BrainBox

Hebbian memory for AI coding agents. Learns which files you access together, which errors lead to which fixes, and which tool chains you use most — then recalls them instantly.

Not a vector database. Not RAG. Procedural memory.

If BrainBox saved you tokens, give it a star — it helps others find it. Built by @thebasedcapital

Session 1:  agent greps for auth.ts, reads it, edits it (2000 tokens)
Session 5:  agent recalls auth.ts directly, skips search (500 tokens saved)
Session 20: auth.ts is a superhighway — instant recall, zero search cost

Install

npm install brainbox-hebbian

That's it. The postinstall script automatically:

1. Adds PostToolUse hook to ~/.claude/settings.json (learns from every file read/edit/search)
2. Adds UserPromptSubmit hook (injects neural recall into prompts automatically)
3. Registers the MCP server via claude mcp add (6 tools for manual recall/recording)
4. Creates ~/.brainbox/ database directory

BrainBox learns passively from your next Claude Code session. No configuration needed.

What does NOT happen automatically

The macOS daemon (system-wide FSEvents file watcher) is completely separate and opt-in:

# Only if you want BrainBox to learn from VS Code, Xcode, vim, shell, etc.
brainbox daemon install   # installs LaunchAgent, starts watching
brainbox daemon status    # check if running
brainbox daemon uninstall # remove completely

The daemon watches file changes across all your editors — not just Claude Code. It requires explicit opt-in because it registers a LaunchAgent and monitors your configured project directories.

Uninstall

brainbox uninstall  # removes hooks + MCP server, preserves database

Seed from git history (recommended)

Kill cold start by bootstrapping from your existing git history:

brainbox bootstrap --repo /path/to/project --imports

This seeds the neural network from git commit co-changes and import graphs so BrainBox starts with knowledge instead of from zero.

How It Works

BrainBox implements neuroscience-inspired learning:

• Neurons — files, tools, and errors you interact with
• Synapses — connections formed when things are accessed together ("neurons that fire together wire together")
• Myelination — frequently-used paths get faster (like muscle memory)
• Spreading activation — recalling one file activates related files
• Decay — unused connections weaken naturally, keeping the network clean

https://github.com/thebasedcapital/brainbox/raw/main/assets/brainbox-animation.mp4

https://github.com/thebasedcapital/brainbox/raw/main/assets/brainbox-spreading.mp4

https://github.com/thebasedcapital/brainbox/raw/main/assets/brainbox-superhighway.mp4

https://github.com/thebasedcapital/brainbox/raw/main/assets/brainbox-immune.mp4

Other Integrations

MCP Server (any agent)

If you're not using Claude Code, you can run the MCP server standalone:

# 6 tools: record, recall, error, predict_next, stats, decay
npx tsx node_modules/brainbox-hebbian/src/mcp.ts

Kilo / OpenCode (native plugin)

Add to ~/.config/kilo/config.json:

{
  "plugin": ["node_modules/brainbox-hebbian/src/kilo-plugin.ts"]
}

OpenClaw (NeuroVault)

BrainBox can be deployed as an OpenClaw memory slot plugin. See NeuroVault for the reference implementation.

| Aspect | Claude Code | OpenClaw | |---|---|---| | Tool names | PascalCase (Read) | Lowercase (read) | | Context injection | UserPromptSubmit hook | before_agent_start lifecycle | | Learning trigger | PostToolUse hook | after_tool_call lifecycle | | Embeddings | all-MiniLM-L6-v2 | Keyword-only (lower confidence gate) |

CLI

brainbox recall "authentication login"
brainbox record src/auth.ts --context "authentication"
brainbox stats
brainbox error "TypeError: cannot read 'token'"
brainbox predict Read
brainbox embed          # add vector embeddings for semantic recall
brainbox hubs           # most connected neurons
brainbox stale          # decaying superhighways
brainbox projects       # list project tags
brainbox sessions       # recent sessions with intents
brainbox streaks        # anti-recall ignore streaks
brainbox graph          # ASCII neural network
brainbox highways       # show superhighways
brainbox decay          # weaken unused connections

Key Features

Hebbian Learning

Files accessed together form synapses. Access auth.ts then session.ts 10 times and BrainBox learns they're related — recalling one activates the other.

Error-Fix Immune System

When you fix a bug, BrainBox remembers which files fixed which errors. Next time a similar error appears, it suggests the fix files immediately.

Tool Sequence Prediction

After 20 Grep-Read-Edit chains, BrainBox predicts you'll Read after Grep and pre-loads likely files.

SNAP Plasticity

Strong synapses resist further strengthening (like real neural synapses). Prevents any single connection from dominating the network.

Anti-Recall Escalation

Files recalled but never opened get progressively stronger decay. Consecutive ignores escalate: 1st = 10%, 2nd = 19%, 3rd = 27%. Opening the file resets the streak.

Hub Detection & Staleness Alerts

Identify the most-connected neurons in your network and detect decaying superhighways before they fade.

Project Tagging

Auto-tag file neurons by project. Recall scoped to current project reduces cross-project noise.

Architecture

src/
  hebbian.ts     # Core engine: record, recall, decay, SNAP, BCM, spreading activation
  db.ts          # SQLite schema: neurons, synapses, access_log, sessions
  embeddings.ts  # Optional vector embeddings (all-MiniLM-L6-v2, 384 dims)
  installer.ts   # Auto-installer: adds hooks + MCP to ~/.claude/settings.json
  mcp.ts         # MCP server (6 tools)
  hook.ts        # Claude Code PostToolUse hook
  prompt-hook.ts # Claude Code UserPromptSubmit hook
  kilo-plugin.ts # Kilo/OpenCode native plugin
  bootstrap.ts   # Git/vault/import seeder
  daemon.ts      # FSEvents file watcher (macOS, opt-in)
  cli.ts         # CLI interface
  test.ts        # 59 tests, all passing

Algorithm Details

| Component | Mechanism | |-----------|-----------| | Synapse formation | Sequential window (25 items), positional decay | | Strengthening | SNAP sigmoid plasticity (midpoint 0.5, steepness 8) | | Myelination | BCM sliding threshold + diminishing returns, 0.95 ceiling | | Confidence | Multiplicative: contextScore * (1 + myelin + recency + path) | | Spreading | 2-hop BFS, fan-out cap 10, fan effect 1/sqrt(degree) | | Decay | Activation -15%, synapses -2%, myelination -0.5% per cycle | | Error learning | 2x boosted learning rate for error neurons | | Anti-recall | Compound decay: 1 - (1 - 0.1)^streak, floor at 0.1 |

Full details in WHITEPAPER.md.

Tests

npm test  # 59 tests, ~2s

Requirements

• Node.js 18+
• macOS or Linux (FSEvents daemon is macOS-only, everything else is cross-platform)

License

MIT