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495
docs/my-website/docs/response_api.md
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@ -14,22 +14,22 @@ LiteLLM provides a BETA endpoint in the spec of [OpenAI's `/responses` API](http
| Fallbacks | ✅ | Works between supported models |
| Loadbalancing | ✅ | Works between supported models |
| Supported LiteLLM Versions | 1.63.8+ | |
| Supported LLM providers | `openai` | |
| Supported LLM providers | **All LiteLLM supported providers** | `openai`, `anthropic`, `bedrock`, `vertex_ai`, `gemini`, `azure`, `azure_ai` etc. |
## Usage
## Create a model response
### LiteLLM Python SDK
<Tabs>
<TabItem value="litellm-sdk" label="LiteLLM SDK">
<TabItem value="openai" label="OpenAI">
#### Non-streaming
```python showLineNumbers
```python showLineNumbers title="OpenAI Non-streaming Response"
import litellm
# Non-streaming response
response = litellm.responses(
model="o1-pro",
model="openai/o1-pro",
input="Tell me a three sentence bedtime story about a unicorn.",
max_output_tokens=100
)
@ -38,12 +38,12 @@ print(response)
```
#### Streaming
```python showLineNumbers
```python showLineNumbers title="OpenAI Streaming Response"
import litellm
# Streaming response
response = litellm.responses(
model="o1-pro",
model="openai/o1-pro",
input="Tell me a three sentence bedtime story about a unicorn.",
stream=True
)
@ -53,58 +53,169 @@ for event in response:
```
</TabItem>
<TabItem value="proxy" label="OpenAI SDK with LiteLLM Proxy">
First, add this to your litellm proxy config.yaml:
```yaml showLineNumbers
model_list:
- model_name: o1-pro
litellm_params:
model: openai/o1-pro
api_key: os.environ/OPENAI_API_KEY
```
Start your LiteLLM proxy:
```bash
litellm --config /path/to/config.yaml
# RUNNING on http://0.0.0.0:4000
```
Then use the OpenAI SDK pointed to your proxy:
<TabItem value="anthropic" label="Anthropic">
#### Non-streaming
```python showLineNumbers
from openai import OpenAI
```python showLineNumbers title="Anthropic Non-streaming Response"
import litellm
import os
# Initialize client with your proxy URL
client = OpenAI(
base_url="http://localhost:4000", # Your proxy URL
api_key="your-api-key" # Your proxy API key
)
# Set API key
os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-api-key"
# Non-streaming response
response = client.responses.create(
model="o1-pro",
input="Tell me a three sentence bedtime story about a unicorn."
response = litellm.responses(
model="anthropic/claude-3-5-sonnet-20240620",
input="Tell me a three sentence bedtime story about a unicorn.",
max_output_tokens=100
)
print(response)
```
#### Streaming
```python showLineNumbers
from openai import OpenAI
```python showLineNumbers title="Anthropic Streaming Response"
import litellm
import os
# Initialize client with your proxy URL
client = OpenAI(
base_url="http://localhost:4000", # Your proxy URL
api_key="your-api-key" # Your proxy API key
)
# Set API key
os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-api-key"
# Streaming response
response = client.responses.create(
model="o1-pro",
response = litellm.responses(
model="anthropic/claude-3-5-sonnet-20240620",
input="Tell me a three sentence bedtime story about a unicorn.",
stream=True
)
for event in response:
print(event)
```
</TabItem>
<TabItem value="vertex" label="Vertex AI">
#### Non-streaming
```python showLineNumbers title="Vertex AI Non-streaming Response"
import litellm
import os
# Set credentials - Vertex AI uses application default credentials
# Run 'gcloud auth application-default login' to authenticate
os.environ["VERTEXAI_PROJECT"] = "your-gcp-project-id"
os.environ["VERTEXAI_LOCATION"] = "us-central1"
# Non-streaming response
response = litellm.responses(
model="vertex_ai/gemini-1.5-pro",
input="Tell me a three sentence bedtime story about a unicorn.",
max_output_tokens=100
)
print(response)
```
#### Streaming
```python showLineNumbers title="Vertex AI Streaming Response"
import litellm
import os
# Set credentials - Vertex AI uses application default credentials
# Run 'gcloud auth application-default login' to authenticate
os.environ["VERTEXAI_PROJECT"] = "your-gcp-project-id"
os.environ["VERTEXAI_LOCATION"] = "us-central1"
# Streaming response
response = litellm.responses(
model="vertex_ai/gemini-1.5-pro",
input="Tell me a three sentence bedtime story about a unicorn.",
stream=True
)
for event in response:
print(event)
```
</TabItem>
<TabItem value="bedrock" label="AWS Bedrock">
#### Non-streaming
```python showLineNumbers title="AWS Bedrock Non-streaming Response"
import litellm
import os
# Set AWS credentials
os.environ["AWS_ACCESS_KEY_ID"] = "your-access-key-id"
os.environ["AWS_SECRET_ACCESS_KEY"] = "your-secret-access-key"
os.environ["AWS_REGION_NAME"] = "us-west-2" # or your AWS region
# Non-streaming response
response = litellm.responses(
model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
input="Tell me a three sentence bedtime story about a unicorn.",
max_output_tokens=100
)
print(response)
```
#### Streaming
```python showLineNumbers title="AWS Bedrock Streaming Response"
import litellm
import os
# Set AWS credentials
os.environ["AWS_ACCESS_KEY_ID"] = "your-access-key-id"
os.environ["AWS_SECRET_ACCESS_KEY"] = "your-secret-access-key"
os.environ["AWS_REGION_NAME"] = "us-west-2" # or your AWS region
# Streaming response
response = litellm.responses(
model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
input="Tell me a three sentence bedtime story about a unicorn.",
stream=True
)
for event in response:
print(event)
```
</TabItem>
<TabItem value="gemini" label="Google AI Studio">
#### Non-streaming
```python showLineNumbers title="Google AI Studio Non-streaming Response"
import litellm
import os
# Set API key for Google AI Studio
os.environ["GEMINI_API_KEY"] = "your-gemini-api-key"
# Non-streaming response
response = litellm.responses(
model="gemini/gemini-1.5-flash",
input="Tell me a three sentence bedtime story about a unicorn.",
max_output_tokens=100
)
print(response)
```
#### Streaming
```python showLineNumbers title="Google AI Studio Streaming Response"
import litellm
import os
# Set API key for Google AI Studio
os.environ["GEMINI_API_KEY"] = "your-gemini-api-key"
# Streaming response
response = litellm.responses(
model="gemini/gemini-1.5-flash",
input="Tell me a three sentence bedtime story about a unicorn.",
stream=True
)
@ -116,10 +227,296 @@ for event in response:
</TabItem>
</Tabs>
### LiteLLM Proxy with OpenAI SDK
## **Supported Providers**
First, set up and start your LiteLLM proxy server.
