How to Create APIs for AutoGen (2024)

Since we have gone through quite a lot of techniques on how to create visualization for multi-agent LLM applications, demonstrating these apps under AutoGen has become much more convenient and friendly. However, when a project steps from the demo phase to the production phase, using standalone and unified styling UI will be a weakness, there will be a huge demand for making more customized UI and complicated workflow with the capability to integrate to an existing platform that customers have already deployed.

In this tutorial, we will demonstrate building an API server of AutoGen as a backend using Flask, empowering customized front-end experiences and seamless integration with diverse systems.

Why an APIServer?

Directly using Panel or Streamlit for AutoGen is excellent for prototyping or small projects, especially for a simple chatbot app, but building production-ready applications often demands:

• Customized UI/UX: Tailoring the interface to specific branding and complex user experience requirements.

• Integration: Connecting with existing webpages, databases, and workflows.

  See Also

  22 Example Work Breakdown Structures examples to download now2. Auto-generating OpenAPI docs — tapir 1.x documentation

• Scalability and Security: Managing user sessions, authentication, rate limitation, and handling multiple requests efficiently.

An API server decouples the front end from the implementation of multi-agent core logic powered by the AutoGen framework, which can provide the above advantages.

Architecture Overview

The architecture around a Flask API server that communicates with both a front-end application and the AutoGen engine:

1. Flask API Endpoints

If you don’t know much about the Flask —  Flask is a Python web framework known for its lightweight and flexibility. It provides the various tools you need to build web applications quickly, without imposing strict structures or unnecessary dependencies.

In this project, we can use Flask for:

• Receives user requests and data (e.g., messages, agent configurations).

• Manages the AutoGen environment: Creates agents, handles message queues and orchestrates conversations.

• Sends responses (e.g., chat status, agent outputs) back to the front end.

2. AutoGenCore

This is where the key enabler of multi-agent LLM interaction happens. The API endpoints interact with the AutoGen creation process, triggering conversations, receiving agent outputs, and managing the group chat workflow.

3. Front-End Application

This section will handle user interaction, send requests to the API server, and display responses. I will only demonstrate with a simple React example later. This section is open to you for various ideas, frameworks, and custom designs.

Code Walkthrough for theServer

We’ll be using Python with Flask for our API server. Here’s a breakdown code walkthrough of the key components.

0. Install the libraries

For the start, make sure you have installed AutoGen and Flask packages.

!pip install --upgrade pyautogen==0.2.31 flask==3.0.3 flask_cors==4.0.1

Flask CORS stands for “Cross-Origin Requests” which is very essential for front-end communication from external network.

1. Initialization and Dependencies

import osimport timeimport asyncioimport threadingimport autogenfrom flask import Flask, request, jsonifyfrom flask_cors import CORSfrom autogen.agentchat.contrib.gpt_assistant_agent import GPTAssistantAgentfrom autogen.agentchat import AssistantAgent, UserProxyAgentimport queueapp = Flask(__name__)cors=CORS(app)chat_status = "ended" # Queues for single-user setupprint_queue = queue.Queue()user_queue = queue.Queue()# Replace with your actual OpenAI API keyos.environ["OPENAI_API_KEY"] = "Your_API_Key" 

There are several key definitions we should prepare for the program.

• Flask Setup: Initializes a Flask app, and configures CORS for cross-origin requests.

• Queues: print_queue stores messages to be sent to the front end, while user_queue receives user input. These queues are the key enablers for asynchronous communication.

• Chat Status: chat_status helps track the current state of the AutoGen conversation (e.g., “Chat ongoing”, “inputting”, “ended”).

2. API Endpoints

The Flask server should expose several API endpoints to manage conversations, so before any internal function implementation, we should design these endpoints in advance. By using Flask’s decorator app.route(…), it is very easy to define endpoint with relevant processing methods.

In this demonstration, we define the three most essential endpoints:

a) /api/start_chat (POST)

Responsible for initialing an AutoGen group chat by accepting agent configurations (in the request body request.json), task definitions (also in the request body request.json), queue initialization, etc… Then the handler will create a thread by using Python’s classic threading methods to handle the entire workflow run_chat (We will introduce it later.) of AutoGen for asynchronous operations. Using asynchronous operation, the response and further API requests will not be stuck by AutoGen processing, and the redirection of input and output is feasible as well.

@app.route('/api/start_chat', methods=['POST', 'OPTIONS']) def start_chat(): if request.method == 'OPTIONS': return jsonify({}), 200 elif request.method == 'POST': global chat_status try: if chat_status == 'error': chat_status = 'ended' with print_queue.mutex: print_queue.queue.clear() with user_queue.mutex: user_queue.queue.clear() chat_status = 'Chat ongoing' thread = threading.Thread( target=run_chat, args=(request.json,) ) thread.start() return jsonify({'status': chat_status}) except Exception as e: return jsonify({'status': 'Error occurred', 'error': str(e)})

b) /api/send_message (POST)

Responsible for receiving user messages during an ongoing group chat.

@app.route('/api/send_message', methods=['POST'])def send_message(): user_input = request.json['message'] user_queue.put(user_input) return jsonify({'status': 'Message Received'})

c) /api/get_message (GET)

Responsible for sending output messages from the group chat for the front-end fetching.

@app.route('/api/get_message', methods=['GET'])def get_messages(): global chat_status if not print_queue.empty(): msg = print_queue.get() return jsonify({'message': msg, 'chat_status': chat_status}), 200 else: return jsonify({'message': None, 'chat_status': chat_status}), 200

Here is the message flow that shows how the messages and requests interact.

3. Orchestrating the Conversation

So far, we have implemented the external interface of this API server, it’s time to create the internal process logic for the entire multi-agent generating workflow.