Google’s Agent Development Kit helps developers build multi agent systems.
OpenAI Agents SDK
The OpenAI Agents SDK provides tools for developers building applications with agentic AI.
About OpenAI Agents SDK
The OpenAI Agents SDK is a Python package for building applications where language models use instructions and tools to complete tasks. Its core building blocks are agents, handoffs for delegation between agents, and guardrails for checking inputs and outputs. It manages turns, tool execution, sessions for working context, and tracing for visualizing and debugging workflows. It also supports sandbox workspaces with resumable sessions, voice and realtime agents, MCP server tool calling, function tools, and human involvement during runs. Installation is via pip, and running examples requires setting an OPENAI_API_KEY environment variable.
Key features
- Define agents with instructions, tools, guardrails, and handoffs
- Run specialists in isolated workspaces with resumable sessions
- Build voice pipelines with speech-to-text and text-to-speech
- Turn Python functions into tools with automatic schema generation
- Expose remote MCP tools to agents alongside function tools
- Visualize and debug workflows with built-in tracing
Best for: Developers building Python applications where agents manage tools, delegation, and validation across multiple steps.
Good for
- Small set of primitives centered on agents, handoffs, and guardrails
- Built-in tracing to visualize and debug workflows
- Manages turns, tool execution, sessions, and human input during runs
OpenAI Agents SDK at a glance
- Category
- AI & Machine Learning › AI Agent Frameworks
- Type
- Website
- Pricing
- Not confirmed. Check the site
- Platforms
- Web
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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OpenAI Agents SDK FAQ
When should I use the Agents SDK instead of the Responses API directly?
Use the SDK when you want the runtime to manage turns, tool execution, guardrails, handoffs, or sessions, when work spans multiple coordinated steps or produces artifacts, or when you need a workspace. Use the Responses API directly when you want to own the loop and state handling or have a short-lived workflow mainly returning a response.
How do I install and run a first agent?
Install with pip install openai-agents, set the OPENAI_API_KEY environment variable, then define an Agent and run it with Runner.
What are the core building blocks?
Agents with instructions and tools, handoffs for delegation between agents, and guardrails for input and output validation.
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