Demonstrates extremely low time-to-functional for OpenAI-committed teams. The lowest-friction path for linear handoff agents on OpenAI.
OpenAI Agents SDK
active17.9M PyPI downloads/month — 3× CrewAI, closing on LangGraph. Minimalist API (4 primitives: Agents, Handoffs, Guardrails, Tracing). Now supports 100+ LLMs via Chat Completions API — no longer locked to OpenAI models. TypeScript SDK also available. Fastest star accumulation in category (20K in 12 months). Critical gaps: no checkpointing or state persistence, pre-1.0 API.
Where it wins
17.9M PyPI downloads/month — 3× CrewAI, closing on LangGraph
Minimalist API: 4 primitives (Agents, Handoffs, Guardrails, Tracing) — learnable in an afternoon
Now supports 100+ LLMs via Chat Completions API — no longer OpenAI-locked
Fastest star accumulation in category: 20K in 12 months
Built-in tracing eliminates need for separate observability tooling
TypeScript SDK also available
Rowboat ecosystem (161 HN pts, 51 comments) — strong third-party signal
Where to be skeptical
Pre-1.0 (v0.x) — API still evolving, breaking changes possible
No checkpointing or state persistence — must DIY human-in-the-loop (Temporal required)
No native MCP/A2A protocol support
Editorial verdict
Best for teams that want fast iteration with minimal boilerplate. Minimalist 4-primitive API learnable in an afternoon. Now supports 100+ LLMs — no longer OpenAI-locked. Pre-1.0 API, no state persistence. If your team needs to ship an agent system this week and has never used a framework, start here.
Videos
Reviews, tutorials, and comparisons from the community.
Agents SDK from OpenAI - Full Tutorial
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Public evidence
Strong third-party ecosystem signal — an independent team chose this SDK as the foundation for a multi-agent IDE.
Temporal integration documented as the pattern for adding state persistence. Confirmed structural gap for stateful workflows.
Raw GitHub source
GitHub README peek
Constrained peek so you can sanity-check the source material without leaving the site.
The OpenAI Agents SDK is a lightweight yet powerful framework for building multi-agent workflows. It is provider-agnostic, supporting the OpenAI Responses and Chat Completions APIs, as well as 100+ other LLMs.
<img src="https://cdn.openai.com/API/docs/images/orchestration.png" alt="Image of the Agents Tracing UI" style="max-height: 803px;">[!NOTE] Looking for the JavaScript/TypeScript version? Check out Agents SDK JS/TS.
Core concepts:
- Agents: LLMs configured with instructions, tools, guardrails, and handoffs
- Sandbox Agents: Agents preconfigured to work with a container to perform work over long time horizons.
- Agents as tools / Handoffs: Delegating to other agents for specific tasks
- Tools: Various Tools let agents take actions (functions, MCP, hosted tools)
- Guardrails: Configurable safety checks for input and output validation
- Human in the loop: Built-in mechanisms for involving humans across agent runs
- Sessions: Automatic conversation history management across agent runs
- Tracing: Built-in tracking of agent runs, allowing you to view, debug and optimize your workflows
- Realtime Agents: Build powerful voice agents with
gpt-realtime-1.5and full agent features
Explore the examples directory to see the SDK in action, and read our documentation for more details.
Get started
To get started, set up your Python environment (Python 3.10 or newer required), and then install OpenAI Agents SDK package.
venv
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
pip install openai-agents
For voice support, install with the optional voice group: pip install 'openai-agents[voice]'. For Redis session support, install with the optional redis group: pip install 'openai-agents[redis]'.
uv
If you're familiar with uv, installing the package would be even easier:
uv init
uv add openai-agents
For voice support, install with the optional voice group: uv add 'openai-agents[voice]'. For Redis session support, install with the optional redis group: uv add 'openai-agents[redis]'.
Run your first Sandbox Agent
Sandbox Agents are new in version 0.14.0. A sandbox agent is an agent that uses a computer environment to perform real work with a filesystem, in an environment you configure and control. Sandbox agents are useful when the agent needs to inspect files, run commands, apply patches, or carry workspace state across longer tasks.
from agents import Runner
from agents.run import RunConfig
from agents.sandbox import Manifest, SandboxAgent, SandboxRunConfig
from agents.sandbox.entries import GitRepo
from agents.sandbox.sandboxes import UnixLocalSandboxClient
agent = SandboxAgent(
name="Workspace Assistant",
instructions="Inspect the sandbox workspace before answering.",
default_manifest=Manifest(
entries={
"repo": GitRepo(repo="openai/openai-agents-python", ref="main"),
}
),
)
result = Runner.run_sync(
agent,
"Inspect the repo README and summarize what this project does.",
# Run this agent on the local filesystem
run_config=RunConfig(sandbox=SandboxRunConfig(client=UnixLocalSandboxClient())),
)
print(result.final_output)
# This project provides a Python SDK for building multi-agent workflows.
(If running this, ensure you set the OPENAI_API_KEY environment variable)
(For Jupyter notebook users, see hello_world_jupyter.ipynb)
Explore the examples directory to see the SDK in action, and read our documentation for more details.
Acknowledgements
We'd like to acknowledge the excellent work of the open-source community, especially:
- Pydantic
- Requests
- MCP Python SDK
- Griffe
This library has these optional dependencies:
- websockets
- SQLAlchemy
- any-llm and LiteLLM
We also rely on the following tools to manage the project:
- uv and ruff
- mypy and Pyright
- pytest and Coverage.py
- MkDocs
We're committed to continuing to build the Agents SDK as an open source framework so others in the community can expand on our approach.