Source URL: https://simonwillison.net/2025/Mar/11/openai-agents-sdk/
Source: Simon Willison’s Weblog
Title: OpenAI Agents SDK
Feedly Summary: OpenAI Agents SDK
OpenAI’s other big announcement today (see also) – a Python library (openai-agents) for building “agents", which is a replacement for their previous swarm research project.
In this project, an "agent" is a class that configures an LLM with a system prompt an access to specific tools.
An interesting concept in this one is the concept of handoffs, where one agent can chose to hand execution over to a different system-prompt-plus-tools agent treating it almost like a tool itself. This code example illustrates the idea:
from agents import Agent, handoff
billing_agent = Agent(
name="Billing agent"
)
refund_agent = Agent(
name="Refund agent"
)
triage_agent = Agent(
name="Triage agent",
handoffs=[billing_agent, handoff(refund_agent)]
)
The library also includes guardrails – classes you can add that attempt to filter user input to make sure it fits expected criteria. Bits of this look suspiciously like trying to solve AI security problems with more AI to me.
Tags: python, generative-ai, ai-agents, openai, ai, llms
AI Summary and Description: Yes
Summary: The text introduces OpenAI’s new SDK for building AI agents, which enhances the capabilities of large language models (LLMs) by allowing for flexible execution and integration with various tools. The concept of handoffs between agents is noteworthy, as it allows for more collaborative processing. Moreover, the inclusion of guardrails suggests an attempt to address security challenges by implementing additional layers of AI-driven input filtering.
Detailed Description:
OpenAI has recently announced a Python library named `openai-agents`, aimed at facilitating the development of AI agents that interface with large language models (LLMs). This development highlights several key points of significance for industry professionals:
– **LLM Configuration**: The library enables users to configure LLMs with specific prompts and tools, essentially treating the LLM as a dynamic component of an agent’s functionality.
– **Agent Handoffs**: The ability for one agent to hand off tasks to another agent introduces flexibility in how tasks are processed. This mechanism allows for specialized agents (e.g., billing, refund, triage) to collaborate and react to different scenarios in a responsive manner.
– **Guardrails for Security**: The introduction of guardrails serves as a proactive measure to enhance the security and reliability of interactions with the agents. By filtering user inputs, these guardrails aim to mitigate risks associated with erroneous or malicious input that could potentially exploit vulnerabilities in AI systems.
– **AI Security Implications**: This approach appears to utilize AI itself as a mechanism to solve AI security problems, offering insights into potential future trends in A.I. security where machine learning techniques are employed to enhance system integrity.
Overall, this announcement has implications for various sectors involved in AI, cloud computing, and security:
– **For AI Developers**: The SDK offers a significant improvement in managing AI capabilities, promoting modular design and collaboration between diverse AI functionalities.
– **For Security Professionals**: Highlighting the use of guardrails reaffirms the importance of implementing security measures at multiple levels in AI systems, especially given the increasing reliance on LLMs in critical applications.
– **For Compliance and Governance**: Understanding the operational dynamics within these agent structures may raise questions about accountability and transparency in automated decision-making processes.
This development underlines the ongoing evolution of AI frameworks and the need for continued vigilance in security practices as the technology matures.