NeuralMemory — Brain-Inspired Memory for AI Agents
Description: Reflex-based memory system for AI agents. Retrieval through activation, not search. Multi-hop reasoning, causal chains, temporal awareness.

v1.6.0 — 20 MCP tools, 1649 tests

# Memory for
AI Agents

Reflex-based memory system that retrieves through activation, not search. Multi-hop reasoning. Causal chains. Temporal awareness. Like a brain, not a database.

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## Why Not RAG?

RAG treats memory as a search engine. NeuralMemory treats it as a brain.

Aspect | RAG / Vector Search | NeuralMemory
--- | --- | ---
Model | Search Engine | Human Brain
Query | "Find similar text" | "Recall through association"
Structure | Flat chunks + embeddings | Neural graph + typed synapses
Relationships | None (just cosine similarity) | CAUSED_BY, LEADS_TO, IS_A, ...
Temporal | Timestamp filter | Time as first-class neurons
Multi-hop | Multiple queries needed | Natural graph traversal

Example: "Why did Tuesday's outage happen?"

RAG returns:

"JWT caused outage" (missing why JWT was used)

NeuralMemory traces:

outage ← CAUSED_BY ← JWT ← INVOLVES ← Alice

## Features

### Reflex Activation

Trail-based retrieval through fiber pathways with conductivity. Memories that fire together, wire together.

### Co-Activation

Hebbian binding detects neurons activated by multiple sources. Emergent pattern recognition.

### Time-First Anchoring

Time neurons as primary anchors. "What happened last Tuesday?" just works.

### Typed Relationships

20 synapse types: CAUSED_BY, LEADS_TO, IS_A, CONTAINS, CO_OCCURS. Not just "related".

### Brain Import/Export

Export, import, merge brains. Share learned patterns between agents like Git repos.

### DB-to-Brain Training

Teach brains to understand database schemas. Tables become neurons, FKs become synapses.

## Quick Start

Terminal

# Store memories

$ nmem remember "Fixed auth bug in login.py:42"

$ nmem remember "Use PostgreSQL" --type decision

# Recall through association

$ nmem recall "auth bug fix"

# → "Fixed auth bug in login.py:42"

# Inject context into AI

$ nmem context --limit 10 --json

MCP Server — Claude Code / Cursor

// ~/.claude/mcp_servers.json

{

"neural-memory": {

"command": "nmem-mcp"

}

}

// Then just ask naturally:

// "What did we decide about caching?"

// "Recall the auth bug fix"

// "What happened last Tuesday?"

## Benchmark

NeuralMemory vs cosine-similarity vector search on query types that matter for AI agents.

Query Type | Vector Search | NeuralMemory
--- | --- | ---
Direct fact recall | ~90% | ~92%
Multi-hop reasoning (A→B→C) | ~30% | ~85%
Causal chain ("why did X happen?") | ~15% | ~80%
Temporal query ("what happened Tuesday?") | ~10% | ~88%
Associative recall ("related to auth") | ~50% | ~90%

Synthetic benchmark: 15 memories, 8 queries. Accuracy = correct information retrieved in top-3 results.

## How It Works

1

### Encode

Text is parsed into neurons (entities, events, concepts) connected by typed synapses. Time anchors are created automatically.

2

### Consolidate

Like sleep for the brain. Cross-links related memories, strengthens important paths, prunes weak connections.

3

### Recall

Query activates matching neurons. Activation spreads through synapses. The strongest path = the answer. Multi-hop is free.

## Works Everywhere

20 MCP tools. CLI. Python API. VS Code extension.

Claude Code
Cursor
VS Code
Python
CLI
MCP Server
FastAPI

## Give Your Agents a Brain

Install in 10 seconds. First memory in 30 seconds. Multi-hop reasoning in 60 seconds.

GitHub | Documentation | PyPI