Mem0 provides a smart, self-improving memory layer for Large Language Models, enabling personalized AI experiences across applications.
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 directory.
pip install mem0aifrommem0importMemory# Initialize Mem0m=Memory()
# Store a memory from any unstructured textresult=m.add("I am working on improving my tennis skills. Suggest some online courses.", user_id="alice", metadata={"category": "hobbies"})
print(result)
# Created memory: Improving her tennis skills. Looking for online suggestions.# Retrieve memoriesall_memories=m.get_all()
print(all_memories)
# Search memoriesrelated_memories=m.search(query="What are Alice's hobbies?", user_id="alice")
print(related_memories)
# Update a memoryresult=m.update(memory_id="m1", data="Likes to play tennis on weekends")
print(result)
# Get memory historyhistory=m.history(memory_id="m1")
print(history)- Multi-Level Memory: User, Session, and AI Agent memory retention
- Adaptive Personalization: Continuous improvement based on interactions
- Developer-Friendly API: Simple integration into various applications
- Cross-Platform Consistency: Uniform behavior across devices
- Managed Service: Hassle-free hosted solution
For detailed usage instructions and API reference, visit our documentation at docs.mem0.ai.
For production environments, you can use Qdrant as a vector store:
frommem0importMemoryconfig= {
"vector_store": {
"provider": "qdrant",
"config": {
"host": "localhost",
"port": 6333,
}
},
}
m=Memory.from_config(config)- Integration with various LLM providers
- Support for LLM frameworks
- Integration with AI Agents frameworks
- Customizable memory creation/update rules
- Hosted platform support
Join our Slack or Discord community for support and discussions. If you have any questions, feel free to reach out to us using one of the following methods: