Overview

Bifrost provides Cohere v2 API compatibility through protocol adaptation. The integration accepts Cohere’s native request bodies on /cohere, converts them into Bifrost’s internal format, and rebuilds Cohere’s native response envelopes on the way out. Point the official Cohere SDK at Bifrost and your existing code keeps working, with governance, load balancing, semantic caching, logging and multi-provider routing added underneath. Endpoint: /cohere

Supported routes

Route	Cohere SDK method	Bifrost operation	Streaming
`POST /cohere/v2/chat`	`chat()` / `chat_stream()`	Chat completion	✅
`POST /cohere/v2/embed`	`embed()`	Embedding, including text + image `inputs`	-
`POST /cohere/v2/rerank`	`rerank()`	Rerank	-
`POST /cohere/v1/tokenize`	`tokenize()`	Count tokens	-

Each route works against Cohere models by default and against any other provider when the model is prefixed with the provider name (see Provider/Model Usage).

The Cohere SDKs append the version path themselves (/v2/chat, /v2/embed, /v2/rerank, /v1/tokenize), so the base URL is the bare http://localhost:8080/cohere, without a version suffix.

Setup

Python
JavaScript

import cohere

# Configure client to use Bifrost
co = cohere.ClientV2(
    base_url="http://localhost:8080/cohere",
    api_key="<YOUR-BIFROST-VIRTUAL-KEY>",  # Replace with your actual Bifrost virtual key.
)

# Make requests as usual
response = co.chat(
    model="command-a-03-2025",
    messages=[{"role": "user", "content": "Hello!"}],
)

print(response.message.content[0].text)

import { CohereClientV2 } from "cohere-ai";

// Configure client to use Bifrost
const cohere = new CohereClientV2({
  baseUrl: "http://localhost:8080/cohere",
  token: "<YOUR-BIFROST-VIRTUAL-KEY>", // Replace with your actual Bifrost virtual key.
});

// Make requests as usual
const response = await cohere.chat({
  model: "command-a-03-2025",
  messages: [{ role: "user", content: "Hello!" }],
});

console.log(response.message.content[0].text);

Provider/Model Usage Examples

Use multiple providers through the same Cohere SDK format by prefixing model names with the provider. Bifrost converts the Cohere request into the target provider’s format and converts the result back into Cohere’s response shape.

Python
JavaScript

import cohere

co = cohere.ClientV2(
    base_url="http://localhost:8080/cohere",
    api_key="<YOUR-BIFROST-VIRTUAL-KEY>",  # Replace with your actual Bifrost virtual key.
)

# Cohere models (default)
cohere_response = co.chat(
    model="command-a-03-2025",
    messages=[{"role": "user", "content": "Hello from Cohere!"}],
)

# OpenAI models via Cohere SDK format
openai_response = co.chat(
    model="openai/gpt-4o-mini",
    messages=[{"role": "user", "content": "Hello from OpenAI!"}],
)

# Anthropic models
anthropic_response = co.chat(
    model="anthropic/claude-sonnet-4-20250514",
    messages=[{"role": "user", "content": "Hello from Claude!"}],
)

# Bedrock models
bedrock_response = co.chat(
    model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0",
    messages=[{"role": "user", "content": "Hello from Bedrock!"}],
)

# OpenAI embeddings via Cohere SDK format
openai_embeddings = co.embed(
    model="openai/text-embedding-3-small",
    texts=["hello"],
    input_type="search_document",
    embedding_types=["float"],
)

import { CohereClientV2 } from "cohere-ai";

const cohere = new CohereClientV2({
  baseUrl: "http://localhost:8080/cohere",
  token: "<YOUR-BIFROST-VIRTUAL-KEY>", // Replace with your actual Bifrost virtual key.
});

// Cohere models (default)
const cohereResponse = await cohere.chat({
  model: "command-a-03-2025",
  messages: [{ role: "user", content: "Hello from Cohere!" }],
});

// OpenAI models via Cohere SDK format
const openaiResponse = await cohere.chat({
  model: "openai/gpt-4o-mini",
  messages: [{ role: "user", content: "Hello from OpenAI!" }],
});

