memory-continuity/MEMORY.md

Long-term Memory (curated)

1) Working model / roles

• Tao prefers to send tasks to main; main acts as PM/architect and delegates implementation (often to Codex).
• Dedicated agents and intended responsibilities:
  • tech_geek (Telegram group “技术宅”, workspace tech_home_group): tech/home-lab/ops discussions + GitHub (gh CLI / PR / CI / issues) follow-up.
  • travel_assistant: travel planning and travel-related research/tasks.
  • finance_assistant (“理财小帮手”): personal finance tasks.

2) Dev workflow / merge policy

• Small changes (docs/copy/layout/typos/i18n cleanup): Xiao Qiang can merge after self-review/verification, then notify Tao with PR link + summary.
• Big feature changes: require Tao review/approval before merge.

3) Sub-agents: architecture + token efficiency

• Sub-agents reuse the parent/main agent bot for messaging, but each sub-agent has an independent workspace + SOUL (persona) + MEMORY (memory).
• Goal: prevent context/memory mixing, improve focus/efficiency, and save tokens by keeping contexts smaller.
• Current preferred multi-agent architecture for complex development:
  • main acts as PM/architect/reviewer: requirements, task breakdown, prioritization, risk calls, progress updates, and final summaries to Tao.
  • Execution agents should be role-specialized and do the implementation work.
  • codex_worker is the dedicated Codex execution agent for coding tasks.
  • Default delegation rule: unless a task is truly tiny and can be finished in one short pass, main should not default to personally coding/editing files; implementation work should be delegated to codex_worker first.
  • Good candidates for main to do directly: tiny edits, very small linear fixes, or short actions that are not worth execution-agent handoff.
  • For complex projects, create more specialized agents as needed (e.g. frontend/backend/test/ops/docs/data) instead of overloading one agent.
  • Create multiple execution agents when work is meaningfully parallel, responsibilities are different, contexts are likely to contaminate each other, or independent validation/release tracks are needed.
  • Avoid over-splitting for tiny, highly coupled, or poorly defined tasks.
  • This is the current default pattern, but Tao may change the rules as needs evolve.

4) ACP/Codex delegation constraints

• Telegram channel plugin currently does not support subagent_spawning hooks → cannot bind persistent subagent sessions with thread=true; use sessions_spawn(mode="run") as workaround.

5) OCM (OpenClaw Manager) repo policies

• OCM repo path: ~/.openclaw/ocm.
• Internal-only docs (Project Brief / Architecture / Decisions) must not be uploaded to GitHub; keep them under ~/.openclaw/ocm-internal/docs/.

6) Ops / security notes

• Ensure ~/.openclaw/openclaw.json is not world-readable; prefer permission mode 600.

7) Model policy (current preference)

• Historical temporary preference once was: all subagents primary = openai-codex/gpt-5.2, fallback = github-copilot/claude-opus-4.6.
• Current important exception / newer rule: codex_worker should use primary = openai-codex/gpt-5.4, prefer high/extra/xhigh thinking when available, and should not auto-fallback to another model. If its primary model is unavailable, report to Tao and let Tao decide the replacement model.
• Additional long-lived execution agents initialized locally for repeat use: docs_worker, qa_worker, ops_worker.
• Execution-agent system needs a standardized dispatch/handoff layer: task input template, result format, blocker/escalation rules, and a main-to-Tao forwarding rule.
• Hard reporting/forwarding protocol (must-follow):
  • Worker events that require immediate Tao-visible updates (no “I’ll summarize later”): accepted, milestone result, blocked/failed, completion, agent switch decision, transition to review/commit/release.
  • Ordering constraint: when a worker reports milestone/completion, main’s first action is to update Tao; only then proceed to review/commit/next dispatch.
  • Failure definition: if a worker has already reported completion and main has not forwarded it to Tao, that is a main process failure, not “task still in progress”.
  • Default 4-line update template: who / status / output / next.

8) Watchdog / automation policy

• Tao preference: avoid watchdog-style auto-restart automation.
• The previous watchdog automation was removed:
  • OpenClaw cron job Gateway monitor + auto-restart (main) removed (jobId 139258d6-f675-4f88-b2a7-cbe0b93a0db6).
  • Mac launchd watchdog ai.openclaw.watchdog removed.
  • Pi-side crontab watchdog removed.

9) Webex keep-alive (local script)

• Tao requested a controllable Webex desktop “keep active” option.
• Script installed on Mac: ~/.openclaw/scripts/webex-keepalive.sh (commands: start|stop|status).
• Notes: uses AppleScript/System Events; may require macOS Accessibility permission for Terminal/iTerm. Logs: ~/.openclaw/logs/webex-keepalive.log, pid: ~/.openclaw/run/webex-keepalive.pid.

10) Local memory library organization

• Local memory dir: /Users/taodeng/.openclaw/workspace/main/memory/.
• Added memory/INDEX.md to classify daily notes vs topic notes vs automation state JSON (do not rename/move state JSON without updating jobs).
• Plan: create a separate GitHub KB repo (option A) for shareable/curated knowledge; keep private/sensitive items local and only publish redacted content.