openclaw/docs/concepts/qa-e2e-automation.md

summary, read_when, title

summary	read_when	title
QA stack overview: qa-lab, qa-channel, repo-backed scenarios, live transport lanes, transport adapters, and reporting.	Understanding how the QA stack fits together Extending qa-lab, qa-channel, or a transport adapter Adding repo-backed QA scenarios Building higher-realism QA automation around the Gateway dashboard	QA overview

The private QA stack exercises OpenClaw in a realistic, channel-shaped way that a unit test cannot.

Pieces:

• extensions/qa-channel: synthetic message channel with DM, channel, thread, reaction, edit, and delete surfaces.
• extensions/qa-lab: debugger UI and QA bus for observing the transcript, injecting inbound messages, and exporting a Markdown report.
• extensions/qa-matrix: live-transport adapter that drives the real Matrix plugin inside a child QA gateway.
• qa/: repo-backed seed assets for the kickoff task and baseline QA scenarios.
• Mantis: before/after live verification for bugs that need real transports, browser screenshots, VM state, and PR evidence.

Command surface

Every QA flow runs under pnpm openclaw qa <subcommand>. Many have pnpm qa:* script aliases; both forms work.

Command	Purpose
qa run	Bundled QA self-check without --qa-profile; taxonomy-backed maturity profile runner with --qa-profile smoke-ci, --qa-profile release, or --qa-profile all.
qa suite	Run repo-backed scenarios against the QA gateway lane. --runner multipass uses a disposable Linux VM instead of the host.
qa coverage	Print the YAML scenario-coverage inventory (--json for machine output; --match <query> to find scenarios for a touched behavior; --tools for runtime tool fixture coverage).
qa parity-report	Compare two qa-suite-summary.json files for a model-axis parity gate, or use --runtime-axis --token-efficiency to write Codex-vs-OpenClaw runtime parity and token-efficiency reports.
qa confidence-report	Classify QA proof artifacts against a manifest into a zero-unknown confidence report.
qa confidence-self-test	Write seeded negative-control canaries proving the confidence gate detects drift.
qa jsonl-replay	Replay curated JSONL transcripts through the runtime parity replay harness.
qa character-eval	Run the character QA scenario across multiple live models with a judged report. See Reporting.
qa manual	Run a one-off prompt against the selected provider/model lane.
qa ui	Start the QA debugger UI and local QA bus (alias: pnpm qa:lab:ui).
qa docker-build-image	Build the prebaked QA Docker image.
qa docker-scaffold	Write a docker-compose scaffold for the QA dashboard + gateway lane.
qa up	Build the QA site, start the Docker-backed stack, print the URL (alias: pnpm qa:lab:up; :fast variant adds --use-prebuilt-image --bind-ui-dist --skip-ui-build).
qa aimock	Start only the AIMock provider server.
qa mock-openai	Start only the scenario-aware mock-openai provider server.
qa credentials doctor / add / list / remove	Manage the shared Convex credential pool.
qa discord	Live transport lane against a real private Discord guild channel.
qa matrix	Live transport lane against a disposable Tuwunel homeserver. See Matrix QA.
qa slack	Live transport lane against a real private Slack channel.
qa telegram	Live transport lane against a real private Telegram group.
qa whatsapp	Live transport lane against real WhatsApp Web accounts.
qa mantis	Before/after verification runner for live transport bugs, with Discord status-reactions evidence, Crabbox desktop/browser smoke, and Slack-in-VNC smoke. See Mantis and Mantis Slack Desktop Runbook.

qa matrix is registered as a runner plugin (extensions/qa-matrix); every other lane above is built into qa-lab directly.

Profile-backed qa run

Profile-backed qa run reads membership from taxonomy.yaml, then dispatches the resolved scenarios through qa suite. --surface and --category filter the selected profile instead of defining separate lanes. The resulting qa-evidence.json includes a profile scorecard summary with selected-category counts and missing coverage IDs; the individual evidence entries remain the source of truth for the tests, coverage roles, and results. Taxonomy feature coverage IDs are exact proof targets, not aliases: primary scenario coverage fulfills matching IDs, secondary coverage stays advisory. Coverage IDs use dotted namespace.behavior form with lowercase alphanumeric/dash segments; profile, surface, and category IDs may still use the existing dashed or dotted taxonomy IDs.

Slim evidence omits per-entry execution and sets evidenceMode: "slim"; smoke-ci defaults to slim, and --evidence-mode full restores full entries:

pnpm openclaw qa run \
  --qa-profile smoke-ci \
  --category channel-framework.conversation-routing-and-delivery \
  --provider-mode mock-openai \
  --output-dir .artifacts/qa-e2e/smoke-ci-profile-dispatch

Use smoke-ci for deterministic profile proof with mock model providers and Crabline local provider servers. Use release for Stable/LTS proof against live channels. Use all only for explicit full-taxonomy evidence runs; it selects every active maturity category and can be dispatched through the QA Profile Evidence GitHub Actions workflow with qa_profile=all. When a command also needs an OpenClaw root profile, put the root profile before the QA command:

pnpm openclaw --profile work qa run --qa-profile smoke-ci

Operator flow

The current QA operator flow is a two-pane QA site:

• Left: Gateway dashboard (Control UI) with the agent.
• Right: QA Lab, showing the Slack-ish transcript and scenario plan.

Run it with:

pnpm qa:lab:up

That builds the QA site, starts the Docker-backed gateway lane, and exposes the QA Lab page where an operator or automation loop can give the agent a QA mission, observe real channel behavior, and record what worked, failed, or stayed blocked.

For faster QA Lab UI iteration without rebuilding the Docker image each time, start the stack with a bind-mounted QA Lab bundle:

pnpm openclaw qa docker-build-image
pnpm qa:lab:build
pnpm qa:lab:up:fast
pnpm qa:lab:watch

qa:lab:up:fast keeps the Docker services on a prebuilt image and bind-mounts extensions/qa-lab/web/dist into the qa-lab container. qa:lab:watch rebuilds that bundle on change, and the browser auto-reloads when the QA Lab asset hash changes.

Observability smokes

Observability QA stays source-checkout only. The npm tarball intentionally omits QA Lab (and `qa-channel`/`qa-matrix`), so package Docker release lanes do not run `qa` commands. Run these from a built source checkout when changing diagnostics instrumentation.

