Langchain SDK

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.

Setup

Python
JavaScript

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)

import { ChatOpenAI } from "@langchain/openai";

// Configure client to use Bifrost
const llm = new ChatOpenAI({
  model: "gpt-4o-mini",
  configuration: {
    baseURL: "http://localhost:8080/langchain",  // Point to Bifrost
  },
  openAIApiKey: "dummy-key"  // Keys managed by Bifrost
});

const response = await llm.invoke("Hello!");
console.log(response.content);

Provider/Model Usage Examples

Your existing Langchain provider switching works unchanged through Bifrost:

Python
JavaScript

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!")])

import { ChatOpenAI } from "@langchain/openai";
import { ChatAnthropic } from "@langchain/anthropic";
import { ChatGoogleGenerativeAI } from "@langchain/google-genai";

const baseURL = "http://localhost:8080/langchain";

// OpenAI models via Langchain
const openaiLlm = new ChatOpenAI({
  model: "gpt-4o-mini",
  configuration: { baseURL }
});

// Anthropic models via Langchain
const anthropicLlm = new ChatAnthropic({
  model: "claude-3-sonnet-20240229",
  clientOptions: { baseURL }
});

// Google models via Langchain
const googleLlm = new ChatGoogleGenerativeAI({
  model: "gemini-1.5-flash",
  baseURL
});

// All work the same way
const openaiResponse = await openaiLlm.invoke("Hello GPT!");
const anthropicResponse = await anthropicLlm.invoke("Hello Claude!");
const googleResponse = await googleLlm.invoke("Hello Gemini!");

Adding Custom Headers

Add Bifrost-specific headers for governance and tracking:

Python
JavaScript

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
    }
)

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

import { ChatOpenAI } from "@langchain/openai";

// Add custom headers for Bifrost features
const llm = new ChatOpenAI({
  model: "gpt-4o-mini",
  configuration: {
    baseURL: "http://localhost:8080/langchain",
    defaultHeaders: {
      "x-bf-vk": "your-virtual-key",          // Virtual key for governance
    }
  }
});

const response = await llm.invoke("Hello!");
console.log(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 Key Management for enabling direct API key usage.

Python
JavaScript

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 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!")])

import { ChatOpenAI } from "@langchain/openai";
import { ChatAnthropic } from "@langchain/anthropic";

// Using OpenAI key directly
const openaiLlm = new ChatOpenAI({
  model: "gpt-4o-mini",
  configuration: {
    baseURL: "http://localhost:8080/langchain",
    defaultHeaders: {
      "Authorization": "Bearer sk-your-openai-key"
    }
  }
});

// Using Anthropic key for Claude models
const anthropicLlm = new ChatAnthropic({
  model: "claude-3-sonnet-20240229",
  clientOptions: {
    baseURL: "http://localhost:8080/langchain",
    defaultHeaders: {
      "x-api-key": "sk-ant-your-anthropic-key"
    }
  }
});

// Using Azure with direct Azure key
import { AzureChatOpenAI } from "@langchain/openai";

const azureLlm = new AzureChatOpenAI({
  deploymentName: "gpt-4o-aug",
  apiKey: "your-azure-api-key",
  azureOpenAIEndpoint: "http://localhost:8080/langchain",
  apiVersion: "2024-05-01-preview",
  maxTokens: 100,
  configuration: {
    defaultHeaders: {
      "x-bf-azure-endpoint": "https://your-resource.openai.azure.com",
    }
  }
});

const openaiResponse = await openaiLlm.invoke("Hello GPT!");
const anthropicResponse = await anthropicLlm.invoke("Hello Claude!");
const azureResponse = await azureLlm.invoke("Hello from Azure!");

Reasoning/Thinking Models

Control extended reasoning capabilities for models that support thinking/reasoning modes.

Azure OpenAI Models

For Azure OpenAI reasoning models, use ChatOpenAI with the reasoning parameter and Azure-specific headers:

Python
JavaScript

from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage

# Azure OpenAI with reasoning control
llm = ChatOpenAI(
    model="azure/gpt-5.1",  # Azure deployment name
    base_url="http://localhost:8080/langchain",
    api_key="dummy-key",
    reasoning={
        "effort": "high",      # "minimal" | "low" | "medium" | "high"
        "summary": "detailed"  # "auto" | "concise" | "detailed"
    },
    default_headers={
        "authorization": "Bearer your-azure-api-key",
        "x-bf-azure-endpoint": "https://your-resource.openai.azure.com"
    }
)

response = llm.invoke([HumanMessage(content="Solve this complex problem...")])

import { ChatOpenAI } from "@langchain/openai";

// Azure OpenAI with reasoning control
const llm = new ChatOpenAI({
  model: "azure/gpt-5.1",  // Azure deployment name
  configuration: {
    baseURL: "http://localhost:8080/langchain",
    defaultHeaders: {
      "authorization": "Bearer your-azure-api-key",
      "x-bf-azure-endpoint": "https://your-resource.openai.azure.com"
    }
  },
  openAIApiKey: "dummy-key",
  reasoning: {
    effort: "high",
    summary: "detailed"
  }
});

const response = await llm.invoke("Solve this complex problem...");

OpenAI Models

For OpenAI reasoning models, use ChatOpenAI with the reasoning parameter:

Python
JavaScript

from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage

# OpenAI with reasoning control
llm = ChatOpenAI(
    model="gpt-5",
    base_url="http://localhost:8080/langchain",
    api_key="dummy-key",
    max_tokens=2000,
    reasoning={
        "effort": "high",
        "summary": "detailed"
    }
)

response = llm.invoke([HumanMessage(content="Solve this complex problem...")])

import { ChatOpenAI } from "@langchain/openai";

const llm = new ChatOpenAI({
  model: "gpt-5",
  configuration: {
    baseURL: "http://localhost:8080/langchain"
  },
  openAIApiKey: "dummy-key",
  reasoning: {
    effort: "high",
    summary: "detailed"
  }
});

const response = await llm.invoke("Solve this complex problem...");

Bedrock Models (Anthropic & Nova)

Both Anthropic Claude and Amazon Nova models support reasoning/thinking capabilities via Bedrock. Use ChatBedrockConverse with model-specific configuration formats.

Python

Anthropic Claude Models

import boto3
from langchain_aws import ChatBedrockConverse
from langchain_core.messages import HumanMessage

# Configure Bedrock client to use Bifrost
client_kwargs = {
    "service_name": "bedrock-runtime",
    "region_name": "us-west-2",
    "endpoint_url": "http://localhost:8080/langchain",
}
bedrock_client = boto3.client(**client_kwargs)

# Bedrock Claude with reasoning control
llm = ChatBedrockConverse(
    model="us.anthropic.claude-opus-4-5-20251101-v1:0",
    client=bedrock_client,
    max_tokens=2000,
    additional_model_request_fields={  # Anthropic format
        "reasoning_config": {
            "type": "enabled",
            "budget_tokens": 1500,  # Control thinking token budget
        }
    }
)

response = llm.invoke([HumanMessage(content="Reason through this problem...")])

Amazon Nova Models

import boto3
from langchain_aws import ChatBedrockConverse
from langchain_core.messages import HumanMessage

# Configure Bedrock client to use Bifrost
client_kwargs = {
    "service_name": "bedrock-runtime",
    "region_name": "us-west-2",
    "endpoint_url": "http://localhost:8080/langchain",
}
bedrock_client = boto3.client(**client_kwargs)

# Bedrock Nova with reasoning control
llm = ChatBedrockConverse(
    model="global.amazon.nova-2-lite-v1:0",
    client=bedrock_client,
    max_tokens=2000,
    additional_model_request_fields={  # Nova format
        "reasoningConfig": {
            "type": "enabled",
            "maxReasoningEffort": "high",  # "low" | "medium" | "high"
        }
    }
)

response = llm.invoke([HumanMessage(content="Reason through this problem...")])

