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15 Commits

Author SHA1 Message Date
Kit Langton a211114c8b Merge remote-tracking branch 'origin/dev' into httpapi-codegen 2026-06-23 17:22:24 -04:00
Kit Langton 845c0d47f7 refactor(sdk): extract shared schema and embedded host 2026-06-23 17:22:11 -04:00
Kit Langton bbf1021d4a refactor(client): extract model and protocol 2026-06-23 16:12:56 -04:00
Kit Langton 602c470d37 refactor(client): stabilize datatype names 2026-06-23 15:30:44 -04:00
Kit Langton 9ad36251a7 refactor(client): use canonical session schemas 2026-06-23 15:26:53 -04:00
Kit Langton 7e8f35e29e refactor(client): simplify code generation 2026-06-23 14:18:49 -04:00
Kit Langton 22ee85671b refactor(client): simplify embedded setup 2026-06-23 13:18:36 -04:00
Kit Langton 51e5e4186f feat(client): add embedded OpenCode layer 2026-06-23 13:03:20 -04:00
Kit Langton 9c20973501 refactor(client): generate from hosted session group 2026-06-23 12:53:58 -04:00
Kit Langton 07a65d5a16 fix(client): preserve V2 session contracts 2026-06-23 12:33:28 -04:00
Kit Langton 905cd60017 Merge remote-tracking branch 'origin/dev' into httpapi-codegen
# ------------------------ >8 ------------------------
# Do not modify or remove the line above.
# Everything below it will be ignored.
#
# Conflicts:
#	packages/server/src/groups/session.ts
2026-06-23 12:01:37 -04:00
Kit Langton e03f5dd331 feat(client): add embedded V2 host 2026-06-23 11:56:06 -04:00
Kit Langton 924e00776b feat(client): generate public session API 2026-06-23 09:00:51 -04:00
Kit Langton 66bcddd059 feat(sdk): add Promise client codegen 2026-06-22 22:10:48 -04:00
Kit Langton b241e38cc8 feat(sdk): add Effect HttpApi codegen 2026-06-22 16:48:04 -04:00
2859 changed files with 193790 additions and 492456 deletions
-7
View File
@@ -1,7 +0,0 @@
---
"@opencode-ai/client": patch
"@opencode-ai/protocol": patch
"@opencode-ai/cli": patch
---
Expose background-service lifecycle status, preserve one process-held owner through startup and failure, reconnect TUIs without activating replacement, and stop exact service instances gracefully.
-5
View File
@@ -1,5 +0,0 @@
---
"@opencode-ai/cli": patch
---
Expose a TUI plugin slot at the top of the session view.
-7
View File
@@ -1,7 +0,0 @@
---
"@opencode-ai/client": patch
"@opencode-ai/plugin": patch
"@opencode-ai/protocol": patch
---
Expose transient, read-only session generation through the HTTP API, generated clients, and V2 plugin session context.
-5
View File
@@ -1,5 +0,0 @@
---
"@opencode-ai/cli": patch
---
Expose a TUI plugin slot above the session composer.
-1
View File
@@ -1,3 +1,2 @@
packages/core/migration/**/snapshot.json linguist-generated
packages/core/src/database/migration.gen.ts linguist-generated
packages/core/src/**/*.txt text eol=lf
+1 -5
View File
@@ -1,5 +1,4 @@
adamdotdevin
arvsrn
Brendonovich
fwang
Hona
@@ -8,14 +7,11 @@ jayair
jlongster
kitlangton
kommander
ludvigrask
MrMushrooooom
nexxeln
R44VC0RP
rekram1-node
thdxr
simonklee
Slickstef11
usrnk1
vimtor
StarpTech
starptech
-7
View File
@@ -8,13 +8,6 @@ inputs:
runs:
using: "composite"
steps:
# node-gyp@latest (invoked via bunx for native install scripts) requires Node >=22;
# some runner images ship an older system Node on PATH
- name: Setup Node
uses: actions/setup-node@49933ea5288caeca8642d1e84afbd3f7d6820020 # v4.4.0
with:
node-version: "24"
- name: Get baseline download URL
id: bun-url
shell: bash
-38
View File
@@ -34,48 +34,10 @@ jobs:
const now = Date.now();
const twoHours = 2 * 60 * 60 * 1000;
const orgMemberAssociations = new Set(['OWNER', 'MEMBER']);
const agentLogin = 'opencode-agent[bot]';
const { data: file } = await github.rest.repos.getContent({
owner: context.repo.owner,
repo: context.repo.repo,
path: '.github/TEAM_MEMBERS',
ref: 'dev',
});
const teamMembers = new Set(
Buffer.from(file.content, 'base64')
.toString()
.split('\n')
.map((line) => line.trim().toLowerCase())
.filter(Boolean)
);
function isExempt(item) {
const login = item.user?.login?.toLowerCase();
return (
login === agentLogin ||
orgMemberAssociations.has(item.author_association) ||
(login && teamMembers.has(login))
);
}
for (const item of items) {
const isPR = !!item.pull_request;
const kind = isPR ? 'PR' : 'issue';
const login = item.user?.login;
if (isExempt(item)) {
core.info(`Skipping ${kind} #${item.number}; author ${login || 'unknown'} is exempt`);
try {
await github.rest.issues.removeLabel({
owner: context.repo.owner,
repo: context.repo.repo,
issue_number: item.number,
name: 'needs:compliance',
});
} catch (e) {}
continue;
}
const { data: comments } = await github.rest.issues.listComments({
owner: context.repo.owner,
+1 -45
View File
@@ -17,31 +17,12 @@ jobs:
with:
fetch-depth: 1
- name: Check exempt issue author
id: author
run: |
LOGIN="${{ github.event.issue.user.login }}"
ASSOCIATION="${{ github.event.issue.author_association }}"
if [ "$LOGIN" = "opencode-agent[bot]" ] ||
[ "$ASSOCIATION" = "OWNER" ] ||
[ "$ASSOCIATION" = "MEMBER" ] ||
grep -qxiF "$LOGIN" .github/TEAM_MEMBERS; then
echo "skip=true" >> "$GITHUB_OUTPUT"
echo "Skipping issue automation for exempt author: $LOGIN ($ASSOCIATION)"
else
echo "skip=false" >> "$GITHUB_OUTPUT"
fi
- uses: ./.github/actions/setup-bun
if: steps.author.outputs.skip != 'true'
- name: Install opencode
if: steps.author.outputs.skip != 'true'
run: curl -fsSL https://opencode.ai/install | bash
- name: Check duplicates and compliance
if: steps.author.outputs.skip != 'true'
env:
OPENCODE_API_KEY: ${{ secrets.OPENCODE_API_KEY }}
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
@@ -57,7 +38,6 @@ jobs:
opencode run -m opencode/claude-sonnet-4-6 "A new issue has been created:
Issue number: ${{ github.event.issue.number }}
Issue author association: ${{ github.event.issue.author_association }}
Lookup this issue with gh issue view ${{ github.event.issue.number }}.
@@ -69,8 +49,6 @@ jobs:
Check whether the issue follows our contributing guidelines and issue templates.
If the issue author association is OWNER or MEMBER, skip this compliance check. Do not add the needs:compliance label for organization-owned issues.
This project has three issue templates that every issue MUST use one of:
1. Bug Report - requires a Description field with real content
@@ -105,7 +83,7 @@ jobs:
Based on your findings, post a SINGLE comment on issue #${{ github.event.issue.number }}. Build the comment as follows:
If the issue is NOT compliant and the author association is not OWNER or MEMBER, start the comment with:
If the issue is NOT compliant, start the comment with:
<!-- issue-compliance -->
Then explain what needs to be fixed and that they have 2 hours to edit the issue before it is automatically closed. Also add the label needs:compliance to the issue using: gh issue edit ${{ github.event.issue.number }} --add-label needs:compliance
@@ -151,31 +129,12 @@ jobs:
with:
fetch-depth: 1
- name: Check exempt issue author
id: author
run: |
LOGIN="${{ github.event.issue.user.login }}"
ASSOCIATION="${{ github.event.issue.author_association }}"
if [ "$LOGIN" = "opencode-agent[bot]" ] ||
[ "$ASSOCIATION" = "OWNER" ] ||
[ "$ASSOCIATION" = "MEMBER" ] ||
grep -qxiF "$LOGIN" .github/TEAM_MEMBERS; then
echo "skip=true" >> "$GITHUB_OUTPUT"
echo "Skipping issue automation for exempt author: $LOGIN ($ASSOCIATION)"
else
echo "skip=false" >> "$GITHUB_OUTPUT"
fi
- uses: ./.github/actions/setup-bun
if: steps.author.outputs.skip != 'true'
- name: Install opencode
if: steps.author.outputs.skip != 'true'
run: curl -fsSL https://opencode.ai/install | bash
- name: Recheck compliance
if: steps.author.outputs.skip != 'true'
env:
OPENCODE_API_KEY: ${{ secrets.OPENCODE_API_KEY }}
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
@@ -189,12 +148,9 @@ jobs:
}
run: |
opencode run -m opencode/claude-sonnet-4-6 "Issue #${{ github.event.issue.number }} was previously flagged as non-compliant and has been edited.
Issue author association: ${{ github.event.issue.author_association }}
Lookup this issue with gh issue view ${{ github.event.issue.number }}.
If the issue author association is OWNER or MEMBER, remove the needs:compliance label if present, delete the previous compliance comment if present, and do not post a new comment.
Re-check whether the issue now follows our contributing guidelines and issue templates.
This project has three issue templates that every issue MUST use one of:
+2 -64
View File
@@ -6,7 +6,6 @@ on:
branches:
- ci
- dev
- v2
- beta
- fix/npm-native-binary-install
- snapshot-*
@@ -32,9 +31,6 @@ permissions:
contents: write
packages: write
env:
OPENCODE_CHANNEL: ${{ (github.ref_name == 'v2' && 'next') || '' }}
jobs:
version:
runs-on: blacksmith-4vcpu-ubuntu-2404
@@ -121,61 +117,12 @@ 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
- version
runs-on: blacksmith-4vcpu-windows-2025
if: github.repository == 'anomalyco/opencode' && github.ref_name != 'v2'
if: github.repository == 'anomalyco/opencode'
env:
AZURE_CLIENT_ID: ${{ secrets.AZURE_CLIENT_ID }}
AZURE_TENANT_ID: ${{ secrets.AZURE_TENANT_ID }}
@@ -274,7 +221,7 @@ jobs:
needs:
- build-cli
- version
if: github.repository == 'anomalyco/opencode' && github.ref_name != 'v2'
if: github.repository == 'anomalyco/opencode'
continue-on-error: false
env:
AZURE_CLIENT_ID: ${{ secrets.AZURE_CLIENT_ID }}
@@ -378,7 +325,6 @@ jobs:
run: bun run build
working-directory: packages/desktop
env:
NODE_OPTIONS: --max-old-space-size=4096
OPENCODE_CHANNEL: ${{ (github.ref_name == 'beta' && 'beta') || 'prod' }}
SENTRY_AUTH_TOKEN: ${{ secrets.SENTRY_AUTH_TOKEN }}
SENTRY_ORG: ${{ vars.SENTRY_ORG }}
@@ -462,7 +408,6 @@ jobs:
needs:
- version
- build-cli
- build-node-cli
- sign-cli-windows
- build-electron
if: always() && !failure() && !cancelled()
@@ -501,7 +446,6 @@ jobs:
path: packages/opencode/dist
- uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4.3.0
if: github.ref_name != 'v2'
with:
name: opencode-cli-signed-windows
path: packages/opencode/dist
@@ -511,12 +455,6 @@ jobs:
name: opencode-preview-cli
path: packages/cli/dist
- uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4.3.0
with:
pattern: opencode-node-cli-*
path: packages/cli/dist/node
merge-multiple: true
- uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4.3.0
if: needs.version.outputs.release
with:
-2
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@@ -9,7 +9,6 @@ on:
- "bun.lock"
- "packages/storybook/**"
- "packages/ui/**"
- "packages/session-ui/**"
pull_request:
branches: [dev]
paths:
@@ -18,7 +17,6 @@ on:
- "bun.lock"
- "packages/storybook/**"
- "packages/ui/**"
- "packages/session-ui/**"
workflow_dispatch:
concurrency:
+5 -24
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@@ -4,7 +4,6 @@ on:
push:
branches:
- dev
- v2
pull_request:
workflow_dispatch:
@@ -70,36 +69,18 @@ jobs:
env:
OPENCODE_EXPERIMENTAL_DISABLE_FILEWATCHER: ${{ runner.os == 'Windows' && 'true' || 'false' }}
- name: Verify compiled service lifecycle
if: always()
timeout-minutes: 10
working-directory: packages/cli
run: |
bun run script/build.ts --single --skip-install
bun run script/service-smoke.ts
- name: Setup Node build runtime
if: always()
uses: actions/setup-node@49933ea5288caeca8642d1e84afbd3f7d6820020 # v4.4.0
with:
node-version: "26.4.0"
- name: Verify Node build
if: always()
timeout-minutes: 15
working-directory: packages/cli
run: |
bun run script/build-node.ts --single --skip-install --outdir=dist/node
bun run script/service-smoke.ts --node
- name: Check generated client
if: runner.os == 'Linux'
working-directory: packages/client
run: bun run check:generated
- name: Run HttpApi exerciser gates
if: runner.os == 'Linux'
working-directory: packages/opencode
run: bun run test:httpapi
e2e:
name: e2e (${{ matrix.settings.name }})
if: github.ref_name != 'v2' && github.head_ref != 'v2'
strategy:
fail-fast: false
matrix:
-19
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@@ -16,32 +16,13 @@ jobs:
with:
fetch-depth: 1
- name: Check exempt issue author
id: author
run: |
LOGIN="${{ github.event.issue.user.login }}"
ASSOCIATION="${{ github.event.issue.author_association }}"
if [ "$LOGIN" = "opencode-agent[bot]" ] ||
[ "$ASSOCIATION" = "OWNER" ] ||
[ "$ASSOCIATION" = "MEMBER" ] ||
grep -qxiF "$LOGIN" .github/TEAM_MEMBERS; then
echo "skip=true" >> "$GITHUB_OUTPUT"
echo "Skipping issue automation for exempt author: $LOGIN ($ASSOCIATION)"
else
echo "skip=false" >> "$GITHUB_OUTPUT"
fi
- name: Setup Bun
if: steps.author.outputs.skip != 'true'
uses: ./.github/actions/setup-bun
- name: Install opencode
if: steps.author.outputs.skip != 'true'
run: curl -fsSL https://opencode.ai/install | bash
- name: Triage issue
if: steps.author.outputs.skip != 'true'
env:
OPENCODE_API_KEY: ${{ secrets.OPENCODE_API_KEY }}
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+2 -2
View File
@@ -2,9 +2,9 @@ name: typecheck
on:
push:
branches: [dev, v2]
branches: [dev]
pull_request:
branches: [dev, v2]
branches: [dev]
workflow_dispatch:
jobs:
-3
View File
@@ -11,10 +11,8 @@ node_modules
playground
tmp
dist
dist-node
ts-dist
.turbo
.typecheck-profiles
**/.serena
.serena/
**/.omo
@@ -26,7 +24,6 @@ Session.vim
a.out
target
.scripts
.cache
.direnv/
# Local dev files
-254
View File
@@ -1,254 +0,0 @@
---
name: opencode-drive
description: Use when an agent needs drive OpenCode via a script or interact with an isolated instance
---
# OpenCode Drive
Use `opencode-drive` to launch an isolated OpenCode instance and control it via commands or a script.
There are two modes. Always default to using a script unless specifically directed to be interactive (connect
to an existing running instance, or start a new one, and make a few changes to the UI and read it, and iterate
on changes).
Scripts allow you to run a full walkthrough in one run. When the script is done opencode-drive exits,
stops all processes, and cleans up all artifacts.
# Prepare The Environment
Use `init` when files must be added to the isolated home or project before OpenCode starts. It prints the artifact directory without launching OpenCode. A later `start` with the same name reuses it.
```bash
artifacts=$(opencode-drive init --name demo)
cp -R ./fixtures/home/. "$artifacts/"
cp -R ./fixtures/project/. "$artifacts/files/"
opencode-drive start --name demo --dev ~/projects/opencode
```
The simulated project is under `$artifacts/files`. Running `start` without a prior `init` initializes the artifacts automatically.
# Scripted usage
You can write scripts that walk through entire flows, and gives you full access to controlling
the backend too. See examples of the script API at the bottom of this file.
After creating or editing a script, always typecheck it before running. Never skip this step:
```bash
opencode-drive check ./reproduce-stale-exploring-empty.ts
```
Run it by passing `--script` to start:
```bash
opencode-drive start --name auto-stop-reproduction --script ./reproduce-stale-exploring-empty.ts
```
It will output information about the run, including paths to log files which you can read
to inspect what happened. If you need to dig into failures that aren't clear, read those log
files. If the script is unsuccessful, automatically fix the script and run it again.
Scripts use one typed definition object. `setup` runs before OpenCode starts,
and `fs.writeFile` always writes inside the simulated project.
You can read the full typed API here: https://raw.githubusercontent.com/jlongster/opencode-drive/refs/heads/main/src/script/types.ts
```ts
import { defineScript } from "opencode-drive"
export default defineScript({
async setup({ fs, config }) {
config.autoupdate = false
await fs.writeFile("src/example.ts", "export const value = 1\n")
},
async run({ ui, llm }) {
await ui.submit("Open src/example.ts")
await llm.send(llm.text("The file exports `value`."))
await ui.waitFor("The file exports `value`.")
},
})
```
`setup` receives the current OpenCode config object, which starts from the
default drive config unless the prepared instance already has one. When a script
needs custom config, mutate this `config` parameter instead of generating and
writing a new config object from scratch, so the script keeps the default
provider/model settings unless it intentionally changes them.
Note that the simulated model is a GPT model type, and opencode uses the `patch` tool for working with files Do not use a `edit` or `write` tool to edit files.
Use `launch: "manual"` when the script needs to launch the server and every TUI
itself (this is extremely rare, do not use this unless explicitly asked). In this
mode `ui` is typed as `null`; call `server.launch()` exactly
once before launching clients. Each `clients.launch(name)` result provides the
same UI methods as the automatic client. You can see an example of this API
here: https://raw.githubusercontent.com/jlongster/opencode-drive/refs/heads/main/examples/multiple-clients.ts
Use the exported `wait(milliseconds)` utility for an unconditional delay.
`await llm.send(...)` waits for the next request and resolves after OpenCode
acknowledges its complete response. `llm.queue(...)` declares responses in
advance. Chunks may be built with `text`, `reasoning`, `toolCall`, `raw`,
`finish`, and `disconnect`. A normal response receives `finish("stop")`
automatically unless it yields or queues an explicit terminal event.
`llm.text(text, { delay, chunkSize })` defaults to a 2 ms delay and a
15-character target varied by plus or minus 5 per chunk.
`llm.reasoning` accepts the same options, and `llm.pause(milliseconds)` adds a
delay between any two outputs.
Use `llm.serve` for an ongoing typed response generator:
```ts
llm.serve(async function* (request, index) {
yield llm.reasoning(`Handling request ${index + 1}`)
yield llm.text(`Received ${request.id}`)
yield llm.finish("stop")
})
```
The backend connection, response cleanup, cancellation, and recording
completion are automatic.
You can see some example scripts here:
- https://raw.githubusercontent.com/jlongster/opencode-drive/refs/heads/main/examples/simple.ts
- https://raw.githubusercontent.com/jlongster/opencode-drive/refs/heads/main/examples/serve.ts
## Prune
- `prune` removes artifact directories. These are always cleaned up after running a script
successfully, but leftover on failed runs. Always call this if a script fails.
```bash
opencode-drive prune --name demo
// --force cleans up all artifcat directories
opencode-dirve prune --force
```
# Live interaction usage
- Always give headless instances a unique `--name`. Visible instances may omit it.
- A normal headless `start` detaches automatically and returns after the instance is ready.
- Do not add `&`; the long-running owner already runs in the background.
- Configure simulated model responses after startup when needed.
- Send ordered UI commands with `send`.
- Always stop the instance when finished.
```bash
opencode-drive start --name demo
opencode-drive send --name demo \
--command.ui.type '{"text":"Explain this project"}' \
--command.ui.enter
opencode-drive stop --name demo
```
## Send UI Commands
- Every `send` opens a connection to the named instance, runs its commands in order, and exits.
- Combine typing and Enter in one command when submitting a prompt.
- JSON-valued commands require one JSON argument.
- Multiple command flags execute from left to right.
Commands:
- `--command.ui.type <json>` types into the focused editor. Arguments: `text` string.
- `--command.ui.press <json>` presses a key. Arguments: `key` string; optional `modifiers` object with boolean `ctrl`, `shift`, `meta`, `super`, or `hyper`.
- `--command.ui.enter` presses Enter. Arguments: none.
- `--command.ui.arrow <json>` presses an arrow key. Arguments: `direction` is `up`, `down`, `left`, or `right`.
- `--command.ui.focus <json>` focuses an element. Arguments: `target` is the numeric element `num` returned by `ui.state`.
- `--command.ui.click <json>` clicks an element. Arguments: numeric `target`, `x`, and `y`; use the element `num` returned by `ui.state` as `target`.
- `--command.ui.state` prints focus and interactive element metadata as JSON. Arguments: none.
- `--command.ui.matches <json>` prints whether literal, case-sensitive text appears on screen. Arguments: `text` string.
```bash
opencode-drive send --name demo \
--command.ui.type '{"text":"Find the relevant code and explain it"}' \
--command.ui.enter
opencode-drive send --name demo \
--command.ui.press '{"key":"p","modifiers":{"ctrl":true}}'
opencode-drive send --name demo \
--command.ui.arrow '{"direction":"down"}'
opencode-drive send --name demo \
--command.ui.focus '{"target":12}'
opencode-drive send --name demo \
--command.ui.click '{"target":12,"x":4,"y":1}'
opencode-drive send --name demo \
--command.ui.matches '{"text":"OpenCode"}'
```
To read the UI state and see information about interactable elements, use the `ui.state` command:
```bash
opencode-drive send --name demo --command.ui.state
```
## Configure LLM Responses
- `responses` controls what the LLM responds with
- Only use this if you are wanting to reproduce an exact type of response
- Defaults are `text,reasoning,diff,tool` with `write,apply_patch`.
- Supported types are `text`, `reasoning`, `diff`, and `tool`.
- `--tools` limits generated tool calls to names offered by OpenCode.
```bash
opencode-drive responses --name demo \
--types text,reasoning,diff,tool \
--tools write,apply_patch
opencode-drive responses --name demo \
--types tool \
--tools read,glob,grep
```
## Inspect The UI
- `ui.state` prints focus and interactive element metadata as JSON.
- `ui.matches` checks for literal, case-sensitive screen text.
- `screenshot` prints the generated image path.
```bash
opencode-drive screenshot --name demo
```
## Lifecycle
- `stop` waits for recording export and owner cleanup before returning.
```bash
opencode-drive stop --name demo
```
# Record The UI
- Start with `--record` to capture a headless instance from its first rendered frame.
- `stop` finishes the recording, exports an MP4, and prints its path.
```bash
opencode-drive start --name demo --record
opencode-drive send --name demo \
--command.ui.type '{"text":"Show me the current architecture"}' \
--command.ui.enter
opencode-drive stop --name demo
```
# Artifacts dir
- `dir` prints the artifact directory for the instance.
```bash
opencode-drive dir --name demo
```
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@@ -1,31 +0,0 @@
---
name: sample-skill
description: Use when the user says sample skill, skill demo, or asks how an opencode SKILL.md should be structured; demonstrates a tiny project-local skill with practical assistant workflow guidance.
---
# Sample Skill
This is a minimal project-local opencode skill. It exists as a reference for how a skill is structured and as a tiny reusable workflow the assistant can load when the user asks for a skill example.
## When To Use
- Use when the user asks for a sample skill or skill template.
- Use when demonstrating the required `SKILL.md` frontmatter and body format.
- Do not use for unrelated coding tasks just because a skill exists.
## Workflow
- Confirm the specific outcome the user wants if the request is ambiguous.
- Inspect the relevant files before changing anything.
- Make the smallest correct change.
- Verify the result with a focused read, typecheck, test, or other lightweight check when available.
- Summarize the changed files and any required restart or reload step.
## Example Response Style
When this skill is relevant, keep responses direct and actionable:
```text
I created a project-local skill at .opencode/skills/sample-skill/SKILL.md.
Restart opencode for the new skill to be discovered by future sessions.
```
+1 -1
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@@ -4,7 +4,7 @@ import { tool } from "@opencode-ai/plugin"
const TEAM = {
tui: ["kommander", "simonklee"],
desktop_web: ["Hona", "Brendonovich"],
core: ["jlongster", "rekram1-node", "nexxeln", "kitlangton"],
core: ["jlongster", "rekram1-node", "nexxeln", "kitlangton", "starptech"],
inference: ["fwang", "MrMushrooooom", "starptech"],
windows: ["Hona"],
} as const
-2
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@@ -1,4 +1,2 @@
sst-env.d.ts
packages/desktop/src/bindings.ts
packages/client/src/generated/
packages/client/src/generated-effect/
+7 -15
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@@ -1,7 +1,4 @@
- To regenerate the legacy JavaScript SDK, run `./packages/sdk/js/script/build.ts`.
- After changing the public Protocol or Server `HttpApi`, run `bun run generate` from `packages/client`. Do not edit `src/generated` or `src/generated-effect` directly.
- Keep runtime dependencies directed from Schema to Core and Protocol, then from Core and Protocol to Server. Client runtime code may depend on Schema and Protocol but never Core or Server; `sdk-next` composes Client, Core, and Server.
- Do not modify `packages/opencode` unless the user explicitly asks for V1 work. `packages/opencode` is the V1 implementation and is present for reference only. New implementation changes should land in the V2 package set: `packages/core`, `packages/cli`, `packages/server`, `packages/protocol`, `packages/schema`, and related generated client surfaces when required.
- To regenerate the JavaScript SDK, run `./packages/sdk/js/script/build.ts`.
- The default branch in this repo is `dev`.
- Local `main` ref may not exist; use `dev` or `origin/dev` for diffs.
@@ -25,14 +22,12 @@ Examples: `fix(tui): simplify thinking toggle styling`, `docs: update contributi
- Keep things in one function unless composable or reusable
- Do not extract single-use helpers preemptively. Inline the logic at the call site unless the helper is reused, hides a genuinely complex boundary, or has a clear independent name that improves the caller.
- Before adding complexity for a speculative or vanishingly unlikely race or security edge case, explain the concrete failure mode, likelihood, and complexity cost to the user and get their buy-in. Do not silently expand scope for theoretical robustness.
- Avoid `try`/`catch` where possible
- Avoid using the `any` type
- Use Bun APIs when possible, like `Bun.file()`
- Rely on type inference when possible; avoid explicit type annotations or interfaces unless necessary for exports or clarity
- Prefer functional array methods (flatMap, filter, map) over for loops; use type guards on filter to maintain type inference downstream
- In `src/config`, follow the existing self-export pattern at the top of the file (for example `export * as ConfigAgent from "./agent"`) when adding a new config module.
- In Effect generators, bind services to named variables before calling methods. Do not use nested service yields such as `yield* (yield* Foo.Service).bar()`.
Reduce total variable count by inlining when a value is only used once.
@@ -152,15 +147,12 @@ const table = sqliteTable("session", {
## V2 Session Core
- Keep durable events minimal: record irreducible new facts and do not repeat state derivable by folding the ordered aggregate history. Enrich projections and read models with previous or derived state when consumers need self-contained views.
- Keep durable prompt admission separate from model execution. `SessionV2.prompt(...)` admits one durable `session_pending` row before scheduling advisory `SessionExecution.wake(sessionID)` unless `resume: false` requests admit-only behavior. The serialized runner promotes admitted inputs into visible user messages at safe boundaries, consuming the pending row in the same event transaction; `session_pending` stores only unconsumed work.
- Reusing a Session ID adopts the existing Session. Reusing a prompt message ID reconciles an exact retry only when Session, prompt, and delivery mode match; conflicting reuse fails. Retry of an already-promoted input reconciles against the projected message and the durable admitted event rather than a retained row.
- Keep `SessionExecution` process-global and Session-ID based. Its local implementation owns the process-local Session coordinator and discovers placement through `SessionStore` plus `LocationServiceMap.get(session.location)` only when a drain starts; no layer should take a Session ID. V2 interruption targets the active process-local ownership chain for that Session; interruption of a known but idle or locally unowned Session is a no-op, while the public API rejects an unknown Session.
- Keep durable prompt admission separate from model execution. `SessionV2.prompt(...)` admits one durable `session_input` row before scheduling advisory `SessionExecution.wake(sessionID)` unless `resume: false` requests admit-only behavior. The serialized runner promotes admitted inputs into visible user messages at safe boundaries.
- Reusing a Session ID adopts the existing Session. Reusing a prompt message ID reconciles an exact retry only when Session, prompt, and delivery mode match; conflicting reuse fails. Historical projected prompts lazily synthesize promoted inbox records during exact retry.
- Keep `SessionExecution` process-global and Session-ID based. Its local implementation owns the process-local Session coordinator and discovers placement through `SessionStore` plus `LocationServiceMap.get(session.location)` only when a drain starts; no layer should take a Session ID. V2 interruption targets the active process-local ownership chain for that Session; idle or missing interruption is a no-op.
- Keep `SessionRunner`, model resolution, tool registry, permissions, and filesystem Location-scoped. Omitted `Location.workspaceID` means implicit-local placement; explicit workspace identity remains reserved for future placement semantics.
- Preserve one explicit `llm.stream(request)` call per Physical Attempt and reload projected history before durable continuation. Most Steps have one Physical Attempt; overflow-triggered compaction recovery may rebuild one Step for a second attempt. Do not bridge through legacy `SessionPrompt.loop(...)` or delegate orchestration to an in-memory tool loop.
- Preserve one explicit `llm.stream(request)` call per provider turn and reload projected history before durable continuation. Do not bridge through legacy `SessionPrompt.loop(...)` or delegate orchestration to an in-memory tool loop.
- Keep local Session drains process-local until clustering is implemented. `SessionRunCoordinator` joins explicit same-Session resumes, coalesces prompt wakeups, and allows different Sessions to run concurrently. Advisory wakes drain eligible durable inbox rows only; post-crash continuation recovery requires a separate explicit design before it may retry provider work. A drain has no durable identity or transcript boundary.
- Keep delivery vocabulary explicit. Prompts steer by default and promote at the next safe step boundary while the current drain requires continuation. An explicit `queue` input remains pending until the Session would otherwise become idle; promote one queued input at that boundary, then reevaluate continuation before promoting another. Promoting any new user input resets the selected agent's step allowance; a batch of steers resets it once.
- One step is one logical LLM call; its durable record covers only the model-visible span. Do not write "provider turn", and do not use bare "turn" for a single call: "turn" is reserved for the future assistant-turn unit containing all steps from prompt promotion until the session would go idle.
- Keep delivery vocabulary explicit. Prompts steer by default and promote at the next safe provider-turn boundary while the current drain requires continuation. An explicit `queue` input remains pending until the Session would otherwise become idle; promote one queued input at that boundary, then reevaluate continuation before promoting another. Promoting any new user input resets the selected agent's provider-turn allowance; a batch of steers resets it once.
- Keep EventV2 replay owner claims separate from clustered Session execution ownership.
- Keep the Instructions algebra and built-ins in `src/instructions`; keep instruction producers with their observed domains, and keep Session History selection plus `InstructionState` and `InstructionEntry` persistence Session-owned. `InstructionDiscovery` observes ambient global and upward-project instructions. The runner composes built-ins, discovery, guidance, and entries explicitly in `loadInstructions`; there is no instruction registry.
- `session.instructions.updated` stores only changed source keys and content hashes. Blob values live once in `instruction_blob`; `instruction_state` is a rebuildable fold cache, never primary state. Render initial instructions and chronological updates from values during request assembly. Completed compaction moves the instruction epoch; Session movement and committed revert clear it. Unavailable sources retain the last value and block only the initial complete delta.
- Keep the System Context algebra, registry, and built-ins in `src/system-context`; keep Context Source producers with their observed domains, and keep Session History selection plus Context Epoch persistence Session-owned.
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@@ -0,0 +1,220 @@
# OpenCode Session Runtime
OpenCode sessions preserve durable conversational history while assembling the runtime context an agent needs to act correctly in its current environment.
## Language
**System Context**:
The structured collection of contextual facts presented to the model as initial instructions and chronological updates.
_Avoid_: System prompt
**Session History**:
The projected chronological conversation selected for a provider turn after applying the active compaction and **Context Epoch** cutoffs.
_Avoid_: Session Context
**Context Source**:
One independently observed typed value within the **System Context**, represented by a stable key, JSON codec, infallible loader, pure baseline/update renderers, and an optional removal renderer for dynamic sources.
_Avoid_: Prompt fragment
**System Context Registry**:
The Location-scoped registry of ordered, scoped producers that contribute to the current **System Context**.
**Mid-Conversation System Message**:
A durable chronological instruction that tells the model the newly effective state of a changed **Context Source**.
_Avoid_: System update, system notification, raw text diff
**Context Epoch**:
The span during which one initially rendered **System Context** remains the immutable provider-cache baseline, ending at completed compaction, Session movement, or an incompatible context transition that requires a fresh baseline.
**Baseline System Context**:
The full **System Context** rendered at the start of a **Context Epoch**.
_Avoid_: Live system prompt
**Context Snapshot**:
The overwriteable model-hidden JSON state used to compare each **Context Source** with the value last admitted to a provider turn.
**Unavailable Context**:
An expected temporary inability to observe a **Context Source** value; the runtime retains its prior effective state and emits no update, or omits it until first successfully loaded.
**Safe Provider-Turn Boundary**:
The point immediately before a provider call, after durable input promotion and any required tool settlement, where context changes may be admitted chronologically.
**Admitted Prompt**:
A durable user input accepted into the Session inbox but not yet included in **Session History**.
**Prompt Promotion**:
The durable transition that removes an **Admitted Prompt** from pending input and appends its user message to **Session History**.
**Provider Turn**:
One request to a model provider and the response projected from that request.
**Session Drain**:
One process-local execution span that promotes eligible input and runs required **Provider Turns** until no immediate continuation remains. A Session Drain has no durable identity or transcript boundary.
**Model Tool Output**:
The bounded projection of a Core-executed tool result persisted in Session history and replayed to the model. A tool may shape this projection semantically, but the Tool Registry enforces the final size limit.
**Managed Tool Output File**:
A temporary file created under OpenCode's shared tool-output directory to retain complete output that was too large for Session history.
**Model Request Options**:
Provider-semantic model settings selected from the Catalog and active Session variant before the LLM protocol adapter encodes them for a provider request.
_Avoid_: Request body, wire options
**Generation Controls**:
Provider-neutral sampling and output controls, partitioned from provider semantics and compatibility wire fields when model metadata enters the Catalog.
**PTY Environment**:
The host-supplied environment overlay applied by the server when creating a PTY, observed for the request Location and resolved PTY working directory.
**OpenCode Client**:
The generated Effect API shared by networked and in-process consumers, executed through an `HttpClient` against the same `HttpApi` router and handlers.
_Avoid_: Remote client
**SDK Contract IR**:
The runtime-neutral compiled representation of the authoritative `HttpApi`, preserving encoded and decoded type projections plus transport metadata so independent SDK emitters can choose their public value model and runtime interpreter.
**Embedded OpenCode**:
A scoped in-process host that structurally extends the **OpenCode Client**, supplies an in-memory HTTP transport, and exposes additional same-process capabilities directly.
_Avoid_: Local implementation
**Page**:
A bounded ordered result containing `items` and opaque `previous` and `next` cursor links for navigating the same query in either direction.
_Avoid_: Response envelope
## Relationships
- A **System Context** is an opaque carrier composed from zero or more **Context Sources**.
- **Session History** contains projected conversational messages and admitted **Mid-Conversation System Messages**; the active **Baseline System Context** remains separate provider-request state.
- The **System Context Registry** uses stable-keyed scoped contributions to assemble the current **System Context**; contributor removal naturally removes its sources at the next **Safe Provider-Turn Boundary**.
- A changed **Context Source** may produce one **Mid-Conversation System Message** containing its newly effective state.
- A **Mid-Conversation System Message** persists the exact combined rendered text sent to the model.
- The current **Context Snapshot** advances atomically with the corresponding durable **Mid-Conversation System Message**.
- A **Context Snapshot** stores one codec-encoded JSON value and, for removable dynamic sources, a pre-rendered removal message per stable **Context Source** key.
- Changes from multiple **Context Sources** admitted at one safe boundary combine into one **Mid-Conversation System Message**.
- Context changes are sampled and admitted lazily at a **Safe Provider-Turn Boundary**, never pushed asynchronously when their source changes.
- At a **Safe Provider-Turn Boundary**, newly promoted user input or settled tool results precede any combined **Mid-Conversation System Message**.
- An **Admitted Prompt** is replayable pending input, not yet model-visible **Session History**.
- **Prompt Promotion** atomically consumes the pending inbox entry and appends its model-visible user message.
- Steering prompts promote at the next **Safe Provider-Turn Boundary** while the current **Session Drain** still requires continuation. Promoting any newly admitted user input resets the selected agent's provider-turn allowance; multiple prompts promoted at one boundary reset it once.
- A queued prompt does not promote while the current **Session Drain** requires continuation. The runner promotes one queued prompt when the Session would otherwise become idle, then reevaluates continuation before promoting another.
- A **Session Drain** is process-local coordination rather than a durable domain entity. Durable recovery must reason from prompts, projected history, provider attempts, and tool state rather than inventing an enclosing execution identity.
- The first provider turn renders the latest complete **Baseline System Context** and initializes its **Context Snapshot** without emitting a redundant **Mid-Conversation System Message**; unavailable initial context blocks the turn instead of persisting an incomplete baseline.
- Initial **System Context** preparation precedes the first durable input promotion so an unavailable baseline leaves that input pending and retryable; ordinary reconciliation remains after promotion.
- Compaction starts a new **Context Epoch** with a freshly rendered **Baseline System Context** and **Context Snapshot**; prior **Mid-Conversation System Messages** remain durable audit history but leave projected model history.
- A newly registered core or plugin-defined **Context Source** absent from the current snapshot emits its baseline rendering once at the next **Safe Provider-Turn Boundary**.
- **Context Source** keys are stable and namespaced; duplicate keys fail composition. `SystemContext.combine(...)` preserves caller order; the **System Context Registry** evaluates producers concurrently and combines them in stable contribution-key order so rendered context remains deterministic.
- Each **Context Source** loader returns one coherent typed value. `SystemContext.make(...)` hides that value type so differently typed sources compose uniformly. Its codec compares and stores that value; its pure renderers produce model-visible baseline, update, and removal text only when needed.
- `SystemContext.initialize(...)` observes a composed **System Context** once and produces a fresh **Baseline System Context** with its **Context Snapshot**.
- `SystemContext.reconcile(...)` observes a composed **System Context** once and returns exactly one next action: unchanged, updated, replacement ready, or replacement blocked.
- `SystemContext.replace(...)` renders a fresh generation after completed compaction or another baseline-replacing transition; it reports replacement blocked while previously admitted context is unavailable.
