autogen/python/packages/autogen-core/samples/distributed-group-chat/README.md

# Distributed Group Chat

from autogen_core.application import WorkerAgentRuntimeHost

This example runs a gRPC server using [WorkerAgentRuntimeHost](../../src/autogen_core/application/_worker_runtime_host.py) and instantiates three distributed runtimes using [WorkerAgentRuntime](../../src/autogen_core/application/_worker_runtime.py). These runtimes connect to the gRPC server as hosts and facilitate a round-robin distributed group chat. This example leverages the [Azure OpenAI Service](https://azure.microsoft.com/en-us/products/ai-services/openai-service) to implement writer and editor LLM agents. Agents are instructed to provide concise answers, as the primary goal of this example is to showcase the distributed runtime rather than the quality of agent responses.

## Setup

### Setup Python Environment

1. Create a virtual environment as instructed in [README](../../../../../../../../README.md).
2. Run `uv pip install chainlit` in the same virtual environment

### General Configuration

In the `config.yaml` file, you can configure the `client_config` section to connect the code to the Azure OpenAI Service.

### Authentication

The recommended method for authentication is through Azure Active Directory (AAD), as explained in [Model Clients - Azure AI](https://microsoft.github.io/autogen/dev/user-guide/core-user-guide/framework/model-clients.html#azure-openai). This example works with both the AAD approach (recommended) and by providing the `api_key` in the `config.yaml` file.

## Run

### Run Through Scripts

The [run.sh](./run.sh) file provides commands to run the host and agents using [tmux](https://github.com/tmux/tmux/wiki). The steps for this approach are:

1. Install tmux.
2. Activate the Python environment: `source .venv/bin/activate`.
3. Run the bash script: `./run.sh`.

Here is a screen recording of the execution:

[![Distributed Group Chat Demo with Simple UI Integration](https://img.youtube.com/vi/kLTzI-3VgPQ/0.jpg)](https://youtu.be/kLTzI-3VgPQ)

**Note**: Some `asyncio.sleep` commands have been added to the example code to make the `./run.sh` execution look sequential and visually easy to follow. In practice, these lines are not necessary.

### Run Individual Files

If you prefer to run Python files individually, follow these steps. Note that each step must be run in a different terminal process, and the virtual environment should be activated using `source .venv/bin/activate`.

1. `python run_host.py`: Starts the host and listens for agent connections.
2. `python run_editor.py`: Starts the <img src="./public/avatars/editor.png" width="20" height="20" style="vertical-align:middle"> editor agent and connects it to the host.
3. `python run_writer.py`: Starts the <img src="./public/avatars/writer.png" width="20" height="20" style="vertical-align:middle"> writer agent and connects it to the host.
4. `chainlit run run_group_chat_manager.py --port 8001`: Run chainlit app which starts <img src="./public/avatars/group_chat_manager.png" width="20" height="20" style="vertical-align:middle"> group chat manager agent and sends the initial message to start the conversation. We're using port 8001 as the default port 8000 is used to run host (assuming using same machine to run all of the agents)

## What's Going On?

The general flow of this example is as follows:

1. The <img src="./public/avatars/group_chat_manager.png" width="20" height="20" style="vertical-align:middle"> Group Chat Manager, on behalf of <img src="./public/avatars/user.png" width="20" height="20" style="vertical-align:middle"> `User`, sends a `RequestToSpeak` request to the <img src="./public/avatars/writer.png" width="20" height="20" style="vertical-align:middle"> `writer_agent`.
2. The <img src="./public/avatars/writer.png" width="20" height="20" style="vertical-align:middle"> `writer_agent` writes a short sentence into the group chat topic.
3. The <img src="./public/avatars/editor.png" width="20" height="20" style="vertical-align:middle"> `editor_agent` receives the message in the group chat topic and updates its memory.
4. The <img src="./public/avatars/group_chat_manager.png" width="20" height="20" style="vertical-align:middle"> Group Chat Manager receives the message sent by the writer into the group chat simultaneously and sends the next participant, the <img src="./public/avatars/editor.png" width="20" height="20" style="vertical-align:middle"> `editor_agent`, a `RequestToSpeak` message.
5. The <img src="./public/avatars/editor.png" width="20" height="20" style="vertical-align:middle"> `editor_agent` sends its feedback to the group chat topic.
6. The <img src="./public/avatars/writer.png" width="20" height="20" style="vertical-align:middle"> `writer_agent` receives the feedback and updates its memory.
7. The <img src="./public/avatars/group_chat_manager.png" width="20" height="20" style="vertical-align:middle"> Group Chat Manager receives the message simultaneously and repeats the loop from step 1.

Here is an illustration of the system developed in this example:

```mermaid
graph TD;
    subgraph Host
        A1[GRPC Server]
        wt[Writer Topic]
        et[Editor Topic]
        gct[Group Chat Topic]
    end

    subgraph Distributed Writer Runtime
        writer_agent[<img src="./public/avatars/writer.png" width="50"/> Writer Agent] --> A1
        wt -.->|2 - Subscription| writer_agent
        gct -.->|4 - Subscription| writer_agent
        writer_agent -.->|3 - Publish: Group Chat Message| gct
    end

    subgraph Distributed Editor Runtime
        editor_agent[<img src="./public/avatars/editor.png" width="50"/> Editor Agent] --> A1
        et -.->|6 - Subscription| editor_agent
        gct -.->|4 - Subscription| editor_agent
        editor_agent -.->|7 - Publish: Group Chat Message| gct
    end

    subgraph Distributed Group Chat Manager Runtime
        group_chat_manager[<img src="./public/avatars/group_chat_manager.png" width="50"/> Group Chat Manager Agent] --> A1
        gct -.->|4 - Subscription| group_chat_manager
        group_chat_manager -.->|1 - Request To Speak| wt
        group_chat_manager -.->|5 - Request To Speak| et
    end

    style wt fill:#beb2c3,color:#000
    style et fill:#beb2c3,color:#000
    style gct fill:#beb2c3,color:#000
    style writer_agent fill:#b7c4d7,color:#000
    style editor_agent fill:#b7c4d7,color:#000
    style group_chat_manager fill:#b7c4d7,color:#000

```

## TODO:

- [ ] Properly handle chat restarts. It complains about group chat manager being already registered
- [ ] Send Chainlit messages within each agent (Currently the manager can just sends messages in the group chat topic)
- [ ] Add streaming to the UI like [this example](https://docs.chainlit.io/advanced-features/streaming) but Autogen's Open AI Client [does not supporting streaming yet](https://github.com/microsoft/autogen/blob/0f4dd0cc6dd3eea303ad3d2063979b4b9a1aacfc/python/packages/autogen-ext/src/autogen_ext/models/_openai/_openai_client.py#L81)