在编码代理线束之间保持一层便携式内存和技能，这样切换工具时不会重置代理的工作方式。

一个便携式的.agent/文件夹（内存+技能+协议），可以插入Claude Code、Cursor、Windsurf、OpenCode、OpenClaw、GitHub Copilot CLI、Google Gemini CLI、Hermes、Pi Coding Agent、Codex、Autohand Code CLI、Antigravity或DIY的Python循环——切换时它会保留知识。

它还包含本地数据层，方便监控整个套件 来自一处的代理：利用活动、合同运行、活跃代理、令牌/成本 估算、关键绩效指标摘要、用户定义的资源类别， 可截图的每日仪表盘。

而且它能将经过批准、涂黑的运行变成本地飞轮的遗迹： 追踪记录、上下文卡、评估案例、训练准备好的JSONL和准备度 无需训练模型或发送遥测数据即可获得度量。

v0.20.0 新增内容 — 编排、门控演进、Markdown 内存轨道

大脑现在涵盖了编排、自我进化和便携记忆 采用同样的文件支持、仅 stdlib、主机代理审核的风格：

• DAG 编排 — plan.py 验证目标作为依赖图，

agentic-stack

Keep one portable memory-and-skills layer across coding-agent harnesses, so switching tools doesn’t reset how your agent works.

A portable .agent/ folder (memory + skills + protocols) that plugs into Claude Code, Cursor, Windsurf, OpenCode, OpenClaw, GitHub Copilot CLI, Google Gemini CLI, Hermes, Pi Coding Agent, Codex, Autohand Code CLI, Antigravity, or a DIY Python loop — and keeps its knowledge when you switch.

It also includes a local data layer so you can monitor the whole suite of agents from one place: harness activity, cron runs, active agents, token/cost estimates, KPI summaries, user-defined resource categories, and screenshot-ready daily dashboards.

And it can turn approved, redacted runs into local flywheel artifacts: trace records, context cards, eval cases, training-ready JSONL, and readiness metrics without training a model or sending telemetry.

New in v0.20.0 — orchestration, gated evolution, Markdown memory tracks

The brain now covers orchestration, self-evolution, and portable memory in the same file-backed, stdlib-only, host-agent-reviewed style:

• DAG orchestration — plan.py validates a goal as a dependency graph, matches nodes to agents from protocols/agents.json (Claude Code, Codex, OpenCode, Gemini, Copilot, Pi, Hermes, OpenClaw, shell, custom), dispatches with bounded subprocesses, judges completion by evidence (exit status, truncation, NEED_INPUT: markers, per-node verify), writes exception reports with three resolutions (continue / abandon / replan), keeps every prompt, output, transcript and verdict on disk, and lets later runs consume earlier nodes via ref:. Playbooks save a run as a template; personas assemble a lead role plus specialist roster; campaigns run unattended with a model-free monitor and a report gate. → docs/orchestration.md
• Gated evolution — four strategy seats (memory / planning / capability / action) around the conductor, and evolve.py: diagnose failures from episodic memory, scaffold a candidate, paired screen on a fixed task sample (validity + activation gates, t ≥ 1.96), gene bank per (where, pathology) cell, install with backup. Nothing unscreened touches the live harness. → docs/evolution.md
• Markdown memory tracks — episodes (user), agent cases, foresight with a validity window, weekly model-free reflection that consolidates and deprecates (never deletes), source-backed knowledge pages, a stdio MCP server (mem_context, mem_search, mem_save_turn, mem_save_fact, mem://profile, mem://search) for any host, and Claude Code