# EverMemOS **Repository Path**: ai-tools_4/EverMemOS ## Basic Information - **Project Name**: EverMemOS - **Description**: No description available - **Primary Language**: Python - **License**: Apache-2.0 - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 1 - **Created**: 2026-03-10 - **Last Updated**: 2026-03-10 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README
![banner-gif][banner-gif] [![][arxiv-badge]][arxiv-link] [![Python][python-badge]][python] [![Docker][docker-badge]][docker] [![FastAPI][fastapi-badge]][fastapi] [![MongoDB][mongodb-badge]][mongodb] [![Elasticsearch][elasticsearch-badge]][elasticsearch] [![Milvus][milvus-badge]][milvus] [![Ask DeepWiki][deepwiki-badge]][deepwiki] [![License][license-badge]][license] [Documentation][documentation] โ€ข [API Reference][api-docs] โ€ข [Demo][demo-section]

> [!IMPORTANT] > > ### Memory Genesis Competition 2026 > > Join our AI Memory Competition! Build innovative applications, plugins, or infrastructure improvements powered by EverMemOS. > > **Tracks:** > - **Agent + Memory** - Build intelligent agents with long-term, evolving memories > - **Platform Plugins** - Integrate EverMemOS with VSCode, Chrome, Slack, Notion, LangChain, and more > - **OS Infrastructure** - Optimize core functionality and performance > > **[Get Started with the Competition Starter Kit](docs/STARTER_KIT.md)** > > Join our [Discord](https://discord.gg/gYep5nQRZJ) to find teammates and brainstorm ideas! >
![divider][divider-light] ![divider][divider-dark]
Table of Contents
- [Welcome to EverMemOS][welcome] - [Introduction][introduction] - [Star and stay tuned with us][star-us] - [Why EverMemOS][why-evermemos] - [Quick Start][quick-start] - [Prerequisites][prerequisites] - [Installation][installation] - [API Usage][api-usage] - [Demo][demo-section] - [Run the Demo][run-demo] - [Full Demo Experience][full-demo-experience] - [Evaluation][evaluation-section] - [Documentation][docs-section] - [GitHub Codespaces][codespaces] - [Questions][questions-section] - [Contributing][contributing]
## Welcome to EverMemOS Welcome to EverMemOS! Join our community to help improve the project and collaborate with talented developers worldwide. | Community | Purpose | | :-------- | :------ | | [![Discord][discord-badge]][discord] | Join our Discord community | | [![WeChat][wechat-badge]][wechat] | Join the WeChat discussion group | | [![X][x-badge]][x] | Follow updates on X | | [![LinkedIn][linkedin-badge]][linkedin] | Connect with us on LinkedIn | | [![Hugging Face Space][hugging-face-badge]][hugging-face] | Join our Hugging Face community to explore our spaces and models | | [![Reddit][reddit-badge]][reddit] | Join the Reddit community |
## ๐ŸŒŸ Star and stay tuned with us ![star us gif](https://github.com/user-attachments/assets/0c512570-945a-483a-9f47-8e067bd34484)
## Introduction > ๐Ÿ’ฌ **More than memory โ€” 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.

EverMemOS Architecture Overview

**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). ๐Ÿ“„ [Paper](https://arxiv.org/abs/2601.02163) โ€ข ๐Ÿ“š [Vision & Overview](docs/OVERVIEW.md) โ€ข ๐Ÿ—๏ธ [Architecture](docs/ARCHITECTURE.md) โ€ข ๐Ÿ“– [Full Documentation](docs/) **Latest**: v1.2.0 with API enhancements + DB efficiency improvements ([Changelog](docs/CHANGELOG.md))
## Why EverMemOS? - ๐ŸŽฏ **93% Accuracy** - Best-in-class performance on LoCoMo benchmark - ๐Ÿš€ **Production Ready** - Enterprise-grade with Milvus vector DB, Elasticsearch, MongoDB, and Redis - ๐Ÿ”ง **Easy Integration** - Simple REST API, works with any LLM - ๐Ÿ“Š **Multi-Modal Memory** - Episodes, facts, preferences, relations - ๐Ÿ” **Smart Retrieval** - BM25, embeddings, or agentic search

EverMemOS Benchmark Results
EverMemOS outperforms existing memory systems across all major benchmarks