- **Unavailable Context** uses stale-while-revalidate semantics and is distinct from a successfully loaded absence, which may emit removal text.
- Ordinary **Context Source** loaders return values directly; loaders that intentionally use stale-while-revalidate may explicitly return **Unavailable Context**.
- Nested project instruction discovery after successful reads remains a follow-up; when implemented, discovered instructions must be admitted durably at the next **Safe Provider-Turn Boundary**.
- Location-scoped services naturally re-resolve effective context when a moved session next runs in its destination location.
- Moving a Session clears its active **Context Epoch**, so the destination must initialize a complete baseline before another prompt can promote.
- Instruction discovery, source identity, persistence, and file loading belong to the instruction service; the **System Context** abstraction only composes effectful producers and renders loaded values.
- The first instruction-service slice observes global and upward project `AGENTS.md` files as one ordered aggregate **Context Source** at each **Safe Provider-Turn Boundary**.
- Built-in and instruction context producers register through the **System Context Registry** with stable contribution keys. Plugin-defined context registration and hot-reload lifecycle remain a follow-up built on the same scoped registry seam.
- Selected-agent available-skill guidance is a **Context Source** composed with Location-wide registry sources immediately before Context Epoch admission. It lists only names and descriptions permitted for that agent; skill bodies and locations are exposed only through the permission-checked `skill` tool.
- The selected agent and model are sampled when a provider turn starts. Changes admitted after that boundary apply to the next provider turn and do not restart the current turn.
- Selected-agent available-skill guidance remains a **Context Source**. An agent switch that changes that guidance produces a **Mid-Conversation System Message** while preserving the current baseline.
- Local tool authorization and pending permission requests retain the effective agent of the provider turn that issued the call; a later agent switch cannot change that call's policy.
- Context source changes never wake idle sessions; the next naturally scheduled **Safe Provider-Turn Boundary** loads and compares current values lazily.
- Once admitted, a **Mid-Conversation System Message** remains durable even if the following provider attempt fails and is replayed unchanged on retry.
- **Mid-Conversation System Messages** remain durable Session-message history; normal user-facing transcript surfaces may hide them.
- The date **Context Source** initially preserves host-local calendar-date behavior; a configured user timezone may replace that default later.
- A **Context Epoch** begins with one immutable **Baseline System Context**.
- A **Baseline System Context** is stored durably and reused verbatim across process restarts within its **Context Epoch**.
- A **Baseline System Context** durably preserves the exact joined text used for the active provider-cache prefix.
- Completed compaction starts a new **Context Epoch** on the next provider attempt, folding the current complete **System Context** into a fresh baseline and removing earlier **Mid-Conversation System Messages** from active model history.
- A model/provider switch preserves the current **Context Epoch** and chronological conversation history; the new selection applies to the next provider turn.
- **Model Request Options** remain provider-semantic through Catalog resolution. The Session runner maps them into the LLM package's provider-option namespace; the selected protocol adapter alone owns provider wire encoding.
- **Generation Controls**, protocol-semantic **Model Request Options**, and compatibility request body fields are separate Catalog domains. A shared ingestion adapter partitions legacy and models.dev AI-SDK-shaped options before routing.
- The **PTY Environment** is a server concern rather than a Core PTY concern. PTY creation merges caller values, then the host overlay, then Core-forced terminal invariants such as `TERM` and `OPENCODE_TERMINAL`.
- Networked and **Embedded OpenCode** use the same **OpenCode Client** and preserve the full HTTP encoding, routing, middleware, and decoding boundary; only the `HttpClient` transport differs.
- The Effect-native network constructor obtains `HttpClient.HttpClient` from its environment so callers own transport selection, recording, tracing, retries, and tests. Convenience runtimes may provide a fetch transport separately.
- Creating **Embedded OpenCode** is scoped. Closing its owning Scope releases the in-process server resources, database resources, registrations, and fibers.
- **Embedded OpenCode** exposes shared client capabilities and embedded-only capabilities on one object; consumers do not navigate through a nested `.client` property.
- The beta **OpenCode Client** currently uses plural consumer-facing capability groups such as `sessions`; whether the stable Session namespace should instead be singular `session` must be settled before stabilization. Internal server identifiers do not implicitly define public client names.
- The public `HttpApi` is authoritative for shared **OpenCode Client** capabilities: the server and code generation consume the same hosted `SessionGroup`. Codegen may assign a separate consumer-facing group name without reconstructing group membership or endpoint contracts.
- SDK generation reflects the public `HttpApi` once into an **SDK Contract IR**. Promise and Effect emitters share endpoint structure and transport metadata without being required to expose identical public values: an emitter may select encoded wire types, decoded domain types, compile-time brands, runtime validation, and its own execution abstraction independently.
- The first Effect emitter is the rich projection: it exposes decoded Effect-native values, preserves brands and schema transformations, performs runtime schema decoding, and delegates transport interpretation to `HttpApiClient`. Lighter wire-shaped Effect output remains possible through another emitter policy rather than constraining the shared IR.
- The rich Effect emitter regenerates private executable schemas when the **SDK Contract IR** proves that their transport semantics can be reproduced exactly. Contracts with authoritative custom transformations use the import-based Effect emitter against the canonical Protocol `HttpApi`; the Promise emitter still derives zero-Effect structural wire types from the same IR.
- `@opencode-ai/protocol` owns the authoritative Session `HttpApi`. The server and client generator consume the exact hosted `SessionGroup`, including `compact`, `wait`, and `context`.
- The first Promise emitter targets the same clean domain-oriented method organization rather than Hey API source compatibility. It returns unwrapped values directly, rejects declared and infrastructure failures, and begins with minimal client-level transport configuration; result wrappers, interceptors, and legacy generated signatures are outside the initial surface.
- The first Promise emitter parses response syntax and trusts its generated structural types; it does not perform runtime structural validation. Malformed payload syntax fails, while a syntactically valid shape mismatch is not detected at the SDK boundary. Standalone validator generation remains an optional future emitter policy.
- Declared Promise-client failures retain their tagged structural wire values and have generated type guards. Consumers do not depend on generated `Error` subclass identity, preserving discrimination across package copies and realms while remaining structurally aligned with Effect domain errors.
- Promise-client infrastructure failures use one generated `ClientError` class with a structured reason such as transport failure, unexpected status, unsupported content type, or malformed response. Promise methods reject with either a tagged declared domain failure or `ClientError`, matching the Effect client's conceptual domain/infrastructure error division.
- Promise methods accept a separate optional per-call transport-options argument containing `AbortSignal` and header overrides. Cancellation and transport metadata do not enter the domain input object; broader interceptor and response-mode APIs remain deferred.
- Promise streaming methods return a lazy `AsyncIterable` directly rather than a Promise-wrapped stream object. Iteration opens the connection, `AbortSignal` cancels it, and ending iteration closes the underlying request; the Effect emitter analogously returns `Stream` directly.
- Promise SSE connection establishment, declared HTTP failures, and infrastructure failures occur during `AsyncIterable` iteration, beginning with its first `next()` call, rather than during synchronous method construction.
- Neither generated streaming runtime automatically reconnects after disconnection. Promise `AsyncIterable` and Effect `Stream` fail explicitly; live consumers refresh and resubscribe, while durable sequence-based resume remains explicit composition above the generated client.
- Promise client construction is synchronous and network-free. It requires `baseUrl`, defaults to `globalThis.fetch`, accepts client-level headers, and merges them with per-call header overrides.
- Effect client construction accepts an explicit `baseUrl` and obtains `HttpClient.HttpClient` from the Effect environment. It does not install fetch or duplicate per-call transport policy; callers transform/provide the client for headers, tracing, retries, recording, and tests, while fiber interruption owns cancellation.
- Promise and Effect emitters each own their generated public type modules. The **SDK Contract IR**, not a physically shared generated type package, is the common source; this permits zero-Effect wire types and rich decoded Effect types to evolve independently.
- Promise and Effect network clients ship from `@opencode-ai/client` behind isolated root and `/effect` exports. The root has no runtime path to Effect; `/effect` imports only Effect, Schema, and Protocol.
- The Effect-native scoped host belongs to `@opencode-ai/sdk-next`, which will assume the existing `@opencode-ai/sdk` name after legacy consumers migrate. Client remains network-only and SDK depends one-way on Client.
- SDK executes Server's assembled `HttpRouter` in memory. It opens no listener and performs no network I/O, while preserving Server routing, middleware, codecs, handlers, and errors.
- The Effect Client and SDK re-export their decoded datatype facade from Schema so callers do not depend on internal package locations or Core's versioned names.
- A capability intended for both networked and **Embedded OpenCode** belongs in the authoritative public `HttpApi`; embedded-only same-process capabilities extend **Embedded OpenCode** separately.
- `sessions.events({ sessionID, after })` is a public durable Session event stream. It verifies the Session, replays durable events after the optional aggregate sequence, continues with newly committed durable events, excludes live-only fragments, and is transported as SSE in both networked and embedded modes.
- `events.subscribe()` is a distinct public instance-wide live stream for Session and non-Session activity. It has no replay guarantee and includes connection, heartbeat, and instance-disposal lifecycle events; consumers recover from disconnection by refreshing authoritative state.
- A Session ID is not an optional filter on `events.subscribe()`: instance-wide live events and durable Session events have different schemas, replay guarantees, cursors, lifecycle events, and failure behavior.
- The initial common OpenCode Client does not expose server-global event aggregation. `events.subscribe()` is bounded to the connected OpenCode instance or workspace; any future cross-instance administrative stream requires a separately designed API.
- `events.subscribe()` does not automatically reconnect after transport loss. The live-only stream fails with `ClientError`; consumers refresh authoritative state before explicitly opening a new subscription because events missed during disconnection cannot be replayed.
- `sessions.events({ sessionID, after })` returns the generated HTTP client's cold durable event stream and does not build reconnection policy into the endpoint or client constructor. Transport loss fails the stream with `ClientError`. Callers may compose an explicit resuming stream above it by retaining the last observed durable sequence and opening a new subscription with `after`; any reusable resume helper remains a separate API design question.
- The stable `sessions.list(...)` design returns a **Page** in both networked and **Embedded OpenCode**; embedded execution does not define a separate unbounded array-returning list operation. The beta client currently preserves the existing HTTP `{ data, cursor }` envelope until emitter-level Page projection is implemented.
- Session list cursors are opaque branded values carrying continuation query and ordering state. Consumers pass them back unchanged and do not inspect storage anchors or encoded filter fields.
- A Session list continuation accepts only its opaque cursor. Scope, filters, ordering, and page size are fixed by the initial query and carried by that cursor.
- `sessions.messages(...)` returns a **Page** and uses the same cursor discipline as `sessions.list(...)`: the initial request supplies `sessionID`, ordering, and page size; continuation supplies `sessionID` plus only an opaque branded message cursor carrying ordering, page size, direction, and message anchor. Using a cursor with another Session is invalid.
- `sessions.message({ sessionID, messageID })` is a required resource lookup. An unknown Session fails with `SessionNotFoundError`; a known Session with an absent or differently owned message fails with `SessionMessageNotFoundError` without disclosing cross-Session ownership. Absence is not represented as `undefined` across the public HTTP boundary.
- `sessions.interrupt({ sessionID })` first verifies that the durable Session exists, failing with `SessionNotFoundError` otherwise. For a known Session, interruption is idempotent: idle, already-settled, or locally unowned execution is a no-op.
- `sessions.context({ sessionID })` preserves the existing message-only operation. It returns projected conversational messages selected as Session context; it does not include or represent the complete provider request context, whose baseline system context and other contributions remain separate.
- **Open question**: Should a future, separately named operation expose the complete provider request context, including baseline system context, selected source contributions, and context-epoch metadata?
- `sessions.prompt(...)` exposes `resume?: boolean`. Omitting it preserves durable admission followed by an advisory execution wake; `resume: false` requests durable admit-only behavior.
- The public operation remains `sessions.prompt(...)`; `SessionInput.admit` is the internal primitive, while the public `Admission` result and `resume` option express its durable admission semantics.
- `sessions.create(...)` accepts an optional `location`. Omission resolves through the connected OpenCode instance's default or current location; an explicit value selects a known location. Networked and embedded transports use the same handler semantics.
- `sessions.switchAgent({ sessionID, agent })` is part of the common client alongside `sessions.switchModel(...)`. It affects subsequent Session activity and fails with `SessionNotFoundError` for an unknown Session.
- The **Embedded OpenCode** Layer delegates to the same scoped creation path; it does not define a second implementation.
- A **PTY Environment** adapter observes plugins in the request Location while passing the resolved PTY working directory to the hook; standalone servers use an empty adapter.
- A **Mid-Conversation System Message** lowers to the provider's native chronological instruction role when supported and to a wrapped chronological fallback otherwise.
- When the effective aggregate instruction set changes, its **Mid-Conversation System Message** includes the complete current ordered set and supersedes the prior aggregate value; when no ambient instructions remain, the message states that previously loaded instructions no longer apply.
- Ambient project instruction discovery honors `OPENCODE_DISABLE_PROJECT_CONFIG`; global instructions remain eligible.
- Oversized textual **Model Tool Output** retains a bounded preview in Session history while its complete text moves to managed tool-output storage. Arbitrary structured-result size is a separate concern.
- One tool settlement receives one aggregate textual limit, using the configured maximum lines or UTF-8 bytes, whichever is reached first. The limit is provider-independent; token pressure belongs to context assembly and compaction.
- Generic truncation preserves the beginning and end of textual output. Tools may apply a more meaningful strategy before the Tool Registry enforces the final limit.
- A truncated **Model Tool Output** identifies its complete text both in the bounded model-visible preview and as a typed managed output path. Managed output paths do not modify the tool's validated structured result.
- A **Managed Tool Output File** is temporary and may expire after its retention period. The bounded **Model Tool Output**, not the file, is the durable replayable record.
- Failure to retain a **Managed Tool Output File** does not change a successful tool operation into a failed one. The Session records an explicitly lossy bounded output without a path, while operators receive diagnostics for the storage failure.
- Once a tool operation succeeds, bounding its **Model Tool Output** and publishing its one durable settlement form an interruption-safe completion region. Raw oversized success is never published before a later correction.
- When a structured-only result would exceed the **Model Tool Output** limit, its validated structured value remains unchanged for Session consumers while model replay uses a bounded textual JSON preview and optional managed output path.
- Existing tool-managed output paths survive generic bounding. A fallback file retains exactly the complete projected text received by the Tool Registry and never claims to reconstruct output already discarded by tool-specific shaping.
- **Managed Tool Output Files** use globally unique names in one shared flat directory. Their absolute paths are readable and searchable by ordinary tools; other absolute paths remain outside Location-scoped filesystem authority.
- Provider-executed tool results remain provider-native transcript facts outside generic Tool Registry bounding. Their context control requires provider-aware pruning or compaction because some providers require exact structured round-trip payloads.
## Client contract architecture
Semantic values that mean the same thing internally and publicly live in the lightweight Schema leaf. Core consumes Schema for domain behavior; Protocol composes Schema values into paths, payloads, envelopes, errors, cursors, and streams; Server imports both, hosts Protocol's exact groups, and owns protocol/domain adaptation. The root Promise client remains zero-Effect, `/effect` depends on Effect plus Schema and Protocol, and `@opencode-ai/sdk-next` composes the scoped in-process host above Client, Core, and Server.
Shared public records are plain objects declared with `Schema.Struct`. A same-name inferred interface gives object records readable TypeScript signatures without constructors, prototypes, or nominal identity; unions retain explicit type aliases.
Before stabilizing the client API:
- Keep additional public schemas in Schema and additional network groups in Protocol; neither package may transitively load databases, Drizzle, Session execution, providers, watchers, native modules, or WASM.
- Replace the transitional server-internal Session location middleware tag when the remaining location-scoped groups move to Protocol or gain a narrow request-location service contract.
- Project the existing list response envelope to the stable client **Page** shape and enforce separate initial-query and cursor-continuation inputs without changing the hosted V2 wire contract.
- Settle the stable consumer namespace (`session` versus the current beta `sessions`) and use an explicit codegen annotation if the consumer name should differ from the server group identifier.
- Preserve V2 route paths, operation IDs, codecs, errors, middleware behavior, and OpenAPI output while making this change.
- Preserve browser-safe `@opencode-ai/client` and `@opencode-ai/client/effect` bundles through import-boundary tests.
- Define embedded-host placement before supporting multiple hosts over one database. Hosts that share durable Session storage must also share process-local Session execution coordination, or each host must receive isolated storage explicitly.
- Keep an embedded request scope alive until any streamed response body finishes. The initial non-streaming Session surface does not exercise this lifetime boundary; Session and instance event streams must do so before joining the embedded client.
## Example dialogue
> **Dev:** "The date changed while the session was active. Should the **Mid-Conversation System Message** say what the old date was?"
> **Domain expert:** "No. Emit the newly effective date so the agent can act on the current **System Context**."
## Flagged ambiguities
- Legacy `experimental.chat.system.transform` can mutate the assembled baseline system prompt arbitrarily, but V2 plugins do not yet expose an equivalent hook. Decide separately whether to port it, replace dynamic uses with plugin-defined **Context Sources**, or narrow its semantics.
+1 -1
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@@ -125,4 +125,4 @@ OpenCode 内置两种 Agent,可用 `Tab` 键快速切换:
---
**加入我们的社区** [飞书](https://applink.feishu.cn/client/chat/chatter/add_by_link?link_token=52ao9352-5623-4fa0-b7dd-3407c392c1af&qr_code=true) | [X.com](https://x.com/opencode)
**加入我们的社区** [飞书](https://applink.feishu.cn/client/chat/chatter/add_by_link?link_token=738j8655-cd59-4633-a30a-1124e0096789&qr_code=true) | [X.com](https://x.com/opencode)
+1 -1
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@@ -125,4 +125,4 @@ OpenCode 內建了兩種 Agent,您可以使用 `Tab` 鍵快速切換。
---
**加入我們的社群** [飞书](https://applink.feishu.cn/client/chat/chatter/add_by_link?link_token=52ao9352-5623-4fa0-b7dd-3407c392c1af&qr_code=true) | [X.com](https://x.com/opencode)
**加入我們的社群** [飞书](https://applink.feishu.cn/client/chat/chatter/add_by_link?link_token=738j8655-cd59-4633-a30a-1124e0096789&qr_code=true) | [X.com](https://x.com/opencode)
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node_modules
.remotion
out/frame-*.png
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@@ -1,483 +0,0 @@
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"configVersion": 1,
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"react": "^19.2.3",
"react-dom": "^19.2.3",
"remotion": "^4.0.384",
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"@types/react": "^19.2.8",
"@types/react-dom": "^19.2.3",
"typescript": "^5.8.2",
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}
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{
"name": "glm52-rise-video",
"private": true,
"type": "module",
"scripts": {
"render": "remotion render src/index.tsx GLM52Rise out/glm-52-broke-out.mp4 --codec h264 --pixel-format yuv420p",
"render:sheep": "remotion render src/index.tsx NZSheep out/nz-sheep.mp4 --codec h264 --pixel-format yuv420p",
"render:novel": "remotion render src/index.tsx NovelTokens out/novel-1984.mp4 --codec h264 --pixel-format yuv420p",
"render:flash": "remotion render src/index.tsx FlashShare out/flash-share.mp4 --codec h264 --pixel-format yuv420p",
"render:minimax": "remotion render src/index.tsx MiniMaxClimb out/minimax-climb.mp4 --codec h264 --pixel-format yuv420p",
"still:june": "remotion still src/index.tsx JuneTotals out/june-totals.png --frame=0"
},
"dependencies": {
"@remotion/cli": "^4.0.384",
"react": "^19.2.3",
"react-dom": "^19.2.3",
"remotion": "^4.0.384"
},
"devDependencies": {
"@types/react": "^19.2.8",
"@types/react-dom": "^19.2.3",
"typescript": "^5.8.2"
}
}
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@@ -1,156 +0,0 @@
// Verified against the production opencode-stats PlanetScale DB.
// Scope: tier='Go' (OpenCode Go), dataset='zen', client='all', source='all', grain='day'.
// metric = total_tokens. Daily token volume per model; GLM-5.2 (zhipu) launched Jun 17.
// Segments per day (tokens): glm-5.2, deepseek-v4-flash, deepseek-v4-pro, minimax-m3, all others.
export type Day = {
date: string
total: number
glm: number
dsf: number
dsp: number
mm: number
others: number
}
export const days: Day[] = [
{
date: "Jun 12",
total: 2_283_799_449_383,
glm: 0,
dsf: 1_176_701_653_509,
dsp: 569_527_034_307,
mm: 159_016_250_684,
others: 378_554_510_883,
},
{
date: "Jun 13",
total: 2_008_462_388_420,
glm: 0,
dsf: 995_338_131_997,
dsp: 445_817_536_548,
mm: 211_743_241_967,
others: 355_563_477_908,
},
{
date: "Jun 14",
total: 2_007_785_405_251,
glm: 0,
dsf: 983_954_176_228,
dsp: 428_151_999_341,
mm: 262_476_527_930,
others: 333_202_701_752,
},
{
date: "Jun 15",
total: 2_694_736_103_062,
glm: 0,
dsf: 1_255_893_953_859,
dsp: 632_223_338_376,
mm: 352_507_442_991,
others: 454_111_367_836,
},
{
date: "Jun 16",
total: 2_838_153_758_908,
glm: 0,
dsf: 1_336_625_283_800,
dsp: 676_480_415_730,
mm: 305_268_829_013,
others: 519_779_230_365,
},
{
date: "Jun 17",
total: 2_778_964_711_109,
glm: 70_095_977_043,
dsf: 1_339_831_523_555,
dsp: 660_414_395_220,
mm: 251_302_096_157,
others: 457_320_719_134,
},
{
date: "Jun 18",
total: 2_806_992_430_656,
glm: 201_130_231_172,
dsf: 1_295_599_996_869,
dsp: 595_665_008_776,
mm: 322_205_104_324,
others: 392_392_089_515,
},
{
date: "Jun 19",
total: 2_419_611_630_232,
glm: 199_086_413_910,
dsf: 1_115_750_468_802,
dsp: 475_965_869_304,
mm: 303_586_698_735,
others: 325_222_179_481,
},
{
date: "Jun 20",
total: 2_188_278_916_865,
glm: 193_931_516_396,
dsf: 1_050_194_681_012,
dsp: 395_303_435_278,
mm: 281_998_000_337,
others: 266_851_283_842,
},
{
date: "Jun 21",
total: 2_042_309_961_344,
glm: 181_894_043_118,
dsf: 985_164_570_580,
dsp: 368_194_079_542,
mm: 259_812_551_324,
others: 247_244_716_780,
},
{
date: "Jun 22",
total: 2_893_934_325_663,
glm: 301_759_048_475,
dsf: 1_298_124_282_989,
dsp: 581_012_596_194,
mm: 371_581_117_839,
others: 341_457_280_166,
},
{
date: "Jun 23",
total: 3_109_009_321_480,
glm: 282_277_235_158,
dsf: 1_423_571_678_821,
dsp: 627_374_654_587,
mm: 429_416_300_508,
others: 346_369_452_406,
},
{
date: "Jun 24",
total: 2_939_149_971_595,
glm: 256_497_442_533,
dsf: 1_373_583_023_234,
dsp: 601_270_997_775,
mm: 391_586_493_231,
others: 316_212_014_822,
},
{
date: "Jun 25",
total: 3_029_641_552_948,
glm: 256_279_657_734,
dsf: 1_481_084_002_776,
dsp: 602_077_167_287,
mm: 375_985_302_874,
others: 314_215_422_277,
},
]
export const launchIndex = 5 // Jun 17, first day of GLM-5.2 usage
// GLM-5.2 weekly token volume, Jun 19-25 (sum of glm): 1,671,725,357,324 = 1.67T
export const glmWeekTokensT = 1.672
// stacked segments, bottom -> top. GLM-5.2 is the hero (blue); the rest are the field it cut into.
export const segments = [
{ key: "glm", label: "GLM-5.2", color: "#3b5cf6", hero: true },
{ key: "dsf", label: "deepseek-v4-flash", color: "#9ca3ad" },
{ key: "dsp", label: "deepseek-v4-pro", color: "#b3b9c1" },
{ key: "mm", label: "minimax-m3", color: "#c8cdd3" },
{ key: "others", label: "other models", color: "#dde0e4" },
] as const
-185
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@@ -1,185 +0,0 @@
import React from "react"
import { AbsoluteFill, Easing, interpolate, spring, useCurrentFrame, useVideoConfig } from "remotion"
// stats.opencode.ai design tokens (light theme)
const c = {
bg: "#ffffff",
ink: "#161616",
muted: "#5c5c5c",
faint: "#808080",
line: "#e6e6e6",
dot: "#e4e4e4",
gray: "#aab0b8",
accent: "#3b5cf6",
accentHi: "#5b78ff",
}
const MONO = '"IBM Plex Mono", ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, monospace'
const DOT_MASK =
"url(\"data:image/svg+xml,%3Csvg viewBox='0 0 6 6' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M0 0H2V2H0V0Z' fill='black'/%3E%3C/svg%3E\")"
// verified: OpenCode Go, week of Jun 22-28, 2026 (2026-W26). share = % of 19.64T total Go tokens.
const bars = [
{ label: "deepseek-v4-flash", share: 48.3, hero: true },
{ label: "deepseek-v4-pro", share: 19.4 },
{ label: "minimax-m3", share: 13.0 },
{ label: "glm-5.2", share: 8.3 },
{ label: "mimo-v2.5", share: 4.3 },
{ label: "kimi-k2.7-code", share: 2.6 },
{ label: "other models", share: 4.1 },
]
function DataWordmark({ height = 30 }: { height?: number }) {
return (
<svg width={(height * 66) / 20} height={height} viewBox="0 0 66 20" fill="none" style={{ color: c.ink }}>
<path opacity="0.2" d="M12 16H4V8H12V16Z" fill="currentColor" />
<path d="M12 4H4V16H12V4ZM16 20H0V0H16V20Z" fill="currentColor" />
<path
d="M63.3543 16L62.5119 12.8711H58.6437L57.8013 16H55.7383L59.2454 4H61.9618L65.4689 16H63.3543ZM61.0678 7.851L60.6896 5.94269H60.4489L60.0707 7.851L59.1595 11.1347H61.9962L61.0678 7.851Z"
fill="currentColor"
/>
<path d="M52.5951 5.87392V16H50.4461V5.87392H47.4375V4H55.6209V5.87392H52.5951Z" fill="currentColor" />
<path
d="M45.2059 16L44.3635 12.8711H40.4953L39.6529 16H37.5898L41.097 4H43.8133L47.3205 16H45.2059ZM42.9194 7.851L42.5411 5.94269H42.3004L41.9222 7.851L41.011 11.1347H43.8477L42.9194 7.851Z"
fill="currentColor"
/>
<path
d="M28 4H32.0917C32.8138 4 33.4556 4.11461 34.0172 4.34384C34.5903 4.5616 35.0716 4.9169 35.4613 5.40974C35.8625 5.89112 36.1662 6.51003 36.3725 7.26648C36.5788 8.02292 36.6819 8.9341 36.6819 10C36.6819 11.0659 36.5788 11.9771 36.3725 12.7335C36.1662 13.49 35.8625 14.1146 35.4613 14.6075C35.0716 15.0888 34.5903 15.4441 34.0172 15.6734C33.4556 15.8911 32.8138 16 32.0917 16H28V4ZM32.0917 14.1261C32.8252 14.1261 33.3926 13.9026 33.7937 13.4556C34.1948 12.9971 34.3954 12.3152 34.3954 11.4097V8.59026C34.3954 7.68481 34.1948 7.0086 33.7937 6.5616C33.3926 6.10315 32.8252 5.87392 32.0917 5.87392H30.149V14.1261H32.0917Z"
fill="currentColor"
/>
</svg>
)
}
export function FlashShare() {
const frame = useCurrentFrame()
const { fps } = useVideoConfig()
const grow = (i: number) =>
Math.min(
1,
Math.max(0, spring({ frame: frame - 18 - i * 7, fps, config: { damping: 18, stiffness: 120, mass: 0.6 } })),
)
return (
<AbsoluteFill style={{ background: c.bg, color: c.ink, fontFamily: MONO, padding: 72, boxSizing: "border-box" }}>
<div style={{ height: "100%", display: "flex", flexDirection: "column" }}>
{/* header */}
<div style={{ display: "flex", justifyContent: "space-between", alignItems: "center" }}>
<DataWordmark height={30} />
<div style={{ fontSize: 20, fontWeight: 500, color: c.faint, letterSpacing: 1 }}>JUN 2228, 2026</div>
</div>
{/* headline (static) */}
<div style={{ marginTop: 50 }}>
<div style={{ fontSize: 23, fontWeight: 600, color: c.muted, letterSpacing: 2 }}>
OPENCODE GO · SHARE OF TOKENS
</div>
<div
style={{ display: "flex", justifyContent: "space-between", alignItems: "baseline", gap: 24, marginTop: 14 }}
>
<div style={{ fontSize: 62, fontWeight: 600, letterSpacing: -2, lineHeight: 1 }}>DeepSeek V4 Flash</div>
<div
style={{
fontSize: 88,
fontWeight: 600,
letterSpacing: -2,
lineHeight: 1,
color: c.accent,
fontVariantNumeric: "tabular-nums",
}}
>
48%
</div>
</div>
</div>
{/* bar chart */}
<div style={{ flex: 1, display: "flex", flexDirection: "column", justifyContent: "center", gap: 16 }}>
{bars.map((b, i) => {
const g = grow(i)
const pct = b.share * g
return (
<div key={b.label} style={{ display: "flex", alignItems: "center", gap: 18 }}>
<div
style={{
width: 268,
fontSize: 23,
fontWeight: b.hero ? 600 : 500,
color: b.hero ? c.ink : c.muted,
textAlign: "right",
whiteSpace: "nowrap",
}}
>
{b.label}
</div>
<div style={{ position: "relative", flex: 1, height: 48 }}>
{/* dotted 100% track — height is a multiple of the 12px tile, anchored bottom, so dots never clip */}
<div
style={{
position: "absolute",
inset: 0,
background: c.dot,
WebkitMaskImage: DOT_MASK,
maskImage: DOT_MASK,
WebkitMaskSize: "12px 12px",
maskSize: "12px 12px",
WebkitMaskRepeat: "repeat",
maskRepeat: "repeat",
WebkitMaskPosition: "left bottom",
maskPosition: "left bottom",
}}
/>
{/* fill */}
<div
style={{
position: "absolute",
left: 0,
top: 0,
bottom: 0,
width: `${pct}%`,
background: b.hero ? c.accent : c.gray,
borderRight: b.hero ? `2px solid ${c.accentHi}` : "none",
}}
/>
</div>
<div
style={{
width: 84,
fontSize: 24,
fontWeight: b.hero ? 600 : 500,
color: b.hero ? c.accent : c.muted,
textAlign: "right",
fontVariantNumeric: "tabular-nums",
}}
>
{Math.round(pct)}%
</div>
</div>
)
})}
</div>
{/* footer */}
<div
style={{
display: "flex",
justifyContent: "space-between",
alignItems: "center",
marginTop: 24,
paddingTop: 22,
borderTop: `1px solid ${c.line}`,
fontSize: 20,
fontWeight: 500,
}}
>
<div style={{ display: "inline-flex", alignItems: "center", gap: 9, color: c.muted }}>
<span style={{ width: 13, height: 13, background: c.accent, display: "inline-block" }} />
DeepSeek V4 Flash · 9.48T tokens · 83.6M requests
</div>
<div style={{ color: c.ink }}>opencode.ai/data</div>
</div>
</div>
</AbsoluteFill>
)
}
-36
View File
@@ -1,36 +0,0 @@
import { Composition, registerRoot } from "remotion"
import { GLM52Rise } from "./video"
import { NZSheep } from "./sheep"
import { NovelTokens } from "./novel"
import { FlashShare } from "./flash"
import { MiniMaxClimb } from "./minimax"
import { JuneTotals } from "./june"
function Root() {
return (
<>
<Composition id="GLM52Rise" component={GLM52Rise} durationInFrames={240} fps={30} width={1080} height={1080} />
<Composition id="NZSheep" component={NZSheep} durationInFrames={150} fps={30} width={1080} height={1080} />
<Composition
id="NovelTokens"
component={NovelTokens}
durationInFrames={150}
fps={30}
width={1080}
height={1080}
/>
<Composition id="FlashShare" component={FlashShare} durationInFrames={165} fps={30} width={1080} height={1080} />
<Composition
id="MiniMaxClimb"
component={MiniMaxClimb}
durationInFrames={165}
fps={30}
width={1080}
height={1080}
/>
<Composition id="JuneTotals" component={JuneTotals} durationInFrames={1} fps={30} width={1080} height={1080} />
</>
)
}
registerRoot(Root)
-144
View File
@@ -1,144 +0,0 @@
import React from "react"
import { AbsoluteFill } from "remotion"
const c = {
bg: "#ffffff",
ink: "#161616",
muted: "#5c5c5c",
faint: "#808080",
line: "#e6e6e6",
dot: "#dcdcdc",
accent: "#3b5cf6",
}
const MONO = '"IBM Plex Mono", ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, monospace'
const DOT_MASK =
"url(\"data:image/svg+xml,%3Csvg viewBox='0 0 6 6' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M0 0H2V2H0V0Z' fill='black'/%3E%3C/svg%3E\")"
// verified: OpenCode Go (tier=Go, dataset=zen), June 1-30, 2026.
// 72.78T tokens · 651.4M requests · 11.42M sessions -> rounded headline figures.
function DataWordmark({ height = 30 }: { height?: number }) {
return (
<svg width={(height * 66) / 20} height={height} viewBox="0 0 66 20" fill="none" style={{ color: c.ink }}>
<path opacity="0.2" d="M12 16H4V8H12V16Z" fill="currentColor" />
<path d="M12 4H4V16H12V4ZM16 20H0V0H16V20Z" fill="currentColor" />
<path
d="M63.3543 16L62.5119 12.8711H58.6437L57.8013 16H55.7383L59.2454 4H61.9618L65.4689 16H63.3543ZM61.0678 7.851L60.6896 5.94269H60.4489L60.0707 7.851L59.1595 11.1347H61.9962L61.0678 7.851Z"
fill="currentColor"
/>
<path d="M52.5951 5.87392V16H50.4461V5.87392H47.4375V4H55.6209V5.87392H52.5951Z" fill="currentColor" />
<path
d="M45.2059 16L44.3635 12.8711H40.4953L39.6529 16H37.5898L41.097 4H43.8133L47.3205 16H45.2059ZM42.9194 7.851L42.5411 5.94269H42.3004L41.9222 7.851L41.011 11.1347H43.8477L42.9194 7.851Z"
fill="currentColor"
/>
<path
d="M28 4H32.0917C32.8138 4 33.4556 4.11461 34.0172 4.34384C34.5903 4.5616 35.0716 4.9169 35.4613 5.40974C35.8625 5.89112 36.1662 6.51003 36.3725 7.26648C36.5788 8.02292 36.6819 8.9341 36.6819 10C36.6819 11.0659 36.5788 11.9771 36.3725 12.7335C36.1662 13.49 35.8625 14.1146 35.4613 14.6075C35.0716 15.0888 34.5903 15.4441 34.0172 15.6734C33.4556 15.8911 32.8138 16 32.0917 16H28V4ZM32.0917 14.1261C32.8252 14.1261 33.3926 13.9026 33.7937 13.4556C34.1948 12.9971 34.3954 12.3152 34.3954 11.4097V8.59026C34.3954 7.68481 34.1948 7.0086 33.7937 6.5616C33.3926 6.10315 32.8252 5.87392 32.0917 5.87392H30.149V14.1261H32.0917Z"
fill="currentColor"
/>
</svg>
)
}
function DotBand() {
return (
<div
style={{
height: 12,
background: c.dot,
WebkitMaskImage: DOT_MASK,
maskImage: DOT_MASK,
WebkitMaskSize: "12px 12px",
maskSize: "12px 12px",
WebkitMaskRepeat: "repeat",
maskRepeat: "repeat",
WebkitMaskPosition: "left top",
maskPosition: "left top",
}}
/>
)
}
function Metric({ value, label }: { value: string; label: string }) {
return (
<div>
<div
style={{
fontSize: 92,
fontWeight: 600,
letterSpacing: -3,
lineHeight: 1,
fontVariantNumeric: "tabular-nums",
marginLeft: -8, // align the glyph's visual left edge to the column
}}
>
{value}
</div>
<div style={{ marginTop: 16, fontSize: 24, fontWeight: 500, color: c.muted, letterSpacing: 1 }}>{label}</div>
</div>
)
}
export function JuneTotals() {
return (
<AbsoluteFill style={{ background: c.bg, color: c.ink, fontFamily: MONO, padding: 72, boxSizing: "border-box" }}>
<div style={{ height: "100%", display: "flex", flexDirection: "column" }}>
{/* header */}
<div style={{ display: "flex", justifyContent: "space-between", alignItems: "center", marginBottom: 22 }}>
<DataWordmark height={30} />
<div style={{ fontSize: 20, fontWeight: 500, color: c.faint, letterSpacing: 1 }}>MONTHLY RECAP</div>
</div>
<DotBand />
{/* hero */}
<div style={{ flex: 1, display: "flex", flexDirection: "column", justifyContent: "center" }}>
<div style={{ fontSize: 25, fontWeight: 600, color: c.muted, letterSpacing: 3 }}>OPENCODE GO · JUNE 2026</div>
<div style={{ marginTop: 14 }}>
<div
style={{
fontSize: 300,
fontWeight: 600,
letterSpacing: -10,
lineHeight: 0.82,
color: c.accent,
marginLeft: -22,
}}
>
73T
</div>
<div style={{ fontSize: 50, fontWeight: 600, color: c.ink, marginTop: 10 }}>tokens processed</div>
</div>
{/* supporting metrics */}
<div
style={{
display: "grid",
gridTemplateColumns: "1fr 1fr",
marginTop: 60,
paddingTop: 40,
borderTop: `1px solid ${c.line}`,
}}
>
<Metric value="650M" label="requests" />
<Metric value="11M" label="sessions" />
</div>
</div>
{/* footer */}
<DotBand />
<div
style={{
display: "flex",
justifyContent: "flex-end",
alignItems: "center",
marginTop: 22,
fontSize: 20,
fontWeight: 500,
}}
>
<div style={{ color: c.ink }}>opencode.ai/data</div>
</div>
</div>
</AbsoluteFill>
)
}
-201
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@@ -1,201 +0,0 @@
import React from "react"
import { AbsoluteFill, interpolate, spring, useCurrentFrame, useVideoConfig } from "remotion"
const c = {
bg: "#ffffff",
ink: "#161616",
muted: "#5c5c5c",
faint: "#808080",
line: "#e6e6e6",
dot: "#e4e4e4",
accent: "#3b5cf6",
accentHi: "#5b78ff",
accentDim: "#aebcf3",
}
const MONO = '"IBM Plex Mono", ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, monospace'
const DOT_MASK =
"url(\"data:image/svg+xml,%3Csvg viewBox='0 0 6 6' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M0 0H2V2H0V0Z' fill='black'/%3E%3C/svg%3E\")"
// verified: minimax-m3, OpenCode Go (tier=Go, dataset=zen), weekly total_tokens.
