Build Multi-Agent AI Systems in Python with OpenAI SDK (2025)

In 2025, AI developers are no longer satisfied with chatbots that forget context after a few messages. A new approach is emerging: multi-agent systems that collaborate, remember, and act like a team of specialists. OpenAI’s Agents SDK provides the tools to build these systems in Python today. Here’s how to get started.

Why Chatbots Alone Can’t Handle Complex Tasks

Most AI assistants excel at answering isolated questions — draft an email, summarize a document, or explain a concept. But when you ask an AI to plan a project, coordinate multiple steps, or remember context across interactions, the limitations become obvious.

Consider a routine workflow: “Research three SaaS companies, compare their pricing, and draft an email to the best one.” A traditional chatbot would require you to manually copy-paste results between prompts, repeatedly re-explain your requirements, and hope it doesn’t lose track of your budget constraints halfway through.

Single-prompt AI lacks the ability to retain context, break tasks into logical steps, or use tools beyond text generation. The result? Frustration and manual work that defeats the purpose of automation.

How Agents Work: From LLM to Task Completer

OpenAI’s Agents SDK transforms large language models into agents — autonomous systems capable of reasoning, planning, and execution. Unlike chatbots, agents follow a structured workflow:

• Perception: Receive input (e.g., your task description).
• Reasoning: Analyze the goal and current state.
• Action: Use tools, APIs, or handoffs to make progress.
• Observation: Evaluate results and adapt.
• Repeat: Continue until the task is complete.

This loop mirrors how humans solve problems: define the goal, break it into steps, take action, assess outcomes, and refine the approach. By codifying this process, agents move from “answering questions” to “getting things done.”

Core Components: Agents, Tools, and Handoffs

Building effective agent systems requires three foundational elements:

• Agents: Specialized entities with defined roles and instructions. For example, a Research Agent might be tasked with gathering company data, while a Writer Agent drafts emails. Each agent operates within a clear scope, reducing errors and improving reliability.

• Tools: Functions that agents can call to interact with the real world. Tools bridge the gap between text generation and action. In the SDK, a tool is a Python function decorated with a description. When the agent decides a tool is needed, it calls the function and receives real-time data or performs an action.

  from openai.agents import function_tool

  @function_tool
  def fetch_company_data(company_name: str) -> dict:
      """Retrieve pricing, features, and reviews for a SaaS company."""
      # Integration with pricing APIs or web scraping
      return {"name": company_name, "price": "$99/month", "rating": 4.5}

• Handoffs: The process of transferring control between agents. Instead of one overloaded agent, handoffs ensure each task is handled by the most qualified specialist. For instance, a Triage Agent might greet the user, classify the request, and route it to a Billing Agent or Technical Support Agent. This modular design improves accuracy and simplifies debugging.

Building Your First Multi-Agent Pipeline

Let’s walk through a practical example: a multi-agent system that researches SaaS companies, compares pricing, and drafts an email to the best choice. The system will include three agents:

• Triage Agent: Classifies the request and initiates the workflow.
• Research Agent: Fetches data on specified companies.
• Writer Agent: Composes an email based on the research.

First, install the OpenAI Agents SDK:

pip install openai-agents

Define the agents and their tools. The Research Agent uses the fetch_company_data tool to gather pricing and features:

from openai.agents import Agent, function_tool

@function_tool
def fetch_company_data(company_name: str) -> dict:
    # Mock implementation for demonstration
    return {
        "name": company_name,
        "price": "$129/month",
        "features": ["API access", "24/7 support"],
        "rating": 4.7
    }

research_agent = Agent(
    name="Research Agent",
    instructions="You are a SaaS researcher. Use tools to fetch pricing and features.",
    tools=[fetch_company_data]
)

Next, create the Writer Agent to draft the email:

writer_agent = Agent(
    name="Writer Agent",
    instructions="You write concise, professional emails to recommend a SaaS product based on research.",
    tools=[]  # No tools needed for this role
)

Finally, set up the handoff logic. The Triage Agent starts the process, calls the Research Agent, and passes the results to the Writer Agent:

from openai.agents import handoff, AgentRunner

@handoff
def start_research(task: str) -> Agent:
    return research_agent

@handoff
def transfer_to_writer(results: list) -> Agent:
    return writer_agent

# Run the system
runner = AgentRunner(
    start=start_research,
    handoffs={
        research_agent: transfer_to_writer
    }
)

result = runner.run("Find the best SaaS for API development under $150/month")
print(result.final_output)

When executed, this pipeline autonomously researches companies, compares pricing, and generates a recommendation email. The agents collaborate without manual intervention, demonstrating the power of multi-agent systems.

Key Considerations for Production Systems

While the SDK simplifies agent development, production systems require careful design:

• Context Management: Each agent loop consumes tokens. Keep prompts concise and use summarization to avoid hitting context limits.

• Error Handling: Define fallback behaviors for tool failures or unexpected inputs. For example, retry failed API calls or notify a human operator.

• Testing: Validate each agent’s outputs and handoffs in isolation before integration. Unit tests ensure tools return expected data formats.

• Performance: Monitor token usage and execution time. Optimize tool descriptions to reduce reasoning overhead.

As AI continues to evolve, the shift from static chatbots to dynamic agent systems marks a pivotal advancement. OpenAI’s Agents SDK provides the foundation, but the real innovation lies in how developers design workflows that leverage these capabilities. The era of AI assistants that merely answer questions is ending — the future belongs to systems that get things done.