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MCP verified Apache-2.0 Self-run

Brainlayer

mcp-etanhey-brainlayer · by EtanHey

Persistent memory MCP for AI agents — SQLite, knowledge graph, 12 tools with ToolAnnotations

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Install

$ agentstack add mcp-etanhey-brainlayer

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution Used
  • Environment & secrets Used
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

Security review passed
0 installs to date
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2mo ago

Declared compatibility

Claude CodeClaude DesktopCursorWindsurf

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.

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About

BrainLayer

> Your AI has amnesia. BrainLayer fixes that.

[](https://pypi.org/project/brainlayer/) [](https://github.com/EtanHey/brainlayer/actions/workflows/ci.yml) [](LICENSE) [](https://modelcontextprotocol.io) [](#testing) [](https://brainlayer.etanheyman.com)

Every architecture decision, every debugging session, every preference you've expressed — gone between sessions. You repeat yourself constantly. Your agent rediscovers bugs it already fixed.

BrainLayer gives any MCP-compatible AI agent persistent memory across conversations. One SQLite file. No cloud. No Docker. Just pip install.

"What approach did I use for auth last month?"     →  brain_search
"Remember this decision for later"                 →  brain_store
"What was I working on yesterday?"                 →  brain_recall
"Ingest this meeting transcript"                   →  brain_digest
"What do we know about this person?"               →  brain_get_person

Quick Start

pip install brainlayer

Add to your MCP config (~/.claude.json for Claude Code):

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

That's it. Your agent now remembers everything.

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"
    }
  }
}

MCP Tools (13)

Every tool includes ToolAnnotations so agents know which calls are safe to run without confirmation.

| Tool | Type | What it does | |------|------|-------------| | brain_search | read | Semantic + keyword hybrid search across all memories. Lifecycle-aware. | | brain_store | write | Persist decisions, learnings, mistakes. Auto-importance scoring. Per-agent scoping via agent_id. | | brain_recall | read | Proactive retrieval — session context, summaries, recent work. | | brain_resume | read | Recover recent PreCompact checkpoints for explicit session restoration. | | brain_tags | read | Browse tags and discover what's in memory without a query. | | brain_digest | write | Ingest raw content — entity extraction, relations, action items. | | brain_entity | read | Look up knowledge graph entities — type, relations, evidence. | | brain_expand | read | Get a chunk with N surrounding chunks for full context. | | brain_update | write | Update importance, tags, or archive existing memories. | | brain_get_person | read | Person lookup — entity details, interactions, preferences. | | brain_enrich | write | Run LLM enrichment — Gemini, Groq, or local MLX/Ollama. | | brain_supersede | destructive | Replace old memory with new. Safety gate on personal data. | | brain_archive | destructive | Soft-delete with timestamp. Recoverable via direct lookup. |

All 14 legacy brainlayer_* tool names still work as aliases.

Architecture

graph LR
    A["Claude Code / Cursor / Zed"] -->|MCP| B["BrainLayer13 tools"]
    B --> C["Hybrid Searchvector + FTS5"]
    C --> D["SQLite + sqlite-vecsingle .db file"]
    B --> KG["Knowledge Graphentities + relations"]
    KG --> D
    E["JSONL conversations"] --> W["Real-time Watcher~1s latency"]
    W --> D
    I["BrainBar UINSStatusItem + NSPopover"] -->|UDS /tmp/brainbar.sock| BB["BrainBarDaemonMCP + brain bus"]
    BB -->|MCP socket protocol| B

Everything runs locally. Cloud enrichment (Gemini/Groq) and Axiom telemetry are optional.

| Layer | Implementation | |-------|---------------| | Storage | SQLite + sqlite-vec, WAL mode, single .db file | | Embeddings | bge-large-en-v1.5 (1024 dims, CPU/MPS) | | Search | Vector similarity + FTS5, merged with Reciprocal Rank Fusion | | Watcher | Real-time JSONL indexing (~1s), 4-layer content filters, offset-persistent | | Enrichment | 10 metadata fields per chunk — Groq, Gemini, MLX, or Ollama | | Knowledge Graph | Entities, relations, co-occurrence extraction, person lookup |

Why BrainLayer?

| | BrainLayer | Mem0 | Zep/Graphiti | Letta | |---|:---:|:---:|:---:|:---:| | MCP tools | 13 | 1 | 1 | 0 | | Local-first | SQLite | Cloud-first | Cloud-only | Docker+PG | | Zero infra | pip install | API key | API key | Docker | | Real-time indexing | ~1s | No | No | No | | Knowledge lifecycle | Supersede/archive | Auto-dedup | No | No | | Open source | Apache 2.0 | Apache 2.0 | Source-available | Apache 2.0 |

