# Baseten integration > For the complete documentation index, see [llms.txt](https://docs.temporal.io/llms.txt). > Any documentation page is available as raw Markdown by appending `.md` to its URL. > For related questions, query https://docs.temporal.io/api/retrieve?q=&jtb=&src=md-page&agent= (no auth; returns JSON `{content, source_url, page_url}`, with `source_url` pointing to Markdown for docs pages). Don't include personal data or credentials. [Baseten](https://www.baseten.co/) 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](/develop/python/integrations/openai-agents) 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](https://github.com/temporalio/samples-python/tree/main/openai_agents/model_providers), which run the same agent on Baseten, Fireworks AI, or Together AI from one Worker. ## Prerequisites - This guide assumes you are already familiar with the [OpenAI Agents SDK integration](/develop/python/integrations/openai-agents) and with Baseten's [OpenAI-compatible API](https://docs.baseten.co/inference/model-apis/overview). - If you are new to Temporal, we recommend reading [Understanding Temporal](/evaluate/understanding-temporal) or taking the [Temporal 101](https://learn.temporal.io/courses/temporal_101/) course. - Set up your local development environment by following [Set up your local development environment](/develop/python/set-up-your-local-python) and leave the Temporal development server running. - Create a [Baseten API key](https://app.baseten.co/settings/api_keys) and export it as `BASETEN_API_KEY` in the Worker's environment. The key stays in the Worker process; it is never part of Workflow input or Event History. ## Install ```bash 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](https://github.com/temporalio/samples-python/blob/main/openai_agents/model_providers/openai_compatible_providers.py) ```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 `/`, 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](https://www.baseten.co/library/gpt-oss-120b/) and `GET https://inference.baseten.co/v1/models` describe the catalog. A [dedicated deployment](https://docs.baseten.co/inference/calling-your-model) has its own base URL, `https://model-.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 `/` name to the matching endpoint and returns the Chat Completions model that the Agents SDK calls: [openai_agents/model_providers/openai_compatible_providers.py](https://github.com/temporalio/samples-python/blob/main/openai_agents/model_providers/openai_compatible_providers.py) ```py class OpenAICompatibleModelProvider(ModelProvider): """Routes ``/`` 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 / 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](https://github.com/temporalio/samples-python/blob/main/openai_agents/model_providers/run_openai_compatible_worker.py) ```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](https://github.com/temporalio/samples-python/blob/main/openai_agents/model_providers/workflows/openai_compatible_workflow.py) ```py @dataclass class OpenAICompatibleAgentInput: prompt: str # "/", 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](/develop/python/integrations/openai-agents#activity-backed-tools). ## Run the sample Export your Baseten key and start the Worker: ```bash 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: ```bash 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](https://docs.baseten.co/inference/model-apis/pricing-and-limits) 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](https://docs.baseten.co/inference/errors) 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](https://www.baseten.co/talk-to-us/increase-rate-limits/). ## 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](/develop/python/integrations/openai-agents#opentelemetry) path works as usual. ## Baseten notes - A dedicated deployment is called at `https://model-.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](https://docs.baseten.co/deployment/autoscaling/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](https://docs.baseten.co/inference/model-apis/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](https://docs.baseten.co/inference/function-calling) and [structured outputs](https://docs.baseten.co/inference/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 - [openai_agents/model_providers](https://github.com/temporalio/samples-python/tree/main/openai_agents/model_providers): the same agent on Baseten, Fireworks AI, or Together AI from one Worker, selected per Workflow by the model name. - [OpenAI Agents SDK integration](/develop/python/integrations/openai-agents): tools, MCP servers, conversation history, and tracing with the plugin.