// W26 2.559T is +23.2% / +482.3B vs W25 2.077T.
const weeks = [
{ label: "May 25", t: 0.008 },
{ label: "Jun 1", t: 0.429 },
{ label: "Jun 8", t: 1.192 },
{ label: "Jun 15", t: 2.077 },
{ label: "Jun 22", t: 2.559, latest: true },
]
const AXIS_MAX = 3.0
function DataWordmark({ height = 30 }: { height?: number }) {
return (
<svg width={(height * 66) / 20} height={height} viewBox="0 0 66 20" fill="none" style={{ color: c.ink }}>
<path opacity="0.2" d="M12 16H4V8H12V16Z" fill="currentColor" />
<path d="M12 4H4V16H12V4ZM16 20H0V0H16V20Z" fill="currentColor" />
<path
d="M63.3543 16L62.5119 12.8711H58.6437L57.8013 16H55.7383L59.2454 4H61.9618L65.4689 16H63.3543ZM61.0678 7.851L60.6896 5.94269H60.4489L60.0707 7.851L59.1595 11.1347H61.9962L61.0678 7.851Z"
fill="currentColor"
/>
<path d="M52.5951 5.87392V16H50.4461V5.87392H47.4375V4H55.6209V5.87392H52.5951Z" fill="currentColor" />
<path
d="M45.2059 16L44.3635 12.8711H40.4953L39.6529 16H37.5898L41.097 4H43.8133L47.3205 16H45.2059ZM42.9194 7.851L42.5411 5.94269H42.3004L41.9222 7.851L41.011 11.1347H43.8477L42.9194 7.851Z"
fill="currentColor"
/>
<path
d="M28 4H32.0917C32.8138 4 33.4556 4.11461 34.0172 4.34384C34.5903 4.5616 35.0716 4.9169 35.4613 5.40974C35.8625 5.89112 36.1662 6.51003 36.3725 7.26648C36.5788 8.02292 36.6819 8.9341 36.6819 10C36.6819 11.0659 36.5788 11.9771 36.3725 12.7335C36.1662 13.49 35.8625 14.1146 35.4613 14.6075C35.0716 15.0888 34.5903 15.4441 34.0172 15.6734C33.4556 15.8911 32.8138 16 32.0917 16H28V4ZM32.0917 14.1261C32.8252 14.1261 33.3926 13.9026 33.7937 13.4556C34.1948 12.9971 34.3954 12.3152 34.3954 11.4097V8.59026C34.3954 7.68481 34.1948 7.0086 33.7937 6.5616C33.3926 6.10315 32.8252 5.87392 32.0917 5.87392H30.149V14.1261H32.0917Z"
fill="currentColor"
/>
</svg>
)
}
const CHART_H = 420 // multiple of 12 so the dotted tracks never clip
export function MiniMaxClimb() {
const frame = useCurrentFrame()
const { fps } = useVideoConfig()
const grow = (i: number) =>
Math.min(
1,
Math.max(0, spring({ frame: frame - 22 - i * 9, fps, config: { damping: 18, stiffness: 110, mass: 0.6 } })),
)
return (
<AbsoluteFill style={{ background: c.bg, color: c.ink, fontFamily: MONO, padding: 72, boxSizing: "border-box" }}>
<div style={{ height: "100%", display: "flex", flexDirection: "column" }}>
{/* header */}
<div style={{ display: "flex", justifyContent: "space-between", alignItems: "center" }}>
<DataWordmark height={30} />
<div style={{ fontSize: 20, fontWeight: 500, color: c.faint, letterSpacing: 1 }}>JUN 2228, 2026</div>
</div>
{/* headline (static) */}
<div style={{ marginTop: 50 }}>
<div style={{ fontSize: 23, fontWeight: 600, color: c.muted, letterSpacing: 2 }}>
OPENCODE GO · WEEKLY TOKENS
</div>
<div
style={{ display: "flex", justifyContent: "space-between", alignItems: "baseline", gap: 24, marginTop: 14 }}
>
<div style={{ fontSize: 62, fontWeight: 600, letterSpacing: -2, lineHeight: 1 }}>MiniMax M3</div>
<div style={{ textAlign: "right", flexShrink: 0 }}>
<div
style={{
fontSize: 84,
fontWeight: 600,
letterSpacing: -2,
lineHeight: 1,
color: c.accent,
fontVariantNumeric: "tabular-nums",
}}
>
+23.2%
</div>
<div style={{ fontSize: 19, fontWeight: 500, color: c.muted, letterSpacing: 1, marginTop: 6 }}>
WEEK OVER WEEK
</div>
</div>
</div>
</div>
{/* column chart */}
<div style={{ flex: 1, display: "flex", alignItems: "flex-end", marginTop: 30 }}>
<div style={{ width: "100%", display: "flex", alignItems: "flex-end", gap: 30 }}>
{weeks.map((w, i) => {
const g = grow(i)
const h = Math.round((w.t / AXIS_MAX) * CHART_H * g)
return (
<div key={w.label} style={{ flex: 1, display: "flex", flexDirection: "column", alignItems: "center" }}>
{/* value + bar */}
<div style={{ position: "relative", width: "100%", height: CHART_H }}>
{/* dotted track */}
<div
style={{
position: "absolute",
inset: 0,
background: c.dot,
WebkitMaskImage: DOT_MASK,
maskImage: DOT_MASK,
WebkitMaskSize: "12px 12px",
maskSize: "12px 12px",
WebkitMaskRepeat: "repeat",
maskRepeat: "repeat",
WebkitMaskPosition: "left bottom",
maskPosition: "left bottom",
}}
/>
{/* fill */}
<div
style={{
position: "absolute",
left: 0,
right: 0,
bottom: 0,
height: h,
background: w.latest ? c.accent : c.accentDim,
borderTop: w.latest ? `3px solid ${c.accentHi}` : "none",
}}
/>
{/* value label */}
<div
style={{
position: "absolute",
left: 0,
right: 0,
bottom: h + 10,
textAlign: "center",
fontSize: 26,
fontWeight: w.latest ? 600 : 500,
color: w.latest ? c.accent : c.muted,
fontVariantNumeric: "tabular-nums",
opacity: interpolate(g, [0.5, 1], [0, 1], {
extrapolateLeft: "clamp",
extrapolateRight: "clamp",
}),
}}
>
{w.t.toFixed(2)}T
</div>
</div>
{/* week label */}
<div
style={{
marginTop: 14,
fontSize: 18,
fontWeight: 500,
color: w.latest ? c.ink : c.faint,
}}
>
{w.label}
</div>
</div>
)
})}
</div>
</div>
{/* footer */}
<div
style={{
display: "flex",
justifyContent: "space-between",
alignItems: "center",
marginTop: 22,
paddingTop: 22,
borderTop: `1px solid ${c.line}`,
fontSize: 20,
fontWeight: 500,
}}
>
<div style={{ display: "inline-flex", alignItems: "center", gap: 9, color: c.muted }}>
<span style={{ width: 13, height: 13, background: c.accent, display: "inline-block" }} />
2.56T tokens last week · +482.3B added
</div>
<div style={{ color: c.ink }}>opencode.ai/data</div>
</div>
</div>
</AbsoluteFill>
)
}
-135
View File
@@ -1,135 +0,0 @@
import React from "react"
import { AbsoluteFill, Easing, Img, interpolate, staticFile, useCurrentFrame } from "remotion"
const c = {
white: "#ffffff",
dim: "rgba(255,255,255,0.74)",
}
const MONO = '"IBM Plex Mono", ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, monospace'
// verified: OpenCode Go, week of Jun 22-28, 2026 (2026-W26)
// 19,642,742,937,105 tokens / 173,651,197 requests = 113,116 tokens/request
const AVG = 113116
const K = Math.round(AVG / 1000) // 113
const nf = new Intl.NumberFormat("en-US")
function DataWordmark({ height = 30 }: { height?: number }) {
return (
<svg width={(height * 66) / 20} height={height} viewBox="0 0 66 20" fill="none" style={{ color: c.white }}>
<path opacity="0.35" d="M12 16H4V8H12V16Z" fill="currentColor" />
<path d="M12 4H4V16H12V4ZM16 20H0V0H16V20Z" fill="currentColor" />
<path
d="M63.3543 16L62.5119 12.8711H58.6437L57.8013 16H55.7383L59.2454 4H61.9618L65.4689 16H63.3543ZM61.0678 7.851L60.6896 5.94269H60.4489L60.0707 7.851L59.1595 11.1347H61.9962L61.0678 7.851Z"
fill="currentColor"
/>
<path d="M52.5951 5.87392V16H50.4461V5.87392H47.4375V4H55.6209V5.87392H52.5951Z" fill="currentColor" />
<path
d="M45.2059 16L44.3635 12.8711H40.4953L39.6529 16H37.5898L41.097 4H43.8133L47.3205 16H45.2059ZM42.9194 7.851L42.5411 5.94269H42.3004L41.9222 7.851L41.011 11.1347H43.8477L42.9194 7.851Z"
fill="currentColor"
/>
<path
d="M28 4H32.0917C32.8138 4 33.4556 4.11461 34.0172 4.34384C34.5903 4.5616 35.0716 4.9169 35.4613 5.40974C35.8625 5.89112 36.1662 6.51003 36.3725 7.26648C36.5788 8.02292 36.6819 8.9341 36.6819 10C36.6819 11.0659 36.5788 11.9771 36.3725 12.7335C36.1662 13.49 35.8625 14.1146 35.4613 14.6075C35.0716 15.0888 34.5903 15.4441 34.0172 15.6734C33.4556 15.8911 32.8138 16 32.0917 16H28V4ZM32.0917 14.1261C32.8252 14.1261 33.3926 13.9026 33.7937 13.4556C34.1948 12.9971 34.3954 12.3152 34.3954 11.4097V8.59026C34.3954 7.68481 34.1948 7.0086 33.7937 6.5616C33.3926 6.10315 32.8252 5.87392 32.0917 5.87392H30.149V14.1261H32.0917Z"
fill="currentColor"
/>
</svg>
)
}
export function NovelTokens() {
const frame = useCurrentFrame()
const k = Math.round(
K *
interpolate(frame, [18, 92], [0, 1], {
extrapolateLeft: "clamp",
extrapolateRight: "clamp",
easing: Easing.out(Easing.cubic),
}),
)
const zoom = interpolate(frame, [0, 150], [1.06, 1.12], {
extrapolateRight: "clamp",
easing: Easing.inOut(Easing.quad),
})
return (
<AbsoluteFill style={{ background: "#0c0c0c", overflow: "hidden" }}>
<Img
src={staticFile("book.jpg")}
style={{
position: "absolute",
width: "100%",
height: "100%",
objectFit: "cover",
objectPosition: "center 34%",
transform: `scale(${zoom})`,
transformOrigin: "center 30%",
}}
/>
{/* legibility scrims */}
<div
style={{
position: "absolute",
inset: 0,
background:
"linear-gradient(to bottom, rgba(0,0,0,0.55) 0%, rgba(0,0,0,0) 20%, rgba(0,0,0,0.1) 44%, rgba(0,0,0,0.86) 100%)",
}}
/>
<div
style={{
position: "absolute",
inset: 0,
boxSizing: "border-box",
padding: 64,
color: c.white,
fontFamily: MONO,
display: "flex",
flexDirection: "column",
justifyContent: "space-between",
}}
>
{/* header */}
<div style={{ display: "flex", justifyContent: "space-between", alignItems: "center" }}>
<DataWordmark height={30} />
<div style={{ fontSize: 20, fontWeight: 500, color: c.dim, letterSpacing: 1 }}>JUN 2228, 2026</div>
</div>
{/* bottom block */}
<div>
<div style={{ fontSize: 23, fontWeight: 600, color: c.dim, letterSpacing: 2, marginBottom: 8 }}>
OPENCODE GO · LAST WEEK
</div>
<div
style={{
fontSize: 168,
fontWeight: 600,
lineHeight: 0.92,
letterSpacing: -5,
fontVariantNumeric: "tabular-nums",
}}
>
{k}K
</div>
<div style={{ fontSize: 50, fontWeight: 600, letterSpacing: -1, marginTop: 4 }}>tokens per request</div>
<div
style={{
display: "flex",
justifyContent: "space-between",
alignItems: "center",
marginTop: 30,
fontSize: 20,
fontWeight: 500,
}}
>
<div style={{ color: c.dim }}>{nf.format(AVG)} tokens / request · last week</div>
<div style={{ color: c.white }}>opencode.ai/data</div>
</div>
</div>
</div>
</AbsoluteFill>
)
}
-139
View File
@@ -1,139 +0,0 @@
import React from "react"
import { AbsoluteFill, Easing, Img, interpolate, staticFile, useCurrentFrame } from "remotion"
const c = {
white: "#ffffff",
dim: "rgba(255,255,255,0.72)",
faint: "rgba(255,255,255,0.55)",
}
const MONO = '"IBM Plex Mono", ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, monospace'
// verified: NZ OpenCode Go, week of Jun 22-28, 2026 (2026-W26)
const TOKENS = 40_915_594_381 // 40.9B
const SHEEP = 23_600_000 // 23.6M
const PER_SHEEP = Math.round(TOKENS / SHEEP) // 1,734
const nf = new Intl.NumberFormat("en-US")
// the correct opencode "DATA" wordmark (white, over photo)
function DataWordmark({ height = 30 }: { height?: number }) {
return (
<svg width={(height * 66) / 20} height={height} viewBox="0 0 66 20" fill="none" style={{ color: c.white }}>
<path opacity="0.35" d="M12 16H4V8H12V16Z" fill="currentColor" />
<path d="M12 4H4V16H12V4ZM16 20H0V0H16V20Z" fill="currentColor" />
<path
d="M63.3543 16L62.5119 12.8711H58.6437L57.8013 16H55.7383L59.2454 4H61.9618L65.4689 16H63.3543ZM61.0678 7.851L60.6896 5.94269H60.4489L60.0707 7.851L59.1595 11.1347H61.9962L61.0678 7.851Z"
fill="currentColor"
/>
<path d="M52.5951 5.87392V16H50.4461V5.87392H47.4375V4H55.6209V5.87392H52.5951Z" fill="currentColor" />
<path
d="M45.2059 16L44.3635 12.8711H40.4953L39.6529 16H37.5898L41.097 4H43.8133L47.3205 16H45.2059ZM42.9194 7.851L42.5411 5.94269H42.3004L41.9222 7.851L41.011 11.1347H43.8477L42.9194 7.851Z"
fill="currentColor"
/>
<path
d="M28 4H32.0917C32.8138 4 33.4556 4.11461 34.0172 4.34384C34.5903 4.5616 35.0716 4.9169 35.4613 5.40974C35.8625 5.89112 36.1662 6.51003 36.3725 7.26648C36.5788 8.02292 36.6819 8.9341 36.6819 10C36.6819 11.0659 36.5788 11.9771 36.3725 12.7335C36.1662 13.49 35.8625 14.1146 35.4613 14.6075C35.0716 15.0888 34.5903 15.4441 34.0172 15.6734C33.4556 15.8911 32.8138 16 32.0917 16H28V4ZM32.0917 14.1261C32.8252 14.1261 33.3926 13.9026 33.7937 13.4556C34.1948 12.9971 34.3954 12.3152 34.3954 11.4097V8.59026C34.3954 7.68481 34.1948 7.0086 33.7937 6.5616C33.3926 6.10315 32.8252 5.87392 32.0917 5.87392H30.149V14.1261H32.0917Z"
fill="currentColor"
/>
</svg>
)
}
export function NZSheep() {
const frame = useCurrentFrame()
const count = Math.round(
PER_SHEEP *
interpolate(frame, [18, 90], [0, 1], {
extrapolateLeft: "clamp",
extrapolateRight: "clamp",
easing: Easing.out(Easing.cubic),
}),
)
// slow Ken Burns push-in (scale up only — never reveals an edge)
const zoom = interpolate(frame, [0, 150], [1.06, 1.12], {
extrapolateRight: "clamp",
easing: Easing.inOut(Easing.quad),
})
return (
<AbsoluteFill style={{ background: "#0c0c0c", overflow: "hidden" }}>
{/* the sheep, staring */}
<Img
src={staticFile("sheep.jpg")}
style={{
position: "absolute",
width: "100%",
height: "100%",
objectFit: "cover",
objectPosition: "center 38%",
transform: `scale(${zoom})`,
transformOrigin: "center 35%",
}}
/>
{/* legibility scrims */}
<div
style={{
position: "absolute",
inset: 0,
background:
"linear-gradient(to bottom, rgba(0,0,0,0.5) 0%, rgba(0,0,0,0) 22%, rgba(0,0,0,0) 48%, rgba(0,0,0,0.78) 100%)",
}}
/>
{/* content */}
<div
style={{
position: "absolute",
inset: 0,
boxSizing: "border-box",
padding: 64,
color: c.white,
fontFamily: MONO,
display: "flex",
flexDirection: "column",
justifyContent: "space-between",
}}
>
{/* header */}
<div style={{ display: "flex", justifyContent: "space-between", alignItems: "center" }}>
<DataWordmark height={30} />
<div style={{ fontSize: 20, fontWeight: 500, color: c.dim, letterSpacing: 1 }}>JUN 2228, 2026</div>
</div>
{/* bottom block */}
<div>
<div style={{ fontSize: 23, fontWeight: 600, color: c.dim, letterSpacing: 2, marginBottom: 8 }}>
OPENCODE GO · NEW ZEALAND
</div>
<div
style={{
fontSize: 168,
fontWeight: 600,
lineHeight: 0.92,
letterSpacing: -5,
fontVariantNumeric: "tabular-nums",
}}
>
{nf.format(count)}
</div>
<div style={{ fontSize: 50, fontWeight: 600, letterSpacing: -1, marginTop: 4 }}>tokens per sheep</div>
<div
style={{
display: "flex",
justifyContent: "space-between",
alignItems: "center",
marginTop: 34,
fontSize: 20,
fontWeight: 500,
}}
>
<div style={{ color: c.dim }}>40.9B tokens ÷ 23.6M sheep · last week</div>
<div style={{ color: c.white }}>opencode.ai/data</div>
</div>
</div>
</div>
</AbsoluteFill>
)
}
-254
View File
@@ -1,254 +0,0 @@
import React from "react"
import { AbsoluteFill, Easing, interpolate, useCurrentFrame, useVideoConfig } from "remotion"
import { days, launchIndex, glmWeekTokensT, segments } from "./data"
// stats.opencode.ai design tokens (light theme)
const c = {
bg: "#ffffff",
ink: "#161616",
muted: "#5c5c5c",
faint: "#808080",
line: "#e6e6e6",
dot: "#ededed",
accent: "#3b5cf6",
accentHi: "#5b78ff",
}
const MONO = '"IBM Plex Mono", ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, monospace'
const W = 1080
const DOT_MASK =
"url(\"data:image/svg+xml,%3Csvg viewBox='0 0 6 6' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M0 0H2V2H0V0Z' fill='black'/%3E%3C/svg%3E\")"
const field = segments.filter((s) => !s.hero)
const glmColor = segments.find((s) => s.hero)!.color
const clamp = (v: number, lo: number, hi: number) => Math.min(hi, Math.max(lo, v))
// the correct opencode "DATA" wordmark (from stats.opencode.ai header)
function DataWordmark({ height = 30, color = c.ink }: { height?: number; color?: string }) {
return (
<svg width={(height * 66) / 20} height={height} viewBox="0 0 66 20" fill="none" style={{ color }}>
<path opacity="0.2" d="M12 16H4V8H12V16Z" fill="currentColor" />
<path d="M12 4H4V16H12V4ZM16 20H0V0H16V20Z" fill="currentColor" />
<path
d="M63.3543 16L62.5119 12.8711H58.6437L57.8013 16H55.7383L59.2454 4H61.9618L65.4689 16H63.3543ZM61.0678 7.851L60.6896 5.94269H60.4489L60.0707 7.851L59.1595 11.1347H61.9962L61.0678 7.851Z"
fill="currentColor"
/>
<path d="M52.5951 5.87392V16H50.4461V5.87392H47.4375V4H55.6209V5.87392H52.5951Z" fill="currentColor" />
<path
d="M45.2059 16L44.3635 12.8711H40.4953L39.6529 16H37.5898L41.097 4H43.8133L47.3205 16H45.2059ZM42.9194 7.851L42.5411 5.94269H42.3004L41.9222 7.851L41.011 11.1347H43.8477L42.9194 7.851Z"
fill="currentColor"
/>
<path
d="M28 4H32.0917C32.8138 4 33.4556 4.11461 34.0172 4.34384C34.5903 4.5616 35.0716 4.9169 35.4613 5.40974C35.8625 5.89112 36.1662 6.51003 36.3725 7.26648C36.5788 8.02292 36.6819 8.9341 36.6819 10C36.6819 11.0659 36.5788 11.9771 36.3725 12.7335C36.1662 13.49 35.8625 14.1146 35.4613 14.6075C35.0716 15.0888 34.5903 15.4441 34.0172 15.6734C33.4556 15.8911 32.8138 16 32.0917 16H28V4ZM32.0917 14.1261C32.8252 14.1261 33.3926 13.9026 33.7937 13.4556C34.1948 12.9971 34.3954 12.3152 34.3954 11.4097V8.59026C34.3954 7.68481 34.1948 7.0086 33.7937 6.5616C33.3926 6.10315 32.8252 5.87392 32.0917 5.87392H30.149V14.1261H32.0917Z"
fill="currentColor"
/>
</svg>
)
}
export function GLM52Rise() {
const frame = useCurrentFrame()
const { fps } = useVideoConfig()
// ---------- virtual camera ----------
// open zoomed-in on the field, pan right while the blue fills, then pull back to reveal.
const K = [0, 32, 150, 206, 240]
const ease = Easing.inOut(Easing.cubic)
const opt = { extrapolateLeft: "clamp" as const, extrapolateRight: "clamp" as const, easing: ease }
const s = interpolate(frame, K, [1.82, 1.72, 1.72, 1.0, 1.0], opt)
let fx = interpolate(frame, K, [420, 438, 760, 540, 540], opt)
let fy = interpolate(frame, K, [664, 664, 664, 540, 540], opt)
// keep the framing inside the 1080 canvas so edges never reveal black
fx = clamp(fx, 540 / s, W - 540 / s)
fy = clamp(fy, 540 / s, W - 540 / s)
const camera = `translate(${540 - fx * s}px, ${540 - fy * s}px) scale(${s})`
// ---------- blue sweep (synced to the pan) ----------
const p = interpolate(frame, [32, 150], [0, 1], { extrapolateLeft: "clamp", extrapolateRight: "clamp", easing: ease })
const revealAmount = p * (days.length - launchIndex) + 0.35
const fillOf = (i: number) => clamp(revealAmount - (i - launchIndex), 0, 1)
// token number climbs as the camera pulls back and the headline re-enters frame
const tokensT =
glmWeekTokensT *
interpolate(frame, [150, 202], [0, 1], {
extrapolateLeft: "clamp",
extrapolateRight: "clamp",
easing: Easing.out(Easing.cubic),
})
// chart geometry
const chartH = 440
const chartW = 936
const gap = 14
const EXAGGERATE = 2.876 // broken y-axis: GLM-5.2 magnified ~2.9x for emphasis
return (
<AbsoluteFill style={{ background: c.bg, overflow: "hidden" }}>
<div
style={{
position: "absolute",
width: W,
height: W,
background: c.bg,
transformOrigin: "0 0",
transform: camera,
}}
>
<div
style={{
width: W,
height: W,
boxSizing: "border-box",
padding: 72,
color: c.ink,
fontFamily: MONO,
display: "flex",
flexDirection: "column",
}}
>
{/* header */}
<div style={{ display: "flex", justifyContent: "space-between", alignItems: "center" }}>
<DataWordmark height={30} />
<div style={{ fontSize: 20, fontWeight: 500, color: c.faint, letterSpacing: 1 }}>JUN 1225, 2026</div>
</div>
{/* headline (static) */}
<div style={{ marginTop: 52 }}>
<div style={{ fontSize: 92, fontWeight: 600, lineHeight: 0.98, letterSpacing: -2 }}>
GLM-5.2 <span style={{ color: c.accent }}>broke out</span>
</div>
<div style={{ marginTop: 22, fontSize: 30, fontWeight: 500, color: c.muted }}>
From 0 to <span style={{ color: c.ink }}>{tokensT.toFixed(2)}T tokens</span> in a week.
</div>
</div>
{/* stacked chart */}
<div style={{ flex: 1, display: "flex", alignItems: "flex-end", marginTop: 40 }}>
<div
style={{
position: "relative",
width: chartW,
height: chartH,
margin: "0 auto",
borderBottom: `2px solid ${c.ink}`,
boxSizing: "border-box",
}}
>
{/* faint dotted backdrop */}
<div
style={{
position: "absolute",
left: -8,
right: -8,
top: -8,
bottom: 0,
background: c.dot,
WebkitMaskImage: DOT_MASK,
maskImage: DOT_MASK,
WebkitMaskSize: "12px 12px",
maskSize: "12px 12px",
WebkitMaskRepeat: "repeat",
maskRepeat: "repeat",
}}
/>
{/* columns */}
<div style={{ position: "absolute", inset: 0, display: "flex", gap, alignItems: "flex-end" }}>
{days.map((d, i) => {
const glmShare = d.glm / d.total
const blueH = Math.round(glmShare * EXAGGERATE * chartH)
const filled = Math.round(blueH * fillOf(i))
const slotGap = filled > 2 ? 5 : 0
const fieldH = chartH - filled - slotGap
const fieldTotal = d.dsf + d.dsp + d.mm + d.others
return (
<div key={i} style={{ position: "relative", flex: 1, height: chartH }}>
{/* gray field of other models (already in place) */}
<div
style={{
position: "absolute",
top: 0,
left: 0,
right: 0,
height: fieldH,
display: "flex",
flexDirection: "column-reverse",
}}
>
{field.map((seg) => (
<div
key={seg.key}
style={{
height: Math.round(((d[seg.key as keyof typeof d] as number) / fieldTotal) * fieldH),
background: seg.color,
borderTop: `2px solid ${c.bg}`,
}}
/>
))}
</div>
{/* GLM-5.2, animates in */}
{filled > 2 && (
<div
style={{
position: "absolute",
left: 0,
right: 0,
bottom: 0,
height: filled,
background: glmColor,
borderTop: `2px solid ${c.accentHi}`,
}}
/>
)}
</div>
)
})}
</div>
</div>
</div>
{/* day axis */}
<div style={{ display: "flex", gap, width: chartW, margin: "14px auto 0" }}>
{days.map((d, i) => (
<div
key={i}
style={{
flex: 1,
textAlign: "center",
fontSize: 15,
fontWeight: 500,
color: i === days.length - 1 ? c.ink : c.faint,
}}
>
{i === 0 || i === launchIndex || i === days.length - 1 ? d.date : ""}
</div>
))}
</div>
{/* footer */}
<div
style={{
display: "flex",
justifyContent: "space-between",
alignItems: "center",
marginTop: 28,
paddingTop: 22,
borderTop: `1px solid ${c.line}`,
fontSize: 20,
fontWeight: 500,
}}
>
<div style={{ display: "inline-flex", alignItems: "center", gap: 9, color: c.muted }}>
<span style={{ width: 13, height: 13, background: c.accent, display: "inline-block" }} />
GLM-5.2
</div>
<div style={{ color: c.ink }}>opencode.ai/data</div>
</div>
</div>
</div>
</AbsoluteFill>
)
}
+307 -2580
View File
@@ -14,11 +14,8 @@
},
"devDependencies": {
"@actions/artifact": "5.0.1",
"@ast-grep/cli": "0.44.0",
"@tsconfig/bun": "catalog:",
"@types/mime-types": "3.0.1",
"@types/react": "19.2.17",
"@types/react-dom": "19.2.3",
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@@ -27,44 +24,16 @@
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@@ -89,7 +58,7 @@
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@@ -97,7 +66,6 @@
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@@ -110,7 +78,6 @@
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@@ -119,59 +86,31 @@
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@@ -185,29 +124,15 @@
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@@ -243,7 +168,7 @@
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@@ -270,7 +195,7 @@
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@@ -292,7 +217,7 @@
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@@ -316,7 +241,7 @@
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@@ -336,7 +261,7 @@
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@@ -344,7 +269,7 @@
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@@ -353,27 +278,25 @@
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@@ -386,11 +309,10 @@
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@@ -401,10 +323,9 @@
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@@ -433,7 +354,7 @@
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@@ -485,15 +406,9 @@
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@@ -507,7 +422,7 @@
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@@ -519,11 +434,10 @@
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@@ -551,7 +465,7 @@
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@@ -567,12 +481,12 @@
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@@ -581,12 +495,11 @@
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@@ -597,9 +510,27 @@
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@@ -610,8 +541,8 @@
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@@ -619,13 +550,13 @@
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@@ -636,13 +567,8 @@
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@@ -674,7 +600,7 @@
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@@ -698,7 +624,7 @@
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@@ -732,14 +658,10 @@
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@@ -747,16 +669,15 @@
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@@ -766,7 +687,6 @@
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@@ -775,20 +695,18 @@
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@@ -806,14 +724,10 @@
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@@ -821,7 +735,7 @@
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@@ -836,12 +750,10 @@
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@@ -851,70 +763,9 @@
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@@ -927,10 +778,9 @@
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@@ -961,7 +811,7 @@
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-685
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@@ -1,685 +0,0 @@
# Service Lifecycle: Election, Restart, and Reconnect
Status: in progress
Incident: [#36688](https://github.com/anomalyco/opencode/issues/36688)
## Summary
The managed V2 service keeps its current update policy: the background updater
may install a new package, but only a freshly launched TUI activates that update
after finding an older running service. Existing TUIs never replace a service;
they only reconnect.
The restart path changes in three places:
1. A process-held OS lock, not the HTTP port or registration file, elects
exactly one server owner for its lifetime.
2. The elected process binds and registers a minimal lifecycle surface before
it initializes the application, so clients can distinguish a slow winner
from an absent server.
3. TUIs rediscover and reconnect indefinitely. Transport loss is never a
terminal error by itself.
Several clients may spawn small contenders during a restart. This is safe and
intentional: one contender acquires the lock and initializes, while every loser
exits before expensive server boot. The design does not require clients to
agree on a single initiator.
This proposal does not introduce a supervisor process, warm candidate server,
protocol negotiation, idle background restart, or general execution-recovery
framework.
## Architecture at a Glance
```text
╭───────────────────╮
│ CLI ServiceConfig │
╰─────────┬─────────╯
╭──────────────────────╮
│ CLI ServerConnection │
╰───────────┬──────────╯
╭──────────────────╰───────────────────╮
▼ ▼
╭──────────────────────────╮ ╭─────────────────────────╮
│ Client Service lifecycle │ │ CLI runPromiseWith seam │
╰─────────────┬────────────╯ ╰─────────────┬───────────╯
╰─────╮ │
▼ ▼
╭────────────────────────────╮ ╭─────────────╮
│ Background service process │ │ TUI / Solid │
╰──────────────┬─────────────╯ ╰──────┬──────╯
│ │
╰────────────◀────────────────────╯
╭───────────────────────╮
│ Server HTTP transport │
╰───────────┬───────────╯
╭──────────────────╮
│ Core application │
╰──────────────────╯
```
| Owner | Responsibility |
| ------------------------------------------------ | --------------------------------------------------------------------------------------------------- |
| `packages/client/src/effect/service.ts` | Effect-native discovery, start, and stop lifecycle operations |
| `packages/cli/src/services/service-config.ts` | CLI registration path, installed version, and daemon command |
| `packages/cli/src/services/server-connection.ts` | Resolve an endpoint and, only for the shared service, grouped reconnect and restart Effects |
| `packages/cli/src/server-process.ts` | Daemon election, registration, and server process boot |
| `packages/server/src/process.ts` | HTTP lifecycle shell and application transport |
| `packages/core` | Application behavior behind the transport |
| CLI default handler | Convert lifecycle Effects with the outer `FileSystem` context and pass grouped Promise capabilities |
| `packages/tui` Solid client context | Own event-stream reconnect, endpoint replacement, status, and user-triggered restart UI |
## Implementation Status
| Area | State |
| ------------------------- | --------------------------------------------------------------------- |
| Lifetime ownership | Implemented on this branch with a scoped OS lock |
| Contender behavior | Implemented; losers exit before the server module is imported |
| Registration repair | Implemented; the owner reasserts deleted or corrupt discovery |
| Channel isolation | Implemented with no-clobber migration for legacy preview discovery |
| Client startup waiting | Implemented; slow winners are not killed and waiting is indefinite |
| Lifecycle shell | Implemented; the owner binds and registers before application boot |
| Failed-state latching | Implemented; deterministic boot failure stays bound and actionable |
| Recovery diagnostics | Implemented; the TUI shows status instead of transport internals |
| Cross-platform validation | macOS runtime verified; Linux and Windows run in the unit-test matrix |
## Context
The V2 CLI runs a shared managed service that owns Sessions, location graphs,
plugins, permissions, and tool execution. The service updater can replace the
installed package while the current process continues running the old image.
A later TUI launch then detects the version mismatch and replaces the service.
Incident #36688 showed four failures in that replacement path:
- Multiple TUIs spawned heavyweight server contenders.
- A winner remained unobservable while it cold-booted, so another wave treated
it as absent and displaced it.
- A fresh TUI exhausted its reconnect budget and crashed with an unhandled
transport defect.
- A losing contender remained alive and consumed about 1 GB of RSS.
The `origin/v2` baseline serializes service startup with `EffectFlock`. A
contender acquires a three-second heartbeat lease, checks whether another
service became discoverable, and only the winner crosses the application-boot
boundary. This already prevents simultaneous heavy boots and makes startup
losers exit.
The lease is released immediately after registration, however, so it is not
lifetime ownership. Registration then reverts to last-writer-wins authority: a
deleted or corrupt registration can admit a second boot, a displaced server
terminates itself through its 10-second registration self-check, and a stalled
lease holder can be displaced after the three-second service staleness timeout.
`Flock` and `EffectFlock` live in `packages/core/src/util` and are also used for
config writes, MCP auth, npm installs, and repository caching. Despite the
name, the primitive is an atomic-mkdir lease with heartbeat and staleness
takeover, not an OS-held lock. It remains appropriate for bounded critical
sections, including today's startup fence, but is not lifetime service
ownership.
The current implementation also mixes three different concepts:
- **Ownership:** which process is allowed to be the managed server.
- **Discovery:** where clients can reach that process.
- **Lifecycle:** whether that process is starting, ready, stopping, or failed.
This design gives each concept one authority.
```definitions
[
{
"term": "Owner",
"definition": "The one process holding the process-held OS service lock."
},
{
"term": "Contender",
"definition": "A small serve process attempting to acquire the service lock. It must not initialize the application before winning."
},
{
"term": "Registration",
"definition": "An atomic discovery record containing the elected owner's identity and endpoint. Registration never grants ownership."
},
{
"term": "Lifecycle shell",
"definition": "The minimal HTTP surface bound by the elected process before application initialization. It serves health and retryable startup responses."
},
{
"term": "Application",
"definition": "The full server routes and global or location-scoped modules used for normal OpenCode work."
}
]
```
## Goals
- At most one process initializes and serves the managed application.
- Losing contenders exit before database, route, plugin, MCP, or location boot.
- A slow winner becomes observable before expensive initialization.
- Existing and freshly launched TUIs survive retryable service unavailability.
- Reconnect follows service state instead of displaying retry counts or raw
transport failures.
- Version-mismatch replacement remains triggered by a fresh TUI launch.
- A stale or malformed registration cannot create a second owner.
- An unresponsive owner is never killed automatically by an arbitrary TUI.
- Every spawned contender has a bounded path to ownership or exit.
## Non-goals
- Restarting automatically when a background update finds an idle window.
- Running old and candidate application servers concurrently.
- Adding a permanent steward, proxy, or supervisor process.
- Zero-downtime worker handoff or automatic rollback.
- Application protocol negotiation or automatic TUI self-restart.
- General hard-crash recovery for active Sessions.
- Defining recovery semantics for provider attempts, tools, shells, sub-agents,
permissions, questions, or background jobs.
- Automatically killing a frozen owner.
- Bounding concurrent location cold boots after clients reconnect.
- Multi-machine or clustered service placement.
## Invariants
1. **The service lock is ownership.** Exactly one process may hold the OS lock
for one installation channel and service profile.
2. **Ownership precedes boot.** A contender performs no expensive application
initialization before it acquires the lock.
3. **Ownership lasts for the process lifetime.** The owner holds an open lock
handle until the managed server exits. The OS releases it on process death
without a cleanup callback.
4. **The port is transport, not election.** The owner may select a dynamic port
after acquiring the lock.
5. **Registration is discovery, not election.** Deleting, corrupting, or
replacing registration does not invalidate a live owner's lock.
6. **Only a fresh launch enforces package version.** Existing TUIs reconnect to
the current owner without initiating version replacement.
7. **Transport loss is retryable.** It never terminates a TUI without a separate
diagnosed, non-retryable cause.
8. **Clients do not kill an unresponsive owner automatically.** Destructive
recovery requires the explicit `service restart` command.
9. **Lifecycle does not promise execution semantics.** Graceful replacement
invokes Session suspension and resumption hooks, but tool-level continuity
belongs to a separate design.
## System Model
```text
╭───────────────────────╮ ╭──────────────────────────────╮
│ Fresh or existing TUI │ │ Process-held OS service lock │
╰───────────┬───────────╯ ╰───────────────┬──────────────╯
╰─────┬ normal requests observe ───────────────────────╮ │
│ discover │ ├──╯ authorizes one owner
▼ │ ▼
╭───────────────────╮ │ ╭─────────────────╮
│ Registration file │ │ │ Lifecycle shell │
╰───────────────────╯ │ ╰────────┬────────╯
│ │
├────────────────────────╯
╭──────────────────────╮
│ OpenCode application │
╰──────────────────────╯
```
The lifecycle shell and application run in the same process. The distinction is
initialization order and responsibility, not process topology.
## Service Status
The server reports one small status value:
```typescript
type ServiceStatus =
| {
type: "starting"
}
| {
type: "ready"
}
| {
type: "stopping"
targetVersion?: string
}
| {
type: "failed"
message: string
action: string
}
```
The client adds only the discovery states needed by callers:
```typescript
type Status = { type: "missing" } | { type: "unreachable" } | { type: "unresponsive" } | ServiceStatus
```
The health response retains the existing fields for old clients and adds the
status discriminant:
```typescript
type ServiceHealth = {
healthy: true
version: string
pid: number
instanceID: string
status: ServiceStatus
}
```
`healthy: true` means the registered lifecycle shell is responding and its
identity matches registration. New clients use `status.type === "ready"` as
the application-readiness signal.
During `starting` or `stopping`, application requests are not held in memory.
They receive an immediate retryable response:
```http
HTTP/1.1 503 Service Unavailable
Retry-After: 1
Content-Type: application/json
```
`stopping` uses `service_stopping`. A failed application boot uses
`service_failed` and includes a safe diagnostic message.
A failed owner remains bound and keeps holding the service lock. Exiting on
failure would let every waiting client's `ensureRunning` loop elect a new
contender that repeats the same heavy failing boot, so staying bound turns a
deterministic boot failure into one observable `failed` state instead of a
client-driven respawn loop. Recovery still works: a fresh launch observes the
failed instance through the stop path, and explicit `service restart` replaces
it.
## Registration Contract
Registration contains only discovery identity:
```typescript
type ServiceRegistration = {
schema: 1
instanceID: string
version: string
url: string
pid: number
}
```
Authentication continues to use the existing private service credential
storage. The registration schema does not change that policy.
The owner writes registration only after the lifecycle shell has bound:
1. Bind the lifecycle shell.
2. Write a temporary registration file with mode `0600`.
3. Atomically rename it over the old registration.
4. Serve lifecycle health as `starting`.
On shutdown, the owner removes registration only if the current file still has
its `instanceID`. An old finalizer can never remove a successor's registration.
While running, the owner periodically asserts its registration. Because the
lock guarantees exactly one live owner, any registration that does not name the
owner is stale or corrupt, and the owner rewrites it. A deleted or clobbered
registration therefore heals within one assertion interval instead of leaving
clients waiting on absent discovery. This inverts today's self-check loop,
which terminates the displaced process instead of repairing discovery.
Legacy registration shapes are decoded by a compatibility adapter. The new
domain type does not make fields optional to represent old formats.
## Election
This design promotes today's startup fence into lifetime ownership.
Last-writer-wins registration is replaced by a process-held OS lock that is
acquired before any expensive boot work and held for the entire service
lifetime.
A heartbeat-and-staleness lease, including the existing `Flock` utility, is not
sufficient for service ownership: the service configures a three-second stale
timeout, after which its lock can be broken and recreated. An event-loop stall,
a suspended machine, or a debugger pause can therefore make a live owner appear
stale and allow a contender to displace it. Service ownership requires a
process-held OS lock: `flock` on Unix and an exclusively bound named pipe on
Windows. It cannot be broken because a heartbeat exceeded a timeout. Process
death releases the lock through the OS.
Neither Bun nor Node exposes `flock` directly, the existing `Flock` utility is
an mkdir-plus-heartbeat lease rather than an OS-held lock, and the common
lockfile packages are staleness-based leases as well. The platform layer uses
`bun:ffi` to call `flock` on POSIX and Node's named-pipe server support on
Windows, where Bun FFI is not available on every shipped architecture. It lives
alongside the existing utility in `packages/core/src/util`. This primitive is
the foundation of the design, so the delivery sequence spikes it first.
```text
Contender Lock Lifecycle Application
│ │ │ │
├─ try acquire ───▶ │ │
│ │ │ │
╭─ alt: lock held ────────────────────────────────────────────────╮
│ │ │ │ │ │
│ ◀─ busy ──────────┤ │ │ │
│ │ │ │ │ │
│ ├─────────╮ │ │ │ │
│ │ exit │ │ │ │ │
│ ◀─────────╯ │ │ │ │
│ │ │ │ │ │
├─ else: lock acquired ───────────────────────────────────────────┤
│ │ │ │ │ │
│ ◀─ owner ─────────┤ │ │ │
│ │ │ │ │ │
│ ├─ bind, register, starting ────────▶ │ │
│ │ │ │ │ │
│ ├─ initialize ──────────────────────────────────────────────▶ │
│ │ │ │ │ │
│╭─ alt: boot succeeds ──────────────────────────────────────────╮│
││ │ │ │ │ ││
││ │ │ ◀─ ready ───────────────┤ ││
││ │ │ │ │ ││
│├─ else: boot fails ────────────────────────────────────────────┤│
││ │ │ │ │ ││
││ │ │ ◀─ failed, stay bound ──┤ ││
││ │ │ │ │ ││
│╰───────────────────────────────────────────────────────────────╯│
│ │ │ │ │ │
╰─────────────────────────────────────────────────────────────────╯
│ │ │ │
```
Lock acquisition by a contender is nonblocking or tightly bounded. A loser
must exit before constructing application routes or importing startup-heavy
modules.
Several clients may spawn contenders concurrently. The design guarantees one
heavy winner, not one process spawn. If the winner crashes during startup, the
OS releases the lock and a later client retry starts another election.
The lock is scoped by installation channel and service profile. Local, preview,
and stable installations cannot displace one another.
## Update Activation
Background update behavior remains unchanged:
1. The running service checks for an update.
2. The updater installs the package in the background.
3. The running process continues using its existing process image.
4. No idle check or automatic restart occurs.
A fresh TUI launch activates the installed update:
1. Read registration and authenticate the responding service.
2. If its package version matches the fresh client, attach normally.
3. If the version differs, request graceful stop of that exact registered
instance using the existing authenticated stop path.
4. Re-check instance identity before every signal or escalation in that path.
5. Wait for the old process to exit and release the service lock.
6. Call `ensureRunning` until a compatible service becomes ready.
Concurrent fresh launchers may all observe the same old instance. Stopping that
exact instance must be idempotent. Once registration names a different instance,
a stale launcher stops signaling and returns to discovery.
No durable restart-transition record is introduced. The initiating fresh TUI
already knows the source and target versions and can display its update
preflight. Existing TUIs may display `Updating...` if they observed `stopping`;
otherwise `Waiting for background service...` is the honest fallback.
## Fresh Launch Versus Reconnect
Fresh launch and reconnect deliberately have different version policies:
```typescript
type ManagedConnection =
| {
type: "launch"
requiredVersion: string
}
| {
type: "reconnect"
}
```
- `launch` requires the installed package version and may activate replacement.
- `reconnect` accepts the current owner and never activates replacement.
This preserves today's permissive reconnect behavior. Explicit application
protocol negotiation and automatic TUI re-exec remain follow-ups.
## Client Reconnect
Fresh and existing TUIs use the same status loop after startup:
1. Read registration on every attempt. Do not retry a stale URL indefinitely.
2. If registration is absent, call `ensureRunning` and continue waiting.
3. If registration is unreachable, call `ensureRunning`. A live owner prevents
contenders from acquiring the lock; a dead owner does not.
4. If status is `starting` or `stopping`, wait.
5. If status is `failed`, show its actionable message.
6. If status is `ready`, rebuild HTTP and event-stream clients for the new
endpoint and perform authoritative state reconciliation.
Retry cadence is internal policy. Retry counts are telemetry, not user-facing
state. The TUI waits until the service is ready or the user exits.
Transport failures are handled at the TUI run boundary. A raw client transport
error or Effect defect must not escape to the terminal. Hard exit is reserved
for diagnosed causes such as invalid local configuration, failed authentication,
or a foreign process occupying an explicitly configured port.
The UI derives text from status:
| Status | User-facing state |
| ------------------------ | ----------------------------------- |
| No registration | `Starting background service...` |
| Registration unreachable | `Waiting for background service...` |
| `starting` | `Starting OpenCode vX...` |
| `stopping` | `Updating to vX...` |
| `failed` | Actionable failure message |
| `ready` | Normal TUI |
## Graceful Session Continuity
Version-mismatch replacement uses the existing graceful Session suspension and
resumption hooks:
1. The old server snapshots active Session IDs during graceful teardown.
2. The successor schedules those Sessions for continuation.
3. The runner reloads durable Session history before continuing.
This lifecycle design does not define what an interrupted physical provider
attempt or tool invocation means. It does not promise that external side effects
did not occur, replay the exact interrupted tool, preserve an in-memory form, or
recover process-local background work.
Those concerns require a separate execution-continuity design covering tools,
shells, sub-agents, permissions, questions, provider attempts, and hard-crash
recovery.
## Unresponsive Owner
An unreachable registration does not prove that the owner is dead. A contender
attempts the service lock:
- If the lock is free, the contender starts a replacement.
- If the lock is held, the contender exits and the client keeps waiting.
After a bounded diagnostic threshold, the client may show:
```text
The background service owns the service lock but is not responding.
Run `opencode service restart` to recover it.
```
Only explicit `service restart` may perform destructive recovery. It verifies
the complete registration and process instance before signaling, waits for
graceful exit, re-checks identity before escalation, and refuses to kill a
process it cannot positively identify.
Automatic frozen-owner recovery is deferred.
## Failure Walkthroughs
### Update with open TUIs
1. The old service installs vNext but keeps running.
2. A fresh vNext TUI finds the healthy vOld service and requests graceful stop.
3. The old service reports `stopping`, suspends active Sessions, and exits.
4. Open TUIs enter their indefinite status loops.
5. One or more clients spawn contenders.
6. One contender acquires the service lock. Losers exit before heavy boot.
7. The winner binds and registers the lifecycle shell as `starting`.
8. Clients stop spawning and wait on the observable winner.
9. The winner initializes the application and reports `ready`.
10. TUIs rebuild clients, reconcile state, and resume.
### Server crashes while ready
1. The endpoint becomes unreachable and registration may remain stale.
2. Clients call `ensureRunning`.
3. Process death has released the service lock.
4. One contender wins, replaces registration, and starts normally.
5. Detailed active-execution recovery is outside this design.
### Winner crashes during startup
1. Clients observed `starting` and remain alive.
2. Process death releases the service lock.
3. A later reconnect attempt starts another election.
4. One new contender wins; all other contenders exit.
### Registration is deleted while the owner is healthy
1. Clients may call `ensureRunning` because discovery is absent.
2. Every contender fails to acquire the owner's lock and exits.
3. No second application initializes.
4. The owner's next registration assertion republishes discovery.
### Owner is alive but unresponsive
1. Health fails, but the process still holds the service lock.
2. Contenders fail lock acquisition and exit.
3. Clients wait and eventually show explicit recovery guidance.
4. No TUI kills the owner automatically.
## TDD Verification
Implementation should proceed test-first with real subprocesses and real locks.
Mocks cannot establish process death, lock release, loser cleanup, or port
behavior.
### Election tests
| Scenario | Required result |
| ----------------------------------------------------- | ------------------------------------------------------- |
| Ten contenders start simultaneously | Exactly one crosses the application-boot boundary |
| Winner pauses after lock acquisition | No loser initializes or remains alive |
| Winner event loop pauses beyond the old stale timeout | Ownership is not displaced |
| Winner crashes before bind | Lock releases; a later attempt wins |
| Winner crashes after bind but before registration | Lock releases; a later attempt replaces stale discovery |
| Registration is deleted while owner runs | No second owner initializes |
| Registration is malformed | Lock still prevents a second owner |
| Registration names a dead PID | New contender can acquire the released lock |
| Two installation channels start | Each elects an independent owner |
| Explicit configured port is foreign-owned | Fail diagnostically; do not kill the foreign process |
The fixture records a marker immediately before application initialization. The
tests assert that only one process writes that marker and that every loser exits
within a bounded interval. The harness should also assert that a loser's peak
RSS stays an order of magnitude below an application boot, since import weight
was the observed incident cost.
### Lifecycle tests
| Scenario | Required result |
| ----------------------------------------------- | ---------------------------------------------------------------- |
| Winner owns lock but application boot is paused | Health reports `starting` |
| Application request arrives during startup | Immediate retryable `503` |
| Application becomes ready | Status changes once from `starting` to `ready` |
| Graceful replacement begins | Status reports `stopping` before disconnect |
| Application initialization fails | Actionable `failed` status; owner stays bound and holds the lock |
| Registration is deleted while owner runs | Owner republishes it within one assertion interval |
| Owner exits | Registration is removed only if it still names that owner |
### Update tests
| Scenario | Required result |
| -------------------------------------- | -------------------------------------------------------- |
| Background update installs vNext | Running vOld service does not restart |
| Fresh vNext launch finds vOld | Exact old instance stops; vNext eventually becomes ready |
| Two fresh vNext launches race | One heavy successor; both clients attach |
| Existing vOld TUI reconnects to vNext | It never requests replacement |
| Stale launcher observes a new instance | It does not signal the new instance |
### Reconnect tests
| Scenario | Required result |
| --------------------------------------------------- | -------------------------------------------------- |
| Endpoint disappears and changes port | TUI rediscovers and rebuilds clients |
| Service remains unavailable beyond old retry budget | TUI remains alive |
| Event stream reconnects | Client performs authoritative state reconciliation |
| Transport returns an unexpected defect | TUI formats it; no raw stack escapes |
| Owner remains unresponsive | TUI waits and shows explicit restart guidance |
## Delivery Sequence
1. **Spike the lock primitive.** Prove a nonblocking, process-held OS lock
under Bun on macOS, Linux, and Windows (`bun:ffi` to `flock` on POSIX and a
named pipe on Windows), including release on hard kill and behavior across
containers and network filesystems used in CI.
2. **Expand the subprocess test harness.** Begin from the baseline
two-contender test and cover ten contenders, lock release on crash, a paused
winner, deleted or corrupt registration, and bounded loser exit before
changing ownership.
3. **Contain client failure.** Make transport loss nonterminal, rediscover on
every cycle, and format unexpected failures at the TUI boundary.
4. **Promote the startup fence to process-held ownership.** Preserve the
existing pre-boot acquisition seam, replace its lease with the OS lock, hold
it until process exit, and invert the registration self-check from
self-termination to reassertion.
5. **Bind the lifecycle shell first.** Publish registration and `starting`,
return retryable `503` for application requests, then initialize the app.
The health contract change is public API: regenerate clients from
`packages/client` with `bun run generate`.
6. **Codify launch versus reconnect.** Fresh launch enforces installed version;
reconnect never activates replacement.
7. **Integrate graceful replacement.** Preserve current background-install and
fresh-launch activation behavior while invoking Session continuity hooks.
8. **Harden explicit recovery.** Verify exact process identity during explicit
`service restart`; never automatically kill an unresponsive owner.
9. **Run the full multi-process suite.** Include repeated restart cycles and
assert that no contender or child process remains afterward.
## Acceptance Criteria
- Ten concurrent restart observers produce one application initialization.
- No losing contender survives or builds a location graph.
- A 30-second application boot remains continuously observable as `starting`.
- A TUI remains alive through a service outage longer than the previous retry
budget.
- A service endpoint change does not require restarting an existing TUI.
- Background installation alone does not restart the service.
- A fresh mismatched TUI eventually attaches to the installed service version.
- Existing reconnecting TUIs never replace the current owner.
- Registration corruption cannot produce two owners.
- A deleted registration heals without restarting the owner or any client.
- An unresponsive owner is not killed without an explicit recovery command.
- Raw transport defects never escape to the terminal.
## Follow-ups
- Idle background update activation with an admission fence.
- Application protocol compatibility and automatic local TUI re-exec.
- Durable execution recovery for provider attempts and tools.
- Shell, sub-agent, permission, question, and background-job continuity.
- Automatic recovery for a positively identified frozen owner.
- Cold-boot concurrency limits and interaction-prioritized location loading.
- A steward or socket-handoff architecture if zero-downtime replacement becomes
a real requirement.
a real requirement.
+1
View File
@@ -495,6 +495,7 @@ async function subscribeSessionEvents() {
console.log("Subscribing to session events...")
const TOOL: Record<string, [string, string]> = {
todowrite: ["Todo", "\x1b[33m\x1b[1m"],
bash: ["Bash", "\x1b[31m\x1b[1m"],
edit: ["Edit", "\x1b[32m\x1b[1m"],
glob: ["Glob", "\x1b[34m\x1b[1m"],
-1
View File
@@ -256,7 +256,6 @@ new sst.cloudflare.x.SolidStart("Console", {
SECRET.UpstashRedisRestToken,
AUTH_API_URL,
STRIPE_WEBHOOK_SECRET,
SECRET.SupportApiKey,
DISCORD_INCIDENT_WEBHOOK_URL,
SECRET.HoneycombWebhookSecret,
STRIPE_SECRET_KEY,
-1
View File
@@ -7,7 +7,6 @@ new sst.cloudflare.x.SolidStart("Teams", {
domain: shortDomain,
path: "packages/enterprise",
buildCommand: "bun run build:cloudflare",
link: [SECRET.SupportApiKey],
environment: {
OPENCODE_STORAGE_ADAPTER: "r2",
OPENCODE_STORAGE_ACCOUNT_ID: sst.cloudflare.DEFAULT_ACCOUNT_ID,
-4
View File
@@ -67,10 +67,6 @@ const athenaWorkgroup = new aws.athena.Workgroup("LakeAthenaWorkgroup", {
configuration: {
enforceWorkgroupConfiguration: true,
publishCloudwatchMetricsEnabled: true,
// Athena bills $5/TB scanned; kill any query that would scan more than 2 TB
// so a regression cannot silently burn money. Stats sync full passes scan
// ~250 GB as of 2026-07.
bytesScannedCutoffPerQuery: 2 * 1024 ** 4,
resultConfiguration: {
outputLocation: $interpolate`s3://${athenaResultsBucket.bucket}/`,
},
-1
View File
@@ -9,7 +9,6 @@ export const SECRET = {
R2SecretKey: new sst.Secret("R2SecretKey", "unknown"),
HoneycombApiKey: new sst.Secret("HONEYCOMB_API_KEY"),
HoneycombWebhookSecret: new random.RandomPassword("HoneycombWebhookSecret", { length: 24 }),
SupportApiKey: new sst.Secret("SUPPORT_API_KEY"),
UpstashRedisRestUrl: new sst.Secret("UpstashRedisRestUrl"),
UpstashRedisRestToken: new sst.Secret("UpstashRedisRestToken"),
}
+2 -5
View File
@@ -42,12 +42,11 @@ const inferenceEventTable = new aws.s3tables.Table(
{ name: "request", type: "string", required: false },
{ name: "client", type: "string", required: false },
{ name: "user_agent", type: "string", required: false },
{ name: "model", type: "string", required: false },
{ name: "model_tier", type: "string", required: false },
{ name: "model_variant", type: "string", required: false },
{ name: "source", type: "string", required: false },
{ name: "provider", type: "string", required: false },
{ name: "provider_model", type: "string", required: false },
{ name: "model", type: "string", required: false },
{ name: "llm_error_code", type: "int", required: false },
{ name: "llm_error_message", type: "string", required: false },
{ name: "error_response", type: "string", required: false },
@@ -185,9 +184,7 @@ export const statSync = new sst.aws.Service("StatsSyncService", {
cluster: lakeCluster,
architecture: "arm64",
cpu: "0.25 vCPU",
// 0.5 GB caused an OOM crash loop: every restart immediately re-ran the 4 Athena
// stats queries (~$5/pass) every ~5 minutes instead of hourly.
memory: "2 GB",
memory: "0.5 GB",
image: {
context: ".",
dockerfile: "packages/stats/server/Dockerfile",
+4 -4
View File
@@ -1,8 +1,8 @@
{
"nodeModules": {
"x86_64-linux": "sha256-F1luclnqCPQk9yxfmeSYGaM/nScf28yBu9K3Fv+Xd24=",
"aarch64-linux": "sha256-XW0XZnsCRkU3MFJH9TjMRYZHffzVy3cQyiNCkec2gl4=",
"aarch64-darwin": "sha256-bf8kvORs3Fs2UYLp3PekF+AJR7NKOcHb+fIQA79RtMk=",
"x86_64-darwin": "sha256-sBdQPkzd7JXNW6Lbi9JHiAsfHwdLwTKWY+uPeXAv2Nw="
"x86_64-linux": "sha256-oWSGu+SP66Aquy/0Vaq7Bgp8404ZdOWbQX+O7h3jxHU=",
"aarch64-linux": "sha256-UsS0+c+GwtIukmWwQeFbY/3Oaz3t4Q7C6cFMGkmlyAY=",
"aarch64-darwin": "sha256-CArz92ewPmXO+ORFCBkCH8LzMpU/DjyaO4ic7QL0UpI=",
"x86_64-darwin": "sha256-rhnz9gmG6L06wIzfMhTaXDDEf6IbMD32CavqwXoqcUs="
}
}
+16 -29
View File
@@ -2,30 +2,23 @@
"$schema": "https://json.schemastore.org/package.json",
"name": "opencode",
"description": "AI-powered development tool",
"version": "0.0.0",
"private": true,
"type": "module",
"packageManager": "bun@1.3.14",
"scripts": {
"dev": "bun run --cwd packages/cli --conditions=browser src/index.ts",
"dev": "bun run --cwd packages/opencode --conditions=browser src/index.ts",
"dev:desktop": "bun --cwd packages/desktop dev",
"dev:web": "bun --cwd packages/app dev",
"dev:console": "ulimit -n 10240 2>/dev/null; bun run --cwd packages/console/app dev",
"dev:stats": "bun sst shell --stage=production -- bun run --cwd packages/stats/app dev",
"dev:www": "bun run --cwd packages/www dev",
"dev:storybook": "bun --cwd packages/storybook storybook",
"lint": "oxlint",
"lint:effect-patterns": "ast-grep scan -c script/ast-grep/sgconfig.yml packages/core/src packages/server/src packages/protocol/src packages/cli/src",
"test:lint-rules": "ast-grep test -c script/ast-grep/sgconfig.yml",
"typecheck": "bun turbo typecheck --concurrency=3",
"typecheck:profile": "bun script/profile-typecheck.ts",
"typecheck:profile:packages": "bun script/profile-typecheck-packages.ts",
"typecheck": "bun turbo typecheck",
"upgrade-opentui": "bun run script/upgrade-opentui.ts",
"postinstall": "bun run --cwd packages/core fix-node-pty",
"prepare": "husky",
"random": "echo 'Random script'",
"sso": "aws sso login --sso-session=opencode --no-browser",
"translate:app": "bun run script/translate-app.ts",
"test": "echo 'do not run tests from root' && exit 1"
},
"workspaces": {
@@ -37,23 +30,23 @@
"packages/slack"
],
"catalog": {
"@effect/opentelemetry": "4.0.0-beta.98",
"@effect/platform-node": "4.0.0-beta.98",
"@effect/sql-sqlite-bun": "4.0.0-beta.98",
"@effect/opentelemetry": "4.0.0-beta.83",
"@effect/platform-node": "4.0.0-beta.83",
"@effect/sql-sqlite-bun": "4.0.0-beta.83",
"@npmcli/arborist": "9.4.0",
"@noble/hashes": "2.2.0",
"@types/bun": "1.3.13",
"@types/cross-spawn": "6.0.6",
"@octokit/rest": "22.0.0",
"@hono/standard-validator": "0.2.0",
"@hono/zod-validator": "0.4.2",
"@opentui/core": "0.4.5",
"@opentui/keymap": "0.4.5",
"@opentui/solid": "0.4.5",
"@tanstack/solid-virtual": "3.13.32",
"@opentui/core": "0.3.4",
"@opentui/keymap": "0.3.4",
"@opentui/solid": "0.3.4",
"@tanstack/solid-virtual": "3.13.28",
"@shikijs/stream": "4.2.0",
"ulid": "3.0.1",
"@kobalte/core": "0.13.11",
"@corvu/drawer": "0.2.4",
"@types/luxon": "3.7.1",
"@types/node": "24.12.2",
"@types/semver": "7.7.1",
@@ -69,15 +62,14 @@
"dompurify": "3.3.1",
"drizzle-kit": "1.0.0-rc.2",
"drizzle-orm": "1.0.0-rc.2",
"effect": "4.0.0-beta.98",
"effect": "4.0.0-beta.83",
"ai": "6.0.168",
"cross-spawn": "7.0.6",
"hono": "4.10.7",
"hono-openapi": "1.1.2",
"fuzzysort": "3.1.0",
"get-east-asian-width": "1.6.0",
"luxon": "3.6.1",
"marked": "17.0.6",
"marked": "17.0.1",
"marked-shiki": "1.2.1",
"remend": "1.3.0",
"@playwright/test": "1.59.1",
@@ -86,11 +78,9 @@
"@typescript/native-preview": "7.0.0-dev.20251207.1",
"zod": "4.1.8",
"remeda": "2.26.0",
"resolve.exports": "2.0.3",
"sst": "4.13.1",
"shiki": "4.2.0",
"solid-list": "0.3.0",
"string-width": "7.2.0",
"tailwindcss": "4.1.11",
"vite": "7.1.4",
"@solidjs/meta": "0.29.4",
@@ -105,9 +95,6 @@
},
"devDependencies": {
"@actions/artifact": "5.0.1",
"@ast-grep/cli": "0.44.0",
"@types/react": "19.2.17",
"@types/react-dom": "19.2.3",
"@tsconfig/bun": "catalog:",
"@types/mime-types": "3.0.1",
"@typescript/native-preview": "catalog:",
@@ -118,7 +105,7 @@
"prettier": "3.6.2",
"semver": "^7.6.0",
"sst": "catalog:",
"turbo": "2.10.2"
"turbo": "2.8.13"
},
"dependencies": {
"@aws-sdk/client-s3": "3.933.0",
@@ -160,13 +147,13 @@
"@silvia-odwyer/photon-node@0.3.4": "patches/@silvia-odwyer%2Fphoton-node@0.3.4.patch",
"@standard-community/standard-openapi@0.2.9": "patches/@standard-community%2Fstandard-openapi@0.2.9.patch",
"solid-js@1.9.10": "patches/solid-js@1.9.10.patch",
"@ai-sdk/xai@3.0.102": "patches/@ai-sdk%2Fxai@3.0.102.patch",
"@ai-sdk/xai@3.0.82": "patches/@ai-sdk%2Fxai@3.0.82.patch",
"gcp-metadata@8.1.2": "patches/gcp-metadata@8.1.2.patch",
"pacote@21.5.0": "patches/pacote@21.5.0.patch",
"@ai-sdk/google@3.0.73": "patches/@ai-sdk%2Fgoogle@3.0.73.patch",
"@tanstack/solid-virtual@3.13.28": "patches/@tanstack%2Fsolid-virtual@3.13.28.patch",
"@pierre/trees@1.0.0-beta.4": "patches/@pierre%2Ftrees@1.0.0-beta.4.patch",
"@modelcontextprotocol/sdk@1.29.0": "patches/@modelcontextprotocol%2Fsdk@1.29.0.patch",
"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"
"@tanstack/virtual-core@3.17.0": "patches/@tanstack%2Fvirtual-core@3.17.0.patch"
}
}
-1113
View File
@@ -1,1113 +0,0 @@
# AI Library Design
> Discussion draft. This document describes an intended clean-break redesign of
> the current private `@opencode-ai/ai` API. Names and exact TypeScript signatures
> are illustrative until implementation, but the domain boundaries and defaults
> are deliberate.
## Status
- Proposed package: `@opencode-ai/ai`
- Initial stable domain: `LLM`
- Release posture: pre-1.0, with a stable-core intent
- Migration posture: clean break; do not preserve compatibility aliases
- Primary audience: general-purpose TypeScript developers using Effect
- Secondary audience: OpenCode and other durable agent runtimes
The package name leaves room for future domains such as embeddings, images, and
speech. Those domains are not part of this design and should not be forced into
the LLM run/turn model.
## Goals
1. Make a useful model call require very little code.
2. Make the default behavior good enough that most callers do not configure it.
3. Let advanced callers inspect, transform, or replace every important stage.
4. Keep provider quirks behind provider and protocol boundaries.
5. Preserve one provider turn as an explicit primitive for durable runtimes.
6. Keep serializable request data separate from process-local execution behavior.
7. Make unsupported combinations fail locally with useful typed errors.
8. Stay Effect-native without making package-specific service provisioning part
of every call site.
## Non-goals
- A global provider or model registry
- Durable agent orchestration or persistence
- Session history ownership
- Permission handling
- Cost billing or accounting guarantees
- Runtime model-catalog network requests
- Compatibility with the current private API
- Designing embeddings, image generation, speech, or transcription now
## Design Principles
### Progressive disclosure
The API has four layers:
1. **Run a model** with `LLM.generate` or `LLM.stream`.
2. **Control one provider turn** with `LLM.generateTurn` or `LLM.streamTurn`.
3. **Customize execution** with model defaults, call options, hooks, and provider
configuration.
4. **Author providers** with experimental provider definitions and protocols.
Normal documentation should teach only the first layer initially.
### Values over registries
Provider definitions, configured providers, models, protocols, tools, and hooks
are immutable values. Importing a provider does not register anything globally.
### Portable data, local behavior
Requests, messages, tool definitions, events, usage, and result projections are
plain immutable data with schemas. Configured models, executable tools, hooks,
and provider definitions may contain functions and Effect requirements and are
not serializable.
### Strong defaults, explicit overrides
Defaults should make common calls correct without hiding where behavior comes
from. Overrides compose in a documented order and never require patching
installed dependencies.
## Domain Model
### Provider Definition
An immutable, declarative description of a provider integration. It owns model
selection, option schemas, catalog corrections, protocols, and provider-wide
hooks. It is an experimental provider-authoring API.
### Configured Provider
A provider definition bound to deployment concerns such as credentials,
endpoint, transport, and provider headers.
`configure(...)` is intentionally deployment-only. It does not establish hidden
generation defaults.
### Model
A process-local executable model value selected from a configured provider. It
contains identity, capabilities, pricing metadata, provider-specific option
types, reusable request-behavior defaults, and hidden execution behavior.
Normal users do not need to learn the current `Route` composite. Protocol,
endpoint, auth, transport, and hooks are bound behind `Model`.
### Request
Portable, model-independent input for a model call. It may contain system
instructions, messages, tool definitions, generation controls, output intent,
cache policy, and metadata. It does not contain a configured model, executable
tool handlers, or hooks.
### Provider Turn
Exactly one request to a model provider and its normalized response. It does not
execute local tools or continue the conversation.
### Model Run
A complete interaction consisting of one or more provider turns. A run executes
local tools, appends their results, and continues until the model completes or a
stopping condition matches.
### TurnResult
The result of exactly one provider turn.
### GenerateResult
The result of a complete model run. It preserves every turn, tool activity,
aggregate usage, and estimated cost while exposing shortcuts to the final output.
### Protocol
The provider-wire contract that lowers portable requests into provider-native
bodies and raises provider-native stream events into normalized turn events.
Protocols are public, reusable, fully inspectable, and immutably patchable, but
the entire protocol-authoring API is experimental.
## Happy Path
### Effect
```ts
import { Effect } from "effect"
import { LLM } from "@opencode-ai/ai"
import { OpenAI } from "@opencode-ai/ai/providers/openai"
// Environment-based credentials are a provider default. No LLMClient layer is
// required: the Effect exposes standard runtime dependencies directly.
const model = OpenAI.model("gpt-4.1-mini")
const program = Effect.gen(function* () {
const result = yield* LLM.generate({
model,
system: "You are concise.",
prompt: "Explain Effect in one sentence.",
})
// `generate` always returns GenerateResult, even when the run has one turn.
console.log(result.text)
console.log(result.turns.length) // 1
console.log(result.usage)
console.log(result.cost) // Estimated cost, or undefined if any turn is unpriced.
})
```
The required Effect environment should contain standard services plus services
required by tools and hooks. It should not contain an `LLMClient` wrapper service.
### Current API
The current README appears similarly small but omits the package-specific service
and layer required at runtime:
```ts
// Current API: this request contains an executable model/route value.
const request = LLM.request({
model: OpenAI.configure({ apiKey }).responses("gpt-4o-mini"),
prompt: "Say hello.",
})
// Current API: this performs one provider turn, despite the broad name.
const response = yield * LLM.generate(request)
// Current API: execution also needs LLMClient.layer and RequestExecutor services.
```
The proposal removes mandatory request construction, removes package-specific
runtime provisioning, and makes `generate` mean a complete run.
## Provider And Model Selection
### Environment defaults
```ts
import { OpenAI } from "@opencode-ai/ai/providers/openai"
// Open strings receive autocomplete for IDs from the generated models.dev
// snapshot but continue to accept newly released and fine-tuned model IDs.
const model = OpenAI.model("gpt-4.1-mini")
```
### Deployment configuration
```ts
const openai = OpenAI.configure({
apiKey,
baseURL: "https://gateway.example.com/openai/v1",
headers: {
"x-tenant": "acme",
},
})
const model = openai.model("gpt-4.1-mini")
```
`configure(...)` owns deployment concerns only:
- Credentials and authentication
- Base URL and deployment location
- Transport selection
- Provider/deployment headers
- Other provider-specific connection setup
It does not own temperature, maximum output tokens, cache policy, retry policy,
tools, output schema, or system instructions.
### Reusable model defaults
```ts
const model = OpenAI.model("gpt-4.1-mini", {
generation: {
temperature: 0.2,
maxTokens: 2_000,
},
cache: "auto",
provider: {
store: false,
},
})
```
The second argument may default request behavior but not prompt/history or
executable tools. Call-level values override model defaults.
Provider-specific options are inferred from the concrete model:
```ts
yield *
LLM.generate({
model: OpenAI.model("gpt-4.1-mini"),
prompt: "Hello",
provider: {
store: false,
// OpenAI-specific autocomplete here; no `{ openai: ... }` nesting.
},
})
```
Code choosing between providers dynamically must narrow the model before using
provider-specific options. Portable generation controls remain available without
narrowing.
### Current API
```ts
// Current API mixes deployment configuration and reusable request behavior.
const model = OpenAI.configure({
apiKey,
generation: { maxTokens: 160 },
providerOptions: {
openai: { store: false },
},
}).model("gpt-4o-mini")
```
The proposal separates deployment configuration from selected-model behavior and
removes provider-keyed option bags when a concrete model already identifies the
provider.
## Requests
### Inline input
```ts
const result =
yield *
LLM.generate({
model,
system: "You are concise.",
prompt: "Summarize this pull request.",
generation: { maxTokens: 500 },
})
```
### Reusable portable request
```ts
const request = LLM.request({
system: "You are concise.",
prompt: "Summarize this pull request.",
generation: { maxTokens: 500 },
})
// Bind process-local execution behavior only when running.
const result = yield * LLM.generate({ model, request })
```
`LLM.request(...)` returns a plain immutable object. Use ordinary object spread
to derive another request:
```ts
const longer = {
...request,
generation: {
...request.generation,
maxTokens: 1_000,
},
}
```
There is no `LLM.updateRequest(...)` helper and no request Schema class.
### Conversation history
```ts
import { Message } from "@opencode-ai/ai"
const request = LLM.request({
system: "You are concise.",
messages: [
Message.user("What is Effect?"),
Message.assistant("A TypeScript library for typed functional effects."),
Message.user("Why would I use it?"),
],
})
```
Message helpers return plain immutable data. Object literals remain valid when
they satisfy the same input type.
`system` stays separate from chronological messages because it is the initial
privileged instruction. A chronological system message represents an instruction
change at a specific point in history.
## Complete Runs
### Automatic local tool loop
```ts
import { Effect, Schema } from "effect"
import { LLM, Tool } from "@opencode-ai/ai"
const tools = {
getWeather: Tool.make({
description: "Get current weather for a city.",
parameters: Schema.Struct({ city: Schema.String }),
success: Schema.Struct({ forecast: Schema.String }),
// Tool service requirements and typed errors flow into LLM.generate's
// Effect environment/error model instead of being erased.
execute: ({ city }) => Weather.get(city),
// Expected domain failures need an explicit model-visible representation.
formatError: (error) => ({
type: "text",
text: `Weather lookup failed: ${error.message}`,
}),
}),
}
const result =
yield *
LLM.generate({
model,
prompt: "What is the weather in London?",
tools,
})
// The runtime advertises definitions, dispatches calls, records results, and
// continues provider turns automatically.
console.log(result.text)
console.log(result.turns)
console.log(result.toolExecutions)
```
The default stopping condition is equivalent to:
```ts
stopWhen: StopWhen.turnCount(20)
```
This matches the Vercel AI SDK `ToolLoopAgent` default. Reaching the limit is a
successful result with `stopReason: "max-turns"`, not an Effect failure.
### Custom stopping
```ts
const result =
yield *
LLM.generate({
model,
prompt,
tools,
stopWhen: StopWhen.any(StopWhen.turnCount(8), StopWhen.hasToolCall("finalize")),
})
```
`stopWhen` accepts one predicate. Composition is explicit through combinators
such as `StopWhen.any`, `StopWhen.all`, and `StopWhen.not`.
Successful run stop reasons are closed:
```ts
type RunStopReason = "completed" | "max-turns" | "stop-condition"
```
### Tool concurrency
Independent tool calls emitted in one turn run concurrently with a bounded,
configurable concurrency limit. Results are appended in deterministic emitted
order. The runtime does not infer dependencies between tool calls; the model must
request dependent calls in separate turns.
Tools may declare an optional timeout. The overall run timeout still applies.
### Current API
Today callers must manually bridge every layer:
```ts
const request = LLM.request({
model,
prompt,
tools: Tool.toDefinitions(tools),
})
const events = yield * LLM.stream(request).pipe(Stream.runCollect)
const call = Array.from(events).find(LLMEvent.is.toolCall)
if (call && !call.providerExecuted) {
const dispatched = yield * ToolRuntime.dispatch(tools, call)
const followUp = LLM.updateRequest(request, {
messages: [...request.messages, Message.assistant([call]), Message.tool({ ...call, result: dispatched.result })],
})
// Caller must invoke the provider again and repeat the loop.
}
```
That explicit flow remains possible through turn APIs, but it is no longer the
only tool experience.
## One Provider Turn
OpenCode and other durable runtimes need to own persistence, tool settlement,
and continuation. They use the explicit turn API:
```ts
const result =
yield *
LLM.generateTurn({
model,
request,
// Definitions only. generateTurn never dispatches local handlers.
tools: {
getWeather: Tool.definition({
description: "Get current weather for a city.",
parameters: WeatherInput,
}),
},
})
// Persist the TurnResult and settle calls durably before the next turn.
for (const call of result.toolCalls) {
// Application-owned dispatch and persistence.
}
```
`generateTurn` and `streamTurn` make exactly one provider request. They never
execute a local tool and never continue automatically.
This separation is load-bearing:
- `generate` / `stream`: complete Model Run
- `generateTurn` / `streamTurn`: one Provider Turn
## Portable Tool Definitions
A portable request may declare serializable definitions, but executable handlers
are bound at run time:
```ts
const request = LLM.request({
prompt: "What is the weather in London?",
tools: {
getWeather: Tool.definition({
description: "Get current weather for a city.",
parameters: WeatherInput,
}),
},
})
const result =
yield *
LLM.generate({
model,
request,
tools: {
getWeather: Tool.make({
description: "Get current weather for a city.",
parameters: WeatherInput,
success: WeatherOutput,
execute: getWeather,
formatError,
}),
},
})
```
Definitions and handlers match by record key. Before the first provider call,
the runtime validates that every local definition has a compatible executable
binding. Missing or incompatible bindings fail with a typed tool-binding error.
Provider-hosted tools are distinct typed values:
```ts
const result =
yield *
LLM.generate({
model: OpenAI.model("gpt-4.1"),
prompt: "Find today's relevant announcements.",
tools: {
search: OpenAI.tool.webSearch({ searchContextSize: "medium" }),
},
})
```
Hosted tools do not pretend to have local handlers, and callers do not inspect a
`providerExecuted` boolean to decide whether dispatch is safe.
## Streaming
### Run stream
`LLM.stream` returns an Effect `Stream<RunEvent, LLMError, Requirements>`.
Run events explicitly expose orchestration boundaries:
```ts
const program = LLM.stream({ model, prompt, tools }).pipe(
Stream.tap((event) =>
Effect.sync(() => {
switch (event.type) {
case "run-start":
break
case "turn-start":
break
case "turn-event":
// Normalized text, reasoning, tool-call, usage, and finish events.
if (event.event.type === "text-delta") {
process.stdout.write(event.event.text)
}
break
case "tool-start":
break
case "tool-finish":
break
case "turn-finish":
break
case "run-finish":
// Contains the same full GenerateResult returned by LLM.generate.
console.log(event.result.usage)
break
}
}),
),
Stream.runDrain,
)
```
Exact event tag spelling remains an implementation detail to finalize, but the
algebra is settled:
- A separate `RunEvent` union for run, turn, and tool lifecycle
- A focused `TurnEvent` union for normalized provider output
- `streamTurn` emits only `TurnEvent`
- The terminal run event contains the full `GenerateResult`
External cancellation remains Effect interruption. It does not fabricate a
successful result with an `interrupted` stop reason.
## Structured Output
Structured output is an option on `generate`, not a separate operation:
```ts
const Weather = Schema.Struct({
city: Schema.String,
forecast: Schema.String,
highCelsius: Schema.Number,
})
const result =
yield *
LLM.generate({
model,
prompt: "Give me today's weather for London.",
output: Weather,
})
// Inferred from Weather.
result.output.city
```
The model declaration and protocol select the best reliable strategy:
1. Provider-native structured output when supported and reliable
2. Forced tool output when required as a compatibility fallback
3. Typed unsupported-capability failure before network execution when neither is
available
Advanced callers may override the strategy when exact provider semantics matter.
### Current API
```ts
// Current API is a separate operation and always forces a synthetic tool.
const result =
yield *
LLM.generateObject({
model,
prompt,
schema: Weather,
})
```
The proposal unifies generation and lets capabilities choose the strategy rather
than permanently encoding one cross-provider workaround.
## Model Catalog
`models.dev` is the release-time source for:
- Model ID suggestions
- Capabilities and modalities
- Context and output limits
- Pricing
- Other available model metadata
The package ships a generated, versioned snapshot. Normal execution performs no
catalog network requests.
Provider definitions may correct generated metadata where protocol-specific
knowledge is more accurate. Precedence is:
```text
models.dev snapshot
< provider-definition correction
< provider configuration override
< model-selection override
< call override
```
Unknown model IDs inherit only capabilities guaranteed by the selected protocol.
Unsupported request capabilities fail before network execution unless the caller
explicitly overrides the model declaration.
## Usage And Cost
`GenerateResult` aggregates normalized usage across every turn, including cache
read/write usage where providers report it.
It also exposes estimated cost using the generated models.dev pricing snapshot:
```ts
result.usage.inputTokens
result.usage.outputTokens
result.usage.cacheReadInputTokens
result.usage.cacheWriteInputTokens
result.cost?.total
result.cost?.currency // e.g. "USD"
```
Cost is an estimate, not a billing guarantee. If reliable pricing is unavailable
for any turn, aggregate run cost is unavailable rather than partial or silently
zero. Per-turn metadata should retain the catalog/pricing identity used so an
estimate can be explained.
## Caching
Prompt caching remains `"auto"` by default. The library places protocol-aware
cache boundaries where explicit caching is supported and does nothing on the wire
where providers cache implicitly.
```ts
yield *
LLM.generate({
model,
prompt,
cache: "none", // Explicit opt-out.
})
```
Granular cache policy remains available as an advanced request option.
## Retries, Timeouts, And Cancellation
### Retries
The default retry policy is deliberately conservative:
- Retry bounded transient transport and rate-limit failures
- Retry only before observable output
- Never silently retry after ambiguous tool execution or other side effects
- Allow each call to override or disable retry behavior
Retry configuration is call-scoped only. Provider and model configuration do not
silently inherit custom retry policies.
### Timeouts
```ts
yield *
LLM.generate({
model,
prompt,
timeout: "2 minutes", // Entire run, including tools.
turnTimeout: "30 seconds", // Each provider turn.
tools,
})
```
Exact Duration input spelling follows Effect conventions. Individual tools may
also declare optional timeouts.
### Cancellation
- Effect API: fiber interruption
- Promise API: `AbortSignal`, rejecting with a recognizable abort error
- Cancellation is not a successful run stop reason
## Hooks
Stable high-level hooks exist at five named stages:
1. Canonical request
2. Provider-native body
3. Prepared transport request
4. Normalized event
5. Error
Hooks are Effectful. They may transform the stage value or fail with a typed
error. They may not secretly short-circuit execution, synthesize a response,
retry, or redirect control flow.
```ts
const model = OpenAI.model("gpt-4.1", {
hooks: {
request: (request) =>
Effect.succeed({
...request,
metadata: { ...request.metadata, tenant: "acme" },
}),
body: (body, context) => auditBody(body, context),
transport: (request) => signInternalGatewayRequest(request),
event: (event) => redactProviderMetadata(event),
error: (error) => classifyInternalError(error),
},
})
```
Hook scopes compose in this order:
```text
provider-definition hooks -> model hooks -> call hooks
```
Each hook sees the prior hook's output. Replacement requires an explicit
definition-level patch, not accidental last-writer-wins semantics.
Provider-definition hooks are authored by provider integrations. They are not
passed through `Provider.configure(...)`, which remains deployment-only.
## HTTP And Provider Escape Hatches
The request customization ladder is:
1. Portable generation controls
2. Model-typed `provider` options
3. Stable staged hooks
4. Serializable HTTP/body overlays
5. Experimental provider-definition or protocol patching
```ts
yield *
LLM.generate({
model,
prompt,
http: {
headers: { "x-experimental": "1" },
query: { debug: "true" },
body: { newlyReleasedProviderField: true },
},
})
```
Raw overlays are intentional last-resort support for provider features that ship
before the library has a typed option.
## Provider-Native Metadata
Normalized message/content/event unions remain closed and exhaustive. Unknown or
provider-required round-trip data lives in caller-writable `providerMetadata`.
```ts
const assistant = Message.assistant([
{
type: "reasoning",
text: "...",
providerMetadata: {
openai: {
// Opaque provider data needed for replay or continuation.
},
},
},
])
```
Protocols validate metadata they consume. The field is an escape hatch, not a
portable semantic guarantee.
## Error Model
The Effect error channel is a tagged domain union rather than one `LLMError`
wrapper with nested reasons. Illustrative categories:
```ts
type LLMError =
| AuthenticationError
| InvalidRequestError
| UnsupportedCapabilityError
| ToolBindingError
| TransportError
| ProviderResponseError
| InvalidProviderOutputError
| HookError
```
Each error retains relevant provider/model/turn/stage context and its underlying
cause where available.
Expected tool errors keep their own typed error channel. `Tool.make` requires an
explicit mapping before such errors become model-visible tool results. Expected
mapped failures let the model recover; defects and interruption fail the run.
## Observability
The core library emits Effect-native spans and metrics for:
- Model runs
- Provider turns
- Provider requests
- Retries
- Tool executions
Default telemetry records metadata only:
- Provider and model identity
- Timing
- Token/cache usage
- Estimated cost availability
- Finish and stop reasons
- Retry counts
- Tool names
Prompts, model output, tool arguments, and tool results are never recorded by
default. Explicit hooks or telemetry configuration may opt into content capture.
## Promise API
Promise wrappers live at a separate subpath so the root remains unambiguously
Effect-first:
```ts
import { LLM } from "@opencode-ai/ai/promise"
import { OpenAI } from "@opencode-ai/ai/providers/openai"
const result = await LLM.generate({
model: OpenAI.model("gpt-4.1-mini"),
prompt: "Explain Effect in one sentence.",
signal: abortController.signal,
})
```
Streaming returns an `AsyncIterable<RunEvent>`:
```ts
for await (const event of LLM.stream({ model, prompt, signal })) {
if (event.type === "turn-event" && event.event.type === "text-delta") {
process.stdout.write(event.event.text)
}
}
```
Top-level Promise functions use a default runtime for built-in services. Custom
Effect service requirements use a configured client:
```ts
const client = LLM.makeClient({
layer: Layer.mergeAll(WeatherLive, AuditLive),
})
const result = await client.generate({ model, prompt, tools })
```
The Promise API mirrors Effect semantics. It does not invent different run,
error, stopping, or cancellation behavior.
## Schemas
Schemas live in a dedicated namespace/subpath instead of flooding root exports:
```ts
import { LLMSchema } from "@opencode-ai/ai/schema"
const request = yield * Schema.decodeUnknown(LLMSchema.Request)(input)
```
Schemas cover only serializable domain values:
- Requests and messages
- Portable tool definitions
- Turn and run events
- Serializable result projections
- Usage and cost estimates
- Tagged errors where serializable
- Provider metadata containers
Configured models, executable tools, hooks, provider definitions, and protocols
are process-local behavior and do not receive fake serialization schemas.
## Provider Authoring
Provider authoring is public but experimental.
### Declarative provider definition
```ts
import { Provider, Protocol } from "@opencode-ai/ai/provider"
export const ExampleAI = Provider.define({
id: "example",
options: ExampleProviderOptions,
configure: configureExampleDeployment,
protocols: {
responses: ExampleResponses,
},
models: ({ deployment, catalog }) => ({
model: (id, defaults) =>
Provider.model({
id,
deployment,
protocol: ExampleResponses,
metadata: catalog.model(id),
defaults,
}),
}),
catalog: generatedExampleCatalog,
corrections: exampleCatalogCorrections,
hooks: exampleProviderHooks,
})
```
The exact builder fields need implementation design, but it must remain one
declarative immutable object, infer provider option types, and support `.with(...)`
patching. It must not register globally.
Built-ins export their immutable definition for advanced forking:
```ts
import { OpenAI } from "@opencode-ai/ai/providers/openai"
const PatchedOpenAI = OpenAI.definition.with({
protocols: {
responses: OpenAI.protocols.responses.with({
// Explicit immutable stage patch.
body: {
fromRequest: patchResponsesBody,
},
}),
},
})
```
### Protocols
A protocol exposes all native types and stages:
- Provider-native request body and schema
- Transport frame type
- Provider-native event and schema
- Parser state
- Request lowering
- Event stepping
- Terminal detection and final flushing
Every stage is immutably patchable. This is deliberately more open than the AI
SDK integrations that motivated this package.
```ts
const PatchedResponses = OpenAIResponses.with({
body: {
fromRequest: (request) =>
OpenAIResponses.body.fromRequest(request).pipe(Effect.map((body) => ({ ...body, custom_field: true }))),
},
stream: {
step: patchResponsesStep,
},
})
```
Protocol body, frame, native event, and parser-state types are exported. Because
provider wire formats change often, these types and patch APIs are explicitly
experimental and do not receive the high-level API's compatibility promise.
## Package Surface
Illustrative export layout:
```text
@opencode-ai/ai
LLM
Message
Tool
StopWhen
stable domain types
@opencode-ai/ai/promise
Promise/AsyncIterable LLM facade
@opencode-ai/ai/schema
serializable domain schemas
@opencode-ai/ai/provider
experimental Provider and Protocol authoring APIs
@opencode-ai/ai/providers/openai
@opencode-ai/ai/providers/anthropic
@opencode-ai/ai/providers/google
...
```
Providers are imported through individual subpaths. The root does not export all
providers, and there is no preferred all-providers barrel.
## Defaults
| Concern | Default |
| ---------------------------- | --------------------------------------------------- |
| `LLM.generate` semantics | Complete Model Run |
| `LLM.generateTurn` semantics | Exactly one Provider Turn |
| Maximum turns | 20 |
| Turn-limit outcome | Successful `max-turns` result |
| Tool execution | Automatic in runs |
| Tool concurrency | Concurrent, bounded, deterministic result order |
| Prompt caching | `auto` |
| Retries | Conservative, pre-output transient failures only |
| Structured output | Capability-selected native or tool strategy |
| Capability mismatch | Typed failure before network execution |
| Unknown model capability | Conservative protocol baseline |
| Telemetry content | Metadata only |
| Cost | Estimated aggregate or unavailable |
| Cancellation | Interruption/rejection, never successful completion |
## Clean-break Migration
The redesign intentionally removes or changes these current concepts:
| Current | Proposed |
| --------------------------------------- | ----------------------------------------------------------- |
| Mandatory `LLM.request({ model, ... })` | Inline calls or model-free portable requests |
| `LLM.generate` means one turn | `LLM.generate` means complete run |
| `LLMClient.generate/stream` | `LLM.generateTurn/streamTurn` for one turn |
| `LLMClient.layer` requirement | Standard Effect requirements exposed directly |
| Public `Route` mental model | Hidden behind executable `Model` |
| `Provider.make` structural helper | Experimental declarative `Provider.define` |
| Schema classes as canonical values | Plain immutable values plus schema subpath |
| `LLM.updateRequest` | Object spread |
| `Tool.toDefinitions` in normal calls | Named executable tool records |
| Manual `ToolRuntime.dispatch` loop | Automatic run dispatch; explicit turn API for orchestration |
| `providerOptions: { openai: ... }` | Model-typed `provider: ...` |
| `generateObject` | Typed `output` option on `generate` |
| One event union for provider output | Separate `TurnEvent` and `RunEvent` unions |
| `providerExecuted` dispatch check | Distinct hosted-tool constructors |
| One wrapped `LLMError` | Tagged domain error union |
OpenCode should migrate to `generateTurn` / `streamTurn`, preserving its durable
prompt admission, persistence, permission, tool settlement, and continuation
boundaries. It should not use the automatic run API for Session orchestration.
## Remaining Implementation-level Questions
These do not reopen the main design:
1. Exact `RunEvent` and `TurnEvent` tag names and payloads
2. Exact `GenerateResult` shortcut fields for text, reasoning, output, and messages
3. Exact Provider definition TypeScript shape needed for strong inference
4. Exact protocol `.with(...)` patch syntax and replacement semantics
5. Exact Duration input fields and names
6. Exact models.dev generation pipeline and correction-file format
7. Exact cost representation and decimal arithmetic strategy
8. Exact default retry schedule and bounded tool concurrency number
9. Whether request-level serializable HTTP overlays belong in the stable schema
10. Which tagged errors are serializable versus process-local
These should be resolved with call-site sketches and implementation spikes rather
than by changing the domain boundaries above.
-108
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@@ -1,108 +0,0 @@
# LLM Provider Parity Status
Last reviewed: 2026-07-16
This file tracks the gap between the native `@opencode-ai/ai` package and the AI SDK provider packages that opencode still depends on for many catalog/runtime paths.
## Existing Status Sources
| File | What it tracks | Limitation |
| ----------------------------------- | ----------------------------------------------------------------------- | ------------------------------------------------------- |
| `packages/ai/DESIGN.md` | Future clean-break API proposal for `@opencode-ai/ai`. | Not a provider parity tracker. |
| `packages/ai/example/call-sites.md` | Route/value/provider-facade migration checklist and call-site sketches. | Architecture migration only; not AI SDK package parity. |
## Current Implementation Snapshot
| Native slice | Source | Current state | Main gaps |
| ---------------------------------- | ---------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| OpenAI Chat | `src/protocols/openai-chat.ts`, `src/providers/openai.ts` | Usable. Streams text, reasoning deltas, tool calls, usage, images, and common generation controls. | No typed structured-output / `response_format` path. Limited typed OpenAI option surface compared with SDK escape hatches. |
| OpenAI Responses HTTP | `src/protocols/openai-responses.ts`, `src/providers/openai.ts` | Usable. Supports hosted-tool event surfacing, reasoning replay metadata, GPT-5 defaults, and cache usage. | No explicit `previous_response_id` path. Typed options cover only a subset of Responses fields. Structured output is still mostly synthetic-tool based. |
| OpenAI Responses WebSocket | `src/protocols/openai-responses.ts`, `src/route/transport/websocket.ts` | Present as `OpenAI.responsesWebSocket(...)`. | Runner/catalog support explicitly must not downgrade WebSocket routes; broader runtime selection is not complete. |
| OpenAI-compatible Chat | `src/protocols/openai-compatible-chat.ts`, `src/providers/openai-compatible.ts` | Usable for generic Chat and several profiles: Baseten, Cerebras, DeepInfra, DeepSeek, Fireworks, Groq, TogetherAI. | Family quirks are mostly endpoint defaults, not full typed behavior. |
| OpenAI-compatible Responses | `src/protocols/openai-compatible-responses.ts`, `src/providers/openai-compatible-responses.ts` | Usable for deployments that implement the OpenAI Responses wire protocol. | No named family profiles or recorded deployment coverage yet. |
| Anthropic-compatible Messages | `src/protocols/anthropic-messages.ts`, `src/providers/anthropic-compatible.ts` | Usable for deployments that implement the Anthropic Messages wire protocol. Named Anthropic composes this base. | No named compatible family profiles or recorded deployment coverage yet. |
| Anthropic Messages | `src/protocols/anthropic-messages.ts`, `src/providers/anthropic.ts` | Usable. Supports tools, thinking, cache control, images, server-hosted tool events, and usage. | Provider option surface is small. Beta/header handling, metadata, and newer Messages fields need a typed parity pass. |
| Gemini Developer API | `src/protocols/gemini.ts`, `src/providers/google.ts` | Usable for Google API key flow. Supports text, images, tools, thinking signatures, and cache usage. | This is not Vertex. Typed provider options are narrow; many Gemini request fields currently require raw `http.body` overlays. |
| Vertex Gemini | `src/protocols/gemini.ts`, `src/providers/google-vertex.ts` | Usable through API-key express mode, explicit OAuth tokens, or ADC with project/location endpoint derivation, including tuned `endpoints/...` deployments. | Core runner/catalog mapping and recorded provider coverage are missing. |
| Vertex Chat | `src/protocols/openai-chat.ts`, `src/providers/google-vertex-chat.ts` | Usable for MaaS models through OpenAI-compatible Chat Completions with explicit OAuth tokens or ADC and project/location endpoint derivation. | Core runner/catalog mapping and recorded provider coverage are missing; MaaS family-specific request parity needs review. |
| Vertex Responses | `src/protocols/openai-responses.ts`, `src/providers/google-vertex-responses.ts` | Usable for Grok models through OpenAI-compatible Responses with explicit OAuth tokens or ADC, project/location endpoint derivation, and storage disabled by default. | Core runner/catalog mapping and recorded provider coverage are missing; stateful continuation is not supported by Vertex. |
| Vertex Messages | `src/protocols/anthropic-messages.ts`, `src/providers/google-vertex-messages.ts` | Usable through explicit OAuth tokens or ADC, including global, regional, and `eu`/`us` multi-region endpoints. | Core runner/catalog mapping and recorded provider coverage are missing; Vertex-specific hosted-tool parity needs review. |
| Bedrock Converse | `src/protocols/bedrock-converse.ts`, `src/providers/amazon-bedrock.ts` | Partial but real. Supports AWS event-stream framing, SigV4 with supplied credentials, bearer auth, tools, reasoning signatures, media, cache points, and recorded tests. | Native facade does not mirror the AI SDK plugin's default AWS credential chain/profile behavior. Runner/catalog mapping is missing. Guardrails, inference profiles, region-specific model ID fixes, and model-specific request fields need a parity pass. |
| Azure OpenAI | `src/providers/azure.ts` using OpenAI Chat/Responses protocols | Partial. Supports resource/base URL setup, API key auth, API version query, Chat, and Responses selectors. | Core runner does not map `@ai-sdk/azure` to this native facade. AAD/token auth and Azure-specific endpoint variants need review. |
| Cloudflare AI Gateway / Workers AI | `src/providers/cloudflare.ts` | Present via OpenAI-compatible Chat routes. | Useful but not part of the critical AI SDK replacement set yet. Needs per-product recorded coverage before relying on it broadly. |
| OpenRouter | `src/providers/openrouter.ts` | Present with OpenRouter-specific usage/reasoning/prompt-cache options over Chat. | Responses-style OpenRouter support is absent. |
| xAI | `src/providers/xai.ts` | Present with Responses and Chat selectors. | Needs package-parity review against the AI SDK xAI provider. |
| GitHub Copilot | `src/providers/github-copilot.ts` | Present as explicit-base-URL OpenAI Chat/Responses facade. | Runtime/catalog integration remains specialized and should stay separate from public OpenAI-compatible defaults. |
## V2 Runner Status
`packages/core/src/session/runner/model.ts` currently resolves only this native subset from catalog `aisdk` metadata:
| Catalog API | Native route used today |
| --------------------------------------------------- | ---------------------------- |
| `aisdk:@ai-sdk/openai` | `OpenAIResponses.route` |
| `aisdk:@ai-sdk/anthropic` | `AnthropicMessages.route` |
| `aisdk:@ai-sdk/openai-compatible` with explicit URL | `OpenAICompatibleChat.route` |
Other `aisdk:` packages, including Google Vertex, Azure, and Bedrock, currently fall back through the AI SDK loader in the production runner. The dependency-free resolver seam rejects them with `SessionRunnerModel.UnsupportedPackageError`; they are not native route mappings yet.
## AI SDK Package Parity Matrix
| AI SDK package | Intended native target | Status | Biggest gaps |
| --------------------------------- | -------------------------------------------------------------- | ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `@ai-sdk/openai` | `OpenAI.chat`, `OpenAI.responses`, `OpenAI.responsesWebSocket` | Partial / usable | Add complete typed option coverage, structured output strategy, explicit Responses continuation support, and runner route selection between Chat/Responses/WebSocket. |
| `@ai-sdk/openai-compatible` | Generic OpenAI-compatible Chat and Responses | Partial / usable | Decide per-family namespace/profile behavior and runner API selection for providers that support Responses versus Chat only. |
| `@ai-sdk/anthropic` | `AnthropicMessages` | Partial / usable | Finish Messages API parity for headers/betas/metadata/newer fields and document hosted-tool continuation expectations. |
| `@ai-sdk/google` | Gemini Developer API | Partial / usable | Add typed options for safety, response schema/modalities, cached content, grounding/search/code execution, and non-text output modes where supported. |
| `@ai-sdk/google-vertex` | Vertex Gemini namespace/facade | Partial / usable | Add runner/catalog mapping, recorded coverage, and broader provider-option parity. |
| `@ai-sdk/google-vertex/anthropic` | Anthropic Messages over Vertex namespace/facade | Partial / usable | Add runner/catalog mapping, recorded coverage, and Vertex-specific hosted-tool parity. |
| `@ai-sdk/google-vertex/maas` | Vertex Chat | Partial / usable | Add runner/catalog mapping, recorded coverage, and MaaS family-specific request parity. |
| `@ai-sdk/google-vertex/xai` | Vertex Chat / Responses | Partial / usable | Decide Chat/Responses selection for catalog models, add runner mapping and recorded coverage, and review xAI-specific request options. |
| `@ai-sdk/azure` | Azure OpenAI Chat/Responses facade | Partial | Map runner/catalog metadata to native Azure, handle resourceName/baseURL/apiVersion variants, add AAD/token auth story, and verify Chat vs Responses deployment selection. |
| `@ai-sdk/amazon-bedrock` | Bedrock Converse | Partial | Add default AWS credential chain/profile support, region/inference-profile model ID handling, provider option parity via `additionalModelRequestFields`, guardrails/performance config, and runner/catalog mapping. |
| `@ai-sdk/amazon-bedrock/mantle` | Bedrock Mantle OpenAI-compatible Chat/Responses namespace | Missing | Decide native Mantle shape, likely separate from Converse because it uses OpenAI-compatible Chat/Responses semantics over Bedrock. Add package mapping and tests. |
## Highest-Risk Gaps
1. Runner support is narrower than the LLM package. The package has native provider facades for Google, Azure, and Bedrock, but the V2 Session runner only maps OpenAI, Anthropic, and explicit OpenAI-compatible Chat from `aisdk` catalog metadata.
2. OpenAI-compatible Responses is available as a separate package entrypoint, but the V2 runner still maps `@ai-sdk/openai-compatible` to Chat only. Catalog selection must become API-aware before Responses deployments can use it.
3. Bedrock native auth is not AI SDK parity. The AI SDK plugin uses the default AWS provider chain, profile, container credentials, and Bedrock bearer token env behavior. Native Bedrock currently expects explicit credentials or bearer auth on the facade.
4. Vertex Gemini, Vertex Chat, Vertex Responses, and Vertex Messages now have native package entrypoints, but the core runner does not map catalog metadata to them yet and recorded provider coverage is still missing.
5. Azure is only a provider facade, not a full runtime replacement. Native Azure exists, but the catalog runner does not select it, and token auth/resource variants need review.
6. Provider option typing is uneven. OpenAI, Anthropic, Gemini, Bedrock, and OpenRouter each expose a small typed subset plus raw HTTP overlays; this is useful but not equivalent to AI SDK provider option coverage.
7. Structured output is not provider-native yet. `LLM.generateObject` still uses a synthetic tool strategy, while the future design expects native structured output where reliable and tool fallback where needed.
8. Package/namespace boundaries for the current native loading set are explicit in docs and exports. Other exported provider facades are not catalog package entrypoints until they implement the contract. Vertex xAI still needs catalog API selection; the missing native boundary is Bedrock Mantle.
9. Recorded coverage is uneven. OpenAI, Anthropic, Gemini, Bedrock Converse, Cloudflare, OpenRouter, and several OpenAI-compatible Chat providers have cassettes. Azure, Vertex, and Mantle need first-class recorded scenarios before switching defaults.
## Native Namespace Shape
These are implementation/API slices, not separate npm packages.
| API slice | Package-like entrypoint | Purpose |
| ----------------------------- | ------------------------------------------------------- | ---------------------------------------------------------------------------- |
| OpenAI Chat | `@opencode-ai/ai/providers/openai/chat` | OpenAI `/chat/completions` semantics. |
| OpenAI Responses | `@opencode-ai/ai/providers/openai/responses` | OpenAI `/responses` semantics with HTTP/WebSocket selected through settings. |
| OpenAI-compatible Chat | `@opencode-ai/ai/providers/openai-compatible` | Generic OpenAI-compatible `/chat/completions`. |
| OpenAI-compatible Responses | `@opencode-ai/ai/providers/openai-compatible/responses` | Generic OpenAI-compatible `/responses`. |
| Anthropic-compatible Messages | `@opencode-ai/ai/providers/anthropic-compatible` | Generic Anthropic-compatible `/messages`. |
| Anthropic Messages | `@opencode-ai/ai/providers/anthropic` | Anthropic Messages API. |
| Gemini Developer API | `@opencode-ai/ai/providers/google` | Google AI Studio Gemini API. |
| Vertex Gemini | `@opencode-ai/ai/providers/google-vertex/gemini` | Vertex Gemini API; `providers/google-vertex` is the default alias. |
| Vertex Chat | `@opencode-ai/ai/providers/google-vertex/chat` | Vertex OpenAI-compatible Chat Completions for MaaS models. |
| Vertex Responses | `@opencode-ai/ai/providers/google-vertex/responses` | Vertex OpenAI-compatible Responses for Grok models. |
| Vertex Messages | `@opencode-ai/ai/providers/google-vertex/messages` | Vertex-hosted Anthropic Messages API. |
| Bedrock Converse | `@opencode-ai/ai/providers/amazon-bedrock` | AWS Bedrock Converse API. |
| Bedrock Mantle | Missing | AWS Bedrock Mantle OpenAI-compatible APIs. |
| Azure OpenAI Chat | `@opencode-ai/ai/providers/azure/chat` | Azure specialization of OpenAI Chat. |
| Azure OpenAI Responses | `@opencode-ai/ai/providers/azure/responses` | Azure specialization of OpenAI Responses. |
## Suggested Next Work Slices
1. Add native runner/catalog mappings for `@ai-sdk/azure`, `@ai-sdk/google`, and `@ai-sdk/amazon-bedrock` where the existing native facades are already close.
2. Add API-aware runner/catalog selection between OpenAI-compatible Chat and Responses.
3. Bring Bedrock native auth/config to AI SDK parity: region, profile, default AWS credential chain, bearer token env, endpoint override, and cross-region inference profile handling.
4. Add runner/catalog mappings and recorded scenarios for the native Vertex Gemini, Chat, Responses, and Messages entrypoints.
5. Decide Chat/Responses selection for `@ai-sdk/google-vertex/xai` catalog models.
6. Add Bedrock Mantle as a separate OpenAI-compatible Bedrock namespace after deciding whether it uses Chat, Responses, or both by model.
7. Expand typed provider options from the existing V1 lowerer knowledge in `packages/core/src/v1/config/provider-options.ts` before adding more raw overlay examples.
8. Add recorded provider tests for Azure, Vertex Gemini, Vertex Chat, Vertex Responses, Vertex Messages, Bedrock credential-chain behavior, and Mantle before making native runtime the default for those packages.
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@@ -1,37 +0,0 @@
{
"$schema": "https://json.schemastore.org/package.json",
"version": "1.17.20",
"name": "@opencode-ai/ai",
"type": "module",
"license": "MIT",
"scripts": {
"setup:recording-env": "bun run script/setup-recording-env.ts",
"test": "bun test --timeout 30000 --only-failures",
"typecheck": "tsgo --noEmit",
"build": "tsc -p tsconfig.build.json"
},
"files": [
"dist"
],
"exports": {
".": "./src/index.ts",
"./*": "./src/*.ts"
},
"devDependencies": {
"@clack/prompts": "1.0.0-alpha.1",
"@effect/platform-node": "catalog:",
"@opencode-ai/http-recorder": "workspace:*",
"@tsconfig/bun": "catalog:",
"@types/bun": "catalog:",
"@typescript/native-preview": "catalog:",
"typescript": "catalog:"
},
"dependencies": {
"@smithy/eventstream-codec": "4.2.14",
"@smithy/util-utf8": "4.2.2",
"@opencode-ai/schema": "workspace:*",
"aws4fetch": "1.0.20",
"effect": "catalog:",
"google-auth-library": "10.5.0"
}
}
-38
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@@ -1,38 +0,0 @@
#!/usr/bin/env bun
import { Script } from "@opencode-ai/script"
import { $ } from "bun"
import { fileURLToPath } from "url"
const dir = fileURLToPath(new URL("..", import.meta.url))
process.chdir(dir)
async function published(name: string, version: string) {
return (await $`npm view ${name}@${version} version`.nothrow()).exitCode === 0
}
await $`bun run build`
const originalText = await Bun.file("package.json").text()
const pkg = JSON.parse(originalText) as {
name: string
version: string
exports: Record<string, string>
}
if (await published(pkg.name, pkg.version)) {
console.log(`already published ${pkg.name}@${pkg.version}`)
} else {
for (const [key, value] of Object.entries(pkg.exports)) {
const file = value.replace("./src/", "./dist/").replace(".ts", "")
// @ts-ignore
pkg.exports[key] = {
import: file + ".js",
types: file + ".d.ts",
}
}
await Bun.write("package.json", JSON.stringify(pkg, null, 2))
try {
await $`bun pm pack`
await $`npm publish *.tgz --tag ${Script.channel} --access public`
} finally {
await Bun.write("package.json", originalText)
}
}
-1
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@@ -1 +0,0 @@
export * from "./protocols/index"
@@ -1,23 +0,0 @@
import { Route, type RouteRoutedModelInput } from "../route/client"
import { Endpoint } from "../route/endpoint"
import { OpenAIResponses } from "./openai-responses"
const ADAPTER = "openai-compatible-responses"
export type OpenAICompatibleResponsesModelInput = RouteRoutedModelInput
/**
* Route for providers that expose an OpenAI Responses-compatible `/responses`
* endpoint. Provider helpers configure identity, endpoint, and auth before
* model selection while this route reuses the OpenAI Responses protocol.
*/
export const route = Route.make({
id: ADAPTER,
providerMetadataKey: "openai",
protocol: OpenAIResponses.protocol,
endpoint: Endpoint.path(OpenAIResponses.PATH),
transport: OpenAIResponses.httpTransport,
defaults: { providerOptions: { openai: { store: false } } },
})
export * as OpenAICompatibleResponses from "./openai-compatible-responses"
@@ -1,86 +0,0 @@
import type { JsonSchema, ModelToolSchemaCompatibility } from "../../schema"
import { isRecord } from "../../utils/record"
import { GeminiToolSchema } from "./gemini-tool-schema"
const removeNullSchemas = (value: unknown): unknown => {
if (Array.isArray(value)) return value.map(removeNullSchemas)
if (!isRecord(value)) return value
const fields = Object.fromEntries(
Object.entries(value)
.filter(([key]) => key !== "anyOf")
.map(([key, field]) => [key, removeNullSchemas(field)]),
)
if (!Array.isArray(value.anyOf)) return fields
const variants = value.anyOf.filter((variant) => !isRecord(variant) || variant.type !== "null").map(removeNullSchemas)
if (variants.length === 1 && isRecord(variants[0])) return { ...fields, ...variants[0] }
return { ...fields, anyOf: variants }
}
const tupleItemsSchema = (items: ReadonlyArray<unknown>) => {
const projected = items.map(moonshotNode)
if (projected.length === 0) return {}
if (projected.length === 1) return projected[0]
return { anyOf: projected }
}
const moonshotNode = (schema: unknown): unknown => {
if (Array.isArray(schema)) return schema.map(moonshotNode)
if (!isRecord(schema)) return schema
if (typeof schema.$ref === "string") return { $ref: schema.$ref }
return Object.fromEntries(
Object.entries(schema).flatMap(([key, value]) => {
if (key === "items" && Array.isArray(value)) return [[key, tupleItemsSchema(value)]]
if (key === "prefixItems") {
if ("items" in schema) return []
return [["items", tupleItemsSchema(Array.isArray(value) ? value : [])]]
}
if (key === "unevaluatedItems") return []
return [[key, moonshotNode(value)]]
}),
)
}
const moonshot = (schema: JsonSchema): JsonSchema => {
const projected = moonshotNode(schema)
return isRecord(projected) ? projected : {}
}
const openAI = (schema: JsonSchema): JsonSchema => {
const variants = Array.isArray(schema.anyOf) ? schema.anyOf.filter(isRecord) : []
const flattened =
variants.length === 0
? { ...schema, type: "object" }
: {
...Object.fromEntries(Object.entries(schema).filter(([key]) => key !== "anyOf")),
type: "object",
properties: variants.reduce(
(properties, variant) => ({ ...(isRecord(variant.properties) ? variant.properties : {}), ...properties }),
{},
),
additionalProperties: false,
}
const normalized = removeNullSchemas(flattened)
return isRecord(normalized) ? normalized : { type: "object" }
}
const gemini = (schema: JsonSchema): JsonSchema => GeminiToolSchema.convert(schema) ?? {}
const modelCompatibility = (
schema: JsonSchema,
compatibility: ModelToolSchemaCompatibility | undefined,
): JsonSchema => {
if (compatibility === undefined) return schema
switch (compatibility) {
case "gemini":
return gemini(schema)
case "moonshot":
return moonshot(schema)
}
}
export const ToolSchemaProjection = {
gemini,
modelCompatibility,
moonshot,
openAI,
} as const
-198
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@@ -1,198 +0,0 @@
import { Option, Schema } from "effect"
import {
AuthenticationReason,
ContentPolicyReason,
InvalidRequestReason,
LLMError,
ProviderErrorEvent,
ProviderInternalReason,
QuotaExceededReason,
RateLimitReason,
UnknownProviderReason,
type HttpContext,
type HttpRateLimitDetails,
type ProviderMetadata,
} from "./schema"
const patterns = [
/prompt is too long/i,
/input is too long for requested model/i,
/exceeds the context window/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 limit of \d+/i,
/exceeds the available context size/i,
/greater than the context length/i,
/context window exceeds limit/i,
/exceeded model token limit/i,
/context[_ ]length[_ ]exceeded/i,
/request entity too large/i,
/context length is only \d+ tokens/i,
/input length.*exceeds.*context length/i,
/prompt too long; exceeded (?:max )?context length/i,
/too large for model with \d+ maximum context length/i,
/model_context_window_exceeded/i,
]
export const isContextOverflow = (message: string) =>
patterns.some((pattern) => pattern.test(message)) || /^4(00|13)\s*(status code)?\s*\(no body\)/i.test(message)
export const isContextOverflowFailure = (failure: unknown) =>
failure instanceof LLMError
? failure.reason._tag === "InvalidRequest" && failure.reason.classification === "context-overflow"
: Schema.is(ProviderErrorEvent)(failure) && failure.classification === "context-overflow"
const decodeJson = Schema.decodeUnknownOption(Schema.UnknownFromJsonString)
const CONTENT_POLICY_CODES = new Set(["content_filter", "content_policy_violation", "safety"])
const QUOTA_CODES = new Set(["insufficient_quota", "usage_not_included", "billing_error"])
const RATE_LIMIT_CODES = new Set(["resource_exhausted", "throttlingexception", "too_many_requests"])
const SERVER_CODES = new Set([
"api_error",
"internal",
"internal_error",
"internal_server_error",
"internalserverexception",
"modelstreamerrorexception",
"modeltimeoutexception",
"overloaded_error",
"response_error",
"server_error",
"server_is_overloaded",
"serviceunavailableexception",
])
const INVALID_REQUEST_CODES = new Set([
"invalid_argument",
"invalid_prompt",
"invalid_request_error",
"model_not_found",
"not_found",
"not_found_error",
"resourcenotfoundexception",
"request_too_large",
"validationexception",
])
const RATE_LIMIT_TEXT = /rate increased too quickly|rate[-_\s]?limit|throttl|too[_\s]?many[_\s]?requests/i
const QUOTA_TEXT = /insufficient[-_\s]?quota|quota[-_\s]?exceeded/i
const CONTENT_POLICY_TEXT =
/content[-_\s]?(?:filter|policy)|safety (?:filter|policy|rating)|blocked (?:by|due to) safety/i
const INVALID_REQUEST_TEXT = /validation (?:error|exception)/i
const SERVER_TEXT = /internal server error|service unavailable/i
export interface ProviderFailure {
readonly message: string
/** Provider text used only for classification and never retained on the resulting reason. */
readonly evidence?: string | undefined
readonly status?: number | undefined
readonly code?: string | undefined
readonly retryAfterMs?: number | undefined
readonly rateLimit?: HttpRateLimitDetails | undefined
readonly http?: HttpContext | undefined
readonly providerMetadata?: ProviderMetadata | undefined
}
// Keep HTTP failures and provider-reported stream failures on one typed path so
// session retry policy never needs provider-specific string matching.