Alias	What it runs
pnpm qa:otel:smoke	Local OpenTelemetry receiver plus the otel-trace-smoke scenario with diagnostics-otel enabled.
pnpm qa:otel:collector-smoke	Same lane behind a real OpenTelemetry Collector Docker container. Use it when changing endpoint wiring or collector/OTLP compatibility.
pnpm qa:prometheus:smoke	The docker-prometheus-smoke scenario with diagnostics-prometheus enabled.
pnpm qa:observability:smoke	qa:otel:smoke followed by qa:prometheus:smoke.
pnpm qa:observability:collector-smoke	qa:otel:collector-smoke followed by qa:prometheus:smoke.

qa:otel:smoke starts a local OTLP/HTTP receiver, runs a minimal QA-channel agent turn, then asserts traces, metrics, and logs are exported. It decodes the exported protobuf trace spans and checks the release-critical shape: openclaw.run, openclaw.harness.run, a latest GenAI semantic-convention model-call span, openclaw.context.assembled, and openclaw.message.delivery must all be present. The smoke forces OTEL_SEMCONV_STABILITY_OPT_IN=gen_ai_latest_experimental, so the model-call span must use the {gen_ai.operation.name} {gen_ai.request.model} name; model calls must not export StreamAbandoned on successful turns; raw diagnostic IDs and openclaw.content.* attributes must stay out of the trace. The scenario prompt asks the model to reply with a fixed marker and to withhold a fixed secret string; the raw OTLP payloads must not contain either, or the QA session key derived from the scenario id. It writes otel-smoke-summary.json next to the QA suite artifacts.

qa:prometheus:smoke verifies unauthenticated scrapes are rejected, then checks the authenticated scrape includes release-critical metric families without prompt content, response content, raw diagnostic identifiers, auth tokens, or local paths.

Matrix smoke lanes

For a transport-real Matrix smoke lane that does not require model-provider credentials, run the fast profile with the deterministic mock OpenAI provider:

OPENCLAW_QA_MATRIX_NO_REPLY_WINDOW_MS=3000 \
  pnpm openclaw qa matrix --provider-mode mock-openai --profile fast --fail-fast

For the live-frontier provider lane, supply OpenAI-compatible credentials explicitly:

OPENCLAW_LIVE_OPENAI_KEY="${OPENAI_API_KEY}" \
OPENCLAW_QA_MATRIX_NO_REPLY_WINDOW_MS=3000 \
  pnpm openclaw qa matrix --provider-mode live-frontier --profile fast --fail-fast

The full CLI reference, profile/scenario catalog, env vars, and artifact layout for this lane live in Matrix QA. At a glance: it provisions a disposable Tuwunel homeserver in Docker, registers temporary driver/SUT/observer users, runs the real Matrix plugin inside a child QA gateway scoped to that transport (no qa-channel), then writes a Markdown report, JSON summary, observed-events artifact, and combined output log under .artifacts/qa-e2e/matrix-<timestamp>/.

The scenarios cover transport behavior that unit tests cannot prove end to end: mention gating, allow-bot policies, allowlists, top-level and threaded replies, DM routing, reaction handling, inbound edit suppression, restart replay dedupe, homeserver interruption recovery, approval metadata delivery, media handling, and Matrix E2EE bootstrap/recovery/verification flows. The E2EE CLI profile also drives openclaw matrix encryption setup and verification commands through the same disposable homeserver before checking gateway replies.

CI uses the same command surface in .github/workflows/qa-live-transports-convex.yml. Scheduled and default manual runs execute the fast Matrix profile with QA-provided live-frontier credentials, --fast, and OPENCLAW_QA_MATRIX_NO_REPLY_WINDOW_MS=3000. Manual matrix_profile=all fans out into five profile shards: transport, media, e2ee-smoke, e2ee-deep, and e2ee-cli.

Discord Mantis scenarios

Discord also has Mantis-only opt-in scenarios for bug reproduction. Use --scenario discord-status-reactions-tool-only for the explicit status reaction timeline, or --scenario discord-thread-reply-filepath-attachment to create a real Discord thread and verify that message.thread-reply preserves a filePath attachment. These scenarios stay out of the default live Discord lane because they are before/after repro probes rather than broad smoke coverage. The thread-attachment Mantis workflow can also add a logged-in Discord Web witness video when MANTIS_DISCORD_VIEWER_CHROME_PROFILE_DIR or MANTIS_DISCORD_VIEWER_CHROME_PROFILE_TGZ_B64 is configured in the QA environment. That viewer profile is only for visual capture; the pass/fail decision still comes from the Discord REST oracle.

For transport-real Discord, Slack, Telegram, and WhatsApp smoke lanes:

pnpm openclaw qa discord
pnpm openclaw qa slack
pnpm openclaw qa telegram
pnpm openclaw qa whatsapp

They target a pre-existing real channel with two bots or accounts (driver + SUT). Required env vars, scenario lists, output artifacts, and the Convex credential pool are documented in Discord, Slack, Telegram, and WhatsApp QA reference below.

Mantis Slack desktop and visual-task runners

For a full Slack desktop VM run with VNC rescue, run:

pnpm openclaw qa mantis slack-desktop-smoke \
  --gateway-setup \
  --scenario slack-canary \
  --keep-lease

That command leases a Crabbox desktop/browser machine, runs the Slack live lane inside the VM, opens Slack Web in the VNC browser, captures the desktop, and copies slack-qa/, slack-desktop-smoke.png, and slack-desktop-smoke.mp4 (when video capture is available) back to the Mantis artifact directory. Crabbox desktop/browser leases provide the capture tools and browser/native-build helper packages up front, so the scenario should only install fallbacks on older leases. Mantis reports total and per-phase timings in mantis-slack-desktop-smoke-report.md so slow runs show whether time went into lease warmup, credential acquisition, remote setup, or artifact copy. Reuse --lease-id <cbx_...> after logging in to Slack Web manually through VNC; reused leases also keep Crabbox's pnpm store cache warm. The default --hydrate-mode source verifies from a source checkout and runs install/build inside the VM. Use --hydrate-mode prehydrated only when the reused remote workspace already has node_modules and a built dist/; that mode skips the expensive install/build step and fails closed when the workspace is not ready. With --gateway-setup, Mantis leaves a persistent OpenClaw Slack gateway running inside the VM on port 38973; without it, the command runs the normal bot-to-bot Slack QA lane and exits after artifact capture.

