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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
| Date | Stars |
|---|---|
| 2026-07-31 | 461 |
| 2026-08-01 | 462 |
| 2026-08-02 | 464 |
| 2026-08-05 | 466 |
| 2026-08-06 | 466 |
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# LangGraph ReAct Memory Agent [](https://github.com/langchain-ai/memory-agent/actions/workflows/unit-tests.yml) [](https://langgraph-studio.vercel.app/templates/open?githubUrl=https://github.com/langchain-ai/memory-agent) This repo provides a simple example of a ReAct-style agent with a tool to save memories. This is a simple way to let an agent persist important information to reuse later. In this case, we save all memories scoped to a configurable `user_id`, which lets the bot learn a user's preferences across conversational threads.  ## Getting Started This quickstart will get your memory service deployed on [LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/). Once created, you can interact with it from any API. Assuming you have already [installed LangGraph Studio](https://github.com/langchain-ai/langgraph-studio?tab=readme-ov-file#download), to set up: 1. Create a `.env` file. ```bash cp .env.example .env ``` 2. Define required API keys in your `.env` file. ### Setup Model The defaults values for `model` are shown below: ```yaml model: anthropic/claude-3-5-sonnet-20240620 ``` Follow the instructions below to get set up, or pick one of the additional options. #### Anthropic To use Anthropic's chat models: 1. Sign up for an [Anthropic API key](https://console.anthropic.com/) if you haven't already. 2. Once you have your API key, add it to your `.env` file: ``` ANTHROPIC_API_KEY=your-api-key ``` #### OpenAI To use OpenAI's chat models: 1. Sign up for an [OpenAI API key](https://platform.openai.com/signup). 2. Once you have your API key, add it to your `.env` file: ``` OPENAI_API_KEY=your-api-key ``` 3. Open in LangGraph studio. Navigate to the `memory_agent` graph and have a conversation with it! Try sending some messages saying your name and other things the bot should remember. Assuming the bot saved some memories, create a _new_ thread using the `+` icon. Then chat with the bot again - if you've completed your setup correctly, the bot should now have access to the memories you've saved! You can review the saved memories by clicking the "memory" button.  ## How it works This chat bot reads from your memory graph's `Store` to easily list extracted memories. If it calls a tool, LangGraph will route to the `store_memory` node to save the information to the store. ## How to evaluate Memory management can be challenging to get right, especially if you add additional tools for the bot to choose between. To tune the frequency and quality of memories your bot is saving, we recommend start
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f332d2c5689bb3ba, llm:Repository name 'langchain-ai/memory-agent' and association with LangChain imply an agent that handles memory for LLM applications; likely an agent framework component for conversational memory management.
matched fp:f332d2c5689bb3ba, llm:Repository name 'langchain-ai/memory-agent' and association with LangChain imply an agent that handles memory for LLM applications; likely an agent framework component for conversational memory management.
matched fp:f332d2c5689bb3ba, llm:Repository name 'langchain-ai/memory-agent' and association with LangChain imply an agent that handles memory for LLM applications; likely an agent framework component for conversational memory management.
