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Basic Information

This repository provides a minimal example user interface and reference implementation for building chat-style frontends that interact with AutoGen AgentChat multi-agent systems. It demonstrates how to wire a simple manager that runs a predefined agent team configuration, a FastAPI backend that exposes a /generate endpoint and streams results, and a Next.js frontend that offers a chat interface. The project is described as a hello-world starting point updated to use the AutoGen AgentChat 0.4x API. Included artifacts shown in the README include a manager implementation, a default_team.json agent team specification, and a tutorial notebook illustrating how to load and run team specs. The README documents required environment variables such as OPENAI_API_KEY and the basic install and run commands for both packaged and source installs.

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Features
Provides autogenui.manager with a simple run method that accepts a prompt and returns responses from a configured agent team. Ships a FastAPI backend that creates a manager, exposes a /generate API endpoint, and streams task run results to the client. Includes a Next.js frontend that implements a chat UI and communicates with the backend via an API server variable. Supplies a default_team.json agent team configuration and a tutorial notebook to show usage patterns. Offers a CLI entrypoint for running the UI server, instructions for development mode with hot-reload for backend and frontend, and build steps for rebuilding the frontend. Installation is supported via pip or editable source install. The README also lists extension ideas like multiple team configs, interaction history storage, and auth improvements.
Use Cases
This repo serves as a practical starter kit for developers who want to prototype interfaces for multi-agent LLM applications using the AutoGen AgentChat API. It demonstrates end-to-end wiring of agent configuration, backend orchestration, streaming responses, and a browser chat UI, which helps developers learn integration patterns and accelerate prototyping. The included default team JSON and tutorial notebook make it easier to experiment with agent teams without building orchestration from scratch. Development and build instructions reduce setup friction, and suggested next steps such as persisting history or adding authentication make it a useful scaffold for evolving a prototype into a more production-ready system. The README also points readers to a fuller example project for more advanced use cases.

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