Skip to content

Reddit r/LocalLLaMA Post

Title: Open-source graph-based memory for AI coding agents — spreading activation instead of RAG (no API keys needed)

Body:

I've been working on an alternative approach to giving AI agents persistent memory. Instead of the usual RAG pipeline (embed → vector search → return chunks), I built a system that stores memories as a neural graph and retrieves them through spreading activation.

The problem with RAG for agent memory

RAG treats memory as a search problem: "find text similar to this query." It works, but it loses structure. When you ask "why did the outage happen?", RAG returns "JWT caused the outage" — but not why you chose JWT, who suggested it, or what it replaced.

Spreading activation approach

Neural Memory stores everything as typed neurons connected by typed synapses (CAUSED_BY, LEADS_TO, SUGGESTED_BY, RESOLVED_BY, etc.). Recall works by:

  1. Activate seed neurons matching your query
  2. Activation spreads through synapses (weighted, with decay)
  3. Most-activated neurons form the response context

This gives you multi-hop reasoning for free. "Why did the outage happen?" traces: outage ← CAUSED_BY ← JWT ← SUGGESTED_BY ← Alice ← DECIDED_AT ← Tuesday meeting.

No embedding API calls needed. Core recall is pure graph traversal with O(n) complexity on the local subgraph. Embeddings are optional (supports Ollama, sentence-transformers, Gemini free tier, OpenAI).

Technical details

  • Storage: SQLite via aiosqlite (async), FTS5 for text search
  • Graph: 14 neuron types, 24 synapse types, fiber bundles for episodic grouping
  • Retrieval: Spreading activation with configurable decay, threshold, and max hops
  • Consolidation: Memory lifecycle — decay, reinforcement, pruning of orphan nodes
  • Extraction: Entity/keyword/temporal extraction, Vietnamese NLP support
  • MCP server: 55 tools (incl. cognitive reasoning), stdio + HTTP transport, works with Claude Code, Cursor, etc.
  • Tests: 3,976+, 67% coverage, CI with mypy + ruff + pytest

What makes it interesting for local LLM users

  • Zero API cost: core recall doesn't need embeddings or LLM calls
  • Ollama integration: if you want embeddings, use your local Ollama instance
  • Works with any MCP client: not locked to Claude — any agent that speaks MCP
  • Light: single SQLite file, ~23MB for 1000+ memories with full graph

Install

pip install neural-memory

# With local embeddings via Ollama
pip install neural-memory[embeddings]

Config for Ollama embeddings: 

# ~/.neuralmemory/config.toml
[embedding]
enabled = true
provider = "ollama"
model = "nomic-embed-text"

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

The codebase is MIT licensed. Contributions welcome — especially around alternative graph backends (Neo4j, FalkorDB adapters exist but are early).