// Anthropic models
const anthropicResponse = await cohere.chat({
  model: "anthropic/claude-sonnet-4-20250514",
  messages: [{ role: "user", content: "Hello from Claude!" }],
});

// OpenAI embeddings via Cohere SDK format
const openaiEmbeddings = await cohere.embed({
  model: "openai/text-embedding-3-small",
  texts: ["hello"],
  inputType: "search_document",
  embeddingTypes: ["float"],
});

Chat

/cohere/v2/chat accepts the full Cohere v2 chat body: messages, tools, response_format, thinking, max_tokens, temperature, p, k, stop_sequences and the rest. Bifrost maps these onto each provider’s own parameters. See the Cohere provider page for the exact parameter mapping.

Streaming

Python
JavaScript

import cohere

co = cohere.ClientV2(
    base_url="http://localhost:8080/cohere",
    api_key="<YOUR-BIFROST-VIRTUAL-KEY>",  # Replace with your actual Bifrost virtual key.
)

for event in co.chat_stream(
    model="command-a-03-2025",
    messages=[{"role": "user", "content": "Write a haiku about gateways."}],
):
    if event.type == "content-delta":
        print(event.delta.message.content.text, end="", flush=True)

import { CohereClientV2 } from "cohere-ai";

const cohere = new CohereClientV2({
  baseUrl: "http://localhost:8080/cohere",
  token: "<YOUR-BIFROST-VIRTUAL-KEY>", // Replace with your actual Bifrost virtual key.
});

const stream = await cohere.chatStream({
  model: "command-a-03-2025",
  messages: [{ role: "user", content: "Write a haiku about gateways." }],
});

for await (const event of stream) {
  if (event.type === "content-delta") {
    process.stdout.write(event.delta?.message?.content?.text ?? "");
  }
}

Streaming on /cohere/v2/chat returns Cohere’s native event stream only when both hold: the model is served by Cohere, and raw responses are enabled for the Cohere provider. Bifrost replays the raw Cohere events rather than rebuilding them, so without raw responses the stream is emitted in Bifrost’s OpenAI-style chunk format even for Cohere models, and the Cohere SDK’s stream parser will not understand it.Enable raw responses either in the provider config or per request:

{
  "providers": {
    "cohere": {
      "keys": [{ "name": "cohere-key", "value": "env.COHERE_API_KEY", "weight": 1.0 }],
      "send_back_raw_response": true
    }
  }
}

x-bf-send-back-raw-response: true

For a provider/model that routes elsewhere, the stream is always emitted in Bifrost’s chunk format. Use non-streaming calls for cross-provider chat through this route, or use the OpenAI SDK integration for cross-provider streaming. See Request Options for the full list of x-bf-* headers.

Embeddings

/cohere/v2/embed accepts all three of Cohere’s input fields and returns Cohere’s native envelope (embeddings, response_type, meta.billed_units) for every provider.

Field	Content	Notes
`texts`	Array of strings	Works against any provider that supports embeddings.
`images`	Array of base64 data URIs	Image-only embeddings. `input_type` should be `image`.
`inputs`	Array of `{ "content": [ ... ] }` with `text` and `image_url` blocks	Mixed text + image in one vector. Cohere Embed v4 and other multimodal models.

input_type, embedding_types, truncate and output_dimension are forwarded. When embedding_types lists several formats, each comes back under its own key in embeddings (float, int8, uint8, binary, ubinary, base64).

Python
JavaScript

import base64
import cohere

co = cohere.ClientV2(
    base_url="http://localhost:8080/cohere",
    api_key="<YOUR-BIFROST-VIRTUAL-KEY>",  # Replace with your actual Bifrost virtual key.
)

# Text embeddings
response = co.embed(
    model="embed-v4.0",
    texts=["a golden retriever playing fetch", "a cat asleep on a sofa"],
    input_type="search_document",
    embedding_types=["float", "int8"],
)
print(len(response.embeddings.float_[0]), len(response.embeddings.int8[0]))

