brain
A personal memory engine and MCP server that stores durable facts in markdown files managed via git, enabling hybrid search (lexical + semantic) through an MCP interface for persistent context across LLM sessions.
README
brain
A personal memory engine and MCP server: hybrid RAG (SQLite FTS5 + vector search) over a git-backed markdown store.
Replace
OWNERin the CI badge with your GitHub account once you push this to a repo.
Why
An LLM's context is amnesiac: everything it learns about you, your projects,
and your decisions evaporates when the session ends. brain fixes that by
persisting durable facts as plain markdown in git — the kind of store you can
read, edit, grep, and diff by hand — and making it recallable to any MCP
client through hybrid search. The markdown is the source of truth; the search
index is a rebuildable cache you can delete at any time.
Architecture
SOURCE OF TRUTH (markdown + git) DERIVED INDEX (rebuildable)
┌───────────────────────────────────┐ ┌─────────────────────────┐
│ memories/ curated facts │ │ cache/brain.db │
│ summaries/ session digests │─────▶│ ── FTS5 (BM25) │
│ ~/.claude/… ingested transcripts│ ingest ── sqlite-vec (256-d │
│ (episodes) │ │ nomic-embed vectors)│
└───────────────────────────────────┘ └───────────┬─────────────┘
▲ │
│ writes auto-commit │ hybrid recall
│ (optional push to a private remote) ▼
┌───────────┴───────────┐ ┌─────────────────────────┐
│ remember(fact,type) │◀──── MCP ────────▶│ recall(query,k,scope) │
│ │ (stdio) │ get_episode(id) │
└───────────────────────┘ └─────────────────────────┘
any MCP client (Claude Code, …)
- Three kinds of memory.
memories/*.mdare curated, durable facts (preferences, runbooks, per-project state notes).summaries/YYYY-MM/*.mdare per-session digests. Episodes are ingested Claude Code transcripts (read from~/.claude/projects/**.jsonl). The first two are git-tracked text you own; episodes derive from live transcripts. - The index is a cache.
cache/brain.dbholds an FTS5 table and asqlite-vectable of 256-dimension nomic-embed-text-v1.5 vectors (Matryoshka-truncated from 768). It is always rebuildable and never committed — delete it freely andbrain-ingest --fullrecreates it. - Hybrid recall. Each query runs both a lexical (FTS5/BM25) and a semantic
(vector) leg; the two rankings fuse via reciprocal-rank fusion, then
a prior re-weights by kind (a curated memory outranks a raw episode at
equal evidence) and recency (exponential decay with a per-kind
half-life, floored so age never fully erases relevance). Every hit carries a
scorein(0, 1]. The whole ranking surface is env-tunable — see Configuration. - Durability & sync. Memory writes auto-commit, so a fact is safe the
moment it is written. Point
originat any private git remote you control and the markdown store syncs across machines; the index never leaves the box. Sync is off unless you configure a remote, and hard-disabled withBRAIN_SYNC=0.
Quickstart
Requires uv and Python 3.12+. This repo ships a
tiny synthetic store under examples/ so you can try recall without any data
of your own.
uv sync # install deps into .venv
# Build the index over the shipped examples only.
# BRAIN_CLAUDE_PROJECTS points at an empty dir so no real transcripts are read;
# drop it to also ingest your own ~/.claude/projects transcripts.
BRAIN_DIR=$PWD/examples BRAIN_CLAUDE_PROJECTS=$(mktemp -d) \
uv run brain-ingest --full
# Hybrid recall from the shell.
BRAIN_DIR=$PWD/examples uv run brain-recall "postgres backup"
Expected top hit:
scope=all
mem_… memory 2026-01-12 … score=0.67… How the demo acme-webapp Postgres database is backed up each night. …
Register the MCP server with Claude Code (or any MCP client that speaks stdio):
claude mcp add brain -- uv run --directory "$PWD" brain-server
Write a fact mid-session from the shell (brain-remember reads one JSON object
from stdin — only fact is required):
echo '{"fact": "Staging DB resets nightly at 03:00 UTC.", "type": "reference"}' \
| uv run brain-remember
First run downloads the embedding model (nomic-embed-text-v1.5, a few hundred MB) to
$FASTEMBED_CACHE_PATH(defaultcache/fastembed/, gitignored). Subsequent runs are instant. Everything runs locally — no API key, no external inference calls.
