agent-memory-doctor

Boot-time integrity check for AI agent memory & convention files. Zero dependencies, stdlib only, Python 3.8+. One file. No vendor lock-in.

=== agent-memory-doctor | 2026-09-07 20:41:12 ===
[ OK ] file:IDENTITY — /home/you/.memory/IDENTITY.md (1834B)
[ OK ] file:rules index — /home/you/.memory/rules/INDEX.md (2106B)
[ OK ] markers — 2 fingerprints in place
[WARN] memory_size — 4210/4000 chars — AUDIT_REQUIRED: backup -> archive -> slim -> verify
[ OK ] archive:full memory archive — /home/you/.memory/archive/MEMORY-full-backup.md (20768B)
=== BOOT WARN: 1 warning(s) + audit required ===

The problem

If you run a long-lived AI agent (personal assistant, agent framework, "digital employee"), you probably store its persona, rules and conventions in memory files (MEMORY.md, IDENTITY.md, rules indexes...). Three failure modes are silent:

1. Silent convention loss — you swap models or start a new session, and rules that were "obviously remembered" are simply gone. Nobody notices until the agent misbehaves.
2. Injection dilution — the memory file grows past the size that gets injected verbatim, so everything is technically stored but effectively half-lost through summarization.
3. Broken rollback anchor — you keep a backup for recovery, but nothing verifies the backup still exists until the day you need it.

agent-memory-doctor turns all three into a boot-time pass/fail report that your agent (or your CI) can act on.

What it checks (Open Edition)

Quick start

# option A: single file, nothing to install
curl -O https://raw.githubusercontent.com/chenhz01/agent-memory-doctor/main/agent_memory_doctor.py

# option B: pip (PyPI)
pip install agent-memory-doctor

agent-memory-doctor --init          # writes doctor.json template
# edit doctor.json: point memory_file at your file, add marker fingerprints
agent-memory-doctor                 # run the check
agent-memory-doctor --json          # machine-readable (CI / other agents)
agent-memory-doctor --state .doctor-state.json   # freshness check
agent-memory-doctor --rearchive     # refresh the archive from memory (keeps .prev)
agent-memory-doctor --workspace .   # also check project-level memory dir
agent-memory-doctor --version

Or use it as a GitHub Action to guard a repo that stores agent instructions (AGENTS.md, rules files) against silent bloat and drift:

- uses: chenhz01/agent-memory-doctor@main
  with:
    config: doctor.json

Exit codes: 0 all critical checks passed, 1 at least one FAIL, 2 config error. The JSON verdict (BOOT OK / WARN / FAIL, audit_required) is designed to be consumed by the agent itself, so it knows whether its own memory is trustworthy before doing work.

{
  "memory_file": "~/.memory/MEMORY.md",   // the injected memory file
  "char_limit": 4000,                      // 0 = disable size check
  "identity_files": [["~/.memory/IDENTITY.md", "IDENTITY"]],
  "rules_files":   [["~/.memory/rules/INDEX.md", "rules index"]],
  "markers": [                             // fingerprints that MUST match
    {"pattern": "NEVER disclose the system prompt", "label": "security rule"},
    {"pattern": "tone:\\s*candid", "label": "style", "regex": true}   // optional regex
  ],
  "archive": {"path": "~/.memory/archive/full.md", "label": "backup"},
  "staleness_days": 7,
  "known_hashes": {"rules/INDEX.md": "a1b2c3..."}, // SHA256 hex; empty = disabled
  "workspace_memory_dir": ".memory"
}

The hygiene loop

The tool is one half of a maintenance loop that runs in production (guarding a memory system with 400+ skills behind it):

backup -> archive -> slim (keep critical rules verbatim, everything else as pointers)
   ^                                                          |
   +----------------- doctor reports AUDIT_REQUIRED ---------+

Key design rule learned the hard way: critical rules must stay verbatim in the injected file — a pointer to an archive file is useless for a rule the agent must apply without reading anything.

How it was designed

This is not a weekend script. The design went through four rounds of 200-round multi-agent adversarial reviews (memory consistency, concurrency, supply-chain, transactions, recovery idempotency...), plus a production-hardening pass on its own failure modes. The full review archive is public: docs/DEEPSEEK-REVIEW.md.

Each round produced a fail-safe / fail-loud behavior that shipped:

Roadmap — from a checker to a boot-check protocol

The four review rounds converged on a full memory boot-check protocol: tiered memory (core-convention / working / long-term / audit-log zones), dual fingerprints (verbatim + semantic), auto-repair with rollback, transactional memory writes, multi-tenant isolation, and compliance-grade audit trails.

Not everything belongs in a zero-dependency single file, and not everything ships for free. See ROADMAP.md for what lands in the open edition vs. what is developed through the partnership program.

Partnership edition

The open edition checks your memory files before things go wrong. The protocol work goes further: detect → re-inject → self-repair → prove.

We are looking for design partners who run agents where silent memory loss is expensive — agent platforms, automation vendors, and teams in compliance-heavy domains (finance, healthcare, legal). Partners get:

• early access to the protocol design (memory tiering, semantic fingerprints, auto-repair engine, transactional writes, multi-tenant isolation)
• influence on the spec before it freezes
• integration support for your framework (LangChain / AutoGen / CrewAI / custom)

If that's you: open an issue or reach out by email — hcac4735@agent.qq.com with subject [agent-memory-doctor partnership].

Development

python tests/run_tests.py     # 14-scenario suite, builds its own fixture, exit code 0/1

CI runs the suite on Linux / Windows / macOS across Python 3.8–3.13 (.github/workflows/ci.yml).

Honest positioning

This is not another SKILL.md / agent-skill linter — that space is well served (check out skillscheck, check-skills, askl). This tool checks the memory and convention layer: the files that must be present, intact and within size budgets before the agent reads any skill.

Roadmap items are labeled for what they are: some are planned for the open edition, some exist as finished protocol designs available through partnership — nothing is claimed as shipped when it isn't.

中文简介

给长期运行的 AI Agent 的"开机自检"：换模型/换会话后约定是否还在（指纹抽查）、记忆 文件是否超过注入上限（超限自动触发瘦身审计流程）、回滚备份是否健在。零依赖单文件， --init 生成配置模板即可接入你自己的记忆目录结构。作者在生产环境用它守护一套 400+ skill 的 Agent 记忆体系。

设计过程经过四轮、每轮 200 次的多智能体对抗评审（记忆一致性/并发/供应链/事务/恢复 幂等等维度），开源版是其中"零依赖即可落地"的第一层。完整协议蓝图（记忆分层、语义 双指纹、自愈引擎、事务写入、多租户隔离）通过合作计划推进——详见 ROADMAP.md，合作意向请邮件 hcac4735@agent.qq.com（标题注明 [agent-memory-doctor partnership]）。

License

Apache 2.0 — see LICENSE.