# Image embeddings
with open("photo.jpg", "rb") as f:
    image_uri = "data:image/jpeg;base64," + base64.b64encode(f.read()).decode()

response = co.embed(
    model="embed-v4.0",
    images=[image_uri],
    input_type="image",
    embedding_types=["float"],
)

# Mixed text + image in one vector
response = co.embed(
    model="embed-v4.0",
    inputs=[
        cohere.EmbedInput(
            content=[
                {"type": "text", "text": "a golden retriever in a park"},
                {"type": "image_url", "image_url": {"url": image_uri}},
            ]
        )
    ],
    input_type="search_document",
    embedding_types=["float"],
)

import fs from "node:fs";
import { CohereClientV2 } from "cohere-ai";

const cohere = new CohereClientV2({
  baseUrl: "http://localhost:8080/cohere",
  token: "<YOUR-BIFROST-VIRTUAL-KEY>", // Replace with your actual Bifrost virtual key.
});

// Text embeddings
const textResponse = await cohere.embed({
  model: "embed-v4.0",
  texts: ["a golden retriever playing fetch", "a cat asleep on a sofa"],
  inputType: "search_document",
  embeddingTypes: ["float", "int8"],
});

// Image embeddings
const imageUri = "data:image/jpeg;base64," + fs.readFileSync("photo.jpg").toString("base64");

const imageResponse = await cohere.embed({
  model: "embed-v4.0",
  images: [imageUri],
  inputType: "image",
  embeddingTypes: ["float"],
});

// Mixed text + image in one vector
const mixedResponse = await cohere.embed({
  model: "embed-v4.0",
  inputs: [
    {
      content: [
        { type: "text", text: "a golden retriever in a park" },
        { type: "image_url", imageUrl: { url: imageUri } },
      ],
    },
  ],
  inputType: "search_document",
  embeddingTypes: ["float"],
});

Images must be inline data URIs; Cohere does not fetch remote URLs. Which modalities a non-Cohere model accepts through this route follows the model, not the SDK: see the multimodal embeddings support matrix.

Rerank

/cohere/v2/rerank accepts string documents or object documents, top_n and max_tokens_per_doc, and returns Cohere’s results[] with index and relevance_score. Map index back onto the documents you sent to recover the text. Cross-provider rerank (for example a vLLM or custom OpenAI-compatible reranker) is converted into the same shape.

Python
JavaScript

import cohere

co = cohere.ClientV2(
    base_url="http://localhost:8080/cohere",
    api_key="<YOUR-BIFROST-VIRTUAL-KEY>",  # Replace with your actual Bifrost virtual key.
)

documents = [
    "Paris is the capital and largest city of France.",
    "Berlin is the capital of Germany.",
    "The Eiffel Tower is in Paris.",
]

response = co.rerank(
    model="rerank-v3.5",
    query="What is the capital of France?",
    documents=documents,
    top_n=2,
)

for result in response.results:
    print(result.index, result.relevance_score, documents[result.index])

import { CohereClientV2 } from "cohere-ai";

const cohere = new CohereClientV2({
  baseUrl: "http://localhost:8080/cohere",
  token: "<YOUR-BIFROST-VIRTUAL-KEY>", // Replace with your actual Bifrost virtual key.
});

const documents = [
  "Paris is the capital and largest city of France.",
  "Berlin is the capital of Germany.",
  "The Eiffel Tower is in Paris.",
];

const response = await cohere.rerank({
  model: "rerank-v3.5",
  query: "What is the capital of France?",
  documents,
  topN: 2,
});

for (const result of response.results) {
  console.log(result.index, result.relevanceScore, documents[result.index]);
}

See the Reranking guide for the provider-neutral /v1/rerank endpoint and the models that support it.

Tokenize

/cohere/v1/tokenize takes model and text and maps to Bifrost’s count-tokens operation. Cohere’s native response (tokens, token_strings, meta) is returned only when the model is served by Cohere and raw responses are enabled (send_back_raw_response on the provider, or the x-bf-send-back-raw-response: true header, as in the streaming note above). In every other case, including Cohere models without raw responses, the response is Bifrost’s count-tokens shape, which the SDK’s typed tokenize() client does not parse.