```bash title="Start LiteLLM Proxy Server"
litellm --config /path/to/config.yaml
# RUNNING on http://0.0.0.0:4000
```
<Tabs>
<TabItem value="openai" label="OpenAI">
First, add this to your litellm proxy config.yaml:
```yaml showLineNumbers title="OpenAI Proxy Configuration"
model_list:
- model_name: openai/o1-pro
litellm_params:
model: openai/o1-pro
api_key: os.environ/OPENAI_API_KEY
```
#### Non-streaming
```python showLineNumbers title="OpenAI Proxy Non-streaming Response"
from openai import OpenAI
# Initialize client with your proxy URL
client = OpenAI(
base_url="http://localhost:4000", # Your proxy URL
api_key="your-api-key" # Your proxy API key
)
# Non-streaming response
response = client.responses.create(
model="openai/o1-pro",
input="Tell me a three sentence bedtime story about a unicorn."
)
print(response)
```
#### Streaming
```python showLineNumbers title="OpenAI Proxy Streaming Response"
from openai import OpenAI
# Initialize client with your proxy URL
client = OpenAI(
base_url="http://localhost:4000", # Your proxy URL
api_key="your-api-key" # Your proxy API key
)
# Streaming response
response = client.responses.create(
model="openai/o1-pro",
input="Tell me a three sentence bedtime story about a unicorn.",
stream=True
)
for event in response:
print(event)
```
</TabItem>
<TabItem value="anthropic" label="Anthropic">
First, add this to your litellm proxy config.yaml:
```yaml showLineNumbers title="Anthropic Proxy Configuration"
model_list:
- model_name: anthropic/claude-3-5-sonnet-20240620
litellm_params:
model: anthropic/claude-3-5-sonnet-20240620
api_key: os.environ/ANTHROPIC_API_KEY
```
#### Non-streaming
```python showLineNumbers title="Anthropic Proxy Non-streaming Response"
from openai import OpenAI
# Initialize client with your proxy URL
client = OpenAI(
base_url="http://localhost:4000", # Your proxy URL
api_key="your-api-key" # Your proxy API key
)
# Non-streaming response
response = client.responses.create(
model="anthropic/claude-3-5-sonnet-20240620",
input="Tell me a three sentence bedtime story about a unicorn."
)
print(response)
```
#### Streaming
```python showLineNumbers title="Anthropic Proxy Streaming Response"
from openai import OpenAI
# Initialize client with your proxy URL
client = OpenAI(
base_url="http://localhost:4000", # Your proxy URL
api_key="your-api-key" # Your proxy API key
)
# Streaming response
response = client.responses.create(
model="anthropic/claude-3-5-sonnet-20240620",
input="Tell me a three sentence bedtime story about a unicorn.",
stream=True
)
for event in response:
print(event)
```
</TabItem>
<TabItem value="vertex" label="Vertex AI">
First, add this to your litellm proxy config.yaml:
```yaml showLineNumbers title="Vertex AI Proxy Configuration"
model_list:
- model_name: vertex_ai/gemini-1.5-pro
litellm_params:
model: vertex_ai/gemini-1.5-pro
vertex_project: your-gcp-project-id
vertex_location: us-central1
```
#### Non-streaming
```python showLineNumbers title="Vertex AI Proxy Non-streaming Response"
from openai import OpenAI
# Initialize client with your proxy URL
client = OpenAI(
base_url="http://localhost:4000", # Your proxy URL
api_key="your-api-key" # Your proxy API key
)
# Non-streaming response
response = client.responses.create(
model="vertex_ai/gemini-1.5-pro",
input="Tell me a three sentence bedtime story about a unicorn."
)
print(response)
```
#### Streaming
```python showLineNumbers title="Vertex AI Proxy Streaming Response"
from openai import OpenAI
# Initialize client with your proxy URL
client = OpenAI(
base_url="http://localhost:4000", # Your proxy URL
api_key="your-api-key" # Your proxy API key
)
# Streaming response
response = client.responses.create(
model="vertex_ai/gemini-1.5-pro",
input="Tell me a three sentence bedtime story about a unicorn.",
stream=True
)
for event in response:
print(event)
```
</TabItem>
<TabItem value="bedrock" label="AWS Bedrock">
First, add this to your litellm proxy config.yaml:
```yaml showLineNumbers title="AWS Bedrock Proxy Configuration"
model_list:
- model_name: bedrock/anthropic.claude-3-sonnet-20240229-v1:0
litellm_params:
model: bedrock/anthropic.claude-3-sonnet-20240229-v1:0
aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID
aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY
aws_region_name: us-west-2
```
#### Non-streaming
```python showLineNumbers title="AWS Bedrock Proxy Non-streaming Response"
from openai import OpenAI
# Initialize client with your proxy URL
client = OpenAI(
base_url="http://localhost:4000", # Your proxy URL
api_key="your-api-key" # Your proxy API key
)
# Non-streaming response
response = client.responses.create(
model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
input="Tell me a three sentence bedtime story about a unicorn."