BrainBar — macOS Companion

Optional native Swift menu bar companion split into two launchd-managed processes:

flowchart LR
    UI["BrainBarLSUIElement UI"] -->|"watch-brain-bus + commands/tmp/brainbar.sock"| D["BrainBarDaemonheadless MCP server"]
    D -->|"single writer queue + reads"| DB["SQLite WAL~/.local/share/brainlayer/brainlayer.db"]
    D -->|"helper subprocess IPC"| H["Hybrid search helper"]

BrainBarDaemon owns the MCP server, /tmp/brainbar.sock, the single-writer path, the watch-brain-bus stream, and helper subprocess lifecycle. BrainBar owns only the NSStatusItem, transient NSPopover, SwiftUI surfaces, hotkey routing, and a reconnecting socket subscriber. Killing the UI does not stop the daemon socket.

bash brain-bar/build-app.sh    # Build, sign, install LaunchAgent

The build script builds both BrainBar and BrainBarDaemon, embeds both binaries in BrainBar.app, then installs com.brainlayer.brainbar.plist and com.brainlayer.brainbar-daemon.plist with ProcessType=Interactive. It refuses non-canonical checkouts and dirty trees by default (#265) and stamps each bundle with GitCommit, GitDescribe, and BuildTimeUTC in Info.plist (#264) so a stale install is diagnosable in seconds.

Writer Arbitration

Background producers run with BRAINLAYER_ARBITRATED=1 and append writes to ~/.brainlayer/queue/; com.brainlayer.drain.plist drains that queue every 500ms as the single writer. Trigram FTS maintenance is explicit via brainlayer repair-fts and the weekly com.brainlayer.repair-fts.plist, not synchronous startup work. See [docs/arbitration.md](docs/arbitration.md).

Recent Hardening (2026-04-15 → 2026-05-17)

Two-week stability sprint behind the next presentation. Every line below traces to a merged PR.

Search recall & dedup

  • FTS recall hardened across Python, Swift BrainBar, and the watcher pipeline (#263).
  • Lexical defense dictionary exports for fragile-token recovery (#262).
  • MMR post-retrieval dedup on brain_search (#242).
  • Legacy unique content_hash index dropped — was blocking re-enrichment writes (#245).
  • Swift brain_store queue fallback so BrainBar can persist when the daemon is mid-restart (#261).

BrainBar reliability & UX

  • MenuBarExtra(.window) rewrite with live-state sparklines and full-width hero (#248).
  • Dashboard UX overhaul (#246).
  • MCP initialize handshake preserved under backpressure (#247).
  • KG force-sim early-exit + onAppear timer reset — kills CPU pegging when the graph tab is idle (#249).

Phase B preventive infra (2026-05-01) — one canonical artifact per environment

  • /post-merge-deploy-check skill + initial canonical-deploy-registry.json (orchestrator#60) cross-checks GitHub merge metadata, the registry, and the deployed app's Info.plist so a merged PR cannot be declared shipped while the local bundle still points at the wrong build.
  • Canonical app paths corrected in the deploy registry schema (orchestrator#58).
  • Build-stamp + canonical-build guards land together so future BrainBar bundles carry provenance and refuse silent worktree overwrites (#264, #265).

Test gates — pre-push gate is mandatory before any push to main

  • Pre-push regression gate (#257) plus exit-0 fix on the success path (#260).
  • scripts/run_tests.sh orchestrator unifies Python + Swift + isolation test runs (#256).
  • Stale-index regression fixture (#255) and Deepchecks regression harness (#259).

Security

  • All 11 Swift MCPRouter tools exposed via BrainBar now ship ToolAnnotations (cyberMaster H1) (#253).

In flight (2026-05-02 reliability sprint)PR #251

  • Restores the resizable dashboard panel via a floating NSPanel (BrainBarDashboardPanelController) instead of MenuBarExtra(.window).
  • Adds trigram FTS5 (chunks_fts_trigram) with a startup-safety guard: synchronous backfill is skipped when the desynced trigram table exceeds 10K chunks, so BrainBar never blocks the live ~360K-chunk database before /tmp/brainbar.sock opens.
  • KG atlas presentation (importance-based altitude filtering, region backdrops, deterministic seeding) and AgentActivityMonitor for live CLI presence on the dashboard.
  • Pub/sub plane on /tmp/brainbar.sock is explicitly preserved (brain_subscribe, brain_unsubscribe, notifications/claude/channel) — only search/store handlers move to the Python MCP path.

