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OpenAI Agents SDK
OpenAI Agents SDK is a developer framework for running agents that call tools or delegate work. The SDK runs the agent loop inside your application; your application owns deployment, tool implementations, storage and approval decisions.
The primary repository linked here is the Python SDK. OpenAI also publishes a separate TypeScript SDK; exact APIs differ by language. Use this as a first read, not a recommendation. Open the original project before trusting details like terms, limits, privacy, cost, setup, or safety.
What it is
An agent loop inside your application
The framework runs model calls, tools and handoffs until the turn reaches a stopping point. It is a library for builders, rather than a finished assistant.
Why it stands out
Two ways to use a specialist
A specialist can take over the conversation through a handoff or work as a tool for an agent that keeps responsibility for the reply.
Availability
Python repository and language-specific guides
The Python README requires Python 3.10 or newer and links installation instructions and examples. Voice and Redis session support use optional package groups.
Why it matters
What makes it useful
OpenAI's orchestration guide separates two ways to involve a specialist: a handoff moves control to that specialist, while an agent called as a tool leaves the manager responsible for the answer. For a support workflow, that is the difference between transferring the conversation to a refund agent and asking it for information the main agent uses in its reply.
What to know
Where it fits
For an ongoing conversation, the Python session guide separates SDK-managed history from OpenAI server-managed continuation. A session loads and saves history through its configured storage; previous_response_id chains Responses API calls. The guide says a session cannot be combined with the run-level conversation_id, previous_response_id or auto_previous_response_id options in the same run. When connecting the result to an interface or another program, the Python results guide makes final_output depend on the finishing agent: once final output is produced, it is text without an output_type, or an object of that type when one is defined. The guide also documents None when a run pauses before producing final output, such as an approval interruption, and while a streamed result is unfinished. A handoff can change which agent finishes. That gives a builder a concrete output-contract choice: accept several possible result shapes, or define the finishing agents to return the shape the receiving application expects. The current README also documents sandbox agents and separate voice and realtime paths. Choose the integration and its requirements for the workflow you are building; the Python and TypeScript APIs differ.
Notable points
What stands out
The Python tracing guide says tracing is enabled by default and generation and function spans can include model and tool inputs and outputs. It distinguishes setting RunConfig.trace_include_sensitive_data to False from disabling tracing itself: the former omits that captured content, while the latter stops recording traces. These are tracing controls, not a promise about what the model, tools or session storage receive.
Before using
What to review
For checks that must finish before an agent starts, the Python guardrail guide describes run_in_parallel=False. With the default parallel mode, the agent may already have used tokens or tools before a triggered guardrail cancels it.
The same guide places agent input checks at the first agent and output checks at the final agent. It directs checks around individual function-tool calls to tool guardrails; handoffs and hosted tools do not use that pipeline.
Choose deliberately what model calls, tools, session storage and traces receive or retain. Review deployment permissions and approval decisions for the application you build; changing a trace setting does not control those other data paths.
Reader fit
Who may find it relevant
Developers building tool-using assistants or coordinating specialist agents can use this as the agent loop inside their own application. For a tool call that needs a person's decision, the Python human-review guide uses needs_approval and returns pending calls in interruptions. Its next step is to approve or reject them on result.to_state(), then resume the original top-level run with that state. If a session is in use, the guide retains the same session ID and backing storage: approval continues the paused turn rather than starting a new one.
Editorial note
Why LifeHubber lists it
An agent often needs more application data than the model needs to read. The Python context guide keeps the run's local context object available to your tools and callbacks without sending that object to the model automatically. Instructions, inputs and tool outputs expose the information you choose. A support app can therefore keep its account lookup and business logic together while deciding which details enter the conversation. That separation makes the SDK useful for building an agent into an existing application, beyond deciding which agent answers or where the conversation is stored.
Source links
Source materials
Reader note
Before relying on this entry
LifeHubber lists entries to help readers inspect AI projects, not to endorse them or prove they are safe, suitable, accurate, maintained, or right for a specific use. We do not verify every entry in depth. Before relying on anything listed, review the original materials, terms, privacy practices, limits, and risks that matter for your situation.
What to explore next
Compare the framework or step back to the model.
For a framework choice, compare how other projects organise agent work. For the underlying concept, separate the model from the tools and task loop around it. Exact SDK implementation steps remain in the publisher guides above.
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