)
print(response)
```
#### Streaming
```python showLineNumbers title="AWS Bedrock Proxy Streaming Response"
from openai import OpenAI
# Initialize client with your proxy URL
client = OpenAI(
base_url="http://localhost:4000", # Your proxy URL
api_key="your-api-key" # Your proxy API key
)
# Streaming response
response = client.responses.create(
model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
input="Tell me a three sentence bedtime story about a unicorn.",
stream=True
)
for event in response:
print(event)
```
</TabItem>
<TabItem value="gemini" label="Google AI Studio">
First, add this to your litellm proxy config.yaml:
```yaml showLineNumbers title="Google AI Studio Proxy Configuration"
model_list:
- model_name: gemini/gemini-1.5-flash
litellm_params:
model: gemini/gemini-1.5-flash
api_key: os.environ/GEMINI_API_KEY
```
#### Non-streaming
```python showLineNumbers title="Google AI Studio Proxy Non-streaming Response"
from openai import OpenAI
# Initialize client with your proxy URL
client = OpenAI(
base_url="http://localhost:4000", # Your proxy URL
api_key="your-api-key" # Your proxy API key
)
# Non-streaming response
response = client.responses.create(
model="gemini/gemini-1.5-flash",
input="Tell me a three sentence bedtime story about a unicorn."
)
print(response)
```
#### Streaming
```python showLineNumbers title="Google AI Studio Proxy Streaming Response"
from openai import OpenAI
# Initialize client with your proxy URL
client = OpenAI(
base_url="http://localhost:4000", # Your proxy URL
api_key="your-api-key" # Your proxy API key
)
# Streaming response
response = client.responses.create(
model="gemini/gemini-1.5-flash",
input="Tell me a three sentence bedtime story about a unicorn.",
stream=True
)
for event in response:
print(event)
```
</TabItem>
</Tabs>
## Supported Responses API Parameters
| Provider | Supported Parameters |
|----------|---------------------|
| `openai` | [All Responses API parameters are supported](https://github.com/BerriAI/litellm/blob/7c3df984da8e4dff9201e4c5353fdc7a2b441831/litellm/llms/openai/responses/transformation.py#L23) |
| `azure` | [All Responses API parameters are supported](https://github.com/BerriAI/litellm/blob/7c3df984da8e4dff9201e4c5353fdc7a2b441831/litellm/llms/openai/responses/transformation.py#L23) |
| `anthropic` | [See supported parameters here](https://github.com/BerriAI/litellm/blob/f39d9178868662746f159d5ef642c7f34f9bfe5f/litellm/responses/litellm_completion_transformation/transformation.py#L57) |
| `bedrock` | [See supported parameters here](https://github.com/BerriAI/litellm/blob/f39d9178868662746f159d5ef642c7f34f9bfe5f/litellm/responses/litellm_completion_transformation/transformation.py#L57) |
| `gemini` | [See supported parameters here](https://github.com/BerriAI/litellm/blob/f39d9178868662746f159d5ef642c7f34f9bfe5f/litellm/responses/litellm_completion_transformation/transformation.py#L57) |
| `vertex_ai` | [See supported parameters here](https://github.com/BerriAI/litellm/blob/f39d9178868662746f159d5ef642c7f34f9bfe5f/litellm/responses/litellm_completion_transformation/transformation.py#L57) |
| `azure_ai` | [See supported parameters here](https://github.com/BerriAI/litellm/blob/f39d9178868662746f159d5ef642c7f34f9bfe5f/litellm/responses/litellm_completion_transformation/transformation.py#L57) |
| All other llm api providers | [See supported parameters here](https://github.com/BerriAI/litellm/blob/f39d9178868662746f159d5ef642c7f34f9bfe5f/litellm/responses/litellm_completion_transformation/transformation.py#L57) |
| Provider | Link to Usage |
|-------------|--------------------|
| OpenAI| [Usage](#usage) |
| Azure OpenAI| [Usage](../docs/providers/azure#azure-responses-api) |

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1:null

4
litellm/proxy/auth/auth_checks.py
View file

@ -88,7 +88,7 @@ async def common_checks(
9. Check if request body is safe
10. [OPTIONAL] Organization checks - is user_object.organization_id is set, run these checks
"""
_model = request_body.get("model", None)
_model: Optional[str] = cast(Optional[str], request_body.get("model", None))
# 1. If team is blocked
if team_object is not None and team_object.blocked is True:
@ -112,7 +112,7 @@ async def common_checks(
)
## 2.1 If user can call model (if personal key)
if team_object is None and user_object is not None:
if _model and team_object is None and user_object is not None:
await can_user_call_model(
model=_model,
llm_router=llm_router,

61
litellm/responses/litellm_completion_transformation/streaming_iterator.py
View file

@ -7,15 +7,18 @@ from litellm.responses.litellm_completion_transformation.transformation import (
)
from litellm.responses.streaming_iterator import ResponsesAPIStreamingIterator
from litellm.types.llms.openai import (
OutputTextDeltaEvent,
ResponseCompletedEvent,
ResponseInputParam,
ResponsesAPIOptionalRequestParams,
ResponsesAPIStreamEvents,
ResponsesAPIStreamingResponse,
)
from litellm.types.utils import Delta as ChatCompletionDelta
from litellm.types.utils import (
ModelResponse,
ModelResponseStream,
StreamingChoices,
TextCompletionResponse,
)
@ -38,7 +41,7 @@ class LiteLLMCompletionStreamingIterator(ResponsesAPIStreamingIterator):
self.responses_api_request: ResponsesAPIOptionalRequestParams = (
responses_api_request
)
self.collected_chunks: List[ModelResponseStream] = []
self.collected_chat_completion_chunks: List[ModelResponseStream] = []
self.finished: bool = False
async def __anext__(
@ -51,7 +54,14 @@ class LiteLLMCompletionStreamingIterator(ResponsesAPIStreamingIterator):
# Get the next chunk from the stream
try:
chunk = await self.litellm_custom_stream_wrapper.__anext__()
self.collected_chunks.append(chunk)
self.collected_chat_completion_chunks.append(chunk)
response_api_chunk = (
self._transform_chat_completion_chunk_to_response_api_chunk(
chunk
)
)
if response_api_chunk:
return response_api_chunk
except StopAsyncIteration:
self.finished = True
response_completed_event = self._emit_response_completed_event()
@ -74,28 +84,65 @@ class LiteLLMCompletionStreamingIterator(ResponsesAPIStreamingIterator):
try:
while True:
if self.finished is True:
raise StopAsyncIteration
raise StopIteration
# Get the next chunk from the stream
try:
chunk = self.litellm_custom_stream_wrapper.__next__()
self.collected_chunks.append(chunk)
except StopAsyncIteration:
self.collected_chat_completion_chunks.append(chunk)
response_api_chunk = (
self._transform_chat_completion_chunk_to_response_api_chunk(
chunk
)
)
if response_api_chunk:
return response_api_chunk
except StopIteration:
self.finished = True
response_completed_event = self._emit_response_completed_event()
if response_completed_event:
return response_completed_event
else:
raise StopAsyncIteration
raise StopIteration
except Exception as e:
# Handle HTTP errors
self.finished = True
raise e
def _transform_chat_completion_chunk_to_response_api_chunk(
self, chunk: ModelResponseStream
) -> Optional[ResponsesAPIStreamingResponse]:
"""
Transform a chat completion chunk to a response API chunk.
