, { recursive: true, force: true })
await rm(archive, { force: true })
return realpath(executable)
}
async function smoke(output: string) {
const root = await mkdtemp(path.join(os.tmpdir(), "opencode-node-smoke-"))
const executable = path.join(root, path.basename(output))
await copyFile(output, executable)
if (process.platform !== "win32") await chmod(executable, 0o755)
run(executable, ["--version"], root)
run(executable, ["--help"], root)
await rm(root, { recursive: true, force: true })
}
function targetName(target: NodeTarget) {
return `${target.platform === "win32" ? "windows" : target.platform}-${target.arch}`
}
function run(command: string, args: readonly string[], cwd = dir) {
const result = spawnSync(command, args, { cwd, stdio: "inherit", env: process.env })
if (result.error) throw result.error
if (result.status !== 0) throw new Error(`${command} exited with status ${result.status ?? "unknown"}`)
}

22
packages/cli/script/build.ts
View file

@ -1,7 +1,6 @@
#!/usr/bin/env bun
import { $ } from "bun"
import fs from "fs"
import { rm } from "fs/promises"
import path from "path"
import { Script } from "@opencode-ai/script"
@ -11,9 +10,14 @@ import { modelsData } from "./generate"
const dir = path.resolve(import.meta.dirname, "..")
const binary = "opencode2"
const outdir = path.resolve(
dir,
process.argv.find((arg) => arg.startsWith("--outdir="))?.slice("--outdir=".length) ?? "dist",
)
if (outdir === dir) throw new Error("--outdir must not be the package directory")
process.chdir(dir)
await rm("dist", { recursive: true, force: true })
await rm(outdir, { recursive: true, force: true })
const singleFlag = process.argv.includes("--single")
const baselineFlag = process.argv.includes("--baseline")
@ -50,10 +54,6 @@ const targets = singleFlag
if (!skipInstall) await $`bun install --os="*" --cpu="*" @opentui/core@${pkg.dependencies["@opentui/core"]}`
const localParserWorker = path.resolve(dir, "node_modules/@opentui/core/parser.worker.js")
const rootParserWorker = path.resolve(dir, "../../node_modules/@opentui/core/parser.worker.js")
const parserWorker = fs.realpathSync(fs.existsSync(localParserWorker) ? localParserWorker : rootParserWorker)
for (const item of targets) {
const target = [
binary,
@ -67,7 +67,7 @@ for (const item of targets) {
const name = target.replace(binary, "cli")
console.log(`building ${name}`)
const result = await Bun.build({
entrypoints: ["./src/index.ts", parserWorker],
entrypoints: ["./src/index.ts"],
tsconfig: "./tsconfig.json",
plugins: [plugin],
external: ["node-gyp"],
@ -81,7 +81,7 @@ for (const item of targets) {
autoloadTsconfig: true,
autoloadPackageJson: true,
target: target.replace(binary, "bun") as Bun.Build.CompileTarget,
outfile: `./dist/${name}/bin/${binary}`,
outfile: path.join(outdir, name, "bin", binary),
execArgv: [`--user-agent=${binary}/${Script.version}`, "--use-system-ca", "--"],
windows: {},
},
@ -93,10 +93,6 @@ for (const item of targets) {
OPENCODE_LIBC: item.os === "linux" ? `'${item.abi ?? "glibc"}'` : "undefined",
// FFF_LIBC selects the fff native lib variant: "musl" or "gnu".
FFF_LIBC: item.os === "linux" ? `'${item.abi ?? "gnu"}'` : "undefined",
OTUI_TREE_SITTER_WORKER_PATH:
(item.os === "win32" ? '"B:/~BUN/root/' : '"/$bunfs/root/') +
path.relative(dir, parserWorker).replaceAll("\\", "/") +
'"',
...(item.os === "linux" ? { "process.env.OPENTUI_LIBC": JSON.stringify(item.abi ?? "glibc") } : {}),
},
})
@ -107,7 +103,7 @@ for (const item of targets) {
}
await Bun.write(
`./dist/${name}/package.json`,
path.join(outdir, name, "package.json"),
JSON.stringify(
{
name: `@opencode-ai/${name}`,

4
packages/cli/script/generate.ts
View file

@ -1,7 +1,9 @@
import { readFile } from "node:fs/promises"
const modelsUrl = process.env.OPENCODE_MODELS_URL || "https://models.dev"
export const modelsData = process.env.MODELS_DEV_API_JSON
? await Bun.file(process.env.MODELS_DEV_API_JSON).text()
? await readFile(process.env.MODELS_DEV_API_JSON, "utf8")
: await fetch(`${modelsUrl}/api.json`).then((response) => response.text())
console.log("Loaded models.dev snapshot")

83
packages/cli/script/node-assets.ts Normal file
View file

@ -0,0 +1,83 @@
import { createHash } from "node:crypto"
import { copyFile, mkdir, readdir, readFile, stat } from "node:fs/promises"
import { createRequire } from "node:module"
import path from "node:path"
import { fileURLToPath } from "node:url"
import { getNodeAssets } from "@opentui/core/node-assets"
import { attentionSoundAssets, type NodeTarget, photonWasmAsset } from "../src/node/target"
const dir = path.resolve(import.meta.dirname, "..")
// Bun's compiler discovers file imports and embeds them in its virtual filesystem. Vite only bundles the JavaScript
// portion of the Node executable, while SEA embeds only the assets explicitly listed in its build configuration.
// Collect and stage those files under stable keys so the SEA prelude can extract them to real paths at startup;
// native addons, helper executables, and other path-based consumers cannot use assets directly from SEA memory.
export type NodeAsset = {
readonly key: string
readonly source: string
}
async function files(root: string, current = root): Promise<string[]> {
return (
await Promise.all(
(await readdir(current, { withFileTypes: true })).map((entry) => {
const target = path.join(current, entry.name)
return entry.isDirectory() ? files(root, target) : [path.relative(root, target)]
}),
)
).flat()
}
export async function collectNodeAssets(target: NodeTarget) {
const ptyEntry = fileURLToPath(import.meta.resolve(target.nodePtyPackage))
const ptyRoot = path.resolve(path.dirname(ptyEntry), "..")
const assets: NodeAsset[] = [
...getNodeAssets({
platform: target.platform,
arch: target.arch,
...(target.platform === "linux" ? { libc: "glibc" as const } : {}),
}),
{ key: target.parcelWatcherAsset, source: fileURLToPath(import.meta.resolve(target.parcelWatcherPackage)) },
{
key: photonWasmAsset,
source: createRequire(path.resolve(dir, "../core/package.json")).resolve(photonWasmAsset),
},
...attentionSoundAssets.map((key) => ({
key,
source: path.resolve(dir, "../ui/src/assets/audio", path.basename(key)),
})),
...(await files(ptyRoot))
.filter((relative) => !relative.endsWith(".map") && !relative.endsWith(".pdb"))
.map((relative) => ({
key: `${target.nodePtyPackage}/${relative}`,
source: path.join(ptyRoot, relative),
})),
]
await Promise.all(assets.map((asset) => stat(asset.source)))
return assets
}
export async function hashNodeAssets(assets: readonly NodeAsset[]) {
const hash = createHash("sha256")
for (const asset of assets.toSorted((left, right) => left.key.localeCompare(right.key))) {
hash.update(asset.key)
hash.update(await readFile(asset.source))
}
return hash.digest("hex").slice(0, 16)
}
export async function copyNodeAssets(assets: readonly NodeAsset[]) {
const root = path.join(dir, "dist-node", "assets")
await Promise.all(
assets.map(async (asset) => {
const target = path.join(root, asset.key)
await mkdir(path.dirname(target), { recursive: true })
await copyFile(asset.source, target)
}),
)
}
export async function seaAssetMap() {
const root = path.join(dir, "dist-node", "assets")
return Object.fromEntries((await files(root)).map((key) => [key.replaceAll(path.sep, "/"), path.join(root, key)]))
}

161
packages/cli/script/postinstall.mjs Normal file
View file

@ -0,0 +1,161 @@
#!/usr/bin/env node
import childProcess from "node:child_process"
import fs from "node:fs"
import os from "node:os"
import path from "node:path"
import { createRequire } from "node:module"
import { fileURLToPath } from "node:url"
const directory = path.dirname(fileURLToPath(import.meta.url))
const require = createRequire(import.meta.url)
const packageJson = JSON.parse(fs.readFileSync(path.join(directory, "package.json"), "utf8"))
const command = Object.keys(packageJson.bin ?? {})[0]
if (!command) throw new Error("OpenCode package does not declare a binary")
const platform = { darwin: "darwin", linux: "linux", win32: "windows" }[os.platform()] ?? os.platform()
const arch = { x64: "x64", arm64: "arm64", arm: "arm" }[os.arch()] ?? os.arch()
const sourceBinary = platform === "windows" ? `${command}.exe` : command
const targetBinary = path.resolve(directory, packageJson.bin[command])
const dependencies = packageJson.optionalDependencies ?? {}
const base = Object.keys(dependencies).find((name) => name.endsWith(`-${platform}-${arch}`))
if (!base) throw new Error(`OpenCode does not provide a binary for ${platform}-${arch}`)
function supportsAvx2() {
if (arch !== "x64") return false
if (platform === "linux") {
try {
return /(^|\s)avx2(\s|$)/i.test(fs.readFileSync("/proc/cpuinfo", "utf8"))
} catch {
return false
}
}
if (platform === "darwin") {
try {
const result = childProcess.spawnSync("sysctl", ["-n", "hw.optional.avx2_0"], {
encoding: "utf8",
timeout: 1500,
})
return result.status === 0 && (result.stdout || "").trim() === "1"
} catch {
return false
}
}
if (platform === "windows") {
const script =
'(Add-Type -MemberDefinition "[DllImport(""kernel32.dll"")] public static extern bool IsProcessorFeaturePresent(int ProcessorFeature);" -Name Kernel32 -Namespace Win32 -PassThru)::IsProcessorFeaturePresent(40)'
for (const executable of ["powershell.exe", "pwsh.exe", "pwsh", "powershell"]) {
try {
const result = childProcess.spawnSync(executable, ["-NoProfile", "-NonInteractive", "-Command", script], {
encoding: "utf8",
timeout: 3000,
windowsHide: true,
})
if (result.status !== 0) continue
const output = (result.stdout || "").trim().toLowerCase()
if (output === "true" || output === "1") return true
if (output === "false" || output === "0") return false
} catch {
continue
}
}
}
return false
}
function isMusl() {
if (platform !== "linux") return false
try {
if (fs.existsSync("/etc/alpine-release")) return true
const result = childProcess.spawnSync("ldd", ["--version"], { encoding: "utf8" })
return `${result.stdout || ""}${result.stderr || ""}`.toLowerCase().includes("musl")
} catch {
return false
}
}
function packageNames() {
const baseline = arch === "x64" && !supportsAvx2()
const names =
platform === "linux"
? isMusl()
? arch === "x64"
? baseline
? [`${base}-baseline-musl`, `${base}-musl`, `${base}-baseline`, base]
: [`${base}-musl`, `${base}-baseline-musl`, base, `${base}-baseline`]
: [`${base}-musl`, base]
: arch === "x64"
? baseline
? [`${base}-baseline`, base, `${base}-baseline-musl`, `${base}-musl`]
: [base, `${base}-baseline`, `${base}-musl`, `${base}-baseline-musl`]
: [base, `${base}-musl`]
: arch === "x64"
? baseline
? [`${base}-baseline`, base]
: [base, `${base}-baseline`]
: [base]
return names.filter((name) => dependencies[name])
}
function copyBinary(source) {
if (!fs.existsSync(source)) throw new Error(`Binary not found at ${source}`)
fs.mkdirSync(path.dirname(targetBinary), { recursive: true })
if (fs.existsSync(targetBinary)) fs.unlinkSync(targetBinary)
try {
fs.linkSync(source, targetBinary)
} catch {
fs.copyFileSync(source, targetBinary)
}
fs.chmodSync(targetBinary, 0o755)
}
function resolveBinary(name) {
const packagePath = require.resolve(`${name}/package.json`)
return path.join(path.dirname(packagePath), "bin", sourceBinary)
}
function installPackage(name) {
const temp = fs.mkdtempSync(path.join(os.tmpdir(), "opencode-install-"))
try {
const result = childProcess.spawnSync(
"npm",
["install", "--ignore-scripts", "--no-save", "--loglevel=error", "--prefix", temp, `${name}@${dependencies[name]}`],
{ stdio: "inherit", windowsHide: true },
)
if (result.status !== 0) return false
copyBinary(path.join(temp, "node_modules", name, "bin", sourceBinary))
return true
} finally {
fs.rmSync(temp, { recursive: true, force: true })
}
}
function verifyBinary() {
return (
childProcess.spawnSync(targetBinary, ["--version"], {
stdio: "ignore",
windowsHide: true,
}).status === 0
)
}
function main() {
const names = packageNames()
for (const name of names) {
try {
copyBinary(resolveBinary(name))
if (verifyBinary()) return
} catch {
if (installPackage(name) && verifyBinary()) return
}
}
throw new Error(`Failed to install OpenCode. Try manually installing ${names.map((name) => JSON.stringify(name)).join(" or ")}.`)
}
try {
main()
} catch (error) {
console.error(error instanceof Error ? error.message : String(error))
process.exit(1)
}

93
packages/cli/script/publish.ts
View file

@ -18,37 +18,66 @@ async function publish(dir: string, name: string, version: string) {
await $`npm publish *.tgz --access public --tag ${Script.channel}`.cwd(dir)
}
const binaries: Record<string, string> = {}
for (const filepath of new Bun.Glob("*/package.json").scanSync({ cwd: "./dist" })) {
const item = await Bun.file(`./dist/${filepath}`).json()
binaries[item.name] = item.version
async function publishDistribution(input: { root: string; name: string; binary: string; packagePrefix: string }) {
const binaries: Record<string, string> = {}
for (const filepath of new Bun.Glob("*/package.json").scanSync({ cwd: input.root })) {
const item = await Bun.file(`${input.root}/${filepath}`).json()
if (!item.name.startsWith(input.packagePrefix)) continue
binaries[item.name] = item.version
}
console.log(input.name, "binaries", binaries)
const versions = new Set(Object.values(binaries))
if (versions.size > 1) throw new Error(`Binary package versions do not match for ${input.name}`)
const version = versions.values().next().value
if (!version) throw new Error(`No binary packages found for ${input.name}`)
await $`mkdir -p ${input.root}/${input.name}/bin`
await $`cp ./script/postinstall.mjs ${input.root}/${input.name}/postinstall.mjs`
await Bun.file(`${input.root}/${input.name}/bin/${input.binary}.exe`).write(
[
`echo "Error: ${input.name}'s postinstall script was not run." >&2`,
'echo "" >&2',
'echo "This occurs when installation scripts are disabled." >&2',
'echo "Run the package postinstall script or reinstall with scripts enabled." >&2',
"exit 1",
"",
].join("\n"),
)
await Bun.file(`${input.root}/${input.name}/package.json`).write(
JSON.stringify(
{
name: input.name,
bin: { [input.binary]: `./bin/${input.binary}.exe` },
scripts: { postinstall: "node ./postinstall.mjs" },
version,
license: pkg.license,
repository: { type: "git", url: "git+https://github.com/anomalyco/opencode.git" },
os: ["darwin", "linux", "win32"],
cpu: ["arm64", "x64"],
optionalDependencies: binaries,
},
null,
2,
),
)
await Promise.all(
Object.entries(binaries).map(([name, version]) =>
publish(`${input.root}/${name.replace("@opencode-ai/", "")}`, name, version),
),
)
await publish(`${input.root}/${input.name}`, input.name, version)
}
console.log("binaries", binaries)
const version = Object.values(binaries)[0]
const name = pkg.name
await $`mkdir -p ./dist/${name}/bin`
await $`cp ./bin/opencode2.cjs ./dist/${name}/bin/opencode2`
await Bun.file(`./dist/${name}/package.json`).write(
JSON.stringify(
{
name,
bin: { opencode2: "./bin/opencode2" },
version,
license: pkg.license,
repository: { type: "git", url: "git+https://github.com/anomalyco/opencode.git" },
os: ["darwin", "linux", "win32"],
cpu: ["arm64", "x64"],
optionalDependencies: binaries,
},
null,
2,
),
)
await Promise.all(
Object.entries(binaries).map(([name, version]) =>
publish(`./dist/${name.replace("@opencode-ai/", "")}`, name, version),
),
)
await publish(`./dist/${name}`, name, version)
await publishDistribution({
root: "./dist",
name: pkg.name,
binary: "opencode2",
packagePrefix: "@opencode-ai/cli-",
})
await publishDistribution({
root: "./dist/node",
name: "opencode-node",
binary: "opencode2-node",
packagePrefix: "@opencode-ai/cli-node-",
})

