Since Langchain already provides multi-provider abstraction and chaining capabilities, Bifrost adds enterprise features like governance, semantic caching, MCP tools, observability, etc, on top of your existing setup. Endpoint: /langchain
Provider Compatibility: This integration only works for AI providers that both Langchain and Bifrost support. If you’re using a provider specific to Langchain that Bifrost doesn’t support (or vice versa), those requests will fail.

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Setup

from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage

# Configure client to use Bifrost
llm = ChatOpenAI(
    model="gpt-4o-mini",
    openai_api_base="http://localhost:8080/langchain",  # Point to Bifrost
    openai_api_key="dummy-key"  # Keys managed by Bifrost
)

response = llm.invoke([HumanMessage(content="Hello!")])
print(response.content)

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Provider/Model Usage Examples

Your existing Langchain provider switching works unchanged through Bifrost: 
from langchain_openai import ChatOpenAI
from langchain_anthropic import ChatAnthropic
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.messages import HumanMessage

base_url = "http://localhost:8080/langchain"

# OpenAI models via Langchain
openai_llm = ChatOpenAI(
    model="gpt-4o-mini",
    openai_api_base=base_url
)

# Anthropic models via Langchain  
anthropic_llm = ChatAnthropic(
    model="claude-3-sonnet-20240229",
    anthropic_api_url=base_url
)

# Google models via Langchain
google_llm = ChatGoogleGenerativeAI(
    model="gemini-1.5-flash",
    google_api_base=base_url
)

# All work the same way
openai_response = openai_llm.invoke([HumanMessage(content="Hello GPT!")])
anthropic_response = anthropic_llm.invoke([HumanMessage(content="Hello Claude!")])
google_response = google_llm.invoke([HumanMessage(content="Hello Gemini!")])

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Adding Custom Headers

Add Bifrost-specific headers for governance and tracking: 
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage

# Add custom headers for Bifrost features
llm = ChatOpenAI(
    model="gpt-4o-mini",
    openai_api_base="http://localhost:8080/langchain",
    default_headers={
        "x-bf-vk": "your-virtual-key",          # Virtual key for governance
        "x-bf-user-id": "user123",              # User tracking
        "x-bf-team-id": "team-ai",              # Team tracking  
        "x-bf-trace-id": "trace-456"            # Custom trace ID
    }
)

response = llm.invoke([HumanMessage(content="Hello!")])
print(response.content)

​
Using Direct Keys

Pass API keys directly to bypass Bifrost’s key management. You can pass any provider’s API key since Bifrost only looks for Authorization or x-api-key headers. This requires the Allow Direct API keys option to be enabled in Bifrost configuration.
Learn more: See Quickstart Configuration for enabling direct API key usage. 
from langchain_openai import ChatOpenAI
from langchain_anthropic import ChatAnthropic
from langchain_core.messages import HumanMessage

# Using OpenAI key directly
openai_llm = ChatOpenAI(
    model="gpt-4o-mini",
    openai_api_base="http://localhost:8080/langchain",
    default_headers={
        "Authorization": "Bearer sk-your-openai-key"
    }
)

# Using Anthropic key for Claude models
anthropic_llm = ChatAnthropic(
    model="claude-3-sonnet-20240229",
    anthropic_api_url="http://localhost:8080/langchain",
    default_headers={
        "x-api-key": "sk-ant-your-anthropic-key"
    }
)

# Using Azure OpenAI with direct Azure key
from langchain_openai import AzureChatOpenAI

azure_llm = AzureChatOpenAI(
    deployment_name="gpt-4o-aug",
    api_key="your-azure-api-key",
    azure_endpoint="http://localhost:8080/langchain",
    api_version="2024-05-01-preview",
    max_tokens=100,
    default_headers={
        "x-bf-azure-endpoint": "https://your-resource.openai.azure.com",
    }
)

openai_response = openai_llm.invoke([HumanMessage(content="Hello GPT!")])
anthropic_response = anthropic_llm.invoke([HumanMessage(content="Hello Claude!")])
azure_response = azure_llm.invoke([HumanMessage(content="Hello from Azure!")])

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Supported Features

The Langchain integration supports all features that are available in both the Langchain SDK and Bifrost core functionality. Your existing Langchain chains and workflows work seamlessly with Bifrost’s enterprise features. 😄

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Next Steps