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To expose an OpenAI-powered agent through FastAPI, define typed request and response models, keep OPENAI_API_KEY on the server, and call the agent asynchronously from an endpoint. Use the OpenAI Agents SDK when you want its runtime to manage agent turns and tool workflows; call the Responses API directly when your application should manage orchestration and state.
Choose the OpenAI Agents SDK or a direct API call
Both approaches let a FastAPI endpoint send a user’s input to OpenAI. The difference is how much of the agent workflow you want a library to handle.
| Approach | Who manages turns and tools? | Best fit |
|---|---|---|
| OpenAI Agents SDK | The SDK provides a higher-level runtime for agent runs and tool workflows. | You want agent-oriented features such as handoffs, guardrails, or sessions and prefer to implement less orchestration yourself. |
| Direct OpenAI Python client | Your application manages its loop, tool dispatch, and state. | You want direct control over orchestration and are prepared to implement and maintain that state. |
The Agents SDK uses the Responses API by default. The choice can be made per workflow rather than once for an entire application. See OpenAI’s Agents guide and Responses API guide.
Build a minimal FastAPI endpoint with the Agents SDK
This is an illustrative integration pattern, not a tested, version-pinned application. FastAPI’s tutorial says its own code blocks are tested; that does not establish that this combined example has been tested against particular versions of FastAPI, openai-agents, or their dependencies.
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Install packages and configure the credential
In a virtual environment, install the Agents SDK and FastAPI. The official quickstart uses pip install openai-agents; FastAPI’s current tutorial recommends uv add "fastapi[standard]". Set OPENAI_API_KEY in the server process environment or an appropriate deployment secret store before the first model call. The Agents SDK resolves the key when it first creates its OpenAI client.
Do not accept the API key in the endpoint request, log it, or include it in a response. Keep it in server-side configuration, not in code shipped to clients.
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Define request and response contracts
Save the following as an illustrative starting point, then verify imports and asynchronous behavior against the package versions you pin:
from fastapi import FastAPI
from pydantic import BaseModel
from agents import Agent, Runner
app = FastAPI()
agent = Agent(
name="Helpful assistant",
instructions="Answer the user's question clearly and concisely.",
)
class AskRequest(BaseModel):
question: str
class AskResponse(BaseModel):
answer: str
@app.post("/ask", response_model=AskResponse)
async def ask(payload: AskRequest) -> AskResponse:
result = await Runner.run(agent, payload.question)
return AskResponse(answer=str(result.final_output))The endpoint accepts JSON with a question field, runs the agent, and returns an answer field. For example, a request body is {"question":"How do I sort a list in Python?"}. The response model describes the public output shape; include only fields clients should receive.
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What the FastAPI models provide
FastAPI uses Pydantic models to validate incoming data and document the endpoint. A response model validates and serializes returned data, generates schema documentation, and filters out fields not declared in the model. That filtering is useful for preventing internal data from leaking into a public response. FastAPI also generates OpenAPI 3.1 schemas, which can support interactive documentation and client-generation workflows.
Use the direct OpenAI Python client when you need more control
The alternative is to call the Responses API with AsyncOpenAI from the openai package inside the endpoint. This makes sense when your application needs to own tool dispatch, turn limits, and conversation state rather than use the Agents SDK runtime.
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Use the current OpenAI Python SDK reference for the method names and request and response fields that match your pinned version. The relevant official guides establish the Responses API direction but do not provide a complete method-level reference for a copy-paste direct-call example here. Avoid relying on unpinned snippets: confirm the current API signature and explicitly choose a model supported by your selected version and account.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Plan for agent runs that take longer than a simple request
An agent turn may include multiple steps or tool calls, so a production endpoint needs more than a request model. The right settings depend on your application and deployment; there is no universal timeout or concurrency value that fits every agent.
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- Timeouts and cancellation: Decide how long a client request should wait and what should happen if the client disconnects while work is in progress.
- Retries and rate limits: Handle transient failures deliberately and account for provider and application limits without creating duplicate work.
- Concurrency: Set limits appropriate to your service capacity and expected workload.
- Persistence: Decide whether conversation or tool state must survive beyond a single request.
- Background jobs: For work that may outlast a normal HTTP request, consider returning a job identifier and processing the task separately rather than holding the request open.
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