https://github.com/sammyne/langmem0
LangChain memory toolkit powered by https://mem0.ai
https://github.com/sammyne/langmem0
agentic-memory ai-agents langchain
Last synced: 5 months ago
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LangChain memory toolkit powered by https://mem0.ai
- Host: GitHub
- URL: https://github.com/sammyne/langmem0
- Owner: sammyne
- Created: 2026-01-05T05:30:07.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2026-01-10T12:21:29.000Z (5 months ago)
- Last Synced: 2026-01-11T03:26:57.643Z (5 months ago)
- Topics: agentic-memory, ai-agents, langchain
- Language: Python
- Homepage: https://sammyne.github.io/langmem0/
- Size: 121 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Agents: AGENTS.md
Awesome Lists containing this project
README
# LangMem0
📚 [Documentation](https://sammyne.github.io/langmem0/)
[](https://pypi.org/project/langmem0/)
[](https://github.com/sammyne/langmem0/actions/workflows/lint.yml)
## Installation
Install via `uv`
```bash
uv add langmem0
```
Install via `pip`
```bash
pip install langmem0
```
## Examples quickstart
### 0. Environment
- uv >= 0.9
### 1. Clone the repository
```bash
git clone https://github.com/sammyne/langmem0.git
cd langmem0
```
### 2. Install dependencies
```bash
uv pip install -e .
```
### 3. Set up environment variables
Create a `.env` file in the project root with your OpenAI configuration:
```bash
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.
```bash
# 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.
```bash
# 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](https://langchain-ai.github.io/langmem/hot_path_quickstart/#agent).
## References
- [LangChain Chatbot](https://chat.langchain.com/?threadId=e7011d54-12ce-4c14-8d2a-324996e0d0de)
- [LangChain forum](https://forum.langchain.com/c/oss-product-help-lc-and-lg/langchain/14)
- [Mem0 Open Source](https://docs.mem0.ai/open-source/overview)