Neural Memory

Reflex-based memory system for AI agents — stores experiences as interconnected neurons and recalls them through spreading activation, mimicking how the human brain works.

What It Does

Neural Memory gives AI agents persistent, associative memory across sessions. Instead of keyword search, it uses spreading activation through a neural graph — memories that fire together, wire together.

Key Features

• 39 MCP tools for persistent memory + cognitive reasoning
• Spreading activation recall — not keyword search, memories activate related memories
• Cognitive reasoning — hypotheses, evidence, predictions, schema evolution
• Knowledge base training from PDF, DOCX, PPTX, HTML, JSON, XLSX, CSV
• Multi-device sync with neural-aware conflict resolution
• 4 embedding providers — Sentence Transformers, Gemini, Ollama, OpenAI
• Retrieval pipeline — RRF score fusion, graph expansion, Personalized PageRank
• Session intelligence — topic EMA tracking, LRU eviction, auto-expiry
• React dashboard — 7 pages: health, evolution, graph, timeline, settings
• VS Code extension — status bar, graph explorer, CodeLens, memory tree
• Fernet encryption for sensitive content
• Brain versioning — snapshots, rollback, export/import
• Telegram backup — send brain .db to chat/group/channel

Installation

pip install neural-memory

Or with embeddings:

pip install neural-memory[embeddings]

MCP Configuration

{
  "mcpServers": {
    "neural-memory": {
      "command": "uvx",
      "args": ["--from", "neural-memory", "nmem-mcp"]
    }
  }
}

Usage

Neural Memory works automatically once configured. The agent should:

1. Session start: nmem_recall("current project context") to load past context
2. After each task: nmem_remember("Chose X over Y because Z") to save decisions
3. Session end: nmem_auto(action="process", text="summary") to flush context

Memory Types

Links

• GitHub
• Documentation
• VS Code Extension