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Baseten integration

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Baseten serves open-weight models such as gpt-oss, DeepSeek, GLM, and Kimi, both on its shared Model APIs and on dedicated deployments, through an API compatible with the OpenAI Chat Completions API.

Temporal's OpenAI Agents SDK integration runs each model call as an Activity. A call to Baseten gets retries with backoff, a timeout, and a durable record in Event History, and the agent loop around it survives Worker restarts. Because Baseten speaks the OpenAI Chat Completions API, the integration's model provider hook is all you need: a model provider that maps a model name such as baseten/openai/gpt-oss-120b to Baseten's endpoint. One Worker can serve Baseten alongside other providers, and each Workflow picks its provider through the model name it gives its agent.

This integration is a sample pattern rather than a plugin. Code snippets in this guide are taken from the OpenAI Agents SDK model provider samples, which run the same agent on Baseten, Fireworks AI, or Together AI from one Worker.

Prerequisites​

Install​

uv add temporalio-openai-agents

The package installs the Temporal Python SDK, the OpenAI Agents SDK, and the openai client. The Baseten CLI and the truss package are for deploying models to Baseten; calling Model APIs needs neither.

Configure the model provider​

The sample describes each provider as a base URL, the environment variable that holds its API key, and an example model with tool calling:

openai_agents/model_providers/openai_compatible_providers.py

BASETEN = OpenAICompatibleProvider(
    name="baseten",
    base_url="https://inference.baseten.co/v1",
    api_key_env="BASETEN_API_KEY",
    default_model="openai/gpt-oss-120b",
)

Model APIs name models the way Hugging Face does, as <organization>/<model>, so the example here is openai/gpt-oss-120b, named baseten/openai/gpt-oss-120b from a Workflow: an open-weight model under the Apache 2.0 license that Baseten serves on Model APIs, where tool calling is supported for every model. Its model library page and GET https://inference.baseten.co/v1/models describe the catalog. A dedicated deployment has its own base URL, https://model-<id>.api.baseten.co/environments/production/sync/v1, takes the same API key, and expects the model name that the deployment serves, so in the sample it is a second entry in PROVIDERS.

A ModelProvider routes a <provider>/<model> name to the matching endpoint and returns the Chat Completions model that the Agents SDK calls:

openai_agents/model_providers/openai_compatible_providers.py

class OpenAICompatibleModelProvider(ModelProvider):
    """Routes ``<provider>/<model>`` names to the matching OpenAI-compatible endpoint.

    One Worker serves every provider whose API key is set, so Workflows on
    different providers run side by side on the same Task Queue. Client
    retries are off: the plugin runs each model call as a Temporal Activity,
    and the Activity's Retry Policy owns the retries.
    """

    def __init__(self, providers: Iterable[OpenAICompatibleProvider]) -> None:
        self._providers = {provider.name: provider for provider in providers}
        self._clients: dict[str, AsyncOpenAI] = {}
        for provider in self._providers.values():
            api_key = os.environ.get(provider.api_key_env)
            if api_key:
                self._clients[provider.name] = AsyncOpenAI(
                    base_url=provider.base_url, api_key=api_key, max_retries=0
                )

    @property
    def configured(self) -> list[str]:
        """Names of the providers whose API key is set."""
        return sorted(self._clients)

    def get_model(self, model_name: str | None) -> Model:
        provider_name, _, model = (model_name or "").partition("/")
        provider = self._providers.get(provider_name)
        if provider is None or not model:
            # A mistake in the Workflow's input: fail the Activity without retrying.
            raise ApplicationError(
                f"Model name {model_name!r} must be <provider>/<model> with a "
                f"provider in {sorted(self._providers)}",
                non_retryable=True,
            )
        client = self._clients.get(provider_name)
        if client is None:
            raise ApplicationError(
                f"Set {provider.api_key_env} and restart the Worker to use {provider_name}",
                non_retryable=True,
            )
        # Chat Completions is the API all of these providers serve. The Agents
        # SDK defaults to the Responses API, which not every provider offers.
        return OpenAIChatCompletionsModel(model=model, openai_client=client)

