chore: update merge branch with latest v2

.changeset/calm-sessions-header.md

@@ -0,0 +1,5 @@
+---
+"@opencode-ai/cli": patch
+---
+
+Expose a TUI plugin slot at the top of the session view.

.changeset/clean-sessions-generate.md

@@ -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.

.changeset/fresh-composers-slot.md

@@ -0,0 +1,5 @@
+---
+"@opencode-ai/cli": patch
+---
+
+Expose a TUI plugin slot above the session composer.

.github/workflows/publish.yml

@@ -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:

.github/workflows/test.yml

@@ -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

.gitignore

@@ -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

bun.lock

@@ -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/core": ">=0.4.5",
-        "@opentui/keymap": ">=0.4.3",
+        "@opentui/keymap": ">=0.4.5",
-        "@opentui/solid": ">=0.4.3",
+        "@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/opentelemetry": "4.0.0-beta.98",
-    "@effect/platform-node": "4.0.0-beta.83",
+    "@effect/platform-node": "4.0.0-beta.98",
-    "@effect/sql-sqlite-bun": "4.0.0-beta.83",
+    "@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/core": "0.4.5",
-    "@opentui/keymap": "0.4.3",
+    "@opentui/keymap": "0.4.5",
-    "@opentui/solid": "0.4.3",
+    "@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 @@
 
     "@drizzle-team/brocli": ["@drizzle-team/brocli@0.11.0", "", {}, "sha512-hD3pekGiPg0WPCCGAZmusBBJsDqGUR66Y452YgQsZOnkdQ7ViEPKuyP4huUGEZQefp8g34RRodXYmJ2TbCH+tg=="],
 
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+    "@effect/opentelemetry": ["@effect/opentelemetry@4.0.0-beta.98", "", { "peerDependencies": { "@opentelemetry/api": "^1.9", "@opentelemetry/api-logs": ">=0.203.0 <0.300.0", "@opentelemetry/resources": "^2.0.0", "@opentelemetry/sdk-logs": ">=0.203.0 <0.300.0", "@opentelemetry/sdk-metrics": "^2.0.0", "@opentelemetry/sdk-trace-base": "^2.0.0", "@opentelemetry/sdk-trace-node": "^2.0.0", "@opentelemetry/sdk-trace-web": "^2.0.0", "@opentelemetry/semantic-conventions": "^1.33.0", "effect": "^4.0.0-beta.98" }, "optionalPeers": ["@opentelemetry/api", "@opentelemetry/api-logs", "@opentelemetry/resources", "@opentelemetry/sdk-logs", "@opentelemetry/sdk-metrics", "@opentelemetry/sdk-trace-base", "@opentelemetry/sdk-trace-node", "@opentelemetry/sdk-trace-web"] }, "sha512-ITfK8xhcl+9GXOvPwzADWkOQ+dgUGZrJNefT3r2+uLFmzjyKRLtHzhLOl6lZaLSsf5io13+nmt8adfMRQPq+oA=="],
 
-    "@effect/platform-node": ["@effect/platform-node@4.0.0-beta.83", "", { "dependencies": { "@effect/platform-node-shared": "^4.0.0-beta.83", "mime": "^4.1.0", "undici": "^8.2.0" }, "peerDependencies": { "effect": "^4.0.0-beta.83", "ioredis": "^5.7.0" } }, "sha512-RmpVGu/+X/Bif3/g1Rzj8oFzTOknoVB3yHCa0b179vytPpKe+Kj9ZwKNcAnKWqHUDkbSPBq1Ca60mvOHr2/+LQ=="],
+    "@effect/platform-node": ["@effect/platform-node@4.0.0-beta.98", "", { "dependencies": { "@effect/platform-node-shared": "^4.0.0-beta.98", "mime": "^4.1.0", "undici": "^8.7.0" }, "peerDependencies": { "effect": "^4.0.0-beta.98", "ioredis": "^5.7.0" } }, "sha512-IQu1TiLXQEDSGkDBllyYjVadf+UqdjptryqX4mmktVTTbGDq7X4uVxe7cSgXuqZvyfG6kagTzwj2lfynxOaKQg=="],
 
-    "@effect/platform-node-shared": ["@effect/platform-node-shared@4.0.0-beta.83", "", { "dependencies": { "@types/ws": "^8.18.1", "ws": "^8.20.0" }, "peerDependencies": { "effect": "^4.0.0-beta.83" } }, "sha512-+yr/+PJmKTgmJq1QOINSBPgLu7Cjc4CZcotBXnGjyDEizOmimFgTkN2B8PBJAKIKUWYWfobjXqC+58/VhhPKAw=="],
+    "@effect/platform-node-shared": ["@effect/platform-node-shared@4.0.0-beta.98", "", { "dependencies": { "@types/ws": "^8.18.1", "ws": "^8.21.0" }, "peerDependencies": { "effect": "^4.0.0-beta.98" } }, "sha512-iySXaffnCJX1sNAIp79ghhIeui9E5qwUQyqd1VLPkB9UNO4vdpd9B5fTEXwe7S/GusL4jsk9vSvX38XJgRFG1w=="],
 
