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 is meant to exercise OpenClaw in a more realistic, channel-shaped way than a single unit test can.

Current 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, future runner plugins: live-transport adapters that drive a real channel inside a child QA gateway.
• qa/: repo-backed seed assets for the kickoff task and baseline QA scenarios.
• Mantis: before and 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 are supported.

Command	Purpose
qa run	Bundled QA self-check; writes a Markdown report.
qa suite	Run repo-backed scenarios against the QA gateway lane. Aliases: pnpm openclaw qa suite --runner multipass for a disposable Linux VM.
qa coverage	Print the markdown scenario-coverage inventory (--json for machine output).
qa parity-report	Compare two qa-suite-summary.json files and write the agentic parity report.
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 matrix	Live transport lane against a disposable Tuwunel homeserver. See Matrix QA.
qa telegram	Live transport lane against a real private Telegram group.
qa discord	Live transport lane against a real private Discord guild channel.
qa slack	Live transport lane against a real private Slack channel.
qa mantis	Before and after verification runner for live transport bugs, with Discord status-reactions evidence, Crabbox desktop/browser smoke, and Slack-in-VNC smoke. See Mantis.

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.

For a local OpenTelemetry trace smoke, run:

pnpm qa:otel:smoke

That script starts a local OTLP/HTTP trace receiver, runs the otel-trace-smoke QA scenario with the diagnostics-otel plugin enabled, then decodes the exported protobuf spans and asserts the release-critical shape: openclaw.run, openclaw.harness.run, openclaw.model.call, openclaw.context.assembled, and openclaw.message.delivery must be present; model calls must not export StreamAbandoned on successful turns; raw diagnostic IDs and openclaw.content.* attributes must stay out of the trace. It writes otel-smoke-summary.json next to the QA suite artifacts.

Observability QA stays source-checkout only. The npm tarball intentionally omits QA Lab, so package Docker release lanes do not run qa commands. Use pnpm qa:otel:smoke from a built source checkout when changing diagnostics instrumentation.

For a transport-real Matrix smoke lane, run:

pnpm openclaw qa matrix --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>/.

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

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

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

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. Reuse --lease-id <cbx_...> after logging in to Slack Web manually through VNC. 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.

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-5.4

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 and only passes when the model reports positive visible evidence; a negative response that merely quotes the target text 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.

Before using pooled live credentials, run:

pnpm openclaw qa credentials doctor

The doctor checks Convex broker env, validates endpoint settings, and verifies admin/list reachability when the maintainer secret is present. It reports only set/missing status for secrets.

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.

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

This keeps qa-channel as the broad product-behavior suite while Matrix, Telegram, and future 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. 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.

Telegram, Discord, and Slack QA reference

Matrix has a dedicated page because of its scenario count and Docker-backed homeserver provisioning. Telegram, Discord, and Slack are smaller — a handful of scenarios each, no profile system, against pre-existing real channels — 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/{telegram,discord,slack}-<timestamp>	Where reports/summary/observed messages 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 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

Optional:

• OPENCLAW_QA_TELEGRAM_CAPTURE_CONTENT=1 keeps message bodies in observed-message artifacts (default redacts).

Scenarios (extensions/qa-lab/src/live-transports/telegram/telegram-live.runtime.ts:44):

• telegram-canary
• telegram-mention-gating
• telegram-mentioned-message-reply
• telegram-help-command
• telegram-commands-command
• telegram-tools-compact-command
• telegram-whoami-command
• telegram-context-command
• telegram-long-final-reuses-preview
• telegram-long-final-three-chunks

Output artifacts:

• telegram-qa-report.md
• telegram-qa-summary.json — includes per-reply RTT (driver send → observed SUT reply) starting with the canary.
• telegram-qa-observed-messages.json — bodies redacted unless OPENCLAW_QA_TELEGRAM_CAPTURE_CONTENT=1.

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.

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

• discord-canary
• discord-mention-gating
• discord-native-help-command-registration
• 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.

Run the Mantis status-reaction scenario explicitly:

pnpm openclaw qa discord \
  --scenario discord-status-reactions-tool-only \
  --provider-mode live-frontier \
  --model openai/gpt-5.4 \
  --alt-model openai/gpt-5.4 \
  --fast

Output artifacts:

• discord-qa-report.md
• discord-qa-summary.json
• 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.

Scenarios (extensions/qa-lab/src/live-transports/slack/slack-live.runtime.ts:39):

• slack-canary
• slack-mention-gating

Output artifacts:

• slack-qa-report.md
• slack-qa-summary.json
• slack-qa-observed-messages.json — bodies redacted unless OPENCLAW_QA_SLACK_CAPTURE_CONTENT=1.

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 mirrors the bundled Slack plugin's production install (extensions/slack/src/setup-shared.ts:10). 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. The scope set mirrors the bundled Slack plugin's production install (extensions/slack/src/setup-shared.ts:10):

{
  "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",
        "reactions:read",
        "reactions: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",
        "reaction_added",
        "reaction_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.

Convex credential pool

Telegram, Discord, and Slack 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 "telegram", "discord", and "slack".

Payload shapes the broker validates on admin/add:

• Telegram (kind: "telegram"): { groupId: string, driverToken: string, sutToken: string } — groupId must be a numeric chat-id string.
• Discord (kind: "discord"): { guildId: string, channelId: string, driverBotToken: string, sutBotToken: string, sutApplicationId: string }.
• Slack (kind: "slack"): { channelId: string, driverBotToken: string, sutBotToken: string, sutAppToken: string } — channelId must match ^[A-Z][A-Z0-9]+$ (a Slack 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 Discord support; the broker semantics are identical for both kinds).

Repo-backed seeds

Seed assets live in qa/:

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

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

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

• scenario metadata
• optional category, capability, lane, and risk metadata
• docs and code refs
• optional plugin requirements
• optional gateway config patch
• the executable qa-flow

The reusable runtime surface that backs qa-flow is allowed to stay generic and cross-cutting. For example, markdown 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 should route through the provider registry instead of branching on provider names.

Transport adapters

qa-lab owns a generic transport seam for markdown QA scenarios. qa-channel is the first adapter on that seam, but the design target is wider: future real or synthetic channels should plug into the same suite runner instead of adding a transport-specific QA runner.

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.
• Markdown 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 markdown QA system requires exactly two things:

1. A transport adapter for the channel.
2. A scenario pack that exercises the channel contract.

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 markdown 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).

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-4-6,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-4-6,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>. --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. Add ,fast, ,no-fast, or ,fast=false inline when a single candidate or judge needs an override. 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-4-6, anthropic/claude-sonnet-4-6, zai/glm-5.1, moonshot/kimi-k2.5, and google/gemini-3.1-pro-preview when no --model is passed. When no --judge-model is passed, the judges default to openai/gpt-5.5,thinking=xhigh,fast and anthropic/claude-opus-4-6,thinking=high.

Related docs

• Matrix QA
• QA Channel
• Testing
• Dashboard