Three details matter here. The provider builds a client only for the providers whose API key is set, and a model name for a provider without one, or with an unknown prefix, fails the Activity without retrying. Client retries are off (max_retries=0), so every attempt is a visible Activity attempt governed by the Activity's Retry Policy rather than a hidden retry inside the HTTP client. And the provider builds an OpenAIChatCompletionsModel rather than the Agents SDK's default Responses API model, which Baseten does not serve: Model APIs expose Chat Completions and, in beta, the Anthropic Messages API.

Configure the Worker​

Pass the provider to the plugin. The Worker serves every provider whose key is set and prints which ones at startup. A custom model provider must set a model Activity timeout; the sample uses 60 seconds. Model APIs need no deployment of your own to warm up; a dedicated deployment that has scaled to zero parks a request until a replica starts, for up to 1200 seconds by default, so cover that cold start in the timeout or keep min_replica above zero. The Heartbeat Timeout lets Temporal notice a Worker that died mid-call within seconds instead of after the full Start-To-Close Timeout; the plugin heartbeats on its own.

openai_agents/model_providers/run_openai_compatible_worker.py

# One provider for every endpoint whose API key is set. The model name on
# each Workflow's agent selects the endpoint.
model_provider = OpenAICompatibleModelProvider(PROVIDERS.values())
if not model_provider.configured:
    env_vars = ", ".join(provider.api_key_env for provider in PROVIDERS.values())
    sys.exit(f"No provider API key is set; export at least one of {env_vars}.")
print(f"Serving providers: {', '.join(model_provider.configured)}", flush=True)

client = await Client.connect(
    "localhost:7233",
    plugins=[
        OpenAIAgentsPlugin(
            model_params=ModelActivityParameters(
                # A custom model provider must set a timeout. Raise it for
                # slow models or deployments that scale up from zero.
                start_to_close_timeout=timedelta(seconds=60),
                # The plugin heartbeats, so a Worker that dies mid-call is
                # noticed in seconds rather than after the full timeout.
                heartbeat_timeout=timedelta(seconds=10),
            ),
            model_provider=model_provider,
        ),
    ],
)

worker = Worker(
    client,
    task_queue="openai-agents-model-providers-task-queue",
    workflows=[OpenAICompatibleAgentWorkflow],
)
await worker.run()

The Client that starts the Workflow also needs OpenAIAgentsPlugin(), for its Data Converter, but no model provider: only the Worker talks to Baseten.

Write the Workflow​

The Workflow is ordinary Agents SDK code. The model name arrives in the Workflow input and goes on the Agent, so one Worker runs Workflows on Baseten and on other providers at the same time. Any model Baseten serves works, prefixed with baseten/.

openai_agents/model_providers/workflows/openai_compatible_workflow.py

@dataclass
class OpenAICompatibleAgentInput:
    prompt: str
    # "<provider>/<model>", such as "together/openai/gpt-oss-120b". The Worker's
    # model provider routes the agent's model calls to that provider.
    model: str


@workflow.defn
class OpenAICompatibleAgentWorkflow:
    @workflow.run
    async def run(self, input: OpenAICompatibleAgentInput) -> str:
        # Tools that run inside the Workflow must be async: the Agents SDK runs
        # sync tools in a thread, which the Workflow sandbox does not allow.
        @function_tool
        async def get_weather(city: str) -> str:
            workflow.logger.debug(f"Getting weather for {city}")
            return f"The weather in {city} is sunny."

        agent = Agent(
            name="Assistant",
            instructions="You only respond in haikus. When asked about the weather always use the tool to get the current weather.",
            model=input.model,
            tools=[get_weather],
        )

        result = await Runner.run(agent, input.prompt)
        return result.final_output

Name the model on the agent, as the sample does. An agent that names no model inherits the Agents SDK's default settings for OpenAI's current default model, which include verbosity and a reasoning effort; temporalio-openai-agents 1.1.0 forwards them to the provider unchanged, and Fireworks AI, for example, rejects verbosity with a 400. On that release, give such an agent an explicit model_settings=ModelSettings().

