langmem0 0.1.0

Add your description here

LangMem0

Environment

• uv >= 0.9

Quickstart

1. Clone the repository

git clone https://github.com/sammyne/langmem0.git
cd langmem0

2. Install dependencies

uv pip install -e .

3. Set up environment variables

Create a .env file in the project root with your OpenAI configuration:

OPENAI_API_KEY=your_api_key_here
OPENAI_API_BASE_URL=https://api.openai.com/v1
OPENAI_MODEL=gpt-4o-mini

4. Run examples

LangMem0 provides two main ways to integrate memory into your LangChain applications:

Middleware Examples

Use Mem0Middleware to enable memory in LangChain agent conversations. This approach provides fine-grained control over memory operations.

# Synchronous example
uv run examples/middleware/helloworld.py

# Asynchronous example
uv run examples/middleware/helloworld_async.py

These examples demonstrate:

• Using Mem0Middleware to intercept and augment agent messages with memories
• How agents can remember and retrieve information across conversations
• Different thread contexts for separate conversation memories
• Integration with LangGraph checkpoints

Chat Examples

Use ChatOpenAI with built-in Mem0 memory capabilities for a simpler integration path.

# Basic synchronous example
uv run examples/chat/helloworld.py

# Basic asynchronous example
uv run examples/chat/helloworld_async.py

# Advanced synchronous with RunnableConfig:**
uv run examples/chat/runnable_config.py

# Advanced asynchronous with RunnableConfig:**
uv run examples/chat/runnable_config_async.py

These examples demonstrate:

• Direct integration with OpenAI-compatible chat models
• Memory-enabled conversations with minimal setup
• Using RunnableConfig for thread management
• Both sync and async API patterns

FAQ

Why not provide tool-based API like langmem

Tool-based API has a problem: system prompt needs augumenting with memories using a separate middleware as demonstrated by LangMem's Hot Path Quickstart.

References

• LangChain Chatbot
• LangChain forum
• Mem0 Open Source