-    "@effect/sql-sqlite-bun": ["@effect/sql-sqlite-bun@4.0.0-beta.83", "", { "peerDependencies": { "effect": "^4.0.0-beta.83" } }, "sha512-6OaxLsWffxkh9pXYUSyj/AxjVb9URY2rG9U6atjxClWy30Jx77R9Pm3Rrc7cQ63kQurePavEw1bQbzQ/SILiQQ=="],
+    "@effect/sql-sqlite-bun": ["@effect/sql-sqlite-bun@4.0.0-beta.98", "", { "peerDependencies": { "effect": "^4.0.0-beta.98" } }, "sha512-cc41uLhYBqexdbTNu4dlui+31E8hcVLEapLySa0C8d60FmBY8IEAV/RD3oF+6pqPslKEZ9p1+XVLdDm0iflw5Q=="],
 
     "@electron/asar": ["@electron/asar@3.4.1", "", { "dependencies": { "commander": "^5.0.0", "glob": "^7.1.6", "minimatch": "^3.0.4" }, "bin": { "asar": "bin/asar.js" } }, "sha512-i4/rNPRS84t0vSRa2HorerGRXWyF4vThfHesw0dmcWHp+cspK743UanA0suA5Q5y8kzY2y6YKrvbIUn69BCAiA=="],
 
@@ -1775,6 +1796,12 @@
 
     "@fontsource/inter": ["@fontsource/inter@5.2.8", "", {}, "sha512-P6r5WnJoKiNVV+zvW2xM13gNdFhAEpQ9dQJHt3naLvfg+LkF2ldgSLiF4T41lf1SQCM9QmkqPTn4TH568IRagg=="],
 
+    "@fontsource/noto-sans-math": ["@fontsource/noto-sans-math@5.2.5", "", {}, "sha512-1bxEvVlF51Vfgpju32mRZzI/CHvsfqjXjI2+sAuEyHYvXABUAIyj+93sCO3QZIoMG5drWyrzgoCqRQRaL6wQ8Q=="],
+
+    "@fontsource/noto-sans-symbols": ["@fontsource/noto-sans-symbols@5.2.5", "", {}, "sha512-mxoIRstsmZpZFzd/SRWiD+l6T7TGhpgCrGs7TEnnuGSQIfjVMrQT9Zej2enh9pkfmPNAFyeaGJkHkszJ1hH++w=="],
+
+    "@fontsource/noto-sans-symbols-2": ["@fontsource/noto-sans-symbols-2@5.2.5", "", {}, "sha512-F4O9WLifwoZS1quNzY1ebjMNo2cQPe/UP68Dmud0ONi2lOxaR6xp6fFPO2gG17MI7DwAnfMyQFl64A2tAd28hg=="],
+
     "@fuma-translate/react": ["@fuma-translate/react@1.0.2", "", { "peerDependencies": { "@types/react": "*", "react": "^19.2.0", "react-dom": "^19.2.0" }, "optionalPeers": ["@types/react"] }, "sha512-uOiOtBx3nRXR8Nu1GzBf1tApgF1FErDBTHxRIAQeyQdyOoZbrNRN6H4kDCWObY4qyGeGbHydG0DHzgeUgFDMIw=="],
 
     "@fumadocs/tailwind": ["@fumadocs/tailwind@0.1.0", "", { "peerDependencies": { "tailwindcss": "^4.0.0" }, "optionalPeers": ["tailwindcss"] }, "sha512-nF/DCAwOR21HZ4AkjIOv3Iqwyqywzb6pdyeMcoa+aZzirXj5ntvNZbe3jJ0v3ehhtrRfYYeXBezvjn8ZmV+fuQ=="],
@@ -2273,27 +2300,27 @@
 
     "@opentelemetry/semantic-conventions": ["@opentelemetry/semantic-conventions@1.41.1", "", {}, "sha512-/UhIkaZgPutTFmQ7RnIJGgDXZmtEJ7Dvi86xNTFWcnRxVRNk/aotsqDJYeEvDP+FSMB2SdW+pQzNMcWP0rwuNA=="],
 
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+    "@opentui/solid": ["@opentui/solid@0.4.5", "", { "dependencies": { "@babel/core": "7.28.0", "@babel/preset-typescript": "7.27.1", "@opentui/core": "0.4.5", "babel-plugin-module-resolver": "5.0.2", "babel-preset-solid": "1.9.12", "entities": "7.0.1", "s-js": "^0.4.9" }, "peerDependencies": { "solid-js": "1.9.12" } }, "sha512-B0RSkXnrtPVfEJOX+Hj+axjLJ3lzbG1BZw5I7Pvb9OPp48Vzg2cW2a3cSa86/q48ndLt647i/XwFPIw/jqnI5g=="],
 
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@@ -4017,7 +4044,7 @@
 
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+    "effect": ["effect@4.0.0-beta.98", "", { "dependencies": { "@standard-schema/spec": "^1.1.0", "fast-check": "^4.9.0", "find-my-way-ts": "^0.1.6", "ini": "^7.0.0", "kubernetes-types": "^1.30.0", "msgpackr": "^2.0.4", "multipasta": "^0.2.8", "toml": "^4.1.2", "uuid": "^14.0.1", "yaml": "^2.9.0" } }, "sha512-oz+bsG5h+6RNrw4t5GMfQrk/xBS8ROoqkYsuvRhBr5O7mCOrpvH/hbw+QrDzvKIpX4HJClwm86F94c87W0sJxg=="],
 
