AG-2 in Practice #3 – Creating a Multi-Agent Workflow (Researcher + Writer)

Welcome back to the series!

So far, we’ve learned what AG-2 is and created a basic single-agent app. Now we’re moving to the fun part: collaboration between agents.

In this post, we’ll build a simple multi-agent pipeline:

• A Researcher agent that finds information
• A Writer agent that turns that info into a summary

This will show you how to chain agents, pass messages between them, and use AG-2's orchestration features.

What We’re Building

We’ll simulate this conversation:

You: “Write a summary about climate change.”
Researcher: “Climate change refers to long-term shifts…”
Writer: “Sure! Here's a concise summary of that…”

Step 1: Install or Update AG-2 (if needed)

Make sure you're using the latest AG-2:

pip install --upgrade ag2 

Step 2: Define Two Agents

Create a file called multi_agent_pipeline.py:

from ag2 import Agent, Orchestrator, Conversation import os os.environ["OPENAI_API_KEY"] = "your-api-key-here" # Researcher agent researcher = Agent( name="researcher", llm="openai/gpt-4", system_message="You are a helpful researcher. Given a topic, find relevant and accurate information.", ) # Writer agent writer = Agent( name="writer", llm="openai/gpt-4", system_message="You are a professional writer. Based on research findings, write clear summaries.", ) 

Step 3: Orchestrate the Conversation

Now we’ll define the flow between them.

# Define orchestrator logic orchestrator = Orchestrator( agents=[researcher, writer], rules=[ {"from": "user", "to": "researcher"}, {"from": "researcher", "to": "writer"}, {"from": "writer", "to": "user"}, ] ) # Start the conversation conv = Conversation(orchestrator=orchestrator) conv.send("Please write a short summary about climate change.") 

This defines a simple linear message flow from the user to Researcher → Writer → back to user.

Step 4: Run It

python multi_agent_pipeline.py 

You should see something like:

User: Please write a short summary about climate change.
Researcher: Climate change refers to...
Writer: Sure! Here's a summary: ...

Success! You just built a multi-agent reasoning chain.

What’s Next?

In the next post, we’ll:

• Explore Patterns in AG-2 (like debate, delegation, human review)
• Add tool use in multi-agent chains
• Start designing more dynamic workflows

Keep coding