To prove native Slack approval UI with desktop evidence, run the Mantis approval checkpoint mode:

pnpm openclaw qa mantis slack-desktop-smoke \
  --approval-checkpoints \
  --credential-source convex \
  --credential-role maintainer

This mode is mutually exclusive with --gateway-setup. It runs the Slack approval scenarios, rejects non-approval scenario ids, waits at each pending and resolved approval state, renders the observed Slack API message into approval-checkpoints/<scenario>-pending.png and approval-checkpoints/<scenario>-resolved.png, then fails if any checkpoint, message evidence, acknowledgement, or rendered screenshot is missing or empty. Cold CI leases may still show Slack sign-in in slack-desktop-smoke.png; the approval checkpoint images are the visual proof for this lane.

The default checkpoint run keeps the two standard Slack approval scenarios. To capture either opt-in Codex approval route, select it explicitly with --scenario slack-codex-approval-exec-native or --scenario slack-codex-approval-plugin-native; Mantis accepts both and emits the same pending/resolved screenshot pair. The runner expands its checkpoint and remote-command deadlines for each selected Codex route so the full approval, agent completion, and resolved-update sequence can finish.

The operator checklist, GitHub workflow dispatch command, evidence-comment contract, hydrate-mode decision table, timing interpretation, and failure handling steps live in Mantis Slack Desktop Runbook.

For an agent/CV style desktop task, run:

pnpm openclaw qa mantis visual-task \
  --browser-url https://example.net \
  --expect-text "Example Domain" \
  --vision-model openai/gpt-5.5

visual-task leases or reuses a Crabbox desktop/browser machine, starts crabbox record --while, drives the visible browser through a nested visual-driver, captures visual-task.png, runs openclaw infer image describe against the screenshot when --vision-mode image-describe is selected, and writes visual-task.mp4, mantis-visual-task-summary.json, mantis-visual-task-driver-result.json, and mantis-visual-task-report.md. When --expect-text is set, the vision prompt asks for a structured JSON verdict (visible, evidence, reason) and only passes when the model reports visible: true with evidence that cites the expected text; a visible: false response that merely quotes the target text still fails the assertion. Use --vision-mode metadata for a no-model smoke that proves the desktop, browser, screenshot, and video plumbing without calling an image-understanding provider. Recording is a required artifact for visual-task; if Crabbox records no non-empty visual-task.mp4, the task fails even when the visual driver passed. On failure, Mantis keeps the lease for VNC unless the task had already passed and --keep-lease was not set.

Credential pool health check

Before using pooled live credentials, run:

pnpm openclaw qa credentials doctor

The doctor checks Convex broker env (OPENCLAW_QA_CONVEX_SITE_URL, OPENCLAW_QA_CONVEX_ENDPOINT_PREFIX), validates endpoint settings, reports only set/missing status for OPENCLAW_QA_CONVEX_SECRET_CI and OPENCLAW_QA_CONVEX_SECRET_MAINTAINER, and verifies admin/list reachability when the maintainer secret is present.

Live transport coverage

Live transport lanes share one contract instead of each inventing their own scenario list shape. qa-channel is the broad synthetic product-behavior suite and is not part of the live transport coverage matrix.

Live transport runners import the shared scenario ids, baseline coverage helpers, and scenario-selection helper from openclaw/plugin-sdk/qa-live-transport-scenarios.

Lane	Canary	Mention gating	Bot-to-bot	Allowlist block	Top-level reply	Quote reply	Restart resume	Thread follow-up	Thread isolation	Reaction observation	Help command	Native command registration
Discord	x	x	x									x
Matrix	x	x	x	x	x		x	x	x	x
Slack	x	x	x	x	x		x	x	x
Telegram	x	x	x								x
WhatsApp	x	x		x	x	x	x			x	x

This keeps qa-channel as the broad product-behavior suite while Matrix, Telegram, and the other live transports share one explicit transport-contract checklist.

For a disposable Linux VM lane without bringing Docker into the QA path, run:

pnpm openclaw qa suite --runner multipass --scenario channel-chat-baseline

This boots a fresh Multipass guest, installs dependencies, builds OpenClaw inside the guest, runs qa suite, then copies the normal QA report and summary back into .artifacts/qa-e2e/... on the host. It reuses the same scenario-selection behavior as qa suite on the host.

Host and Multipass suite runs execute multiple selected scenarios in parallel with isolated gateway workers by default. qa-channel defaults to concurrency 4, capped by the selected scenario count. Use --concurrency <count> to tune the worker count, or --concurrency 1 for serial execution. Use --pack personal-agent to run the personal assistant benchmark pack (10 scenarios). The pack selector is additive with repeated --scenario flags: explicit scenarios run first, then pack scenarios run in pack order with duplicates removed. Use --pack observability to select the otel-trace-smoke and docker-prometheus-smoke scenarios together when a custom QA runner already supplies the OpenTelemetry collector setup.

The command exits non-zero when any scenario fails. Use --allow-failures when you want artifacts without a failing exit code.

Live runs forward the supported QA auth inputs that are practical for the guest: env-based provider keys, the QA live provider config path, and CODEX_HOME when present. Keep --output-dir under the repo root so the guest can write back through the mounted workspace.

Discord, Slack, Telegram, and WhatsApp QA reference

Matrix has a dedicated page because of its scenario count and Docker-backed homeserver provisioning. Discord, Slack, Telegram, and WhatsApp run against pre-existing real transports, so their reference lives here.

Shared CLI flags

These lanes register through extensions/qa-lab/src/live-transports/shared/live-transport-cli.ts and accept the same flags:

Flag	Default	Description
--scenario <id>	-	Run only this scenario. Repeatable.
--output-dir <path>	<repo>/.artifacts/qa-e2e/<transport>-<timestamp>	Where reports, summaries, evidence, transport-specific artifacts, and the output log are written. Relative paths resolve against --repo-root.
--repo-root <path>	process.cwd()	Repository root when invoking from a neutral cwd.
--sut-account <id>	sut	Temporary account id inside the QA gateway config.
--provider-mode <mode>	live-frontier	mock-openai or live-frontier (legacy live-openai still works).
--model <ref> / --alt-model <ref>	provider default	Primary/alternate model refs.
--fast	off	Provider fast mode where supported.
--credential-source <env|convex>	env	See Convex credential pool.
--credential-role <maintainer|ci>	ci in CI, maintainer otherwise	Role used when --credential-source convex.

Each lane exits non-zero on any failed scenario. --allow-failures writes artifacts without setting a failing exit code. Telegram also accepts --list-scenarios to print available scenario ids and exit; the other lanes do not expose that flag.