export function classifyProviderFailure(input: ProviderFailure): LLMError["reason"] {
const body = input.http?.body ?? ""
const evidence = input.evidence ?? ""
const codes = [input.code, ...providerCodes(body), ...providerCodes(input.message), ...providerCodes(evidence)]
.filter((code): code is string => code !== undefined)
.map((code) => code.toLowerCase())
const texts = [body, input.message, evidence]
const common = { message: input.message, providerMetadata: input.providerMetadata, http: input.http }
const clientScoped = input.status === undefined || (input.status >= 400 && input.status < 500)
if (
clientScoped &&
(codes.includes("context_length_exceeded") ||
codes.includes("model_context_window_exceeded") ||
texts.some(isContextOverflow))
)
return new InvalidRequestReason({ ...common, classification: "context-overflow" })
if (codes.some((code) => CONTENT_POLICY_CODES.has(code)) || texts.some((text) => CONTENT_POLICY_TEXT.test(text)))
return new ContentPolicyReason(common)
if (
codes.some((code) => QUOTA_CODES.has(code)) ||
(input.status === 429 && texts.some((text) => QUOTA_TEXT.test(text)))
)
return new QuotaExceededReason(common)
if (input.status === 401) return new AuthenticationReason({ ...common, kind: "invalid" })
if (input.status === 403) return new AuthenticationReason({ ...common, kind: "insufficient-permissions" })
if (codes.some((code) => code === "authentication_error" || code === "unauthenticated"))
return new AuthenticationReason({ ...common, kind: "invalid" })
if (
codes.some(
(code) => code === "accessdeniedexception" || code === "permission_error" || code === "permission_denied",
)
)
return new AuthenticationReason({ ...common, kind: "insufficient-permissions" })
if (codes.some((code) => code.includes("rate_limit") || RATE_LIMIT_CODES.has(code)))
return new RateLimitReason({
...common,
retryAfterMs: input.retryAfterMs,
rateLimit: input.rateLimit,
})
if (texts.some((text) => RATE_LIMIT_TEXT.test(text)))
return new RateLimitReason({
...common,
retryAfterMs: input.retryAfterMs,
rateLimit: input.rateLimit,
})
if (codes.some((code) => INVALID_REQUEST_CODES.has(code)) || texts.some((text) => INVALID_REQUEST_TEXT.test(text)))
return new InvalidRequestReason(common)
if (
codes.some((code) => SERVER_CODES.has(code) || code.includes("unavailable")) ||
texts.some((text) => SERVER_TEXT.test(text))
)
return new ProviderInternalReason({
...common,
status: input.status,
retryAfterMs: input.retryAfterMs,
})
if (input.status === 429) {
return new RateLimitReason({
...common,
retryAfterMs: input.retryAfterMs,
rateLimit: input.rateLimit,
})
}
if (input.status !== undefined && input.status >= 500)
return new ProviderInternalReason({
...common,
status: input.status,
retryAfterMs: input.retryAfterMs,
})
if (
input.status === 400 ||
input.status === 404 ||
input.status === 409 ||
input.status === 413 ||
input.status === 422
)
return new InvalidRequestReason(common)
return new UnknownProviderReason({ ...common, status: input.status })
}
function providerCodes(value: string) {
const decoded = Option.getOrUndefined(decodeJson(value))
if (!isRecord(decoded)) return []
const error = isRecord(decoded.error) ? decoded.error : undefined
const response = isRecord(decoded.response) ? decoded.response : undefined
const responseError = isRecord(response?.error) ? response.error : undefined
return [
decoded.code,
decoded.status,
error?.code,
error?.type,
error?.status,
responseError?.code,
responseError?.type,
].filter((value): value is string => typeof value === "string")
}
function isRecord(value: unknown): value is Record<string, unknown> {
return typeof value === "object" && value !== null
}
-16
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@@ -1,16 +0,0 @@
import type { Model } from "./schema"
export interface Settings extends Readonly<Record<string, unknown>> {
readonly headers?: Readonly<Record<string, string>>
readonly body?: Readonly<Record<string, unknown>>
readonly limits?: {
readonly context: number
readonly output: number
}
}
export interface Definition<ProviderSettings extends Settings = Settings> {
readonly model: (modelID: string, settings: ProviderSettings) => Model
}
export * as ProviderPackage from "./provider-package"
-1
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@@ -1 +0,0 @@
export * from "./providers/index"
@@ -1,67 +0,0 @@
import type { ProviderPackage } from "../provider-package"
import { AnthropicMessages } from "../protocols/anthropic-messages"
import { Auth } from "../route/auth"
import type { ProviderAuthOption } from "../route/auth-options"
import type { RouteDefaultsInput } from "../route/client"
import { ProviderID, type ModelID } from "../schema"
export const id = ProviderID.make("anthropic-compatible")
export type Config = RouteDefaultsInput &
ProviderAuthOption<"optional"> & {
readonly provider?: string
readonly baseURL: string
}
export type Settings = ProviderPackage.Settings &
(
| { readonly apiKey?: string; readonly authToken?: never }
| { readonly apiKey?: never; readonly authToken?: string }
) & {
readonly baseURL: string
readonly provider?: string
}
export const routes = [AnthropicMessages.route]
const auth = (input: ProviderAuthOption<"optional">) => {
if ("auth" in input && input.auth) return input.auth
return Auth.optional("apiKey" in input ? input.apiKey : undefined, "apiKey").pipe(Auth.header("x-api-key"))
}
export const configure = (input: Config) => {
if (!input.baseURL) throw new Error("Anthropic-compatible providers require a baseURL")
const provider = input.provider ?? "anthropic-compatible"
const { provider: _, baseURL, apiKey: _apiKey, auth: _auth, ...rest } = input
const route = AnthropicMessages.route.with({
...rest,
provider,
endpoint: { baseURL },
auth: auth(input),
})
return {
id: ProviderID.make(provider),
model: (modelID: string | ModelID) => route.model({ id: modelID }),
configure,
}
}
export const provider = {
id,
configure,
}
export const model: ProviderPackage.Definition<Settings>["model"] = (modelID, settings) => {
if (settings.apiKey !== undefined && settings.authToken !== undefined)
throw new Error("Anthropic-compatible apiKey cannot be combined with authToken")
return configure({
...(settings.authToken === undefined ? { apiKey: settings.apiKey } : { auth: Auth.bearer(settings.authToken) }),
baseURL: settings.baseURL,
headers: settings.headers === undefined ? undefined : { ...settings.headers },
http: settings.body === undefined ? undefined : { body: { ...settings.body } },
limits: settings.limits,
provider: settings.provider,
}).model(modelID)
}
export * as AnthropicCompatible from "./anthropic-compatible"
-56
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@@ -1,56 +0,0 @@
import type { RouteDefaultsInput } from "../route/client"
import { Auth } from "../route/auth"
import type { ProviderAuthOption } from "../route/auth-options"
import type { ProviderPackage } from "../provider-package"
import { ProviderID, type ModelID } from "../schema"
import { AnthropicMessages } from "../protocols/anthropic-messages"
import { AnthropicCompatible } from "./anthropic-compatible"
export const id = ProviderID.make("anthropic")
export const routes = [AnthropicMessages.route]
export type Config = RouteDefaultsInput & ProviderAuthOption<"optional"> & { readonly baseURL?: string }
export type Settings = ProviderPackage.Settings &
(
| { readonly apiKey?: string; readonly authToken?: never }
| { readonly apiKey?: never; readonly authToken?: string }
) & {
readonly baseURL?: string
}
const auth = (options: ProviderAuthOption<"optional">) => {
if ("auth" in options && options.auth) return options.auth
return Auth.optional("apiKey" in options ? options.apiKey : undefined, "apiKey")
.orElse(Auth.config("ANTHROPIC_API_KEY"))
.pipe(Auth.header("x-api-key"))
}
export const configure = (input: Config = {}) => {
const { apiKey: _, auth: _auth, baseURL, ...rest } = input
const compatible = AnthropicCompatible.configure({
...rest,
auth: auth(input),
baseURL: baseURL ?? AnthropicMessages.DEFAULT_BASE_URL,
provider: id,
})
return {
id,
model: (modelID: string | ModelID) => compatible.model(modelID),
configure,
}
}
export const provider = configure()
export const model: ProviderPackage.Definition<Settings>["model"] = (modelID, settings) => {
if (settings.apiKey !== undefined && settings.authToken !== undefined)
throw new Error("Anthropic apiKey cannot be combined with authToken")
return configure({
...(settings.authToken === undefined ? { apiKey: settings.apiKey } : { auth: Auth.bearer(settings.authToken) }),
baseURL: settings.baseURL,
headers: settings.headers === undefined ? undefined : { ...settings.headers },
http: settings.body === undefined ? undefined : { body: { ...settings.body } },
limits: settings.limits,
}).model(modelID)
}
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@@ -1,2 +0,0 @@
export { chatModel as model } from "../azure"
export type { Settings } from "../azure"
@@ -1,2 +0,0 @@
export { responsesModel as model } from "../azure"
export type { Settings } from "../azure"
@@ -1,81 +0,0 @@
import type { ProviderPackage } from "../provider-package"
import { OpenAICompatibleChat } from "../protocols/openai-compatible-chat"
import type { RouteDefaultsInput } from "../route/client"
import { ProviderID, type ModelID, type ProviderOptions } from "../schema"
import { GoogleVertexShared } from "./google-vertex-shared"
export const id = ProviderID.make("google-vertex")
export type Config = RouteDefaultsInput &
GoogleVertexShared.OAuthOptions & {
readonly baseURL?: string
readonly location?: string
readonly project?: string
}
export interface Settings extends ProviderPackage.Settings {
readonly accessToken?: string
readonly apiKey?: never
readonly baseURL?: string
readonly location?: string
readonly project?: string
readonly providerOptions?: ProviderOptions
}
const route = OpenAICompatibleChat.route.with({
id: "google-vertex-chat",
provider: id,
})
export const routes = [route]
const configuredRoute = (input: Config) => {
if ("apiKey" in input && input.apiKey !== undefined) throw new Error("Google Vertex Chat does not support API keys")
const {
accessToken: _accessToken,
auth: _auth,
baseURL,
location: inputLocation,
project: inputProject,
...rest
} = input
const location = GoogleVertexShared.location(inputLocation, "global")
const project = GoogleVertexShared.project(inputProject)
return route.with({
...rest,
endpoint: {
baseURL:
baseURL ??
`https://aiplatform.googleapis.com/v1/projects/${GoogleVertexShared.requireProject(project)}/locations/${location}/endpoints/openapi`,
},
auth: GoogleVertexShared.oauth(input, project),
})
}
export const configure = (input: Config = {}) => {
const route = configuredRoute(input)
return {
id,
model: (modelID: string | ModelID) => route.model({ id: modelID }),
configure,
}
}
export const provider = {
id,
configure,
}
export const model: ProviderPackage.Definition<Settings>["model"] = (modelID, settings) => {
if (settings.apiKey !== undefined) throw new Error("Google Vertex Chat does not support API keys")
return configure({
accessToken: settings.accessToken,
baseURL: settings.baseURL,
headers: settings.headers === undefined ? undefined : { ...settings.headers },
http: settings.body === undefined ? undefined : { body: { ...settings.body } },
limits: settings.limits,
location: settings.location,
project: settings.project,
providerOptions: settings.providerOptions,
}).model(modelID)
}
@@ -1,111 +0,0 @@
import { Effect, Schema, Struct } from "effect"
import type { ProviderPackage } from "../provider-package"
import { AnthropicMessages } from "../protocols/anthropic-messages"
import { Auth } from "../route/auth"
import { Route, type RouteDefaultsInput } from "../route/client"
import { Endpoint } from "../route/endpoint"
import { Framing } from "../route/framing"
import { Protocol } from "../route/protocol"
import { ProviderID, type ModelID, type ProviderOptions } from "../schema"
import { GoogleVertexShared } from "./google-vertex-shared"
const VERSION = "vertex-2023-10-16" as const
// models.dev uses this provider id even though the API contract is Anthropic Messages.
export const id = ProviderID.make("google-vertex-anthropic")
export type Config = RouteDefaultsInput &
GoogleVertexShared.OAuthOptions & {
readonly baseURL?: string
readonly location?: string
readonly project?: string
}
export interface Settings extends ProviderPackage.Settings {
readonly accessToken?: string
readonly apiKey?: never
readonly baseURL?: string
readonly location?: string
readonly project?: string
readonly providerOptions?: ProviderOptions
}
const route = Route.make({
id: "google-vertex-messages",
provider: id,
providerMetadataKey: "anthropic",
protocol: Protocol.make({
id: AnthropicMessages.protocol.id,
body: {
schema: Schema.Struct({
...Struct.omit(AnthropicMessages.AnthropicMessagesBody.fields, ["model"]),
anthropic_version: Schema.Literal(VERSION),
}),
from: (request) =>
AnthropicMessages.protocol.body.from(request).pipe(
Effect.map((body) => ({
...Struct.omit(body, ["model"]),
anthropic_version: VERSION,
})),
),
},
stream: AnthropicMessages.protocol.stream,
}),
endpoint: Endpoint.path(({ request }) => `/${request.model.id}:streamRawPredict`),
auth: Auth.none,
framing: Framing.sse,
})
export const routes = [route]
const configuredRoute = (input: Config) => {
if ("apiKey" in input && input.apiKey !== undefined)
throw new Error("Google Vertex Messages does not support API keys")
const {
accessToken: _accessToken,
auth: _auth,
baseURL,
location: inputLocation,
project: inputProject,
...rest
} = input
const location = GoogleVertexShared.location(inputLocation, "global")
const project = GoogleVertexShared.project(inputProject)
return route.with({
...rest,
endpoint: {
baseURL:
baseURL ??
`https://${GoogleVertexShared.host(location)}/v1/projects/${GoogleVertexShared.requireProject(project)}/locations/${location}/publishers/anthropic/models`,
},
auth: GoogleVertexShared.oauth(input, project),
})
}
export const configure = (input: Config = {}) => {
const route = configuredRoute(input)
return {
id,
model: (modelID: string | ModelID) => route.model({ id: modelID }),
configure,
}
}
export const provider = {
id,
configure,
}
export const model: ProviderPackage.Definition<Settings>["model"] = (modelID, settings) => {
if (settings.apiKey !== undefined) throw new Error("Google Vertex Messages does not support API keys")
return configure({
accessToken: settings.accessToken,
baseURL: settings.baseURL,
headers: settings.headers === undefined ? undefined : { ...settings.headers },
http: settings.body === undefined ? undefined : { body: { ...settings.body } },
limits: settings.limits,
location: settings.location,
project: settings.project,
providerOptions: settings.providerOptions,
}).model(modelID)
}
@@ -1,82 +0,0 @@
import type { ProviderPackage } from "../provider-package"
import { OpenAICompatibleResponses } from "../protocols/openai-compatible-responses"
import type { RouteDefaultsInput } from "../route/client"
import { ProviderID, type ModelID, type ProviderOptions } from "../schema"
import { GoogleVertexShared } from "./google-vertex-shared"
export const id = ProviderID.make("google-vertex")
export type Config = RouteDefaultsInput &
GoogleVertexShared.OAuthOptions & {
readonly baseURL?: string
readonly location?: string
readonly project?: string
}
export interface Settings extends ProviderPackage.Settings {
readonly accessToken?: string
readonly apiKey?: never
readonly baseURL?: string
readonly location?: string
readonly project?: string
readonly providerOptions?: ProviderOptions
}
const route = OpenAICompatibleResponses.route.with({
id: "google-vertex-responses",
provider: id,
})
export const routes = [route]
const configuredRoute = (input: Config) => {
if ("apiKey" in input && input.apiKey !== undefined)
throw new Error("Google Vertex Responses does not support API keys")
const {
accessToken: _accessToken,
auth: _auth,
baseURL,
location: inputLocation,
project: inputProject,
...rest
} = input
const location = GoogleVertexShared.location(inputLocation, "global")
const project = GoogleVertexShared.project(inputProject)
return route.with({
...rest,
endpoint: {
baseURL:
baseURL ??
`https://aiplatform.googleapis.com/v1/projects/${GoogleVertexShared.requireProject(project)}/locations/${location}/endpoints/openapi`,
},
auth: GoogleVertexShared.oauth(input, project),
})
}
export const configure = (input: Config = {}) => {
const route = configuredRoute(input)
return {
id,
model: (modelID: string | ModelID) => route.model({ id: modelID }),
configure,
}
}
export const provider = {
id,
configure,
}
export const model: ProviderPackage.Definition<Settings>["model"] = (modelID, settings) => {
if (settings.apiKey !== undefined) throw new Error("Google Vertex Responses does not support API keys")
return configure({
accessToken: settings.accessToken,
baseURL: settings.baseURL,
headers: settings.headers === undefined ? undefined : { ...settings.headers },
http: settings.body === undefined ? undefined : { body: { ...settings.body } },
limits: settings.limits,
location: settings.location,
project: settings.project,
providerOptions: settings.providerOptions,
}).model(modelID)
}
@@ -1,77 +0,0 @@
import type { AnyAuthClient } from "google-auth-library"
import { Effect, Redacted } from "effect"
import { Auth, MissingCredentialError } from "../route/auth"
const SCOPE = "https://www.googleapis.com/auth/cloud-platform"
export type OAuthOptions =
| { readonly accessToken?: string; readonly auth?: never }
| { readonly accessToken?: never; readonly auth?: Auth.Definition }
export type ApiKeyOptions =
| (OAuthOptions & { readonly apiKey?: never })
| { readonly accessToken?: never; readonly apiKey?: string; readonly auth?: never }
export const project = (value?: string) =>
value ??
process.env.GOOGLE_VERTEX_PROJECT ??
process.env.GOOGLE_CLOUD_PROJECT ??
process.env.GCP_PROJECT ??
process.env.GCLOUD_PROJECT
export const location = (value: string | undefined, fallback: string) =>
value ??
process.env.GOOGLE_VERTEX_LOCATION ??
process.env.GOOGLE_CLOUD_LOCATION ??
process.env.VERTEX_LOCATION ??
fallback
export const host = (location: string) => {
if (location === "global") return "aiplatform.googleapis.com"
// Jurisdictional multi-regions use Regional Endpoint Platform domains.
if (location === "eu" || location === "us") return `aiplatform.${location}.rep.googleapis.com`
return `${location}-aiplatform.googleapis.com`
}
export const requireProject = (value: string | undefined) => {
if (value) return value
throw new Error("Google Vertex requires a project when baseURL is not configured")
}
export const apiKey = (input: ApiKeyOptions) => {
if (input.apiKey !== undefined && (input.accessToken !== undefined || input.auth !== undefined))
throw new Error("Google Vertex apiKey cannot be combined with accessToken or auth")
if (input.accessToken !== undefined || input.auth !== undefined) return undefined
return input.apiKey ?? process.env.GOOGLE_VERTEX_API_KEY
}
const adc = (project?: string) => {
let client: Promise<AnyAuthClient> | undefined
const loadClient = () => {
if (client) return client
client = import("google-auth-library").then(({ GoogleAuth }) =>
new GoogleAuth({ projectId: project, scopes: [SCOPE] }).getClient(),
)
return client
}
return Auth.effect(
Effect.tryPromise({
try: async () => {
const token = await (await loadClient()).getAccessToken()
if (!token.token) throw new Error("Google ADC returned an empty access token")
return Redacted.make(token.token)
},
catch: () => new MissingCredentialError("Google Application Default Credentials"),
}),
).bearer()
}
export const oauth = (input: OAuthOptions, project?: string) => {
if (input.accessToken !== undefined && input.auth !== undefined)
throw new Error("Google Vertex accessToken cannot be combined with auth")
if (input.auth) return input.auth
if (input.accessToken !== undefined) return Auth.bearer(input.accessToken)
return adc(project)
}
export * as GoogleVertexShared from "./google-vertex-shared"
@@ -1,98 +0,0 @@
import type { ProviderPackage } from "../provider-package"
import { Gemini } from "../protocols/gemini"
import { Auth } from "../route/auth"
import { Route, type RouteDefaultsInput } from "../route/client"
import { Endpoint } from "../route/endpoint"
import { Framing } from "../route/framing"
import { ProviderID, type ModelID, type ProviderOptions } from "../schema"
import { GoogleVertexShared } from "./google-vertex-shared"
export const id = ProviderID.make("google-vertex")
export type Config = RouteDefaultsInput &
GoogleVertexShared.ApiKeyOptions & {
readonly baseURL?: string
readonly location?: string
readonly project?: string
}
export type Settings = ProviderPackage.Settings &
(
| { readonly accessToken?: string; readonly apiKey?: never }
| { readonly accessToken?: never; readonly apiKey?: string }
) & {
readonly baseURL?: string
readonly location?: string
readonly project?: string
readonly providerOptions?: ProviderOptions
}
const route = Route.make({
id: "google-vertex-gemini",
provider: id,
providerMetadataKey: "google",
protocol: Gemini.protocol,
endpoint: Endpoint.path(({ request }) => {
const model = String(request.model.id)
return `/${model.startsWith("endpoints/") ? model : `models/${model}`}:streamGenerateContent?alt=sse`
}),
auth: Auth.none,
framing: Framing.sse,
})
export const routes = [route]
const configuredRoute = (input: Config, modelID: string | ModelID) => {
const {
accessToken: _accessToken,
apiKey: _apiKey,
auth: _auth,
baseURL,
location: inputLocation,
project: inputProject,
...rest
} = input
const apiKey = GoogleVertexShared.apiKey(input)
const endpointModel = String(modelID).startsWith("endpoints/")
if (apiKey !== undefined && endpointModel)
throw new Error("Google Vertex tuned models do not support Express Mode API keys")
const location = GoogleVertexShared.location(inputLocation, "us-central1")
const project = GoogleVertexShared.project(inputProject)
const endpoint =
baseURL ??
(apiKey
? "https://aiplatform.googleapis.com/v1/publishers/google"
: `https://${GoogleVertexShared.host(location)}/v1beta1/projects/${GoogleVertexShared.requireProject(project)}/locations/${location}${endpointModel ? "" : "/publishers/google"}`)
return route.with({
...rest,
endpoint: { baseURL: endpoint },
auth: apiKey === undefined ? GoogleVertexShared.oauth(input, project) : Auth.header("x-goog-api-key", apiKey),
})
}
export const configure = (input: Config = {}) => {
return {
id,
model: (modelID: string | ModelID) => configuredRoute(input, modelID).model({ id: modelID }),
configure,
}
}
export const provider = {
id,
configure,
}
export const model: ProviderPackage.Definition<Settings>["model"] = (modelID, settings) => {
if (settings.apiKey !== undefined && settings.accessToken !== undefined)
throw new Error("Google Vertex apiKey cannot be combined with accessToken or auth")
return configure({
...(settings.apiKey === undefined ? { accessToken: settings.accessToken } : { apiKey: settings.apiKey }),
baseURL: settings.baseURL,
headers: settings.headers === undefined ? undefined : { ...settings.headers },
http: settings.body === undefined ? undefined : { body: { ...settings.body } },
limits: settings.limits,
location: settings.location,
project: settings.project,
providerOptions: settings.providerOptions,
}).model(modelID)
}
@@ -1,2 +0,0 @@
export { model } from "../google-vertex-chat"
export type { Settings } from "../google-vertex-chat"
@@ -1,2 +0,0 @@
export { model } from "../google-vertex"
export type { Settings } from "../google-vertex"
@@ -1,2 +0,0 @@
export { model } from "../google-vertex-messages"
export type { Settings } from "../google-vertex-messages"
@@ -1,2 +0,0 @@
export { model } from "../google-vertex-responses"
export type { Settings } from "../google-vertex-responses"
@@ -1,55 +0,0 @@
import type { ProviderPackage } from "../provider-package"
import { OpenAICompatibleResponses } from "../protocols/openai-compatible-responses"
import { AuthOptions, type ProviderAuthOption } from "../route/auth-options"
import type { RouteDefaultsInput } from "../route/client"
import { ProviderID, type ModelID } from "../schema"
import type { OpenAIProviderOptionsInput } from "./openai-options"
export const id = ProviderID.make("openai-compatible")
export type Config = RouteDefaultsInput &
ProviderAuthOption<"optional"> & {
readonly provider?: string
readonly baseURL: string
}
export interface Settings extends ProviderPackage.Settings {
readonly apiKey?: string
readonly baseURL: string
readonly provider?: string
readonly providerOptions?: OpenAIProviderOptionsInput
}
export const routes = [OpenAICompatibleResponses.route]
export const configure = (input: Config) => {
const provider = input.provider ?? "openai-compatible"
const { provider: _, baseURL, apiKey: _apiKey, auth: _auth, ...rest } = input
const route = OpenAICompatibleResponses.route.with({
...rest,
provider,
endpoint: { baseURL },
auth: AuthOptions.bearer(input, []),
})
return {
id: ProviderID.make(provider),
model: (modelID: string | ModelID) => route.model({ id: modelID }),
configure,
}
}
export const provider = {
id,
configure,
}
export const model: ProviderPackage.Definition<Settings>["model"] = (modelID, settings) =>
configure({
apiKey: settings.apiKey,
baseURL: settings.baseURL,
headers: settings.headers === undefined ? undefined : { ...settings.headers },
http: settings.body === undefined ? undefined : { body: { ...settings.body } },
limits: settings.limits,
provider: settings.provider,
providerOptions: settings.providerOptions,
}).model(modelID)
@@ -1 +0,0 @@
export * from "../openai-compatible-responses"
-2
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@@ -1,2 +0,0 @@
export { chatModel as model } from "../openai"
export type { Settings } from "../openai"
@@ -1,2 +0,0 @@
export { model } from "../openai"
export type { Settings } from "../openai"
-1
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@@ -1 +0,0 @@
export * from "./route/index"
-10
View File
@@ -1,10 +0,0 @@
/* This file is auto-generated by SST. Do not edit. */
/* tslint:disable */
/* eslint-disable */
/* deno-fmt-ignore-file */
/* biome-ignore-all lint: auto-generated */
/// <reference path="../../sst-env.d.ts" />
import "sst"
export {}
-178
View File
@@ -1,178 +0,0 @@
import { describe, expect, test } from "bun:test"
import { Effect, Schema } from "effect"
import { HttpClientRequest } from "effect/unstable/http"
import { LLM, mergeProviderOptions } from "../src"
import { AnthropicMessages, OpenAIChat } from "../src/protocols"
import { Auth, LLMClient } from "../src/route"
import { it } from "./lib/effect"
import { dynamicResponse } from "./lib/http"
import { deltaChunk } from "./lib/openai-chunks"
import { sseEvents } from "./lib/sse"
const TargetJson = Schema.fromJsonString(Schema.Unknown)
const decodeJson = Schema.decodeUnknownSync(TargetJson)
describe("request option precedence", () => {
test("deep-merges provider option records and replaces arrays, primitives, and null", () => {
const merged = mergeProviderOptions(
{
openai: {
include: ["route"],
metadata: { route: true, shared: "route" },
nullable: "route",
primitive: "route",
},
},
{
openai: {
include: ["model"],
metadata: { model: true, shared: "model" },
nullable: null,
primitive: "model",
},
},
{ openai: { metadata: { request: true }, primitive: false } },
)
expect(merged).toEqual({
openai: {
include: ["model"],
metadata: { route: true, model: true, request: true, shared: "model" },
nullable: null,
primitive: false,
},
})
})
it.effect("prepares bodies with route defaults, model defaults, and call options in order", () =>
Effect.gen(function* () {
const route = OpenAIChat.route.with({
endpoint: { baseURL: "https://api.openai.test/v1/" },
auth: Auth.bearer("test"),
generation: { maxTokens: 10, temperature: 1, stop: ["route"] },
providerOptions: { openai: { store: false, reasoningEffort: "low" } },
})
const model = route.model({
id: "gpt-4o-mini",
defaults: {
generation: { maxTokens: 20, temperature: 0.5, frequencyPenalty: 0.25, stop: ["model"] },
providerOptions: { openai: { reasoningEffort: "medium" } },
},
})
const prepared = yield* LLMClient.prepare<OpenAIChat.OpenAIChatBody>(
LLM.request({
model,
prompt: "Say hello.",
generation: { maxTokens: 30, topP: 0.9, stop: ["request"] },
providerOptions: { openai: { store: true } },
}),
)
expect(prepared.body).toMatchObject({
model: "gpt-4o-mini",
stream: true,
max_tokens: 30,
temperature: 0.5,
top_p: 0.9,
frequency_penalty: 0.25,
store: true,
reasoning_effort: "medium",
})
expect(prepared.body.stop).toEqual(["request"])
}),
)
it.effect("applies model HTTP defaults before request HTTP overlays", () =>
LLMClient.generate(
LLM.request({
model: OpenAIChat.route
.with({
endpoint: { baseURL: "https://api.openai.test/v1/" },
auth: Auth.bearer("fresh-key"),
http: {
body: { metadata: { route: true, shared: "route" }, value: "route" },
headers: { "x-route": "route", "x-shared": "route" },
query: { route: "1", shared: "route" },
},
})
.model({
id: "gpt-4o-mini",
defaults: {
http: {
body: { metadata: { model: true, shared: "model" }, value: "model" },
headers: { "x-model": "model", "x-shared": "model" },
query: { model: "1", shared: "model" },
},
},
}),
prompt: "Say hello.",
http: {
body: { metadata: { request: true }, value: null },
headers: { "x-request": "request" },
query: { request: "1" },
},
}),
).pipe(
Effect.provide(
dynamicResponse((input) =>
Effect.gen(function* () {
const web = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
expect(web.url).toBe("https://api.openai.test/v1/chat/completions?route=1&shared=model&model=1&request=1")
expect(web.headers.get("authorization")).toBe("Bearer fresh-key")
expect(web.headers.get("x-route")).toBe("route")
expect(web.headers.get("x-model")).toBe("model")
expect(web.headers.get("x-request")).toBe("request")
expect(web.headers.get("x-shared")).toBe("model")
expect(decodeJson(input.text)).toMatchObject({
metadata: { route: true, model: true, request: true, shared: "model" },
value: null,
})
return input.respond(sseEvents(deltaChunk({}, "stop")), {
headers: { "content-type": "text/event-stream" },
})
}),
),
),
),
)
it.effect("rejects raw body overlays for protocol-owned roots", () =>
Effect.gen(function* () {
const model = OpenAIChat.route
.with({ endpoint: { baseURL: "https://api.openai.test/v1/" }, auth: Auth.bearer("test") })
.model({ id: "gpt-4o-mini" })
const error = yield* LLMClient.prepare(
LLM.request({
model,
prompt: "Say hello.",
http: { body: { model: "gpt-5", messages: [], tools: [] } },
}),
).pipe(Effect.flip)
expect(error.reason).toMatchObject({
_tag: "InvalidRequest",
message: "http.body cannot overlay protocol-owned field(s): model, messages, tools",
})
}),
)
it.effect("uses model output limits after route limits and before call maxTokens", () =>
Effect.gen(function* () {
const route = AnthropicMessages.route.with({
endpoint: { baseURL: "https://api.anthropic.test/v1/" },
auth: Auth.header("x-api-key", "test"),
limits: { output: 128 },
})
const model = route.model({ id: "claude-sonnet-4-5", defaults: { limits: { output: 64 } } })
const withoutMaxTokens = yield* LLMClient.prepare<AnthropicMessages.AnthropicMessagesBody>(
LLM.request({ model, prompt: "Say hello.", cache: "none" }),
)
const withMaxTokens = yield* LLMClient.prepare<AnthropicMessages.AnthropicMessagesBody>(
LLM.request({ model, prompt: "Say hello.", cache: "none", generation: { maxTokens: 32 } }),
)
expect(withoutMaxTokens.body.max_tokens).toBe(64)
expect(withMaxTokens.body.max_tokens).toBe(32)
}),
)
})
-118
View File
@@ -1,118 +0,0 @@
import { describe, expect, test } from "bun:test"
import { HttpContext, HttpRequestDetails, isContextOverflow } from "../src"
import { classifyProviderFailure } from "../src/provider-error"
describe("provider error classification", () => {
test("classifies Z.AI GLM token limit messages as context overflow", () => {
expect(isContextOverflow("tokens in request more than max tokens allowed")).toBe(true)
})
test("checks overflow evidence in the message when the response body is uninformative", () => {
expect(
classifyProviderFailure({
message: "Input is too long for requested model",
status: 400,
http: new HttpContext({
request: new HttpRequestDetails({ method: "POST", url: "https://provider.test", headers: {} }),
body: "{}",
}),
}),
).toMatchObject({ classification: "context-overflow" })
})
test("lets semantic invalid-request codes override server status", () => {
expect(classifyProviderFailure({ message: "too large", status: 500, code: "request_too_large" })._tag).toBe(
"InvalidRequest",
)
})
test("does not treat incidental safety text as a content-policy failure", () => {
expect(classifyProviderFailure({ message: "Internal safety check failed", status: 500 })._tag).toBe(
"ProviderInternal",
)
expect(classifyProviderFailure({ message: "Blocked by safety policy", status: 400 })._tag).toBe("ContentPolicy")
expect(classifyProviderFailure({ message: "Blocked", status: 400, code: "SAFETY" })._tag).toBe("ContentPolicy")
})
test("classifies V1 plain-text rate limit fallbacks", () => {
expect(
[
"Request rate increased too quickly",
"Rate limit exceeded, please try again later",
"Too many requests, please slow down",
].map((message) => classifyProviderFailure({ message })._tag),
).toEqual(["RateLimit", "RateLimit", "RateLimit"])
})
test("classifies V1 JSON rate limit fallbacks", () => {
expect(
[
'{"type":"error","error":{"type":"too_many_requests"}}',
'{"type":"error","error":{"code":"rate_limit_exceeded"}}',
'{"code":"bad_request","error":{"code":"rate_limit_exceeded"}}',
'{"type":"error","error":{"code":"unknown","type":"too_many_requests"}}',
].map((message) => classifyProviderFailure({ message })._tag),
).toEqual(["RateLimit", "RateLimit", "RateLimit", "RateLimit"])
})
test("classifies canonical provider retry codes", () => {
expect(
['{"code":"resource_exhausted"}', '{"code":"service_unavailable"}'].map(
(message) => classifyProviderFailure({ message })._tag,
),
).toEqual(["RateLimit", "ProviderInternal"])
})
test("keeps temporary per-minute quota wording retryable", () => {
expect(classifyProviderFailure({ message: "You exceeded your per-minute quota", status: 429 })._tag).toBe(
"RateLimit",
)
})
test("classifies canonical Google error codes", () => {
expect(
["UNAUTHENTICATED", "PERMISSION_DENIED", "INVALID_ARGUMENT", "NOT_FOUND", "INTERNAL"].map(
(code) => classifyProviderFailure({ message: "Provider failed", code })._tag,
),
).toEqual(["Authentication", "Authentication", "InvalidRequest", "InvalidRequest", "ProviderInternal"])
})
test("classifies stripped Bedrock stream errors from their messages", () => {
expect(
["Internal server error", "Throttling exception", "Validation error: invalid input"].map(
(message) => classifyProviderFailure({ message })._tag,
),
).toEqual(["ProviderInternal", "RateLimit", "InvalidRequest"])
})
test("classifies documented Bedrock exception codes", () => {
expect(
["accessDeniedException", "modelTimeoutException", "resourceNotFoundException"].map(
(code) => classifyProviderFailure({ message: "Bedrock failed", code })._tag,
),
).toEqual(["Authentication", "ProviderInternal", "InvalidRequest"])
})
test("classifies Anthropic not-found stream errors as invalid requests", () => {
expect(classifyProviderFailure({ message: "Model unavailable", code: "not_found_error" })._tag).toBe(
"InvalidRequest",
)
})
test("classifies nested provider codes when a top-level code is also present", () => {
expect(
[
'{"code":"bad_request","error":{"code":"usage_not_included"}}',
'{"code":"bad_request","error":{"code":"server_error"}}',
'{"code":"bad_request","error":{"type":"invalid_request_error"}}',
'{"type":"response.failed","response":{"error":{"code":"server_error"}}}',
].map((message) => classifyProviderFailure({ message })._tag),
).toEqual(["QuotaExceeded", "ProviderInternal", "InvalidRequest", "ProviderInternal"])
})
test("keeps unknown and malformed provider payloads non-retryable", () => {
expect(classifyProviderFailure({ message: '{"error":{"message":"no_kv_space"}}' })._tag).toBe("UnknownProvider")
expect(classifyProviderFailure({ message: '{"type":"error","error":{"code":123}}' })._tag).toBe("UnknownProvider")
expect(classifyProviderFailure({ message: "not-json" })._tag).toBe("UnknownProvider")
})
})
-296
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@@ -1,296 +0,0 @@
import { describe, expect, test } from "bun:test"
import { model } from "@opencode-ai/ai/providers/openai"
describe("provider package entrypoints", () => {
test("semantic API aliases expose the same contract", async () => {
const modules = await Promise.all([
import("@opencode-ai/ai/providers/openai"),
import("@opencode-ai/ai/providers/openai/responses"),
import("@opencode-ai/ai/providers/openai/chat"),
import("@opencode-ai/ai/providers/anthropic"),
import("@opencode-ai/ai/providers/anthropic-compatible"),
import("@opencode-ai/ai/providers/openai-compatible"),
import("@opencode-ai/ai/providers/openai-compatible/responses"),
import("@opencode-ai/ai/providers/amazon-bedrock"),
import("@opencode-ai/ai/providers/azure"),
import("@opencode-ai/ai/providers/azure/responses"),
import("@opencode-ai/ai/providers/azure/chat"),
import("@opencode-ai/ai/providers/google"),
import("@opencode-ai/ai/providers/google-vertex"),
import("@opencode-ai/ai/providers/google-vertex/gemini"),
import("@opencode-ai/ai/providers/google-vertex/chat"),
import("@opencode-ai/ai/providers/google-vertex/responses"),
import("@opencode-ai/ai/providers/google-vertex/messages"),
])
for (const module of modules) expect(module.model).toBeFunction()
expect(modules[0].model).toBe(modules[1].model)
expect(modules[8].model).toBe(modules[9].model)
expect(modules[12].model).toBe(modules[13].model)
})
test("maps package settings onto the executable model", () => {
const selected = model("gpt-5", {
apiKey: "fixture",
baseURL: "https://api.openai.test/v1",
headers: { "x-application": "opencode" },
body: { service_tier: "priority" },
limits: { context: 200_000, output: 64_000 },
unrelatedInheritedSetting: true,
})
expect(selected.route.id).toBe("openai-responses")
expect(selected.route.defaults.headers).toEqual({ "x-application": "opencode" })
expect(selected.route.defaults.http?.body).toEqual({ service_tier: "priority" })
expect(selected.route.defaults.limits).toEqual({ context: 200_000, output: 64_000 })
})
test("selects transport without changing the semantic API", () => {
expect(model("gpt-5", { apiKey: "fixture" }).route.id).toBe("openai-responses")
expect(model("gpt-5", { apiKey: "fixture", transport: "websocket" }).route.id).toBe("openai-responses-websocket")
})
test("maps OpenAI-compatible Responses settings onto the executable model", async () => {
const OpenAICompatibleResponses = await import("@opencode-ai/ai/providers/openai-compatible/responses")
const selected = OpenAICompatibleResponses.model("custom-model", {
apiKey: "fixture",
baseURL: "https://responses.example.test/v1",
provider: "example",
headers: { "x-application": "opencode" },
body: { service_tier: "priority" },
limits: { context: 200_000, output: 64_000 },
providerOptions: { openai: { reasoningEffort: "low", store: true } },
})
expect(String(selected.provider)).toBe("example")
expect(selected.route.id).toBe("openai-compatible-responses")
expect(selected.route.endpoint).toMatchObject({
baseURL: "https://responses.example.test/v1",
path: "/responses",
})
expect(selected.route.defaults.headers).toEqual({ "x-application": "opencode" })
expect(selected.route.defaults.http?.body).toEqual({ service_tier: "priority" })
expect(selected.route.defaults.limits).toEqual({ context: 200_000, output: 64_000 })
expect(selected.route.defaults.providerOptions).toEqual({
openai: { reasoningEffort: "low", store: true },
})
})
test("maps Anthropic-compatible settings onto the executable model", async () => {
const AnthropicCompatible = await import("@opencode-ai/ai/providers/anthropic-compatible")
const selected = AnthropicCompatible.model("compatible-model", {
apiKey: "fixture",
baseURL: "https://messages.example.test/v1",
provider: "example",
headers: { "x-application": "opencode" },
body: { metadata: { user_id: "user_1" } },
limits: { context: 200_000, output: 64_000 },
})
expect(String(selected.provider)).toBe("example")
expect(selected.route.id).toBe("anthropic-messages")
expect(selected.route.endpoint).toMatchObject({
baseURL: "https://messages.example.test/v1",
path: "/messages",
})
expect(selected.route.defaults.headers).toEqual({ "x-application": "opencode" })
expect(selected.route.defaults.http?.body).toEqual({ metadata: { user_id: "user_1" } })
expect(selected.route.defaults.limits).toEqual({ context: 200_000, output: 64_000 })
})
test("requires an Anthropic-compatible base URL at runtime", async () => {
const AnthropicCompatible = await import("@opencode-ai/ai/providers/anthropic-compatible")
expect(() =>
Reflect.apply(AnthropicCompatible.model, undefined, ["compatible-model", { apiKey: "fixture" }]),
).toThrow("Anthropic-compatible providers require a baseURL")
})
test("rejects conflicting Anthropic-compatible auth settings at runtime", async () => {
const Anthropic = await import("@opencode-ai/ai/providers/anthropic")
const AnthropicCompatible = await import("@opencode-ai/ai/providers/anthropic-compatible")
expect(() =>
Reflect.apply(AnthropicCompatible.model, undefined, [
"compatible-model",
{
apiKey: "fixture",
authToken: "token",
baseURL: "https://messages.example.test/v1",
},
]),
).toThrow("Anthropic-compatible apiKey cannot be combined with authToken")
expect(() =>
Reflect.apply(Anthropic.model, undefined, ["claude-sonnet-4-6", { apiKey: "fixture", authToken: "token" }]),
).toThrow("Anthropic apiKey cannot be combined with authToken")
})
test("maps legacy OpenAI organization and project settings to headers", () => {
const selected = model("gpt-5", {
apiKey: "fixture",
organization: "org_123",
project: "proj_123",
})
expect(selected.route.defaults.headers).toMatchObject({
"OpenAI-Organization": "org_123",
"OpenAI-Project": "proj_123",
})
})
test("selects Azure API entrypoints with the same model contract", async () => {
const Azure = await import("@opencode-ai/ai/providers/azure")
const AzureChat = await import("@opencode-ai/ai/providers/azure/chat")
const AzureResponses = await import("@opencode-ai/ai/providers/azure/responses")
const settings = {
apiKey: "fixture",
resourceName: "opencode-test",
headers: { "x-application": "opencode" },
body: { service_tier: "priority" },
limits: { context: 200_000, output: 64_000 },
}
const responses = AzureResponses.model("deployment", settings)
const chat = AzureChat.model("deployment", settings)
expect(Azure.model("deployment", settings).route.id).toBe("azure-openai-responses")
expect(responses.route.id).toBe("azure-openai-responses")
expect(responses.route.endpoint.baseURL).toBe("https://opencode-test.openai.azure.com/openai/v1")
expect(responses.route.defaults.headers).toEqual({ "x-application": "opencode" })
expect(responses.route.defaults.http?.body).toEqual({ service_tier: "priority" })
expect(responses.route.defaults.limits).toEqual({ context: 200_000, output: 64_000 })
expect(chat.route.id).toBe("azure-openai-chat")
})
test("maps Google package settings onto the Gemini model", async () => {
const Google = await import("@opencode-ai/ai/providers/google")
const selected = Google.model("gemini-2.5-flash", {
apiKey: "fixture",
baseURL: "https://generativelanguage.test/v1beta",
headers: { "x-application": "opencode" },
body: { safetySettings: [] },
limits: { context: 1_000_000, output: 65_536 },
providerOptions: { gemini: { thinkingConfig: { thinkingBudget: 1_024 } } },
})
expect(selected.route.id).toBe("gemini")
expect(selected.route.endpoint.baseURL).toBe("https://generativelanguage.test/v1beta")
expect(selected.route.defaults.headers).toEqual({ "x-application": "opencode" })
expect(selected.route.defaults.http?.body).toEqual({ safetySettings: [] })
expect(selected.route.defaults.limits).toEqual({ context: 1_000_000, output: 65_536 })
expect(selected.route.defaults.providerOptions).toEqual({
gemini: { thinkingConfig: { thinkingBudget: 1_024 } },
})
})
test("selects Vertex entrypoints with the same model contract", async () => {
const GoogleVertex = await import("@opencode-ai/ai/providers/google-vertex")
const GoogleVertexGemini = await import("@opencode-ai/ai/providers/google-vertex/gemini")
const GoogleVertexChat = await import("@opencode-ai/ai/providers/google-vertex/chat")
const GoogleVertexResponses = await import("@opencode-ai/ai/providers/google-vertex/responses")
const GoogleVertexMessages = await import("@opencode-ai/ai/providers/google-vertex/messages")
const gemini = GoogleVertex.model("gemini-3.5-flash", {
apiKey: "fixture",
headers: { "x-application": "opencode" },
body: { safetySettings: [] },
limits: { context: 1_000_000, output: 65_536 },
})
const messages = GoogleVertexMessages.model("claude-sonnet-4-6", {
accessToken: "fixture",
location: "global",
project: "vertex-project",
})
const chat = GoogleVertexChat.model("deepseek-ai/deepseek-v3.2-maas", {
accessToken: "fixture",
location: "global",
project: "vertex-project",
})
const responses = GoogleVertexResponses.model("xai/grok-4.20-reasoning", {
accessToken: "fixture",
location: "global",
project: "vertex-project",
})
expect(GoogleVertexGemini.model).toBe(GoogleVertex.model)
expect(gemini.route.id).toBe("google-vertex-gemini")
expect(gemini.route.protocol).toBe("gemini")
expect(gemini.route.endpoint.baseURL).toBe("https://aiplatform.googleapis.com/v1/publishers/google")
expect(gemini.route.defaults.headers).toEqual({ "x-application": "opencode" })
expect(gemini.route.defaults.http?.body).toEqual({ safetySettings: [] })
expect(gemini.route.defaults.limits).toEqual({ context: 1_000_000, output: 65_536 })
expect(
GoogleVertex.model("gemini-3.5-flash", {
accessToken: "fixture",
location: "eu",
project: "vertex-project",
}).route.endpoint.baseURL,
).toBe("https://aiplatform.eu.rep.googleapis.com/v1beta1/projects/vertex-project/locations/eu/publishers/google")
expect(messages.route.id).toBe("google-vertex-messages")
expect(messages.route.protocol).toBe("anthropic-messages")
expect(messages.route.endpoint.baseURL).toBe(
"https://aiplatform.googleapis.com/v1/projects/vertex-project/locations/global/publishers/anthropic/models",
)
expect(chat.route.id).toBe("google-vertex-chat")
expect(chat.route.protocol).toBe("openai-chat")
expect(chat.route.endpoint).toMatchObject({
baseURL: "https://aiplatform.googleapis.com/v1/projects/vertex-project/locations/global/endpoints/openapi",
path: "/chat/completions",
})
expect(responses.route.id).toBe("google-vertex-responses")
expect(responses.route.protocol).toBe("openai-responses")
expect(responses.route.endpoint).toMatchObject({
baseURL: "https://aiplatform.googleapis.com/v1/projects/vertex-project/locations/global/endpoints/openapi",
path: "/responses",
})
expect(responses.route.defaults.providerOptions).toEqual({ openai: { store: false } })
})
test("rejects conflicting Vertex auth settings at runtime", async () => {
const GoogleVertex = await import("@opencode-ai/ai/providers/google-vertex")
const GoogleVertexChat = await import("@opencode-ai/ai/providers/google-vertex/chat")
const GoogleVertexMessages = await import("@opencode-ai/ai/providers/google-vertex/messages")
const GoogleVertexResponses = await import("@opencode-ai/ai/providers/google-vertex/responses")
const Providers = await import("@opencode-ai/ai/providers")
expect(() =>
Reflect.apply(GoogleVertex.model, undefined, [
"gemini-3.5-flash",
{ accessToken: "token", apiKey: "fixture", project: "vertex-project" },
]),
).toThrow("Google Vertex apiKey cannot be combined with accessToken or auth")
const configured = Reflect.apply(GoogleVertex.configure, undefined, [
{ accessToken: "token", auth: {}, project: "vertex-project" },
])
expect(() => configured.model("gemini-3.5-flash")).toThrow("Google Vertex accessToken cannot be combined with auth")
expect(() =>
Reflect.apply(GoogleVertexMessages.model, undefined, [
"claude-sonnet-4-6",
{ apiKey: "fixture", project: "vertex-project" },
]),
).toThrow("Google Vertex Messages does not support API keys")
expect(() =>
Reflect.apply(Providers.GoogleVertexMessages.configure, undefined, [
{ apiKey: "fixture", project: "vertex-project" },
]),
).toThrow("Google Vertex Messages does not support API keys")
expect(() =>
Reflect.apply(GoogleVertexChat.model, undefined, [
"deepseek-ai/deepseek-v3.2-maas",
{ apiKey: "fixture", project: "vertex-project" },
]),
).toThrow("Google Vertex Chat does not support API keys")
expect(() =>
Reflect.apply(Providers.GoogleVertexChat.configure, undefined, [
{ apiKey: "fixture", project: "vertex-project" },
]),
).toThrow("Google Vertex Chat does not support API keys")
expect(() =>
Reflect.apply(GoogleVertexResponses.model, undefined, [
"xai/grok-4.20-reasoning",
{ apiKey: "fixture", project: "vertex-project" },
]),
).toThrow("Google Vertex Responses does not support API keys")
expect(() =>
Reflect.apply(Providers.GoogleVertexResponses.configure, undefined, [
{ apiKey: "fixture", project: "vertex-project" },
]),
).toThrow("Google Vertex Responses does not support API keys")
})
})
@@ -1,246 +0,0 @@
import { describe, expect } from "bun:test"
import { Effect } from "effect"
import { HttpClientRequest } from "effect/unstable/http"
import { LLM } from "../../src"
import { GoogleVertex, GoogleVertexChat, GoogleVertexMessages, GoogleVertexResponses } from "../../src/providers"
import { LLMClient } from "../../src/route"
import { it } from "../lib/effect"
import { dynamicResponse } from "../lib/http"
import { deltaChunk, finishChunk } from "../lib/openai-chunks"
import { sseEvents } from "../lib/sse"
describe("Google Vertex providers", () => {
it.effect("sends Gemini requests to the global Vertex endpoint", () =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(
LLM.request({
model: GoogleVertex.configure({
accessToken: "vertex-token",
location: "global",
project: "vertex-project",
}).model("gemini-3.5-flash"),
prompt: "Say hello.",
}),
).pipe(
Effect.provide(
dynamicResponse((input) =>
Effect.gen(function* () {
const request = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
expect(request.url).toBe(
"https://aiplatform.googleapis.com/v1beta1/projects/vertex-project/locations/global/publishers/google/models/gemini-3.5-flash:streamGenerateContent?alt=sse",
)
expect(request.headers.get("authorization")).toBe("Bearer vertex-token")
expect(yield* Effect.promise(() => request.json())).toMatchObject({
contents: [{ role: "user", parts: [{ text: "Say hello." }] }],
})
return input.respond(
sseEvents({
candidates: [
{
content: { role: "model", parts: [{ text: "Hello." }] },
finishReason: "STOP",
},
],
}),
{ headers: { "content-type": "text/event-stream" } },
)
}),
),
),
)
expect(response.text).toBe("Hello.")