# offline=False is required: the Python SDK defaults to offline=True and tokenizes
# locally with a downloaded tokenizer, never calling Bifrost at all.
response = co.tokenize(model="command-a-03-2025", text="Hello, world!", offline=False)
print(len(response.tokens))

Adding Custom Headers

Pass custom headers required by Bifrost plugins (like governance, telemetry, etc.):

Python
JavaScript

import cohere
import httpx

# Sending the virtual key through both fields requires dual_credential_conflict_behavior to be set to "prefer_vk".
co = cohere.ClientV2(
    base_url="http://localhost:8080/cohere",
    api_key="<YOUR-BIFROST-VIRTUAL-KEY>",  # Replace with your actual Bifrost virtual key.
    httpx_client=httpx.Client(
        headers={
            "x-bf-vk": "<YOUR-BIFROST-VIRTUAL-KEY>",  # Replace with your actual Bifrost virtual key.
        }
    ),
)

response = co.chat(
    model="command-a-03-2025",
    messages=[{"role": "user", "content": "Hello with custom headers!"}],
)

import { CohereClientV2 } from "cohere-ai";

// Sending the virtual key through both fields requires dual_credential_conflict_behavior to be set to "prefer_vk".
const cohere = new CohereClientV2({
  baseUrl: "http://localhost:8080/cohere",
  token: "<YOUR-BIFROST-VIRTUAL-KEY>", // Replace with your actual Bifrost virtual key.
  headers: {
    "x-bf-vk": "<YOUR-BIFROST-VIRTUAL-KEY>", // Replace with your actual Bifrost virtual key.
  },
});

// Headers can also be set per request
const response = await cohere.chat(
  {
    model: "command-a-03-2025",
    messages: [{ role: "user", content: "Hello with custom headers!" }],
  },
  { headers: { "x-bf-vk": "<YOUR-BIFROST-VIRTUAL-KEY>" } }
);

Supported Features

The Cohere integration supports the four routes above for any model Bifrost can reach. Operations the Cohere API does not expose (text completions, image generation, speech, transcription, files, batch) are not available on /cohere; use the OpenAI SDK integration or the native /v1 endpoints for those. Known differences from calling Cohere directly:

Streaming and tokenize need raw responses. Native Cohere stream events and the native tokenize response are passed through only when send_back_raw_response is enabled for the Cohere provider (or the x-bf-send-back-raw-response header is set). Otherwise both fall back to Bifrost’s own shape, even for Cohere models.
Cross-provider streaming on /v2/chat always emits Bifrost’s chunk format rather than Cohere’s event stream, regardless of the raw-response setting.
Cross-provider tokenize always returns Bifrost’s count-tokens response rather than Cohere’s tokens / token_strings shape.
Native passthrough: with raw responses enabled and the model served by Cohere, chat, embed, rerank and tokenize responses are returned exactly as Cohere sent them. Without raw responses, non-streaming chat, embed and rerank are rebuilt from Bifrost’s format into Cohere’s shape, so they still parse in the SDK.

Next Steps

OpenAI SDK - GPT integration patterns
Anthropic SDK - Claude integration patterns
Google GenAI SDK - Gemini integration patterns
Cohere Provider - Parameter mapping and provider configuration
Multimodal Embeddings - Image, audio, video and PDF embeddings
Configuration - Bifrost setup and configuration

Runbooks

CLI Agents & Editors

SDKs & Frameworks

Identity Providers (SSO)

Content Safety (Guardrails)

Observability

Vector Databases

Overview

Overview

Supported routes

Setup

Provider/Model Usage Examples

Chat

Streaming

Embeddings

Rerank

Tokenize

Adding Custom Headers

Supported Features

Next Steps

​Overview

​Supported routes

​Setup

​Provider/Model Usage Examples

​Chat

​Streaming

​Embeddings

​Rerank

​Tokenize

​Adding Custom Headers

​Supported Features

​Next Steps

Overview

Supported routes

Setup

Provider/Model Usage Examples

Chat

Streaming

Embeddings

Rerank

Tokenize

Adding Custom Headers

Supported Features

Next Steps