Telegram QA

pnpm openclaw qa telegram

Targets one real private Telegram group with two distinct bots (driver + SUT). The SUT bot must have a Telegram username; bot-to-bot observation works best when both bots have Bot-to-Bot Communication Mode enabled in @BotFather.

Required env when --credential-source env:

• OPENCLAW_QA_TELEGRAM_GROUP_ID - numeric chat id (string).
• OPENCLAW_QA_TELEGRAM_DRIVER_BOT_TOKEN
• OPENCLAW_QA_TELEGRAM_SUT_BOT_TOKEN

Scenarios (extensions/qa-lab/src/live-transports/telegram/telegram-live.runtime.ts):

• telegram-canary
• telegram-mention-gating
• telegram-mentioned-message-reply
• telegram-help-command
• telegram-commands-command
• telegram-tools-compact-command
• telegram-whoami-command
• telegram-status-command
• telegram-repeated-command-authorization
• telegram-other-bot-command-gating
• telegram-context-command
• telegram-current-session-status-tool
• telegram-tool-only-usage-footer
• telegram-reply-chain-exact-marker
• telegram-stream-final-single-message
• telegram-long-final-reuses-preview
• telegram-long-final-three-chunks

The implicit default set always covers canary, mention gating, native command replies, command addressing, and bot-to-bot group replies. mock-openai defaults also include deterministic reply-chain and final-message streaming checks. telegram-current-session-status-tool and telegram-tool-only-usage-footer remain opt-in: the former is only stable when threaded directly after canary, and the latter is a real-Telegram proof of the /usage footer on tool-only replies. Use pnpm openclaw qa telegram --list-scenarios --provider-mode mock-openai to print the current default/optional split with regression refs.

Output artifacts:

• telegram-qa-report.md
• qa-evidence.json - evidence entries for the live transport checks, including profile, coverage, provider, channel, artifacts, result, and RTT fields.

Package Telegram runs use the same Telegram credential contract. Repeated RTT measurement is part of the normal package Telegram live lane; the RTT distribution is folded into qa-evidence.json under result.timing for the selected RTT check.

OPENCLAW_QA_CREDENTIAL_SOURCE=convex \
pnpm test:docker:npm-telegram-live

When OPENCLAW_QA_CREDENTIAL_SOURCE=convex is set, the package live wrapper leases a kind: "telegram" credential, exports the leased group/driver/SUT bot env into the installed-package run, heartbeats the lease, and releases it on shutdown. The package wrapper defaults to 20 RTT checks of telegram-mentioned-message-reply, a 30s RTT timeout, and Convex role maintainer outside CI when Convex is selected. Override OPENCLAW_NPM_TELEGRAM_RTT_SAMPLES, OPENCLAW_NPM_TELEGRAM_RTT_TIMEOUT_MS, or OPENCLAW_NPM_TELEGRAM_RTT_MAX_FAILURES to tune RTT measurement without creating a separate RTT command or Telegram-specific summary format.

Discord QA

pnpm openclaw qa discord

Targets one real private Discord guild channel with two bots: a driver bot controlled by the harness and a SUT bot started by the child OpenClaw gateway through the bundled Discord plugin. Verifies channel mention handling, that the SUT bot has registered the native /help command with Discord, and opt-in Mantis evidence scenarios.

Required env when --credential-source env:

• OPENCLAW_QA_DISCORD_GUILD_ID
• OPENCLAW_QA_DISCORD_CHANNEL_ID
• OPENCLAW_QA_DISCORD_DRIVER_BOT_TOKEN
• OPENCLAW_QA_DISCORD_SUT_BOT_TOKEN
• OPENCLAW_QA_DISCORD_SUT_APPLICATION_ID - must match the SUT bot user id returned by Discord (the lane fails fast otherwise).

Optional:

• OPENCLAW_QA_DISCORD_CAPTURE_CONTENT=1 keeps message bodies in observed-message artifacts.
• OPENCLAW_QA_DISCORD_VOICE_CHANNEL_ID selects the voice/stage channel for discord-voice-autojoin; without it, the scenario picks the first visible voice/stage channel for the SUT bot.

Scenarios (extensions/qa-lab/src/live-transports/discord/discord-live.runtime.ts:36):

• discord-canary
• discord-mention-gating
• discord-native-help-command-registration
• discord-voice-autojoin - opt-in voice scenario. Runs by itself, enables channels.discord.voice.autoJoin, and verifies the SUT bot's current Discord voice state is the target voice/stage channel. Convex Discord credentials may include optional voiceChannelId; otherwise the runner discovers the first visible voice/stage channel in the guild.
• discord-status-reactions-tool-only - opt-in Mantis scenario. Runs by itself because it switches the SUT to always-on, tool-only guild replies with messages.statusReactions.enabled=true, then captures a REST reaction timeline plus HTML/PNG visual artifacts. Mantis before/after reports also preserve scenario-provided MP4 artifacts as baseline.mp4 and candidate.mp4.
• discord-thread-reply-filepath-attachment - opt-in Mantis scenario; see Discord Mantis scenarios.

Run the Discord voice auto-join scenario explicitly:

pnpm openclaw qa discord \
  --scenario discord-voice-autojoin \
  --provider-mode mock-openai

Run the Mantis status-reaction scenario explicitly:

pnpm openclaw qa discord \
  --scenario discord-status-reactions-tool-only \
  --provider-mode live-frontier \
  --model openai/gpt-5.5 \
  --alt-model openai/gpt-5.5 \
  --fast

Output artifacts:

• discord-qa-report.md
• qa-evidence.json - evidence entries for the live transport checks.
• discord-qa-observed-messages.json - bodies redacted unless OPENCLAW_QA_DISCORD_CAPTURE_CONTENT=1.
• discord-qa-reaction-timelines.json and discord-status-reactions-tool-only-timeline.png when the status-reaction scenario runs.

Slack QA

pnpm openclaw qa slack

Targets one real private Slack channel with two distinct bots: a driver bot controlled by the harness and a SUT bot started by the child OpenClaw gateway through the bundled Slack plugin.

