AG2 Integration

Deploy AG2 (AutoGen 2.0) multi-agent systems with RunAgent

Prerequisites

Basic understanding of AG2/AutoGen
Completed Deploy Your First Agent tutorial
Python 3.8 or higher

Overview

AG2 (AutoGen 2.0) is a framework for building multi-agent conversational systems with automated agent collaboration. RunAgent makes it easy to deploy AG2 agents and access them from any programming language while maintaining conversation flow.

Installation & Setup

1. Install AG2

pip install ag2>=0.9.6

2. Set Environment Variables

AG2 requires API keys for LLM providers:

export OPENAI_API_KEY=your_openai_api_key_here

3. Quick Start with RunAgent

runagent init my-ag2-agent --framework ag2
cd my-ag2-agent

Quick Start

1. Project Structure

After initialization, your project will have:

my-ag2-agent/
├── conversation.py          # Main agent code
├── .env                     # Environment variables
├── requirements.txt         # Python dependencies
└── runagent.config.json     # RunAgent configuration

2. Configuration

The generated runagent.config.json:

{
  "agent_name": "my-ag2-agent",
  "description": "AG2 multi-agent conversation system",
  "framework": "ag2",
  "version": "1.0.0",
  "agent_architecture": {
    "entrypoints": [
      {
        "file": "conversation.py",
        "module": "invoke",
        "tag": "ag2_invoke"
      },
      {
        "file": "conversation.py",
        "module": "stream",
        "tag": "ag2_stream"
      }
    ]
  }
}

3. Create `.env` File

OPENAI_API_KEY=your_openai_api_key_here

Basic AG2 Agent

Here’s a simple AG2 agent with two conversational agents:

# conversation.py
from autogen import ConversableAgent, LLMConfig

# Configure LLM using environment variables
llm_config = LLMConfig(
    api_type="openai",
    model="gpt-4o-mini"
)

# Create agents within the LLM config context
with llm_config:
    assistant = ConversableAgent(
        name="assistant",
        system_message="You are a helpful assistant that responds concisely and accurately.",
    )

    fact_checker = ConversableAgent(
        name="fact_checker",
        system_message="You are a fact-checking assistant. Verify claims and provide accurate corrections when needed.",
    )


def invoke(message: str, max_turns: int = 5):
    """
    Non-streaming conversation between assistant and fact checker.
    
    Args:
        message: The user's message to start the conversation
        max_turns: Maximum number of conversation turns (default: 5)
        
    Returns:
        Conversation result with chat history
    """
    try:
        result = assistant.initiate_chat(
            recipient=fact_checker,
            message=message,
            max_turns=max_turns
        )
        
        return {
            "status": "success",
            "result": result,
            "message": "Conversation completed successfully"
        }
        
    except Exception as e:
        return {
            "status": "error",
            "error": str(e),
            "message": f"Error in AG2 conversation: {str(e)}"
        }


def stream(message: str, max_turns: int = 5):
    """
    Streaming conversation between assistant and fact checker.
    
    Args:
        message: The user's message to start the conversation
        max_turns: Maximum number of conversation turns (default: 5)
        
    Yields:
        Conversation events as they occur
    """
    try:
        # Start the conversation
        response = assistant.run(
            recipient=fact_checker,
            message=message,
            max_turns=max_turns
        )
        
        # Stream events
        for event in response.events:
            if event.type == "text":
                yield {
                    "type": "text",
                    "content": event.content,
                    "sender": getattr(event, 'sender', 'unknown')
                }
            else:
                yield {
                    "type": event.type,
                    "data": event.model_dump() if hasattr(event, 'model_dump') else str(event)
                }
                
    except Exception as e:
        yield {
            "type": "error",
            "error": str(e),
            "message": f"Error in AG2 streaming: {str(e)}"
        }

Advanced AG2 Patterns

1. Multi-Agent Collaboration

# advanced_conversation.py
from autogen import ConversableAgent, LLMConfig

llm_config = LLMConfig(api_type="openai", model="gpt-4o-mini")

with llm_config:
    # Research agent
    researcher = ConversableAgent(
        name="researcher",
        system_message="You are a research specialist. Gather and present factual information.",
    )
    
    # Analyst agent
    analyst = ConversableAgent(
        name="analyst",
        system_message="You are a data analyst. Analyze information and provide insights.",
    )
    
    # Writer agent
    writer = ConversableAgent(
        name="writer",
        system_message="You are a technical writer. Synthesize information into clear, concise reports.",
    )


def research_workflow(topic: str, max_turns: int = 10):
    """
    Multi-agent research workflow.
    