}),
)
it.effect("projects Anthropic Messages onto the Vertex raw-predict API", () =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(
LLM.request({
model: GoogleVertexMessages.configure({
accessToken: "vertex-token",
location: "eu",
project: "vertex-project",
}).model("claude-sonnet-4-6"),
prompt: "Say hello.",
}),
).pipe(
Effect.provide(
dynamicResponse((input) =>
Effect.gen(function* () {
const request = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
expect(request.url).toBe(
"https://aiplatform.eu.rep.googleapis.com/v1/projects/vertex-project/locations/eu/publishers/anthropic/models/claude-sonnet-4-6:streamRawPredict",
)
expect(request.headers.get("authorization")).toBe("Bearer vertex-token")
expect(request.headers.get("anthropic-version")).toBeNull()
const body = yield* Effect.promise(() => request.json())
expect(body).toMatchObject({
anthropic_version: "vertex-2023-10-16",
messages: [{ role: "user", content: [{ type: "text", text: "Say hello." }] }],
stream: true,
})
expect(body).not.toHaveProperty("model")
return input.respond(
sseEvents(
{ type: "content_block_start", index: 0, content_block: { type: "text", text: "" } },
{ type: "content_block_delta", index: 0, delta: { type: "text_delta", text: "Hello." } },
{ type: "content_block_stop", index: 0 },
{ type: "message_delta", delta: { stop_reason: "end_turn" }, usage: { output_tokens: 2 } },
{ type: "message_stop" },
),
{ headers: { "content-type": "text/event-stream" } },
)
}),
),
),
)
expect(response.text).toBe("Hello.")
}),
)
it.effect("sends MaaS requests through Vertex Chat Completions", () =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(
LLM.request({
model: GoogleVertexChat.configure({
accessToken: "vertex-token",
location: "global",
project: "vertex-project",
}).model("deepseek-ai/deepseek-v3.2-maas"),
prompt: "Say hello.",
}),
).pipe(
Effect.provide(
dynamicResponse((input) =>
Effect.gen(function* () {
const request = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
expect(request.url).toBe(
"https://aiplatform.googleapis.com/v1/projects/vertex-project/locations/global/endpoints/openapi/chat/completions",
)
expect(request.headers.get("authorization")).toBe("Bearer vertex-token")
expect(yield* Effect.promise(() => request.json())).toMatchObject({
model: "deepseek-ai/deepseek-v3.2-maas",
messages: [{ role: "user", content: "Say hello." }],
stream: true,
stream_options: { include_usage: true },
})
return input.respond(sseEvents(deltaChunk({ content: "Hello." }), finishChunk("stop")), {
headers: { "content-type": "text/event-stream" },
})
}),
),
),
)
expect(response.text).toBe("Hello.")
}),
)
it.effect("sends Grok requests through Vertex Responses", () =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(
LLM.request({
model: GoogleVertexResponses.configure({
accessToken: "vertex-token",
location: "global",
project: "vertex-project",
}).model("xai/grok-4.20-reasoning"),
prompt: "Say hello.",
}),
).pipe(
Effect.provide(
dynamicResponse((input) =>
Effect.gen(function* () {
const request = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
expect(request.url).toBe(
"https://aiplatform.googleapis.com/v1/projects/vertex-project/locations/global/endpoints/openapi/responses",
)
expect(request.headers.get("authorization")).toBe("Bearer vertex-token")
expect(yield* Effect.promise(() => request.json())).toMatchObject({
model: "xai/grok-4.20-reasoning",
input: [{ role: "user", content: [{ type: "input_text", text: "Say hello." }] }],
store: false,
stream: true,
})
return input.respond(
sseEvents(
{ type: "response.output_text.delta", item_id: "msg_1", delta: "Hello." },
{ type: "response.completed", response: { id: "resp_1" } },
),
{ headers: { "content-type": "text/event-stream" } },
)
}),
),
),
)
expect(response.text).toBe("Hello.")
}),
)
it.effect("protects the Vertex Messages API version from body overlays", () =>
Effect.gen(function* () {
const error = yield* LLMClient.prepare(
LLM.request({
model: GoogleVertexMessages.configure({
accessToken: "vertex-token",
http: { body: { anthropic_version: "wrong" } },
project: "vertex-project",
}).model("claude-sonnet-4-6"),
prompt: "Say hello.",
}),
).pipe(Effect.flip)
expect(error.message).toContain("http.body cannot overlay protocol-owned field(s): anthropic_version")
}),
)
it.effect("routes tuned Gemini models through their deployed endpoint", () =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(
LLM.request({
model: GoogleVertex.configure({
accessToken: "vertex-token",
location: "us-central1",
project: "vertex-project",
}).model("endpoints/1234567890"),
prompt: "Say hello.",
}),
).pipe(
Effect.provide(
dynamicResponse((input) =>
Effect.gen(function* () {
const request = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
expect(request.url).toBe(
"https://us-central1-aiplatform.googleapis.com/v1beta1/projects/vertex-project/locations/us-central1/endpoints/1234567890:streamGenerateContent?alt=sse",
)
return input.respond(
sseEvents({
candidates: [
{
content: { role: "model", parts: [{ text: "Hello." }] },
finishReason: "STOP",
},
],
}),
{ headers: { "content-type": "text/event-stream" } },
)
}),
),
),
)
expect(response.text).toBe("Hello.")
}),
)
it.effect("rejects tuned Gemini models in express mode", () =>
Effect.sync(() => {
expect(() => GoogleVertex.configure({ apiKey: "fixture" }).model("endpoints/1234567890")).toThrow(
"Google Vertex tuned models do not support Express Mode API keys",
)
}),
)
})
@@ -1,53 +0,0 @@
import { describe, expect } from "bun:test"
import { Effect } from "effect"
import { LLM } from "../../src"
import { configure } from "../../src/providers/openai-compatible-responses"
import { OpenAICompatibleResponses } from "../../src/protocols/openai-compatible-responses"
import { OpenAIResponses } from "../../src/protocols/openai-responses"
import { LLMClient } from "../../src/route"
import { it } from "../lib/effect"
describe("OpenAI-compatible Responses route", () => {
it.effect("reuses the OpenAI Responses protocol for a configured deployment", () =>
Effect.gen(function* () {
expect(OpenAICompatibleResponses.route.body).toBe(OpenAIResponses.protocol.body)
expect(OpenAICompatibleResponses.route.transport).toBe(OpenAIResponses.httpTransport)
const model = configure({
apiKey: "test-key",
baseURL: "https://responses.example.test/v1",
provider: "example",
}).model("example-model")
const prepared = yield* LLMClient.prepare(
LLM.request({
model,
system: "You are concise.",
prompt: "Say hello.",
}),
)
expect(prepared.route).toBe("openai-compatible-responses")
expect(prepared.protocol).toBe("openai-responses")
expect(prepared.model).toMatchObject({
id: "example-model",
provider: "example",
route: {
id: "openai-compatible-responses",
endpoint: {
baseURL: "https://responses.example.test/v1",
path: "/responses",
},
},
})
expect(prepared.body).toEqual({
model: "example-model",
input: [
{ role: "system", content: "You are concise." },
{ role: "user", content: [{ type: "input_text", text: "Say hello." }] },
],
store: false,
stream: true,
})
}),
)
})
-98
View File
@@ -1,98 +0,0 @@
import { describe, expect, test } from "bun:test"
import { LLMEvent, LLMResponse } from "../src"
const reduce = (events: ReadonlyArray<LLMEvent>) => events.reduce(LLMResponse.reduce, LLMResponse.empty())
const finishEvents = (events: ReadonlyArray<LLMEvent>) => events.filter(LLMEvent.is.finish)
describe("LLMResponse reducer", () => {
test("assembles interleaved reasoning and text with end metadata", () => {
const events = [
LLMEvent.reasoningStart({ id: "r1" }),
LLMEvent.reasoningDelta({ id: "r1", text: "I should " }),
LLMEvent.textStart({ id: "t1" }),
LLMEvent.reasoningDelta({ id: "r1", text: "compare..." }),
LLMEvent.reasoningEnd({ id: "r1", providerMetadata: { anthropic: { signature: "sig" } } }),
LLMEvent.textDelta({ id: "t1", text: "Answer" }),
LLMEvent.textEnd({ id: "t1" }),
LLMEvent.finish({ reason: "stop", usage: { outputTokens: 5 } }),
]
const response = LLMResponse.fromEvents(events)
expect(response?.finishReason).toBe("stop")
expect(response?.usage).toMatchObject({ outputTokens: 5 })
expect(response?.events).toEqual(events)
expect(response?.events.map((event) => event.type)).toEqual([
"reasoning-start",
"reasoning-delta",
"text-start",
"reasoning-delta",
"reasoning-end",
"text-delta",
"text-end",
"finish",
])
expect(finishEvents(response?.events ?? [])).toHaveLength(1)
expect(response?.message.content).toEqual([
{
type: "reasoning",
text: "I should compare...",
providerMetadata: { anthropic: { signature: "sig" } },
},
{ type: "text", text: "Answer" },
])
})
test("preserves partial content without completing a failed stream", () => {
const state = reduce([LLMEvent.textStart({ id: "t1" }), LLMEvent.textDelta({ id: "t1", text: "partial" })])
expect(LLMResponse.complete(state)).toBeUndefined()
expect(state.message.content).toEqual([{ type: "text", text: "partial" }])
})
test("does not complete ended content without a terminal finish", () => {
const state = reduce([
LLMEvent.textStart({ id: "t1" }),
LLMEvent.textDelta({ id: "t1", text: "partial" }),
LLMEvent.textEnd({ id: "t1" }),
])
expect(LLMResponse.complete(state)).toBeUndefined()
expect(state.message.content).toEqual([{ type: "text", text: "partial" }])
})
test("uses terminal usage when present and keeps prior usage when finish omits it", () => {
const withFinishUsage = LLMResponse.fromEvents([
LLMEvent.stepFinish({ index: 0, reason: "stop", usage: { inputTokens: 3 } }),
LLMEvent.finish({ reason: "stop", usage: { outputTokens: 2 } }),
])
const withoutFinishUsage = LLMResponse.fromEvents([
LLMEvent.stepFinish({ index: 0, reason: "stop", usage: { inputTokens: 3 } }),
LLMEvent.finish({ reason: "stop" }),
])
expect(withFinishUsage?.usage).toMatchObject({ outputTokens: 2 })
expect(withoutFinishUsage?.usage).toMatchObject({ inputTokens: 3 })
})
test("assembles tool-call content only after the completed tool call event", () => {
const pending = reduce([
LLMEvent.toolInputStart({ id: "call_1", name: "lookup" }),
LLMEvent.toolInputDelta({ id: "call_1", name: "lookup", text: '{"query"' }),
])
expect(pending.message.content).toEqual([])
expect(pending.toolInputs.call_1?.text).toBe('{"query"')
const response = LLMResponse.fromEvents([
...pending.events,
LLMEvent.toolInputDelta({ id: "call_1", name: "lookup", text: ':"weather"}' }),
LLMEvent.toolInputEnd({ id: "call_1", name: "lookup" }),
LLMEvent.toolCall({ id: "call_1", name: "lookup", input: { query: "weather" } }),
LLMEvent.finish({ reason: "tool-calls" }),
])
expect(response?.message.content).toEqual([
{ type: "tool-call", id: "call_1", name: "lookup", input: { query: "weather" } },
])
})
})
@@ -1,117 +0,0 @@
import { describe, expect, test } from "bun:test"
import { Effect } from "effect"
import { LLM } from "../src"
import { OpenAIChat } from "../src/protocols"
import { ToolSchemaProjection } from "../src/protocols/utils/tool-schema"
import { Auth, LLMClient } from "../src/route"
import { it } from "./lib/effect"
describe("tool schema projections", () => {
test("moonshot strips $ref siblings and converts tuple arrays to a schema object", () => {
expect(
ToolSchemaProjection.moonshot({
type: "object",
properties: {
linked: { $ref: "#/$defs/Linked", description: "drop me" },
tuple: { type: "array", items: [{ type: "string" }, { type: "number" }] },
prefixTuple: { type: "array", prefixItems: [{ type: "boolean" }, { type: "string" }] },
},
}),
).toEqual({
type: "object",
properties: {
linked: { $ref: "#/$defs/Linked" },
tuple: { type: "array", items: { anyOf: [{ type: "string" }, { type: "number" }] } },
prefixTuple: { type: "array", items: { anyOf: [{ type: "boolean" }, { type: "string" }] } },
},
})
})
test("gemini handles numeric enums, dangling required fields, untyped arrays, and scalar object keys", () => {
expect(
ToolSchemaProjection.gemini({
type: "object",
required: ["status", "missing"],
properties: {
status: { type: "integer", enum: [1, 2] },
tags: { type: "array" },
name: { type: "string", properties: { ignored: { type: "string" } }, required: ["ignored"] },
},
}),
).toEqual({
type: "object",
required: ["status"],
properties: {
status: { type: "string", enum: ["1", "2"] },
tags: { type: "array", items: { type: "string" } },
name: { type: "string" },
},
})
})
test("openai keeps one flat object top-level schema", () => {
expect(
ToolSchemaProjection.openAI({
anyOf: [
{
type: "object",
properties: {
path: { type: "string" },
maybe: { anyOf: [{ type: "string" }, { type: "null" }] },
},
},
{ type: "object", properties: { resource: { type: "string" } } },
],
}),
).toEqual({
type: "object",
properties: {
path: { type: "string" },
maybe: { type: "string" },
resource: { type: "string" },
},
additionalProperties: false,
})
})
it.effect("applies model compatibility before protocol projection", () =>
Effect.gen(function* () {
const model = OpenAIChat.route
.with({ endpoint: { baseURL: "https://api.openai.test/v1/" }, auth: Auth.bearer("test") })
.model({ id: "kimi-k2", compatibility: { toolSchema: "moonshot" } })
const prepared = yield* LLMClient.prepare<OpenAIChat.OpenAIChatBody>(
LLM.request({
model,
prompt: "Use the tool.",
tools: [
{
name: "lookup",
description: "Lookup data.",
inputSchema: {
type: "object",
anyOf: [
{
type: "object",
properties: {
tuple: { type: "array", items: [{ type: "string" }, { type: "number" }] },
linked: { $ref: "#/$defs/Linked", description: "drop me" },
},
},
],
},
},
],
}),
)
expect(prepared.body.tools?.[0]?.function.parameters).toEqual({
type: "object",
properties: {
tuple: { type: "array", items: { anyOf: [{ type: "string" }, { type: "number" }] } },
linked: { $ref: "#/$defs/Linked" },
},
additionalProperties: false,
})
}),
)
})
-8
View File
@@ -1,8 +0,0 @@
{
"$schema": "https://json.schemastore.org/tsconfig",
"extends": "./tsconfig.json",
"compilerOptions": {
"allowImportingTsExtensions": false,
"noEmit": false
}
}
-12
View File
@@ -1,12 +0,0 @@
{
"$schema": "https://json.schemastore.org/tsconfig.json",
"extends": "@tsconfig/bun/tsconfig.json",
"compilerOptions": {
"rootDir": "src",
"outDir": "dist",
"declaration": true,
"lib": ["ESNext", "DOM", "DOM.Iterable"],
"noUncheckedIndexedAccess": false
},
"include": ["src"]
}
-2
View File
@@ -18,8 +18,6 @@ bun run test:bench
The suite contains:
- cold and hot session-tab timing
- home-session click timing split between content and titlebar-tab paint
- single-session tab close timing through stable home restoration
- cached session repaint and mutation tracing
- streaming timeline throughput, RAF-gap, long-task, geometry, and remount diagnostics
@@ -1,61 +0,0 @@
# Timeline Layout Continuity
Run from `packages/app`:
```sh
bun run test:stability
```
The suite runs a production build in one Chromium worker. Selected scenarios use deterministic 4x CPU stress after application readiness. This is a stress profile, not emulation of a specific device.
## What It Proves
The continuity probe samples DOM-derived layout and visibility state across browser render opportunities. Tests declare explicit contracts such as:
- Preserve a visible semantic anchor while the user is away from the bottom.
- Preserve end anchoring while active content grows or new content appears.
- Keep adjacent visible rows ordered without material overlap.
- Keep user-selected disclosure state through updates and virtualization.
- Avoid a sampled blank interval while one visible surface replaces another.
- Preserve logical row and control identity where local state or focus depends on it.
- Keep keyboard, wheel, and nested-scroll ownership consistent during remeasurement.
The suite exercises real browser reducer, projection, component, virtualizer, layout, focus, and interaction code. The backend and event producer are controlled fixtures.
## What It Does Not Prove
The pass/fail oracle does not inspect every compositor-presented pixel. A sample taken after `requestAnimationFrame` is a DOM/layout observation, not proof that every sampled state was displayed or that every displayed frame was sampled.
The suite does not provide complete coverage for:
- Compositor-only or raster-only glitches.
- Color, contrast, canvas, WebGL, masks, irregular clips, or arbitrary occlusion.
- Physical display refresh rates, native OS scaling, or a named low-end device.
- TCP packetization, proxy buffering, or the complete real server/provider pipeline.
Playwright video, trace, screenshots, and observation JSON are diagnostic evidence. They are not pixel baselines and do not participate in normal pass/fail decisions.
For optional before/violation/after screenshots, set `OPENCODE_STABILITY_CAPTURE=1`. Capture is opt-in because compositor readback can perturb timing.
## Test Layers
- **Projection:** admitted rows, grouping, labels, and final visible states.
- **Local state:** disclosure state, identity, duplicate delivery, and virtualization restoration.
- **Interaction:** wheel, keyboard, nested scrolling, actionability, and focus behavior.
- **Layout continuity:** anchoring, adjacency, responsive reflow, and visible surface handoffs.
- **Reducer hardening:** validly shaped but intentionally reordered, duplicated, removed, or replaced events.
- **Oracle contract:** pure analyzer and browser sampler calibration tests.
Production-lifecycle fixtures should model states emitted by the current producer. Impossible or reordered sequences belong in reducer-hardening tests and must not be described as normal provider behavior.
## Diagnostics
Failures retain:
- `video.webm`
- `trace.zip`
- failure screenshot
- sampled DOM/layout trace JSON
- event markers and summarized violations
The analyzer records both unclipped layout bounds and ancestor-clipped visible intersections. Scrollbar and raw `scrollTop` changes alone do not fail continuity checks; user-visible semantic anchor movement does.
@@ -1,250 +0,0 @@
import { expect, test } from "@playwright/test"
import {
defineVisualRegions,
reportVisualStability,
startVisualProbe,
stopVisualProbe,
visualPlan,
} from "../../utils/visual-stability"
import {
assistantMessage,
partUpdated,
setupTimeline,
shell,
textPart,
toolPart,
userMessage,
waitForVisualSettle,
type TimelineMessage,
} from "./fixture"
test.describe("timeline adverse visual stability", () => {
test("does not pull a scrolled-away user while an active shell grows", async ({ page }, testInfo) => {
const activeShellID = "prt_adverse_01_shell"
const messages = [
...history(24),
userMessage(),
assistantMessage([shell(activeShellID, "running")], { completed: false }),
]
const timeline = await setupTimeline(page, {
messages,
settings: { shellToolPartsExpanded: true },
cpuRate: 4,
eventRetry: 30,
})
const scroller = page.locator(".scroll-view__viewport", {
has: page.locator('[data-timeline-row="AssistantPart"]'),
})
await scroller.evaluate((element) => {
element.dispatchEvent(new WheelEvent("wheel", { bubbles: true, cancelable: true, deltaY: -450 }))
element.scrollTop = Math.max(0, element.scrollHeight - element.clientHeight - 450)
})
await page.waitForTimeout(150)
await expect
.poll(() => scroller.evaluate((element) => element.scrollHeight - element.clientHeight - element.scrollTop))
.toBeGreaterThan(100)
const anchor = await scroller.evaluate((element) => {
const view = element.getBoundingClientRect()
return [...element.querySelectorAll<HTMLElement>("[data-timeline-key]")].find((row) => {
const rect = row.getBoundingClientRect()
return rect.top >= view.top + 40 && rect.bottom <= view.bottom - 40
})?.dataset.timelineKey
})
expect(anchor).toBeTruthy()
await waitForVisualSettle(page, [`[data-timeline-key="${anchor}"]`])
const regions = defineVisualRegions({
anchor: { selector: `[data-timeline-key="${anchor}"]` },
})
await startVisualProbe(page, regions)
await timeline.send(partUpdated(shell(activeShellID, "running", lines(1))), 180)
await timeline.send(partUpdated(shell(activeShellID, "running", lines(10))), 90)
await timeline.send(partUpdated(shell(activeShellID, "running", lines(50))), 350)
await timeline.send(partUpdated(shell(activeShellID, "completed", lines(50))), 500)
const trace = await stopVisualProbe<keyof typeof regions>(page)
await reportVisualStability(
testInfo,
"scrolled-away-shell",
trace,
visualPlan(regions, [
{ type: "required", regions: ["anchor"] },
{ type: "unique", regions: ["anchor"] },
{ type: "stable", regions: ["anchor"] },
{ type: "fixed", regions: ["anchor"] },
{ type: "opacity", regions: "all" },
{ type: "continuity", regions: "all" },
{ type: "motion", regions: "all", maxPositionReversals: 0 },
{ type: "label-stability", regions: "all" },
]),
)
})
test("preserves an explicit shell state across virtualization", async ({ page }) => {
const targetID = "prt_virtual_shell"
const messages = [
userMessage(undefined, { id: "msg_0000_virtual_user", created: 1700000000000 }),
assistantMessage([shell(targetID, "completed", lines(20))], {
id: "msg_0001_virtual_assistant",
parentID: "msg_0000_virtual_user",
created: 1700000001000,
}),
...history(35, 10),
]
await setupTimeline(page, { messages, settings: { shellToolPartsExpanded: false } })
const scroller = page.locator(".scroll-view__viewport", { has: page.locator("[data-timeline-row]") })
await scroller.evaluate((element) => {
element.dispatchEvent(new WheelEvent("wheel", { bubbles: true, cancelable: true, deltaY: -1_000 }))
element.scrollTop = 0
})
await page.waitForTimeout(300)
const trigger = page.locator(`[data-timeline-part-id="${targetID}"] [data-slot="collapsible-trigger"]`)
await expect(trigger).toBeVisible()
await trigger.click()
await expect(trigger).toHaveAttribute("aria-expanded", "true")
await scroller.evaluate((element) => (element.scrollTop = element.scrollHeight))
await expect(page.locator(`[data-timeline-part-id="${targetID}"]`)).toHaveCount(0)
await scroller.evaluate((element) => (element.scrollTop = 0))
await expect(trigger).toBeVisible()
await expect(trigger).toHaveAttribute("aria-expanded", "true")
})
test("keeps narrow viewport rows ordered during long shell growth", async ({ page }, testInfo) => {
const shellID = "prt_narrow_01_shell"
const followingID = "prt_narrow_02_following"
const timeline = await setupTimeline(page, {
messages: [
userMessage(),
assistantMessage(
[shell(shellID, "running"), textPart(followingID, "A narrow following row that wraps across lines.")],
{
completed: false,
},
),
],
settings: { shellToolPartsExpanded: true },
viewport: { width: 430, height: 800 },
cpuRate: 4,
})
await waitForVisualSettle(page, [
`[data-timeline-part-id="${shellID}"]`,
`[data-timeline-part-id="${followingID}"]`,
])
const regions = defineVisualRegions({
shell: { selector: `[data-timeline-part-id="${shellID}"]`, closest: '[data-timeline-row="AssistantPart"]' },
following: {
selector: `[data-timeline-part-id="${followingID}"]`,
closest: '[data-timeline-row="AssistantPart"]',
},
})
await startVisualProbe(page, regions)
await timeline.send(partUpdated(shell(shellID, "running", wideLines(10))), 100)
await timeline.send(partUpdated(shell(shellID, "running", wideLines(50))), 300)
await timeline.send(partUpdated(shell(shellID, "completed", wideLines(50))), 500)
const trace = await stopVisualProbe<keyof typeof regions>(page)
await reportVisualStability(
testInfo,
"narrow-shell",
trace,
visualPlan(
regions,
[
{ type: "required", regions: ["shell", "following"] },
{ type: "unique", regions: ["shell", "following"] },
{ type: "stable", regions: ["shell", "following"] },
{ type: "opacity", regions: "all" },
{ type: "continuity", regions: "all" },
{ type: "motion", regions: "all", maxPositionReversals: 0 },
{ type: "label-stability", regions: "all" },
{ type: "preserve-bottom-anchor" },
{ type: "flow", regions: ["shell", "following"] },
],
{ perMarker: true },
),
)
})
test("keeps visible rows ordered while resizing desktop to narrow and back", async ({ page }, testInfo) => {
const shellID = "prt_resize_01_shell"
const contextIDs = ["prt_resize_02_read", "prt_resize_03_glob"]
const followingID = "prt_resize_04_following"
await setupTimeline(page, {
messages: [
userMessage(),
assistantMessage([
shell(shellID, "completed", wideLines(15)),
toolPart(contextIDs[0]!, "read", "completed", { filePath: "src/a.ts" }),
toolPart(contextIDs[1]!, "glob", "completed", { path: ".", pattern: "**/*.ts" }),
textPart(followingID, "Following responsive timeline content that wraps on narrow screens."),
]),
],
settings: { shellToolPartsExpanded: true },
cpuRate: 4,
seedHistory: true,
})
const group = `[data-timeline-part-ids="${contextIDs.join(",")}"]`
const regions = defineVisualRegions({
shell: { selector: `[data-timeline-part-id="${shellID}"]`, closest: '[data-timeline-row="AssistantPart"]' },
context: { selector: group, closest: '[data-timeline-row="AssistantPart"]' },
following: {
selector: `[data-timeline-part-id="${followingID}"]`,
closest: '[data-timeline-row="AssistantPart"]',
},
})
await startVisualProbe(page, regions)
await page.setViewportSize({ width: 430, height: 800 })
await page.waitForTimeout(500)
await page.setViewportSize({ width: 900, height: 800 })
await page.waitForTimeout(500)
await page.setViewportSize({ width: 1400, height: 900 })
await page.waitForTimeout(500)
const trace = await stopVisualProbe<keyof typeof regions>(page)
await reportVisualStability(
testInfo,
"responsive-resize",
trace,
visualPlan(regions, [
{ type: "required", regions: ["shell", "context", "following"] },
{ type: "unique", regions: ["shell", "context", "following"] },
{ type: "stable", regions: ["shell", "context", "following"] },
{ type: "opacity", regions: "all" },
{ type: "continuity", regions: "all" },
{ type: "motion", regions: "all", maxPositionReversals: 4, maxReversals: 4 },
{ type: "label-stability", regions: "all" },
{ type: "flow", regions: ["shell", "context", "following"] },
]),
)
})
})
function history(count: number, offset = 0): TimelineMessage[] {
return Array.from({ length: count }, (_, index) => {
const value = index + offset
const prefix = `msg_0${String(value).padStart(3, "0")}_history`
const userID = `${prefix}_a_user`
return [
userMessage(undefined, { id: userID, created: 1699990000000 + value * 10_000 }),
assistantMessage(
[
textPart(
`prt_history_${String(value).padStart(3, "0")}`,
`Historical response ${value}. ${"Stable history content. ".repeat(8)}`,
),
],
{
id: `${prefix}_b_assistant`,
parentID: userID,
created: 1699990001000 + value * 10_000,
},
),
]
}).flat()
}
function lines(count: number) {
return Array.from({ length: count }, (_, index) => `line ${index + 1}`).join("\n")
}
function wideLines(count: number) {
return Array.from({ length: count }, (_, index) => `line ${index + 1} ${"wide-output-".repeat(20)}`).join("\n")
}
@@ -1,192 +0,0 @@
import { expect, test } from "@playwright/test"
import {
defineVisualRegions,
reportVisualStability,
startVisualProbe,
stopVisualProbe,
visualPlan,
} from "../../utils/visual-stability"
import {
assistantID,
assistantMessage,
event,
partUpdated,
setupTimeline,
textPart,
toolPart,
userMessage,
waitForVisualSettle,
} from "./fixture"
const inputs = {
read: { filePath: "src/a.ts", offset: 0, limit: 120 },
glob: { path: ".", pattern: "**/*.ts" },
grep: { path: ".", pattern: "stable", include: "*.ts" },
list: { path: "src" },
}
test("appends context operations while the group is expanded", async ({ page }, testInfo) => {
const firstID = "prt_append_01_read"
const followingID = "prt_append_99_following"
const timeline = await setupTimeline(page, {
messages: [
userMessage(),
assistantMessage([toolPart(firstID, "read", "running", inputs.read), textPart(followingID, "Following append")], {
completed: false,
}),
],
cpuRate: 4,
})
const initialGroup = `[data-timeline-part-ids="${firstID}"]`
await page.locator(`${initialGroup} [data-slot="collapsible-trigger"]`).click()
await waitForVisualSettle(page, [initialGroup, `[data-timeline-part-id="${followingID}"]`])
const regions = defineVisualRegions({
context: {
selector: '[data-timeline-part-ids^="prt_append_01_read"]',
closest: '[data-timeline-row="AssistantPart"]',
},
following: { selector: `[data-timeline-part-id="${followingID}"]`, closest: '[data-timeline-row="AssistantPart"]' },
})
await startVisualProbe(page, regions)
await timeline.send(partUpdated(toolPart("prt_append_02_glob", "glob", "running", inputs.glob)), 180)
await timeline.send(partUpdated(toolPart("prt_append_03_grep", "grep", "completed", inputs.grep)), 240)
await timeline.send(partUpdated(toolPart("prt_append_04_list", "list", "completed", inputs.list)), 500)
const trace = await stopVisualProbe<keyof typeof regions>(page)
await reportVisualStability(
testInfo,
"context-append",
trace,
visualPlan(
regions,
[
{ type: "required", regions: ["context", "following"] },
{ type: "unique", regions: ["context", "following"] },
{ type: "stable", regions: ["context", "following"] },
{ type: "opacity", regions: "all" },
{ type: "continuity", regions: "all" },
{ type: "motion", regions: ["following"], maxPositionReversals: 0 },
{ type: "label-stability", regions: "all" },
{ type: "preserve-bottom-anchor" },
{ type: "flow", regions: ["context", "following"] },
],
{ perMarker: true },
),
)
await expect(
page.locator(
'[data-timeline-part-ids="prt_append_01_read,prt_append_02_glob,prt_append_03_grep,prt_append_04_list"]',
),
).toBeVisible()
await expect(
page.locator('[data-timeline-part-ids^="prt_append_01_read"] [data-slot="collapsible-trigger"]'),
).toHaveAttribute("aria-expanded", "true")
})
test("splits and merges context groups when a middle text part changes", async ({ page }, testInfo) => {
const textID = "prt_split_02_text"
const followingID = "prt_split_99_following"
const timeline = await setupTimeline(page, {
messages: [
userMessage(),
assistantMessage([
toolPart("prt_split_01_read", "read", "completed", inputs.read),
textPart(textID, "Boundary"),
toolPart("prt_split_03_glob", "glob", "completed", inputs.glob),
textPart(followingID, "Following split groups"),
]),
],
cpuRate: 4,
})
const regions = defineVisualRegions({
following: { selector: `[data-timeline-part-id="${followingID}"]`, closest: '[data-timeline-row="AssistantPart"]' },
})
await startVisualProbe(page, regions)
await timeline.send(
event("message.part.removed", { sessionID: "ses_timeline_stability", messageID: assistantID, partID: textID }),
500,
)
await expect(page.locator('[data-timeline-part-ids="prt_split_01_read,prt_split_03_glob"]')).toBeVisible()
await timeline.send(partUpdated(textPart(textID, "Boundary restored")), 500)
await expect(page.locator('[data-timeline-part-ids="prt_split_01_read"]')).toBeVisible()
await expect(page.locator('[data-timeline-part-ids="prt_split_03_glob"]')).toBeVisible()
const trace = await stopVisualProbe<keyof typeof regions>(page)
await reportVisualStability(
testInfo,
"context-split-merge",
trace,
visualPlan(
regions,
[
{ type: "required", regions: ["following"] },
{ type: "unique", regions: ["following"] },
{ type: "stable", regions: ["following"] },
{ type: "opacity", regions: "all" },
{ type: "continuity", regions: "all" },
{ type: "motion", regions: "all", maxPositionReversals: 1 },
{ type: "label-stability", regions: "all" },
],
{ perMarker: true },
),
)
})
test("removing the first context member replaces the group once without overlapping following content", async ({
page,
}, testInfo) => {
const ids = ["prt_key_01_read", "prt_key_02_glob", "prt_key_03_grep"]
const followingID = "prt_key_99_following"
const timeline = await setupTimeline(page, {
messages: [
userMessage(),
assistantMessage([
toolPart(ids[0]!, "read", "completed", inputs.read),
toolPart(ids[1]!, "glob", "completed", inputs.glob),
toolPart(ids[2]!, "grep", "completed", inputs.grep),
textPart(followingID, "Following replaced group"),
]),
],
cpuRate: 4,
})
const original = page.locator(`[data-timeline-part-ids="${ids.join(",")}"]`)
const originalRowKey = await original.evaluate((element) =>
element.closest("[data-timeline-key]")?.getAttribute("data-timeline-key"),
)
await original.locator('[data-slot="collapsible-trigger"]').click()
await expect(original.locator('[data-slot="collapsible-trigger"]')).toHaveAttribute("aria-expanded", "true")
const regions = defineVisualRegions({
context: {
selector: '[data-timeline-part-ids*="prt_key_02_glob"]',
closest: '[data-timeline-row="AssistantPart"]',
},
following: { selector: `[data-timeline-part-id="${followingID}"]`, closest: '[data-timeline-row="AssistantPart"]' },
})
await startVisualProbe(page, regions)
await timeline.send(
event("message.part.removed", { sessionID: "ses_timeline_stability", messageID: assistantID, partID: ids[0] }),
500,
)
const trace = await stopVisualProbe<keyof typeof regions>(page)
await reportVisualStability(
testInfo,
"context-first-remove",
trace,
visualPlan(regions, [
{ type: "required", regions: ["context", "following"] },
{ type: "unique", regions: ["context", "following"] },
{ type: "opacity", regions: "all" },
{ type: "continuity", regions: "all" },
{ type: "motion", regions: "all", maxPositionReversals: 0 },
{ type: "label-stability", regions: "all" },
{ type: "flow", regions: ["context", "following"] },
]),
)
await expect(page.locator(`[data-timeline-part-ids="${ids.slice(1).join(",")}"]`)).toBeVisible()
expect(
await page
.locator(`[data-timeline-part-ids="${ids.slice(1).join(",")}"]`)
.evaluate((element) => element.closest("[data-timeline-key]")?.getAttribute("data-timeline-key")),
).toBe(originalRowKey)
await expect(
page.locator(`[data-timeline-part-ids="${ids.slice(1).join(",")}"] [data-slot="collapsible-trigger"]`),
).toHaveAttribute("aria-expanded", "true")
})
@@ -1,113 +0,0 @@
import { test } from "@playwright/test"
import {
defineVisualRegions,
reportVisualStability,
startVisualProbe,
stopVisualProbe,
visualPlan,
} from "../../utils/visual-stability"
import {
assistantMessage,
partUpdated,
setupTimeline,
shell,
textPart,
userMessage,
waitForVisualSettle,
} from "./fixture"
// Fractional scaling exercises different browser rounding than the baseline.
