111 lines
3.1 KiB
Markdown
111 lines
3.1 KiB
Markdown
<p align="center">
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<img src="docs/images/mem0-bg.png" width="500px" alt="Mem0 Logo">
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</p>
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<p align="center">
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<a href="https://mem0.ai/slack">
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<img src="https://img.shields.io/badge/slack-mem0-brightgreen.svg?logo=slack" alt="Mem0 Slack">
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</a>
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<a href="https://mem0.ai/discord">
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<img src="https://dcbadge.vercel.app/api/server/6PzXDgEjG5?style=flat" alt="Mem0 Discord">
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</a>
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<a href="https://x.com/mem0ai">
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<img src="https://img.shields.io/twitter/follow/mem0ai" alt="Mem0 Twitter">
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</a>
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</p>
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# Mem0: The Memory Layer for Personalized AI
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Mem0 provides a smart, self-improving memory layer for Large Language Models, enabling personalized AI experiences across applications.
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> Note: The Mem0 repository now also includes the Embedchain project. We continue to maintain and support Embedchain ❤️. You can find the Embedchain codebase in the [embedchain](https://github.com/mem0ai/mem0/tree/main/embedchain) directory.
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## 🚀 Quick Start
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### Installation
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```bash
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pip install mem0ai
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```
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### Basic Usage
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```python
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import os
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from mem0 import Memory
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os.environ["OPENAI_API_KEY"] = "xxx"
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# Initialize Mem0
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m = Memory()
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# Store a memory from any unstructured text
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result = m.add("I am working on improving my tennis skills. Suggest some online courses.", user_id="alice", metadata={"category": "hobbies"})
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print(result)
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# Created memory: Improving her tennis skills. Looking for online suggestions.
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# Retrieve memories
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all_memories = m.get_all()
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print(all_memories)
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# Search memories
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related_memories = m.search(query="What are Alice's hobbies?", user_id="alice")
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print(related_memories)
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# Update a memory
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result = m.update(memory_id="m1", data="Likes to play tennis on weekends")
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print(result)
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# Get memory history
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history = m.history(memory_id="m1")
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print(history)
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```
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## 🔑 Core Features
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- **Multi-Level Memory**: User, Session, and AI Agent memory retention
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- **Adaptive Personalization**: Continuous improvement based on interactions
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- **Developer-Friendly API**: Simple integration into various applications
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- **Cross-Platform Consistency**: Uniform behavior across devices
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- **Managed Service**: Hassle-free hosted solution
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## 📖 Documentation
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For detailed usage instructions and API reference, visit our documentation at [docs.mem0.ai](https://docs.mem0.ai).
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## 🔧 Advanced Usage
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For production environments, you can use Qdrant as a vector store:
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```python
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from mem0 import Memory
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config = {
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"vector_store": {
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"provider": "qdrant",
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"config": {
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"host": "localhost",
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"port": 6333,
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}
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},
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}
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m = Memory.from_config(config)
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```
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## 🗺️ Roadmap
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- Integration with various LLM providers
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- Support for LLM frameworks
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- Integration with AI Agents frameworks
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- Customizable memory creation/update rules
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- Hosted platform support
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## 🙋♂️ Support
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Join our Slack or Discord community for support and discussions.
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If you have any questions, feel free to reach out to us using one of the following methods:
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- [Join our Discord](https://embedchain.ai/discord)
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- [Join our Slack](https://embedchain.ai/slack)
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- [Follow us on Twitter](https://twitter.com/mem0ai)
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- [Email us](mailto:founders@mem0.ai)
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