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BrainBox

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from thebasedcapital

Hebbian memory for AI agents β€” learns file access patterns, builds neural pathways, predicts next tools/files, saves tokens

πŸ”₯πŸ”₯πŸ”₯βœ“ VerifiedFreeQuick setup

BrainBox

<p align="center"> <img src="assets/brainbox-hero.png" alt="BrainBox β€” AI with muscle memory" width="800" /> </p> <p align="center"> <a href="https://www.npmjs.com/package/brainbox-hebbian"><img src="https://img.shields.io/npm/v/brainbox-hebbian.svg" alt="npm version" /></a> <a href="https://github.com/thebasedcapital/brainbox/stargazers"><img src="https://img.shields.io/github/stars/thebasedcapital/brainbox?style=social" alt="GitHub stars" /></a> <a href="https://github.com/thebasedcapital/brainbox/blob/main/LICENSE"><img src="https://img.shields.io/badge/license-MIT-blue.svg" alt="License" /></a> </p>

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 β€” then recalls them instantly.

Not a vector database. Not RAG. Procedural memory.

If BrainBox saved you tokens, give it a star β€” 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 β€” instant recall, zero search cost

How It Works

BrainBox implements neuroscience-inspired learning:

  • Neurons β€” files, tools, and errors you interact with
  • Synapses β€” connections formed when things are accessed together ("neurons that fire together wire together")
  • Myelination β€” frequently-used paths get faster (like muscle memory)
  • Spreading activation β€” recalling one file activates related files
  • Decay β€” unused connections weaken naturally, keeping the network clean
<details> <summary><strong>Hebbian Learning in Action</strong> (click to play)</summary>

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

</details> <details> <summary><strong>Spreading Activation</strong> β€” recalling one file activates related files through synaptic connections</summary>

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

</details> <details> <summary><strong>Superhighway Formation</strong> β€” frequently-used pathways become instant-recall superhighways</summary>

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

</details> <details> <summary><strong>Error-Fix Immune System</strong> β€” remembers which files fixed which errors</summary>

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

</details>

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.

AspectClaude CodeOpenClaw
Tool namesPascalCase (Read)Lowercase (read)
Context injectionUserPromptSubmit hookbefore_agent_start lifecycle
Learning triggerPostToolUse hookafter_tool_call lifecycle
Embeddingsall-MiniLM-L6-v2Keyword-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 β€” 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

ComponentMechanism
Synapse formationSequential window (25 items), positional decay
StrengtheningSNAP sigmoid plasticity (midpoint 0.5, steepness 8)
MyelinationBCM sliding threshold + diminishing returns, 0.95 ceiling
ConfidenceMultiplicative: contextScore * (1 + myelin + recency + path)
Spreading2-hop BFS, fan-out cap 10, fan effect 1/sqrt(degree)
DecayActivation -15%, synapses -2%, myelination -0.5% per cycle
Error learning2x boosted learning rate for error neurons
Anti-recallCompound decay: 1 - (1 - 0.1)^streak, floor at 0.1

Full details in WHITEPAPER.md.

Tests

npm test  # 59 tests, ~2s

License

MIT