Neural Memory

NeuralMemory — Associative Memory for AI Agents

A biologically-inspired memory system that uses spreading activation instead of keyword/vector search. Memories form a neural graph where neurons connect via 20 typed synapses. Frequently co-accessed memories strengthen their connections (Hebbian learning). Stale memories decay naturally. Contradictions are auto-detected.

Why not just vector search? Vector search finds documents similar to your query. NeuralMemory finds conceptually related memories through graph traversal — even when there's no keyword or embedding overlap. "What decision did we make about auth?" activates time + entity + concept neurons simultaneously and finds the intersection.

Setup

1. Install NeuralMemory

pip install neural-memory

The brain and config at ~/.neuralmemory/ are auto-created on first use.

2. Install the OpenClaw Plugin (Recommended)

The plugin occupies the exclusive memory slot — auto-injects context before each agent run and auto-captures memories after.

# Install from npm
npm install -g neuralmemory

Add to ~/.openclaw/openclaw.json:

{
  "plugins": {
    "load": {
      "paths": [""]
    },
    "entries": {
      "neuralmemory": {
        "enabled": true,
        "config": {
          "pythonPath": "python",
          "brain": "default",
          "autoContext": true,
          "autoCapture": true
        }
      }
    },
    "slots": {
      "memory": "neuralmemory"
    }
  }
}

Plugin features:

• 6 tools registered automatically (nmem_remember, nmem_recall, nmem_context, nmem_todo, nmem_stats, nmem_health)

• before_agent_start hook: injects tool instructions + relevant memories as context (persists across /new)

• agent_end hook: auto-extracts facts, decisions, and TODOs from the conversation

• Configurable: contextDepth (0-3), maxContextTokens (100-10000)

After installing, build the plugin:

cd
npm run build

This compiles TypeScript to JavaScript in dist/. The plugin entry point is dist/index.js.

#### Windows Installation

On Windows, use forward slashes or escaped backslashes in openclaw.json paths:

{
  "plugins": {
    "load": {
      "paths": ["C:/Users/ /AppData/Roaming/npm/node_modules/neuralmemory"]
    }
  }
}

To find the installed path:

npm list -g neuralmemory --parseable

If openclaw plugins list doesn't show the plugin:

• Verify the path in openclaw.json points to the package root (where package.json is)

• Ensure npm run build was run (the dist/ folder must exist with compiled .js files)

• Use python instead of python3 in the plugin config (Windows default)

Alternative: MCP Configuration (Manual)

If you prefer MCP over the plugin, add to ~/.openclaw/mcp.json:

{
  "mcpServers": {
    "neural-memory": {
      "command": "python",
      "args": ["-m", "neural_memory.mcp"],
      "env": {
        "NEURALMEMORY_BRAIN": "default"
      }
    }
  }
}

On Windows, use "python" (not "python3"). This gives you all 63 MCP tools but without the auto-context/auto-capture hooks.

3. Verify

nmem stats

You should see brain statistics (neurons, synapses, fibers).

Troubleshooting

Tools Reference

Core Memory Tools

Intelligence Tools

Management Tools

Workflow

At Session Start

• Call nmem_context to inject recent memories into your awareness

• If user mentions a specific topic, call nmem_recall with that topic

During Conversation

• When a decision is made: nmem_remember with type="decision"

• When an error occurs: nmem_remember with type="error"

• When user states a preference: nmem_remember with type="preference"

• When asked about past events: nmem_recall with appropriate depth

At Session End

• Call nmem_auto with action="process" on important conversation segments

• This auto-extracts facts, decisions, errors, and TODOs

Examples

Remember a decision

nmem_remember(
  content="Use PostgreSQL for production, SQLite for development",
  type="decision",
  tags=["database", "infrastructure"],
  priority=8
)

Recall with spreading activation

nmem_recall(
  query="database configuration for production",
  depth=1,
  max_tokens=500
)

Returns memories found via graph traversal, not keyword matching. Related memories (e.g., "deploy uses Docker with pg_dump backups") surface even without shared keywords.

Trace causal chains

nmem_recall(
  query="why did the deployment fail last week?",
  depth=2
)

Follows CAUSED_BY and LEADS_TO synapses to trace cause-and-effect chains.

Auto-capture from conversation

nmem_auto(
  action="process",
  text="We decided to switch from REST to GraphQL because the frontend needs flexible queries. The migration will take 2 sprints. TODO: update API docs."
)

Automatically extracts: 1 decision, 1 fact, 1 TODO.

Key Features

• Zero LLM dependency — Pure algorithmic: regex, graph traversal, Hebbian learning

• Spreading activation — Associative recall through neural graph, not keyword/vector search

• 20 synapse types — Temporal (BEFORE/AFTER), causal (CAUSED_BY/LEADS_TO), semantic (IS_A/HAS_PROPERTY), emotional (FELT/EVOKES), conflict (CONTRADICTS)

• Memory lifecycle — Short-term → Working → Episodic → Semantic with Ebbinghaus decay

• Contradiction detection — Auto-detects conflicting memories, deprioritizes outdated ones

• Hebbian learning — "Neurons that fire together wire together" — memory improves with use

• Temporal reasoning — Causal chain traversal, event sequences, temporal range queries

• Brain versioning — Snapshot, rollback, diff brain state

• Brain transplant — Transfer filtered knowledge between brains

• Vietnamese + English — Full bilingual support for extraction and sentiment

Depth Levels

Related skills

[More in Agents](/skills?category=agents)

Downloads

Security audit [Pass](/nhadaututtheky/skills/neural-memory/security-audit)

Last updated 1d ago

Current version v4.59.0

License MIT-0

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