BrainLayer

Persistent memory for AI agents. Search, think, recall — across every conversation you've ever had.

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Your AI agent forgets everything between sessions. Every architecture decision, every debugging session, every preference you've expressed — gone. You repeat yourself constantly.

BrainLayer fixes this. It's a local-first memory layer that gives any MCP-compatible AI agent the ability to remember, think, and recall across conversations.

"What approach did I use for auth last month?"     →  brain_search
"Show me everything about this file's history"     →  brain_recall
"What was I working on yesterday?"                 →  brain_recall
"Remember this decision for later"                 →  brain_store

Quick Start

pip install brainlayer
brainlayer init              # Interactive setup wizard
brainlayer index             # Index your Claude Code conversations

Then add to your editor's MCP config:

Claude Code (~/.claude.json):

{
  "mcpServers": {
    "brainlayer": {
      "command": "brainlayer-mcp"
    }
  }
}

Other editors (Cursor, Zed, VS Code)

Cursor (MCP settings):

{
  "mcpServers": {
    "brainlayer": {
      "command": "brainlayer-mcp"
    }
  }
}

Zed (settings.json):

{
  "context_servers": {
    "brainlayer": {
      "command": { "path": "brainlayer-mcp" }
    }
  }
}

VS Code (.vscode/mcp.json):

{
  "servers": {
    "brainlayer": {
      "command": "brainlayer-mcp"
    }
  }
}

That's it. Your agent now has persistent memory across every conversation.

Architecture

graph LR
    A["Claude Code / Cursor / Zed"] -->|MCP| B["BrainLayer MCP Server<br/>3 tools"]
    B --> C["Hybrid Search<br/>semantic + keyword (RRF)"]
    C --> D["SQLite + sqlite-vec<br/>single .db file"]

    E["Claude Code JSONL<br/>conversations"] --> F["Pipeline"]
    F -->|extract → classify → chunk → embed| D
    G["Local LLM<br/>Ollama / MLX"] -->|enrich| D

Loading

Everything runs locally. No cloud accounts, no API keys, no Docker, no database servers.

Component	Implementation
Storage	SQLite + sqlite-vec (single .db file, WAL mode)
Embeddings	bge-large-en-v1.5 via sentence-transformers (1024 dims, runs on CPU/MPS)
Search	Hybrid: vector similarity + FTS5 keyword, merged with Reciprocal Rank Fusion
Enrichment	Local LLM via Ollama or MLX — 10-field metadata per chunk
MCP Server	stdio-based, MCP SDK v1.26+, compatible with any MCP client
Clustering	HDBSCAN + UMAP for brain graph visualization (optional)

MCP Tools (3)

Tool	Description
brain_search	Semantic search — unified search across query, file_path, chunk_id, filters.
brain_store	Persist memories — ideas, decisions, learnings, mistakes. Auto-type/auto-importance.
brain_recall	Proactive retrieval — current context, sessions, session summaries.

Backward Compatibility

Old brainlayer_* names still work as aliases.

• brain_search aliases: brainlayer_search, brainlayer_context, brainlayer_stats, brainlayer_list_projects, brainlayer_file_timeline, brainlayer_operations, brainlayer_regression, brainlayer_plan_links, brainlayer_think
• brain_store alias: brainlayer_store
• brain_recall aliases: brainlayer_recall, brainlayer_current_context, brainlayer_sessions, brainlayer_session_summary

Enrichment

BrainLayer enriches each chunk with 10 structured metadata fields using a local LLM:

Field	Example
summary	"Debugging Telegram bot message drops under load"
tags	"telegram, debugging, performance"
importance	8 (architectural decision) vs 2 (directory listing)
intent	debugging, designing, implementing, configuring, deciding, reviewing
primary_symbols	"TelegramBot, handleMessage, grammy"
resolved_query	"How does the Telegram bot handle rate limiting?"
epistemic_level	hypothesis, substantiated, validated
version_scope	"grammy 1.32, Node 22"
debt_impact	introduction, resolution, none
external_deps	"grammy, Supabase, Railway"

Two local LLM backends:

Backend	Best for	Speed
MLX (Apple Silicon)	M1/M2/M3 Macs	21-87% faster than Ollama
Ollama	Any platform	~1s/chunk (short), ~13s (long)

brainlayer enrich                              # Default backend (auto-detects)
BRAINLAYER_ENRICH_BACKEND=mlx brainlayer enrich --batch-size=100

Why BrainLayer?