7
packages/cli/script/service-smoke.ts
View file

@ -7,9 +7,10 @@ import fs from "node:fs/promises"
import os from "node:os"
import path from "node:path"
const target = `cli-${process.platform === "win32" ? "windows" : process.platform}-${process.arch}`
const directory = path.join(import.meta.dir, "..", "dist", target, "bin")
const binary = path.join(directory, `opencode2${process.platform === "win32" ? ".exe" : ""}`)
const nodeBuild = process.argv.includes("--node")
const target = `cli${nodeBuild ? "-node" : ""}-${process.platform === "win32" ? "windows" : process.platform}-${process.arch}`
const directory = path.join(import.meta.dir, "..", "dist", ...(nodeBuild ? ["node"] : []), target, "bin")
const binary = path.join(directory, `opencode2${nodeBuild ? "-node" : ""}${process.platform === "win32" ? ".exe" : ""}`)
if (!(await Bun.file(binary).exists())) throw new Error(`Missing compiled CLI in ${directory}`)
const root = await fs.mkdtemp(path.join(os.tmpdir(), "opencode-service-smoke-"))

4
packages/cli/src/commands/commands.ts
View file

@ -114,6 +114,10 @@ export const Commands = Spec.make(typeof OPENCODE_CLI_NAME === "string" ? OPENCO
}),
],
}),
Spec.make("plugin", {
description: "Manage plugins",
commands: [Spec.make("list", { description: "List active plugins" })],
}),
Spec.make("migrate", { description: "Migrate v1 data to v2" }),
Spec.make("mini", {
description: "Start the minimal interactive interface",

4
packages/cli/src/commands/handlers/default.ts
View file

@ -1,4 +1,4 @@
import { AppNodeBuilder } from "@opencode-ai/core/effect/app-node-builder"
import { LayerNode } from "@opencode-ai/core/effect/layer-node"
import { Global } from "@opencode-ai/core/global"
import { run } from "@opencode-ai/tui"
import { Commands } from "../commands"
@ -75,6 +75,6 @@ export default Runtime.handler(Commands, (input) =>
: Effect.logInfo(message, tags)
runFork(effect)
},
}).pipe(Effect.provide(AppNodeBuilder.build(Global.node)))
}).pipe(Effect.provide(LayerNode.compile(Global.node)))
}),
)

19
packages/cli/src/commands/handlers/mcp/add.ts
View file

@ -1,5 +1,6 @@
import { EOL } from "node:os"
import path from "node:path"
import { readFile, stat, writeFile } from "node:fs/promises"
import { Effect, Option } from "effect"
import { applyEdits, modify } from "jsonc-parser"
import { Global } from "@opencode-ai/core/global"
@ -35,7 +36,7 @@ export default Runtime.handler(
}),
)
async function resolveConfigPath(directory: string) {
export async function resolveConfigPath(directory: string) {
const candidates = [
path.join(directory, "opencode.json"),
path.join(directory, "opencode.jsonc"),
@ -43,16 +44,24 @@ async function resolveConfigPath(directory: string) {
path.join(directory, ".opencode", "opencode.jsonc"),
]
for (const candidate of candidates) {
if (await Bun.file(candidate).exists()) return candidate
if (
await stat(candidate).then(
(info) => info.isFile(),
() => false,
)
)
return candidate
}
return candidates[0]
}
async function write(configPath: string, name: string, server: unknown) {
const file = Bun.file(configPath)
const text = (await file.exists()) ? await file.text() : "{}"
const text = await readFile(configPath, "utf8").catch((error) => {
if (typeof error === "object" && error !== null && "code" in error && error.code === "ENOENT") return "{}"
throw error
})
const edits = modify(text, ["mcp", "servers", name], server, {
formattingOptions: { tabSize: 2, insertSpaces: true },
})
await Bun.write(configPath, applyEdits(text, edits))
await writeFile(configPath, applyEdits(text, edits))
}

15
packages/cli/src/commands/handlers/mini.ts
View file

@ -1,7 +1,9 @@
import { Effect, Option } from "effect"
import { Context, Effect, FileSystem, Option } from "effect"
import { Commands } from "../commands"
import { Runtime } from "../../framework/runtime"
import { ServerConnection } from "../../services/server-connection"
import { Config } from "../../config"
import { resolve } from "@opencode-ai/tui/config"
export default Runtime.handler(Commands.commands.mini, (input) =>
Effect.gen(function* () {
@ -9,9 +11,17 @@ export default Runtime.handler(Commands.commands.mini, (input) =>
yield* Effect.promise(async () => validateMiniTerminal())
const serverURL = Option.getOrUndefined(input.server)
const server = yield* ServerConnection.resolve({ server: serverURL, standalone: input.standalone })
const config = yield* Config.Service
const resolved = resolve(yield* config.get(), { terminalSuspend: process.platform !== "win32" })
const fileSystem = yield* FileSystem.FileSystem
const runServicePromise = Effect.runPromiseWith(Context.make(FileSystem.FileSystem, fileSystem))
const service = server.service
yield* Effect.promise(() =>
runMini({
server,
server: {
endpoint: server.endpoint,
reconnect: service ? (signal) => runServicePromise(service.reconnect(), { signal }) : undefined,
},
continue: input.continue,
session: Option.getOrUndefined(input.session),
fork: input.fork,
@ -21,6 +31,7 @@ export default Runtime.handler(Commands.commands.mini, (input) =>
replay: input.replay,
replayLimit: Option.getOrUndefined(input.replayLimit),
demo: input.demo,
tuiConfig: resolved,
}),
)
}),

24
packages/cli/src/commands/handlers/plugin/list.ts Normal file
View file

@ -0,0 +1,24 @@
import { EOL } from "node:os"
import { Effect } from "effect"
import { OpenCode } from "@opencode-ai/client"
import { Service } from "@opencode-ai/client/effect/service"
import { Commands } from "../../commands"
import { Runtime } from "../../../framework/runtime"
import { ServiceConfig } from "../../../services/service-config"
export default Runtime.handler(
Commands.commands.plugin.commands.list,
Effect.fn("cli.plugin.list")(function* () {
const options = yield* ServiceConfig.options()
const found = yield* Service.discover(options)
const endpoint = found ?? (yield* Service.ensure(options))
const client = OpenCode.make({ baseUrl: endpoint.url, headers: Service.headers(endpoint) })
const response = yield* Effect.promise(() => client.plugin.list({ location: { directory: process.cwd() } }))
const plugins = response.data.toSorted((a, b) => a.id.localeCompare(b.id))
if (plugins.length === 0) {
process.stdout.write("No plugins loaded" + EOL)
return
}
process.stdout.write(plugins.map((plugin) => plugin.id).join(EOL) + EOL)
}),
)

2
packages/cli/src/commands/handlers/run.ts
View file

@ -5,7 +5,7 @@ import { ServerConnection } from "../../services/server-connection"
export default Runtime.handler(Commands.commands.run, (input) =>
Effect.gen(function* () {
const { runNonInteractive } = yield* Effect.promise(() => import("../../mini"))
const { runNonInteractive } = yield* Effect.promise(() => import("../../run/run"))
const separator = process.argv.indexOf("--", 2)
const server = yield* ServerConnection.resolve({
server: Option.getOrUndefined(input.server),

2
packages/cli/src/commands/handlers/serve.ts
View file

@ -5,7 +5,7 @@ import { ServerProcess } from "../../server-process"
export default Runtime.handler(
Commands.commands.serve,
Effect.fn("cli.serve")(function* (input) {
Effect.fnUntraced(function* (input) {
if (input.service && input.stdio) return yield* Effect.fail(new Error("--service and --stdio cannot be combined"))
return yield* ServerProcess.run({
mode: input.service ? "service" : input.stdio ? "stdio" : "default",

6
packages/cli/src/index.ts
View file

@ -7,7 +7,6 @@ import { Runtime } from "./framework/runtime"
import { Observability } from "@opencode-ai/core/observability"
import { Updater } from "./services/updater"
import { InstallationChannel, InstallationVersion, InstallationLocal } from "@opencode-ai/core/installation/version"
import { AppNodeBuilder } from "@opencode-ai/core/effect/app-node-builder"
import { LayerNode } from "@opencode-ai/core/effect/layer-node"
import { Global } from "@opencode-ai/core/global"
import { AppProcess } from "@opencode-ai/core/process"
@ -32,6 +31,9 @@ const Handlers = Runtime.handlers(Commands, {
auth: () => import("./commands/handlers/mcp/auth"),
logout: () => import("./commands/handlers/mcp/logout"),
},
plugin: {
list: () => import("./commands/handlers/plugin/list"),
},
migrate: () => import("./commands/handlers/migrate"),
mini: () => import("./commands/handlers/mini"),
run: () => import("./commands/handlers/run"),
@ -58,7 +60,7 @@ Effect.logInfo("cli starting", {
Effect.annotateLogs({ role: "cli" }),
Effect.provide(Config.layer),
Effect.provide(Updater.layer),
Effect.provide(AppNodeBuilder.build(LayerNode.group([Global.node, AppProcess.node, Npm.node]))),
Effect.provide(LayerNode.compile(LayerNode.group([Global.node, AppProcess.node, Npm.node]))),
Effect.provide(Observability.layer),
Effect.provide(NodeServices.layer),
Effect.scoped,

176
packages/cli/src/mini-host.ts Normal file
View file

@ -0,0 +1,176 @@
import type { MiniFrontendInput } from "@opencode-ai/tui/mini"
import { createModelPreferenceRepository } from "@opencode-ai/tui/model-preference"
import { Flag } from "@opencode-ai/core/flag/flag"
import { Global } from "@opencode-ai/core/global"
import fs from "node:fs"
import { readFile } from "node:fs/promises"
import path from "node:path"
import { ReadStream } from "node:tty"
export const INTERACTIVE_INPUT_ERROR = "opencode mini requires a controlling terminal for input"
export type InteractiveStdin = {
stdin: NodeJS.ReadStream
cleanup(): void
}
type MiniHost = MiniFrontendInput["host"]
function preferences(statePath: string): MiniHost["preferences"] {
const repository = createModelPreferenceRepository(path.join(statePath, "model.json"))
return {
async resolveVariant(model) {
if (!model) return
return repository.resolveVariant(model)
},
async saveVariant(model, variant) {
if (!model) return
await repository.saveVariant(model, variant).catch(() => undefined)
},
}
}
function signal(name: "SIGINT" | "SIGUSR2"): MiniHost["signals"]["sigint"] {
return {
subscribe(listener) {
let subscribed = true
process.on(name, listener)
return () => {
if (!subscribed) return
subscribed = false
process.off(name, listener)
}
},
}
}
function createTrace(
logPath: string,
diagnostics: { pid: number; cwd: string; argv: string[] },
): MiniHost["diagnostics"]["trace"] {
if (!process.env.OPENCODE_DIRECT_TRACE) return
const stamp = new Date()
.toISOString()
.replace(/[-:]/g, "")
.replace(/\.\d+Z$/, "Z")
const target = path.join(logPath, "direct", `${stamp}-${diagnostics.pid}.jsonl`)
const text = (data: unknown) =>
JSON.stringify(data, (_key, value) => (typeof value === "bigint" ? String(value) : value), 0)
fs.mkdirSync(path.dirname(target), { recursive: true })
fs.writeFileSync(
path.join(logPath, "direct", "latest.json"),
text({
time: new Date().toISOString(),
...diagnostics,
path: target,
}) + "\n",
)
const trace = {
write(type: string, data?: unknown) {
fs.appendFileSync(
target,
text({
time: new Date().toISOString(),
pid: diagnostics.pid,
type,
data,
}) + "\n",
)
},
}
trace.write("trace.start", {
argv: diagnostics.argv,
cwd: diagnostics.cwd,
path: target,
})
return trace
}
function openTerminalStdin(target: string): NodeJS.ReadStream {
return new ReadStream(fs.openSync(target, "r"))
}
export function resolveInteractiveStdin(
stdin: NodeJS.ReadStream = process.stdin,
open: (target: string) => NodeJS.ReadStream = openTerminalStdin,
platform: NodeJS.Platform = process.platform,
): InteractiveStdin {
if (stdin.isTTY) return { stdin, cleanup() {} }
const target = platform === "win32" ? "CONIN$" : "/dev/tty"
try {
const source = open(target)
let cleaned = false
return {
stdin: source,
cleanup() {
if (cleaned) return
cleaned = true
source.destroy()
},
}
} catch (error) {
throw new Error(INTERACTIVE_INPUT_ERROR, { cause: error })
}
}
/** @internal Exported for owner-local resource cleanup tests. */
export async function usingInteractiveStdin<T>(
run: (terminal: InteractiveStdin) => Promise<T>,
resolve: () => InteractiveStdin = resolveInteractiveStdin,
) {
const terminal = resolve()
try {
return await run(terminal)
} finally {
terminal.cleanup()
}
}
/** @internal Exported for owner-local host capability tests. */
export function createMiniHost(input: {
terminal: InteractiveStdin
directory: string
paths?: { home: string; state: string; log: string }
}): MiniHost {
const paths = input.paths ?? {
home: Global.Path.home,
state: Global.Path.state,
log: Global.Path.log,
}
const diagnostics = {
pid: process.pid,
cwd: input.directory,
argv: process.argv.slice(2),
}
return {
terminal: { stdin: input.terminal.stdin },
platform: process.platform,
stdout: {
write(value) {
process.stdout.write(value)
},
},
files: {
readText: (url) => readFile(new URL(url), "utf8"),
},
editor: {
async open(options) {
const { openEditor } = await import("@opencode-ai/tui/editor")
return openEditor(options)
},
},
paths: { home: paths.home },
signals: {
sigint: signal("SIGINT"),
sigusr2: signal("SIGUSR2"),
},
startup: {
showTiming: Flag.OPENCODE_SHOW_TTFD,
now: () => performance.now(),
},
diagnostics: {
trace: createTrace(paths.log, diagnostics),
},
preferences: preferences(paths.state),
}
}

