anamnesis
Persistent session memory for Claude Code, providing episodic recall across sessions via structured markdown logs and BM25 search.
README
Anamnesis MCP
"All inquiry and all learning is but recollection." — Plato, Meno 81d
Persistent session memory for Claude Code.
Claude Code already has persistence primitives — CLAUDE.md for project context, memory files for user preferences, skills for reusable workflows. What it doesn't have is episodic memory: what happened across sessions, what was tried, what worked, what's still open. The gap between episodic recall (what happened) and procedural knowledge (what to do) resets to zero every time a context window closes.
Anamnesis bridges that gap. It logs each session as structured markdown — Plan, Done, Open — and serves it back via an MCP server. Future sessions can search and cross-reference the past.
Features
- BM25 full-text search over session logs with date/tag/host filtering
- Structured entry search — find Plan/Done/Open items across all sessions
- Obsidian-first — plain markdown vault, works with graph view and Obsidian Sync
save_sessiontool — capture sessions from any MCP client (Cursor, VS Code, etc.)- Auto session capture — SessionEnd hook summarizes conversations automatically
- Corpus analytics — word counts, tag distributions, open item tracking (Zeigarnik stats)
- Section extraction — heading-level retrieval and cross-session section search
- No database — just markdown files in a directory, audit/edit/delete anytime
Requirements
- Python 3.13+
- uv (package manager)
- Claude Code (or any MCP client)
ANTHROPIC_API_KEY(only needed for the optional SessionEnd hook)
Quick Start
git clone https://github.com/chaosisnotrandomitisrhythmic/anamnesis-mcp.git
cd anamnesis-mcp && uv sync
Add the MCP server to ~/.claude.json:
{ "mcpServers": { "anamnesis": {
"command": "uv",
"args": ["--directory", "/path/to/anamnesis-mcp", "run", "anamnesis"]
}}}
Add the SessionEnd hook to ~/.claude/settings.json:
{ "hooks": { "SessionEnd": [{ "matcher": "", "hooks": [{
"type": "command",
"command": "bash /path/to/anamnesis-mcp/scripts/session-summary.sh",
"timeout": 10000
}]}]}}
| Variable | Default | Description |
|---|---|---|
ANAMNESIS_VAULT |
~/Documents/Anamnesis |
Directory where session files are stored |
ANTHROPIC_API_KEY |
— | Required by the hook to summarize transcripts |
ANAMNESIS_MODEL |
claude-opus-4-6 |
Model used for summaries |
ANAMNESIS_DAILY_DIR |
<vault>/../Daily Logs |
Directory for daily summary files |
ANAMNESIS_DAILY_HOUR |
20 |
Hour for daily summary cron schedule |
ANAMNESIS_DAILY_MINUTE |
0 |
Minute for daily summary cron schedule |
Optionally, copy examples/skill/SKILL.md to ~/.claude/skills/anamnesis/SKILL.md for a /anamnesis slash command that teaches Claude how to search and cross-reference sessions.
Tools
search_sessions— BM25 full-text search over session logsget_session— retrieve full session by IDlist_sessions— browse/paginate with date/tag/host filterssearch_entries— cross-session search for Plan/Done/Open itemsget_section/list_sections/search_sections— heading-level extractionanalyze_corpus— corpus-wide statistics and open item countsrun_analysis— execute Python against session data (local/stdio only)
Vault Format
Plain markdown with YAML frontmatter. No database — just files in a directory.
---
session_id: "abc123-..."
date: "2026-03-14"
host: "myhost"
cwd: "/home/user/project"
tags: []
---
# Session Title
Summary paragraph describing the full arc of work.
---
## 2026-03-14 14:30
- **Plan**: What the user set out to do
- **Done**: What was accomplished
- **Open**: Unfinished items or next steps
Works with any markdown viewer. If you use Obsidian, point ANAMNESIS_VAULT at a folder inside your vault for graph view and sync.
Daily Summary
Anamnesis includes an optional daily summary script — the third compression layer in the hierarchy:
Transcript (raw) → Session file (Opus summary) → Daily summary (residual symbols)
Instead of appending per-session blocks, a cron job synthesizes all sessions from the day into a cohesive narrative with thematic threads and consolidated open loops. Each layer is a lossy compression that produces what Hofstadter calls residual symbols — not shorter text, but the stable attractors that emerge after recursive compression.
Setup:
# Add to crontab (runs at 8:03 PM daily)
3 20 * * * /Users/you/.local/bin/uv run scripts/daily_summary.py >> /tmp/anamnesis-daily-summary.log 2>&1
Configuration:
| Variable | Default | Description |
|---|---|---|
ANAMNESIS_DAILY_DIR |
<vault>/../Daily Logs |
Directory for daily summary files |
ANAMNESIS_DAILY_HOUR |
20 |
Hour for cron schedule |
ANAMNESIS_DAILY_MINUTE |
3 |
Minute for cron schedule |
You can also run it manually for any date: uv run scripts/daily_summary.py 2026-03-31
Uninstallation
- Remove the
anamnesisentry frommcpServersin~/.claude.json - Remove the SessionEnd hook from
~/.claude/settings.json(if configured) - Optionally delete the vault directory (
~/Documents/Anamnesisby default) - Optionally remove the skill:
rm -rf ~/.claude/skills/anamnesis/ - Remove the repo:
rm -rf /path/to/anamnesis-mcp
Your session logs in the vault are plain markdown — keep them, move them, or delete them as you see fit.
Why "Anamnesis"
Anamnesis (ἀνάμνησις) — Plato's word for recollection, literally un-forgetting. His claim was that knowledge is not acquired but recovered: what appears as new learning is the recall of what was already known but inaccessible. Not the acquisition of something foreign, but the recognition of something familiar.
Claude Code's existing persistence — CLAUDE.md, memory files, skills — is procedural. It encodes how to behave: project conventions, user preferences, workflow patterns. What it doesn't capture is the episodic layer underneath: the specific sessions where those conventions were discovered, the failed approaches that led to the current ones, the open threads that haven't converged yet. Procedural memory feels like knowing. Episodic memory feels like remembering. Anamnesis adds the remembering.
The Strange Loop
The same model that had the conversation summarizes it. That summary enters a searchable index. The next session reads its own past summaries and continues the work — reconstructing itself from its own compressed artifacts.
Session N happens
→ SessionEnd hook fires
→ The model summarizes Session N
→ Summary enters the searchable index
→ Session N+1 starts
→ User says "search my past sessions"
→ Claude reads its own summary of Session N
→ That reading becomes part of Session N+1's context
→ SessionEnd hook fires
→ The model summarizes Session N+1
(which now includes a summary of Session N)
→ Session N+2 reads THAT...
Summaries of summaries of summaries, each layer lossy, until what remains is not the conversation but its shape. Hofstadter called these stable residues symbols — not the raw data, but the attractor that emerges when a system references itself enough times. The rolling summary that gets rewritten each session is literally this process: not the conversation, but the residue of the conversation after recursive compression.
The model doing the summarizing is the same model that will later read the summary — writing notes for a future self that isn't itself. The reading instance reconstructs a past self from compressed artifacts. Hofstadter would recognize this: we don't replay experiences, we reconstruct them from lossy symbols, and the reconstruction is shaped by our current context.
Inspired by Douglas Hofstadter's Gödel, Escher, Bach: an Eternal Golden Braid and I Am a Strange Loop. For the full exploration — Gödel's incompleteness, Escher's self-referential architecture, Bach's fugue in the Plan/Done/Open format — see research/strange-loops.md.
License
MIT
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。