langchain-litellm

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🤔 What is this?

This package contains the LangChain integration with LiteLLM. LiteLLM is a library that simplifies calling Anthropic, Azure, Huggingface, Replicate, etc.

📖 Documentation

For conceptual guides, tutorials, and examples on using these classes, see the LangChain Docs.

Advanced Features

Vertex AI Grounding (Google Search)

Supported in v0.3.5+

You can use Google Search grounding with Vertex AI models (e.g., gemini-2.5-flash). Citations and metadata are returned in response_metadata (Batch) or additional_kwargs (Streaming).

Setup

import os
from langchain_litellm import ChatLiteLLM

os.environ["VERTEX_PROJECT"] = "your-project-id"
os.environ["VERTEX_LOCATION"] = "us-central1"

llm = ChatLiteLLM(model="vertex_ai/gemini-2.5-flash", temperature=0)

Batch Usage

# Invoke with Google Search tool enabled
response = llm.invoke(
    "What is the current stock price of Google?",
    tools=[{"googleSearch": {}}]
)

# Access Citations & Metadata
provider_fields = response.response_metadata.get("provider_specific_fields")
if provider_fields:
    # Vertex returns a list; the first item contains the grounding info
    print(provider_fields[0])

Streaming Usage

stream = llm.stream(
    "What is the current stock price of Google?",
    tools=[{"googleSearch": {}}]
)

for chunk in stream:
    print(chunk.content, end="", flush=True)
    # Metadata is injected into the chunk where it arrives
    if "provider_specific_fields" in chunk.additional_kwargs:
        print("\n[Metadata Found]:", chunk.additional_kwargs["provider_specific_fields"])

📕 Releases & Versioning

See our Releases and Versioning policies.

💁 Contributing

As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.

For detailed information on how to contribute, see the Contributing Guide.