277
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import { Service, type Endpoint } from "@opencode-ai/client/effect/service"
import { ClientError, OpenCode, type OpenCodeClient } from "@opencode-ai/client/promise"
import type { MiniFrontendInput } from "@opencode-ai/tui/mini"
import { setTimeout } from "node:timers/promises"
import { waitForCatalogReady } from "./services/catalog"
import { readStdin } from "./util/io"
import { createMiniHost, INTERACTIVE_INPUT_ERROR, usingInteractiveStdin } from "./mini-host"
import { parseSessionTargetModel, resolveSessionTarget, type SessionTargetPreparation } from "./session-target"
export type MiniCommandInput = {
server: {
endpoint: Endpoint
reconnect?: (signal: AbortSignal) => Promise<Endpoint>
}
continue?: boolean
session?: string
fork?: boolean
model?: string
agent?: string
prompt?: string
replay?: boolean
replayLimit?: number
demo?: boolean
tuiConfig?: MiniFrontendInput["tuiConfig"]
}
type Model = MiniFrontendInput["model"]
class MiniInputError extends Error {}
export async function runMini(input: MiniCommandInput) {
try {
validate(input)
const result = await usingInteractiveStdin(async (terminal) => {
const initialInput = mergeInput(process.stdin.isTTY ? undefined : await readStdin(), input.prompt)
const frontendTask = import("@opencode-ai/tui/mini")
const directory = localDirectory()
const connection = createMiniConnection(input.server)
const sdk = connection.sdk
const requested = parseModel(input.model)
const model = requested ? { providerID: requested.providerID, modelID: requested.id } : undefined
const prepare = prepareTarget(input.agent)
const resolveTarget = async (initial: OpenCodeClient, signal: AbortSignal) => {
const resolved = await resolveMiniTarget({
sdk: initial,
reconnect: connection.reconnect,
signal,
resolve: (client) =>
resolveSessionTarget({
client,
location: { directory },
continue: input.continue,
session: input.session,
fork: input.fork,
model: requested,
agent: input.agent,
prepare,
signal,
}).catch((error) => {
if (error instanceof Error && error.message === "Session not found")
throw new MiniInputError(error.message)
throw error
}),
})
const target = resolved.value
return {
sdk: resolved.sdk,
sessionID: target.session.id,
sessionTitle: target.session.title,
location: target.location,
model: target.model ? { providerID: target.model.providerID, modelID: target.model.id } : undefined,
variant: target.model?.variant,
agent: target.agent,
resume: target.resume,
}
}
const create = (
client: OpenCodeClient,
next: {
location: { directory: string; workspaceID?: string }
agent: string | undefined
model: Model
variant: string | undefined
},
signal?: AbortSignal,
) =>
resolveSessionTarget({
client,
location: { directory: next.location.directory, workspace: next.location.workspaceID },
agent: next.agent,
model: next.model
? { providerID: next.model.providerID, id: next.model.modelID, variant: next.variant }
: undefined,
prepare,
signal,
}).then((target) => ({
sessionID: target.session.id,
sessionTitle: target.session.title,
location: target.location,
model: target.model ? { providerID: target.model.providerID, modelID: target.model.id } : undefined,
variant: target.model?.variant,
agent: target.agent,
resume: false,
}))
const frontend = await frontendTask
return frontend.runMiniFrontend({
host: createMiniHost({ terminal, directory }),
sdk,
directory,
target: resolveTarget,
reconnect: connection.reconnect,
createSession: create,
agent: input.agent,
model,
variant: requested?.variant,
files: [],
initialInput,
replay: input.replay ?? true,
replayLimit: input.replayLimit,
demo: input.demo,
tuiConfig: input.tuiConfig,
})
})
if (result.exitCode !== 0) process.exit(result.exitCode)
} catch (error) {
if (error instanceof MiniInputError || (error instanceof Error && error.message === INTERACTIVE_INPUT_ERROR))
fail(error.message)
throw error
}
}
/** @internal Exported for CLI boundary tests. */
export function createMiniConnection(input: MiniCommandInput["server"]) {
const make = (endpoint: Endpoint) =>
OpenCode.make({
baseUrl: endpoint.url,
headers: Service.headers(endpoint),
})
const reconnect = input.reconnect
return {
sdk: make(input.endpoint),
reconnect: reconnect
? async (signal: AbortSignal) => {
const endpoint = await reconnect(signal)
return make(endpoint)
}
: undefined,
}
}
/** @internal Exported for reconnect lifecycle tests. */
export async function resolveMiniTarget<A>(input: {
sdk: OpenCodeClient
reconnect?: (signal: AbortSignal) => Promise<OpenCodeClient>
signal: AbortSignal
resolve: (sdk: OpenCodeClient) => Promise<A>
}) {
let sdk = input.sdk
while (true) {
try {
return { sdk, value: await input.resolve(sdk) }
} catch (error) {
if (!input.reconnect || !(error instanceof ClientError) || error.reason !== "Transport") throw error
while (true) {
try {
sdk = await input.reconnect(input.signal)
break
} catch (resolveError) {
if (input.signal.aborted) throw resolveError
await setTimeout(250, undefined, { signal: input.signal })
}
}
}
}
}
export function validateMiniTerminal() {
if (!process.stdout.isTTY) fail("opencode mini requires a TTY stdout")
}
/** @internal Exported for testing. */
export function mergeInput(piped: string | undefined, prompt: string | undefined) {
if (!prompt) return piped || undefined
if (!piped) return prompt
return piped + "\n" + prompt
}
function validate(input: MiniCommandInput) {
validateMiniTerminal()
if (input.replayLimit !== undefined && (!Number.isInteger(input.replayLimit) || input.replayLimit <= 0)) {
fail("--replay-limit must be a positive integer")
}
if (input.fork && !input.continue && !input.session) fail("--fork requires --continue or --session")
}
function localDirectory(): string {
const root = process.env.PWD ?? process.cwd()
try {
process.chdir(root)
return process.cwd()
} catch {
throw new MiniInputError(`Failed to change directory to ${root}`)
}
}
function parseModel(value?: string) {
try {
return parseSessionTargetModel(value)
} catch {
throw new MiniInputError("--model must use the format provider/model[#variant]")
}
}
function prepareTarget(requestedAgent?: string): SessionTargetPreparation {
return async (input) => {
if (input.model)
await waitForCatalogReady({
sdk: input.client,
directory: input.location.directory,
workspace: input.location.workspaceID,
model: { providerID: input.model.providerID, modelID: input.model.id },
signal: input.signal,
})
return {
model: input.model,
agent: requestedAgent
? await validateAgent(
input.client,
input.location.directory,
input.location.workspaceID,
requestedAgent,
input.signal,
)
: input.agent,
}
}
}
async function validateAgent(
sdk: OpenCodeClient,
directory: string,
workspace: string | undefined,
name?: string,
signal?: AbortSignal,
) {
if (!name) return
const deadline = Date.now() + 5_000
let agents: Awaited<ReturnType<OpenCodeClient["agent"]["list"]>> | undefined
while (Date.now() < deadline && !signal?.aborted) {
agents = await sdk.agent.list({ location: { directory, workspace } }, { signal }).catch((error) => {
if (signal && error instanceof ClientError && error.reason === "Transport") throw error
return undefined
})
const agent = agents?.data.find((item) => item.id === name)
if (agent?.mode === "subagent") {
warning(`agent "${name}" is a subagent, not a primary agent. Falling back to default agent`)
return
}
if (agent) return name
await setTimeout(25, undefined, { signal }).catch(() => {})
}
if (signal?.aborted) return
if (!agents) {
warning("failed to list agents. Falling back to default agent")
return
}
warning(`agent "${name}" not found. Falling back to default agent`)
}
function warning(message: string) {
process.stderr.write(`\x1b[93m\x1b[1m!\x1b[0m ${message}\n`)
}
function fail(message: string): never {
process.stderr.write(`\x1b[91m\x1b[1mError: \x1b[0m${message}\n`)
process.exit(1)
}

159
packages/cli/src/mini/catalog.shared.ts
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@ -1,159 +0,0 @@
import type {
AgentListOutput,
CommandListOutput,
ModelListOutput,
OpenCodeClient,
ProviderListOutput,
SkillListOutput,
} from "@opencode-ai/client/promise"
import type { RunAgent, RunCommand, RunProvider, RunReference } from "./types"
type CurrentAgent = AgentListOutput["data"][number]
type CurrentCommand = CommandListOutput["data"][number]
type CurrentSkill = SkillListOutput["data"][number]
type CurrentProvider = ProviderListOutput["data"][number]
type CurrentModel = ModelListOutput["data"][number]
function location(directory: string, workspace?: string) {
return {
location: {
directory,
workspace,
},
}
}
function defaultCost(model: CurrentModel) {
const picked = model.cost.find((cost) => cost.tier === undefined) ?? model.cost[0]
if (!picked) {
return undefined
}
return {
...picked,
input: model.cost.every((cost) => cost.input === 0) ? 0 : picked.input,
}
}
export function runAgent(input: CurrentAgent): RunAgent {
return {
id: input.id,
name: input.name,
description: input.description,
mode: input.mode,
hidden: input.hidden,
}
}
export function runCommand(input: CurrentCommand): RunCommand {
return {
name: input.name,
description: input.description,
}
}
export function runSkill(input: CurrentSkill): RunCommand {
return {
name: input.id,
description: input.description,
source: "skill",
}
}
export function runProviders(providers: CurrentProvider[], models: CurrentModel[]): RunProvider[] {
const grouped = new Map<string, RunProvider>()
for (const provider of providers) {
grouped.set(provider.id, {
id: provider.id,
name: provider.name,
models: {},
})
}
for (const model of models) {
const provider = grouped.get(model.providerID) ?? {
id: model.providerID,
name: model.providerID,
models: {},
}
provider.models[model.id] = {
id: model.id,
providerID: model.providerID,
name: model.name,
capabilities: model.capabilities,
cost: defaultCost(model),
limit: model.limit,
status: model.status,
variants: Object.fromEntries((model.variants ?? []).map((variant) => [variant.id, {}])),
}
grouped.set(provider.id, provider)
}
return [...grouped.values()]
}
// A location boots its plugins in a deferred background batch after the layer
// is built, so first-turn model resolution can observe empty catalog state.
// For explicit --model flows, wait for that exact ref to appear before prompt
// admission. On timeout, return and let the real execution error surface.
export async function waitForCatalogReady(input: {
sdk: OpenCodeClient
directory: string
workspace?: string
model: { providerID: string; modelID: string }
timeoutMs?: number
}) {
const deadline = Date.now() + (input.timeoutMs ?? 5_000)
while (Date.now() < deadline) {
const models = await input.sdk.model
.list(location(input.directory, input.workspace))
.then((result) => result.data)
.catch(() => undefined)
if (models?.some((model) => model.providerID === input.model.providerID && model.id === input.model.modelID)) return
await new Promise((resolve) => setTimeout(resolve, 25))
}
}
export async function waitForDefaultModel(input: {
sdk: OpenCodeClient
directory: string
timeoutMs?: number
active?: () => boolean
}): Promise<{ providerID: string; modelID: string } | undefined> {
const deadline = Date.now() + (input.timeoutMs ?? 5_000)
while (Date.now() < deadline && (input.active?.() ?? true)) {
const model = await input.sdk.model
.default(location(input.directory))
.then((result) => result.data)
.catch(() => undefined)
if (model) return { providerID: model.providerID, modelID: model.id }
await new Promise((resolve) => setTimeout(resolve, 25))
}
}
export async function loadRunAgents(sdk: OpenCodeClient, directory: string): Promise<RunAgent[]> {
const result = await sdk.agent.list(location(directory))
return result.data.map(runAgent)
}
export async function loadRunCommands(sdk: OpenCodeClient, directory: string): Promise<RunCommand[]> {
const [commands, skills] = await Promise.all([
sdk.command.list(location(directory)),
sdk.skill.list(location(directory)),
])
return [...commands.data.map(runCommand), ...skills.data.filter((skill) => skill.slash !== false).map(runSkill)]
}
export async function loadRunReferences(sdk: OpenCodeClient, directory: string): Promise<RunReference[]> {
const result = await sdk.reference.list(location(directory))
return result.data.filter((reference) => !reference.hidden)
}
export async function loadRunProviders(sdk: OpenCodeClient, directory: string): Promise<RunProvider[]> {
const [providers, models] = await Promise.all([
sdk.provider.list(location(directory)),
sdk.model.list(location(directory)),
])
return runProviders([...providers.data], [...models.data])
}