     "ejs": ["ejs@3.1.10", "", { "dependencies": { "jake": "^10.8.5" }, "bin": { "ejs": "bin/cli.js" } }, "sha512-UeJmFfOrAQS8OJWPZ4qtgHyWExa088/MtK5UEyoJGFH67cDEXkZSviOiKRCZ4Xij0zxI3JECgYs3oKx+AizQBA=="],
 
@@ -4193,7 +4220,7 @@
 
     "extsprintf": ["extsprintf@1.4.1", "", {}, "sha512-Wrk35e8ydCKDj/ArClo1VrPVmN8zph5V4AtHwIuHhvMXsKf73UT3BOD+azBIW+3wOJ4FhEH7zyaJCFvChjYvMA=="],
 
-    "fast-check": ["fast-check@4.8.0", "", { "dependencies": { "pure-rand": "^8.0.0" } }, "sha512-GOJ158CUMnN6cSahsv4+ExARvIDuzzinFjkp0E9WtiBa5zcVeLozVkWaE4IzFcc+Y48Wp1EDlUZsXRyAztQcSg=="],
+    "fast-check": ["fast-check@4.9.0", "", { "dependencies": { "pure-rand": "^8.0.0" } }, "sha512-7ms6T7SybUev/PQITciI0yLM2pOSFy5zpG8Ty7tQofcVaQUvrMXp6CBwqF6fThLCLOrfBtuHAtwq6Yu4XPCllg=="],
 
     "fast-decode-uri-component": ["fast-decode-uri-component@1.0.1", "", {}, "sha512-WKgKWg5eUxvRZGwW8FvfbaH7AXSh2cL+3j5fMGzUMCxWBJ3dV3a7Wz8y2f/uQ0e3B6WmodD3oS54jTQ9HVTIIg=="],
 
@@ -5057,7 +5084,7 @@
 
     "ms": ["ms@2.1.3", "", {}, "sha512-6FlzubTLZG3J2a/NVCAleEhjzq5oxgHyaCU9yYXvcLsvoVaHJq/s5xXI6/XXP6tz7R9xAOtHnSO/tXtF3WRTlA=="],
 
-    "msgpackr": ["msgpackr@2.0.2", "", { "optionalDependencies": { "msgpackr-extract": "^3.0.4" } }, "sha512-c5hYOXFbP79Slh6Dzd2wzk+jnV7mX1UxfMYtilnY1NmalXPqG8DGb5cYCMBrW4AsH3zekBBZd4QrKz9NhtvYLQ=="],
+    "msgpackr": ["msgpackr@2.0.4", "", { "optionalDependencies": { "msgpackr-extract": "^3.0.4" } }, "sha512-o1C5KRmuRt+apqMr1HuGSqWStZoRBUpEsCsl15uM9VdAF1qHLtvMOU2En747EnTyEl6c4pzPewRMFF31s1CNbA=="],
 
     "msgpackr-extract": ["msgpackr-extract@3.0.4", "", { "dependencies": { "node-gyp-build-optional-packages": "5.2.2" }, "optionalDependencies": { "@msgpackr-extract/msgpackr-extract-darwin-arm64": "3.0.4", "@msgpackr-extract/msgpackr-extract-darwin-x64": "3.0.4", "@msgpackr-extract/msgpackr-extract-linux-arm": "3.0.4", "@msgpackr-extract/msgpackr-extract-linux-arm64": "3.0.4", "@msgpackr-extract/msgpackr-extract-linux-x64": "3.0.4", "@msgpackr-extract/msgpackr-extract-win32-x64": "3.0.4" }, "bin": { "download-msgpackr-prebuilds": "bin/download-prebuilds.js" } }, "sha512-4kmO/MdyUIkLIvTPr8VHLil4AtoKIoniWPIEk5+CDy0xnWC84azhSFmuJ7PxZdsYtiP5kEeQsORAVIeMgxT+Hw=="],
 
@@ -5065,7 +5092,7 @@
 
     "multicast-dns": ["multicast-dns@7.2.5", "", { "dependencies": { "dns-packet": "^5.2.2", "thunky": "^1.0.2" }, "bin": { "multicast-dns": "cli.js" } }, "sha512-2eznPJP8z2BFLX50tf0LuODrpINqP1RVIm/CObbTcBRITQgmC/TjcREF1NeTBzIcR5XO/ukWo+YHOjBbFwIupg=="],
 
-    "multipasta": ["multipasta@0.2.7", "", {}, "sha512-KPA58d68KgGil15oDqXjkUBEBYc00XvbPj5/X+dyzeo/lWm9Nc25pQRlf1D+gv4OpK7NM0J1odrbu9JNNGvynA=="],
+    "multipasta": ["multipasta@0.2.8", "", {}, "sha512-ZPWuMKyv0cSO29f7hozp+k6+crZbQijV8ipMvxNxRf2SwtYGTX1ZX89Kd20VV4H9Znonx+EQn+iy1wGQsJ+b+Q=="],
 
