memex-chats 0.1.0

Local MCP server that indexes your Claude.ai chats and exposes them to Claude Code (and, soon, to Claude.ai via remote MCP).

Memex

Local-first MCP server that indexes your Claude.ai chat history and exposes it to Claude Code (and, soon, to Claude.ai via remote MCP). The goal: give Claude Code the same context Claude.ai already has.

Status: alpha (0.1.0). Phases 0 to 3 closed. Phase 3 quality pass shipped: on-demand auto-summaries, chat ↔ project/repo association, SessionStart hook, and find_related tool. Next is Phase 5 (release polish) so an external user can install without git clone.

End-to-end demo: a chat on claude.ai captured seconds earlier by the Chrome extension, recalled from Claude Code via list_recent_chats and get_chat. No extra prompting, no manual context handoff.

The problem

Brainstorming and planning happen in Claude.ai. Execution happens in Claude Code. The two worlds do not talk to each other: Claude Code cannot read a chat of yours from Claude.ai, not even the one that originated the task it is currently working on. The memory Anthropic shipped on Claude.ai (March 2026) is curated, not full history, and lives isolated inside Claude.ai.

Memex fills that gap: runs locally, indexes the entire corpus of your chats, and exposes them as MCP tools so Claude can search and pull past context whenever it needs to.

How it works

[Claude.ai]
    ↓  (official JSON export / Chrome ext)
[Ingestor]  →  [SQLite + sqlite-vec]  →  [local embeddings (fastembed / Ollama)]
                                    ↓
                          [core: storage + retrieval]
                                    ↓
                  [MCP stdio]  ───→  Claude Code, Claude Desktop
                  [MCP SSE/HTTP] ──→ Claude.ai (coming soon)

Design: pure core (storage, ingest, embeddings, retrieval) decoupled from transport. The same engine serves both stdio and remote MCP without a rewrite.

Requirements

• Python 3.12 or newer
• uv (package manager)

Embeddings: zero-config by default (uses fastembed with a quantized 130 MB model that downloads itself the first time).

If you would rather route through your local Ollama (because you already run it for other models), set:

export MEMEX_EMBED_BACKEND=ollama
# and optionally:
ollama pull nomic-embed-text

Quickstart

Option A: install from PyPI (recommended)

# One-time install (zero-config: includes fastembed for local embeddings).
uvx --from memex-chats memex --help
# Or with pipx if you prefer:
pipx install memex-chats

Then:

1. Request your official Claude.ai export (Settings → Privacy → Export data).
2. Ingest it (the first run downloads the local embedding model, takes ~30s):

   memex ingest /path/to/your-export.zip
   

3. Verify the install:

   memex doctor
   

4. Search from the CLI or wire it into Claude Code (see Wiring it into Claude Code below):

   memex search "your query" -n 5
   memex stats
   

To enable the always-on live capture and the chat ↔ repo features, see the corresponding sections below.

Option B: from source

git clone https://github.com/dioniipereyraa/memex
cd memex
uv sync
uv run memex doctor          # verify setup
uv run memex ingest data/exports/<your-export>.zip
uv run memex search "your query" -n 5

Diagnostics

Run memex doctor any time something is not working. It checks Python version, database, embedder, live-capture server, summarizer config, registered repos, and indexed corpus. Reports OK / WARN / FAIL per check.

MCP server tools (v1)

• search_chats(query, limit=5, source?, mode="hybrid", repo?) searches the corpus. Modes: hybrid (default, combines vector search + FTS5 BM25 via Reciprocal Rank Fusion), semantic (vectors only), lexical (FTS5 only, ideal for proper nouns or exact terms). source filters by origin (conversations, design_chat, memory). repo boosts results associated to a registered repo (see "Repo associations" below). Deduplicated per conversation.
• get_chat(uuid, messages_limit=10, messages_offset=0) fetches a conversation with its messages, paginated. raw_content is omitted; each message is truncated to 1500 chars to stay inside the client's token budget (worst-case response ~17k chars). Long chats are paginated with messages_offset; max messages_limit is 100.
• list_recent_chats(limit=10, source?) lists the latest chats ordered by last update.
• find_related(context, limit=5, repo?) takes free-form text (a paragraph, a file contents, the current discussion) and returns chats that are semantically related. Pure vector search, no FTS, capped at 4000 input chars. Useful when you want "more like this" without typing a keyword query.