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packages/cli/src/mini/footer.question.tsx
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@ -1,573 +0,0 @@
// Question UI body for the direct-mode footer.
//
// Renders inside the footer when the reducer pushes a FooterView of type
// "question". Supports single-question and multi-question flows:
//
// Single question: options list with up/down selection, digit shortcuts,
// and optional custom text input.
//
// Multi-question: tabbed interface where each question is a tab, plus a
// final "Confirm" tab that shows all answers for review. Tab/shift-tab
// or left/right to navigate between questions.
//
// All state logic lives in question.shared.ts as a pure state machine.
// This component just renders it and dispatches keyboard events.
/** @jsxImportSource @opentui/solid */
import type { TextareaRenderable } from "@opentui/core"
import { useKeyboard, useTerminalDimensions } from "@opentui/solid"
import { For, Show, createEffect, createMemo, createSignal } from "solid-js"
import type { QuestionV2Request } from "@opencode-ai/client/promise"
import {
createQuestionBodyState,
questionConfirm,
questionCustom,
questionInfo,
questionInput,
questionMove,
questionOther,
questionPicked,
questionReject,
questionSave,
questionSelect,
questionSetEditing,
questionSetSelected,
questionSetSubmitting,
questionSetTab,
questionSingle,
questionStoreCustom,
questionSubmit,
questionSync,
questionTabs,
questionTotal,
} from "./question.shared"
import { footerWidthPolicy } from "./footer.width"
import type { RunFooterTheme } from "./theme"
import type { QuestionReject, QuestionReply } from "./types"
export function RunQuestionBody(props: {
request: QuestionV2Request
theme: RunFooterTheme
onReply: (input: QuestionReply) => void | Promise<void>
onReject: (input: QuestionReject) => void | Promise<void>
}) {
const dims = useTerminalDimensions()
const [state, setState] = createSignal(createQuestionBodyState(props.request.id))
const single = createMemo(() => questionSingle(props.request))
const confirm = createMemo(() => questionConfirm(props.request, state()))
const info = createMemo(() => questionInfo(props.request, state()))
const input = createMemo(() => questionInput(state()))
const other = createMemo(() => questionOther(props.request, state()))
const picked = createMemo(() => questionPicked(state()))
const disabled = createMemo(() => state().submitting)
const narrow = createMemo(() => footerWidthPolicy(dims().width).dialog.narrow)
const verb = createMemo(() => {
if (confirm()) {
return "submit"
}
if (info()?.multiple) {
return "toggle"
}
if (single()) {
return "submit"
}
return "confirm"
})
let area: TextareaRenderable | undefined
createEffect(() => {
setState((prev) => questionSync(prev, props.request.id))
})
const setTab = (tab: number) => {
setState((prev) => questionSetTab(prev, tab))
}
const move = (dir: -1 | 1) => {
setState((prev) => questionMove(prev, props.request, dir))
}
const beginReply = async (input: QuestionReply) => {
setState((prev) => questionSetSubmitting(prev, true))
try {
await props.onReply(input)
} catch {
setState((prev) => questionSetSubmitting(prev, false))
}
}
const beginReject = async (input: QuestionReject) => {
setState((prev) => questionSetSubmitting(prev, true))
try {
await props.onReject(input)
} catch {
setState((prev) => questionSetSubmitting(prev, false))
}
}
const saveCustom = () => {
const cur = state()
const next = questionSave(cur, props.request)
if (next.state !== cur) {
setState(next.state)
}
if (!next.reply) {
return
}
void beginReply(next.reply)
}
const choose = (selected: number) => {
const base = state()
const cur = questionSetSelected(base, selected)
const next = questionSelect(cur, props.request)
if (next.state !== base) {
setState(next.state)
}
if (!next.reply) {
return
}
void beginReply(next.reply)
}
const mark = (selected: number) => {
setState((prev) => questionSetSelected(prev, selected))
}
const select = () => {
const cur = state()
const next = questionSelect(cur, props.request)
if (next.state !== cur) {
setState(next.state)
}
if (!next.reply) {
return
}
void beginReply(next.reply)
}
const submit = () => {
void beginReply(questionSubmit(props.request, state()))
}
const reject = () => {
void beginReject(questionReject(props.request))
}
useKeyboard((event) => {
const cur = state()
if (cur.submitting) {
event.preventDefault()
return
}
if (cur.editing) {
if (event.name === "escape") {
setState((prev) => questionSetEditing(prev, false))
event.preventDefault()
return
}
return
}
if (!single() && (event.name === "left" || event.name === "h")) {
setTab((cur.tab - 1 + questionTabs(props.request)) % questionTabs(props.request))
event.preventDefault()
return
}
if (!single() && (event.name === "right" || event.name === "l")) {
setTab((cur.tab + 1) % questionTabs(props.request))
event.preventDefault()
return
}
if (!single() && event.name === "tab") {
const dir = event.shift ? -1 : 1
setTab((cur.tab + dir + questionTabs(props.request)) % questionTabs(props.request))
event.preventDefault()
return
}
if (questionConfirm(props.request, cur)) {
if (event.name === "return") {
submit()
event.preventDefault()
return
}
if (event.name === "escape") {
reject()
event.preventDefault()
}
return
}
const total = questionTotal(props.request, cur)
const max = Math.min(total, 9)
const digit = Number(event.name)
if (!Number.isNaN(digit) && digit >= 1 && digit <= max) {
choose(digit - 1)
event.preventDefault()
return
}
if (event.name === "up" || event.name === "k") {
move(-1)
event.preventDefault()
return
}
if (event.name === "down" || event.name === "j") {
move(1)
event.preventDefault()
return
}
if (event.name === "return") {
select()
event.preventDefault()
return
}
if (event.name === "escape") {
reject()
event.preventDefault()
}
})
createEffect(() => {
if (!state().editing || !area || area.isDestroyed) {
return
}
if (area.plainText !== input()) {
area.setText(input())
area.cursorOffset = input().length
}
queueMicrotask(() => {
if (!area || area.isDestroyed || !state().editing) {
return
}
area.focus()
area.cursorOffset = area.plainText.length
})
})
return (
<box width="100%" height="100%" flexDirection="column">
<box
flexDirection="column"
gap={1}
paddingLeft={1}
paddingRight={3}
paddingTop={1}
flexGrow={1}
flexShrink={1}
backgroundColor={props.theme.surface}
>
<Show when={!single()}>
<box flexDirection="row" gap={1} paddingLeft={1} flexShrink={0}>
<For each={props.request.questions}>
{(item, index) => {
const active = () => state().tab === index()
const answered = () => (state().answers[index()]?.length ?? 0) > 0
return (
<box
paddingLeft={1}
paddingRight={1}
backgroundColor={active() ? props.theme.highlight : props.theme.surface}
onMouseUp={() => {
if (!disabled()) setTab(index())
}}
>
<text fg={active() ? props.theme.surface : answered() ? props.theme.text : props.theme.muted}>
{item.header}
</text>
</box>
)
}}
</For>
<box
paddingLeft={1}
paddingRight={1}
backgroundColor={confirm() ? props.theme.highlight : props.theme.surface}
onMouseUp={() => {
if (!disabled()) setTab(props.request.questions.length)
}}
>
<text fg={confirm() ? props.theme.surface : props.theme.muted}>Confirm</text>
</box>
</box>
</Show>
<Show
when={!confirm()}
fallback={
<box width="100%" flexGrow={1} flexShrink={1} paddingLeft={1}>
<scrollbox
width="100%"
height="100%"
verticalScrollbarOptions={{
trackOptions: {
backgroundColor: props.theme.surface,
foregroundColor: props.theme.line,
},
}}
>
<box width="100%" flexDirection="column" gap={1}>
<box paddingLeft={1}>
<text fg={props.theme.text}>Review</text>
</box>
<For each={props.request.questions}>
{(item, index) => {
const value = () => state().answers[index()]?.join(", ") ?? ""
const answered = () => Boolean(value())
return (
<box paddingLeft={1}>
<text wrapMode="word">
<span style={{ fg: props.theme.muted }}>{item.header}:</span>{" "}
<span style={{ fg: answered() ? props.theme.text : props.theme.error }}>
{answered() ? value() : "(not answered)"}
</span>
</text>
</box>
)
}}
</For>
</box>
</scrollbox>
</box>
}
>
<box width="100%" flexGrow={1} flexShrink={1} paddingLeft={1} gap={1}>
<box>
<text fg={props.theme.text} wrapMode="word">
{info()?.question}
{info()?.multiple ? " (select all that apply)" : ""}
</text>
</box>
<box flexGrow={1} flexShrink={1}>
<scrollbox
width="100%"
height="100%"
verticalScrollbarOptions={{
trackOptions: {
backgroundColor: props.theme.surface,
foregroundColor: props.theme.line,
},
}}
>
<box width="100%" flexDirection="column">
<For each={info()?.options ?? []}>
{(item, index) => {
const active = () => state().selected === index()
const hit = () => state().answers[state().tab]?.includes(item.label) ?? false
return (
<box
flexDirection="column"
gap={0}
onMouseOver={() => {
if (!disabled()) {
mark(index())
}
}}
onMouseDown={() => {
if (!disabled()) {
mark(index())
}
}}
onMouseUp={() => {
if (!disabled()) {
choose(index())
}
}}
>
<box flexDirection="row">
<box backgroundColor={active() ? props.theme.line : undefined} paddingRight={1}>
<text fg={active() ? props.theme.highlight : props.theme.muted}>{`${index() + 1}.`}</text>
</box>
<box backgroundColor={active() ? props.theme.line : undefined}>
<text
fg={active() ? props.theme.highlight : hit() ? props.theme.success : props.theme.text}
>
{info()?.multiple ? `[${hit() ? "✓" : " "}] ${item.label}` : item.label}
</text>
</box>
<Show when={!info()?.multiple}>
<text fg={props.theme.success}>{hit() ? " ✓" : ""}</text>
</Show>
</box>
<box paddingLeft={3}>
<text fg={props.theme.muted} wrapMode="word">
{item.description}
</text>
</box>
</box>
)
}}
</For>
<Show when={questionCustom(props.request, state())}>
<box
flexDirection="column"
gap={0}
onMouseOver={() => {
if (!disabled()) {
mark(info()?.options.length ?? 0)
}
}}
onMouseDown={() => {
if (!disabled()) {
mark(info()?.options.length ?? 0)
}
}}
onMouseUp={() => {
if (!disabled()) {
choose(info()?.options.length ?? 0)
}
}}
>
<box flexDirection="row">
<box backgroundColor={other() ? props.theme.line : undefined} paddingRight={1}>
<text
fg={other() ? props.theme.highlight : props.theme.muted}
>{`${(info()?.options.length ?? 0) + 1}.`}</text>
</box>
<box backgroundColor={other() ? props.theme.line : undefined}>
<text
fg={other() ? props.theme.highlight : picked() ? props.theme.success : props.theme.text}
>
{info()?.multiple
? `[${picked() ? "✓" : " "}] Type your own answer`
: "Type your own answer"}
</text>
</box>
<Show when={!info()?.multiple}>
<text fg={props.theme.success}>{picked() ? " ✓" : ""}</text>
</Show>
</box>
<Show
when={state().editing}
fallback={
<Show when={input()}>
<box paddingLeft={3}>
<text fg={props.theme.muted} wrapMode="word">
{input()}
</text>
</box>
</Show>
}
>
<box paddingLeft={3}>
<textarea
width="100%"
minHeight={1}
maxHeight={4}
wrapMode="word"
placeholder="Type your own answer"
placeholderColor={props.theme.muted}
textColor={props.theme.text}
focusedTextColor={props.theme.text}
backgroundColor={props.theme.surface}
focusedBackgroundColor={props.theme.surface}
cursorColor={props.theme.text}
focused={!disabled()}
onSubmit={saveCustom}
onContentChange={() => {
if (!area || area.isDestroyed || disabled()) {
return
}
const text = area.plainText
setState((prev) => questionStoreCustom(prev, prev.tab, text))
}}
ref={(item) => {
area = item
}}
/>
</box>
</Show>
</box>
</Show>
</box>
</scrollbox>
</box>
</box>
</Show>
</box>
<box
flexDirection={narrow() ? "column" : "row"}
flexShrink={0}
gap={1}
paddingLeft={2}
paddingRight={3}
paddingBottom={1}
justifyContent={narrow() ? "flex-start" : "space-between"}
alignItems={narrow() ? "flex-start" : "center"}
>
<Show
when={!disabled()}
fallback={
<text fg={props.theme.muted} wrapMode="word">
Waiting for question event...
</text>
}
>
<box
flexDirection={narrow() ? "column" : "row"}
gap={narrow() ? 1 : 2}
flexShrink={0}
width={narrow() ? "100%" : undefined}
>
<Show
when={!state().editing}
fallback={
<>
<text fg={props.theme.text}>
enter <span style={{ fg: props.theme.muted }}>save</span>
</text>
<text fg={props.theme.text}>
esc <span style={{ fg: props.theme.muted }}>cancel</span>
</text>
</>
}
>
<Show when={!single()}>
<text fg={props.theme.text}>
{"⇆"} <span style={{ fg: props.theme.muted }}>tab</span>
</text>
</Show>
<Show when={!confirm()}>
<text fg={props.theme.text}>
{"↑↓"} <span style={{ fg: props.theme.muted }}>select</span>
</text>
</Show>
<text fg={props.theme.text}>
enter <span style={{ fg: props.theme.muted }}>{verb()}</span>
</text>
<text fg={props.theme.text}>
esc <span style={{ fg: props.theme.muted }}>dismiss</span>
</text>
</Show>
</box>
</Show>
</box>
</box>
)
}

8
packages/cli/src/mini/index.ts
View file

@ -1,8 +0,0 @@
export { runMini, validateMiniTerminal, mergeInput as mergeInteractiveInput, type MiniCommandInput } from "./mini"
export {
runNonInteractive,
mergeInput as mergeNonInteractiveInput,
pickRunModel,
parseRunModel,
type RunCommandInput,
} from "./run"

173
packages/cli/src/mini/mini.ts
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@ -1,173 +0,0 @@
import { Service } from "@opencode-ai/client/effect/service"
import { OpenCode, type OpenCodeClient } from "@opencode-ai/client/promise"
import { ServerConnection } from "../services/server-connection"
import { waitForCatalogReady } from "./catalog.shared"
import { INTERACTIVE_INPUT_ERROR, resolveInteractiveStdin } from "./runtime.stdin"
import type { RunInput, RunTuiConfig } from "./types"
export type MiniCommandInput = {
server: ServerConnection.Resolved
continue?: boolean
session?: string
fork?: boolean
model?: string
agent?: string
prompt?: string
replay?: boolean
replayLimit?: number
demo?: boolean
tuiConfig?: RunTuiConfig | Promise<RunTuiConfig>
}
type Session = Awaited<ReturnType<OpenCodeClient["session"]["get"]>>
export async function runMini(input: MiniCommandInput) {
validate(input)
const initialInput = mergeInput(process.stdin.isTTY ? undefined : await Bun.stdin.text(), input.prompt)
const runtimeTask = import("./runtime")
const directory = localDirectory()
try {
const sdk = OpenCode.make({
baseUrl: input.server.endpoint.url,
headers: Service.headers(input.server.endpoint),
})
const model = parseModel(input.model)
let agentTask: Promise<string | undefined> | undefined
const resolveAgent = () => {
agentTask ??= validateAgent(sdk, directory, input.agent)
return agentTask
}
const resolveSession = async () => {
const [agent, selected] = await Promise.all([resolveAgent(), selectSession(sdk, directory, input)])
const readyModel =
model ?? (selected?.model ? { providerID: selected.model.providerID, modelID: selected.model.id } : undefined)
if (readyModel) await waitForCatalogReady({ sdk, directory, model: readyModel })
const session = selected ?? (await createSession(sdk, directory, agent, model))
return { id: session.id, title: session.title, resume: selected !== undefined }
}
const create = (
_sdk: OpenCodeClient,
next: { agent: string | undefined; model: RunInput["model"]; variant: string | undefined },
) => createSession(sdk, directory, next.agent, next.model, next.variant)
const runtime = await runtimeTask
await runtime.runInteractiveDeferredMode({
sdk,
directory,
resolveAgent,
session: resolveSession,
createSession: create,
agent: input.agent,
model,
variant: undefined,
files: [],
initialInput,
thinking: true,
replay: input.replay ?? true,
replayLimit: input.replayLimit,
demo: input.demo,
tuiConfig: input.tuiConfig,
})
} catch (error) {
if (error instanceof Error && error.message === INTERACTIVE_INPUT_ERROR) fail(error.message)
throw error
}
}
export function validateMiniTerminal() {
if (!process.stdout.isTTY) fail("opencode mini requires a TTY stdout")
}
/** @internal Exported for testing. */
export function mergeInput(piped: string | undefined, prompt: string | undefined) {
if (!prompt) return piped || undefined
if (!piped) return prompt
return piped + "\n" + prompt
}
function validate(input: MiniCommandInput) {
validateMiniTerminal()
if (input.replayLimit !== undefined && (!Number.isInteger(input.replayLimit) || input.replayLimit <= 0)) {
fail("--replay-limit must be a positive integer")
}
if (input.fork && !input.continue && !input.session) fail("--fork requires --continue or --session")
resolveInteractiveStdin().cleanup?.()
}
function localDirectory(): string {
const root = process.env.PWD ?? process.cwd()
try {
process.chdir(root)
return process.cwd()
} catch {
fail(`Failed to change directory to ${root}`)
}
}
function parseModel(value?: string): RunInput["model"] {
if (!value) return
const [providerID, ...rest] = value.split("/")
const modelID = rest.join("/")
if (!providerID || !modelID) fail("--model must use the format provider/model")
return { providerID, modelID }
}
async function validateAgent(sdk: OpenCodeClient, directory: string, name?: string) {
if (!name) return
const deadline = Date.now() + 5_000
let agents: Awaited<ReturnType<OpenCodeClient["agent"]["list"]>> | undefined
while (Date.now() < deadline) {
agents = await sdk.agent.list({ location: { directory } }).catch(() => undefined)
const agent = agents?.data.find((item) => item.id === name)
if (agent?.mode === "subagent") {
warning(`agent "${name}" is a subagent, not a primary agent. Falling back to default agent`)
return
}
if (agent) return name
await Bun.sleep(25)
}
if (!agents) {
warning("failed to list agents. Falling back to default agent")
return
}
warning(`agent "${name}" not found. Falling back to default agent`)
}
async function selectSession(sdk: OpenCodeClient, directory: string, input: MiniCommandInput, preselected?: Session) {
const selected =
preselected ??
(input.session
? await sdk.session.get({ sessionID: input.session }).catch(() => undefined)
: input.continue
? await sdk.session
.list({ directory, parentID: null, limit: 1, order: "desc" })
.then((result) => result.data[0])
: undefined)
if (input.session && !selected) fail("Session not found")
if (!selected) return
if (!input.fork) return selected
return sdk.session.fork({ sessionID: selected.id })
}
async function createSession(
sdk: OpenCodeClient,
directory: string,
agent: string | undefined,
model: RunInput["model"],
variant?: string,
): Promise<Session> {
if (model) await waitForCatalogReady({ sdk, directory, model })
return sdk.session.create({
agent,
model: model ? { providerID: model.providerID, id: model.modelID, variant } : undefined,
location: { directory },
})
}
function warning(message: string) {
process.stderr.write(`\x1b[93m\x1b[1m!\x1b[0m ${message}\n`)
}
function fail(message: string): never {
process.stderr.write(`\x1b[91m\x1b[1mError: \x1b[0m${message}\n`)
process.exit(1)
}

