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_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** — 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

| 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/<brain>.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