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Reddit r/ClaudeAI Post

Title: I built a persistent memory system for Claude Code that works like a brain — Neural Memory (open source, 56 MCP tools)

Body:

Claude Code forgets everything between sessions. I got tired of re-explaining project context every time, so I built Neural Memory — an MCP server that gives Claude a persistent, associative memory.

How it's different from other memory tools

Most memory MCP servers use RAG (embed text → vector search → return chunks). Neural Memory doesn't. It stores memories as a neural graph and retrieves them through spreading activation — the same mechanism the human brain uses for recall.

When you remember "Alice", it doesn't just find text containing "Alice". It activates the Alice neuron, which spreads to connected concepts: the meeting where you discussed rate limiting → the outage it caused → the JWT decision that led to it. You get the full causal chain, not just keyword matches.

No LLM/embedding API required for core recall. It's pure algorithmic graph traversal. Embeddings are optional for cross-language search.

What it does

  • 56 MCP tools: nmem_remember, nmem_recall, nmem_context, nmem_explain, nmem_habits, and more
  • Spreading activation retrieval: memories surface through association, not search
  • Connection explainer: ask "how are X and Y connected?" and get the exact path through the knowledge graph
  • Habits tracking: detects recurring patterns in your workflow
  • Multi-brain: separate memory spaces for different projects
  • Proactive auto-save: captures memories during session + saves summary on exit
  • Local-first: SQLite, zero external deps, fully offline

Quick start

pip install neural-memory

Add to Claude Code: 

/plugin marketplace add nhadaututtheky/neural-memory

Or configure MCP manually: 

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

Numbers

  • 3,500+ tests, 68% coverage
  • v2.29.0, production-stable
  • 14 memory types, 24 synapse types
  • Python 3.11+, async, MIT license
  • Optional embeddings: Ollama, Gemini (free), OpenAI, sentence-transformers

GitHub: https://github.com/nhadaututtheky/neural-memory Docs: https://nhadaututtheky.github.io/neural-memory/

Happy to answer questions about the architecture or how spreading activation works in practice.