    Flow: User → Researcher → Analyst → Writer
    """
    try:
        # Stage 1: Research
        research_result = researcher.initiate_chat(
            recipient=analyst,
            message=f"Research the following topic: {topic}",
            max_turns=max_turns // 2
        )
        
        # Stage 2: Analysis and Writing
        final_result = analyst.initiate_chat(
            recipient=writer,
            message="Based on the research, create a comprehensive report.",
            max_turns=max_turns // 2
        )
        
        return {
            "status": "success",
            "research": research_result,
            "final_report": final_result,
            "topic": topic
        }
        
    except Exception as e:
        return {
            "status": "error",
            "error": str(e),
            "topic": topic
        }

2. Agent with Custom Tools

# tool_agent.py
from autogen import ConversableAgent, LLMConfig
from typing import Annotated

llm_config = LLMConfig(api_type="openai", model="gpt-4o-mini")


def calculate(
    expression: Annotated[str, "Mathematical expression to evaluate"]
) -> str:
    """Calculate a mathematical expression."""
    try:
        # Safe evaluation with limited scope
        import math
        allowed = {
            "abs": abs, "round": round, "min": min, "max": max,
            "sum": sum, "pow": pow, **{k: v for k, v in math.__dict__.items() if not k.startswith("__")}
        }
        result = eval(expression, {"__builtins__": {}}, allowed)
        return f"Result: {result}"
    except Exception as e:
        return f"Error: {str(e)}"


def search_info(
    query: Annotated[str, "Search query"]
) -> str:
    """Search for information (mock implementation)."""
    # In production, integrate with actual search API
    return f"Search results for '{query}': [Mock data - integrate with real search API]"


with llm_config:
    # Tool-enabled assistant
    assistant = ConversableAgent(
        name="assistant",
        system_message="You are a helpful assistant with access to tools. Use them when appropriate.",
    )
    
    # Register tools
    assistant.register_function(calculate)
    assistant.register_function(search_info)
    
    # User proxy
    user_proxy = ConversableAgent(
        name="user",
        system_message="You represent the user.",
        human_input_mode="NEVER",  # Automated for RunAgent
    )


def invoke_with_tools(message: str, max_turns: int = 5):
    """Conversation with tool access."""
    try:
        result = user_proxy.initiate_chat(
            recipient=assistant,
            message=message,
            max_turns=max_turns
        )
        
        return {
            "status": "success",
            "result": result,
            "tools_used": ["calculate", "search_info"]
        }
        
    except Exception as e:
        return {
            "status": "error",
            "error": str(e)
        }

3. Conditional Conversation Flow

# conditional_flow.py
from autogen import ConversableAgent, LLMConfig

llm_config = LLMConfig(api_type="openai", model="gpt-4o-mini")

with llm_config:
    classifier = ConversableAgent(
        name="classifier",
        system_message="Classify user queries into categories: technical, general, urgent.",
    )
    
    technical_expert = ConversableAgent(
        name="technical_expert",
        system_message="You are a technical expert. Provide detailed technical answers.",
    )
    
    general_assistant = ConversableAgent(
        name="general_assistant",
        system_message="You are a general assistant. Provide helpful, friendly responses.",
    )


def smart_route(message: str, max_turns: int = 5):
    """Route queries to appropriate agents based on classification."""
    try:
        # First, classify the query
        classification = classifier.initiate_chat(
            recipient=general_assistant,
            message=f"Classify this query: {message}",
            max_turns=2
        )
        
