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BytesAgain β€Ί Skills β€Ί Neural Memory Enhanced
πŸ¦€ ClawHub

Neural Memory Enhanced

by @zhuyu28

Associative memory with spreading activation for persistent, intelligent recall. Use PROACTIVELY when: (1) You need to remember facts, decisions, errors, or...

Versionv1.0.0
Downloads1,121
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TERMINAL
clawhub install neural-memory-enhanced

πŸ“– About This Skill

name: neural-memory description: | Associative memory with spreading activation for persistent, intelligent recall. Use PROACTIVELY when: (1) You need to remember facts, decisions, errors, or context across sessions (2) User asks "do you remember..." or references past conversations (3) Starting a new task β€” inject relevant context from memory (4) After making decisions or encountering errors β€” store for future reference (5) User asks "why did X happen?" β€” trace causal chains through memory Zero LLM dependency. Neural graph with Hebbian learning, memory decay, contradiction detection, and temporal reasoning. homepage: https://github.com/nhadaututtheky/neural-memory metadata: {"openclaw":{"emoji":"brain","primaryEnv":"NEURALMEMORY_BRAIN","requires":{"bins":["python3"],"env":["NEURALMEMORY_BRAIN"]},"os":["darwin","linux","win32"],"install":[{"id":"pip","kind":"node","package":"neural-memory","bins":["nmem"],"label":"pip install neural-memory"}]}}

NeuralMemory β€” 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 β€” 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
nmem init

This creates ~/.neuralmemory/ with a default brain and configures MCP automatically.

2. Configure MCP for OpenClaw

Add to your OpenClaw MCP configuration (~/.openclaw/mcp.json or project openclaw.json):

{
  "mcpServers": {
    "neural-memory": {
      "command": "python3",
      "args": ["-m", "neural_memory.mcp"],
      "env": {
        "NEURALMEMORY_BRAIN": "default"
      }
    }
  }
}

3. Verify

nmem stats

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

Tools Reference

Core Memory Tools

| Tool | Purpose | When to Use | |------|---------|-------------| | nmem_remember | Store a memory | After decisions, errors, facts, insights, user preferences | | nmem_recall | Query memories | Before tasks, when user references past context, "do you remember..." | | nmem_context | Get recent memories | At session start, inject fresh context | | nmem_todo | Quick TODO with 30-day expiry | Task tracking |

Intelligence Tools

| Tool | Purpose | When to Use | |------|---------|-------------| | nmem_auto | Auto-extract memories from text | After important conversations β€” captures decisions, errors, TODOs automatically | | nmem_recall (depth=3) | Deep associative recall | Complex questions requiring cross-domain connections | | nmem_habits | Workflow pattern suggestions | When user repeats similar action sequences |

Management Tools

| Tool | Purpose | When to Use | |------|---------|-------------| | nmem_health | Brain health diagnostics | Periodic checkup, before sharing brain | | nmem_stats | Brain statistics | Quick overview of memory counts | | nmem_version | Brain snapshots and rollback | Before risky operations, version checkpoints | | nmem_transplant | Transfer memories between brains | Cross-project knowledge sharing |

Workflow

At Session Start

1. Call nmem_context to inject recent memories into your awareness 2. If user mentions a specific topic, call nmem_recall with that topic

During Conversation

3. When a decision is made: nmem_remember with type="decision" 4. When an error occurs: nmem_remember with type="error" 5. When user states a preference: nmem_remember with type="preference" 6. When asked about past events: nmem_recall with appropriate depth

At Session End

7. Call nmem_auto with action="process" on important conversation segments 8. 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 β€” Pure algorithmic: regex, graph traversal, Hebbian learning
  • Spreading activation β€” Associative recall through neural graph, not keyword/vector search
  • 20 synapse types β€” Temporal (BEFORE/AFTER), causal (CAUSED_BY/LEADS_TO), semantic (IS_A/HAS_PROPERTY), emotional (FELT/EVOKES), conflict (CONTRADICTS)
  • Memory lifecycle β€” Short-term β†’ Working β†’ Episodic β†’ Semantic with Ebbinghaus decay
  • Contradiction detection β€” Auto-detects conflicting memories, deprioritizes outdated ones
  • Hebbian learning β€” "Neurons that fire together wire together" β€” memory improves with use
  • Temporal reasoning β€” Causal chain traversal, event sequences, temporal range queries
  • Brain versioning β€” Snapshot, rollback, diff brain state
  • Brain transplant β€” Transfer filtered knowledge between brains
  • Vietnamese + English β€” Full bilingual support for extraction and sentiment

    • Depth Levels

    | Depth | Name | Speed | Use Case | |-------|------|-------|----------| | 0 | Instant | <10ms | Quick facts, recent context | | 1 | Context | ~50ms | Standard recall (default) | | 2 | Habit | ~200ms | Pattern matching, workflow suggestions | | 3 | Deep | ~500ms | Cross-domain associations, causal chains |

    Notes

  • Memories are stored locally in SQLite at ~/.neuralmemory/brains/.db
  • No data is sent to external services (unless optional embedding provider is configured)
  • Brain isolation: each brain is independent, no cross-contamination
  • nmem_remember returns fiber_id for reference tracking
  • Priority scale: 0 (trivial) to 10 (critical), default 5
  • Memory types: fact, decision, preference, todo, insight, context, instruction, error, workflow, reference

    • πŸ’‘ 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.

    βš™οΈ Configuration

    1. Install NeuralMemory

    pip install neural-memory
nmem init

    This creates ~/.neuralmemory/ with a default brain and configures MCP automatically.

    2. Configure MCP for OpenClaw

    Add to your OpenClaw MCP configuration (~/.openclaw/mcp.json or project openclaw.json):

    {
  "mcpServers": {
    "neural-memory": {
      "command": "python3",
      "args": ["-m", "neural_memory.mcp"],
      "env": {
        "NEURALMEMORY_BRAIN": "default"
      }
    }
  }
}

    3. Verify

    nmem stats

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

    πŸ“‹ Tips & Best Practices

  • Memories are stored locally in SQLite at ~/.neuralmemory/brains/.db
  • No data is sent to external services (unless optional embedding provider is configured)
  • Brain isolation: each brain is independent, no cross-contamination
  • nmem_remember returns fiber_id for reference tracking
  • Priority scale: 0 (trivial) to 10 (critical), default 5
  • Memory types: fact, decision, preference, todo, insight, context, instruction, error, workflow, reference