> ## 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.

# Passthrough

> Forward provider-native requests through Bifrost with full core pipeline processing, including logs and observability.

## Overview

Passthrough integrations let you call provider-native API paths and payloads through Bifrost without route-level request/response conversion.

When you use passthrough endpoints, the request still flows through Bifrost core logic. You keep Bifrost features such as logging and observability while sending provider-native paths and bodies.

***

## Endpoints

* `/openai_passthrough`
  Default provider: `openai`
* `/anthropic_passthrough`
  Default provider: `anthropic`
* `/azure_passthrough`
  Default provider: `azure`
* `/genai_passthrough`
  Default provider: `gemini` (with automatic Vertex detection for clients configured to use Vertex)

***

## How It Works

1. Send your request to a passthrough endpoint (OpenAI, Anthropic, Azure, or GenAI passthrough).
2. The integration strips the passthrough prefix and forwards the remaining provider-native path/body.
3. Bifrost handles provider execution through core inference and plugin pipelines.
4. Response status, headers, and body are returned as passthrough output (for both stream and non-stream requests).

***

## Provider Selection Rules

### OpenAI Passthrough

* Uses `openai` as the default provider.

### Anthropic Passthrough

* Uses `anthropic` as the default provider.

### Azure Passthrough

* Uses `azure` as the default provider.
* Requires an Azure key with `endpoint` configured. `api-version` is injected automatically:
  * **Key config `api_version`** takes priority (consistent with how auth is handled).
  * Falls back to any `api-version` the client supplied in the query string.

### GenAI Passthrough

* Uses `gemini` by default.
* Automatically switches to `vertex` when Vertex patterns are detected, such as:
  * URL path containing `/projects/{PROJECT_ID}/locations/{LOCATION}/`
  * Request body `model` containing a Vertex resource path
  * OAuth token pattern typically used for Vertex (`Bearer ya29...`)

***

## Usage Examples

### OpenAI Passthrough

<Tabs group="openai-passthrough">
  <Tab title="Python SDK">
    ```python theme={null}
    import openai

    client = openai.OpenAI(
        base_url="http://localhost:8080/openai_passthrough/v1",
        api_key="dummy-key"
    )

    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": "hello from passthrough"}]
    )

    print(response.choices[0].message.content)
    ```
  </Tab>

  <Tab title="cURL">
    ```bash theme={null}
    curl -X POST "http://localhost:8080/openai_passthrough/v1/chat/completions" \
      -H "content-type: application/json" \
      -H "authorization: Bearer sk-your-openai-key" \
      -d '{
        "model": "gpt-4o-mini",
        "messages": [{"role":"user","content":"hello from passthrough"}]
      }'
    ```
  </Tab>
</Tabs>

### Anthropic Passthrough

<Tabs group="anthropic-passthrough">
  <Tab title="Python SDK">
    ```python theme={null}
    import anthropic

    client = anthropic.Anthropic(
        base_url="http://localhost:8080/anthropic_passthrough",
        api_key="dummy-key"
    )

    response = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=1024,
        messages=[{"role": "user", "content": "hello from passthrough"}]
    )

    print(response.content[0].text)
    ```
  </Tab>

  <Tab title="cURL">
    ```bash theme={null}
    curl -X POST "http://localhost:8080/anthropic_passthrough/v1/messages" \
      -H "content-type: application/json" \
      -H "x-api-key: your-anthropic-key" \
      -H "anthropic-version: 2023-06-01" \
      -d '{
        "model": "claude-sonnet-4-20250514",
        "max_tokens": 1024,
        "messages": [{"role":"user","content":"hello from passthrough"}]
      }'
    ```
  </Tab>
</Tabs>

### Azure Passthrough

<Tabs group="azure-passthrough">
  <Tab title="Azure OpenAI SDK">
    ```python theme={null}
    from openai import AzureOpenAI

    client = AzureOpenAI(
        azure_endpoint="http://localhost:8080/azure_passthrough",
        api_key="dummy-key",
        api_version="2024-10-21",  # overridden by key config api_version if set
    )

    response = client.chat.completions.create(
        model="gpt-4o",  # your Azure deployment name
        messages=[{"role": "user", "content": "hello from azure passthrough"}]
    )

    print(response.choices[0].message.content)
    ```
  </Tab>

  <Tab title="OpenAI SDK">
    ```python theme={null}
    from openai import OpenAI

    client = OpenAI(
        base_url="http://localhost:8080/azure_passthrough/openai/v1/",
        api_key="dummy-key",
    )

    response = client.responses.create(
        model="gpt-4.1",  # your Azure deployment name
        input="hello from azure passthrough",
    )

    print(response.output_text)
    ```
  </Tab>

  <Tab title="Anthropic SDK (Anthropic on Azure)">
    ```python theme={null}
    import anthropic

    client = anthropic.Anthropic(
        base_url="http://localhost:8080/azure_passthrough",
        api_key="dummy-key",
    )

    response = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=1024,
        messages=[{"role": "user", "content": "hello from azure passthrough"}]
    )

    print(response.content[0].text)
    ```
  </Tab>

  <Tab title="cURL">
    ```bash theme={null}
    curl -X POST "http://localhost:8080/azure_passthrough/openai/deployments/gpt-4o/chat/completions" \
      -H "content-type: application/json" \
      -d '{
        "messages": [{"role": "user", "content": "hello from azure passthrough"}]
      }'
    ```
  </Tab>
</Tabs>

### GenAI Passthrough (Gemini)

<Tabs group="genai-passthrough">
  <Tab title="Python SDK">
    ```python theme={null}
    from google import genai
    from google.genai.types import HttpOptions

    client = genai.Client(
        api_key="dummy-key",
        http_options=HttpOptions(base_url="http://localhost:8080/genai_passthrough")
    )

    response = client.models.generate_content(
        model="gemini-2.5-flash",
        contents="hello from passthrough"
    )

    print(response.text)
    ```
  </Tab>

  <Tab title="cURL">
    ```bash theme={null}
    curl -X POST "http://localhost:8080/genai_passthrough/v1beta/models/gemini-2.5-flash:generateContent" \
      -H "content-type: application/json" \
      -H "x-goog-api-key: your-gemini-key" \
      -d '{
        "contents":[{"parts":[{"text":"hello from passthrough"}]}]
      }'
    ```
  </Tab>
</Tabs>

### GenAI Passthrough (Vertex-style request)

<Tabs group="vertex-passthrough">
  <Tab title="Python SDK">
    ```python theme={null}
    from google import genai
    from google.genai.types import HttpOptions

    client = genai.Client(
        vertexai=True,
        api_key="dummy-key",
        http_options=HttpOptions(base_url="http://localhost:8080/genai_passthrough")
    )

    response = client.models.generate_content(
        model="gemini-2.5-flash",
        contents="hello from vertex passthrough"
    )

    print(response.text)
    ```
  </Tab>

  <Tab title="cURL">
    ```bash theme={null}
    curl -X POST "http://localhost:8080/genai_passthrough/v1/projects/my-project/locations/us-central1/publishers/google/models/gemini-2.5-flash:generateContent" \
      -H "content-type: application/json" \
      -H "authorization: Bearer ya29.your-vertex-token" \
      -d '{
        "contents":[{"parts":[{"text":"hello from vertex passthrough"}]}]
      }'
    ```
  </Tab>
</Tabs>

***

## Notes

* Use passthrough when you need a provider endpoint that is not directly supported by Bifrost integration routes yet.
* For Azure passthrough, auth headers (`api-key`, `x-api-key`, OAuth token) are always sourced from the Bifrost key config and never forwarded from the client request.