## Quick Start ### Prerequisites - Python 3.10+ โ€ข Docker 20.10+ โ€ข uv package manager โ€ข 4GB RAM **Verify Prerequisites:** ```bash # Verify you have the required versions python --version # Should be 3.10+ docker --version # Should be 20.10+ ``` ### Installation ```bash # 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 # 6. Verify installation curl http://localhost:1995/health # Expected response: {"status": "healthy", ...} ``` โœ… Server running at `http://localhost:1995` โ€ข [Full Setup Guide](docs/installation/SETUP.md)
## Basic Usage Store and retrieve memories with simple Python code: ```python import requests API_BASE = "http://localhost:1995/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}") ``` ๐Ÿ“– [More Examples](docs/usage/USAGE_EXAMPLES.md) โ€ข ๐Ÿ“š [API Reference](docs/api_docs/memory_api.md) โ€ข ๐ŸŽฏ [Interactive Demos](docs/usage/DEMOS.md)
## Demo ### Run the Demo ```bash # Terminal 1: Start the API server uv run python src/run.py # Terminal 2: Run the simple demo uv run python src/bootstrap.py demo/simple_demo.py ``` **Try it now**: Follow the [Demo Guide](docs/usage/DEMOS.md) for step-by-step instructions. ### Full Demo Experience ```bash # Extract memories from sample data uv run python src/bootstrap.py demo/extract_memory.py # Start interactive chat with memory uv run python src/bootstrap.py demo/chat_with_memory.py ``` See the [Demo Guide](docs/usage/DEMOS.md) for details.
## Advanced Techniques - **[Group Chat Conversations](docs/advanced/GROUP_CHAT_GUIDE.md)** - Combine messages from multiple speakers - **[Conversation Metadata Control](docs/advanced/METADATA_CONTROL.md)** - Fine-grained control over conversation context - **[Memory Retrieval Strategies](docs/advanced/RETRIEVAL_STRATEGIES.md)** - Lightweight vs Agentic retrieval modes - **[Batch Operations](docs/usage/BATCH_OPERATIONS.md)** - Process multiple messages efficiently
## Documentation | Guide | Description | | ----- | ----------- | | [Quick Start][getting-started] | Installation and configuration | | [Configuration Guide][config-guide] | Environment variables and services | | [API Usage Guide][api-usage-guide] | Endpoints and data formats | | [Development Guide][dev-guide] | Architecture and best practices | | [Memory API][api-docs] | Complete API reference | | [Demo Guide][demo-guide] | Interactive examples | | [Evaluation Guide][evaluation-guide] | Benchmark testing |
## Evaluation & Benchmarking EverMemOS achieves **93% overall accuracy** on the LoCoMo benchmark, outperforming comparable memory systems. ### Benchmark Results

EverMemOS Benchmark Results

### Supported Benchmarks - **[LoCoMo](https://github.com/snap-research/locomo)** - Long-context memory benchmark with single/multi-hop reasoning - **[LongMemEval](https://huggingface.co/datasets/xiaowu0162/longmemeval-cleaned)** - Multi-session conversation evaluation - **[PersonaMem](https://huggingface.co/datasets/bowen-upenn/PersonaMem)** - Persona-based memory evaluation ### Quick Start ```bash # 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 ``` ๐Ÿ“Š [Full Evaluation Guide](evaluation/README.md) โ€ข ๐Ÿ“ˆ [Complete Results](https://huggingface.co/datasets/EverMind-AI/EverMemOS_Eval_Results)
## GitHub Codespaces EverMemOS supports [GitHub Codespaces](https://github.com/features/codespaces) for cloud-based development. This eliminates the need to set up Docker, manage local network configurations, or worry about environment compatibility issues. [![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/EverMind-AI/EverMemOS) ![divider][divider-light] ![divider][divider-dark] ### Requirements | Machine Type | Status | Notes | | ------------ | ------ | ----- | | 2-core (Free tier) | โŒ Not supported | Insufficient resources for infrastructure services | | 4-core | โœ… Minimum | Works but may be slow under load | | 8-core | โœ… Recommended | Good performance with all services | | 16-core+ | โœ… Optimal | Best for heavy development workloads | > **Note:** If your company provides GitHub Codespaces, hardware limitations typically won't be an issue since enterprise plans often include access to larger machine types. ### Getting Started with Codespaces 1. Click the "Open in GitHub Codespaces" button above 2. Select a **4-core or larger** machine when prompted 3. Wait for the container to build and services to start 4. Update API keys in `.env` (LLM_API_KEY, VECTORIZE_API_KEY, etc.) 5. Run `make run` to start the server All infrastructure services (MongoDB, Elasticsearch, Milvus, Redis) start automatically and are pre-configured to work together.
## Questions EverMemOS is available on these AI-powered Q&A platforms. They can help you find answers quickly and accurately in multiple languages, covering everything from basic setup to advanced implementation details. | Service | Link | | ------- | ---- | | DeepWiki | [![Ask DeepWiki][deepwiki-badge]][deepwiki] |
[![][back-to-top]][readme-top]
## Contributing We love open-source energy! Whether youโ€™re squashing bugs, shipping features, sharpening docs, or just tossing in wild ideas, every PR moves EverMemOS forward. Browse [Issues](https://github.com/EverMind-AI/EverMemOS/issues) to find your perfect entry pointโ€”then show us what youโ€™ve got. Letโ€™s build the future of memory together.
> [!TIP] > > **Welcome all kinds of contributions** ๐ŸŽ‰ > > Join us in building EverMemOS better! Every contribution makes a difference, from code to documentation. Share your projects on social media to inspire others! > > Connect with one of the EverMemOS maintainers [@elliotchen200](https://x.com/elliotchen200) on ๐• or [@cyfyifanchen](https://github.com/cyfyifanchen) on GitHub for project updates, discussions, and collaboration opportunities. ![divider][divider-light] ![divider][divider-dark] ### Code Contributors [![EverMemOS][contributors-image]][contributors] ![divider][divider-light] ![divider][divider-dark] ### Contribution Guidelines Read our [Contribution Guidelines](CONTRIBUTING.md) for code standards and Git workflow. ![divider][divider-light] ![divider][divider-dark] ### License & Citation & Acknowledgments [Apache 2.0](LICENSE) โ€ข [Citation](docs/CITATION.md) โ€ข [Acknowledgments](docs/ACKNOWLEDGMENTS.md)
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