Search is also reachable from the CLI with memex search "query" --mode {hybrid|semantic|lexical}. For databases created before the hybrid FTS5 work, run memex reindex-fts once to populate the lexical index.

Wiring it into Claude Code

Once your local database is populated (memex ingest), start the MCP server with uv run memex-mcp. For Claude Code to discover it automatically, add a .mcp.json file at the root of your project (or a user-level server in ~/.claude.json):

{
  "mcpServers": {
    "memex": {
      "command": "uv",
      "args": ["run", "memex-mcp"],
      "cwd": "/absolute/path/to/the/memex/repo"
    }
  }
}

Set cwd to the absolute path where you cloned Memex (where pyproject.toml lives). Restart Claude Code and the tools search_chats, get_chat, list_recent_chats will show up in the session.

The same searches are also available from the CLI via uv run memex search "..." if you prefer them outside Claude Code.

Auto-summaries (Phase 3, optional)

When search_chats returns a chat that does not have a summary yet, Memex can generate one on-the-fly using Claude Haiku. The summary is persisted, so the next search of the same chat hits cache and does not pay the API again. This way you only pay for chats you actually look at, not for the whole corpus.

Opt-in. Off by default. Cap: at most 3 summaries generated per search_chats call (parallel), to bound latency and per-query cost.

1. Install the extra (adds the anthropic SDK):

   uv sync --extra summaries
   

2. In your .env (or as env vars), set:

   ANTHROPIC_API_KEY=sk-ant-...
   MEMEX_SUMMARY_ENABLED=true
   

3. Use search_chats from Claude Code as usual. The first time a chat appears in results without a cached summary, Memex generates one. Subsequent searches return the cached summary instantly.

Configurable via env: MEMEX_SUMMARY_MODEL (default claude-haiku-4-5-20251001), MEMEX_SUMMARY_MAX_TOKENS (default 200). If the API fails for a particular chat (no key, rate limit, network), that one result comes back without a summary, the search itself never aborts, and the warning is logged.

Cost model: bulk ingest of an export with 74 chats costs $0 (no summaries are generated at ingest time). Each unique chat you actually open in a search costs roughly $0.01 (Haiku, ~5-10k input + ~200 output tokens). $5 of API credits comfortably covers months of use.

Repo associations (Phase 3)

Memex can associate each chat with the local code repos it touches, and boost those chats when search_chats is invoked from inside a repo. So when you ask Claude Code something like "remember the auth refactor we discussed?", chats that touched the current repo rank higher than unrelated chats with the same keywords.

How it works:

1. Register a repo:

   uv run memex repos add /path/to/your/repo
   

   Memex reads .git/config (origin URL), and pyproject.toml / package.json / Cargo.toml (package name). The canonical key prefers the git remote (stable across clones) over the path.

2. Run a one-time scan over chats you already ingested:

   uv run memex repos scan
   

   Each chat gets matched against every registered repo. The matcher uses 4 signals (highest wins): remote URL literal in chat text (confidence 1.0), absolute path literal (0.9), manifest name word-bounded (0.8), display name word-bounded (0.5). Anything below 0.5 is dropped. New chats ingested after registering the repo are auto-scanned at ingest time.

3. From Claude Code, search with the repo argument:

   search_chats(query="auth refactor", repo="d:/dionisio/memex")
   

   Or pass the git remote URL, or the canonical key from memex repos list. Chats associated to the repo get their distance reduced by 0.3 * confidence. Chats outside the repo still appear lower down (it is a boost, not a filter).

CLI helpers:

uv run memex repos list           # show registered repos
uv run memex repos remove <key>   # unregister + cascade-remove associations
uv run memex tag <chat-uuid> <repo-key>    # manual override (sticky vs auto-scan)
uv run memex untag <chat-uuid> <repo-key>  # remove an association

A manual tag survives subsequent auto-scans: once you tag a chat by hand, the matcher will not overwrite it.