     "mustache": ["mustache@4.2.0", "", { "bin": { "mustache": "bin/mustache" } }, "sha512-71ippSywq5Yb7/tVYyGbkBggbU8H3u5Rz56fH60jGFgr8uHwxs+aSKeqmluIVzM0m0kB7xQjKS6qPfd0b2ZoqQ=="],
 
@@ -5595,6 +5622,8 @@
 
     "resolve-pkg-maps": ["resolve-pkg-maps@1.0.0", "", {}, "sha512-seS2Tj26TBVOC2NIc2rOe2y2ZO7efxITtLZcGSOnHHNOQ7CkiUBfw0Iw2ck6xkIhPwLhKNLS8BO+hEpngQlqzw=="],
 
+    "resolve.exports": ["resolve.exports@2.0.3", "", {}, "sha512-OcXjMsGdhL4XnbShKpAcSqPMzQoYkYyhbEaeSko47MjRP9NfEQMhZkXL1DoFlt9LWQn4YttrdnV6X2OiyzBi+A=="],
+
     "responselike": ["responselike@2.0.1", "", { "dependencies": { "lowercase-keys": "^2.0.0" } }, "sha512-4gl03wn3hj1HP3yzgdI7d3lCkF95F21Pz4BPGvKHinyQzALR5CapwC8yIi0Rh58DEMQ/SguC03wFj2k0M/mHhw=="],
 
     "restore-cursor": ["restore-cursor@4.0.0", "", { "dependencies": { "onetime": "^5.1.0", "signal-exit": "^3.0.2" } }, "sha512-I9fPXU9geO9bHOt9pHHOhOkYerIMsmVaWB0rA2AI9ERh/+x/i7MV5HKBNrg+ljO5eoPVgCcnFuRjJ9uH6I/3eg=="],
@@ -5963,7 +5992,7 @@
 
     "toidentifier": ["toidentifier@1.0.1", "", {}, "sha512-o5sSPKEkg/DIQNmH43V0/uerLrpzVedkUh8tGNvaeXpfpuwjKenlSox/2O/BTlZUtEe+JG7s5YhEz608PlAHRA=="],
 
-    "toml": ["toml@4.1.1", "", {}, "sha512-EBJnVBr3dTXdA89WVFoAIPUqkBjxPMwRqsfuo1r240tKFHXv3zgca4+NJib/h6TyvGF7vOawz0jGuryJCdNHrw=="],
+    "toml": ["toml@4.3.0", "", {}, "sha512-lVb8X9BsPVuH0M4BKeS91tXAmJvCjQ5UIyAbQFaxkKGyUFK2RPkhwaFSQH8vbpl1d23eu/IBH+dwVMHWaq9A5A=="],
 
     "toolbeam-docs-theme": ["toolbeam-docs-theme@0.4.8", "", { "peerDependencies": { "@astrojs/starlight": "^0.34.3", "astro": "^5.7.13" } }, "sha512-b+5ynEFp4Woe5a22hzNQm42lD23t13ZMihVxHbzjA50zdcM9aOSJTIjdJ0PDSd4/50HbBXcpHiQsz6rM4N88ww=="],
 
@@ -6049,7 +6078,7 @@
 
     "uncrypto": ["uncrypto@0.1.3", "", {}, "sha512-Ql87qFHB3s/De2ClA9e0gsnS6zXG27SkTiSJwjCc9MebbfapQfuPzumMIUMi38ezPZVNFcHI9sUIepeQfw8J8Q=="],
 
-    "undici": ["undici@8.3.0", "", {}, "sha512-TkUDgb6tl7KOGZ+7e8E3d2FYgUQgF6z5YypqjWmixVQSQERFcVrVg0ySADm2LVLRh5ljAaHTCR5Fmz3Q34rB7Q=="],
+    "undici": ["undici@8.7.0", "", {}, "sha512-N7iQtfyLhIMOFgQubvmLV26svHpO0bqKnAiWotTQCVKCmWrcGbBotPuW1x+xwYZ2VHdSTVUfPQQnlEt1/LouTQ=="],
 
     "undici-types": ["undici-types@7.16.0", "", {}, "sha512-Zz+aZWSj8LE6zoxD+xrjh4VfkIG8Ya6LvYkZqtUQGJPZjYl53ypCaUwWqo7eI0x66KBGeRo+mlBEkMSeSZ38Nw=="],
 
@@ -6131,7 +6160,7 @@
 
     "utils-merge": ["utils-merge@1.0.1", "", {}, "sha512-pMZTvIkT1d+TFGvDOqodOclx0QWkkgi6Tdoa8gC8ffGAAqz9pzPTZWAybbsHHoED/ztMtkv/VoYTYyShUn81hA=="],
 
-    "uuid": ["uuid@14.0.0", "", { "bin": { "uuid": "dist-node/bin/uuid" } }, "sha512-Qo+uWgilfSmAhXCMav1uYFynlQO7fMFiMVZsQqZRMIXp0O7rR7qjkj+cPvBHLgBqi960QCoo/PH2/6ZtVqKvrg=="],
+    "uuid": ["uuid@14.0.1", "", { "bin": { "uuid": "dist-node/bin/uuid" } }, "sha512-6ZxzVpzDXDa3bJWaHilVayA+BH/1zmxCJoVgvmqJnid/gPoKHxUrS/aC/T6LGQtNHT+XHG9fXPJB4d+IrU30Ew=="],
 