This currently only handles emitting the OutputTextDeltaEvent, which is used by other tools using the responses API.
"""
return OutputTextDeltaEvent(
type=ResponsesAPIStreamEvents.OUTPUT_TEXT_DELTA,
item_id=chunk.id,
output_index=0,
content_index=0,
delta=self._get_delta_string_from_streaming_choices(chunk.choices),
)
def _get_delta_string_from_streaming_choices(
self, choices: List[StreamingChoices]
) -> str:
"""
Get the delta string from the streaming choices
For now this collected the first choice's delta string.
It's unclear how users expect litellm to translate multiple-choices-per-chunk to the responses API output.
"""
choice = choices[0]
chat_completion_delta: ChatCompletionDelta = choice.delta
return chat_completion_delta.content or ""
def _emit_response_completed_event(self) -> Optional[ResponseCompletedEvent]:
litellm_model_response: Optional[
Union[ModelResponse, TextCompletionResponse]
] = stream_chunk_builder(chunks=self.collected_chunks)
] = stream_chunk_builder(chunks=self.collected_chat_completion_chunks)
if litellm_model_response and isinstance(litellm_model_response, ModelResponse):
return ResponseCompletedEvent(

33
litellm/responses/litellm_completion_transformation/transformation.py
View file

@ -7,6 +7,7 @@ from typing import Any, Dict, List, Optional, Union
from openai.types.responses.tool_param import FunctionToolParam
from litellm.caching import InMemoryCache
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.responses.litellm_completion_transformation.session_handler import (
ResponsesAPISessionElement,
SessionHandler,
@ -52,6 +53,26 @@ RESPONSES_API_SESSION_HANDLER = SessionHandler()
class LiteLLMCompletionResponsesConfig:
@staticmethod
def get_supported_openai_params(model: str) -> list:
"""
LiteLLM Adapter from OpenAI Responses API to Chat Completion API supports a subset of OpenAI Responses API params
"""
return [
"input",
"model",
"instructions",
"max_output_tokens",
"metadata",
"parallel_tool_calls",
"previous_response_id",
"stream",
"temperature",
"tool_choice",
"tools",
"top_p",
"user",
]
@staticmethod
def transform_responses_api_request_to_chat_completion_request(
@ -88,6 +109,18 @@ class LiteLLMCompletionResponsesConfig:
"custom_llm_provider": custom_llm_provider,
}
# Responses API `Completed` events require usage, we pass `stream_options` to litellm.completion to include usage
if stream is True:
stream_options = {
"include_usage": True,
}
litellm_completion_request["stream_options"] = stream_options
litellm_logging_obj: Optional[LiteLLMLoggingObj] = kwargs.get(
"litellm_logging_obj"
)
if litellm_logging_obj:
litellm_logging_obj.stream_options = stream_options
# only pass non-None values
litellm_completion_request = {
k: v for k, v in litellm_completion_request.items() if v is not None

84
litellm/responses/streaming_iterator.py
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@ -11,7 +11,9 @@ from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging
from litellm.litellm_core_utils.thread_pool_executor import executor
from litellm.llms.base_llm.responses.transformation import BaseResponsesAPIConfig
from litellm.types.llms.openai import (
OutputTextDeltaEvent,
ResponseCompletedEvent,
ResponsesAPIResponse,
ResponsesAPIStreamEvents,
ResponsesAPIStreamingResponse,
)
@ -212,9 +214,14 @@ class SyncResponsesAPIStreamingIterator(BaseResponsesAPIStreamingIterator):
class MockResponsesAPIStreamingIterator(BaseResponsesAPIStreamingIterator):
"""
mock iterator - some models like o1-pro do not support streaming, we need to fake a stream
Mock iterator—fake a stream by slicing the full response text into
5 char deltas, then emit a completed event.
Models like o1-pro don't support streaming, so we fake it.
