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💬 More than memory — it's foresight.

EverMemOS enables AI to not only remember what happened, but understand the meaning behind memories and use them to guide decisions. Achieving 93% reasoning accuracy on the LoCoMo benchmark, EverMemOS provides long-term memory capabilities for conversational AI agents through structured extraction, intelligent retrieval, and progressive profile building.

How it works: EverMemOS extracts structured memories from conversations (Encoding), organizes them into episodes and profiles (Consolidation), and intelligently retrieves relevant context when needed (Retrieval).

📄 Paper • 📚 Vision & Overview • 🏗️ Architecture • 📖 Full Documentation

Latest: v1.2.0 with API enhancements + DB efficiency improvements (Changelog)

• 🎯 93% Accuracy - Best-in-class performance on LoCoMo benchmark
• 🚀 Production Ready - Enterprise-grade with Milvus vector DB, Elasticsearch, MongoDB, and Redis
• 🔧 Easy Integration - Simple REST API, works with any LLM
• 📊 Multi-Modal Memory - Episodes, facts, preferences, relations
• 🔍 Smart Retrieval - BM25, embeddings, or agentic search

EverMemOS outperforms existing memory systems across all major benchmarks

• Python 3.10+ • Docker 20.10+ • uv package manager • 4GB RAM

Verify Prerequisites:

# Verify you have the required versions
python --version  # Should be 3.10+
docker --version  # Should be 20.10+

# 1. Clone and navigate
git clone https://github.com/EverMind-AI/EverMemOS.git
cd EverMemOS

# 2. Start Docker services
docker-compose up -d

# 3. Install uv and dependencies
curl -LsSf https://astral.sh/uv/install.sh | sh
uv sync

# 4. Configure API keys
cp env.template .env
# Edit .env and set:
#   - LLM_API_KEY (for memory extraction)
#   - VECTORIZE_API_KEY (for embedding/rerank)

# 5. Start server
uv run python src/run.py --port 8001

# 6. Verify installation
curl http://localhost:8001/health
# Expected response: {"status": "healthy", ...}

✅ Server running at http://localhost:8001 • Full Setup Guide

Store and retrieve memories with simple Python code:

import requests

API_BASE = "http://localhost:8001/api/v1"

# 1. Store a conversation memory
requests.post(f"{API_BASE}/memories", json={
    "message_id": "msg_001",
    "create_time": "2025-02-01T10:00:00+00:00",
    "sender": "user_001",
    "content": "I love playing soccer on weekends"
})

# 2. Search for relevant memories
response = requests.get(f"{API_BASE}/memories/search", json={
    "query": "What sports does the user like?",
    "user_id": "user_001",
    "memory_types": ["episodic_memory"],
    "retrieve_method": "hybrid"
})

result = response.json().get("result", {})
for memory_group in result.get("memories", []):
    print(f"Memory: {memory_group}")

Try it now: uv run python src/bootstrap.py demo/simple_demo.py (Demo Guide)

📖 More Examples • 📚 API Reference • 🎯 Interactive Demos

• Group Chat Conversations - Combine messages from multiple speakers
• Conversation Metadata Control - Fine-grained control over conversation context
• Memory Retrieval Strategies - Lightweight vs Agentic retrieval modes
• Batch Operations - Process multiple messages efficiently

EverMemOS achieves 93% overall accuracy on the LoCoMo benchmark, outperforming comparable memory systems.

• LoCoMo - Long-context memory benchmark with single/multi-hop reasoning
• LongMemEval - Multi-session conversation evaluation
• PersonaMem - Persona-based memory evaluation

# Install evaluation dependencies
uv sync --group evaluation

# Run smoke test (quick verification)
uv run python -m evaluation.cli --dataset locomo --system evermemos --smoke

# Run full evaluation
uv run python -m evaluation.cli --dataset locomo --system evermemos

# View results
cat evaluation/results/locomo-evermemos/report.txt

📊 Full Evaluation Guide • 📈 Complete Results

We welcome contributions! Here's how you can help:

• 🐛 Report Bugs - Help us improve
• ✨ Request Features - Share your ideas
• 💻 Submit PRs - Read our contribution guide
• 💬 Join Discord - Connect with the community
• 📧 Email Us - General inquiries

Community: GitHub Discussions • Reddit • X/Twitter

📖 Read our Contribution Guidelines for code standards and Git workflow.

Licensed under Apache 2.0 • Citation Info • Acknowledgments