AI Agent with LangGraph, LangSmith, Claude, and Tavily

This repository demonstrates how to create an AI Agent using LangGraph with LangSmith, Claude and Tavily to build AI-powered applications using multimodal capabilities. It's designed as a starting point for developers looking to explore and experiment with LangGraph AI Agents.

🚀 Features

• Beginner-friendly: Simple setup to get started quickly.
• LLM Integration: Powered by Claude for advanced reasoning and natural language capabilities.
• Search Integration: Employs Tavily as a tool, allowing the agent to query the web, retrieve information, and enhance its knowledge of current events or data outside the training corpus.
• Extensible: Easily adapt the agent to incorporate new tools, models, or additional data sources.

🛠️ Setup Instructions

Follow these steps to set up and run the project on your local machine:

1. Clone the Repository

git clone https://github.com/hmshb/langgraph-ai-agent-claude.git
cd langgraph-ai-agent-claude

2. Create a Virtual Environment

python -m venv venv
source venv/bin/activate  # For Linux/Mac

.\venv\Scripts\activate # For Windows

3. Install LangGraph CLI

pip install -U "langgraph-cli[inmem]"

4. Install Other Dependencies

pip install -e .

5. Generate LangSmith API Key

1. Visit LangSmith.
2. Create an API key for accessing LangSmith logs.
3. Copy the generated API key.

6. Generate Anthropic Claude API Key

1. Visit Anthropic.
2. Create an API key for accessing Claude.
3. Copy the generated API key.

7. Generate Tavily API Key

1. Visit Tavily.
2. Create an API key for accessing tavily.
3. Copy the generated API key.

8. Update .env File

• Add the following line to the .env file for actual keys

  LANGCHAIN_TRACING_V2=true
  LANGCHAIN_API_KEY=lsv2_pt_*************************
  TAVILY_API_KEY=tvly-*******************************
  ANTHROPIC_API_KEY=sk-ant-**************************
  

9. Run the project

langgraph dev

10. LangGraph of the AI Agent

📂 Project Structure

langchain-ai-agent-google-gemini/
├── .env                 # API key configuration file
├── agent                # Actual AI Agent lives here
├── langgraph.json       # Configuration file for LangGraph
├── pyproject.toml       # configuration file to defines build system requirements and metadata for Python projects.
├── README.md            # Documentation file
├── venv/                # Virtual environment

⭐ Acknowledgments

Special thanks to:

• LangGraph for providing a robust framework for building AI Agents.
• LangSmith for providing a platform for monitoring, debugging, and evaluating applications built with LangChain
• Claude for their powerful multimodal AI capabilities.
• Tavily for allowing the agent to query the web.
• LangGraph Example official example, inspiration behind my example

📜 License

This project is open-source and licensed under the MIT License.

📢 Get Involved!

If you find this repository helpful, please consider:

• ⭐ Starring the Repository to show your support.
• 📤 Forking the Repository to explore further and make your own customizations.
• 💬 Sharing Your Feedback by opening issues or discussions.

📝 Notes

LangGraph, LangSmith, Claude and Tavily is currently in limited or preview release (depending on your region and timing), and integration details may change as the service evolves.

Always refer to official documentation for the most up-to-date guidance.

Let's build amazing AI-powered applications together!