Required env when --credential-source env:

• OPENCLAW_QA_SLACK_CHANNEL_ID
• OPENCLAW_QA_SLACK_DRIVER_BOT_TOKEN
• OPENCLAW_QA_SLACK_SUT_BOT_TOKEN
• OPENCLAW_QA_SLACK_SUT_APP_TOKEN

Optional:

• OPENCLAW_QA_SLACK_CAPTURE_CONTENT=1 keeps message bodies in observed-message artifacts.
• OPENCLAW_QA_SLACK_APPROVAL_CHECKPOINT_DIR enables visual approval checkpoints for Mantis. The runner writes <scenario>.pending.json and <scenario>.resolved.json, then waits for matching .ack.json files.
• OPENCLAW_QA_SLACK_APPROVAL_CHECKPOINT_TIMEOUT_MS overrides the checkpoint acknowledgement timeout. The default is 120000.

Scenarios (extensions/qa-lab/src/live-transports/slack/slack-live.runtime.ts):

• slack-canary
• slack-mention-gating
• slack-allowlist-block
• slack-top-level-reply-shape
• slack-restart-resume
• slack-thread-follow-up
• slack-thread-isolation
• slack-reaction-glyph-native - opt-in live message-tool reaction scenario. Instructs the agent to pass the exact ✅ glyph and confirms Slack stored white_check_mark for the SUT bot on the target message.
• slack-approval-exec-native - opt-in native Slack exec approval scenario. Requests an exec approval through the gateway, verifies the Slack message has native approval buttons, resolves it, and verifies the resolved Slack update.
• slack-approval-plugin-native - opt-in native Slack plugin approval scenario. Enables exec and plugin approval forwarding together so plugin events are not suppressed by exec approval routing, then verifies the same pending/resolved native Slack UI path.
• slack-codex-approval-exec-native - opt-in Codex Guardian command approval scenario. Enables the Codex plugin in Guardian mode, routes a Slack-originated Gateway agent turn through the Codex app-server harness, waits for the native Slack plugin approval prompt for openclaw-codex-app-server, resolves it, and verifies the Codex turn finishes with the expected command-output and assistant markers.
• slack-codex-approval-plugin-native - opt-in Codex Guardian file approval scenario. Uses an outside-workspace apply_patch instruction so Codex emits the app-server file-change approval route, then verifies the same native Slack pending/resolved approval path, final assistant marker, and exact file contents before cleanup.

The Codex approval scenarios require an openai/* or codex/* --model, the normal live model credentials, and Codex auth or API-key auth accepted by the Codex plugin. The Slack report includes the Codex app-server method, selected Codex model key, final Codex turn status, and operation-marker verification alongside the redacted Slack approval metadata.

Output artifacts:

• slack-qa-report.md
• qa-evidence.json - evidence entries for the live transport checks.
• slack-qa-observed-messages.json - bodies redacted unless OPENCLAW_QA_SLACK_CAPTURE_CONTENT=1.
• approval-checkpoints/ - only when Mantis sets OPENCLAW_QA_SLACK_APPROVAL_CHECKPOINT_DIR; contains checkpoint JSON, acknowledgement JSON, and pending/resolved screenshots.

Setting up the Slack workspace

The lane needs two distinct Slack apps in one workspace, plus a channel both bots are members of:

• channelId - the Cxxxxxxxxxx id of a channel both bots have been invited to. Use a dedicated channel; the lane posts on every run.
• driverBotToken - bot token (xoxb-...) of the Driver app.
• sutBotToken - bot token (xoxb-...) of the SUT app, which must be a separate Slack app from the driver so its bot user id is distinct.
• sutAppToken - app-level token (xapp-...) of the SUT app with connections:write, used by Socket Mode so the SUT app can receive events.

Prefer a Slack workspace dedicated to QA over reusing a production workspace.

The SUT manifest below intentionally narrows the bundled Slack plugin's production install (extensions/slack/src/setup-shared.ts:12) to the permissions and events covered by the live Slack QA suite. For the production-channel setup as users see it, see Slack channel quick setup; the QA Driver/SUT pair is intentionally separate because the lane needs two distinct bot user ids in one workspace.

1. Create the Driver app

Go to api.slack.com/apps → Create New App → From a manifest → pick the QA workspace, paste the following manifest, then Install to Workspace:

{
  "display_information": {
    "name": "OpenClaw QA Driver",
    "description": "Test driver bot for OpenClaw QA Slack live lane"
  },
  "features": {
    "bot_user": {
      "display_name": "OpenClaw QA Driver",
      "always_online": true
    }
  },
  "oauth_config": {
    "scopes": {
      "bot": ["chat:write", "channels:history", "groups:history", "users:read"]
    }
  },
  "settings": {
    "socket_mode_enabled": false
  }
}

Copy the Bot User OAuth Token (xoxb-...) - that becomes driverBotToken. The driver only needs to post messages and identify itself; no events, no Socket Mode.

2. Create the SUT app

Repeat Create New App → From a manifest in the same workspace. This QA app intentionally uses a narrower version of the bundled Slack plugin's production manifest (extensions/slack/src/setup-shared.ts:12): reaction scopes and events are omitted because the live Slack QA suite does not cover reaction handling yet.

{
  "display_information": {
    "name": "OpenClaw QA SUT",
    "description": "OpenClaw QA SUT connector for OpenClaw"
  },
  "features": {
    "bot_user": {
      "display_name": "OpenClaw QA SUT",
      "always_online": true
    },
    "app_home": {
      "home_tab_enabled": true,
      "messages_tab_enabled": true,
      "messages_tab_read_only_enabled": false
    }
  },
  "oauth_config": {
    "scopes": {
      "bot": [
        "app_mentions:read",
        "assistant:write",
        "channels:history",
        "channels:read",
        "chat:write",
        "commands",
        "emoji:read",
        "files:read",
        "files:write",
        "groups:history",
        "groups:read",
        "im:history",
        "im:read",
        "im:write",
        "mpim:history",
        "mpim:read",
        "mpim:write",
        "pins:read",
        "pins:write",
        "usergroups:read",
        "users:read"
      ]
    }
  },
  "settings": {
    "socket_mode_enabled": true,
    "event_subscriptions": {
      "bot_events": [
        "app_home_opened",
        "app_mention",
        "channel_rename",
        "member_joined_channel",
        "member_left_channel",
        "message.channels",
        "message.groups",
        "message.im",
        "message.mpim",
        "pin_added",
        "pin_removed"
      ]
    }
  }
}

After Slack creates the app, do two things on its settings page:

• Install to Workspace → copy the Bot User OAuth Token → that becomes sutBotToken.
• Basic Information → App-Level Tokens → Generate Token and Scopes → add scope connections:write → save → copy the xapp-... value → that becomes sutAppToken.