     "valibot": ["valibot@1.4.1", "", { "peerDependencies": { "typescript": ">=5" }, "optionalPeers": ["typescript"] }, "sha512-klCmFTz2jeDluy9RwX+F884TCiogtdBJ/YaxSx1EOBYXa3NXNWj8kR1jjN8rzluwojJVWWaHJ4r1U5LfICnM3g=="],
 
@@ -8341,6 +8370,26 @@
 
     "@solidjs/start/shiki/@shikijs/types": ["@shikijs/types@1.29.2", "", { "dependencies": { "@shikijs/vscode-textmate": "^10.0.1", "@types/hast": "^3.0.4" } }, "sha512-VJjK0eIijTZf0QSTODEXCqinjBn0joAHQ+aPSBzrv4O2d/QSbsMw+ZeSRx03kV34Hy7NzUvV/7NqfYGRLrASmw=="],
 
+    "@standard-community/standard-json/effect/fast-check": ["fast-check@4.8.0", "", { "dependencies": { "pure-rand": "^8.0.0" } }, "sha512-GOJ158CUMnN6cSahsv4+ExARvIDuzzinFjkp0E9WtiBa5zcVeLozVkWaE4IzFcc+Y48Wp1EDlUZsXRyAztQcSg=="],
+
+    "@standard-community/standard-json/effect/msgpackr": ["msgpackr@2.0.2", "", { "optionalDependencies": { "msgpackr-extract": "^3.0.4" } }, "sha512-c5hYOXFbP79Slh6Dzd2wzk+jnV7mX1UxfMYtilnY1NmalXPqG8DGb5cYCMBrW4AsH3zekBBZd4QrKz9NhtvYLQ=="],
+
+    "@standard-community/standard-json/effect/multipasta": ["multipasta@0.2.7", "", {}, "sha512-KPA58d68KgGil15oDqXjkUBEBYc00XvbPj5/X+dyzeo/lWm9Nc25pQRlf1D+gv4OpK7NM0J1odrbu9JNNGvynA=="],
+
+    "@standard-community/standard-json/effect/toml": ["toml@4.1.1", "", {}, "sha512-EBJnVBr3dTXdA89WVFoAIPUqkBjxPMwRqsfuo1r240tKFHXv3zgca4+NJib/h6TyvGF7vOawz0jGuryJCdNHrw=="],
+
+    "@standard-community/standard-json/effect/uuid": ["uuid@14.0.0", "", { "bin": { "uuid": "dist-node/bin/uuid" } }, "sha512-Qo+uWgilfSmAhXCMav1uYFynlQO7fMFiMVZsQqZRMIXp0O7rR7qjkj+cPvBHLgBqi960QCoo/PH2/6ZtVqKvrg=="],
+
+    "@standard-community/standard-openapi/effect/fast-check": ["fast-check@4.8.0", "", { "dependencies": { "pure-rand": "^8.0.0" } }, "sha512-GOJ158CUMnN6cSahsv4+ExARvIDuzzinFjkp0E9WtiBa5zcVeLozVkWaE4IzFcc+Y48Wp1EDlUZsXRyAztQcSg=="],
+
+    "@standard-community/standard-openapi/effect/msgpackr": ["msgpackr@2.0.2", "", { "optionalDependencies": { "msgpackr-extract": "^3.0.4" } }, "sha512-c5hYOXFbP79Slh6Dzd2wzk+jnV7mX1UxfMYtilnY1NmalXPqG8DGb5cYCMBrW4AsH3zekBBZd4QrKz9NhtvYLQ=="],
+
+    "@standard-community/standard-openapi/effect/multipasta": ["multipasta@0.2.7", "", {}, "sha512-KPA58d68KgGil15oDqXjkUBEBYc00XvbPj5/X+dyzeo/lWm9Nc25pQRlf1D+gv4OpK7NM0J1odrbu9JNNGvynA=="],
+
+    "@standard-community/standard-openapi/effect/toml": ["toml@4.1.1", "", {}, "sha512-EBJnVBr3dTXdA89WVFoAIPUqkBjxPMwRqsfuo1r240tKFHXv3zgca4+NJib/h6TyvGF7vOawz0jGuryJCdNHrw=="],
+
+    "@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=="],

package.json

@@ -37,18 +37,18 @@
       "packages/slack"
     ],
     "catalog": {
-      "@effect/opentelemetry": "4.0.0-beta.83",
+      "@effect/opentelemetry": "4.0.0-beta.98",
-      "@effect/platform-node": "4.0.0-beta.83",
+      "@effect/platform-node": "4.0.0-beta.98",
-      "@effect/sql-sqlite-bun": "4.0.0-beta.83",
+      "@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/core": "0.4.5",
-      "@opentui/keymap": "0.4.3",
+      "@opentui/keymap": "0.4.5",
-      "@opentui/solid": "0.4.3",
+      "@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"
   }
 }

packages/ai/README.md

@@ -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
 

packages/ai/STATUS.md

@@ -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.                                                                                                                                                                                   |

