ai-memory-mcp

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Local, Markdown-first shared memory for AI coding agents, served over MCP.

Every session of every tool (Claude Code, Codex CLI, Copilot CLI, ...) starts with amnesia. This gives them one shared place to record decisions, dead ends and handoffs, and to look them up next time, so you stop re-explaining your project.

Status: feature-complete, provided as-is. This is a small tool I use myself. I do not offer support and I am not taking feature requests. Bug reports may go unanswered. Fork it freely (MIT).

Why this one

• Plain Markdown is the source of truth. One .md file per memory: readable, greppable, diffable, committable.

• The index is disposable. SQLite only accelerates search. Delete index.db and it is rebuilt from your notes.

• Local and private. No cloud, no account. Optional semantic search uses a local Ollama model.

• Any language. Substring search works for Chinese/Japanese/Korean text, not just space-separated words.

• Auditable. Every read is logged (who, when, what query; never the results).

Related MCP server: mcp-chest-memory

Install

Requires Python 3.10+.

git clone https://github.com/bargisula/ai-memory-mcp.git
cd ai-memory-mcp
pipx install .          # or: pip install .
ai-memory doctor        # checks your setup

Then register the server with your MCP client. The command is ai-memory-mcp (stdio transport):

# Claude Code
claude mcp add -s user ai-memory -- ai-memory-mcp

# Codex CLI
codex mcp add ai-memory -- ai-memory-mcp

Any client that takes a JSON config (for example ~/.copilot/mcp-config.json):

{ "mcpServers": { "ai-memory": { "command": "ai-memory-mcp" } } }

If you use uv, you can skip the install and point the client at uvx --from git+https://github.com/bargisula/ai-memory-mcp ai-memory-mcp instead.

The server ships usage instructions to the client automatically (check the handoff first, record decisions and failures as you go), so no extra prompt file is required.

Tools

Pass your own name in llm (for example "Claude") so entries and reads can be attributed.

Where your data lives

Default: ~/.ai-memory/ (override with AI_MEMORY_HOME).

~/.ai-memory/
  notes/<project>/2026-01-05-choose-database.md   <- the truth. Back this up / put it in git.
  index.db                                        <- disposable search index + audit log

A note looks like this, and you can write or edit them by hand:

---
project: my-app
type: decision
llm: Claude
tags: database
ts: 2026-01-05T10:20:00+08:00
---

# Use SQLite

Single file, zero setup. Rejected Postgres: no concurrent writers needed.

After hand edits, deletions or copying notes from another machine, run ai-memory reindex. (A missing index.db is rebuilt automatically the first time the server starts.)

Configuration

All optional, all environment variables:

Semantic search (optional)

ollama pull nomic-embed-text
ai-memory reindex --embed     # embed notes written before Ollama was available

Without Ollama everything else works; search_memory_semantic simply reports that it is unavailable. Vectors are compared by brute force in Python, which is fine up to a few thousand notes.

Command line

ai-memory doctor                  check the installation
ai-memory reindex [--embed]       rebuild the index from notes/
ai-memory handoff <project>       print the newest handoff (see hook below)
ai-memory search "<words>"        keyword search, JSON output
ai-memory audit                   read audit log, JSON output
ai-memory web [--port N]          open the read-only audit page in your browser

Audit page

ai-memory web serves a small page at http://127.0.0.1:8765 showing who read what and when, with filters for caller, project, tool, status and query text. It is read-only, listens on 127.0.0.1 only, refuses requests whose Host header is not loopback (DNS-rebinding protection), and follows your browser language (English or 繁體中文).

Make handoffs load automatically (Claude Code)

Relying on the model to remember to call get_handoff is fragile. A SessionStart hook guarantees it. In .claude/settings.json of your project:

{
  "hooks": {
    "SessionStart": [
      { "hooks": [ { "type": "command", "command": "ai-memory handoff my-app" } ] }
    ]
  }
}

Security notes

• Project names are restricted to letters, digits, _, -, .; anything path-like is rejected, so a prompt-injected agent cannot write outside notes/. read_memory is likewise confined to notes/*.md.

• Notes are plain text on disk. Do not store secrets in memories. A write guard enforces this: record_memory refuses (and writes nothing) when title, content or tags contain a token or API key (Telegram, sk-, GitHub, AWS, Google, Slack, JWT, private-key blocks, password=...-style assignments), a Taiwan mobile number, a checksum-valid Taiwan national ID, or a Luhn-valid card number. The refusal names the category, never the matched text. Add your own rules, one regex per line, to <data dir>/guard-block.txt (# starts a comment); every LLM sharing the data directory is covered. Set AI_MEMORY_GUARD=off to disable.

• The audit log stores tool, caller, project and the query text (first 500 characters), never result contents.

Limits (by design)

• Single user, single machine. Concurrent writers are safe (files are created exclusively, SQLite runs in WAL mode), but there is no access control and no built-in sync. To sync, keep notes/ in a git repository.

• No edit or delete tool. Edit or delete the Markdown file, then ai-memory reindex.

• Search is substring matching, not fuzzy or ranked by meaning (use the semantic tool for that).

Coming from a private predecessor?

The note format is unchanged: copy your old notes/ folder into AI_MEMORY_HOME and start the server. The index builds itself.

Development

pip install -e ".[dev]"
pytest

License

MIT. See LICENSE.