"""
CHUNK_SIZE = 5
def __init__(
self,
response: httpx.Response,
@ -222,49 +229,68 @@ class MockResponsesAPIStreamingIterator(BaseResponsesAPIStreamingIterator):
responses_api_provider_config: BaseResponsesAPIConfig,
logging_obj: LiteLLMLoggingObj,
):
self.raw_http_response = response
super().__init__(
response=response,
model=model,
responses_api_provider_config=responses_api_provider_config,
logging_obj=logging_obj,
)
self.is_done = False
# one-time transform
transformed = (
self.responses_api_provider_config.transform_response_api_response(
model=self.model,
raw_response=response,
logging_obj=logging_obj,
)
)
full_text = self._collect_text(transformed)
# build a list of 5‑char delta events
deltas = [
OutputTextDeltaEvent(
type=ResponsesAPIStreamEvents.OUTPUT_TEXT_DELTA,
delta=full_text[i : i + self.CHUNK_SIZE],
item_id=transformed.id,
output_index=0,
content_index=0,
)
for i in range(0, len(full_text), self.CHUNK_SIZE)
]
# append the completed event
self._events = deltas + [
ResponseCompletedEvent(
type=ResponsesAPIStreamEvents.RESPONSE_COMPLETED,
response=transformed,
)
]
self._idx = 0
def __aiter__(self):
return self
async def __anext__(self) -> ResponsesAPIStreamingResponse:
if self.is_done:
if self._idx >= len(self._events):
raise StopAsyncIteration
self.is_done = True
transformed_response = (
self.responses_api_provider_config.transform_response_api_response(
model=self.model,
raw_response=self.raw_http_response,
logging_obj=self.logging_obj,
)
)
return ResponseCompletedEvent(
type=ResponsesAPIStreamEvents.RESPONSE_COMPLETED,
response=transformed_response,
)
evt = self._events[self._idx]
self._idx += 1
return evt
def __iter__(self):
return self
def __next__(self) -> ResponsesAPIStreamingResponse:
if self.is_done:
if self._idx >= len(self._events):
raise StopIteration
self.is_done = True
transformed_response = (
self.responses_api_provider_config.transform_response_api_response(
model=self.model,
raw_response=self.raw_http_response,
logging_obj=self.logging_obj,
)
)
return ResponseCompletedEvent(
type=ResponsesAPIStreamEvents.RESPONSE_COMPLETED,
response=transformed_response,
)
evt = self._events[self._idx]
self._idx += 1
return evt
def _collect_text(self, resp: ResponsesAPIResponse) -> str:
out = ""
for out_item in resp.output:
if out_item.type == "message":
for c in getattr(out_item, "content", []):
out += c.text
return out

56
litellm/router.py
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@ -339,9 +339,9 @@ class Router:
) # names of models under litellm_params. ex. azure/chatgpt-v-2
self.deployment_latency_map = {}
### CACHING ###
cache_type: Literal[
"local", "redis", "redis-semantic", "s3", "disk"
] = "local" # default to an in-memory cache
cache_type: Literal["local", "redis", "redis-semantic", "s3", "disk"] = (
"local" # default to an in-memory cache
)
redis_cache = None
cache_config: Dict[str, Any] = {}
@ -562,9 +562,9 @@ class Router:
)
)
self.model_group_retry_policy: Optional[
Dict[str, RetryPolicy]
] = model_group_retry_policy
self.model_group_retry_policy: Optional[Dict[str, RetryPolicy]] = (
model_group_retry_policy
)
self.allowed_fails_policy: Optional[AllowedFailsPolicy] = None
if allowed_fails_policy is not None:
@ -1104,17 +1104,21 @@ class Router:
) -> None:
"""
Adds default litellm params to kwargs, if set.
Handles inserting this as either "metadata" or "litellm_metadata" depending on the metadata_variable_name
"""
self.default_litellm_params[
metadata_variable_name
] = self.default_litellm_params.pop("metadata", {})
for k, v in self.default_litellm_params.items():
if (
k not in kwargs and v is not None
): # prioritize model-specific params > default router params
kwargs[k] = v
elif k == metadata_variable_name:
kwargs[metadata_variable_name].update(v)
# 1) copy your defaults and pull out metadata
defaults = self.default_litellm_params.copy()
metadata_defaults = defaults.pop("metadata", {}) or {}
# 2) add any non-metadata defaults that aren't already in kwargs
for key, value in defaults.items():
if value is None:
continue
kwargs.setdefault(key, value)
# 3) merge in metadata, this handles inserting this as either "metadata" or "litellm_metadata"
kwargs.setdefault(metadata_variable_name, {}).update(metadata_defaults)
def _handle_clientside_credential(
self, deployment: dict, kwargs: dict
@ -3243,11 +3247,11 @@ class Router:
if isinstance(e, litellm.ContextWindowExceededError):
if context_window_fallbacks is not None:
fallback_model_group: Optional[
List[str]
] = self._get_fallback_model_group_from_fallbacks(
fallbacks=context_window_fallbacks,
model_group=model_group,
fallback_model_group: Optional[List[str]] = (
self._get_fallback_model_group_from_fallbacks(
fallbacks=context_window_fallbacks,
model_group=model_group,
)
)
if fallback_model_group is None:
raise original_exception
@ -3279,11 +3283,11 @@ class Router:
e.message += "\n{}".format(error_message)
elif isinstance(e, litellm.ContentPolicyViolationError):
if content_policy_fallbacks is not None:
fallback_model_group: Optional[
List[str]
] = self._get_fallback_model_group_from_fallbacks(
fallbacks=content_policy_fallbacks,
model_group=model_group,
fallback_model_group: Optional[List[str]] = (
self._get_fallback_model_group_from_fallbacks(
fallbacks=content_policy_fallbacks,
model_group=model_group,
)
)
if fallback_model_group is None:
raise original_exception

15
litellm/types/llms/openai.py
View file

@ -892,6 +892,19 @@ OpenAIAudioTranscriptionOptionalParams = Literal[
OpenAIImageVariationOptionalParams = Literal["n", "size", "response_format", "user"]
class ComputerToolParam(TypedDict, total=False):
display_height: Required[float]
"""The height of the computer display."""
display_width: Required[float]
"""The width of the computer display."""
environment: Required[Union[Literal["mac", "windows", "ubuntu", "browser"], str]]
"""The type of computer environment to control."""
type: Required[Union[Literal["computer_use_preview"], str]]
class ResponsesAPIOptionalRequestParams(TypedDict, total=False):
"""TypedDict for Optional parameters supported by the responses API."""