Commands
Every entry point is a console script; run it with uv run <name>.
| Command | What it does |
|---|---|
brain-ingest |
Incrementally index memories, summaries, and transcripts (--full rebuilds). |
brain-server |
MCP stdio server exposing recall, get_episode, remember. |
brain-recall |
Hybrid search / full-text fetch from the shell (no MCP needed). |
brain-remember |
Write a durable memory from the shell (dedup-guarded, auto-commits). |
brain-sync |
status / pull / push against the optional private remote. |
brain-usage-report |
Recall/open telemetry report (feeds the consolidation pass). |
brain-hook |
UserPromptSubmit hook: ambient auto-recall (Claude-Code-specific). |
brain-session-start |
SessionStart hook: injects a project's state note (Claude-Code-specific). |
brain-autoreflect |
Wrapper that triggers a headless consolidation pass when debt accrues. |
Memory file format
A memory is YAML frontmatter plus a markdown body (the same shape Claude Code uses for auto-memory), so it stays readable and hand-editable:
---
name: postgres-nightly-backup
description: How the demo acme-webapp Postgres database is backed up each night.
metadata:
type: reference
date: 2026-01-12
---
The acme-webapp production Postgres runs a nightly logical backup at 02:00 UTC…
name— kebab-case slug (defaults to the filename).description— one line; indexed alongside the body.metadata.type— one ofuser(preferences),feedback,project(per-project living state notes),reference(facts/runbooks).date— optional ISO date; drives recency decay.
See examples/memories/ for one of each kind, and
examples/summaries/ for the session-digest format.
Configuration
All configuration is environment variables with sensible defaults — see
src/brain/config.py for the full surface. Highlights:
| Variable | Default | Purpose |
|---|---|---|
BRAIN_DIR |
the repo root | Root of the markdown store + cache/. |
BRAIN_CLAUDE_PROJECTS |
~/.claude/projects |
Transcript root ingested as episodes. |
FASTEMBED_CACHE_PATH |
cache/fastembed/ |
Where the embedding model is cached. |
BRAIN_SYNC |
on (if remote set) | 0 hard-disables all git sync. |
BRAIN_RRF_K |
60 |
Reciprocal-rank-fusion damping constant. |
BRAIN_KIND_WEIGHT_{MEMORY,SUMMARY,EPISODE} |
1.0 / 0.85 / 0.70 |
Per-kind rank multipliers. |
BRAIN_HALF_LIFE_{MEMORY,SUMMARY,EPISODE} |
180 / 90 / 45 days |
Per-kind recency half-lives. |
BRAIN_RECENCY_FLOOR |
0.35 |
Floor the recency factor decays toward. |
Optional integrations (Claude Code)
These are conveniences for a Claude Code workflow and are entirely optional —
the core (ingest + recall/remember + MCP server) has no dependency on
them.
- Ambient auto-recall — a
UserPromptSubmithook (brain-hook) that runs a fast, read-only FTS pass on every prompt and silently injects the strongest matching memories as context. - SessionStart injection —
brain-session-startinjects a project's rolling state note when a session opens, stamped with a freshness banner. - Skills —
skills/reflect(consolidation pass),skills/catchup(deep resume), andskills/handoff. - launchd backstop —
scripts/install-launchd.shinstalls a nightly reflection job on macOS (rendered from a template; a no-op backstop to the primary debt-triggered path).
Design docs for these live under specs/.
How multi-machine sync works (optional)
The markdown store can sync across machines through any private git remote you control — there is nothing brain-specific about it:
git remote add origin <your-private-repo> # e.g. git@github.com:you/brain.git
git config user.name "Your Name" # any identity you like
git config user.email you@example.com
git push -u origin main
cache/brain.db is rebuildable and never pushed. Sync auto-detects the
remote: with no origin, brain behaves exactly as a local-only store, zero
config. BRAIN_SYNC=0 disables it entirely regardless of remote. memories/
and proposals/ carry a merge=union attribute so concurrent edits from two
machines concatenate losslessly rather than conflict; the next consolidation
pass dedups them. See specs/git-sync.md for the full
design.
Memory commits record provenance as git trailers (Session, Project,
Host) so you can audit which machine and session produced each fact.
Scope & honesty
- Single-user personal project. Extracted from a working personal system and published with fresh git history — not a hardened multi-tenant service.
- Developed on macOS. The core is pure Python and portable; only the launchd nightly job is macOS-specific, and it is optional.
- Local embeddings. Vectors are computed locally via
fastembed — no API key and no
external inference calls. (CI runs FTS-only, since the model download and
native
sqlite-vecwheel may be unavailable on the runner; recall degrades gracefully to lexical-only when the vector table is absent.) - Sync is bring-your-own and off by default. Nothing leaves your machine unless you add a private remote.
License
MIT.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.