Update README (#1478)
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Deshraj Yadav
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README.md
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README.md
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# Mem0: Long-Term Memory for LLMs
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<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://embedchain.ai/slack">
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<img src="https://img.shields.io/badge/slack-embedchain-brightgreen.svg?logo=slack" alt="Slack">
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</a>
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<a href="https://embedchain.ai/discord">
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<img src="https://dcbadge.vercel.app/api/server/6PzXDgEjG5?style=flat" alt="Discord">
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</a>
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<a href="https://twitter.com/mem0ai">
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<img src="https://img.shields.io/twitter/follow/mem0ai" alt="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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## Features
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- Persistent memory for users, sessions, and agents
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- Self-improving personalization
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- Simple API for easy integration
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- Cross-platform consistency
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## Quick Start
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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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## Usage
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### Instantiate
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### Basic Usage
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```python
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from mem0 import Memory
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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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If you want to use Qdrant in server mode, use the following method to instantiate.
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## 🔑 Core Features
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Run qdrant first:
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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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```bash
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docker pull qdrant/qdrant
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## 📖 Documentation
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docker run -p 6333:6333 -p 6334:6334 \
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-v $(pwd)/qdrant_storage:/qdrant/storage:z \
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qdrant/qdrant
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```
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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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Then, instantiate memory with qdrant server:
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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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@@ -58,140 +88,19 @@ config = {
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m = Memory.from_config(config)
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```
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### Store a Memory
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## 🗺️ Roadmap
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```python
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m.add("Likes to play cricket over weekend", user_id="alex", metadata={"foo": "bar"})
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# Output:
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# [
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# {
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# 'id': 'm1',
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# 'event': 'add',
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# 'data': 'Likes to play cricket over weekend'
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# }
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# ]
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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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# Similarly, you can store a memory for an agent
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m.add("Agent X is best travel agent in Paris", agent_id="agent-x", metadata={"type": "long-term"})
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```
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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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### Retrieve all memories
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#### 1. Get all memories
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```python
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m.get_all()
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# Output:
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# [
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# {
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# 'id': 'm1',
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# 'text': 'Likes to play cricket over weekend',
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# 'metadata': {
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# 'data': 'Likes to play cricket over weekend'
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# }
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# },
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# {
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# 'id': 'm2',
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# 'text': 'Agent X is best travel agent in Paris',
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# 'metadata': {
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# 'data': 'Agent X is best travel agent in Paris'
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# }
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# }
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# ]
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```
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#### 2. Get memories for specific user
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```python
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m.get_all(user_id="alex")
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```
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#### 3. Get memories for specific agent
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```python
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m.get_all(agent_id="agent-x")
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```
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#### 4. Get memories for a user during an agent run
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```python
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m.get_all(agent_id="agent-x", user_id="alex")
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```
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### Retrieve a Memory
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```python
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memory_id = "m1"
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m.get(memory_id)
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# Output:
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# {
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# 'id': '1',
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# 'text': 'Likes to play cricket over weekend',
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# 'metadata': {
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# 'data': 'Likes to play cricket over weekend'
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# }
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# }
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```
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### Search for related memories
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```python
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m.search(query="What is my name", user_id="deshraj")
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```
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### Update a Memory
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```python
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m.update(memory_id="m1", data="Likes to play tennis")
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```
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### Get history of a Memory
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```python
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m.history(memory_id="m1")
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# Output:
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# [
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# {
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# 'id': 'h1',
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# 'memory_id': 'm1',
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# 'prev_value': None,
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# 'new_value': 'Likes to play cricket over weekend',
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# 'event': 'add',
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# 'timestamp': '2024-06-12 21:00:54.466687',
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# 'is_deleted': 0
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# },
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# {
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# 'id': 'h2',
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# 'memory_id': 'm1',
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# 'prev_value': 'Likes to play cricket over weekend',
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# 'new_value': 'Likes to play tennis',
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# 'event': 'update',
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# 'timestamp': '2024-06-12 21:01:17.230943',
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# 'is_deleted': 0
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# }
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# ]
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```
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### Delete a Memory
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#### Delete specific memory
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```python
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m.delete(memory_id="m1")
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```
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#### Delete memories for a user or agent
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```python
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m.delete_all(user_id="alex")
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m.delete_all(agent_id="agent-x")
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```
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#### Delete all Memories
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```python
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m.reset()
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```
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## License
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[Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0)
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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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