packages/ai/package.json

@@ -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": [

packages/ai/script/setup-recording-env.ts

@@ -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",

packages/ai/src/image-client.ts

@@ -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

packages/ai/src/image.ts

@@ -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

packages/ai/src/index.ts

@@ -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"

packages/ai/src/protocols/anthropic-messages.ts

@@ -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,
+        }),
+      ],
     ]
   }
 

packages/ai/src/protocols/bedrock-converse.ts

@@ -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(

packages/ai/src/protocols/index.ts

@@ -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"

packages/ai/src/protocols/openai-chat.ts

@@ -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_content: reasoningContent,
-      reasoning.length > 0
+    reasoning: reasoning.length > 0 && field === "reasoning" ? text : undefined,
-        ? reasoning.map((part) => part.text).join("")
+    reasoning_text: reasoning.length > 0 && field === "reasoning_text" ? text : undefined,
-        : openAICompatibleReasoningContent(message.native?.openaiCompatible),
+    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)
+    const reasoning = reasoningDelta(delta)
-      lifecycle = Lifecycle.reasoningDelta(lifecycle, events, "reasoning-0", delta.reasoning_content)
+    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,
   },

packages/ai/src/protocols/openai-images.ts

@@ -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

packages/ai/src/protocols/openai-responses.ts

@@ -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 => ({
+const nativeImageToolInput = (tool: ToolDefinition) => {
-  type: "function",
+  const native = tool.native?.openai
-  name: tool.name,
+  return ProviderShared.isRecord(native) && native.type === "image_generation" ? native : undefined
-  description: tool.description,
+}
-  parameters: ToolSchemaProjection.openAI(inputSchema),
+
-  // TODO: Read this from OpenAI-specific tool options so direct LLM callers can opt into strict schemas.
+const nativeImageTool = (tool: ToolDefinition) => {
-  strict: false,
+  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
   }
 

packages/ai/src/protocols/utils/lifecycle.ts

@@ -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
 }
 

packages/ai/src/protocols/utils/openai-image.ts

@@ -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

packages/ai/src/protocols/utils/tool-stream.ts

@@ -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) =>
+const toolCall = (route: string, tool: PendingTool, inputOverride?: string) => {
-  parseToolInput(route, tool.name, inputOverride ?? tool.input).pipe(
+  const raw = inputOverride ?? tool.input
-    Effect.map(
+  return parseToolInput(route, tool.name, raw).pipe(
-      (input): ToolCall =>
+    Effect.map((input): ToolCall | ToolInputError =>
-        LLMEvent.toolCall({
+      LLMEvent.toolCall({
-          id: tool.id,
+        id: tool.id,
-          name: tool.name,
+        name: tool.name,
-          input,
+        input,
-          providerExecuted: tool.providerExecuted ? true : undefined,
+        providerExecuted: tool.providerExecuted ? true : undefined,
-          providerMetadata: tool.providerMetadata,
+        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 id = current?.id ?? delta.id
-  const name = delta.name ?? current?.name
+  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
+ * from state, and return either a call or a non-executable local input error.
- * a no-op because some providers emit stop events for non-tool content blocks.
+ * 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: [
+      events: finishEvents(tool, yield* toolCall(route, tool)),
-        LLMEvent.toolInputEnd({ id: tool.id, name: tool.name, providerMetadata: tool.providerMetadata }),
-        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: [
+      events: finishEvents(tool, yield* toolCall(route, tool, input)),
-        LLMEvent.toolInputEnd({ id: tool.id, name: tool.name, providerMetadata: tool.providerMetadata }),
-        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
+ * not emit per-tool stop events, so all accumulated calls finish independently
- * receives a terminal `finish_reason`.
+ * 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(
+        toolCall(route, tool).pipe(Effect.map((event) => finishEvents(tool, event))),
-          Effect.map((call) => [
-            LLMEvent.toolInputEnd({ id: tool.id, name: tool.name, providerMetadata: tool.providerMetadata }),
-            call,
-          ]),
-        ),
       ).pipe(Effect.map((events) => events.flat())),
     }
   })

packages/ai/src/protocols/xai-images.ts

@@ -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

packages/ai/src/provider-error.ts

@@ -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

packages/ai/src/providers/openai.ts

@@ -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

packages/ai/src/providers/openrouter.ts

@@ -41,13 +41,31 @@ export const protocol = Protocol.make({
     schema: OpenRouterBody,
     from: (request) =>
       OpenAIChat.protocol.body.from(request).pipe(
-        Effect.map(
+        Effect.map((body) => {
-          (body) =>
+          const sourceAssistants = request.messages.filter((message) => message.role === "assistant")
-            ({
+          let assistantIndex = 0
-              ...body,
+          const messages = body.messages.map((message) => {
-              ...bodyOptions(request.providerOptions?.openrouter),
+            if (message.role !== "assistant") return message
-            }) as OpenRouterBody,
+            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,

packages/ai/src/providers/xai.ts

@@ -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

packages/ai/src/route/auth.ts

@@ -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

packages/ai/src/schema/events.ts

@@ -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":

packages/ai/test/auth-options.types.ts

@@ -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(
+GoogleVertexMessages.configure({ auth: Auth.bearer("vertex-token"), project: "project" }).model("claude-sonnet-4-6")
-  "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",
 })
 

packages/ai/test/fixtures/recordings/anthropic-compatible-messages/minimax-m3-anthropic-compatible-text.json

@@ -0,0 +1,40 @@
+{
+  "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"
+      }
+    }
+  ]
+}

packages/ai/test/fixtures/recordings/anthropic-compatible-messages/minimax-m3-anthropic-compatible-tool-call.json

@@ -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"
+      }
+    }
+  ]
+}

packages/ai/test/fixtures/recordings/anthropic-compatible-messages/minimax-m3-anthropic-compatible-tool-loop.json

@@ -0,0 +1,60 @@
+{
+  "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"
+      }
+    }
+  ]
+}

packages/ai/test/fixtures/recordings/openrouter-reasoning.json

@@ -0,0 +1,34 @@
+{
+  "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"
+      }
+    }
+  ]
+}

packages/ai/test/image.test.ts

@@ -0,0 +1,127 @@
+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" },
+                }),
+              )
+            }),
+          ),
+        ),
+      ),
+    ),
+  )
+})

packages/ai/test/image.types.ts

@@ -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 } })

packages/ai/test/provider-error.test.ts

@@ -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", () => {
+  test("classifies provider token limit messages as context overflow", () => {
-    expect(isContextOverflow("tokens in request more than max tokens allowed")).toBe(true)
+    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", () => {

packages/ai/test/provider/anthropic-messages.test.ts

@@ -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(

packages/ai/test/provider/bedrock-converse.test.ts

@@ -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(

packages/ai/test/provider/cloudflare.test.ts

@@ -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(

packages/ai/test/provider/golden.recorded.test.ts

@@ -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",

packages/ai/test/provider/openai-chat-reasoning.recorded.test.ts

@@ -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,
+    )
+  })
+}

packages/ai/test/provider/openai-chat.test.ts

@@ -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(
+      const fields = ["reasoning_content", "reasoning", "reasoning_text"] as const
-        { choices: [{ delta: { reasoning_content: "thinking" } }] },
+      for (const field of fields) {
-        { choices: [{ delta: { content: "Hello" } }] },
+        const response = yield* LLMClient.generate(request).pipe(
-        { choices: [{ delta: {}, finish_reason: "stop" }] },
+          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.message.content.find((part) => part.type === "reasoning")?.providerMetadata).toEqual({
-      expect(response.events).toMatchObject([
+        openai: { reasoningDetails: details },
-        { type: "step-start", index: 0 },
+      })
-        { type: "reasoning-start", id: "reasoning-0" },
+
-        { type: "reasoning-delta", id: "reasoning-0", text: "thinking" },
+      const replay = yield* LLMClient.prepare<OpenAIChat.OpenAIChatBody>(
-        { type: "reasoning-end", id: "reasoning-0" },
+        LLM.request({ model, messages: [response.message] }),
-        { type: "text-start", id: "text-0" },
+      )
-        { type: "text-delta", id: "text-0", text: "Hello" },
+      expect(replay.body.messages).toEqual([{ role: "assistant", content: "Hello", reasoning_details: details }])
-        { type: "text-end", id: "text-0" },
+    }),
-        { type: "step-finish", index: 0, reason: "stop" },
+  )
-        { type: "finish", reason: "stop" },
+
+  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(

packages/ai/test/provider/openai-images.recorded.test.ts

@@ -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)
+    }),
+  )
+})

packages/ai/test/provider/openai-responses-images.recorded.test.ts

@@ -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")
+    }),
+  )
+})

packages/ai/test/provider/openai-responses.test.ts

@@ -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 = {

packages/ai/test/provider/openrouter.test.ts

@@ -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 }])
+    }),
+  )
 })

packages/ai/test/provider/xai-images.recorded.test.ts

@@ -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)
+    }),
+  )
+})

packages/ai/test/provider/xai-images.test.ts

@@ -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" },
+                })
+              }),
+            ),
+          ),
+        ),
+      ),
+    ),
+  )
+})

packages/ai/test/recorded-scenarios.ts

@@ -120,29 +120,8 @@ export const runWeatherToolLoop = (request: LLMRequest) =>
     throw new Error("Weather tool loop exceeded 10 steps")
   })
 
-const assistantContent = (events: ReadonlyArray<LLMEvent>) => {
+const assistantContent = (events: ReadonlyArray<LLMEvent>) =>
-  const content: ContentPart[] = []
+  events.reduce(LLMResponse.reduce, LLMResponse.empty()).message.content
-  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
-}
 
 export const expectFinish = (
   events: ReadonlyArray<LLMEvent>,

packages/ai/test/recorded-test.ts

@@ -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)),
+      )
     },
   })

packages/ai/test/response.test.ts

@@ -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([])
+  })
 })

packages/ai/test/tool-stream.test.ts

@@ -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, {

packages/ai/tsconfig.types.json

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

packages/cli/bin/opencode2.cjs

@@ -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 base = `@opencode-ai/cli${nodeBuild ? "-node" : ""}-` + platform + "-" + arch
-const binary = platform === "windows" ? "opencode2.exe" : "opencode2"
+const binary = platform === "windows" ? `${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",
   )