260
packages/cli/src/mini/permission.shared.ts
View file

@ -1,260 +0,0 @@
// Pure state machine for the permission UI.
//
// Lives outside the JSX component so it can be tested independently. The
// machine has three stages:
//
// permission → initial view with Allow once / Always / Reject options
// always → confirmation step (Confirm / Cancel)
// reject → text input for rejection message
//
// permissionRun() is the main transition: given the current state and the
// selected option, it returns a new state and optionally a PermissionReply
// to send to the SDK. The component calls this on enter/click.
//
// permissionInfo() extracts display info (icon, title, lines, diff) from
// the request, delegating to tool.ts for tool-specific formatting.
import type { PermissionV2Request } from "@opencode-ai/client/promise"
import type { PermissionReply } from "./types"
import { toolPath, toolPermissionInfo } from "./tool"
type Dict = Record<string, unknown>
export type PermissionStage = "permission" | "always" | "reject"
export type PermissionOption = "once" | "always" | "reject" | "confirm" | "cancel"
export type PermissionBodyState = {
requestID: string
stage: PermissionStage
selected: PermissionOption
message: string
submitting: boolean
}
export type PermissionInfo = {
icon: string
title: string
lines: string[]
diff?: string
file?: string
}
export type PermissionStep = {
state: PermissionBodyState
reply?: PermissionReply
}
function dict(v: unknown): Dict {
if (!v || typeof v !== "object" || Array.isArray(v)) {
return {}
}
return { ...v }
}
function text(v: unknown): string {
return typeof v === "string" ? v : ""
}
function data(request: PermissionV2Request): Dict {
const meta = dict(request.metadata)
return {
...meta,
...dict(meta.input),
}
}
function patterns(request: PermissionV2Request): string[] {
return request.resources.filter((item): item is string => typeof item === "string")
}
export function createPermissionBodyState(requestID: string): PermissionBodyState {
return {
requestID,
stage: "permission",
selected: "once",
message: "",
submitting: false,
}
}
export function permissionOptions(stage: PermissionStage): PermissionOption[] {
if (stage === "permission") {
return ["once", "always", "reject"]
}
if (stage === "always") {
return ["confirm", "cancel"]
}
return []
}
export function permissionInfo(request: PermissionV2Request): PermissionInfo {
const pats = patterns(request)
const input = data(request)
const info = toolPermissionInfo(request.action, input, dict(request.metadata), pats)
if (info) {
return info
}
if (request.action === "external_directory") {
const meta = dict(request.metadata)
const raw = text(meta.parentDir) || text(meta.filepath) || pats[0] || ""
const dir = raw.includes("*") ? raw.slice(0, raw.indexOf("*")).replace(/[\\/]+$/, "") : raw
return {
icon: "←",
title: `Access external directory ${toolPath(dir, { home: true })}`,
lines: pats.map((item) => `- ${item}`),
}
}
if (request.action === "doom_loop") {
return {
icon: "⟳",
title: "Continue after repeated failures",
lines: ["This keeps the session running despite repeated failures."],
}
}
return {
icon: "⚙",
title: `Call tool ${request.action}`,
lines: [`Tool: ${request.action}`],
}
}
export function permissionAlwaysLines(request: PermissionV2Request): string[] {
const save = request.save ?? []
if (save.length === 1 && save[0] === "*") {
return [`This will allow ${request.action} until OpenCode is restarted.`]
}
return [
"This will allow the following patterns until OpenCode is restarted.",
...save.map((item) => `- ${item}`),
]
}
export function permissionLabel(option: PermissionOption): string {
if (option === "once") return "Allow once"
if (option === "always") return "Allow always"
if (option === "reject") return "Reject"
if (option === "confirm") return "Confirm"
return "Cancel"
}
export function permissionReply(requestID: string, reply: PermissionReply["reply"], message?: string): PermissionReply {
return {
requestID,
reply,
...(message && message.trim() ? { message: message.trim() } : {}),
}
}
export function permissionShift(
state: PermissionBodyState,
dir: -1 | 1,
list = permissionOptions(state.stage),
): PermissionBodyState {
if (list.length === 0) {
return state
}
const idx = Math.max(0, list.indexOf(state.selected))
const selected = list[(idx + dir + list.length) % list.length]
return {
...state,
selected,
}
}
export function permissionHover(state: PermissionBodyState, option: PermissionOption): PermissionBodyState {
return {
...state,
selected: option,
}
}
export function permissionRun(state: PermissionBodyState, requestID: string, option: PermissionOption): PermissionStep {
if (state.submitting) {
return { state }
}
if (state.stage === "permission") {
if (option === "always") {
return {
state: {
...state,
stage: "always",
selected: "confirm",
},
}
}
if (option === "reject") {
return {
state: {
...state,
stage: "reject",
selected: "reject",
},
}
}
return {
state,
reply: permissionReply(requestID, "once"),
}
}
if (state.stage !== "always") {
return { state }
}
if (option === "cancel") {
return {
state: {
...state,
stage: "permission",
selected: "always",
},
}
}
return {
state,
reply: permissionReply(requestID, "always"),
}
}
export function permissionReject(state: PermissionBodyState, requestID: string): PermissionReply | undefined {
if (state.submitting) {
return undefined
}
return permissionReply(requestID, "reject", state.message)
}
export function permissionCancel(state: PermissionBodyState): PermissionBodyState {
return {
...state,
stage: "permission",
selected: "reject",
}
}
export function permissionEscape(state: PermissionBodyState): PermissionBodyState {
if (state.stage === "always") {
return {
...state,
stage: "permission",
selected: "always",
}
}
return {
...state,
stage: "reject",
selected: "reject",
}
}

157
packages/cli/src/mini/prompt.editor.ts
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@ -1,157 +0,0 @@
import type { RunPromptPart } from "./types"
type Mention = Extract<RunPromptPart, { type: "file" | "agent" }>
export function resolveEditorSlashValue(text: string) {
const head = slashHead(text)
if (!head || head.name.toLowerCase() !== "editor") {
return text
}
return head.arguments
}
export function realignEditorPromptParts(content: string, parts: RunPromptPart[]): RunPromptPart[] {
const matches = new Map<number, Mention | undefined>()
const used: Array<{ start: number; end: number }> = []
for (const [index, part] of parts.entries()) {
if (part.type !== "file" && part.type !== "agent") {
continue
}
const text = promptPartText(part)
if (!text) {
continue
}
const start = findPromptPartIndex(content, text, used, promptPartStart(part))
if (start === -1) {
matches.set(index, undefined)
continue
}
const end = start + text.length
used.push({ start, end })
matches.set(index, updatePromptPart(part, start, end, text))
}
const next: RunPromptPart[] = []
for (const [index, part] of parts.entries()) {
if (part.type !== "file" && part.type !== "agent") {
next.push(part)
continue
}
if (!promptPartText(part)) {
next.push(part)
continue
}
const match = matches.get(index)
if (match) {
next.push(match)
}
}
return next
}
function slashHead(text: string) {
if (!text.startsWith("/")) {
return
}
for (let i = 1; i < text.length; i++) {
switch (text[i]) {
case " ":
case "\t":
case "\n":
return {
name: text.slice(1, i),
arguments: text.slice(i + 1),
}
}
}
return {
name: text.slice(1),
arguments: "",
}
}
function promptPartText(part: Mention) {
if (part.type === "agent") {
return part.source?.value
}
return part.source?.text.value
}
function promptPartStart(part: Mention) {
if (part.type === "agent") {
return part.source?.start ?? Number.POSITIVE_INFINITY
}
return part.source?.text.start ?? Number.POSITIVE_INFINITY
}
function findPromptPartIndex(content: string, text: string, used: Array<{ start: number; end: number }>, hint: number) {
let searchFrom = 0
let best = -1
let distance = Number.POSITIVE_INFINITY
const hinted = Number.isFinite(hint)
while (true) {
const start = content.indexOf(text, searchFrom)
if (start === -1) {
return best
}
const end = start + text.length
searchFrom = start + 1
if (used.some((range) => start < range.end && end > range.start)) {
continue
}
if (!hinted) {
return start
}
const nextDistance = Math.abs(start - hint)
if (nextDistance < distance) {
best = start
distance = nextDistance
}
}
}
function updatePromptPart(part: Mention, start: number, end: number, text: string): Mention {
if (part.type === "agent") {
return {
...part,
source: {
start,
end,
value: text,
},
}
}
if (!part.source?.text) {
return part
}
return {
...part,
source: {
...part.source,
text: {
...part.source.text,
start,
end,
value: text,
},
},
}
}

340
packages/cli/src/mini/question.shared.ts
View file

@ -1,340 +0,0 @@
// Pure state machine for the question UI.
//
// Supports both single-question and multi-question flows. Single questions
// submit immediately on selection. Multi-question flows use tabs and a
// final confirmation step.
//
// State transitions:
// questionSelect → picks an option (single: submits, multi: toggles/advances)
// questionSave → saves custom text input
// questionMove → arrow key navigation through options
// questionSetTab → tab navigation between questions
// questionSubmit → builds the final QuestionReply with all answers
//
// Custom answers: if a question has custom=true, an extra "Type your own
// answer" option appears. Selecting it enters editing mode with a text field.
import type { QuestionV2Info, QuestionV2Request } from "@opencode-ai/client/promise"
import type { QuestionReject, QuestionReply } from "./types"
export type QuestionBodyState = {
requestID: string
tab: number
answers: string[][]
custom: string[]
selected: number
editing: boolean
submitting: boolean
}
export type QuestionStep = {
state: QuestionBodyState
reply?: QuestionReply
}
export function createQuestionBodyState(requestID: string): QuestionBodyState {
return {
requestID,
tab: 0,
answers: [],
custom: [],
selected: 0,
editing: false,
submitting: false,
}
}
export function questionSync(state: QuestionBodyState, requestID: string): QuestionBodyState {
if (state.requestID === requestID) {
return state
}
return createQuestionBodyState(requestID)
}
export function questionSingle(request: QuestionV2Request): boolean {
return request.questions.length === 1 && request.questions[0]?.multiple !== true
}
export function questionTabs(request: QuestionV2Request): number {
return questionSingle(request) ? 1 : request.questions.length + 1
}
export function questionConfirm(request: QuestionV2Request, state: QuestionBodyState): boolean {
return !questionSingle(request) && state.tab === request.questions.length
}
export function questionInfo(request: QuestionV2Request, state: QuestionBodyState): QuestionV2Info | undefined {
return request.questions[state.tab]
}
export function questionCustom(request: QuestionV2Request, state: QuestionBodyState): boolean {
return questionInfo(request, state)?.custom !== false
}
export function questionInput(state: QuestionBodyState): string {
return state.custom[state.tab] ?? ""
}
export function questionPicked(state: QuestionBodyState): boolean {
const value = questionInput(state)
if (!value) {
return false
}
return state.answers[state.tab]?.includes(value) ?? false
}
export function questionOther(request: QuestionV2Request, state: QuestionBodyState): boolean {
const info = questionInfo(request, state)
if (!info || info.custom === false) {
return false
}
return state.selected === info.options.length
}
export function questionTotal(request: QuestionV2Request, state: QuestionBodyState): number {
const info = questionInfo(request, state)
if (!info) {
return 0
}
return info.options.length + (questionCustom(request, state) ? 1 : 0)
}
export function questionAnswers(state: QuestionBodyState, count: number): string[][] {
return Array.from({ length: count }, (_, idx) => state.answers[idx] ?? [])
}
export function questionSetTab(state: QuestionBodyState, tab: number): QuestionBodyState {
return {
...state,
tab,
selected: 0,
editing: false,
}
}
export function questionSetSelected(state: QuestionBodyState, selected: number): QuestionBodyState {
return {
...state,
selected,
}
}
export function questionSetEditing(state: QuestionBodyState, editing: boolean): QuestionBodyState {
return {
...state,
editing,
}
}
export function questionSetSubmitting(state: QuestionBodyState, submitting: boolean): QuestionBodyState {
return {
...state,
submitting,
}
}
function storeAnswers(state: QuestionBodyState, tab: number, list: string[]): QuestionBodyState {
const answers = [...state.answers]
answers[tab] = list
return {
...state,
answers,
}
}
export function questionStoreCustom(state: QuestionBodyState, tab: number, text: string): QuestionBodyState {
const custom = [...state.custom]
custom[tab] = text
return {
...state,
custom,
}
}
function questionPick(
state: QuestionBodyState,
request: QuestionV2Request,
answer: string,
custom = false,
): QuestionStep {
const answers = [...state.answers]
answers[state.tab] = [answer]
let next: QuestionBodyState = {
...state,
answers,
editing: false,
}
if (custom) {
const list = [...state.custom]
list[state.tab] = answer
next = {
...next,
custom: list,
}
}
if (questionSingle(request)) {
return {
state: next,
reply: {
requestID: request.id,
answers: [[answer]],
},
}
}
return {
state: questionSetTab(next, state.tab + 1),
}
}
function questionToggle(state: QuestionBodyState, answer: string): QuestionBodyState {
const list = [...(state.answers[state.tab] ?? [])]
const idx = list.indexOf(answer)
if (idx === -1) {
list.push(answer)
} else {
list.splice(idx, 1)
}
return storeAnswers(state, state.tab, list)
}
export function questionMove(state: QuestionBodyState, request: QuestionV2Request, dir: -1 | 1): QuestionBodyState {
const total = questionTotal(request, state)
if (total === 0) {
return state
}
return {
...state,
selected: (state.selected + dir + total) % total,
}
}
export function questionSelect(state: QuestionBodyState, request: QuestionV2Request): QuestionStep {
const info = questionInfo(request, state)
if (!info) {
return { state }
}
if (questionOther(request, state)) {
if (!info.multiple) {
return {
state: questionSetEditing(state, true),
}
}
const value = questionInput(state)
if (value && questionPicked(state)) {
return {
state: questionToggle(state, value),
}
}
return {
state: questionSetEditing(state, true),
}
}
const option = info.options[state.selected]
if (!option) {
return { state }
}
if (info.multiple) {
return {
state: questionToggle(state, option.label),
}
}
return questionPick(state, request, option.label)
}
export function questionSave(state: QuestionBodyState, request: QuestionV2Request): QuestionStep {
const info = questionInfo(request, state)
if (!info) {
return { state }
}
const value = questionInput(state).trim()
const prev = state.custom[state.tab]
if (!value) {
if (!prev) {
return {
state: questionSetEditing(state, false),
}
}
const next = questionStoreCustom(state, state.tab, "")
return {
state: questionSetEditing(
storeAnswers(
next,
state.tab,
(state.answers[state.tab] ?? []).filter((item) => item !== prev),
),
false,
),
}
}
if (info.multiple) {
const answers = [...(state.answers[state.tab] ?? [])]
if (prev) {
const idx = answers.indexOf(prev)
if (idx !== -1) {
answers.splice(idx, 1)
}
}
if (!answers.includes(value)) {
answers.push(value)
}
const next = questionStoreCustom(state, state.tab, value)
return {
state: questionSetEditing(storeAnswers(next, state.tab, answers), false),
}
}
return questionPick(state, request, value, true)
}
export function questionSubmit(request: QuestionV2Request, state: QuestionBodyState): QuestionReply {
return {
requestID: request.id,
answers: questionAnswers(state, request.questions.length),
}
}
export function questionReject(request: QuestionV2Request): QuestionReject {
return {
requestID: request.id,
}
}
export function questionHint(request: QuestionV2Request, state: QuestionBodyState): string {
if (state.submitting) {
return "Waiting for question event..."
}
if (questionConfirm(request, state)) {
return "enter submit esc dismiss"
}
if (state.editing) {
return "enter save esc cancel"
}
const info = questionInfo(request, state)
if (questionSingle(request)) {
return `↑↓ select enter ${info?.multiple ? "toggle" : "submit"} esc dismiss`
}
return `⇆ tab ↑↓ select enter ${info?.multiple ? "toggle" : "confirm"} esc dismiss`
}

