elo-memory 0.1.8

Bio-inspired episodic memory system for AI agents

Elo Memory

Bio-inspired episodic memory system implementing EM-LLM (ICLR 2025).

The missing memory layer for AI agents — automatic event detection, surprise-based encoding, and human-like memory consolidation.

Works with: OpenClaw | Claude Code | OpenCode | Codex | Claude | Any MCP-compatible agent

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Why Elo Memory?

AI agents forget everything between conversations. Elo Memory fixes that.

• Fast — Retrieves relevant memories in ~5ms. Agent queries by similarity, gets top 5 matches. Never reads all memories.
• Smart storage — Bayesian surprise engine decides in <1ms what's worth remembering. Repetitive content is skipped automatically.
• Human-like recall — Two-stage retrieval finds by similarity first, then expands by time context. Like how you remember "that whole day" not just one fact.
• Self-maintaining — Background consolidation extracts patterns. Old irrelevant memories decay naturally. No manual cleanup.
• Works everywhere — Python library, MCP server, or REST API. Drop into any agent framework.

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How It Works

User message → Query memory (5ms) → 5 relevant episodes → Added to prompt → Better response
                                              ↓
                                    Surprise check (1ms) → Novel? Store it. Boring? Skip it.
                                              ↓
                                    Consolidation (background) → Extract patterns, forget noise

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Quick Start

pip install elo-memory

from elo_memory import EpisodicMemoryStore, BayesianSurpriseEngine

# Initialize memory
memory = EpisodicMemoryStore(embedding_dim=768)
surprise = BayesianSurpriseEngine(input_dim=768)

# Store an observation
embedding = encoder.encode("User loves Italian food")
surprise_info = surprise.compute_surprise(embedding)

if surprise_info['is_novel']:
    memory.store_episode(
        content={"text": "User loves Italian food"},
        embedding=embedding,
        surprise=surprise_info['surprise']
    )

# Retrieve relevant memories
results = memory.retrieve(query_embedding, k=5)

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vs Alternatives

	Elo Memory	Mem0/Zep	Plain RAG
Stores	Experiences with surprise	Everything	Documents
Retrieval	Similarity + temporal	Similarity only	Similarity only
Filtering	Automatic (surprise)	Manual	None
Forgetting	Natural decay	Manual cleanup	None
Speed	~5ms query	~50ms	~100ms
Cost	Free	$70+/month	API costs

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Components (8/8 Complete)

Component	Description	Status
Bayesian Surprise Detection	KL divergence-based novelty detection	✅
Event Segmentation	HMM + prediction error boundaries	✅
Episodic Storage	ChromaDB with temporal-spatial indexing	✅
Two-Stage Retrieval	Similarity + temporal expansion	✅
Memory Consolidation	Sleep-like replay + schema extraction	✅
Forgetting & Decay	Power-law activation decay	✅
Interference Resolution	Pattern separation/completion	✅
Online Learning	Experience replay + adaptive thresholds	✅

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Documentation

• Usage Guide — Real-world integration patterns
• MCP Setup — Claude Code integration
• Performance Comparison — Benchmarks vs competitors
• Competitive Analysis — Honest assessment
• Real-World Project — Build an AI assistant
• Strategic Plan — Roadmap & vision

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Installation

From PyPI

pip install elo-memory

From Source

git clone https://github.com/server-elo/elo-memory.git
cd elo-memory
pip install -e ".[dev]"

With API Server

pip install "elo-memory[api]"
elo-memory server --port 8000

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Running Tests

pytest tests/ -v --cov=elo_memory

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Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Quick start:

git clone https://github.com/server-elo/elo-memory.git
cd elo-memory
pip install -e ".[dev]"
pytest

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License

MIT License — see LICENSE for details.

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Acknowledgments

• EM-LLM (ICLR 2025) — Research foundation
• Itti & Baldi (2009) — Bayesian Surprise
• Squire & Alvarez (1995) — Systems Consolidation
• Kirkpatrick et al. (2017) — Catastrophic Forgetting

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Links

• GitHub: https://github.com/server-elo/elo-memory
• PyPI: https://pypi.org/project/elo-memory/
• Documentation: https://github.com/server-elo/elo-memory#readme
• Issues: https://github.com/server-elo/elo-memory/issues

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Status: Production ready ✅

Made with ❤️ by the Elo Memory community.