        # Route based on classification (simplified)
        if "technical" in str(classification).lower():
            agent = technical_expert
        else:
            agent = general_assistant
        
        # Handle the query with appropriate agent
        result = agent.initiate_chat(
            recipient=classifier,
            message=message,
            max_turns=max_turns
        )
        
        return {
            "status": "success",
            "classification": str(classification),
            "result": result,
            "handler": agent.name
        }
        
    except Exception as e:
        return {
            "status": "error",
            "error": str(e)
        }

Testing Your AG2 Agent

Python Client

# test_ag2.py
from runagent import RunAgentClient

# Test non-streaming
client = RunAgentClient(
    agent_id="your_agent_id_here",
    entrypoint_tag="ag2_invoke",
    local=True
)

result = client.run(
    message="The solar system has 8 planets.",
    max_turns=3
)

print(f"Conversation result: {result}")

# Test streaming
stream_client = RunAgentClient(
    agent_id="your_agent_id_here",
    entrypoint_tag="ag2_stream",
    local=True
)

print("\nStreaming conversation:")
for chunk in stream_client.run(
    message="Explain quantum computing",
    max_turns=4
):
    if chunk.get("type") == "text":
        print(f"{chunk.get('sender', 'Agent')}: {chunk.get('content')}")

JavaScript Client

// test_ag2.js
import { RunAgentClient } from 'runagent';

const client = new RunAgentClient({
    agentId: 'your_agent_id_here',
    entrypointTag: 'ag2_invoke',
    local: true
});

await client.initialize();

// Test conversation
const result = await client.run({
    message: 'What is artificial intelligence?',
    max_turns: 3
});

console.log('Result:', result);

// Test streaming
const streamClient = new RunAgentClient({
    agentId: 'your_agent_id_here',
    entrypointTag: 'ag2_stream',
    local: true
});

await streamClient.initialize();

for await (const chunk of streamClient.run({
    message: 'Explain machine learning',
    max_turns: 3
})) {
    if (chunk.type === 'text') {
        console.log(`${chunk.sender}: ${chunk.content}`);
    }
}

Go Client

package main

import (
    "context"
    "fmt"
    "github.com/runagent-dev/runagent-go/pkg/client"
    "encoding/json"
)

func main() {
    client, _ := client.New(
        "your_agent_id_here",
        "ag2_invoke",
        true,
    )
    defer client.Close()

    ctx := context.Background()
    
    result, _ := client.Run(ctx, map[string]interface{}{
        "message": "Explain neural networks",
        "max_turns": 3,
    })
    
    fmt.Printf("Result: %v\n", result)
}

Configuration Examples

Single Conversation Agent

{
  "agent_name": "ag2-assistant",
  "framework": "ag2",
  "agent_architecture": {
    "entrypoints": [
      {
        "file": "conversation.py",
        "module": "invoke",
        "tag": "ag2_invoke"
      },
      {
        "file": "conversation.py",
        "module": "stream",
        "tag": "ag2_stream"
      }
    ]
  }
}

Multi-Agent Workflow

{
  "agent_name": "ag2-research-team",
  "framework": "ag2",
  "agent_architecture": {
    "entrypoints": [
      {
        "file": "conversation.py",
        "module": "invoke",
        "tag": "simple_chat"
      },
      {
        "file": "advanced_conversation.py",
        "module": "research_workflow",
        "tag": "research"
      },
      {
        "file": "tool_agent.py",
        "module": "invoke_with_tools",
        "tag": "tools"
      }
    ]
  }
}

Best Practices

1. Agent Design

Keep system messages clear and specific
Define roles explicitly for each agent
Use appropriate max_turns to prevent infinite loops

2. Conversation Management

Set reasonable max_turns (typically 3-10)
Handle conversation state appropriately
Implement timeout mechanisms for long conversations

3. Error Handling

Always wrap AG2 operations in try-catch blocks
Return structured error responses
Log conversation failures for debugging