166
packages/cli/src/mini/runtime.boot.ts
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@ -1,166 +0,0 @@
// Boot-time resolution for direct interactive mode.
//
// These functions run concurrently at startup to gather everything the runtime
// needs before the first frame: TUI keymap config, diff display style,
// model variant list with context limits, and session history for the prompt
// history ring. All are async because they read config or hit the SDK, but
// none block each other.
import { Context, Effect, Layer } from "effect"
import { resolve } from "@opencode-ai/tui/config/v1"
import { AppNodeBuilder } from "@opencode-ai/core/effect/app-node-builder"
import { makeGlobalNode } from "@opencode-ai/core/effect/app-node"
import { makeRuntime } from "@opencode-ai/core/effect/runtime"
import { loadRunProviders } from "./catalog.shared"
import { resolveCurrentSession, sessionHistory } from "./session.shared"
import type { RunDiffStyle, RunInput, RunPrompt, RunProvider, RunTuiConfig } from "./types"
import { pickVariant } from "./variant.shared"
export type ModelInfo = {
providers: RunProvider[]
variants: string[]
limits: Record<string, number>
}
export type SessionInfo = {
first: boolean
history: RunPrompt[]
model?: NonNullable<RunInput["model"]>
variant: string | undefined
}
type BootService = {
readonly resolveModelInfo: (
sdk: RunInput["sdk"],
directory: string,
model: RunInput["model"],
) => Effect.Effect<ModelInfo>
readonly resolveSessionInfo: (
sdk: RunInput["sdk"],
sessionID: string,
model: RunInput["model"],
) => Effect.Effect<SessionInfo>
}
class Service extends Context.Service<Service, BootService>()("@opencode/RunBoot") {}
function emptyModelInfo(): ModelInfo {
return {
providers: [],
variants: [],
limits: {},
}
}
function emptySessionInfo(): SessionInfo {
return {
first: true,
history: [],
variant: undefined,
}
}
function defaultRunTuiConfig(): RunTuiConfig {
return {
...resolve({}, { terminalSuspend: process.platform !== "win32" }),
diff_style: "auto",
}
}
const layer = Layer.effect(
Service,
Effect.gen(function* () {
const resolveModelInfo = Effect.fn("RunBoot.resolveModelInfo")(function* (
sdk: RunInput["sdk"],
directory: string,
model: RunInput["model"],
) {
const providers = yield* Effect.promise(() => loadRunProviders(sdk, directory))
const limits = Object.fromEntries(
providers.flatMap((provider) =>
Object.entries(provider.models ?? {}).flatMap(([modelID, info]) => {
const limit = info?.limit?.context
if (typeof limit !== "number" || limit <= 0) {
return []
}
return [[`${provider.id}/${modelID}`, limit] as const]
}),
),
)
if (!model) {
return {
providers,
variants: [],
limits,
}
}
const info = providers.find((item) => item.id === model.providerID)?.models?.[model.modelID]
return {
providers,
variants: Object.keys(info?.variants ?? {}),
limits,
}
})
const resolveSessionInfo = Effect.fn("RunBoot.resolveSessionInfo")(function* (
sdk: RunInput["sdk"],
sessionID: string,
model: RunInput["model"],
) {
const session = yield* Effect.promise(() => resolveCurrentSession(sdk, sessionID).catch(() => undefined))
if (!session) {
return emptySessionInfo()
}
return {
first: session.first,
history: sessionHistory(session),
model: session.model,
variant: pickVariant(model ?? session.model, session),
}
})
return Service.of({
resolveModelInfo,
resolveSessionInfo,
})
}),
)
const node = makeGlobalNode({ service: Service, layer, deps: [] })
const runtime = makeRuntime(Service, AppNodeBuilder.build(node))
// Fetches available variants and context limits for every provider/model pair.
export async function resolveModelInfo(
sdk: RunInput["sdk"],
directory: string,
model: RunInput["model"],
): Promise<ModelInfo> {
return runtime.runPromise((svc) => svc.resolveModelInfo(sdk, directory, model)).catch(() => emptyModelInfo())
}
export function resolveModelInfoStrict(sdk: RunInput["sdk"], directory: string, model: RunInput["model"]) {
return runtime.runPromise((svc) => svc.resolveModelInfo(sdk, directory, model))
}
// Fetches session messages to determine if this is the first turn and build prompt history.
export async function resolveSessionInfo(
sdk: RunInput["sdk"],
sessionID: string,
model: RunInput["model"],
): Promise<SessionInfo> {
return runtime.runPromise((svc) => svc.resolveSessionInfo(sdk, sessionID, model)).catch(() => emptySessionInfo())
}
// Reads TUI config once for direct mode keymap setup and display preferences.
export async function resolveRunTuiConfig(
config?: RunTuiConfig | Promise<RunTuiConfig>,
): Promise<RunTuiConfig> {
return Promise.resolve(config).then((value) => value ?? defaultRunTuiConfig()).catch(() => defaultRunTuiConfig())
}
export async function resolveDiffStyle(config?: RunTuiConfig | Promise<RunTuiConfig>): Promise<RunDiffStyle> {
return resolveRunTuiConfig(config).then((value) => value.diff_style ?? "auto")
}

398
packages/cli/src/mini/runtime.lifecycle.ts
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@ -1,398 +0,0 @@
// Lifecycle management for the split-footer renderer.
//
// Creates the OpenTUI CliRenderer in split-footer mode, resolves the theme
// from the terminal palette, writes the entry splash to scrollback, and
// constructs the RunFooter. Returns a Lifecycle handle whose close() writes
// the exit splash and tears everything down in the right order:
// footer.close → footer.destroy → renderer shutdown.
//
// Also wires SIGINT so Ctrl-c clears a live prompt draft first, then falls
// back to the usual two-press exit sequence through RunFooter.requestExit().
import path from "path"
import { CliRenderEvents, createCliRenderer, type CliRenderer, type ScrollbackWriter } from "@opentui/core"
import { createDefaultOpenTuiKeymap } from "@opentui/keymap/opentui"
import { Global } from "@opencode-ai/core/global"
import { registerOpencodeKeymap } from "@opencode-ai/tui/keymap"
import { isDefaultTitle } from "@opencode-ai/tui/util/session"
import { Locale } from "@opencode-ai/tui/util/locale"
import { resolveInteractiveStdin } from "./runtime.stdin"
import { entrySplash, exitSplash, splashMeta } from "./splash"
import { resolveRunTheme } from "./theme"
import type {
FooterApi,
PermissionReply,
QuestionReject,
QuestionReply,
RunAgent,
RunInput,
RunPrompt,
RunReference,
RunTuiConfig,
} from "./types"
import { formatModelLabel } from "./variant.shared"
const FOOTER_HEIGHT = 4
type SplashState = {
entry: boolean
exit: boolean
}
type CycleResult = {
modelLabel?: string
status?: string
variant?: string | undefined
variants?: string[]
}
type FooterLabels = {
agentLabel: string
modelLabel: string
}
export type LifecycleInput = {
directory: string
findFiles: (query: string) => Promise<string[]>
agents: RunAgent[]
references: RunReference[]
sessionID: string
sessionTitle?: string
getSessionID?: () => string | undefined
first: boolean
history: RunPrompt[]
agent: string | undefined
model: RunInput["model"]
variant: string | undefined
tuiConfig: RunTuiConfig | Promise<RunTuiConfig>
onPermissionReply: (input: PermissionReply) => void | Promise<void>
onQuestionReply: (input: QuestionReply) => void | Promise<void>
onQuestionReject: (input: QuestionReject) => void | Promise<void>
onCycleVariant?: () => CycleResult | void
onModelSelect?: (model: NonNullable<RunInput["model"]>) => CycleResult | void | Promise<CycleResult | void>
onVariantSelect?: (variant: string | undefined) => CycleResult | void | Promise<CycleResult | void>
onInterrupt?: () => void
onBackground?: () => void
onSubagentSelect?: (sessionID: string | undefined) => void
onSubagentInterrupt?: (sessionID: string) => void
}
export type Lifecycle = {
footer: FooterApi
onResize(fn: () => void): () => void
refreshTheme(): void
resetForReplay(input: { sessionTitle?: string; sessionID?: string; history: RunPrompt[] }): Promise<void>
close(input: { showExit: boolean; sessionTitle?: string; sessionID?: string; history?: RunPrompt[] }): Promise<void>
}
// Gracefully tears down the renderer. Order matters: switch external output
// back to passthrough before leaving split-footer mode, so pending stdout
// doesn't get captured into the now-dead scrollback pipeline.
function shutdown(renderer: CliRenderer): void {
if (renderer.isDestroyed) {
return
}
if (renderer.externalOutputMode === "capture-stdout") {
renderer.externalOutputMode = "passthrough"
}
if (renderer.screenMode === "split-footer") {
renderer.screenMode = "main-screen"
}
if (!renderer.isDestroyed) {
renderer.destroy()
}
}
function splashInfo(title: string | undefined, history: RunPrompt[]) {
if (title && !isDefaultTitle(title)) {
return {
title,
showSession: true,
}
}
const next = history.find((item) => item.text.trim().length > 0)
return {
title: next?.text ?? title,
showSession: !!next,
}
}
function footerLabels(input: Pick<RunInput, "agent" | "model" | "variant">): FooterLabels {
const agentLabel = Locale.titlecase(input.agent ?? "build")
return {
agentLabel,
modelLabel: input.model ? formatModelLabel(input.model, input.variant) : "",
}
}
function directoryLabel(directory: string) {
const resolved = path.resolve(directory)
const display =
resolved === Global.Path.home
? "~"
: resolved.startsWith(`${Global.Path.home}${path.sep}`)
? resolved.replace(Global.Path.home, "~")
: resolved
return display.replaceAll("\\", "/")
}
function queueSplash(
renderer: Pick<CliRenderer, "writeToScrollback" | "requestRender">,
state: SplashState,
phase: keyof SplashState,
write: ScrollbackWriter | undefined,
): boolean {
if (state[phase]) {
return false
}
if (!write) {
return false
}
state[phase] = true
renderer.writeToScrollback(write)
renderer.requestRender()
return true
}
// Boots the split-footer renderer and constructs the RunFooter.
//
// The renderer starts in split-footer mode with captured stdout so that
// scrollback commits and footer repaints happen in the same frame. After
// the entry splash, RunFooter takes over the footer region.
export async function createRuntimeLifecycle(input: LifecycleInput): Promise<Lifecycle> {
const source = resolveInteractiveStdin()
const footerTask = import("./footer")
let unregisterKeymap: (() => void) | undefined
try {
const renderer = await createCliRenderer({
stdin: source.stdin,
targetFps: 30,
maxFps: 60,
useMouse: false,
autoFocus: false,
openConsoleOnError: false,
exitOnCtrlC: false,
useKittyKeyboard: { events: process.platform === "win32" },
screenMode: "split-footer",
footerHeight: FOOTER_HEIGHT,
externalOutputMode: "capture-stdout",
consoleMode: "disabled",
clearOnShutdown: false,
})
const [theme, tuiConfig] = await Promise.all([resolveRunTheme(renderer), input.tuiConfig])
renderer.setBackgroundColor(theme.background)
const keymap = createDefaultOpenTuiKeymap(renderer)
unregisterKeymap = registerOpencodeKeymap(keymap, renderer, tuiConfig)
const state: SplashState = {
entry: false,
exit: false,
}
const splash = splashInfo(input.sessionTitle, input.history)
const meta = splashMeta({
title: splash.title,
session_id: input.sessionID,
})
const labels = footerLabels({
agent: input.agent,
model: input.model,
variant: input.variant,
})
const wrote = queueSplash(
renderer,
state,
"entry",
entrySplash({
...meta,
theme: theme.splash,
showSession: splash.showSession,
detail: directoryLabel(input.directory),
}),
)
await renderer.idle().catch(() => {})
const { RunFooter } = await footerTask
let closed = false
let sigintRegistered = false
const footer = new RunFooter(renderer, {
directory: input.directory,
findFiles: input.findFiles,
agents: input.agents,
references: input.references,
sessionID: input.getSessionID ?? (() => input.sessionID),
...labels,
model: input.model,
variant: input.variant,
first: input.first,
history: input.history,
theme,
wrote,
keymap,
tuiConfig,
diffStyle: tuiConfig.diff_style ?? "auto",
onPermissionReply: input.onPermissionReply,
onQuestionReply: input.onQuestionReply,
onQuestionReject: input.onQuestionReject,
onCycleVariant: input.onCycleVariant,
onModelSelect: input.onModelSelect,
onVariantSelect: input.onVariantSelect,
onInterrupt: input.onInterrupt,
onBackground: input.onBackground,
onEditorOpen: async ({ value }) => {
if (closed || renderer.isDestroyed) {
return
}
const { openEditor } = await import("@opencode-ai/tui/editor")
await renderer.idle().catch(() => {})
const ignore = () => {}
detachSigint()
process.on("SIGINT", ignore)
try {
return await openEditor({
value,
cwd: input.directory,
renderer,
stdin: source.stdin,
})
} finally {
process.off("SIGINT", ignore)
attachSigint()
}
},
onSubagentSelect: input.onSubagentSelect,
onSubagentInterrupt: input.onSubagentInterrupt,
})
const sigint = () => {
footer.requestExit()
}
const attachSigint = () => {
if (closed || sigintRegistered) {
return
}
process.on("SIGINT", sigint)
sigintRegistered = true
}
const detachSigint = () => {
if (!sigintRegistered) {
return
}
process.off("SIGINT", sigint)
sigintRegistered = false
}
attachSigint()
const close = async (next: {
showExit: boolean
sessionTitle?: string
sessionID?: string
history?: RunPrompt[]
}) => {
if (closed) {
return
}
closed = true
detachSigint()
let wroteExit = false
try {
await footer.idle().catch(() => {})
const show = renderer.isDestroyed ? false : next.showExit
if (!renderer.isDestroyed && show) {
const sessionID = next.sessionID || input.getSessionID?.() || input.sessionID
const splash = splashInfo(next.sessionTitle ?? input.sessionTitle, next.history ?? input.history)
wroteExit = queueSplash(
renderer,
state,
"exit",
exitSplash({
...splashMeta({
title: splash.title,
session_id: sessionID,
}),
theme: footer.currentTheme().splash,
}),
)
await renderer.idle().catch(() => {})
}
} finally {
footer.close()
await footer.idle().catch(() => {})
footer.destroy()
unregisterKeymap?.()
shutdown(renderer)
if (!wroteExit) {
process.stdout.write("\n")
}
source.cleanup?.()
}
}
return {
footer,
refreshTheme() {
footer.refreshTheme()
},
onResize(fn) {
let width = renderer.terminalWidth
let height = renderer.terminalHeight
const resize = () => {
if (width === renderer.terminalWidth && height === renderer.terminalHeight) {
return
}
width = renderer.terminalWidth
height = renderer.terminalHeight
fn()
}
renderer.on(CliRenderEvents.RESIZE, resize)
return () => renderer.off(CliRenderEvents.RESIZE, resize)
},
async resetForReplay(next) {
if (closed || renderer.isDestroyed || footer.isClosed) {
throw new Error("runtime closed")
}
await footer.idle()
if (closed || renderer.isDestroyed || footer.isClosed) {
throw new Error("runtime closed")
}
footer.resetForReplay(true)
renderer.resetSplitFooterForReplay({ clearSavedLines: true })
const splash = splashInfo(next.sessionTitle ?? input.sessionTitle, next.history)
renderer.writeToScrollback(
entrySplash({
...splashMeta({
title: splash.title,
session_id: next.sessionID ?? input.getSessionID?.() ?? input.sessionID,
}),
theme: footer.currentTheme().splash,
showSession: splash.showSession,
detail: directoryLabel(input.directory),
}),
)
renderer.requestRender()
},
close,
}
} catch (error) {
unregisterKeymap?.()
source.cleanup?.()
throw error
}
}

17
packages/cli/src/mini/runtime.shared.ts
View file

@ -1,17 +0,0 @@
type PendingTask<T> = {
current?: Promise<T>
}
export function reusePendingTask<T>(slot: PendingTask<T>, run: () => Promise<T>) {
if (slot.current) {
return slot.current
}
const task = run().finally(() => {
if (slot.current === task) {
slot.current = undefined
}
})
slot.current = task
return task
}

37
packages/cli/src/mini/runtime.stdin.ts
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@ -1,37 +0,0 @@
import fs from "fs"
import * as tty from "node:tty"
export const INTERACTIVE_INPUT_ERROR = "opencode mini requires a controlling terminal for input"
type InteractiveStdin = {
stdin: NodeJS.ReadStream
cleanup?: () => void
}
function openTerminalStdin(path: string): NodeJS.ReadStream {
return new tty.ReadStream(fs.openSync(path, "r"))
}
export function resolveInteractiveStdin(
stdin: NodeJS.ReadStream = process.stdin,
open: (path: string) => NodeJS.ReadStream = openTerminalStdin,
platform = process.platform,
): InteractiveStdin {
if (stdin.isTTY) {
return { stdin }
}
const file = platform === "win32" ? "CONIN$" : "/dev/tty"
try {
const stream = open(file)
return {
stdin: stream,
cleanup: () => {
stream.destroy()
},
}
} catch (error) {
throw new Error(INTERACTIVE_INPUT_ERROR, { cause: error })
}
}

17
packages/cli/src/mini/session-data.ts
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@ -1,17 +0,0 @@
import type { PermissionV2Request, QuestionV2Request } from "@opencode-ai/client/promise"
import type { FooterView } from "./types"
export function pickBlockerView(input: {
permission?: PermissionV2Request
question?: QuestionV2Request
}): FooterView {
if (input.permission) return { type: "permission", request: input.permission }
if (input.question) return { type: "question", request: input.question }
return { type: "prompt" }
}
export function blockerStatus(view: FooterView) {
if (view.type === "permission") return "awaiting permission"
if (view.type === "question") return "awaiting answer"
return ""
}

