neuromem-core 0.2.0

SOTA-level agent memory at zero infrastructure cost.

One SQLite file. Zero cloud. Ten minutes to set up.

🏆 91.5% on LoCoMo · 📦 One SQLite File · ☁️ Zero Cloud · 💰 Zero Infrastructure Cost

Benchmark · Highlights · Base vs Pro · Install · Claude · API

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🔬 Benchmark

Tested on LoCoMo, the standard benchmark for conversational memory. 1,540 questions across 10 conversations. All 8 systems share the same answer model, judge, scoring, top-k, and byte-identical answer prompt — only retrieval differs.

Neuromem achieves state-of-the-art accuracy for fully-local memory systems at zero ongoing infrastructure cost. Base runs entirely offline with no API keys. Pro adds one small LLM call per query for HyDE query expansion.

All scores use the same evaluation pipeline: GPT-4.1-mini answer generation, GPT-4o-mini judge (3x majority vote), temperature=0. Zero errors across 12,320 total answers. Full methodology, per-question results, cost analysis, and reproduction scripts in benchmarks/.

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⚡ Research Highlights

• 30+ percentage points more accurate than Mem0 on LoCoMo (91.5% vs 61.4%)
• 2x more cost-efficient per correct answer than Mem0
• Runs offline on any device with Python 3.10+ and 512MB RAM
• One SQLite file, zero API keys. The entire 6-layer system runs offline.
• Within 3.0pp of EverMemOS, the only higher-scoring system — and EverMemOS requires cloud retrieval.

Neuromem Pro nearly matches EverMemOS across all 4 question categories. Mem0 collapses on multi-hop reasoning (37.7% vs 90.7%).

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🏗️ Base vs Pro

Same features, same 6-layer pipeline. Pro upgrades the embedding model and the cross-encoder reranker for higher retrieval accuracy.

	Base	Pro
LoCoMo	88.2%	91.5%
Runs on	Any machine (CPU only)	4GB+ RAM (CPU or GPU)
First install	~30MB	~1.5GB one-time download
Speed	Ultra-fast	Fast

Base works everywhere. Pro remembers better.

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🚀 Quickstart

Base

pip install neuromem-core

Pro

pip install neuromem-core[gpu]

Usage

from neuromem import Memory

m = Memory()
m.add("Prefers dark mode and TypeScript", user_id="alex")
m.add("Allergic to peanuts", user_id="alex")

results = m.search("What are Alex's preferences?", user_id="alex")
print(results[0]["content"])
# → "Prefers dark mode and TypeScript"

The database is created automatically at ~/.neuromem/memories.db.

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🤖 Claude Integration

Claude forgets you between sessions. Neuromem fixes that.

1. Install with MCP support

Base:

pip install neuromem-core[mcp]

Pro:

pip install neuromem-core[gpu,mcp]

2. Add to your Claude config

Claude Code: add to ~/.claude.json under "mcpServers":

{
  "mcpServers": {
    "neuromem": {
      "command": "python",
      "args": ["-m", "neuromem.mcp_server"],
      "env": {}
    }
  }
}

Claude Desktop: add to claude_desktop_config.json (Settings > Developer > Edit Config), same format.

If you installed in a virtualenv, use the full path to that Python (e.g. "/path/to/venv/bin/python") instead of "python".

Neuromem starts in Base mode by default. On your first session, Claude will ask if you'd like to upgrade to Pro. You can switch at any time. Your existing memories are automatically re-embedded.

3. Make it automatic

Copy CLAUDE.md.example to your project root or home directory as CLAUDE.md. This tells Claude to automatically store your preferences and recall them without being asked.

cp CLAUDE.md.example ~/CLAUDE.md

4. Test it

Start a new Claude session:

• Say "I always prefer dark mode". Claude stores it automatically.
• Open another session and ask "What are my preferences?". It remembers.

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📖 API

Method	What it does
m.add(content, user_id=None)	Store a memory
m.search(query, user_id=None, limit=10)	Search memories
m.search_deep(query, user_id=None, limit=10)	Agentic multi-round search (higher latency + LLM cost; best for ambiguous queries)
m.get(memory_id)	Get one memory
m.get_all(user_id=None, limit=100)	List all memories
m.update(memory_id, content)	Update a memory
m.delete(memory_id)	Delete a memory
m.delete_all(user_id=None)	Delete all

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📊 Full Benchmark Details

Every benchmark script is self-contained and runs on Modal.

• Leaderboard & Reproduction: run any system yourself
• Full Technical Report: per-category breakdowns, latency, cost, hardware
• Evaluation Config: exact models, prompts, parameters

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📝 Citation

@software{neuromem2026,
  title = {Neuromem: State-of-the-Art Local-First Agent Memory},
  author = {@Building\_Josh},
  organization = {Sauron},
  year = {2026},
  url = {https://github.com/buildingjoshbetter/neuromem},
  version = {0.2.0}
}

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⚖️ License

Licensed under Apache 2.0. Free for personal and commercial use.