@ -907,7 +920,7 @@ class ResponsesAPIOptionalRequestParams(TypedDict, total=False):
temperature: Optional[float]
text: Optional[ResponseTextConfigParam]
tool_choice: Optional[ToolChoice]
tools: Optional[List[ToolParam]]
tools: Optional[List[Union[ToolParam, ComputerToolParam]]]
top_p: Optional[float]
truncation: Optional[Literal["auto", "disabled"]]
user: Optional[str]

4
pyproject.toml
View file

@ -1,6 +1,6 @@
[tool.poetry]
name = "litellm"
version = "1.66.3"
version = "1.67.0"
description = "Library to easily interface with LLM API providers"
authors = ["BerriAI"]
license = "MIT"
@ -118,7 +118,7 @@ requires = ["poetry-core", "wheel"]
build-backend = "poetry.core.masonry.api"
[tool.commitizen]
version = "1.66.3"
version = "1.67.0"
version_files = [
"pyproject.toml:^version"
]

54
tests/litellm/test_router.py Normal file
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@ -0,0 +1,54 @@
import copy
import json
import os
import sys
import pytest
from fastapi.testclient import TestClient
sys.path.insert(
0, os.path.abspath("../../..")
) # Adds the parent directory to the system path
import litellm
def test_update_kwargs_does_not_mutate_defaults_and_merges_metadata():
# initialize a real Router (env‑vars can be empty)
router = litellm.Router(
model_list=[
{
"model_name": "gpt-3.5-turbo",
"litellm_params": {
"model": "azure/chatgpt-v-3",
"api_key": os.getenv("AZURE_API_KEY"),
"api_version": os.getenv("AZURE_API_VERSION"),
"api_base": os.getenv("AZURE_API_BASE"),
},
}
],
)
# override to known defaults for the test
router.default_litellm_params = {
"foo": "bar",
"metadata": {"baz": 123},
}
original = copy.deepcopy(router.default_litellm_params)
kwargs = {}
# invoke the helper
router._update_kwargs_with_default_litellm_params(
kwargs=kwargs,
metadata_variable_name="litellm_metadata",
)
# 1) router.defaults must be unchanged
assert router.default_litellm_params == original
# 2) non‑metadata keys get merged
assert kwargs["foo"] == "bar"
# 3) metadata lands under "metadata"
assert kwargs["litellm_metadata"] == {"baz": 123}

38
tests/llm_responses_api_testing/base_responses_api.py
View file

@ -133,11 +133,13 @@ class BaseResponsesAPITest(ABC):
validate_responses_api_response(response, final_chunk=True)
@pytest.mark.parametrize("sync_mode", [True])
@pytest.mark.parametrize("sync_mode", [True, False])
@pytest.mark.asyncio
async def test_basic_openai_responses_api_streaming(self, sync_mode):
litellm._turn_on_debug()
base_completion_call_args = self.get_base_completion_call_args()
collected_content_string = ""
response_completed_event = None
if sync_mode:
response = litellm.responses(
input="Basic ping",
@ -146,6 +148,10 @@ class BaseResponsesAPITest(ABC):
)
for event in response:
print("litellm response=", json.dumps(event, indent=4, default=str))
if event.type == "response.output_text.delta":
collected_content_string += event.delta
elif event.type == "response.completed":
response_completed_event = event
else:
response = await litellm.aresponses(
input="Basic ping",
@ -154,5 +160,35 @@ class BaseResponsesAPITest(ABC):
)
async for event in response:
print("litellm response=", json.dumps(event, indent=4, default=str))
if event.type == "response.output_text.delta":
collected_content_string += event.delta
elif event.type == "response.completed":
response_completed_event = event
# assert the delta chunks content had len(collected_content_string) > 0
# this content is typically rendered on chat ui's
assert len(collected_content_string) > 0
# assert the response completed event is not None
assert response_completed_event is not None
# assert the response completed event has a response
assert response_completed_event.response is not None
# assert the response completed event includes the usage
assert response_completed_event.response.usage is not None
# basic test assert the usage seems reasonable
print("response_completed_event.response.usage=", response_completed_event.response.usage)
assert response_completed_event.response.usage.input_tokens > 0 and response_completed_event.response.usage.input_tokens < 100
assert response_completed_event.response.usage.output_tokens > 0 and response_completed_event.response.usage.output_tokens < 1000
assert response_completed_event.response.usage.total_tokens > 0 and response_completed_event.response.usage.total_tokens < 1000
# total tokens should be the sum of input and output tokens
assert response_completed_event.response.usage.total_tokens == response_completed_event.response.usage.input_tokens + response_completed_event.response.usage.output_tokens

97
tests/llm_responses_api_testing/test_openai_responses_api.py
View file

@ -936,3 +936,100 @@ async def test_openai_o1_pro_response_api_streaming(sync_mode):
assert request_body["model"] == "o1-pro"
assert request_body["max_output_tokens"] == 20
assert "stream" not in request_body
def test_basic_computer_use_preview_tool_call():
"""
Test that LiteLLM correctly handles a computer_use_preview tool call where the environment is set to "linux"
linux is an unsupported environment for the computer_use_preview tool, but litellm users should still be able to pass it to openai
"""
# Mock response from OpenAI
mock_response = {
"id": "resp_67dc3dd77b388190822443a85252da5a0e13d8bdc0e28d88",
"object": "response",
"created_at": 1742486999,
"status": "incomplete",
"error": None,
"incomplete_details": {"reason": "max_output_tokens"},
"instructions": None,
"max_output_tokens": 20,
"model": "o1-pro-2025-03-19",
"output": [
{
"type": "reasoning",
"id": "rs_67dc3de50f64819097450ed50a33d5f90e13d8bdc0e28d88",
"summary": [],
}
],
"parallel_tool_calls": True,
