Building Intelligent Systems with OpenAI's Agents SDK

Unlock the next generation of AI applications with modular, collaborative, and safe agent workflows.

The OpenAI Agents SDK empowers developers to build sophisticated AI systems with ease. By providing clear abstractions—Agents, Tools, Handoffs, Guardrails, and Tracing—the SDK makes it simple to create modular, flexible, and production-ready AI solutions.

Core Concepts of the SDK

Agents

At the heart of the SDK are Agents—AI entities equipped with instructions and the ability to leverage tools. Agents can:

• Interpret and execute tasks
• Use external tools and functions
• Generate structured, actionable responses

This makes them highly adaptable components for any AI workflow.

Tools

Tools extend agent capabilities beyond text, enabling them to:

• Perform database lookups
• Execute external computations
• Integrate with APIs and more

Advanced Patterns: Handoffs, Agent-as-Tool, and Function Tools

Handoffs: Task Delegation Between Agents

Handoffs allow agents to delegate tasks to other specialized agents, keeping your system organized and efficient.

Example: Triage Agent Delegating to Billing or Technical Support

from agents import Agent, Runner

billing_agent = Agent(
  name="Billing Agent",
  instructions="Handle billing-related queries."
)

technical_support_agent = Agent(
  name="Technical Support Agent",
  instructions="Handle technical support queries."
)

triage_agent = Agent(
  name="Triage Agent",
  instructions="Determine query type and delegate appropriately.",
  handoffs=[billing_agent, technical_support_agent]
)

result = Runner.run_sync(triage_agent, "I have an issue with my payment.")
print(result.final_output)

The Triage Agent decides which specialist should handle the request—streamlining support and improving user experience.

Agent-as-Tool Pattern: Modular Collaboration

The Agent-as-Tool pattern lets one agent invoke another as a tool, promoting modularity and reusability.

from agents import Agent, Runner

translation_agent = Agent(
    name="Translation Agent",
    instructions="Translate English text to Spanish."
)

information_agent = Agent(
    name="Information Agent",
    instructions="Provide general information and translate responses into Spanish when requested.",
    tools=[translation_agent]
)

result = Runner.run_sync(information_agent, "Translate 'OpenAI is powerful.' into Spanish.")
print(result.final_output)

The Information Agent seamlessly taps into the Translation Agent's skills—no need for full handoff.

Simplified Tool Integration with @function_tool

The @function_tool decorator makes it effortless to turn Python functions into agent tools—no manual schema writing required!

from agents import Agent, Runner, function_tool

@function_tool
def compute_tax(amount: float, tax_rate: float) -> str:
    total = amount + (amount * tax_rate / 100)
    return f"The total after tax is ${total:.2f}"

finance_agent = Agent(
    name="Finance Agent",
    instructions="Calculate total amounts including tax.",
    tools=[compute_tax]
)

result = Runner.run_sync(finance_agent, "Calculate total for $100 with a 7.5% tax rate.")
print(result.final_output)

Instantly boost your agent's capabilities with simple, type-annotated Python functions.

Guardrails: Ensuring Safe AI Behavior

Guardrails validate agent interactions, ensuring your AI operates safely and within set boundaries. This prevents unintended actions and boosts reliability—especially in production.

Tracing: Transparency and Debugging

Tracing gives you visibility into agent decisions and workflows, making debugging and optimization a breeze.

Conclusion

With the OpenAI Agents SDK, you can rapidly build robust, modular, and collaborative AI systems. By leveraging agents, tools, handoffs, guardrails, and tracing, you'll deliver smarter, safer, and more maintainable AI applications.

Ready to build?
Start experimenting with the OpenAI Agents SDK and unlock new possibilities for intelligent systems!