Phase 5 ship wave (2026-05-17) — ingest hygiene + KG regression fix

  • Diagnostic + PreCompact noise rejection at ingest (#289) — recursive_mcp_output_reason now detects BrainLayer-MCP-unavailable diagnostics and PreCompact checkpoint payloads, rejecting them at the watcher / drain / store ingestion heads so tooling failures do not become durable memory. The hybrid reranker demotes (not removes) any chunk tagged with precompact/quarantine signals so explicit include_checkpoints callers still see them. Pre-push gate: 1995 passed, 9 skipped, 75 deselected, 1 xfailed. A dry-run-first scripts/quarantine_noise.py is available for back-filling existing infra noise — live DB mutation requires explicit --apply.
  • Persist digest LLM entities (#290) — fixes a KG persistence regression where brain_digest silently skipped Gemini entity extraction because process_chunk passed use_llm=llm_caller is not None and the MCP/CLI path never sets llm_caller. Non-seed person entities were never materialized into kg_entities / kg_entity_chunks. The 2026-04-06 entity-recall recurrence root-caused to this code path. RED-first regression test (test_digest_content_persists_llm_people_entities_for_lookup) now guards the fix.
  • Enrichment LaunchAgent recoveredcom.brainlayer.enrichment was silently unloaded since 2026-05-15 11:50 IDT (no entity extraction running). Bootstrapped back on 2026-05-17 against the 56K-chunk backfill; throttled by Gemini 503s on flex tier but actively draining (verified via launchctl list | grep enrichment returning a live PID).

June 2026 search & KG hardening (#433#445)

  • Hook failures are now loud (#433) — BrainLayer hook DB failures raise clearly instead of silently swallowing errors.
  • Drain hardening (#435) — drain is now resilient to DB open locks under writer contention.
  • chunkorigin provenance (#436) — enrichment stamps chunk_origin on every processed chunk; a backfill pass covers existing unknowns, making provenance queryable across the full corpus.
  • MMR diversity is now on by default (#439) — brain_search applies Maximal Marginal Relevance post-retrieval dedup automatically; pass mmr=false to opt out.
  • KG entity dedup tooling (#441#443) — new path-detector and APSW-safe dedup suggestions for cleaning duplicate KG entities; slash-command reclassify collisions also resolved (#444).
  • KG boost reconnected to entity FTS (#445) — entity-aware ranking is now wired end-to-end through the FTS path.

Data Sources

| Source | Indexer | |--------|---------| | Claude Code | brainlayer index (JSONL from ~/.claude/projects/) | | Claude Desktop | brainlayer index --source desktop | | Codex CLI | brainlayer ingest-codex | | WhatsApp | brainlayer index --source whatsapp | | YouTube | brainlayer index --source youtube | | Markdown | brainlayer index --source markdown | | Manual | brain_store MCP tool | | Real-time | brainlayer watch LaunchAgent (~1s, 4-layer filters) |

Enrichment

Each chunk gets 10 structured metadata fields from a local or cloud 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, deciding | | primary_symbols | "TelegramBot, handleMessage, grammy" | | epistemic_level | hypothesis, substantiated, validated |

brainlayer enrich                    # Run enrichment on new chunks
BRAINLAYER_ENRICH_BACKEND=groq brainlayer enrich   # Force Groq

CLI Reference

brainlayer setup              # Create ~/.config/brainlayer/brainlayer.env
brainlayer setup --launchd    # Create config and install launchd agents
brainlayer init               # Interactive setup wizard
brainlayer index              # Batch index conversations
brainlayer watch              # Real-time watcher (persistent, ~1s)
brainlayer search "query"     # Semantic + keyword search
brainlayer enrich             # LLM enrichment on new chunks
brainlayer stats              # Database statistics
brainlayer brain-export       # Brain graph JSON for visualization
brainlayer export-obsidian    # Export to Obsidian vault
brainlayer dashboard          # Interactive TUI

Testing

pip install -e ".[dev]"
git config core.hooksPath .githooks     # install repo pre-push hook once per clone
pytest tests/                           # 1,848 Python tests
pytest tests/ -m "not integration"      # Unit tests only (fast)
ruff check src/ && ruff format src/     # Lint + format
# BrainBar: 54 Swift tests via Xcode

Configuration (environment variables)

| Variable | Default | Description | |----------|---------|-------------| | BRAINLAYER_DB | ~/.local/share/brainlayer/brainlayer.db | Database file path | | BRAINLAYER_ENRICH_BACKEND | auto-detect | Enrichment backend (groq, gemini, mlx, ollama) | | GROQ_API_KEY | (unset) | Groq API

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.