"previous_response_id": None,
"reasoning": {"effort": "medium", "generate_summary": None},
"store": True,
"temperature": 1.0,
"text": {"format": {"type": "text"}},
"tool_choice": "auto",
"tools": [],
"top_p": 1.0,
"truncation": "disabled",
"usage": {
"input_tokens": 73,
"input_tokens_details": {"cached_tokens": 0},
"output_tokens": 20,
"output_tokens_details": {"reasoning_tokens": 0},
"total_tokens": 93,
},
"user": None,
"metadata": {},
}
class MockResponse:
def __init__(self, json_data, status_code):
self._json_data = json_data
self.status_code = status_code
self.text = json.dumps(json_data)
def json(self):
return self._json_data
with patch(
"litellm.llms.custom_httpx.http_handler.HTTPHandler.post",
return_value=MockResponse(mock_response, 200),
) as mock_post:
litellm._turn_on_debug()
litellm.set_verbose = True
# Call the responses API with computer_use_preview tool
response = litellm.responses(
model="openai/computer-use-preview",
tools=[{
"type": "computer_use_preview",
"display_width": 1024,
"display_height": 768,
"environment": "linux" # other possible values: "mac", "windows", "ubuntu"
}],
input="Check the latest OpenAI news on bing.com.",
reasoning={"summary": "concise"},
truncation="auto"
)
# Verify the request was made correctly
mock_post.assert_called_once()
request_body = json.loads(mock_post.call_args.kwargs["data"])
# Validate the request structure
assert request_body["model"] == "computer-use-preview"
assert len(request_body["tools"]) == 1
assert request_body["tools"][0]["type"] == "computer_use_preview"
assert request_body["tools"][0]["display_width"] == 1024
assert request_body["tools"][0]["display_height"] == 768
assert request_body["tools"][0]["environment"] == "linux"
# Check that reasoning was passed correctly
assert request_body["reasoning"]["summary"] == "concise"
assert request_body["truncation"] == "auto"
# Validate the input format
assert isinstance(request_body["input"], str)
assert request_body["input"] == "Check the latest OpenAI news on bing.com."

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@ -1 +1 @@
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ui/litellm-dashboard/out/model_hub.html
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ui/litellm-dashboard/out/model_hub.txt
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ui/litellm-dashboard/out/onboarding.html
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1:null

49
ui/litellm-dashboard/src/components/chat_ui.tsx
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@ -26,6 +26,7 @@ import {
import { message, Select, Spin, Typography, Tooltip, Input } from "antd";
import { makeOpenAIChatCompletionRequest } from "./chat_ui/llm_calls/chat_completion";
import { makeOpenAIImageGenerationRequest } from "./chat_ui/llm_calls/image_generation";
import { makeOpenAIResponsesRequest } from "./chat_ui/llm_calls/responses_api";
import { fetchAvailableModels, ModelGroup } from "./chat_ui/llm_calls/fetch_models";
import { litellmModeMapping, ModelMode, EndpointType, getEndpointType } from "./chat_ui/mode_endpoint_mapping";
import { Prism as SyntaxHighlighter } from "react-syntax-highlighter";
@ -137,20 +138,28 @@ const ChatUI: React.FC<ChatUIProps> = ({
}, [chatHistory]);
const updateTextUI = (role: string, chunk: string, model?: string) => {
setChatHistory((prevHistory) => {
const lastMessage = prevHistory[prevHistory.length - 1];
if (lastMessage && lastMessage.role === role && !lastMessage.isImage) {
console.log("updateTextUI called with:", role, chunk, model);
setChatHistory((prev) => {
const last = prev[prev.length - 1];
// if the last message is already from this same role, append
if (last && last.role === role && !last.isImage) {
// build a new object, but only set `model` if it wasn't there already
const updated: MessageType = {
...last,
content: last.content + chunk,
model: last.model ?? model, // ← only use the passed‐in model on the first chunk
};
return [...prev.slice(0, -1), updated];
} else {
// otherwise start a brand new assistant bubble
return [
...prevHistory.slice(0, prevHistory.length - 1),
{
...lastMessage,
content: lastMessage.content + chunk,
model
...prev,
{
role,
content: chunk,
model, // model set exactly once here
},
];
} else {
return [...prevHistory, { role, content: chunk, model }];
}
});
};
@ -297,7 +306,6 @@ const ChatUI: React.FC<ChatUIProps> = ({
try {
if (selectedModel) {
// Use EndpointType enum for comparison
if (endpointType === EndpointType.CHAT) {
// Create chat history for API call - strip out model field and isImage field
const apiChatHistory = [...chatHistory.filter(msg => !msg.isImage).map(({ role, content }) => ({ role, content })), newUserMessage];
@ -323,6 +331,21 @@ const ChatUI: React.FC<ChatUIProps> = ({
selectedTags,
signal
);
} else if (endpointType === EndpointType.RESPONSES) {
// Create chat history for API call - strip out model field and isImage field
const apiChatHistory = [...chatHistory.filter(msg => !msg.isImage).map(({ role, content }) => ({ role, content })), newUserMessage];
await makeOpenAIResponsesRequest(
apiChatHistory,
(role, delta, model) => updateTextUI(role, delta, model),
selectedModel,
effectiveApiKey,
selectedTags,
signal,
updateReasoningContent,
updateTimingData,
updateUsageData
);
}
}
} catch (error) {
@ -592,7 +615,7 @@ const ChatUI: React.FC<ChatUIProps> = ({
onChange={(e) => setInputMessage(e.target.value)}
onKeyDown={handleKeyDown}
placeholder={
endpointType === EndpointType.CHAT
endpointType === EndpointType.CHAT || endpointType === EndpointType.RESPONSES
? "Type your message... (Shift+Enter for new line)"
: "Describe the image you want to generate..."