4. Tool Integration

Register tools explicitly with agents
Use type annotations for tool parameters
Implement safe tool execution with proper validation

5. Performance

Reuse agent instances when possible
Monitor conversation length and token usage
Implement caching for repeated queries

Common Patterns

Fact-Checking Pattern

Use multiple agents to verify information:

assistant → fact_checker → validator

Research Pattern

Multi-stage information gathering:

researcher → analyst → writer

Routing Pattern

Direct queries to specialized agents:

classifier → [technical_expert | general_assistant]

Tool-Augmented Pattern

Agents with external capabilities:

assistant + [calculator, search, database]

Troubleshooting

Common Issues

1. API Key Not Found

Solution: Ensure OPENAI_API_KEY is set in environment
Check .env file exists and is loaded
Verify key is valid and has credits

2. Conversation Hangs

Solution: Set appropriate max_turns parameter
Reduce conversation complexity
Implement timeout mechanisms

3. Agent Not Responding

Solution: Check system messages are clear
Verify LLM config is correct
Review agent initialization code

4. Tool Execution Fails

Solution: Verify tool registration
Check tool function signatures
Ensure type annotations are correct

5. Streaming Not Working

Solution: Use assistant.run() instead of initiate_chat()
Check event handling in streaming loop
Verify client supports streaming

Debug Tips

Enable verbose logging:

import logging
logging.basicConfig(level=logging.DEBUG)

def invoke(message, max_turns):
    print(f"Debug: Starting conversation with message: {message}")
    print(f"Debug: Max turns: {max_turns}")
    # ... rest of code

Test conversation locally:

# test_local.py
from conversation import invoke

result = invoke("Test message", max_turns=2)
print(f"Result: {result}")

Performance Optimization

1. Agent Reuse

Create agents once and reuse:

# Global agent instances
_assistant = None
_fact_checker = None

def get_agents():
    global _assistant, _fact_checker
    
    if _assistant is None:
        llm_config = LLMConfig(api_type="openai", model="gpt-4o-mini")
        with llm_config:
            _assistant = ConversableAgent(...)
            _fact_checker = ConversableAgent(...)
    
    return _assistant, _fact_checker

2. Conversation Limits

Set appropriate limits:

def invoke(message, max_turns=5):
    # Reasonable max_turns prevents runaway conversations
    if max_turns > 20:
        max_turns = 20
    # ... rest of code

3. Caching

Implement response caching for repeated queries:

from functools import lru_cache

@lru_cache(maxsize=100)
def invoke_cached(message, max_turns):
    return invoke(message, max_turns)

Next Steps

Advanced Patterns - Learn advanced AG2 patterns
Production Deployment - Deploy to production
Multi-Language Access - Access from different languages
Performance Tuning - Optimize for production

Additional Resources

🎉 Great work! You’ve learned how to deploy AG2 multi-agent systems with RunAgent. AG2’s collaborative agent architecture combined with RunAgent’s multi-language access creates powerful, flexible conversational AI systems!

​AG2 Integration

​Prerequisites

​Overview

​Installation & Setup

​1. Install AG2

​2. Set Environment Variables

​3. Quick Start with RunAgent

​Quick Start

​1. Project Structure

​2. Configuration

​3. Create .env File

​Basic AG2 Agent

​Advanced AG2 Patterns

​1. Multi-Agent Collaboration

​2. Agent with Custom Tools

​3. Conditional Conversation Flow

​Testing Your AG2 Agent

​Python Client

​JavaScript Client

​Go Client

​Configuration Examples

​Single Conversation Agent

​Multi-Agent Workflow

​Best Practices

​1. Agent Design

​2. Conversation Management

​3. Error Handling

​4. Tool Integration

​5. Performance

​Common Patterns

​Fact-Checking Pattern

​Research Pattern

​Routing Pattern

​Tool-Augmented Pattern

​Troubleshooting

​Common Issues

​Debug Tips

​Performance Optimization

​1. Agent Reuse

​2. Conversation Limits

​3. Caching

​Next Steps

​Additional Resources