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Neural Memory

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 — inject relevant context from memory (4) After making decisions or encountering errors — store for future reference (5) User asks "why did X happen?" — trace causal chains through memory Zero LLM dependency. Neural graph with Hebbian learning, memory decay, contradiction detection, and temporal reasoning. Skill: Neural Memory Owner: nhadaututtheky Summary: 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 — inject relevant context from memory (4) After making decisions or encountering errors — store

View on ClawHub

Rank

62

Safety

84

Downloads

3.0k

Updated

Apr 15, 2026

Version

1.0.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 3K downloads reported by the source. Last updated 4/15/2026.

Avoid when

  • Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor
Clawhubvendor · observed Apr 15, 2026
Adoption signal
3K downloadsadoption · observed Apr 15, 2026
Latest release
1.0.0release · observed Feb 9, 2026
Handshake status
UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install kn70ngajsmd3pjtems7xrs3kv580v8fj:neural-memory
  1. Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
  2. Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-nhadaututtheky-neural-memory/snapshot"

Documentation

CLAWHUB

8,030 characters of source documentation, loaded on request.

Extracted files

2 files captured from the source.

SKILL.md

---
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 — inject relevant context from memory
  (4) After making decisions or encountering errors — store for future reference
  (5) User asks "why did X happen?" — 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 — 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

```bash
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`):

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

### 3. Verify

```bash
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 — captures decisions, errors, TODOs automatically |
| `nmem_rec

_meta.json

{
  "ownerId": "kn70ngajsmd3pjtems7xrs3kv580v8fj",
  "slug": "neural-memory",
  "version": "1.0.0",
  "publishedAt": 1770632218592
}

Machine-readable data

The same record, as JSON, for agents and crawlers.

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      "observedAt": "2026-04-15T00:45:39.800Z",
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      "label": "Handshake status",
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    }
  ]
}

Record generated Oct 9, 2026.

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