Docs and tests for litellm

README.md

@@ -1,6 +1,6 @@
 # OpenAI Agents SDK
 
-The OpenAI Agents SDK is a lightweight yet powerful framework for building multi-agent workflows.
+The OpenAI Agents SDK is a lightweight yet powerful framework for building multi-agent workflows. It is provider-agnostic, supporting the OpenAI Responses and Chat Completions APIs, as well as 100+ other LLMs.
 
 <img src="https://cdn.openai.com/API/docs/images/orchestration.png" alt="Image of the Agents Tracing UI" style="max-height: 803px;">
 
@@ -13,8 +13,6 @@ The OpenAI Agents SDK is a lightweight yet powerful framework for building multi
 
 Explore the [examples](examples) directory to see the SDK in action, and read our [documentation](https://openai.github.io/openai-agents-python/) for more details.
 
-Notably, our SDK [is compatible](https://openai.github.io/openai-agents-python/models/) with any model providers that support the OpenAI Chat Completions API format.
-
 ## Get started
 
 1. Set up your Python environment

docs/models/index.md

@@ -5,11 +5,40 @@ The Agents SDK comes with out-of-the-box support for OpenAI models in two flavor
 -   **Recommended**: the [`OpenAIResponsesModel`][agents.models.openai_responses.OpenAIResponsesModel], which calls OpenAI APIs using the new [Responses API](https://platform.openai.com/docs/api-reference/responses).
 -   The [`OpenAIChatCompletionsModel`][agents.models.openai_chatcompletions.OpenAIChatCompletionsModel], which calls OpenAI APIs using the [Chat Completions API](https://platform.openai.com/docs/api-reference/chat).
 
+## Non-OpenAI models
+
+You can use most other non-OpenAI models via the [LiteLLM integration](./litellm.md). First, install the litellm dependency group:
+
+```bash
+pip install "openai-agents[litellm]"
+```
+
+Then, use any of the [supported models](https://docs.litellm.ai/docs/providers) with the `litellm/` prefix:
+
+```python
+claude_agent = Agent(model="litellm/anthropic/claude-3-5-sonnet-20240620", ...)
+gemini_agent = Agent(model="litellm/gemini/gemini-2.5-flash-preview-04-17", ...)
+```
+
+### Other ways to use non-OpenAI models
+
+You can integrate other LLM providers in 3 more ways (examples [here](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/)):
+
+1. [`set_default_openai_client`][agents.set_default_openai_client] is useful in cases where you want to globally use an instance of `AsyncOpenAI` as the LLM client. This is for cases where the LLM provider has an OpenAI compatible API endpoint, and you can set the `base_url` and `api_key`. See a configurable example in [examples/model_providers/custom_example_global.py](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/custom_example_global.py).
+2. [`ModelProvider`][agents.models.interface.ModelProvider] is at the `Runner.run` level. This lets you say "use a custom model provider for all agents in this run". See a configurable example in [examples/model_providers/custom_example_provider.py](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/custom_example_provider.py).
+3. [`Agent.model`][agents.agent.Agent.model] lets you specify the model on a specific Agent instance. This enables you to mix and match different providers for different agents. See a configurable example in [examples/model_providers/custom_example_agent.py](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/custom_example_agent.py). An easy way to use most available models is via the [LiteLLM integration](./litellm.md).
+
+In cases where you do not have an API key from `platform.openai.com`, we recommend disabling tracing via `set_tracing_disabled()`, or setting up a [different tracing processor](../tracing.md).
+
+!!! note
+
+    In these examples, we use the Chat Completions API/model, because most LLM providers don't yet support the Responses API. If your LLM provider does support it, we recommend using Responses.
+
 ## Mixing and matching models
 
 Within a single workflow, you may want to use different models for each agent. For example, you could use a smaller, faster model for triage, while using a larger, more capable model for complex tasks. When configuring an [`Agent`][agents.Agent], you can select a specific model by either:
 
-1. Passing the name of an OpenAI model.
+1. Passing the name of a model.
 2. Passing any model name + a [`ModelProvider`][agents.models.interface.ModelProvider] that can map that name to a Model instance.
 3. Directly providing a [`Model`][agents.models.interface.Model] implementation.
 