}

5
ui/litellm-dashboard/src/components/chat_ui/EndpointSelector.tsx
View file

@ -19,8 +19,9 @@ const EndpointSelector: React.FC<EndpointSelectorProps> = ({
}) => {
// Map endpoint types to their display labels
const endpointOptions = [
{ value: EndpointType.CHAT, label: '/chat/completions' },
{ value: EndpointType.IMAGE, label: '/images/generations' }
{ value: EndpointType.CHAT, label: '/v1/chat/completions' },
{ value: EndpointType.RESPONSES, label: '/v1/responses' },
{ value: EndpointType.IMAGE, label: '/v1/images/generations' },
];
return (

131
ui/litellm-dashboard/src/components/chat_ui/llm_calls/responses_api.tsx Normal file
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@ -0,0 +1,131 @@
import openai from "openai";
import { message } from "antd";
import { MessageType } from "../types";
import { TokenUsage } from "../ResponseMetrics";
export async function makeOpenAIResponsesRequest(
messages: MessageType[],
updateTextUI: (role: string, delta: string, model?: string) => void,
selectedModel: string,
accessToken: string | null,
tags: string[] = [],
signal?: AbortSignal,
onReasoningContent?: (content: string) => void,
onTimingData?: (timeToFirstToken: number) => void,
onUsageData?: (usage: TokenUsage) => void
) {
if (!accessToken) {
throw new Error("API key is required");
}
// Base URL should be the current base_url
const isLocal = process.env.NODE_ENV === "development";
if (isLocal !== true) {
console.log = function () {};
}
const proxyBaseUrl = isLocal
? "http://localhost:4000"
: window.location.origin;
const client = new openai.OpenAI({
apiKey: accessToken,
baseURL: proxyBaseUrl,
dangerouslyAllowBrowser: true,
defaultHeaders: tags && tags.length > 0 ? { 'x-litellm-tags': tags.join(',') } : undefined,
});
try {
const startTime = Date.now();
let firstTokenReceived = false;
// Format messages for the API
const formattedInput = messages.map(message => ({
role: message.role,
content: message.content,
type: "message"
}));
// Create request to OpenAI responses API
// Use 'any' type to avoid TypeScript issues with the experimental API
const response = await (client as any).responses.create({
model: selectedModel,
input: formattedInput,
stream: true,
}, { signal });
for await (const event of response) {
console.log("Response event:", event);
// Use a type-safe approach to handle events
if (typeof event === 'object' && event !== null) {
// Handle output text delta
// 1) drop any “role” streams
if (event.type === "response.role.delta") {
continue;
}
// 2) only handle actual text deltas
if (event.type === "response.output_text.delta" && typeof event.delta === "string") {
const delta = event.delta;
console.log("Text delta", delta);
// skip pure whitespace/newlines
if (delta.trim().length > 0) {
updateTextUI("assistant", delta, selectedModel);
// Calculate time to first token
if (!firstTokenReceived) {
firstTokenReceived = true;
const timeToFirstToken = Date.now() - startTime;
console.log("First token received! Time:", timeToFirstToken, "ms");
if (onTimingData) {
onTimingData(timeToFirstToken);
}
}
}
}
// Handle reasoning content
if (event.type === "response.reasoning.delta" && 'delta' in event) {
const delta = event.delta;
if (typeof delta === 'string' && onReasoningContent) {
onReasoningContent(delta);
}
}
// Handle usage data at the response.completed event
if (event.type === "response.completed" && 'response' in event) {
const response_obj = event.response;
const usage = response_obj.usage;
console.log("Usage data:", usage);
if (usage && onUsageData) {
console.log("Usage data:", usage);
// Extract usage data safely
const usageData: TokenUsage = {
completionTokens: usage.output_tokens,
promptTokens: usage.input_tokens,
totalTokens: usage.total_tokens
};
// Add reasoning tokens if available
if (usage.completion_tokens_details?.reasoning_tokens) {
usageData.reasoningTokens = usage.completion_tokens_details.reasoning_tokens;
}
onUsageData(usageData);
}
}
}
}
} catch (error) {
if (signal?.aborted) {
console.log("Responses API request was cancelled");
} else {
message.error(`Error occurred while generating model response. Please try again. Error: ${error}`, 20);
}
throw error; // Re-throw to allow the caller to handle the error
}
}

3
ui/litellm-dashboard/src/components/chat_ui/mode_endpoint_mapping.tsx
View file

@ -4,6 +4,7 @@
export enum ModelMode {
IMAGE_GENERATION = "image_generation",
CHAT = "chat",
RESPONSES = "responses",
// add additional modes as needed
}
@ -11,6 +12,7 @@ export enum ModelMode {
export enum EndpointType {
IMAGE = "image",
CHAT = "chat",
RESPONSES = "responses",
// add additional endpoint types if required
}
@ -18,6 +20,7 @@ export enum ModelMode {
export const litellmModeMapping: Record<ModelMode, EndpointType> = {
[ModelMode.IMAGE_GENERATION]: EndpointType.IMAGE,
[ModelMode.CHAT]: EndpointType.CHAT,
[ModelMode.RESPONSES]: EndpointType.RESPONSES,
};
export const getEndpointType = (mode: string): EndpointType => {

4
ui/litellm-dashboard/src/components/team/team_info.tsx
View file

@ -353,8 +353,8 @@ const TeamInfoView: React.FC<TeamInfoProps> = ({
<Select.Option key="all-proxy-models" value="all-proxy-models">
All Proxy Models
</Select.Option>
{userModels.map((model) => (
<Select.Option key={model} value={model}>
{userModels.map((model, idx) => (
<Select.Option key={idx} value={model}>
{getModelDisplayName(model)}
</Select.Option>
))}

2
ui/litellm-dashboard/src/components/teams.tsx
View file

@ -685,7 +685,7 @@ const Teams: React.FC<TeamProps> = ({
<Form.Item label={
<span>
Models{' '}
<Tooltip title="These are the models that your selected organization has access to">
<Tooltip title="These are the models that your selected team has access to">
<InfoCircleOutlined style={{ marginLeft: '4px' }} />
</Tooltip>
</span>