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Simple Q&A agent that answers questions on demand.

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Code

"""AG2 (formerly AutoGen) agent wrapped with Bindu.

A simple Q&A agent using AG2's ConversableAgent, exposed as a
Bindu-compatible networked service with identity, communication,
and payment capabilities.
"""

import os

from autogen import ConversableAgent, LLMConfig
from bindu.penguin import bindufy
from dotenv import load_dotenv

load_dotenv()

llm_config = LLMConfig(
    {
        "model": os.getenv("LLM_MODEL", "openai/gpt-4o-mini"),
        "api_key": os.environ.get("OPENROUTER_API_KEY", ""),
        "base_url": "https://openrouter.ai/api/v1",
    }
)

config = {
    "author": "ag2-community",
    "name": "ag2-assistant",
    "description": (
        "A research assistant powered by AG2 (formerly AutoGen). "
        "Ask any question and get a clear, concise answer."
    ),
    "deployment": {
        "url": "http://localhost:3773",
        "expose": True,
        "cors_origins": ["http://localhost:5173"],
    },
    
}


def handler(messages: list[dict[str, str]]):
    """Forward messages to the AG2 agent and return its response."""
    if not messages:
        return [{"role": "assistant", "content": "No input provided."}]

    user_input = messages[-1].get("content", "")
    if not user_input:
        return [{"role": "assistant", "content": "Empty message."}]

    # Fresh agents per request to avoid state leakage
    agent = ConversableAgent(
        name="assistant",
        system_message=(
            "You are a helpful research assistant. Provide "
            "clear, concise answers to questions. When you "
            "don't know something, say so."
        ),
        llm_config=llm_config,
    )
    user_proxy = ConversableAgent(
        name="user", human_input_mode="NEVER",
    )

    result = user_proxy.initiate_chat(
        agent, message=user_input, max_turns=1
    )
    if not result.chat_history:
        return [{"role": "assistant", "content": "No response."}]
    reply = result.chat_history[-1].get("content", "")
    return [{"role": "assistant", "content": reply}]


if __name__ == "__main__":
    bindufy(config, handler)

​
How It Works

Instructions
  • Defines agent as helpful research assistant
  • Provides clear, concise answers to questions
  • Admits when information is unknown
  • Maintains professional, helpful tone
Tools
  • ConversableAgent: Multi-agent conversation framework
  • UserProxy: Handles message forwarding without human input
  • Fresh agent instances per request prevent state leakage
Model
  • gpt-4o-mini: Cost-effective model for Q&A
  • Via OpenRouter API integration
  • Configurable through environment variables

​
Dependencies

uv init

uv add bindu pyautogen python-dotenv

​
Environment Setup

Create .env file: 
OPENROUTER_API_KEY=your_openrouter_api_key_here
LLM_MODEL=openai/gpt-4o-mini

​
Run

uv run ag2-agent.py
Examples:
  • “What are the main differences between AG2 and AutoGen?”
  • “How do I create a multi-agent conversation with AG2?”
  • “What are the key features of AG2 agents?”

​
Example API Calls

{
  "jsonrpc": "2.0",
  "method": "message/send",
  "params": {
    "message": {
      "role": "user",
      "kind": "message",
      "messageId": "9f11c870-5616-49ad-b187-d93cbb100001",
      "contextId": "9f11c870-5616-49ad-b187-d93cbb100002",
      "taskId": "9f11c870-5616-49ad-b187-d93cbb100003",
      "parts": [
        {
          "kind": "text",
          "text": "What are the main differences between AG2 and AutoGen?"
        }
      ]
    },
    "configuration": {
      "acceptedOutputModes": ["application/json"]
    }
  },
  "id": "9f11c870-5616-49ad-b187-d93cbb100003"
}

{
  "jsonrpc": "2.0",
  "method": "tasks/get",
  "params": {
    "taskId": "9f11c870-5616-49ad-b187-d93cbb100003"
  },
  "id": "9f11c870-5616-49ad-b187-d93cbb100004"
}

​
Frontend Setup

# Clone the Bindu repository
git clone https://github.com/GetBindu/Bindu

# Navigate to frontend directory
cd frontend

# Install dependencies
npm install

# Start frontend development server
npm run dev
Open http://localhost:5173 and try to chat with the AG2 assistant