agentbrain

Local-first long-term memory for AI agents — a plain Markdown vault + a thin MCP server. AIエージェント向けのローカル長期記憶：純粋なMarkdownナレッジベース + 軽量MCPサーバー。

日本語クイックスタート · English quickstart

Why agentbrain / 設計理念

• Plain Markdown, no lock-in — your memory is a folder of .md files. Open it in Obsidian, grep it, version it with Git. Remove agentbrain and the memory stays.

• Token-efficient by design — index-first retrieval: Index.md is the cheap first layer, BM25 (CJK-aware) only ranks candidates, and query output is compact by default (mode='index'); full text only on demand.

• Append-only for agents — agents may create lessons, never edit or delete them. Consolidation happens through proposals in _consolidations/ that a human approves, which keeps multi-agent writes conflict-free.

• Plug-and-play via MCP — one server, every client: Claude Code, Codex CLI, OpenCode, Cursor, DSH, Open WebUI, ...

• Secrets never enter the vault — credentials live in env/keyring; lessons reference ${ENV:VAR_NAME} placeholders only, resolved at runtime via shell.

Related MCP server: layer-memory

Vault layout

agentbrain/                    # vault root (git-friendly, Obsidian-friendly)
├─ AGENTS.md                     # rules every agent reads at session start
├─ Case-Learnings/
│  ├─ Index.md                   # auto-generated lesson index (retrieval layer 1)
│  ├─ log.md                     # append-only audit log
│  ├─ Learnings/                 # one lesson per file, YAML frontmatter
│  │  └─ case-001-lesson-01.md   # 文件名 = {case_id}-lesson-{NN}，自动生成
│  └─ _consolidations/           # merge/promotion proposals (human approval)
└─ Agent-Profile/
   ├─ Immutable/                 # owner preferences & environment (agent read-only)
   ├─ Mutable-Hints/             # soft preferences (agent read-only)
   └─ _suggestions/              # agent-suggested profile changes

日本語クイックスタート

pip install -e .                 # 需要 Python >= 3.10
agentbrain init ~/agentbrain     # 生成 vault 脚手架（幂等，可重复执行）
agentbrain ingest --case demo --lesson "部署前必须先跑迁移脚本" --tags 部署,运维
agentbrain query "部署 迁移"
agentbrain profile                # 查看个人偏好（Immutable + Mutable-Hints）
agentbrain suggest --title "回复用中文" --change "偏好简洁的中文回复"   # 提交偏好建议
agentbrain lint                    # 体检：重复/过时/无标签/低置信度 → 生成整合提案
agentbrain apply lint-20260820-172206.md   # 人工审核后执行提案（自动归档）
agentbrain distill                 # 分析 log 中重复出现的模式 → 生成提升提案

MCPクライアントでの接続（Claude Codeを例に）：

claude mcp add agentbrain -- agentbrain serve

汎用MCP JSON設定（Cursor / Open WebUI など）：

{
  "mcpServers": {
    "agentbrain": {
      "command": "agentbrain",
      "args": ["serve"],
      "env": { "AGENTBRAIN_VAULT": "D:\\agentbrain" }
    }
  }
}

Vaultパス解決順序：--vault 引数 > AGENTBRAIN_VAULT 環境変数 > ~/agentbrain。

English quickstart

pip install -e .                 # Python >= 3.10
agentbrain init ~/agentbrain     # scaffold the vault (idempotent)
agentbrain ingest --case demo --lesson "Always run migrations before deploy" --tags deploy,ops
agentbrain query "deploy migrations"
agentbrain profile                # print the owner profile
agentbrain suggest --title "Short replies" --change "Keep answers under 3 sentences."
agentbrain lint                    # health check → consolidation proposals
agentbrain apply lint-20260820-172206.md   # execute an approved proposal (archives it)
agentbrain distill                 # recurring-pattern analysis → promotion proposals
agentbrain serve                   # start the MCP server on stdio

Codex CLI (~/.codex/config.toml):

[mcp_servers.agentbrain]
command = "agentbrain"
args = ["serve"]

MCP tools

MCP resources

Agents are expected to follow AGENTS.md in the vault root: read the profile at session start, query at task start, ingest on learnings, never edit existing lessons, never write secrets into the vault. Consolidation proposals carry machine-readable directive blocks (`agentbrain); only the owner executes them via agentbrain apply.

Design notes

• Retrieval scoring: BM25 over summary (×3), tags (×2), case id and body, with a CJK bigram tokenizer so Chinese queries work out of the box; results are boosted by verified, use_count and recent last_verified_at, demoted when stale (> 1 year).

• Self-maintenance signals: every query hit increments use_count; log.md feeds memory_distill pattern analysis; lint refreshes nothing silently — every mutation of history goes through human-approved proposals.

• Single-user, local-first: no daemon, no ports; concurrent writes from several agents are serialized by a transient .vault.lock (auto-cleaned, stale-reclaimed after 60 s), and all file writes are atomic (temp + rename) so readers never see torn files.

Changelog

• 0.3.1 — Data-integrity fixes: concurrent same-case ingests no longer overwrite each other (lesson-id allocation moved inside the vault lock); confidence: 0.0 round-trips correctly (was silently coerced to 0.8); lint/distill proposals are written atomically under the lock with collision-free names; merge proposals now keep the more-used lesson as the keeper; duplicate detection pre-tokenizes (O(n²) without re-tokenizing per pair). Session wrap-up rule added to AGENTS.md. 59 tests.

• 0.3.0 — Concurrency & robustness: cross-process/thread vault write lock (.vault.lock, re-entrant, stale-reclaim), atomic writes (temp + rename), apply is now a single transaction; query no longer rebuilds the index once per hit (one rebuild per query); stray non-lesson .md files in Learnings/ are ignored; confidence clamped to [0,1]; unknown mode falls back to index; same-second suggestions no longer overwrite each other. 54 tests.

• 0.2.0 — Owner profile layer (memory_profile / memory_suggest + MCP resources), lint/distill proposals with machine-readable directive blocks, agentbrain apply with cycle/self-supersede/dangling checks.

• 0.1.0 — Initial MVP: vault + frontmatter + CJK-aware BM25 retrieval, MCP server (query/ingest/lint/distill) + CLI, scaffold templates.

Roadmap

• Hybrid fallback search (SQLite FTS5 + local embedding, RRF fusion) for large vaults

• agentbrain apply <proposal> to execute approved consolidations

• Owner profile layer: memory_profile / memory_suggest + MCP resources

• Temp-layer bridge (Mem0-style short-term memory → distill promotions)

• Keyring-backed ${ENV:...} resolution helper

• Git snapshot hook on ingest/distill

Development

pip install -e ".[dev]"
pytest

License

Apache-2.0 — see LICENSE.