agent-service-toolkit

AI agent service toolkit based on LangGraph

CommonProductProgramming\AI agentLangGraph

The agent-service-toolkit is a comprehensive toolkit for running AI agent services based on LangGraph, including LangGraph agents, FastAPI services, clients, and Streamlit applications, providing a complete setup from agent definition to user interface. It leverages the high control capabilities and rich ecosystem of the LangGraph framework, supporting advanced features such as concurrent execution, graph loops, and streaming results.

LangGraph Agent: Customizable agents built using the LangGraph framework.
FastAPI Service: FastAPI services providing both streaming and non-streaming endpoints.
Advanced Streaming: Innovative methods supporting token-based and message-based streaming.
Streamlit Interface: A user-friendly chat interface for interacting with agents.
Asynchronous Design: Efficiently handles concurrent requests using async/await.
Docker Support: Includes Dockerfile and docker compose files for easy development and deployment.

\Target audience includes developers and teams looking to quickly build and run AI agent services based on the LangGraph framework. This toolkit provides a template that helps users easily get started and develop their own agent services
making it particularly suitable for AI projects that require a high degree of control and customization.\

Using LangGraph agents for complex data analysis and processing.
Providing a RESTful API interface for AI agents through FastAPI services.
Creating an interactive chat interface for AI agents using Streamlit applications.

Clone the repository: Use the git command to clone agent-service-toolkit to your local machine.
Set environment variables: Create a .env file in the root directory of the project and add the necessary API keys.
Run the service and applications using Docker or a Python virtual environment: Docker is recommended for simplifying environment setup and providing instant updates to code changes.
Access the Streamlit application: Enter http://localhost:8501 in your browser to access the application.
Use the API: Access the FastAPI service API via http://localhost:80

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