Verify the two bots have distinct user ids by calling auth.test on each token. The runtime distinguishes driver and SUT by user id; reusing one app for both will fail mention-gating immediately.

3. Create the channel

In the QA workspace, create a channel (e.g. #openclaw-qa) and invite both bots from inside the channel:

/invite @OpenClaw QA Driver
/invite @OpenClaw QA SUT

Copy the Cxxxxxxxxxx id from channel info → About → Channel ID - that becomes channelId. A public channel works; if you use a private channel both apps already have groups:history so the harness's history reads will still succeed.

4. Register the credentials

Two options. Use env vars for single-machine debugging (set the four OPENCLAW_QA_SLACK_* variables and pass --credential-source env), or seed the shared Convex pool so CI and other maintainers can lease them.

For the Convex pool, write the four fields to a JSON file:

{
  "channelId": "Cxxxxxxxxxx",
  "driverBotToken": "xoxb-...",
  "sutBotToken": "xoxb-...",
  "sutAppToken": "xapp-..."
}

With OPENCLAW_QA_CONVEX_SITE_URL and OPENCLAW_QA_CONVEX_SECRET_MAINTAINER exported in your shell, register and verify:

pnpm openclaw qa credentials add \
  --kind slack \
  --payload-file slack-creds.json \
  --note "QA Slack pool seed"

pnpm openclaw qa credentials list --kind slack --status all --json

Expect count: 1, status: "active", no lease field.

5. Verify end to end

Run the lane locally to confirm both bots can talk to each other through the broker:

pnpm openclaw qa slack \
  --credential-source convex \
  --credential-role maintainer \
  --output-dir .artifacts/qa-e2e/slack-local

A green run completes in well under 30 seconds and slack-qa-report.md shows both slack-canary and slack-mention-gating at status pass. If the lane hangs for ~90 seconds and exits with Convex credential pool exhausted for kind "slack", either the pool is empty or every row is leased - qa credentials list --kind slack --status all --json will tell you which.

WhatsApp QA

pnpm openclaw qa whatsapp

Targets two dedicated WhatsApp Web accounts: a driver account controlled by the harness and a SUT account started by the child OpenClaw gateway through the bundled WhatsApp plugin.

Required env when --credential-source env:

• OPENCLAW_QA_WHATSAPP_DRIVER_PHONE_E164
• OPENCLAW_QA_WHATSAPP_SUT_PHONE_E164
• OPENCLAW_QA_WHATSAPP_DRIVER_AUTH_ARCHIVE_BASE64
• OPENCLAW_QA_WHATSAPP_SUT_AUTH_ARCHIVE_BASE64

Optional:

• OPENCLAW_QA_WHATSAPP_GROUP_JID enables group scenarios such as whatsapp-mention-gating, whatsapp-group-pending-history-context, whatsapp-broadcast-group-fanout, whatsapp-group-activation-always, whatsapp-group-reply-to-bot-triggers, group action/media/poll scenarios, and whatsapp-group-allowlist-block.
• OPENCLAW_QA_WHATSAPP_CAPTURE_CONTENT=1 keeps message bodies in observed-message artifacts.

Scenario catalog (extensions/qa-lab/src/live-transports/whatsapp/whatsapp-live.runtime.ts):

• Baseline and group gating: whatsapp-canary, whatsapp-pairing-block, whatsapp-mention-gating, whatsapp-group-pending-history-context, whatsapp-group-activation-always, whatsapp-group-reply-to-bot-triggers, whatsapp-top-level-reply-shape, whatsapp-restart-resume, whatsapp-group-allowlist-block.
• Native commands: whatsapp-help-command, whatsapp-status-command, whatsapp-commands-command, whatsapp-tools-compact-command, whatsapp-whoami-command, whatsapp-context-command, whatsapp-native-new-command.
• Reply and final-output behavior: whatsapp-tool-only-usage-footer, whatsapp-reply-to-message, whatsapp-group-reply-to-message, whatsapp-reply-to-mode-batched, whatsapp-reply-context-isolation, whatsapp-reply-delivery-shape, whatsapp-stream-final-message-accounting.
• User-path message actions: whatsapp-agent-message-action-react starts from a real driver DM, lets the model call the message tool, and observes the native WhatsApp reaction. whatsapp-agent-message-action-upload-file uses the same posture for message(action=upload-file) and observes native WhatsApp media. whatsapp-group-agent-message-action-react and whatsapp-group-agent-message-action-upload-file prove the same user-visible actions in a real WhatsApp group.
• Group fanout: whatsapp-broadcast-group-fanout starts from one mentioned WhatsApp group message and verifies distinct visible replies from main and qa-second.
• Group activation: whatsapp-group-activation-always changes a real group session to /activation always, proves an unmentioned group message wakes the agent, then restores /activation mention. whatsapp-group-reply-to-bot-triggers seeds a bot reply, sends a native quoted reply to it without an explicit mention, and verifies the agent wakes from that reply context.
• Inbound media and structured messages: whatsapp-inbound-image-caption, whatsapp-audio-preflight, whatsapp-inbound-structured-messages, whatsapp-group-audio-gating, whatsapp-inbound-reaction-no-trigger. These send real WhatsApp image, audio, document, location, contact, sticker, and reaction events through the driver.
• Direct Gateway contract probes: whatsapp-outbound-media-matrix, whatsapp-outbound-document-preserves-filename, whatsapp-outbound-poll, whatsapp-outbound-send-serialization, whatsapp-group-outbound-media, whatsapp-group-outbound-poll, whatsapp-message-actions, whatsapp-reply-context-isolation, whatsapp-reply-delivery-shape. These bypass model prompting on purpose and prove deterministic Gateway/channel send, poll, and message.action contracts.
• Access-control coverage: whatsapp-access-control-dm-open, whatsapp-access-control-dm-disabled, whatsapp-access-control-group-open, whatsapp-access-control-group-disabled, whatsapp-group-allowlist-block.
• Native approvals: whatsapp-approval-exec-deny-native, whatsapp-approval-exec-native, whatsapp-approval-exec-reaction-native, whatsapp-approval-exec-group-reaction-native, whatsapp-approval-plugin-native.
• Status reactions: whatsapp-status-reactions, whatsapp-status-reaction-lifecycle.

The catalog currently contains 52 scenarios. The live-frontier default lane is kept small at 10 scenarios for fast smoke coverage. The mock-openai default lane runs 45 scenarios deterministically through the real WhatsApp transport while mocking only model output; approval scenarios and a few heavier/blocking checks remain explicit by scenario id.

