class LitellmModel(Model):
"""This class enables using any model via LiteLLM. LiteLLM allows you to acess OpenAPI,
Anthropic, Gemini, Mistral, and many other models.
See supported models here: [litellm models](https://docs.litellm.ai/docs/providers).
"""
def __init__(
self,
model: str,
base_url: str | None = None,
api_key: str | None = None,
):
self.model = model
self.base_url = base_url
self.api_key = api_key
async def get_response(
self,
system_instructions: str | None,
input: str | list[TResponseInputItem],
model_settings: ModelSettings,
tools: list[Tool],
output_schema: AgentOutputSchemaBase | None,
handoffs: list[Handoff],
tracing: ModelTracing,
previous_response_id: str | None = None, # unused
conversation_id: str | None = None, # unused
prompt: Any | None = None,
) -> ModelResponse:
with generation_span(
model=str(self.model),
model_config=model_settings.to_json_dict()
| {"base_url": str(self.base_url or ""), "model_impl": "litellm"},
disabled=tracing.is_disabled(),
) as span_generation:
response = await self._fetch_response(
system_instructions,
input,
model_settings,
tools,
output_schema,
handoffs,
span_generation,
tracing,
stream=False,
prompt=prompt,
)
assert isinstance(response.choices[0], litellm.types.utils.Choices)
if _debug.DONT_LOG_MODEL_DATA:
logger.debug("Received model response")
else:
logger.debug(
f"""LLM resp:\n{
json.dumps(
response.choices[0].message.model_dump(), indent=2, ensure_ascii=False
)
}\n"""
)
if hasattr(response, "usage"):
response_usage = response.usage
usage = (
Usage(
requests=1,
input_tokens=response_usage.prompt_tokens,
output_tokens=response_usage.completion_tokens,
total_tokens=response_usage.total_tokens,
input_tokens_details=InputTokensDetails(
cached_tokens=getattr(
response_usage.prompt_tokens_details, "cached_tokens", 0
)
or 0
),
output_tokens_details=OutputTokensDetails(
reasoning_tokens=getattr(
response_usage.completion_tokens_details, "reasoning_tokens", 0
)
or 0
),
)
if response.usage
else Usage()
)
else:
usage = Usage()
logger.warning("No usage information returned from Litellm")
if tracing.include_data():
span_generation.span_data.output = [response.choices[0].message.model_dump()]
span_generation.span_data.usage = {
"input_tokens": usage.input_tokens,
"output_tokens": usage.output_tokens,
}
items = Converter.message_to_output_items(
LitellmConverter.convert_message_to_openai(response.choices[0].message)
)
return ModelResponse(
output=items,
usage=usage,
response_id=None,
)
async def stream_response(
self,
system_instructions: str | None,
input: str | list[TResponseInputItem],
model_settings: ModelSettings,
tools: list[Tool],
output_schema: AgentOutputSchemaBase | None,
handoffs: list[Handoff],
tracing: ModelTracing,
previous_response_id: str | None = None, # unused
conversation_id: str | None = None, # unused
prompt: Any | None = None,
) -> AsyncIterator[TResponseStreamEvent]:
with generation_span(
model=str(self.model),
model_config=model_settings.to_json_dict()
| {"base_url": str(self.base_url or ""), "model_impl": "litellm"},
disabled=tracing.is_disabled(),
) as span_generation:
response, stream = await self._fetch_response(
system_instructions,
input,
model_settings,
tools,
output_schema,
handoffs,
span_generation,
tracing,
stream=True,
prompt=prompt,
)
final_response: Response | None = None
async for chunk in ChatCmplStreamHandler.handle_stream(response, stream):
yield chunk
if chunk.type == "response.completed":
final_response = chunk.response
if tracing.include_data() and final_response:
span_generation.span_data.output = [final_response.model_dump()]
if final_response and final_response.usage:
span_generation.span_data.usage = {
"input_tokens": final_response.usage.input_tokens,
"output_tokens": final_response.usage.output_tokens,
}
@overload
async def _fetch_response(
self,
system_instructions: str | None,
input: str | list[TResponseInputItem],
model_settings: ModelSettings,
tools: list[Tool],
output_schema: AgentOutputSchemaBase | None,
handoffs: list[Handoff],
span: Span[GenerationSpanData],
tracing: ModelTracing,
stream: Literal[True],
prompt: Any | None = None,
) -> tuple[Response, AsyncStream[ChatCompletionChunk]]: ...
