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Version: 2.29

LiteLLM

haystack_integrations.components.generators.litellm.chat.chat_generator​

LiteLLMChatGenerator​

Completes chats using any of 100+ LLM providers via LiteLLM.

LiteLLM routes to OpenAI, Anthropic, Google, AWS Bedrock, Azure, Cohere, Mistral, Groq, and many more through a single unified interface.

Model names use LiteLLM format: provider/model-name, e.g. anthropic/claude-sonnet-4-20250514, openai/gpt-4o, bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0.

See https://docs.litellm.ai/docs/providers for the full list.

Usage example:

python
from haystack_integrations.components.generators.litellm import LiteLLMChatGenerator
from haystack.dataclasses import ChatMessage

generator = LiteLLMChatGenerator(
    model="anthropic/claude-sonnet-4-20250514",
    generation_kwargs={"max_tokens": 1024, "temperature": 0.7},
)

messages = [
    ChatMessage.from_system("You are a helpful assistant"),
    ChatMessage.from_user("What's Natural Language Processing?"),
]
result = generator.run(messages=messages)
print(result["replies"][0].text)

init​

python
__init__(
    *,
    api_key: Secret | None = None,
    model: str = "openai/gpt-4o",
    streaming_callback: StreamingCallbackT | None = None,
    api_base_url: str | None = None,
    generation_kwargs: dict[str, Any] | None = None,
    tools: ToolsType | None = None
) -> None

Create a LiteLLMChatGenerator instance.

Parameters:

  • api_key (Secret | None) – The API key for the provider. Optional: when not set, LiteLLM resolves credentials itself from the provider's standard environment variable (e.g. ANTHROPIC_API_KEY, OPENAI_API_KEY). Pass a Secret only when you want Haystack to manage and serialize the key explicitly.
  • model (str) – The model name in LiteLLM format (provider/model-name).
  • streaming_callback (StreamingCallbackT | None) – A callback function invoked with each new StreamingChunk.
  • api_base_url (str | None) – Custom API base URL (e.g. for a self-hosted LiteLLM proxy).
  • generation_kwargs (dict[str, Any] | None) – Additional parameters passed to litellm.completion(). See https://docs.litellm.ai/docs/completion/input for details.
  • tools (ToolsType | None) – A list of Tool / Toolset objects the model can prepare calls for.

run​

python
run(
    messages: list[ChatMessage],
    streaming_callback: StreamingCallbackT | None = None,
    generation_kwargs: dict[str, Any] | None = None,
    *,
    tools: ToolsType | None = None
) -> dict[str, list[ChatMessage]]

Invoke chat completion via LiteLLM.

Parameters:

  • messages (list[ChatMessage]) – Input messages as ChatMessage instances.
  • streaming_callback (StreamingCallbackT | None) – Override the streaming callback for this call.
  • generation_kwargs (dict[str, Any] | None) – Override generation parameters for this call.
  • tools (ToolsType | None) – Override tools for this call.

Returns:

  • dict[str, list[ChatMessage]] – A dict with key replies containing ChatMessage instances.

run_async​

python
run_async(
    messages: list[ChatMessage],
    streaming_callback: StreamingCallbackT | None = None,
    generation_kwargs: dict[str, Any] | None = None,
    *,
    tools: ToolsType | None = None
) -> dict[str, list[ChatMessage]]

Async version of run().

to_dict​

python
to_dict() -> dict[str, Any]

Serialize this component to a dictionary.

from_dict​

python
from_dict(data: dict[str, Any]) -> LiteLLMChatGenerator

Deserialize a component from a dictionary.