packages/cli/package.json

@@ -12,20 +12,12 @@
   ],
   "exports": {
     "./daemon": "./src/daemon.ts",
-    "./mini": "./src/mini/index.ts",
+    "./run": "./src/run/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",
     "./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:"
   }
 }

packages/cli/script/build-node.ts

@@ -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"}`)
+}

packages/cli/script/build.ts

@@ -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}`,

packages/cli/script/generate.ts

@@ -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")

packages/cli/script/node-assets.ts

@@ -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)]))
+}

packages/cli/script/postinstall.mjs

@@ -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)
+}

packages/cli/script/publish.ts

@@ -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> = {}
+async function publishDistribution(input: { root: string; name: string; binary: string; packagePrefix: string }) {
-for (const filepath of new Bun.Glob("*/package.json").scanSync({ cwd: "./dist" })) {
+  const binaries: Record<string, string> = {}
-  const item = await Bun.file(`./dist/${filepath}`).json()
+  for (const filepath of new Bun.Glob("*/package.json").scanSync({ cwd: input.root })) {
-  binaries[item.name] = item.version
+    const item = await Bun.file(`${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 publishDistribution({
-await $`cp ./bin/opencode2.cjs ./dist/${name}/bin/opencode2`
+  root: "./dist",
-await Bun.file(`./dist/${name}/package.json`).write(
+  name: pkg.name,
-  JSON.stringify(
+  binary: "opencode2",
-    {
+  packagePrefix: "@opencode-ai/cli-",
-      name,
+})
-      bin: { opencode2: "./bin/opencode2" },
+await publishDistribution({
-      version,
+  root: "./dist/node",
-      license: pkg.license,
+  name: "opencode-node",
-      repository: { type: "git", url: "git+https://github.com/anomalyco/opencode.git" },
+  binary: "opencode2-node",
-      os: ["darwin", "linux", "win32"],
+  packagePrefix: "@opencode-ai/cli-node-",
-      cpu: ["arm64", "x64"],
+})
-      optionalDependencies: binaries,
-    },
-    null,
-    2,
-  ),
-)
-
-await Promise.all(
-  Object.entries(binaries).map(([name, version]) =>
-    publish(`./dist/${name.replace("@opencode-ai/", "")}`, name, version),
-  ),
-)
-await publish(`./dist/${name}`, name, version)

packages/cli/script/service-smoke.ts

@@ -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 nodeBuild = process.argv.includes("--node")
-const directory = path.join(import.meta.dir, "..", "dist", target, "bin")
+const target = `cli${nodeBuild ? "-node" : ""}-${process.platform === "win32" ? "windows" : process.platform}-${process.arch}`
-const binary = path.join(directory, `opencode2${process.platform === "win32" ? ".exe" : ""}`)
+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-"))

packages/cli/src/commands/commands.ts

@@ -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",

packages/cli/src/commands/handlers/default.ts

@@ -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)))
   }),
 )

packages/cli/src/commands/handlers/mcp/add.ts

@@ -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 readFile(configPath, "utf8").catch((error) => {
-  const text = (await file.exists()) ? await file.text() : "{}"
+    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))
 }

packages/cli/src/commands/handlers/mini.ts

@@ -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,
       }),
     )
   }),

packages/cli/src/commands/handlers/plugin/list.ts

@@ -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)
+  }),
+)

packages/cli/src/commands/handlers/run.ts

@@ -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),

packages/cli/src/commands/handlers/serve.ts

@@ -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",

packages/cli/src/index.ts

@@ -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,

packages/cli/src/mini-host.ts

@@ -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),
+  }
+}

packages/cli/src/mini.ts

@@ -0,0 +1,277 @@
+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)
+}

packages/cli/src/mini/catalog.shared.ts

@@ -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])
-}

packages/cli/src/mini/footer.question.tsx

@@ -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>
-  )
-}

packages/cli/src/mini/index.ts

@@ -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"

packages/cli/src/mini/mini.ts

@@ -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)
-}

packages/cli/src/mini/permission.shared.ts

@@ -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",
-  }
-}

packages/cli/src/mini/prompt.editor.ts

@@ -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,
-      },
-    },
-  }
-}

packages/cli/src/mini/question.shared.ts

@@ -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`
-}

packages/cli/src/mini/runtime.boot.ts

@@ -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")
-}

packages/cli/src/mini/runtime.lifecycle.ts

@@ -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
-  }
-}

packages/cli/src/mini/runtime.shared.ts

@@ -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
-}

packages/cli/src/mini/runtime.stdin.ts

@@ -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 })
-  }
-}

packages/cli/src/mini/session-data.ts

@@ -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 ""
-}

packages/cli/src/mini/stream-v2.subagent.ts

@@ -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: [] }
-    },
-  }
-}

packages/cli/src/mini/trace.ts

@@ -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
-}

packages/cli/src/mini/variant.shared.ts

@@ -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)
-}

packages/cli/src/node/index.ts

@@ -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")

packages/cli/src/node/plugin-runtime.effect.ts

@@ -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,
+}

packages/cli/src/node/plugin-runtime.promise.ts

@@ -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,
+}