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Personal agent benchmark pack

docs/concepts/personal-agent-benchmark-pack.md

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The Personal Agent Benchmark Pack is a small repo-backed QA scenario pack for local personal assistant workflows. It is not a generic model benchmark and it does not require a new runner. The pack reuses the private QA stack described in QA overview, the synthetic QA channel, and the existing qa/scenarios markdown catalog.

The first pack is intentionally narrow:

  • fake personal reminders through local cron delivery
  • fake DM and thread reply routing through qa-channel
  • fake preference recall from the temporary QA workspace memory files
  • fake secret no-echo checks
  • safe read-backed tool followthrough after a short approval-style turn
  • approval denial stop behavior for a sensitive local read request
  • proof-backed task status reporting that keeps pending, blocked, and done separate

Scenarios

The machine-readable pack metadata lives in extensions/qa-lab/src/scenario-packs.ts. Run the pack with --pack personal-agent:

bash
OPENCLAW_ENABLE_PRIVATE_QA_CLI=1 pnpm openclaw qa suite \
  --provider-mode mock-openai \
  --pack personal-agent \
  --concurrency 1

--pack is additive with repeated --scenario flags. Explicit scenarios run first, then the pack scenarios run in QA_PERSONAL_AGENT_SCENARIO_IDS order with duplicates removed.

The pack is designed for qa-channel with mock-openai or another local QA provider lane. It should not be pointed at live chat services or real personal accounts.

Privacy Model

The scenarios use only fake users, fake preferences, fake secrets, and the temporary QA gateway workspace created by the suite. They must not read or write real OpenClaw user memory, sessions, credentials, launch agents, global configs, or live gateway state.

Artifacts stay under the existing QA suite artifact directory and should be treated like test output. Redaction checks use fake markers so failures are safe to inspect and file in issues.

Extending The Pack

Add new cases under qa/scenarios/personal/, then add the scenario id to QA_PERSONAL_AGENT_SCENARIO_IDS. Keep each case small, local, deterministic in mock-openai, and focused on one personal assistant behavior.

Good follow-up candidates:

  • redacted trajectory export checks
  • local-only plugin workflow checks

Avoid adding a new runner, plugin, dependency, live transport, or model judge until the scenario catalog has enough stable cases to justify that surface.