Tools that run inside the Workflow must be async: the Agents SDK runs sync tools in a thread, which the Workflow sandbox does not allow. Tools that do I/O belong in Activities; see Activity-backed tools.

Run the sample​

Export your Baseten key and start the Worker:

export BASETEN_API_KEY="your_api_key"
uv run openai_agents/model_providers/run_openai_compatible_worker.py

Then start a Workflow on Baseten in a second terminal:

uv run openai_agents/model_providers/run_openai_compatible_workflow.py --model baseten/openai/gpt-oss-120b

Export keys for other providers and pass several --model values to run one Workflow per model at the same time on the same Worker.

Retries, rate limits, and timeouts​

The plugin classifies Baseten's HTTP errors. Status 408, 409, 429, and any 5xx retry under the Activity's Retry Policy, and a Retry-After header becomes the next retry delay. Connection errors and client-side timeouts retry as well. Other 4xx responses, such as an invalid API key or an unknown model name, fail the Activity without retrying, as does a model name whose provider has no key on the Worker. Each attempt, its failure, and the final result are in Event History.

Baseten enforces requests-per-minute and tokens-per-minute limits that depend on your account tier and answers 429 when either is exceeded; the x-ratelimit-remaining-requests and x-ratelimit-remaining-tokens headers show which one hit zero. A 529 means Baseten had no serving capacity for the request even within your limits. Both retry under the Activity's Retry Policy. Baseten's error reference asks for exponential backoff and mentions a Retry-After header only as a possibility on 529, so the Retry Policy's backoff, not the provider, usually sets the pace. Sustained 429 responses mean the limit itself needs to go up, through Baseten's rate limit increase form.

What works through Baseten​

Function tools, Activity-backed tools, handoffs, agents as tools, and MCP servers all work unchanged: they run in the Workflow or in Activities, not at the provider. OpenAI-hosted tools such as web search, file search, code interpreter, and hosted MCP are Responses API features of OpenAI's own service, so they are not available through the Chat Completions API that the sample uses. The sample disables the Agents SDK's hosted tracing, which exports to OpenAI; the OpenTelemetry tracing path works as usual.

Baseten notes​

  • A dedicated deployment is called at https://model-<id>.api.baseten.co/environments/production/sync/v1 with the same Authorization: Bearer key; the legacy Api-Key scheme is still accepted. The model name must match what the deployment serves. A deployment whose min_replica is zero releases its replicas when idle, and the first request after that waits for a replica to start; see Baseten's request lifecycle page.
  • GET https://inference.baseten.co/v1/models lists the Model APIs catalog with context windows, pricing, and supported features, as does baseten model-api list from the Baseten CLI. Baseten notes that the catalog can report different limits from the ones the endpoint enforces.
  • gpt-oss-120b reasons by default at medium effort. The reasoning arrives in reasoning_content on the assistant message, and its tokens count as output tokens. Set ModelSettings(reasoning=Reasoning(effort="low")) on the agent to change the effort; Baseten's reasoning page lists the accepted values per model.
  • Tool calling, structured outputs, and JSON mode are available on every Model API through tools and response_format; see Baseten's function calling and structured outputs guides. Only one completion per request is supported, so leave n at its default.
  • Baseten reuses cached input tokens across requests that share an x-session-affinity header. An agent run resends the growing conversation on every turn, so sending the Workflow ID as the session, through ModelSettings(extra_headers=...), lets later turns reuse the cache. Baseten recommends at most about 60 requests per minute per session.
  • Two Baseten features sit outside this pattern: async inference, which delivers results to a webhook and works only on dedicated deployments, and server-side web search tools on Model APIs, which are in preview.

Samples​