Langchain SDK

Use Bifrost as a drop-in proxy for Langchain applications with zero code changes.

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

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)

JavaScript

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

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!")])

JavaScript

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. Different LangChain provider classes support different methods for adding custom headers:

Python

ChatOpenAI

Use default_headers parameter for OpenAI models:

from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage

llm = ChatOpenAI(
    model="gpt-4o-mini",
    openai_api_base="http://localhost:8080/langchain",
    default_headers={
        "x-bf-vk": "your-virtual-key",
    }
)

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

ChatAnthropic

Use default_headers parameter for Anthropic models:

from langchain_anthropic import ChatAnthropic
from langchain_core.messages import HumanMessage

llm = ChatAnthropic(
    model="claude-3-sonnet-20240229",
    anthropic_api_url="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)

ChatGoogleGenerativeAI

Use additional_headers parameter for Google/Gemini models:

from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.messages import HumanMessage

llm = ChatGoogleGenerativeAI(
    model="gemini-2.5-flash",
    google_api_base="http://localhost:8080/langchain",
    additional_headers={
        "x-bf-vk": "your-virtual-key",  # Virtual key for governance
    }
)

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

ChatBedrockConverse

For Bedrock models, there are two approaches:

Method 1: Using the client’s event system (after initialization)

from langchain_aws import ChatBedrockConverse
from langchain_core.messages import HumanMessage

llm = ChatBedrockConverse(
    model="us.anthropic.claude-haiku-4-5-20251001-v1:0",
    region_name="us-west-2",
    endpoint_url="http://localhost:8080/langchain",
    aws_access_key_id="dummy-access-key",
    aws_secret_access_key="dummy-secret-key",
    max_tokens=2000
)

def add_bifrost_headers(request, **kwargs):
    """Add custom headers to Bedrock requests"""
    request.headers.add_header("x-bf-vk", "your-virtual-key")

# Register header injection for all Bedrock operations
llm.client.meta.events.register_first(
    "before-sign.bedrock-runtime.*",
    add_bifrost_headers
)

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

Method 2: Pre-configuring a boto3 client

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

# Create and configure boto3 client
bedrock_client = boto3.client(
    service_name="bedrock-runtime",
    region_name="us-west-2",
    endpoint_url="http://localhost:8080/langchain",
    aws_access_key_id="dummy-access-key",
    aws_secret_access_key="dummy-secret-key"
)

def add_bifrost_headers(request, **kwargs):
    """Add custom headers to Bedrock requests"""
    request.headers.add_header("x-bf-vk", "your-virtual-key")

# Register header injection before creating LLM
bedrock_client.meta.events.register_first(
    "before-sign.bedrock-runtime.*",
    add_bifrost_headers
)

# Pass the configured client to ChatBedrockConverse
llm = ChatBedrockConverse(
    model="us.anthropic.claude-haiku-4-5-20251001-v1:0",
    client=bedrock_client,
    max_tokens=2000
)

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

JavaScript

ChatOpenAI

Use defaultHeaders in configuration for OpenAI models:

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

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);

ChatAnthropic

Use defaultHeaders in clientOptions for Anthropic models:

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

const llm = new ChatAnthropic({
  model: "claude-3-sonnet-20240229",
  clientOptions: {
    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);

ChatGoogleGenerativeAI

Use additionalHeaders for Google/Gemini models:

import { ChatGoogleGenerativeAI } from "@langchain/google-genai";

const llm = new ChatGoogleGenerativeAI({
  model: "gemini-2.5-flash",
  baseURL: "http://localhost:8080/langchain",
  additionalHeaders: {
    "x-bf-vk": "your-virtual-key",  // Virtual key for governance
  }
});

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

---

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

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...")])

JavaScript

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

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...")])

JavaScript

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

from langchain_aws import ChatBedrockConverse
from langchain_core.messages import HumanMessage

# Bedrock Claude with reasoning control
llm = ChatBedrockConverse(
    model="us.anthropic.claude-opus-4-5-20251101-v1:0",
    region_name="dummy-region",
    endpoint_url="http://localhost:8080/langchain",
    aws_access_key_id="dummy-access-key",
    aws_secret_access_key="dummy-secret-key",
    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

from langchain_aws import ChatBedrockConverse
from langchain_core.messages import HumanMessage

# Bedrock Nova with reasoning control
llm = ChatBedrockConverse(
    model="global.amazon.nova-2-lite-v1:0",
    region_name="dummy-region",
    endpoint_url="http://localhost:8080/langchain",
    aws_access_key_id="dummy-access-key",
    aws_secret_access_key="dummy-secret-key",
    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",  # or "vertex/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.

Cross-Provider Embeddings

For embedding models from other providers (Cohere, Bedrock, Gemini, etc.), you can use GoogleGenerativeAIEmbeddings from the langchain_google_genai package. This module sends text strings directly and works across multiple providers:

from langchain_google_genai import GoogleGenerativeAIEmbeddings

# Works with any provider's embedding models
embeddings = GoogleGenerativeAIEmbeddings(
    model="cohere/cohere-embed-v4.0",  # or bedrock/..., gemini/..., etc.
    base_url="http://localhost:8080/langchain",
    api_key="dummy-key"
)

query_embedding = embeddings.embed_query("What is machine learning?")
doc_embeddings = embeddings.embed_documents([
    "Machine learning is a subset of AI",
    "Deep learning uses neural networks"
])

---

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

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