786
packages/cli/src/mini/stream-v2.subagent.ts
View file

@ -1,786 +0,0 @@
// Current-native subagent (child Session) tracking for the mini transport.
//
// Discovers child Sessions of the active parent from four current sources:
// 1. projected subagent tool output (`structured.sessionID`) during hydration
// 2. the current session list filtered by `parentID` during hydration
// 3. the process-local active-session map during hydration
// 4. live events from unknown sessions whose `parentID` matches the parent
//
// Tracks one footer tab per child and a detail transcript for the selected
// child, reduced from the same current live event stream the parent uses.
// Detail transcripts rebuild from projected messages on discovery, selection,
// and reconnect, then continue from live deltas using the same
// projected-prefix dedup the parent transport uses.
//
// Per-child interruption uses `v2.session.interrupt(childID)`. Per-child
// backgrounding is intentionally absent: subagent jobs block the parent
// session, so only whole-session `v2.session.background(parentID)` exists.
import type {
EventSubscribeOutput,
OpenCodeClient,
SessionMessageAssistantTool,
SessionMessageInfo,
} from "@opencode-ai/client/promise"
import { Locale } from "@opencode-ai/tui/util/locale"
import type { FooterSubagentDetail, FooterSubagentState, FooterSubagentTab, MiniToolPart, StreamCommit } from "./types"
const CHILD_MESSAGE_LIMIT = 80
const CHILD_FRAME_LIMIT = 80
const CHILD_EVENT_BUFFER_LIMIT = 64
const FAMILY_LIST_LIMIT = 100
const FALLBACK_LABEL = "Subagent"
type V2Event = EventSubscribeOutput
export function outputText(content: ReadonlyArray<{ type: string; text?: string }>) {
return content.flatMap((item) => (item.type === "text" && item.text ? [item.text] : [])).join("\n")
}
export function miniTool(input: {
sessionID: string
messageID: string
tool: SessionMessageAssistantTool
}): MiniToolPart {
const tool = input.tool
const providerCall =
tool.executed === undefined && tool.providerState === undefined
? undefined
: { executed: tool.executed, state: tool.providerState }
const providerResult =
tool.executed === undefined && tool.providerResultState === undefined
? undefined
: { executed: tool.executed, state: tool.providerResultState }
const base = {
id: `prt_${tool.id}`,
sessionID: input.sessionID,
messageID: input.messageID,
type: "tool" as const,
callID: tool.id,
tool: tool.name,
}
if (tool.state.status === "streaming") {
return {
...base,
state: { status: "pending", input: {}, raw: tool.state.input },
}
}
if (tool.state.status === "running") {
return {
...base,
state: {
status: "running",
input: tool.state.input,
title: tool.name,
metadata: { structured: tool.state.structured, content: tool.state.content, providerCall },
time: { start: tool.time.ran ?? tool.time.created },
},
}
}
if (tool.state.status === "completed") {
return {
...base,
state: {
status: "completed",
input: tool.state.input,
output: outputText(tool.state.content),
title: tool.name,
metadata: {
structured: tool.state.structured,
content: tool.state.content,
result: tool.state.result,
providerCall,
providerResult,
},
time: { start: tool.time.ran ?? tool.time.created, end: tool.time.completed ?? tool.time.created },
},
}
}
return {
...base,
state: {
status: "error",
input: tool.state.input,
error: tool.state.error.message,
metadata: {
structured: tool.state.structured,
content: tool.state.content,
result: tool.state.result,
providerCall,
providerResult,
},
time: { start: tool.time.ran ?? tool.time.created, end: tool.time.completed ?? tool.time.created },
},
}
}
export function toolCommit(part: MiniToolPart, phase: "start" | "progress" | "final"): StreamCommit {
const status = part.state.status
const text =
status === "running"
? part.tool === "task"
? "running task"
: `running ${part.tool}`
: status === "completed"
? part.state.output
: status === "error"
? part.state.error
: ""
return {
kind: "tool",
source: "tool",
text,
phase,
messageID: part.messageID,
partID: part.id,
tool: part.tool,
part,
toolState: status === "error" ? "error" : status === "completed" ? "completed" : "running",
toolError: status === "error" ? part.state.error : undefined,
}
}
type Frame = {
key: string
commit: StreamCommit
}
type ToolTrack = {
name: string
input: Record<string, unknown>
started: number
providerState?: Record<string, unknown>
}
type ChildState = {
sessionID: string
label: string
description: string
status: FooterSubagentTab["status"]
background: boolean
title?: string
callIDs: Set<string>
lastUpdatedAt: number
frames: Frame[]
text: Map<string, string>
projectedText: Map<string, string>
reasoning: Map<string, string>
projectedReasoning: Map<string, string>
tools: Map<string, ToolTrack>
finishedTools: Set<string>
messageIDs: Set<string>
prompts: Map<string, string>
hydrated: boolean
}
export type SubagentTrackerInput = {
sdk: OpenCodeClient
sessionID: string
thinking: boolean
emit: () => void
}
export type SubagentTracker = {
main(event: V2Event): void
foreign(sessionID: string, event: V2Event): void
hydrate(next: { messages: SessionMessageInfo[]; active: Record<string, unknown> }): Promise<void>
select(sessionID: string | undefined): void
snapshot(): FooterSubagentState
}
function record(value: unknown): Record<string, unknown> | undefined {
if (typeof value === "object" && value !== null && !Array.isArray(value)) return value as Record<string, unknown>
return undefined
}
function text(value: unknown): string | undefined {
if (typeof value !== "string") return undefined
const next = value.trim()
return next || undefined
}
function childSessionID(structured: Record<string, unknown> | undefined) {
const sessionID = text(structured?.sessionID)
if (!sessionID || !sessionID.startsWith("ses")) return undefined
const status = structured?.status
if (status !== "running" && status !== "completed") return undefined
return { sessionID, running: status === "running" }
}
function tab(child: ChildState): FooterSubagentTab {
return {
sessionID: child.sessionID,
partID: `subagent:${child.sessionID}`,
callID: `subagent:${child.sessionID}`,
label: child.label,
description: child.description || child.title || "",
status: child.status,
background: child.background ? true : undefined,
title: child.title,
toolCalls: child.callIDs.size > 0 ? child.callIDs.size : undefined,
lastUpdatedAt: child.lastUpdatedAt,
}
}
export function createSubagentTracker(input: SubagentTrackerInput): SubagentTracker {
const children = new Map<string, ChildState>()
// Live subagent tool calls in the parent, so tool.success structured output
// can be joined with the call's input metadata.
const pendingCalls = new Map<string, Record<string, unknown>>()
// Foreign sessions already resolved through session.get. Non-children stay
// cached so unrelated concurrent sessions are checked at most once.
const checked = new Set<string>()
// Foreign events buffered while a session.get discovery is in flight, so a
// fast child (including its settled event) is not lost mid-discovery.
const pendingEvents = new Map<string, V2Event[]>()
const hydrationEvents = new Map<string, V2Event[]>()
const hydrationOverflow = new Set<string>()
const hydrations = new Map<string, Promise<void>>()
let selected: string | undefined
const fragmentKey = (messageID: string, partID: string) => `${messageID}\u0000${partID}`
const ensureChild = (sessionID: string): ChildState => {
const existing = children.get(sessionID)
const child: ChildState = existing ?? {
sessionID,
label: FALLBACK_LABEL,
description: "",
status: "running",
background: false,
callIDs: new Set(),
lastUpdatedAt: Date.now(),
frames: [],
text: new Map(),
projectedText: new Map(),
reasoning: new Map(),
projectedReasoning: new Map(),
tools: new Map(),
finishedTools: new Set(),
messageIDs: new Set(),
prompts: new Map(),
hydrated: false,
}
if (!existing) children.set(sessionID, child)
// Adopting a child while its session.get discovery is still in flight:
// drain the buffered events now. They arrived before whatever the caller
// applies next, so replaying them first preserves bus order, and the
// resolved discovery can no longer replay stale events (e.g. step.started)
// after a terminal settled event was applied directly.
const buffered = pendingEvents.get(sessionID)
if (buffered) {
pendingEvents.delete(sessionID)
for (const event of buffered) reduce(child, event)
}
return child
}
const touch = (child: ChildState, timestamp?: number) => {
child.lastUpdatedAt = Math.max(child.lastUpdatedAt, timestamp ?? Date.now())
}
const notifyDetail = (child: ChildState) => {
if (child.sessionID === selected) input.emit()
}
const setFrame = (child: ChildState, key: string, commit: StreamCommit) => {
const index = child.frames.findIndex((item) => item.key === key)
if (index === -1) {
child.frames.push({ key, commit })
if (child.frames.length > CHILD_FRAME_LIMIT) child.frames.splice(0, child.frames.length - CHILD_FRAME_LIMIT)
return
}
child.frames[index] = { key, commit }
}
const applyMeta = (child: ChildState, meta: Record<string, unknown> | undefined) => {
if (!meta) return
const agent = text(meta.agent)
if (agent) child.label = Locale.titlecase(agent)
const description = text(meta.description)
if (description) child.description = description
if (meta.background === true) child.background = true
}
const userFrame = (child: ChildState, messageID: string, value: string) => {
if (child.messageIDs.has(messageID)) return false
child.messageIDs.add(messageID)
setFrame(child, `user:${messageID}`, {
kind: "user",
source: "system",
text: value,
phase: "start",
messageID,
})
return true
}
const childTool = (child: ChildState, item: SessionMessageAssistantTool, messageID: string) => {
const part = miniTool({
sessionID: child.sessionID,
messageID,
tool: item,
})
if (item.state.status === "streaming") return
child.callIDs.add(item.id)
if (item.state.status === "running") {
setFrame(child, `tool:${item.id}`, toolCommit(part, "start"))
return
}
child.finishedTools.add(item.id)
child.tools.delete(item.id)
setFrame(child, `tool:${item.id}`, toolCommit(part, "final"))
}
const rebuild = (child: ChildState, messages: SessionMessageInfo[]) => {
child.frames = []
child.text.clear()
child.projectedText.clear()
child.reasoning.clear()
child.projectedReasoning.clear()
child.finishedTools.clear()
child.messageIDs.clear()
child.callIDs.clear()
for (const message of messages) {
if (message.type === "user") {
child.prompts.delete(message.id)
userFrame(child, message.id, message.text)
continue
}
if (message.type !== "assistant") continue
child.messageIDs.add(message.id)
let textOrdinal = 0
let reasoningOrdinal = 0
for (const item of message.content) {
if (item.type === "text") {
const id = `text:${textOrdinal++}`
const key = fragmentKey(message.id, id)
child.text.set(key, item.text)
child.projectedText.set(key, item.text)
setFrame(child, key, {
kind: "assistant",
source: "assistant",
text: item.text,
phase: "progress",
messageID: message.id,
partID: id,
})
continue
}
if (item.type === "reasoning") {
const id = `reasoning:${reasoningOrdinal++}`
const key = fragmentKey(message.id, id)
child.reasoning.set(key, item.text)
child.projectedReasoning.set(key, item.text)
if (input.thinking)
setFrame(child, key, {
kind: "reasoning",
source: "reasoning",
text: `Thinking: ${item.text}`,
phase: "progress",
messageID: message.id,
partID: id,
})
continue
}
childTool(child, item, message.id)
}
if (message.error) {
setFrame(child, `error:${message.id}`, {
kind: "error",
source: "system",
text: message.error.message,
phase: "start",
messageID: message.id,
})
}
}
}
const hydrateChild = (child: ChildState): Promise<void> => {
const existing = hydrations.get(child.sessionID)
if (existing) return existing
const pendingPrompts = new Map(child.prompts)
const pendingTools = new Map(child.tools)
let retry = false
const task = input.sdk.message
.list({ sessionID: child.sessionID, limit: CHILD_MESSAGE_LIMIT, order: "desc" })
.then((response) => {
const buffered = hydrationEvents.get(child.sessionID) ?? []
hydrationEvents.delete(child.sessionID)
if (hydrationOverflow.delete(child.sessionID)) {
child.hydrated = false
retry = true
notifyDetail(child)
return
}
for (const [id, prompt] of pendingPrompts) {
if (!child.prompts.has(id)) child.prompts.set(id, prompt)
}
rebuild(child, structuredClone(response.data).toReversed() as SessionMessageInfo[])
for (const [id, tool] of pendingTools) {
if (!child.finishedTools.has(id) && !child.tools.has(id)) child.tools.set(id, tool)
}
for (const event of buffered) reduce(child, event)
child.hydrated = true
notifyDetail(child)
})
.catch(() => {
hydrationEvents.delete(child.sessionID)
hydrationOverflow.delete(child.sessionID)
})
.finally(() => {
hydrations.delete(child.sessionID)
if (retry) queueMicrotask(() => void hydrateChild(child))
})
hydrations.set(child.sessionID, task)
return task
}
const discover = (sessionID: string) => {
if (checked.has(sessionID) || children.has(sessionID) || sessionID === input.sessionID) return
checked.add(sessionID)
if (!pendingEvents.has(sessionID)) pendingEvents.set(sessionID, [])
void input.sdk.session
.get({ sessionID })
.then((session) => {
const buffered = pendingEvents.get(sessionID) ?? []
pendingEvents.delete(sessionID)
if (session.parentID !== input.sessionID) return
const child = ensureChild(sessionID)
if (session.agent) child.label = Locale.titlecase(session.agent)
child.title = session.title
for (const event of buffered) reduce(child, event)
touch(child)
input.emit()
void hydrateChild(child)
})
.catch(() => {
// Allow a later event to retry discovery after transient failures.
pendingEvents.delete(sessionID)
checked.delete(sessionID)
})
}
const reduce = (child: ChildState, event: V2Event) => {
if (event.type === "session.input.admitted") {
if (event.data.input.type === "user") child.prompts.set(event.data.inputID, event.data.input.data.text)
return
}
if (event.type === "session.input.promoted") {
const prompt = child.prompts.get(event.data.inputID)
if (prompt === undefined) return
child.prompts.delete(event.data.inputID)
if (userFrame(child, event.data.inputID, prompt)) {
touch(child, event.created)
notifyDetail(child)
}
return
}
if (event.type === "session.step.started") {
touch(child, event.created)
if (child.label === FALLBACK_LABEL && event.data.agent) child.label = Locale.titlecase(event.data.agent)
if (child.status !== "running") child.status = "running"
input.emit()
return
}
if (event.type === "session.text.started") {
return
}
if (event.type === "session.text.delta") {
const id = `text:${event.data.ordinal}`
const key = fragmentKey(event.data.assistantMessageID, id)
const projected = child.projectedText.get(key)
const covered = projected?.indexOf(event.data.delta) ?? -1
if (projected && covered >= 0) {
child.projectedText.set(key, projected.slice(covered + event.data.delta.length))
return
}
const next = (child.text.get(key) ?? "") + event.data.delta
child.text.set(key, next)
setFrame(child, key, {
kind: "assistant",
source: "assistant",
text: next,
phase: "progress",
messageID: event.data.assistantMessageID,
partID: id,
})
touch(child, event.created)
notifyDetail(child)
return
}
if (event.type === "session.text.ended") {
const id = `text:${event.data.ordinal}`
const key = fragmentKey(event.data.assistantMessageID, id)
child.text.set(key, event.data.text)
child.projectedText.delete(key)
setFrame(child, key, {
kind: "assistant",
source: "assistant",
text: event.data.text,
phase: "progress",
messageID: event.data.assistantMessageID,
partID: id,
})
touch(child, event.created)
notifyDetail(child)
return
}
if (event.type === "session.reasoning.started") {
return
}
if (event.type === "session.reasoning.delta") {
const id = `reasoning:${event.data.ordinal}`
const key = fragmentKey(event.data.assistantMessageID, id)
const projected = child.projectedReasoning.get(key)
const covered = projected?.indexOf(event.data.delta) ?? -1
if (projected && covered >= 0) {
child.projectedReasoning.set(key, projected.slice(covered + event.data.delta.length))
return
}
const next = (child.reasoning.get(key) ?? "") + event.data.delta
child.reasoning.set(key, next)
if (!input.thinking) return
setFrame(child, key, {
kind: "reasoning",
source: "reasoning",
text: `Thinking: ${next}`,
phase: "progress",
messageID: event.data.assistantMessageID,
partID: id,
})
notifyDetail(child)
return
}
if (event.type === "session.reasoning.ended") {
const id = `reasoning:${event.data.ordinal}`
const key = fragmentKey(event.data.assistantMessageID, id)
child.reasoning.set(key, event.data.text)
child.projectedReasoning.delete(key)
if (!input.thinking) return
setFrame(child, key, {
kind: "reasoning",
source: "reasoning",
text: `Thinking: ${event.data.text}`,
phase: "progress",
messageID: event.data.assistantMessageID,
partID: id,
})
notifyDetail(child)
return
}
if (event.type === "session.tool.input.started") {
if (child.finishedTools.has(event.data.callID)) return
child.tools.set(event.data.callID, { name: event.data.name, input: {}, started: event.created })
return
}
if (event.type === "session.tool.called") {
if (child.finishedTools.has(event.data.callID)) return
const current = child.tools.get(event.data.callID)
child.tools.set(event.data.callID, {
name: current?.name ?? "tool",
input: event.data.input,
started: current?.started ?? event.created,
providerState: event.data.state,
})
childTool(
child,
structuredClone({
type: "tool",
id: event.data.callID,
name: current?.name ?? "tool",
executed: event.data.executed,
providerState: event.data.state,
state: { status: "running", input: event.data.input, structured: {}, content: [] },
time: { created: current?.started ?? event.created, ran: event.created },
}) as SessionMessageAssistantTool,
event.data.assistantMessageID,
)
touch(child, event.created)
notifyDetail(child)
return
}
if (event.type === "session.tool.success" || event.type === "session.tool.failed") {
if (child.finishedTools.has(event.data.callID)) return
const current = child.tools.get(event.data.callID)
const failed = event.type === "session.tool.failed"
childTool(
child,
structuredClone({
type: "tool",
id: event.data.callID,
name: current?.name ?? "tool",
executed: event.data.executed,
providerState: current?.providerState,
providerResultState: event.data.resultState,
state: failed
? {
status: "error",
input: current?.input ?? {},
structured: {},
content: [],
error: event.data.error,
result: event.data.result,
}
: {
status: "completed",
input: current?.input ?? {},
structured: event.data.structured,
content: event.data.content,
result: event.data.result,
},
time: {
created: current?.started ?? event.created,
ran: current?.started,
completed: event.created,
},
}) as SessionMessageAssistantTool,
event.data.assistantMessageID,
)
touch(child, event.created)
notifyDetail(child)
return
}
if (event.type === "session.step.ended") return
if (event.type === "session.step.failed") {
setFrame(child, `error:step:${event.data.assistantMessageID}`, {
kind: "error",
source: "system",
text: event.data.error.message,
phase: "start",
messageID: event.data.assistantMessageID,
})
touch(child, event.created)
notifyDetail(child)
return
}
if (event.type === "session.execution.started") {
child.status = "running"
touch(child, event.created)
input.emit()
return
}
if (
event.type === "session.execution.succeeded" ||
event.type === "session.execution.failed" ||
event.type === "session.execution.interrupted"
) {
child.status =
event.type === "session.execution.succeeded"
? "completed"
: event.type === "session.execution.interrupted"
? "cancelled"
: "error"
touch(child, event.created)
input.emit()
}
}
const mainTool = (item: SessionMessageAssistantTool, active?: Record<string, unknown>) => {
if (item.name !== "subagent" || item.state.status !== "completed") return
const found = childSessionID(record(item.state.structured))
if (!found) return
const child = ensureChild(found.sessionID)
applyMeta(child, record(item.state.input))
if (found.running) child.background = true
if (child.status === "running") {
const running = found.running && (!active || found.sessionID in active)
child.status = running ? "running" : "completed"
}
touch(child, item.time.completed ?? item.time.created)
}
return {
main(event) {
if (event.type === "session.tool.input.started") {
if (event.data.name === "subagent") pendingCalls.set(event.data.callID, {})
return
}
if (event.type === "session.tool.called") {
if (pendingCalls.has(event.data.callID)) pendingCalls.set(event.data.callID, event.data.input)
return
}
if (event.type === "session.tool.failed") {
pendingCalls.delete(event.data.callID)
return
}
if (event.type !== "session.tool.success") return
const pending = pendingCalls.get(event.data.callID)
pendingCalls.delete(event.data.callID)
const found = childSessionID(record(event.data.structured))
if (!found) return
const child = ensureChild(found.sessionID)
applyMeta(child, pending)
if (found.running) {
child.background = true
child.status = "running"
}
if (!found.running && child.status === "running") child.status = "completed"
touch(child, event.created)
input.emit()
if (!child.hydrated) void hydrateChild(child)
},
foreign(sessionID, event) {
const child = children.get(sessionID)
if (child) {
if (hydrations.has(sessionID)) {
const buffered = hydrationEvents.get(sessionID) ?? []
if (buffered.length < CHILD_EVENT_BUFFER_LIMIT) buffered.push(event)
else hydrationOverflow.add(sessionID)
hydrationEvents.set(sessionID, buffered)
}
reduce(child, event)
return
}
discover(sessionID)
const buffered = pendingEvents.get(sessionID)
if (buffered && buffered.length < CHILD_EVENT_BUFFER_LIMIT) buffered.push(event)
},
async hydrate(next) {
for (const message of next.messages) {
if (message.type !== "assistant") continue
for (const item of message.content) {
if (item.type === "tool") mainTool(item, next.active)
}
}
// Family index: adopt children directly from the current session list so
// historical subagents beyond the projected message window still get tabs.
const family = await input.sdk.session
.list({ parentID: input.sessionID, limit: FAMILY_LIST_LIMIT, order: "desc" })
.then((response) => response.data)
.catch(() => [])
for (const session of family) {
const child = ensureChild(session.id)
if (session.agent && child.label === FALLBACK_LABEL) child.label = Locale.titlecase(session.agent)
if (!child.title) child.title = session.title
touch(child, session.time.updated)
}
for (const sessionID of Object.keys(next.active)) discover(sessionID)
for (const child of children.values()) {
// Reconnect can miss a child's settled event; the active map is the
// authoritative live signal for still-running children.
if (child.status === "running" && !(child.sessionID in next.active)) child.status = "completed"
}
const current = selected ? children.get(selected) : undefined
if (current) await hydrateChild(current)
if (children.size > 0) input.emit()
},
select(sessionID) {
selected = sessionID
const child = sessionID ? children.get(sessionID) : undefined
if (child && !child.hydrated) void hydrateChild(child)
input.emit()
},
snapshot() {
const tabs = [...children.values()].map(tab).toSorted((a, b) => {
const active = Number(b.status === "running") - Number(a.status === "running")
if (active !== 0) return active
return b.lastUpdatedAt - a.lastUpdatedAt
})
const child = selected ? children.get(selected) : undefined
const details: Record<string, FooterSubagentDetail> = child
? { [child.sessionID]: { sessionID: child.sessionID, commits: child.frames.map((item) => item.commit) } }
: {}
return { tabs, details, permissions: [], questions: [] }
},
}
}

