> ## Documentation Index
> Fetch the complete documentation index at: https://docs.getbifrost.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# 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`

<Warning>
  **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.
</Warning>

***

## Setup

<Tabs group="langchain-sdk">
  <Tab title="Python">
    ```python {7} theme={null}
    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)
    ```
  </Tab>

  <Tab title="JavaScript">
    ```javascript {7} theme={null}
    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);
    ```
  </Tab>
</Tabs>

***

## Provider/Model Usage Examples

Your existing Langchain provider switching works unchanged through Bifrost:

<Tabs group="langchain-sdk">
  <Tab title="Python">
    ```python theme={null}
    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!")])
    ```
  </Tab>

  <Tab title="JavaScript">
    ```javascript theme={null}
    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!");
    ```
  </Tab>
</Tabs>

***

## Adding Custom Headers

Add Bifrost-specific headers for governance and tracking. Different LangChain provider classes support different methods for adding custom headers:

<Tabs group="langchain-sdk">
  <Tab title="Python">
    ### ChatOpenAI

    Use `default_headers` parameter for OpenAI models:

    ```python theme={null}
    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:

    ```python theme={null}
    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:

    ```python theme={null}
    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)**

    ```python theme={null}
    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**

    ```python theme={null}
    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)
    ```
  </Tab>

  <Tab title="JavaScript">
    ### ChatOpenAI

    Use `defaultHeaders` in configuration for OpenAI models:

    ```javascript theme={null}
    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:

    ```javascript theme={null}
    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:

    ```javascript theme={null}
    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);
    ```
  </Tab>
</Tabs>

***

## 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:

<Tabs group="langchain-sdk">
  <Tab title="Python">
    ```python theme={null}
    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...")])
    ```
  </Tab>

  <Tab title="JavaScript">
    ```javascript theme={null}
    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...");
    ```
  </Tab>
</Tabs>

### OpenAI Models

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

<Tabs group="langchain-sdk">
  <Tab title="Python">
    ```python theme={null}
    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...")])
    ```
  </Tab>

  <Tab title="JavaScript">
    ```javascript theme={null}
    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...");
    ```
  </Tab>
</Tabs>

### Bedrock Models (Anthropic & Nova)

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

<Tabs group="langchain-sdk">
  <Tab title="Python">
    #### Anthropic Claude Models

    ```python theme={null}
    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

    ```python theme={null}
    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...")])
    ```
  </Tab>
</Tabs>

<Note>
  **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")
</Note>

### Google/Vertex AI Models

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

<Tabs group="langchain-sdk">
  <Tab title="Python">
    ```python theme={null}
    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...")])
    ```
  </Tab>
</Tabs>

<Warning>
  **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.
</Warning>

***

## Embeddings

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

```python theme={null}
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"
])
```

<Warning>
  **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.
</Warning>

### 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:

```python theme={null}
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. 😄

***

## Next Steps

* **[Governance Features](../features/governance)** - Virtual keys and team management
* **[Semantic Caching](../features/semantic-caching)** - Intelligent response caching
* **[Configuration](../quickstart/README)** - Provider setup and API key management