@overload
async def _fetch_response(
self,
system_instructions: str | None,
input: str | list[TResponseInputItem],
model_settings: ModelSettings,
tools: list[Tool],
output_schema: AgentOutputSchemaBase | None,
handoffs: list[Handoff],
span: Span[GenerationSpanData],
tracing: ModelTracing,
stream: Literal[False],
prompt: Any | None = None,
) -> litellm.types.utils.ModelResponse: ...
async def _fetch_response(
self,
system_instructions: str | None,
input: str | list[TResponseInputItem],
model_settings: ModelSettings,
tools: list[Tool],
output_schema: AgentOutputSchemaBase | None,
handoffs: list[Handoff],
span: Span[GenerationSpanData],
tracing: ModelTracing,
stream: bool = False,
prompt: Any | None = None,
) -> litellm.types.utils.ModelResponse | tuple[Response, AsyncStream[ChatCompletionChunk]]:
# Preserve reasoning messages for tool calls when reasoning is on
# This is needed for models like Claude 4 Sonnet/Opus which support interleaved thinking
preserve_thinking_blocks = (
model_settings.reasoning is not None and model_settings.reasoning.effort is not None
)
converted_messages = Converter.items_to_messages(
input, preserve_thinking_blocks=preserve_thinking_blocks
)
# Fix for interleaved thinking bug: reorder messages to ensure tool_use comes before tool_result # noqa: E501
if preserve_thinking_blocks:
converted_messages = self._fix_tool_message_ordering(converted_messages)
if system_instructions:
converted_messages.insert(
0,
{
"content": system_instructions,
"role": "system",
},
)
converted_messages = _to_dump_compatible(converted_messages)
if tracing.include_data():
span.span_data.input = converted_messages
parallel_tool_calls = (
True
if model_settings.parallel_tool_calls and tools and len(tools) > 0
else False
if model_settings.parallel_tool_calls is False
else None
)
tool_choice = Converter.convert_tool_choice(model_settings.tool_choice)
response_format = Converter.convert_response_format(output_schema)
converted_tools = [Converter.tool_to_openai(tool) for tool in tools] if tools else []
for handoff in handoffs:
converted_tools.append(Converter.convert_handoff_tool(handoff))
converted_tools = _to_dump_compatible(converted_tools)
if _debug.DONT_LOG_MODEL_DATA:
logger.debug("Calling LLM")
else:
messages_json = json.dumps(
converted_messages,
indent=2,
ensure_ascii=False,
)
tools_json = json.dumps(
converted_tools,
indent=2,
ensure_ascii=False,
)
logger.debug(
f"Calling Litellm model: {self.model}\n"
f"{messages_json}\n"
f"Tools:\n{tools_json}\n"
f"Stream: {stream}\n"
f"Tool choice: {tool_choice}\n"
f"Response format: {response_format}\n"
)
reasoning_effort = model_settings.reasoning.effort if model_settings.reasoning else None
stream_options = None
if stream and model_settings.include_usage is not None:
stream_options = {"include_usage": model_settings.include_usage}
extra_kwargs = {}
if model_settings.extra_query:
extra_kwargs["extra_query"] = copy(model_settings.extra_query)
if model_settings.metadata:
extra_kwargs["metadata"] = copy(model_settings.metadata)
if model_settings.extra_body and isinstance(model_settings.extra_body, dict):
extra_kwargs.update(model_settings.extra_body)
# Add kwargs from model_settings.extra_args, filtering out None values
if model_settings.extra_args:
extra_kwargs.update(model_settings.extra_args)
ret = await litellm.acompletion(
model=self.model,
messages=converted_messages,
tools=converted_tools or None,
temperature=model_settings.temperature,
top_p=model_settings.top_p,
frequency_penalty=model_settings.frequency_penalty,
presence_penalty=model_settings.presence_penalty,
max_tokens=model_settings.max_tokens,
tool_choice=self._remove_not_given(tool_choice),
response_format=self._remove_not_given(response_format),
parallel_tool_calls=parallel_tool_calls,
stream=stream,
stream_options=stream_options,
reasoning_effort=reasoning_effort,
top_logprobs=model_settings.top_logprobs,
extra_headers=self._merge_headers(model_settings),
api_key=self.api_key,
base_url=self.base_url,
**extra_kwargs,
)
if isinstance(ret, litellm.types.utils.ModelResponse):
return ret
response = Response(
id=FAKE_RESPONSES_ID,
created_at=time.time(),
model=self.model,
object="response",
output=[],
tool_choice=cast(Literal["auto", "required", "none"], tool_choice)
if tool_choice is not omit
else "auto",
top_p=model_settings.top_p,
temperature=model_settings.temperature,
tools=[],
parallel_tool_calls=parallel_tool_calls or False,
reasoning=model_settings.reasoning,
)
return response, ret
def _fix_tool_message_ordering(
self, messages: list[ChatCompletionMessageParam]
) -> list[ChatCompletionMessageParam]:
"""
Fix the ordering of tool messages to ensure tool_use messages come before tool_result messages.