1093
packages/cli/src/mini/stream-v2.transport.ts
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94
packages/cli/src/mini/trace.ts
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@ -1,94 +0,0 @@
// Dev-only JSONL event trace for direct interactive mode.
//
// Enable with OPENCODE_DIRECT_TRACE=1. Writes one JSON line per event to
// ~/.local/share/opencode/log/direct/<timestamp>-<pid>.jsonl. Also writes
// a latest.json pointer so you can quickly find the most recent trace.
//
// The trace captures the full closed loop: outbound prompts, inbound SDK
// events, reducer output, footer commits, and turn lifecycle markers.
// Useful for debugging stream ordering, permission behavior, and
// footer/transcript mismatches.
//
// Lazy-initialized: the first call to trace() decides whether tracing is
// active based on the env var, and subsequent calls return the cached result.
import fs from "fs"
import path from "path"
import { Global } from "@opencode-ai/core/global"
export type Trace = {
write(type: string, data?: unknown): void
}
let state: Trace | false | undefined
function stamp() {
return new Date()
.toISOString()
.replace(/[-:]/g, "")
.replace(/\.\d+Z$/, "Z")
}
function file() {
return path.join(Global.Path.log, "direct", `${stamp()}-${process.pid}.jsonl`)
}
function latest() {
return path.join(Global.Path.log, "direct", "latest.json")
}
function text(data: unknown) {
return JSON.stringify(
data,
(_key, value) => {
if (typeof value === "bigint") {
return String(value)
}
return value
},
0,
)
}
export function trace(): Trace | undefined {
if (state !== undefined) {
return state || undefined
}
if (!process.env.OPENCODE_DIRECT_TRACE) {
state = false
return undefined
}
const target = file()
fs.mkdirSync(path.dirname(target), { recursive: true })
fs.writeFileSync(
latest(),
text({
time: new Date().toISOString(),
pid: process.pid,
cwd: process.cwd(),
argv: process.argv.slice(2),
path: target,
}) + "\n",
)
state = {
write(type: string, data?: unknown) {
fs.appendFileSync(
target,
text({
time: new Date().toISOString(),
pid: process.pid,
type,
data,
}) + "\n",
)
},
}
state.write("trace.start", {
argv: process.argv.slice(2),
cwd: process.cwd(),
path: target,
})
return state
}

221
packages/cli/src/mini/variant.shared.ts
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@ -1,221 +0,0 @@
// Model variant resolution and persistence.
//
// Variants are provider-specific reasoning effort levels (e.g., "high", "max").
// Resolution priority: CLI --variant flag > saved preference > session history.
//
// The saved variant persists across sessions in ~/.local/state/opencode/model.json
// so your last-used variant sticks. Cycling (ctrl+t) updates both the active
// variant and the persisted file.
import path from "path"
import { FSUtil } from "@opencode-ai/core/fs-util"
import { AppNodeBuilder } from "@opencode-ai/core/effect/app-node-builder"
import { Context, Effect, Layer } from "effect"
import { LayerNode } from "@opencode-ai/core/effect/layer-node"
import { makeGlobalNode } from "@opencode-ai/core/effect/app-node"
import { makeRuntime } from "@opencode-ai/core/effect/runtime"
import { Global } from "@opencode-ai/core/global"
import { createSession, sessionVariant, type RunSession, type SessionMessages } from "./session.shared"
import type { RunInput, RunProvider } from "./types"
const MODEL_FILE = path.join(Global.Path.state, "model.json")
type ModelState = Record<string, unknown> & {
variant?: Record<string, string | undefined>
}
type VariantService = {
readonly resolveSavedVariant: (model: RunInput["model"]) => Effect.Effect<string | undefined>
readonly saveVariant: (model: RunInput["model"], variant: string | undefined) => Effect.Effect<void>
}
type VariantRuntime = {
resolveSavedVariant(model: RunInput["model"]): Promise<string | undefined>
saveVariant(model: RunInput["model"], variant: string | undefined): Promise<void>
}
function isRecord(value: unknown): value is Record<string, unknown> {
return !!value && typeof value === "object" && !Array.isArray(value)
}
class Service extends Context.Service<Service, VariantService>()("@opencode/RunVariant") {}
function modelKey(provider: string, model: string): string {
return `${provider}/${model}`
}
function variantKey(model: NonNullable<RunInput["model"]>): string {
return modelKey(model.providerID, model.modelID)
}
export function modelInfo(providers: RunProvider[] | undefined, model: NonNullable<RunInput["model"]>) {
const provider = providers?.find((item) => item.id === model.providerID)
return {
provider: provider?.name ?? model.providerID,
model: provider?.models[model.modelID]?.name ?? model.modelID,
}
}
export function formatModelLabel(
model: NonNullable<RunInput["model"]>,
variant: string | undefined,
providers?: RunProvider[],
): string {
const names = modelInfo(providers, model)
const label = variant ? ` · ${variant}` : ""
return `${names.model} · ${names.provider}${label}`
}
export function cycleVariant(current: string | undefined, variants: string[]): string | undefined {
if (variants.length === 0) {
return undefined
}
if (!current) {
return variants[0]
}
const idx = variants.indexOf(current)
if (idx === -1 || idx === variants.length - 1) {
return undefined
}
return variants[idx + 1]
}
export function pickVariant(model: RunInput["model"], input: RunSession | SessionMessages): string | undefined {
return sessionVariant(Array.isArray(input) ? createSession(input) : input, model)
}
function fitVariant(value: string | undefined, variants: string[]): string | undefined {
if (!value) {
return undefined
}
if (variants.length === 0 || variants.includes(value)) {
return value
}
return undefined
}
// Picks the active variant. CLI flag wins, then saved preference, then session
// history. fitVariant() checks saved and session values against the available
// variants list -- if the provider doesn't offer a variant, it drops.
export function resolveVariant(
input: string | undefined,
session: string | undefined,
saved: string | undefined,
variants: string[],
): string | undefined {
if (input !== undefined) {
return input
}
const fallback = fitVariant(saved, variants)
const current = fitVariant(session, variants)
if (current !== undefined) {
return current
}
return fallback
}
function state(value: unknown): ModelState {
if (!isRecord(value)) {
return {}
}
const variant = isRecord(value.variant)
? Object.fromEntries(
Object.entries(value.variant).flatMap(([key, item]) => {
if (typeof item !== "string") {
return []
}
return [[key, item] as const]
}),
)
: undefined
return {
...value,
variant,
}
}
const layer = Layer.fresh(
Layer.effect(
Service,
Effect.gen(function* () {
const file = yield* FSUtil.Service
const read = Effect.fn("RunVariant.read")(function* () {
return yield* file.readJson(MODEL_FILE).pipe(
Effect.map(state),
Effect.catchCause(() => Effect.succeed(state(undefined))),
)
})
const resolveSavedVariant = Effect.fn("RunVariant.resolveSavedVariant")(function* (model: RunInput["model"]) {
if (!model) {
return undefined
}
return (yield* read()).variant?.[variantKey(model)]
})
const saveVariant = Effect.fn("RunVariant.saveVariant")(function* (
model: RunInput["model"],
variant: string | undefined,
) {
if (!model) {
return
}
const current = yield* read()
const next = {
...current.variant,
}
const key = variantKey(model)
if (variant) {
next[key] = variant
}
if (!variant) {
delete next[key]
}
yield* file
.writeJson(MODEL_FILE, {
...current,
variant: next,
})
.pipe(Effect.orElseSucceed(() => undefined))
})
return Service.of({
resolveSavedVariant,
saveVariant,
})
}),
),
)
const node = makeGlobalNode({ service: Service, layer, deps: [FSUtil.node] })
/** @internal Exported for testing. */
export function createVariantRuntime(replacements?: readonly LayerNode.Replacement[]): VariantRuntime {
const runtime = makeRuntime(Service, AppNodeBuilder.build(node, replacements))
return {
resolveSavedVariant: (model) => runtime.runPromise((svc) => svc.resolveSavedVariant(model)).catch(() => undefined),
saveVariant: (model, variant) => runtime.runPromise((svc) => svc.saveVariant(model, variant)).catch(() => {}),
}
}
const runtime = createVariantRuntime()
export async function resolveSavedVariant(model: RunInput["model"]): Promise<string | undefined> {
return runtime.resolveSavedVariant(model)
}
export function saveVariant(model: RunInput["model"], variant: string | undefined): void {
void runtime.saveVariant(model, variant)
}

9
packages/cli/src/node/index.ts Normal file
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@ -0,0 +1,9 @@
import "./plugin-runtime.promise"
import "./plugin-runtime.effect"
process.stdout.on("error", (error) => {
if ("code" in error && error.code === "EPIPE") return
throw error
})
await import("../index")

28
packages/cli/src/node/plugin-runtime.effect.ts Normal file
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@ -0,0 +1,28 @@
import {
Agent,
Command,
Connection,
Credential,
Integration,
Model,
Plugin,
Provider,
Reference,
Skill,
} from "@opencode-ai/plugin/v2/effect"
import { Tool } from "@opencode-ai/plugin/v2/effect/tool"
const key = Symbol.for("opencode.plugin.v2.effect")
;(globalThis as typeof globalThis & { [key]?: unknown })[key] = {
Agent,
Command,
Connection,
Credential,
Integration,
Model,
Plugin,
Provider,
Reference,
Skill,
Tool,
}

26
packages/cli/src/node/plugin-runtime.promise.ts Normal file
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@ -0,0 +1,26 @@
import {
Agent,
Command,
Connection,
Credential,
Integration,
Model,
Plugin,
Provider,
Reference,
Skill,
} from "@opencode-ai/plugin/v2"
const key = Symbol.for("opencode.plugin.v2.promise")
;(globalThis as typeof globalThis & { [key]?: unknown })[key] = {
Agent,
Command,
Connection,
Credential,
Integration,
Model,
Plugin,
Provider,
Reference,
Skill,
}

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