Proactive context injection (SessionStart hook)

Claude Code supports hooks that run shell commands at session boundaries. Memex ships memex session-context, designed to be wired into the SessionStart hook so every new Claude Code session in a registered repo starts with a Markdown blob listing the most relevant past chats. No query needed.

To enable it, add to your .claude/settings.json (project-local) or ~/.claude/settings.json (user-global):

{
  "hooks": {
    "SessionStart": [{
      "command": "uv run memex session-context"
    }]
  }
}

The command auto-detects the active repo by walking up from cwd until it finds .git. If the repo is registered in Memex and has associated chats, it prints a short Markdown blob with the top N (default 5) chats, ordered by manual first, then auto by confidence. If anything fails (no .git, repo not registered, no associations), it prints nothing on stdout (only diagnostics on stderr), so the hook is a silent no-op in that case.

You can also run the command by hand to debug: uv run memex session-context [--repo <path>] [--limit N].

Live capture (Phase 2)

So that new Claude.ai chats land in Memex without asking for a manual export:

1. Start the local HTTP server in a terminal:

   uv run memex serve
   

   Listens on 127.0.0.1:5777 by default. Keep it running while you browse claude.ai.

2. Load the Chrome extension:

   • Soon: install from the Chrome Web Store (in review for the alpha).
   • For now (unpacked load):
     • Open chrome://extensions/
     • Enable Developer mode
     • Load unpacked → pick chrome-extension/ from the repo
     • Click the Memex icon and confirm the "Server" chip says responding (green).

3. Use claude.ai normally. Every chat you open or create is ingested automatically. Verify with memex stats or by calling search_chats from Claude Code.

Details in chrome-extension/README.md.

Autostart (Windows + Linux)

So you do not have to run memex serve by hand every time you log in:

memex install-service          # default action: install
memex install-service status   # check current state
memex install-service uninstall

The CLI dispatches to the right installer for your OS:

• Windows: registers a Scheduled Task (MemexServe) that runs uv run memex serve at logon. No admin required, no console window, survives VS Code close. Logs at %LOCALAPPDATA%\Memex\serve.log. Auto-restarts up to 3 times if the wrapper dies.
• Linux: writes a systemd user unit at ~/.config/systemd/user/memex-serve.service, enables it, and starts it now. Logs at ~/.local/state/memex/serve.log. To keep it running across logout: loginctl enable-linger $USER. Status: systemctl --user status memex-serve.
• macOS: not implemented yet in 0.1.0. The command prints manual start instructions (nohup uv run memex serve > ~/memex-serve.log 2>&1 &). Tracked for 0.2.0.

Making Claude use Memex proactively

By default, LLMs are conservative with tools: they prefer to ask before invoking anything. If you say "remember we talked about X?", Claude tends to answer "I don't recall" instead of searching.

The docstrings of the 3 tools already include "USE PROACTIVELY" instructions, but you can reinforce it by adding this snippet to your CLAUDE.md (global at ~/.claude/CLAUDE.md for every session, or local at <project>/CLAUDE.md for a specific one):

## Memex: persistent memory of Claude.ai chats

There is an MCP server `memex` with 3 tools: `search_chats`, `get_chat`, `list_recent_chats`.
They index ALL of the user's Claude.ai history, reachable via hybrid search
(semantic + lexical FTS5).

**Operational rule:** before answering "I have no record", "I don't remember", "this is
the first time I hear about this", or anything equivalent, call `mcp__memex__search_chats`
with the relevant query. Claude Code's native memory starts clean every session; Memex
is the only path into the user's real history.

Typical triggers: "remember when...", "did I tell you about...", "we already talked about...",
"the other day we discussed...", or any reference to a project / person / decision that might
live in history.

Roadmap

See ROADMAP.md for phases, close criteria, and current status (in Spanish, it is the internal journal).

Devlog

See DEVLOG.md for the log of decisions, blockers, and progress (in Spanish).

Inspiration and references

• Official feature request: anthropics/claude-code#12858
• Claude Historian, claude-conversation-extractor: MCP servers for Claude Code / Desktop history. Reference for tool structure.
• claude-conversation-export: Claude.ai exporter using the same capture strategy. Useful as backfill.
• Spin-off of the SyncChat project.

License

MIT.