for (const deviceScaleFactor of [1, 1.25]) {
test(`keeps shell growth ordered at device scale ${deviceScaleFactor}`, async ({ page }, testInfo) => {
const shellID = `prt_dpr_${String(deviceScaleFactor).replace(".", "_")}_01_shell`
const followingID = `prt_dpr_${String(deviceScaleFactor).replace(".", "_")}_02_following`
const timeline = await setupTimeline(page, {
messages: [
userMessage(),
assistantMessage([shell(shellID, "running"), textPart(followingID, "Following scaled shell")], {
completed: false,
}),
],
settings: { shellToolPartsExpanded: true },
cpuRate: 4,
deviceScaleFactor,
seedHistory: true,
})
await waitForVisualSettle(page, [
`[data-timeline-part-id="${shellID}"]`,
`[data-timeline-part-id="${followingID}"]`,
])
const regions = defineVisualRegions({
shell: { selector: `[data-timeline-part-id="${shellID}"]`, closest: '[data-timeline-row="AssistantPart"]' },
following: {
selector: `[data-timeline-part-id="${followingID}"]`,
closest: '[data-timeline-row="AssistantPart"]',
},
})
await startVisualProbe(page, regions)
await timeline.send(partUpdated(shell(shellID, "running", lines(20))), 180)
await timeline.send(partUpdated(shell(shellID, "completed", lines(20))), 500)
const trace = await stopVisualProbe<keyof typeof regions>(page)
await reportVisualStability(testInfo, `dpr-${deviceScaleFactor}`, trace, shellPlan(regions))
})
}
for (const reducedMotion of [true]) {
test(`keeps shell and status transitions ordered with reduced motion ${reducedMotion}`, async ({
page,
}, testInfo) => {
const shellID = `prt_motion_${reducedMotion}_01_shell`
const followingID = `prt_motion_${reducedMotion}_02_following`
const timeline = await setupTimeline(page, {
messages: [
userMessage(),
assistantMessage([shell(shellID, "running"), textPart(followingID, "Following motion profile")], {
completed: false,
}),
],
settings: { shellToolPartsExpanded: true },
reducedMotion,
cpuRate: 4,
seedHistory: true,
})
await waitForVisualSettle(page, [
`[data-timeline-part-id="${shellID}"]`,
`[data-timeline-part-id="${followingID}"]`,
])
const regions = defineVisualRegions({
shell: { selector: `[data-timeline-part-id="${shellID}"]`, closest: '[data-timeline-row="AssistantPart"]' },
following: {
selector: `[data-timeline-part-id="${followingID}"]`,
closest: '[data-timeline-row="AssistantPart"]',
},
})
await startVisualProbe(page, regions)
await timeline.send(partUpdated(shell(shellID, "completed", lines(10))), 500)
const trace = await stopVisualProbe<keyof typeof regions>(page)
await reportVisualStability(testInfo, `reduced-motion-${reducedMotion}`, trace, shellPlan(regions))
})
}
function lines(count: number) {
return Array.from({ length: count }, (_, index) => `line ${index + 1}`).join("\n")
}
function shellPlan<Regions extends ReturnType<typeof defineVisualRegions>>(
regions: Regions & Record<"shell" | "following", { selector: string }>,
) {
return visualPlan(
regions,
[
{ type: "required", regions: ["shell", "following"] },
{ type: "unique", regions: ["shell", "following"] },
{ type: "stable", regions: ["shell", "following"] },
{ type: "opacity", regions: "all" },
{ type: "continuity", regions: "all" },
{ type: "motion", regions: "all", maxPositionReversals: 0 },
{ type: "label-stability", regions: "all" },
{ type: "preserve-bottom-anchor" },
{ type: "flow", regions: ["shell", "following"] },
],
{ perMarker: true },
)
}
@@ -1,121 +0,0 @@
import { test } from "@playwright/test"
import {
defineVisualRegions,
reportVisualStability,
startVisualProbe,
stopVisualProbe,
visualPlan,
} from "../../utils/visual-stability"
import {
assistantMessage,
partUpdated,
setupTimeline,
textPart,
toolPart,
userMessage,
waitForVisualSettle,
} from "./fixture"
const profiles = [
{ name: "edit", tool: "edit", input: { filePath: "src/edit.ts" } },
{
name: "multi patch",
tool: "patch",
input: { files: ["src/a.ts", "src/b.ts", "src/old.ts", "src/moved.ts"] },
},
] as const
for (const profile of profiles) {
test(`stabilizes ${profile.name} pending to completed`, async ({ page }, testInfo) => {
const partID = `prt_file_matrix_${profiles.indexOf(profile)}`
const followingID = `prt_file_matrix_following_${profiles.indexOf(profile)}`
const timeline = await setupTimeline(page, {
messages: [
userMessage(),
assistantMessage(
[
toolPart(partID, profile.tool, "pending", profile.input),
textPart(followingID, `Following ${profile.name}`),
],
{ completed: false },
),
],
settings: { editToolPartsExpanded: true },
cpuRate: 4,
})
await waitForVisualSettle(page, [`[data-timeline-part-id="${partID}"]`, `[data-timeline-part-id="${followingID}"]`])
const regions = defineVisualRegions({
tool: { selector: `[data-timeline-part-id="${partID}"]`, closest: '[data-timeline-row="AssistantPart"]' },
following: {
selector: `[data-timeline-part-id="${followingID}"]`,
closest: '[data-timeline-row="AssistantPart"]',
},
})
await startVisualProbe(page, regions)
await timeline.send(partUpdated(toolPart(partID, profile.tool, "running", profile.input)), 180)
await timeline.send(partUpdated(completedPart(partID, profile)), 900)
const trace = await stopVisualProbe<keyof typeof regions>(page)
await reportVisualStability(
testInfo,
`file-${profile.name}`,
trace,
visualPlan(
regions,
[
{ type: "required", regions: ["tool", "following"] },
{ type: "unique", regions: ["tool", "following"] },
{ type: "stable", regions: ["tool", "following"] },
{ type: "opacity", regions: "all" },
{ type: "continuity", regions: "all" },
{ type: "motion", regions: "all", maxPositionReversals: 0, maxReversals: 1 },
{ type: "label-stability", regions: "all" },
{ type: "preserve-bottom-anchor" },
{ type: "flow", regions: ["tool", "following"] },
],
{ perMarker: true },
),
)
})
}
function completedPart(partID: string, profile: (typeof profiles)[number]) {
if (profile.tool === "edit") {
return toolPart(partID, profile.tool, "completed", profile.input, {
metadata: {
filediff: {
file: "src/edit.ts",
additions: 50,
deletions: 50,
before: source(50, false),
after: source(50, true),
},
},
})
}
const files = [
patchFile("src/a.ts", "update"),
patchFile("src/b.ts", "add"),
patchFile("src/old.ts", "delete"),
{ ...patchFile("src/moved.ts", "move"), move: "src/new-place.ts" },
]
return toolPart(partID, profile.tool, "completed", profile.input, { metadata: { files } })
}
function patchFile(filePath: string, type: "add" | "update" | "delete" | "move") {
return {
filePath,
relativePath: filePath,
type,
additions: type === "delete" ? 0 : 20,
deletions: type === "add" ? 0 : 20,
before: type === "add" ? undefined : source(20, false),
after: type === "delete" ? undefined : source(20, true),
}
}
function source(count: number, changed: boolean) {
return Array.from(
{ length: count },
(_, index) => `export const value${index} = ${changed ? index + 1 : index}\n`,
).join("")
}
@@ -1,113 +0,0 @@
import { expect, test } from "@playwright/test"
import {
defineVisualRegions,
reportVisualStability,
startVisualProbe,
stopVisualProbe,
visualPlan,
} from "../../utils/visual-stability"
import {
assistantMessage,
partUpdated,
setupTimeline,
textPart,
toolPart,
userMessage,
waitForVisualSettle,
} from "./fixture"
test("adds patch files incrementally without resetting outer expansion", async ({ page }, testInfo) => {
const patchID = "prt_incremental_01_patch"
const followingID = "prt_incremental_02_following"
const first = patchFile("src/a.ts", "update")
const timeline = await setupTimeline(page, {
messages: [
userMessage(),
assistantMessage(
[
toolPart(patchID, "patch", "running", { files: [first.filePath] }, { metadata: { files: [first] } }),
textPart(followingID, "Following incremental patch"),
],
{ completed: false },
),
],
settings: { editToolPartsExpanded: true },
cpuRate: 4,
seedHistory: true,
})
const trigger = page.locator(`[data-timeline-part-id="${patchID}"] [data-slot="collapsible-trigger"]`).first()
await expect(trigger).toHaveAttribute("aria-expanded", "true")
await waitForVisualSettle(page, [`[data-timeline-part-id="${patchID}"]`, `[data-timeline-part-id="${followingID}"]`])
const regions = defineVisualRegions({
patch: { selector: `[data-timeline-part-id="${patchID}"]`, closest: '[data-timeline-row="AssistantPart"]' },
following: { selector: `[data-timeline-part-id="${followingID}"]`, closest: '[data-timeline-row="AssistantPart"]' },
})
await startVisualProbe(page, regions)
const second = patchFile("src/b.ts", "add")
const third = patchFile("src/old.ts", "delete")
await timeline.send(
partUpdated(
toolPart(
patchID,
"patch",
"running",
{ files: [first.filePath, second.filePath] },
{ metadata: { files: [first, second] } },
),
),
240,
)
await timeline.send(
partUpdated(
toolPart(
patchID,
"patch",
"completed",
{ files: [first.filePath, second.filePath, third.filePath] },
{ metadata: { files: [first, second, third] } },
),
),
800,
)
const trace = await stopVisualProbe<keyof typeof regions>(page)
await reportVisualStability(
testInfo,
"incremental-patch",
trace,
visualPlan(
regions,
[
{ type: "required", regions: ["patch", "following"] },
{ type: "unique", regions: ["patch", "following"] },
{ type: "stable", regions: ["patch", "following"] },
{ type: "opacity", regions: "all" },
{ type: "continuity", regions: "all" },
{ type: "motion", regions: ["following"], maxPositionReversals: 0 },
{ type: "label-stability", regions: "all" },
{ type: "preserve-bottom-anchor" },
{ type: "flow", regions: ["patch", "following"] },
],
{ perMarker: true },
),
)
await expect(trigger).toHaveAttribute("aria-expanded", "true")
await expect(page.locator('[data-scope="apply-patch"] [data-type="delete"]')).toBeVisible()
})
function patchFile(filePath: string, type: "add" | "update" | "delete") {
return {
filePath,
relativePath: filePath,
type,
additions: type === "delete" ? 0 : 4,
deletions: type === "add" ? 0 : 3,
before: type === "add" ? undefined : source(false),
after: type === "delete" ? undefined : source(true),
}
}
function source(changed: boolean) {
return Array.from({ length: 12 }, (_, index) => `export const value${index} = ${changed ? index + 1 : index}\n`).join(
"",
)
}
@@ -1,68 +0,0 @@
import { describe, expect, test } from "bun:test"
import {
assistantMessage,
event,
toolPart,
userMessage,
validateTimelineEvent,
validateTimelineMessages,
type PartSeed,
} from "./fixture"
describe("timeline fixture validation", () => {
test("accepts a valid timeline", () => {
expect(validateTimelineMessages([userMessage(), assistantMessage()])).toHaveLength(2)
})
test("rejects malformed SDK values at runtime", () => {
expect(() =>
assistantMessage([], {
error: { name: "APIError", data: { message: "failed" } } as never,
}),
).toThrow()
expect(() =>
validateTimelineEvent({
directory: "C:/OpenCode/TimelineStability",
payload: {
id: "evt_invalid_status",
type: "session.status",
properties: { sessionID: "ses_timeline_stability", status: { type: "retry", attempt: 1 } },
},
}),
).toThrow()
})
test("rejects duplicate IDs and orphan assistants", () => {
expect(() => validateTimelineMessages([userMessage(), userMessage()])).toThrow(/duplicate message ID/)
expect(() =>
validateTimelineMessages([userMessage(), assistantMessage([], { parentID: "msg_missing_parent" })]),
).toThrow(/parent user/)
})
test("assigns deterministic event IDs", () => {
const first = event("session.status", { sessionID: "ses_timeline_stability", status: { type: "busy" } })
const second = event("session.status", { sessionID: "ses_timeline_stability", status: { type: "idle" } })
expect(first.payload.id).toMatch(/^evt_timeline_\d{4}$/)
expect(Number(second.payload.id.slice(-4))).toBe(Number(first.payload.id.slice(-4)) + 1)
})
})
if (false) {
const userSeed = { id: "prt_type_user", type: "text", text: "typed" } satisfies PartSeed<"user">
userMessage([userSeed])
// @ts-expect-error Tool completion fields are not valid while pending.
toolPart("prt_invalid_pending", "bash", "pending", {}, { output: "impossible" })
// @ts-expect-error Tool completion fields are not valid while running.
toolPart("prt_invalid_running", "bash", "running", {}, { output: "impossible" })
// @ts-expect-error Tool error fields are not valid after completion.
toolPart("prt_invalid_completed", "bash", "completed", {}, { error: "impossible" })
assistantMessage([
// @ts-expect-error Agent references belong to user messages, not assistant messages.
{ id: "prt_invalid_owner", type: "agent", name: "explore", source: { value: "@explore", start: 0, end: 8 } },
])
// @ts-expect-error Retry status events require message and next.
event("session.status", { sessionID: "ses_timeline_stability", status: { type: "retry", attempt: 1 } })
}
@@ -1,564 +0,0 @@
import { base64Encode } from "@opencode-ai/core/util/encode"
import { Event } from "@opencode-ai/schema/event"
import { SessionStatusEvent } from "@opencode-ai/schema/session-status-event"
import { SessionV1 } from "@opencode-ai/schema/session-v1"
import type {
AssistantMessage,
GlobalEvent,
Message,
Part,
Session,
SessionStatus,
ToolPart,
ToolState,
UserMessage,
} from "@opencode-ai/sdk/v2/client"
import { expect, type Page } from "@playwright/test"
import { Schema } from "effect"
import { mockOpenCodeServer } from "../../utils/mock-server"
import { installSseTransport } from "../../utils/sse-transport"
import { expectSessionTitle } from "../../utils/waits"
export const directory = "C:/OpenCode/TimelineStability"
export const projectID = "proj_timeline_stability"
export const sessionID = "ses_timeline_stability"
export const userID = "msg_1000_timeline_user"
export const assistantID = "msg_1001_timeline_assistant"
export const title = "Timeline visual stability"
export const model = { providerID: "opencode", modelID: "claude-opus-4-6", variant: "max" }
type TimelinePayload = Extract<
GlobalEvent["payload"],
{
type:
| "message.updated"
| "message.removed"
| "message.part.updated"
| "message.part.removed"
| "message.part.delta"
| "session.status"
}
>
type DeepReadonly<Value> = Value extends readonly unknown[]
? { readonly [Key in keyof Value]: DeepReadonly<Value[Key]> }
: Value extends object
? { readonly [Key in keyof Value]: DeepReadonly<Value[Key]> }
: Value
export type TimelineEvent = DeepReadonly<Omit<GlobalEvent, "payload"> & { payload: TimelinePayload }>
export type EventPayload = TimelineEvent
export type ToolStatus = ToolState["status"]
export type TimelineMessage = { info: UserMessage; parts: Part[] } | { info: AssistantMessage; parts: Part[] }
type UserPart = Extract<Part, { type: "text" | "file" | "agent" | "subtask" }>
type AssistantPart = Exclude<Part, { type: "agent" | "subtask" }>
type OwnedPart<Owner extends Message["role"]> = Owner extends "user" ? UserPart : AssistantPart
export type PartSeed<Owner extends Message["role"]> =
OwnedPart<Owner> extends infer Candidate
? Candidate extends Part
? Omit<Candidate, "sessionID" | "messageID">
: never
: never
type ToolOptions<State extends ToolStatus> = State extends "pending"
? { output?: never; title?: never; metadata?: never; error?: never }
: State extends "running"
? { title?: string; metadata?: Record<string, unknown>; output?: never; error?: never }
: State extends "error"
? { error?: string; metadata?: Record<string, unknown>; output?: never; title?: never }
: { output?: string; title?: string; metadata?: Record<string, unknown>; error?: never }
const decodeOptions = { errors: "all", onExcessProperty: "error" } as const
const decodeMessage = Schema.decodeUnknownSync(SessionV1.WithParts)
const decodePart = Schema.decodeUnknownSync(SessionV1.Part)
const decodeStatus = Schema.decodeUnknownSync(SessionStatusEvent.Info)
const timelineEventSchema = Schema.Union([
eventSchema("message.updated", SessionV1.Event.MessageUpdated.data),
eventSchema("message.removed", SessionV1.Event.MessageRemoved.data),
eventSchema("message.part.updated", SessionV1.Event.PartUpdated.data),
eventSchema("message.part.removed", SessionV1.Event.PartRemoved.data),
eventSchema("message.part.delta", SessionV1.Event.PartDelta.data),
eventSchema("session.status", SessionStatusEvent.Status.data),
])
const decodeEvent = Schema.decodeUnknownSync(timelineEventSchema)
let eventSequence = 0
export async function setupTimeline(
page: Page,
input: {
messages?: TimelineMessage[]
settings?: Record<string, boolean>
sessions?: Session[]
cpuRate?: number
viewport?: { width: number; height: number }
eventRetry?: number
reducedMotion?: boolean
locale?: string
deviceScaleFactor?: number
seedHistory?: boolean
} = {},
) {
const sessions = input.sessions ?? [session()]
const messages = validateTimelineMessages([
...(input.seedHistory ? historyMessages(18) : []),
...(input.messages ?? [userMessage(), assistantMessage()]),
])
const active = messages.findLast((message) => message.info.role === "assistant")
const initialStatus = decodeStatus(
active?.info.role === "assistant" && active.info.time.completed === undefined ? { type: "busy" } : { type: "idle" },
decodeOptions,
)
const transport = await installSseTransport<EventPayload>(page, {
server: `http://${process.env.PLAYWRIGHT_SERVER_HOST ?? "127.0.0.1"}:${process.env.PLAYWRIGHT_SERVER_PORT ?? "4096"}`,
retry: input.eventRetry ?? 20,
})
await mockOpenCodeServer(page, {
directory,
project: project(),
provider: provider(),
sessions,
sessionStatus: { [sessionID]: initialStatus },
pageMessages: () => ({
items: messages,
}),
})
await page.addInitScript((settings) => {
localStorage.setItem(
"settings.v3",
JSON.stringify({
general: {
editToolPartsExpanded: false,
shellToolPartsExpanded: false,
showReasoningSummaries: false,
showSessionProgressBar: true,
...settings,
},
}),
)
if (settings.newLayoutDesigns === false) {
localStorage.setItem("app-version.v1", JSON.stringify({ version: "1.17.20" }))
}
}, input.settings ?? {})
if (input.locale) {
await page.addInitScript((locale) => {
localStorage.setItem("opencode.global.dat:language", JSON.stringify({ locale }))
}, input.locale)
}
if (input.reducedMotion) await page.emulateMedia({ reducedMotion: "reduce" })
await page.setViewportSize(input.viewport ?? { width: 1400, height: 900 })
if (input.deviceScaleFactor) {
const devtools = await page.context().newCDPSession(page)
const viewport = input.viewport ?? { width: 1400, height: 900 }
await devtools.send("Emulation.setDeviceMetricsOverride", {
width: viewport.width,
height: viewport.height,
deviceScaleFactor: input.deviceScaleFactor,
mobile: false,
})
}
await page.goto(`/${base64Encode(directory)}/session/${sessionID}`)
await transport.waitForConnection()
await expectSessionTitle(page, title)
if (input.cpuRate && input.cpuRate > 1) {
const devtools = await page.context().newCDPSession(page)
await devtools.send("Emulation.setCPUThrottlingRate", { rate: input.cpuRate })
}
return {
transport,
async send(event: TimelineEvent, delay = 0) {
const valid = validateTimelineEvent(event)
await transport.send(valid, { marker: describeEvent(valid) })
if (delay) await page.waitForTimeout(delay)
},
async sendAll(sequence: { event: TimelineEvent; delay: number }[]) {
for (const item of sequence) {
const valid = validateTimelineEvent(item.event)
await transport.send(valid, { marker: describeEvent(valid) })
await page.waitForTimeout(item.delay)
}
},
async settle(frames = 3) {
await page.evaluate(
(frames) =>
new Promise<void>((resolve) => {
let remaining = frames
const tick = () => {
remaining--
if (remaining <= 0) return resolve()
requestAnimationFrame(tick)
}
requestAnimationFrame(tick)
}),
frames,
)
},
async waitForPart(partID: string) {
await expect(page.locator(`[data-timeline-part-id="${partID}"]`).first()).toBeVisible()
},
}
}
function describeEvent(event: EventPayload) {
if (event.payload.type === "message.part.updated") {
const part = event.payload.properties.part
return [
event.payload.type,
part.id,
part.type === "tool" ? part.tool : part.type,
part.type === "tool" ? part.state.status : undefined,
]
.filter(Boolean)
.join(":")
}
if (event.payload.type === "session.status") {
const status = event.payload.properties.status
return [event.payload.type, status.type, status.type === "retry" ? status.attempt : undefined]
.filter((value) => value !== undefined)
.join(":")
}
return event.payload.type
}
export function event<const Type extends TimelinePayload["type"]>(
type: Type,
properties: Extract<TimelinePayload, { type: Type }>["properties"],
): TimelineEvent
export function event(type: TimelinePayload["type"], properties: TimelinePayload["properties"]): TimelineEvent {
return validateTimelineEvent({
directory,
payload: { id: `evt_timeline_${String(++eventSequence).padStart(4, "0")}`, type, properties },
})
}
export function validateTimelineEvent(input: unknown): TimelineEvent {
return decodeEvent(input, decodeOptions)
}
export function validateTimelineMessages(input: readonly TimelineMessage[]): TimelineMessage[] {
input.forEach((message) => decodeMessage(message, decodeOptions))
const messages = [...input]
const messageIDs = new Set<string>()
const partIDs = new Set<string>()
const users = new Set(messages.filter((message) => message.info.role === "user").map((message) => message.info.id))
messages.forEach((message) => {
if (messageIDs.has(message.info.id))
throw new Error(`Timeline fixture has duplicate message ID: ${message.info.id}`)
messageIDs.add(message.info.id)
if (message.info.role === "assistant" && !users.has(message.info.parentID))
throw new Error(`Timeline assistant ${message.info.id} must reference a parent user in the fixture`)
message.parts.forEach((part) => {
if (part.sessionID !== message.info.sessionID || part.messageID !== message.info.id)
throw new Error(`Timeline part ${part.id} ownership does not match message ${message.info.id}`)
if (message.info.role === "user" && !["text", "file", "agent", "subtask"].includes(part.type))
throw new Error(`Timeline user message ${message.info.id} cannot own ${part.type} part ${part.id}`)
if (message.info.role === "assistant" && ["agent", "subtask"].includes(part.type))
throw new Error(`Timeline assistant message ${message.info.id} cannot own ${part.type} part ${part.id}`)
if (partIDs.has(part.id)) throw new Error(`Timeline fixture has duplicate part ID: ${part.id}`)
partIDs.add(part.id)
})
})
return messages
}
export async function waitForVisualSettle(page: Page, selectors: string[], stableFrames = 3) {
await page.waitForFunction(
({ selectors, stableFrames }) => {
const elements = selectors.map((selector) => document.querySelector<HTMLElement>(selector))
if (elements.some((element) => !element)) return false
return new Promise<boolean>((resolve) => {
let stable = 0
let previous = ""
const sample = () => {
const signature = JSON.stringify(
elements.map((element) => {
const rect = element!.getBoundingClientRect()
return [Math.round(rect.top * 10), Math.round(rect.bottom * 10), Math.round(rect.height * 10)]
}),
)
stable = signature === previous ? stable + 1 : 0
previous = signature
const ordered = elements
.slice(1)
.every(
(element, index) =>
elements[index]!.getBoundingClientRect().bottom <= element!.getBoundingClientRect().top + 0.5,
)
if (stable >= stableFrames && ordered) return resolve(true)
requestAnimationFrame(sample)
}
requestAnimationFrame(sample)
})
},
{ selectors, stableFrames },
)
}
export function historyMessages(count: number): TimelineMessage[] {
return Array.from({ length: count }, (_, index) => {
const value = String(index).padStart(4, "0")
const historyUserID = `msg_0${value}_history_a_user`
return [
userMessage(undefined, { id: historyUserID, created: 1690000000000 + index * 10_000 }),
assistantMessage(
[
{
id: `prt_0${value}_history_text`,
type: "text",
text: `Historical response ${index}. ${"Existing session content keeps the virtual timeline realistic. ".repeat(5)}`,
},
],
{
id: `msg_0${value}_history_b_assistant`,
parentID: historyUserID,
created: 1690000001000 + index * 10_000,
},
),
]
}).flat()
}
export function partUpdated(part: Part | PartSeed<"assistant">) {
const owned = "messageID" in part ? part : { ...part, sessionID, messageID: assistantID }
decodePart(owned, decodeOptions)
return event("message.part.updated", {
sessionID,
part: owned,
time: 1700000002000,
})
}
export function partDelta(partID: string, delta: string, messageID = assistantID) {
return event("message.part.delta", { sessionID, messageID, partID, field: "text", delta })
}
export function messageUpdated(info: Message) {
return event("message.updated", { sessionID, info })
}
export function status(type: SessionStatus["type"], attempt = 1) {
return event("session.status", {
sessionID,
status: type === "retry" ? { type, attempt, message: "Rate limited", next: 1700000010000 } : { type },
})
}
export function userMessage(
parts?: PartSeed<"user">[],
input: { id?: string; summary?: UserMessage["summary"]; created?: number } = {},
): Extract<TimelineMessage, { info: { role: "user" } }> {
const id = input.id ?? userID
const seeds = parts ?? [userText("Build the timeline stability matrix.", { id: `prt_${id}_text` })]
const message = {
info: {
id,
sessionID,
role: "user",
time: { created: input.created ?? 1700000000000 },
summary: input.summary ?? { diffs: [] },
agent: "build",
model,
},
parts: seeds.map((part) => ({
...part,
sessionID,
messageID: id,
})),
} satisfies Extract<TimelineMessage, { info: { role: "user" } }>
decodeMessage(message, decodeOptions)
return message
}
export function assistantMessage(
parts: PartSeed<"assistant">[] = [],
input: {
id?: string
parentID?: string
completed?: boolean
error?: AssistantMessage["error"]
created?: number
} = {},
): Extract<TimelineMessage, { info: { role: "assistant" } }> {
const id = input.id ?? assistantID
const message = {
info: {
id,
sessionID,
role: "assistant",
time: {
created: input.created ?? 1700000001000,
...(input.completed === false ? {} : { completed: (input.created ?? 1700000001000) + 1_000 }),
},
parentID: input.parentID ?? userID,
modelID: model.modelID,
providerID: model.providerID,
mode: "build",
agent: "build",
path: { cwd: directory, root: directory },
cost: 0.01,
tokens: { input: 100, output: 200, reasoning: 0, cache: { read: 0, write: 0 } },
variant: "max",
...(input.error ? { error: input.error } : {}),
},
parts: parts.map((part) => ({ ...part, sessionID, messageID: id })),
} satisfies Extract<TimelineMessage, { info: { role: "assistant" } }>
decodeMessage(message, decodeOptions)
return message
}
export function userText(
text: string,
input: Partial<Omit<Extract<PartSeed<"user">, { type: "text" }>, "type" | "text">> = {},
): Extract<PartSeed<"user">, { type: "text" }> {
return { id: "prt_user_text", type: "text", text, ...input }
}
export function textPart(id: string, text: string): Extract<PartSeed<"assistant">, { type: "text" }> {
return { id, type: "text", text }
}
export function reasoningPart(id: string, text: string): Extract<PartSeed<"assistant">, { type: "reasoning" }> {
return { id, type: "reasoning", text, time: { start: 1700000001000 } }
}
export function toolPart(
id: string,
tool: string,
state: "pending",
input: Record<string, unknown>,
options?: ToolOptions<"pending">,
): Omit<ToolPart, "sessionID" | "messageID">
export function toolPart(
id: string,
tool: string,
state: "running",
input: Record<string, unknown>,
options?: ToolOptions<"running">,
): Omit<ToolPart, "sessionID" | "messageID">
export function toolPart(
id: string,
tool: string,
state: "completed",
input: Record<string, unknown>,
options?: ToolOptions<"completed">,
): Omit<ToolPart, "sessionID" | "messageID">
export function toolPart(
id: string,
tool: string,
state: "error",
input: Record<string, unknown>,
options?: ToolOptions<"error">,
): Omit<ToolPart, "sessionID" | "messageID">
export function toolPart(
id: string,
tool: string,
state: ToolStatus,
input: Record<string, unknown>,
options: ToolOptions<ToolStatus> = {},
): Omit<ToolPart, "sessionID" | "messageID"> {
const base = { id, type: "tool" as const, callID: `call_${id}`, tool }
if (state === "pending") return { ...base, state: { status: state, input, raw: "" } }
if (state === "running")
return {
...base,
state: {
status: state,
input,
title: options.title,
metadata: options.metadata ?? {},
time: { start: 1700000001000 },
},
}
if (state === "error")
return {
...base,
state: {
status: state,
input,
error: options.error ?? "Tool failed",
metadata: options.metadata ?? {},
time: { start: 1700000001000, end: 1700000002000 },
},
}
return {
...base,
state: {
status: state,
input,
output: options.output ?? "Completed",
title: options.title ?? tool,
metadata: options.metadata ?? {},
time: { start: 1700000001000, end: 1700000002000 },
},
}
}
export function shell(
id: string,
state: ToolStatus,
output = "",
command = `echo ${id}`,
): Omit<ToolPart, "sessionID" | "messageID"> {
if (state === "pending") return toolPart(id, "bash", state, { command })
if (state === "running")
return toolPart(id, "bash", state, { command }, { title: command, metadata: { command, output } })
if (state === "error")
return toolPart(id, "bash", state, { command }, { error: output || undefined, metadata: { command, output } })
return toolPart(id, "bash", state, { command }, { title: command, output, metadata: { command, output } })
}
export function completedAssistantInfo(info: AssistantMessage): AssistantMessage {
return { ...info, time: { ...info.time, completed: 1700000003000 } }
}
export function project() {
return {
id: projectID,
worktree: directory,
vcs: "git",
name: "timeline-stability",
time: { created: 1700000000000, updated: 1700000000000 },
sandboxes: [],
}
}
export function session(input: Partial<Session> = {}): Session {
return {
id: sessionID,
slug: "timeline-stability",
projectID,
directory,
title,
version: "dev",
time: { created: 1700000000000, updated: 1700000000000 },
...input,
}
}
function eventSchema<
const Type extends TimelinePayload["type"],
const Properties extends Schema.Codec<unknown, unknown>,
>(type: Type, properties: Properties) {
return Schema.Struct({
directory: Schema.String,
project: Schema.optional(Schema.String),
workspace: Schema.optional(Schema.String),
payload: Schema.Struct({ id: Event.ID, type: Schema.Literal(type), properties }),
})
}
function provider() {
return {
all: [
{
id: "opencode",
name: "OpenCode",
models: { "claude-opus-4-6": { id: "claude-opus-4-6", name: "Claude Opus 4.6", limit: { context: 200_000 } } },
},
],
connected: ["opencode"],
default: { providerID: "opencode", modelID: "claude-opus-4-6" },
}
}

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