Experimental UI for working with AutoGen agents, based on the AutoGen library. The UI is built using Next.js and web apis built using FastApi.

AutoGen is a framework that enables the development of LLM applications using multiple agents that can converse with each other to solve complex tasks. A UI can help in the development of such applications by enabling rapid prototyping and testing and debugging of agents/agent flows (defining, composing etc) inspecting agent behaviors, and agent outcomes.

Note: This is early work in progress.

Note that you will have to setup your OPENAI_API_KEY or general llm config using an environment variable. Also See this article for how Autogen supports multiple llm providers

export
 OPENAI_API_KEY=
<
your key
>

Install dependencies. Python 3.9+ is required. You can install from pypi using pip.

pip install autogenui 
.

or to install from source

git clone git@github.com:victordibia/autogen-ui.git
cd
 autogenui
pip install -e 
.

Run ui server.

Set env vars OPENAI_API_KEY and NEXT_PUBLIC_API_SERVER .

export
 OPENAI_API_KEY=
<
your_key
>

autogenui 
#
 or with --port 8081

Open http://localhost:8081 in your browser.

To modify the source files, make changes in the frontend source files and run npm run build to rebuild the frontend.

autogenui --reload

note: the UI loaded by this CLI in a pre-complied version by running the frontend build command show blow. That means if you make changes the frontend code or change the hostname or port the backend is running on the frontend updated frontend code needs to be rebuilt for it to load through this command.

cd
 frontend

Install dependencies

yarn install

Run in dev mode - with hot-reload

export
 NEXT_PUBLIC_API_SERVER=http://
<
your_backend_hostname
>
/api

your_backend_hostname - is the hostname that autogenui is running on e.g. localhost:8081

yarn dev

(Re)build

Remember to install dependencies and set NEXT_PUBLIC_API_SERVER before building.

yarn build

• FastApi end point for AutoGen . This involves setting up a FastApi endpoint that can respond to end user prompt based requests using a basic two agent format.
• Basic Chat UI Front end UI with a chatbox to enable sending requests and showing responses from the end point for a basic 2 agent format.
  • Debug Tools : enable support for useful debugging capabilities like viewing
    • # of agent turns per request
    • define agent config (e.g. assistant agent + code agent)
    • append conversation history per request
    • display cost of interaction per request (# tokens and $ cost)
• Streaming UI Enable streaming of agent responses to the UI. This will enable the UI to show agent responses as they are generated, instead of waiting for the entire response to be generated.
• Flow based Playground UI
  Explore the use of a tool like React Flow to add agent nodes and compose agent flows. For example, setup an assistant agent + a code agent, click run and view output in a chat window.
  • Create agent nodes
  • Compose agent nodes into flows
  • Run agent flows
• Explore external integrations e.g. with Flowise

• AutoGen .

@inproceedings{wu2023autogen,
      title={AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework},
      author={Qingyun Wu and Gagan Bansal and Jieyu Zhang and Yiran Wu and Shaokun Zhang and Erkang Zhu and Beibin Li and Li Jiang and Xiaoyun Zhang and Chi Wang},
      year={2023},
      eprint={2308.08155},
      archivePrefix={arXiv},
      primaryClass={cs.AI}
}