	BrainLayer	Mem0	Zep/Graphiti	Letta	LangChain Memory
MCP native	3 tools	1 server	1 server	No	No
Think / Recall	Yes	No	No	No	No
Local-first	SQLite	Cloud-first	Cloud-only	Docker+PG	Framework
Zero infra	pip install	API key	API key	Docker	Multiple deps
Multi-source	6 sources	API only	API only	API only	API only
Enrichment	10 fields	Basic	Temporal	Self-write	None
Session analysis	Yes	No	No	No	No
Open source	Apache 2.0	Apache 2.0	Source-available	Apache 2.0	MIT

BrainLayer is the only memory layer that:

1. Thinks before answering — categorizes past knowledge by intent (decisions, bugs, patterns) instead of raw search results
2. Runs on a single file — no database servers, no Docker, no cloud accounts
3. Works with every MCP client — 3 tools, instant integration, zero SDK

CLI Reference

brainlayer init               # Interactive setup wizard
brainlayer index              # Index new conversations
brainlayer search "query"     # Semantic + keyword search
brainlayer enrich             # Run LLM enrichment on new chunks
brainlayer enrich-sessions    # Session-level analysis (decisions, learnings)
brainlayer stats              # Database statistics
brainlayer brain-export       # Generate brain graph JSON
brainlayer export-obsidian    # Export to Obsidian vault
brainlayer dashboard          # Interactive TUI dashboard

Configuration

All configuration is via environment variables:

Variable	Default	Description
BRAINLAYER_DB	~/.local/share/brainlayer/brainlayer.db	Database file path
BRAINLAYER_ENRICH_BACKEND	auto-detect (MLX on Apple Silicon, else Ollama)	Enrichment LLM backend
BRAINLAYER_ENRICH_MODEL	glm-4.7-flash	Ollama model name
BRAINLAYER_MLX_MODEL	mlx-community/Qwen2.5-Coder-14B-Instruct-4bit	MLX model identifier
BRAINLAYER_OLLAMA_URL	http://127.0.0.1:11434/api/generate	Ollama API endpoint
BRAINLAYER_MLX_URL	http://127.0.0.1:8080/v1/chat/completions	MLX server endpoint
BRAINLAYER_STALL_TIMEOUT	300	Seconds before killing a stuck enrichment chunk
BRAINLAYER_HEARTBEAT_INTERVAL	25	Log progress every N chunks during enrichment
BRAINLAYER_SANITIZE_EXTRA_NAMES	(empty)	Comma-separated names to redact from indexed content
BRAINLAYER_SANITIZE_USE_SPACY	true	Use spaCy NER for PII detection

Optional Extras

pip install "brainlayer[brain]"     # Brain graph visualization (HDBSCAN + UMAP)
pip install "brainlayer[cloud]"     # Cloud backfill (Gemini Batch API)
pip install "brainlayer[youtube]"   # YouTube transcript indexing
pip install "brainlayer[ast]"       # AST-aware code chunking (tree-sitter)
pip install "brainlayer[dev]"       # Development: pytest, ruff

Data Sources

BrainLayer can index conversations from multiple sources:

Source	Format	Indexer
Claude Code	JSONL (~/.claude/projects/)	brainlayer index
Claude Desktop	JSON export	brainlayer index --source desktop
WhatsApp	Exported .txt chat	brainlayer index --source whatsapp
YouTube	Transcripts via yt-dlp	brainlayer index --source youtube
Markdown	Any .md files	brainlayer index --source markdown
Manual	Via MCP tool	brain_store

Testing

pip install -e ".[dev]"
pytest tests/                           # Full suite (268 tests)
pytest tests/ -m "not integration"      # Unit tests only (fast)
ruff check src/                         # Linting

Contributing

Contributions welcome! See CONTRIBUTING.md for dev setup, testing, and PR guidelines.

License

Apache 2.0 — see LICENSE.

Origin

BrainLayer was originally developed as "Zikaron" (Hebrew: memory) inside a personal AI agent ecosystem. It was extracted into a standalone project because every developer deserves persistent AI memory — not just the ones building their own agent systems.