The WhatsApp QA driver observes structured live events (text, media, location, reaction, and poll) and can actively send media, polls, contacts, locations, and stickers. QA Lab imports that driver through the @openclaw/whatsapp/api.js package surface instead of reaching into private WhatsApp runtime files. For group observations, fromJid is the group JID while participantJid and fromPhoneE164 identify the participant sender. Message content is redacted by default. Direct Gateway poll, upload-file, media, group poll, group media, and reply-shape probes are transport/API contract checks; they are not treated as proof that a user prompt made the agent choose the same action. User-path action proof comes from scenarios such as whatsapp-agent-message-action-react and whatsapp-group-agent-message-action-react, where the driver sends a normal WhatsApp message and QA Lab observes the resulting native WhatsApp artifact. WhatsApp reports include each scenario's posture (user-path, direct-gateway, or native-approval) so evidence cannot be mistaken for a stronger contract than it actually proves.

Output artifacts:

• whatsapp-qa-report.md
• qa-evidence.json - evidence entries for the live transport checks.
• whatsapp-qa-observed-messages.json - bodies redacted unless OPENCLAW_QA_WHATSAPP_CAPTURE_CONTENT=1.

Convex credential pool

Discord, Slack, Telegram, and WhatsApp lanes can lease credentials from a shared Convex pool instead of reading the env vars above. Pass --credential-source convex (or set OPENCLAW_QA_CREDENTIAL_SOURCE=convex); QA Lab acquires an exclusive lease, heartbeats it for the duration of the run, and releases it on shutdown. Pool kinds are "discord", "slack", "telegram", and "whatsapp".

Payload shapes the broker validates on admin/add:

• Discord (kind: "discord"): { guildId: string, channelId: string, driverBotToken: string, sutBotToken: string, sutApplicationId: string }.
• Telegram (kind: "telegram"): { groupId: string, driverToken: string, sutToken: string } - groupId must be a numeric chat-id string.
• Telegram real user (kind: "telegram-user"): { groupId: string, sutToken: string, testerUserId: string, testerUsername: string, telegramApiId: string, telegramApiHash: string, tdlibDatabaseEncryptionKey: string, tdlibArchiveBase64: string, tdlibArchiveSha256: string, desktopTdataArchiveBase64: string, desktopTdataArchiveSha256: string } - Mantis Telegram Desktop proof only. Generic QA Lab lanes must not acquire this kind.
• WhatsApp (kind: "whatsapp"): { driverPhoneE164: string, sutPhoneE164: string, driverAuthArchiveBase64: string, sutAuthArchiveBase64: string, groupJid?: string } - phone numbers must be distinct E.164 strings.

The Mantis Telegram Desktop proof workflow holds one exclusive Convex telegram-user lease for both the TDLib CLI driver and Telegram Desktop witness, then releases it after publishing proof.

When a PR needs a deterministic visual diff, Mantis can use the same mock model reply on main and on the PR head while the Telegram formatter or delivery layer changes. Capture defaults are tuned for PR comments: standard Crabbox class, 24fps desktop recording, 24fps motion GIF, and 1920px preview width. Before/after comments should publish a clean bundle that contains only the intended GIFs.

Slack lanes can also use the pool. Slack payload shape checks currently live in the Slack QA runner rather than the broker; use { channelId: string, driverBotToken: string, sutBotToken: string, sutAppToken: string }, with a Slack channel id like Cxxxxxxxxxx. See Setting up the Slack workspace for app and scope provisioning.

Operational env vars and the Convex broker endpoint contract live in Testing → Shared Telegram credentials via Convex (the section name predates the multi-channel pool; the lease semantics are shared across kinds).

Repo-backed seeds

Seed assets live in qa/:

• qa/scenarios/index.yaml
• qa/scenarios/<theme>/*.yaml

These are intentionally in git so the QA plan is visible to both humans and the agent.

qa-lab stays a generic YAML scenario runner. Each scenario YAML file is the source of truth for one test run and should define:

• top-level title
• scenario metadata
• optional category, capability, lane, and risk metadata in scenario
• docs and code refs in scenario
• optional plugin requirements in scenario
• optional gateway config patch in scenario
• executable top-level flow for flow scenarios, or scenario.execution.kind / scenario.execution.path for Vitest and Playwright scenarios

The reusable runtime surface that backs flow stays generic and cross-cutting. For example, YAML scenarios can combine transport-side helpers with browser-side helpers that drive the embedded Control UI through the Gateway browser.request seam without adding a special-case runner.

Scenario files should be grouped by product capability rather than source tree folder. Keep scenario IDs stable when files move; use docsRefs and codeRefs for implementation traceability.

The baseline list should stay broad enough to cover:

• DM and channel chat
• thread behavior
• message action lifecycle
• cron callbacks
• memory recall
• model switching
• subagent handoff
• repo-reading and docs-reading
• one small build task such as Lobster Invaders

Provider mock lanes

qa suite has two local provider mock lanes:

• mock-openai is the scenario-aware OpenClaw mock. It remains the default deterministic mock lane for repo-backed QA and parity gates.
• aimock starts an AIMock-backed provider server for experimental protocol, fixture, record/replay, and chaos coverage. It is additive and does not replace the mock-openai scenario dispatcher.

Provider-lane implementation lives under extensions/qa-lab/src/providers/. Each provider owns its defaults, local server startup, gateway model config, auth-profile staging needs, and live/mock capability flags. Shared suite and gateway code routes through the provider registry instead of branching on provider names.

Transport adapters

qa-lab owns a generic transport seam for YAML QA scenarios. qa-channel is the synthetic default. crabline starts local provider-shaped servers and runs OpenClaw's normal channel plugins against them. live is reserved for real provider credentials and external channels.

At the architecture level, the split is:

• qa-lab owns generic scenario execution, worker concurrency, artifact writing, and reporting.
• The transport adapter owns gateway config, readiness, inbound and outbound observation, transport actions, and normalized transport state.
• YAML scenario files under qa/scenarios/ define the test run; qa-lab provides the reusable runtime surface that executes them.

Adding a channel

Adding a channel to the YAML QA system requires the channel implementation plus a scenario pack that exercises the channel contract. For smoke CI coverage, add the matching Crabline local provider server and expose it through the crabline driver.

