# 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: the Worker points an OpenAI client at Baseten's base URL and names a model, and the Workflow code does not change. 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. ## 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 a default model: [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", model="openai/gpt-oss-120b", ) ``` Model APIs name models the way Hugging Face does, as `/`, so the default here is `openai/gpt-oss-120b`: 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. A `ModelProvider` turns that description into 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): """OpenAI Agents SDK model provider for an OpenAI-compatible endpoint. Client retries are off: the Temporal plugin runs each model call as an Activity, and the Activity's Retry Policy owns the retries. An agent that names no model gets the provider's default; an agent can name any other model the provider serves. """ def __init__(self, provider: OpenAICompatibleProvider) -> None: self._provider = provider self._client = AsyncOpenAI( base_url=provider.base_url, api_key=os.environ[provider.api_key_env], max_retries=0, ) def get_model(self, model_name: str | None) -> Model: # 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_name or self._provider.model, openai_client=self._client, ) ``` Two details matter here. 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. 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 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=OpenAICompatibleModelProvider(PROVIDERS[provider_name]), ), ], ) 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. It names no model, so the Worker's provider supplies its default; set `model=` on the `Agent` to any model Baseten serves to override it. [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 @workflow.defn class OpenAICompatibleAgentWorkflow: @workflow.run async def run(self, prompt: str) -> 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." # No model is named here, so the Worker's model provider supplies its # default. Set model= to any model the provider serves to override it. # Start from empty model settings: an agent that names no model gets # the Agents SDK's defaults for OpenAI's default model (a reasoning # effort and `verbosity`), which other providers reject. 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_settings=ModelSettings(), tools=[get_weather], ) result = await Runner.run(agent, prompt) return result.final_output ``` The empty `ModelSettings()` matters. An `Agent` that names no model gets the Agents SDK's default settings for OpenAI's current default model, which include `verbosity` and a reasoning effort, and the plugin forwards them to the provider. Fireworks AI, for example, rejects `verbosity` with a 400. An agent that names its model explicitly does not get those defaults. 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 Start the Worker with Baseten selected: ```bash export BASETEN_API_KEY="your_api_key" uv run openai_agents/model_providers/run_openai_compatible_worker.py --provider baseten ``` Then start the Workflow in a second terminal: ```bash uv run openai_agents/model_providers/run_openai_compatible_workflow.py ``` ## 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. 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, selected with `--provider`. - [OpenAI Agents SDK integration](/develop/python/integrations/openai-agents): tools, MCP servers, conversation history, and tracing with the plugin.