@@ -64,20 +93,6 @@ english_agent = Agent(
 )
 ```
 
-## Using other LLM providers
-
-You can use other LLM providers in 3 ways (examples [here](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/)):
-
-1. [`set_default_openai_client`][agents.set_default_openai_client] is useful in cases where you want to globally use an instance of `AsyncOpenAI` as the LLM client. This is for cases where the LLM provider has an OpenAI compatible API endpoint, and you can set the `base_url` and `api_key`. See a configurable example in [examples/model_providers/custom_example_global.py](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/custom_example_global.py).
-2. [`ModelProvider`][agents.models.interface.ModelProvider] is at the `Runner.run` level. This lets you say "use a custom model provider for all agents in this run". See a configurable example in [examples/model_providers/custom_example_provider.py](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/custom_example_provider.py).
-3. [`Agent.model`][agents.agent.Agent.model] lets you specify the model on a specific Agent instance. This enables you to mix and match different providers for different agents. See a configurable example in [examples/model_providers/custom_example_agent.py](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/custom_example_agent.py). An easy way to use most available models is via the [LiteLLM integration](./litellm.md).
-
-In cases where you do not have an API key from `platform.openai.com`, we recommend disabling tracing via `set_tracing_disabled()`, or setting up a [different tracing processor](../tracing.md).
-
-!!! note
-
-    In these examples, we use the Chat Completions API/model, because most LLM providers don't yet support the Responses API. If your LLM provider does support it, we recommend using Responses.
-
 ## Common issues with using other LLM providers
 
 ### Tracing client error 401
@@ -100,7 +115,17 @@ The SDK uses the Responses API by default, but most other LLM providers don't ye
 Some model providers don't have support for [structured outputs](https://platform.openai.com/docs/guides/structured-outputs). This sometimes results in an error that looks something like this:
 
 ```
+
 BadRequestError: Error code: 400 - {'error': {'message': "'response_format.type' : value is not one of the allowed values ['text','json_object']", 'type': 'invalid_request_error'}}
+
 ```
 
 This is a shortcoming of some model providers - they support JSON outputs, but don't allow you to specify the `json_schema` to use for the output. We are working on a fix for this, but we suggest relying on providers that do have support for JSON schema output, because otherwise your app will often break because of malformed JSON.
+
+## Mixing models across providers
+
+You need to be aware of feature differences between model providers, or you may run into errors. For example, OpenAI supports structured outputs, multimodal input, and hosted file search and web search, but many other providers don't support these features. Be aware of these limitations:
+
+-   Don't send unsupported `tools` to providers that don't understand them
+-   Filter out multimodal inputs before calling models that are text-only
+-   Be aware that providers that don't support structured JSON outputs will occasionally produce invalid JSON.

tests/models/conftest.py

@@ -0,0 +1,11 @@
+import os
+import sys
+
+
+# Skip voice tests on Python 3.9
+def pytest_ignore_collect(collection_path, config):
+    if sys.version_info[:2] == (3, 9):
+        this_dir = os.path.dirname(__file__)
+
+        if str(collection_path).startswith(this_dir):
+            return True

tests/models/test_map.py

@@ -0,0 +1,20 @@
+from agents import Agent, OpenAIResponsesModel, RunConfig, Runner
+from agents.extensions.models.litellm_model import LitellmModel
+
+
+def test_no_prefix_is_openai():
+    agent = Agent(model="gpt-4o", instructions="", name="test")
+    model = Runner._get_model(agent, RunConfig())
+    assert isinstance(model, OpenAIResponsesModel)
+
+
+def openai_prefix_is_openai():
+    agent = Agent(model="openai/gpt-4o", instructions="", name="test")
+    model = Runner._get_model(agent, RunConfig())
+    assert isinstance(model, OpenAIResponsesModel)
+
+
+def test_litellm_prefix_is_litellm():
+    agent = Agent(model="litellm/foo/bar", instructions="", name="test")
+    model = Runner._get_model(agent, RunConfig())
+    assert isinstance(model, LitellmModel)