Do not add a new top-level QA command root when the shared qa-lab host can own the flow.

qa-lab owns the shared host mechanics:

• the openclaw qa command root
• suite startup and teardown
• worker concurrency
• artifact writing
• report generation
• scenario execution
• compatibility aliases for older qa-channel scenarios

Runner plugins own the transport contract:

• how openclaw qa <runner> is mounted beneath the shared qa root
• how the gateway is configured for that transport
• how readiness is checked
• how inbound events are injected
• how outbound messages are observed
• how transcripts and normalized transport state are exposed
• how transport-backed actions are executed
• how transport-specific reset or cleanup is handled

The minimum adoption bar for a new channel:

1. Keep qa-lab as the owner of the shared qa root.
2. Implement the transport runner on the shared qa-lab host seam.
3. Keep transport-specific mechanics inside the runner plugin or channel harness.
4. Mount the runner as openclaw qa <runner> instead of registering a competing root command. Runner plugins should declare qaRunners in openclaw.plugin.json and export a matching qaRunnerCliRegistrations array from runtime-api.ts. Keep runtime-api.ts light; lazy CLI and runner execution should stay behind separate entrypoints.
5. Author or adapt YAML scenarios under the themed qa/scenarios/ directories.
6. Use the generic scenario helpers for new scenarios.
7. Keep existing compatibility aliases working unless the repo is doing an intentional migration.

The decision rule is strict:

• If behavior can be expressed once in qa-lab, put it in qa-lab.
• If behavior depends on one channel transport, keep it in that runner plugin or plugin harness.
• If a scenario needs a new capability that more than one channel can use, add a generic helper instead of a channel-specific branch in suite.ts.
• If a behavior is only meaningful for one transport, keep the scenario transport-specific and make that explicit in the scenario contract.

Scenario helper names

Preferred generic helpers for new scenarios:

• waitForTransportReady
• waitForChannelReady
• injectInboundMessage
• injectOutboundMessage
• waitForTransportOutboundMessage
• waitForChannelOutboundMessage
• waitForNoTransportOutbound
• getTransportSnapshot
• readTransportMessage
• readTransportTranscript
• formatTransportTranscript
• resetTransport

Compatibility aliases remain available for existing scenarios - waitForQaChannelReady, waitForOutboundMessage, waitForNoOutbound, formatConversationTranscript, resetBus - but new scenario authoring should use the generic names. The aliases exist to avoid a flag-day migration, not as the model going forward.

Reporting

qa-lab exports a Markdown protocol report from the observed bus timeline. The report should answer:

• What worked
• What failed
• What stayed blocked
• What follow-up scenarios are worth adding

For the inventory of available scenarios - useful when sizing follow-up work or wiring a new transport - run pnpm openclaw qa coverage (add --json for machine-readable output). When choosing focused proof for a touched behavior or file path, run pnpm openclaw qa coverage --match <query>. The match report searches scenario metadata, docs refs, code refs, coverage IDs, plugins, and provider requirements, then prints matching qa suite --scenario ... targets.

Every qa suite run writes top-level qa-evidence.json, qa-suite-summary.json, and qa-suite-report.md artifacts for the selected scenario set. Scenarios that declare execution.kind: vitest or execution.kind: playwright run the matching test path and also write per-scenario logs. Scenarios that declare execution.kind: script run the evidence producer at execution.path through node --import tsx (with ${outputDir} and ${scenarioId} expanded in execution.args); the producer writes its own qa-evidence.json, whose entries are imported into the suite output and whose artifact paths are resolved relative to that producer qa-evidence.json. When qa suite is reached through qa run --qa-profile, the same qa-evidence.json also includes the profile scorecard summary for the selected taxonomy categories.

Treat coverage output as a discovery aid, not a gate replacement; the selected scenario still needs the right provider mode, live transport, Multipass, Testbox, or release lane for the behavior under test. For scorecard context, see Maturity scorecard.

For character and style checks, run the same scenario across multiple live model refs and write a judged Markdown report:

pnpm openclaw qa character-eval \
  --model openai/gpt-5.5,thinking=medium,fast \
  --model openai/gpt-5.2,thinking=xhigh \
  --model openai/gpt-5,thinking=xhigh \
  --model anthropic/claude-opus-4-8,thinking=high \
  --model anthropic/claude-sonnet-4-6,thinking=high \
  --model zai/glm-5.1,thinking=high \
  --model moonshot/kimi-k2.5,thinking=high \
  --model google/gemini-3.1-pro-preview,thinking=high \
  --judge-model openai/gpt-5.5,thinking=xhigh,fast \
  --judge-model anthropic/claude-opus-4-8,thinking=high \
  --blind-judge-models \
  --concurrency 16 \
  --judge-concurrency 16

The command runs local QA gateway child processes, not Docker. Character eval scenarios should set the persona through SOUL.md, then run ordinary user turns such as chat, workspace help, and small file tasks. The candidate model should not be told that it is being evaluated. The command preserves each full transcript, records basic run stats, then asks the judge models in fast mode with xhigh reasoning where supported to rank the runs by naturalness, vibe, and humor. Use --blind-judge-models when comparing providers: the judge prompt still gets every transcript and run status, but candidate refs are replaced with neutral labels such as candidate-01; the report maps rankings back to real refs after parsing.

Candidate runs default to high thinking, with medium for GPT-5.5 and xhigh for older OpenAI eval refs that support it. Override a specific candidate inline with --model provider/model,thinking=<level>; inline options also support fast, no-fast, and fast=<bool>. --thinking <level> still sets a global fallback, and the older --model-thinking <provider/model=level> form is kept for compatibility. OpenAI candidate refs default to fast mode so priority processing is used where the provider supports it. Pass --fast only when you want to force fast mode on for every candidate model. Candidate and judge durations are recorded in the report for benchmark analysis, but judge prompts explicitly say not to rank by speed. Candidate and judge model runs both default to concurrency 16. Lower --concurrency or --judge-concurrency when provider limits or local gateway pressure make a run too noisy.

When no candidate --model is passed, the character eval defaults to openai/gpt-5.5, openai/gpt-5.2, openai/gpt-5, anthropic/claude-opus-4-8, anthropic/claude-sonnet-4-6, zai/glm-5.1, moonshot/kimi-k2.5, and google/gemini-3.1-pro-preview. When no --judge-model is passed, the judges default to openai/gpt-5.5,thinking=xhigh,fast and anthropic/claude-opus-4-8,thinking=high.

Related docs

• Matrix QA
• Maturity scorecard
• Personal agent benchmark pack
• QA Channel
• Testing
• Dashboard