Model-Specific Configuration:

Anthropic Claude models use reasoning_config (snake_case) with budget_tokens to control the token budget for reasoning
Amazon Nova models use reasoningConfig (camelCase) with maxReasoningEffort to control reasoning intensity (“low”, “medium”, “high”)

Google/Vertex AI Models

For Google Gemini 2.5 models (Pro, Flash) and Gemini 3, use ChatGoogleGenerativeAI with the thinking_budget parameter:

Python

from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.messages import HumanMessage

# Gemini with thinking budget control
llm = ChatGoogleGenerativeAI(
    model="gemini/gemini-2.5-flash",
    base_url="http://localhost:8080/langchain",
    api_key="dummy-key",
    max_tokens=4000,
    thinking_budget=1024,    # 0=disable, -1=dynamic, >0=constrained token budget
    include_thoughts=True,   # Include reasoning in response
)

response = llm.invoke([HumanMessage(content="Reason through this problem...")])

Experimental Module: ChatGoogleGenerativeAI is a recently released module that deprecates ChatVertexAI. It may have some issues or breaking changes. If you encounter problems, you can use ChatAnthropic with model="gemini/..." or model="vertex/..." as an alternative, which provides stable access to Gemini and Vertex AI models through Bifrost.

Embeddings

LangChain’s OpenAIEmbeddings class can be used to generate embeddings through Bifrost:

from langchain_openai import OpenAIEmbeddings

# Create embeddings instance
embeddings = OpenAIEmbeddings(
    model="text-embedding-3-small",
    base_url="http://localhost:8080/langchain",
    api_key="dummy-key"
)

# Embed a single query
query_embedding = embeddings.embed_query("What is machine learning?")

# Embed multiple documents
doc_embeddings = embeddings.embed_documents([
    "Machine learning is a subset of AI",
    "Deep learning uses neural networks",
    "NLP helps computers understand text"
])

Provider Compatibility Limitation: LangChain’s OpenAIEmbeddings class converts text to int array before sending to the API. While OpenAI’s API supports both text strings and int arrays as input, other providers like Cohere, Bedrock, and Gemini only accept text strings.This means OpenAIEmbeddings only works reliably with OpenAI embedding models. Using it with other providers (e.g., model="cohere/embed-v4.0") will fail because those providers cannot process int array inputs.

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. 😄

Next Steps

Governance Features - Virtual keys and team management
Semantic Caching - Intelligent response caching
Configuration - Provider setup and API key management

Quick Start

Provider Integrations

Custom plugins

Open Source Features

Enterprise Features

Setup

Provider/Model Usage Examples

Adding Custom Headers

Using Direct Keys

Reasoning/Thinking Models

Azure OpenAI Models

OpenAI Models

Bedrock Models (Anthropic & Nova)

Anthropic Claude Models

Amazon Nova Models

Google/Vertex AI Models

Embeddings

Supported Features

Next Steps

Quick Start

Provider Integrations

Custom plugins

Open Source Features

Enterprise Features

​Setup

​Provider/Model Usage Examples

​Adding Custom Headers

​Using Direct Keys

​Reasoning/Thinking Models

​Azure OpenAI Models

​OpenAI Models

​Bedrock Models (Anthropic & Nova)

​Anthropic Claude Models

​Amazon Nova Models

​Google/Vertex AI Models

​Embeddings

​Supported Features

​Next Steps

Setup

Provider/Model Usage Examples

Adding Custom Headers

Using Direct Keys

Reasoning/Thinking Models

Azure OpenAI Models

OpenAI Models

Bedrock Models (Anthropic & Nova)

Anthropic Claude Models

Amazon Nova Models

Google/Vertex AI Models

Embeddings

Supported Features

Next Steps