This addresses the interleaved thinking bug where conversation histories may contain
tool results before their corresponding tool calls, causing Anthropic API to reject the request.
""" # noqa: E501
if not messages:
return messages
# Collect all tool calls and tool results
tool_call_messages = {} # tool_id -> (index, message)
tool_result_messages = {} # tool_id -> (index, message)
other_messages = [] # (index, message) for non-tool messages
for i, message in enumerate(messages):
if not isinstance(message, dict):
other_messages.append((i, message))
continue
role = message.get("role")
if role == "assistant" and message.get("tool_calls"):
# Extract tool calls from this assistant message
tool_calls = message.get("tool_calls", [])
if isinstance(tool_calls, list):
for tool_call in tool_calls:
if isinstance(tool_call, dict):
tool_id = tool_call.get("id")
if tool_id:
# Create a separate assistant message for each tool call
single_tool_msg = cast(dict[str, Any], message.copy())
single_tool_msg["tool_calls"] = [tool_call]
tool_call_messages[tool_id] = (
i,
cast(ChatCompletionMessageParam, single_tool_msg),
)
elif role == "tool":
tool_call_id = message.get("tool_call_id")
if tool_call_id:
tool_result_messages[tool_call_id] = (i, message)
else:
other_messages.append((i, message))
else:
other_messages.append((i, message))
# First, identify which tool results will be paired to avoid duplicates
paired_tool_result_indices = set()
for tool_id in tool_call_messages:
if tool_id in tool_result_messages:
tool_result_idx, _ = tool_result_messages[tool_id]
paired_tool_result_indices.add(tool_result_idx)
# Create the fixed message sequence
fixed_messages: list[ChatCompletionMessageParam] = []
used_indices = set()
# Add messages in their original order, but ensure tool_use → tool_result pairing
for i, original_message in enumerate(messages):
if i in used_indices:
continue
if not isinstance(original_message, dict):
fixed_messages.append(original_message)
used_indices.add(i)
continue
role = original_message.get("role")
if role == "assistant" and original_message.get("tool_calls"):
# Process each tool call in this assistant message
tool_calls = original_message.get("tool_calls", [])
if isinstance(tool_calls, list):
for tool_call in tool_calls:
if isinstance(tool_call, dict):
tool_id = tool_call.get("id")
if (
tool_id
and tool_id in tool_call_messages
and tool_id in tool_result_messages
):
# Add tool_use → tool_result pair
_, tool_call_msg = tool_call_messages[tool_id]
tool_result_idx, tool_result_msg = tool_result_messages[tool_id]
fixed_messages.append(tool_call_msg)
fixed_messages.append(tool_result_msg)
# Mark both as used
used_indices.add(tool_call_messages[tool_id][0])
used_indices.add(tool_result_idx)
elif tool_id and tool_id in tool_call_messages:
# Tool call without result - add just the tool call
_, tool_call_msg = tool_call_messages[tool_id]
fixed_messages.append(tool_call_msg)
used_indices.add(tool_call_messages[tool_id][0])
used_indices.add(i) # Mark original multi-tool message as used
elif role == "tool":
# Only preserve unmatched tool results to avoid duplicates
if i not in paired_tool_result_indices:
fixed_messages.append(original_message)
used_indices.add(i)
else:
# Regular message - add it normally
fixed_messages.append(original_message)
used_indices.add(i)
return fixed_messages
def _remove_not_given(self, value: Any) -> Any:
if value is omit or isinstance(value, NotGiven):
return None
return value
def _merge_headers(self, model_settings: ModelSettings):
return {**HEADERS, **(model_settings.extra_headers or {}), **(HEADERS_OVERRIDE.get() or {})}