TeamoRouter API Integration Guide

TeamoRouter supports multiple compatible API formats, including OpenAI, Google Gemini, and Anthropic. Point the official SDK base URL to TeamoRouter and keep your application code unchanged. You can call Claude / Gemini / GPT and other model families through one gateway.

• Base URL: https://api.teamorouter.com
• Authentication: API Key (sk-teamo-...)
• Protocols: Anthropic native format (/v1/messages), OpenAI-compatible format (/v1/chat/completions), OpenAI Responses API (/v1/responses, GPT models only), Gemini native format (/v1beta/models/{model}:generateContent), and image generation (/v1/images)

1. Authentication

Choose the compatible format according to the model you use:

Different formats use different authentication headers. Choose the header according to the protocol:

2. Available models

Fetch the complete model list in real time with GET /v1/models:

curl https://api.teamorouter.com/v1/models \
  -H "Authorization: Bearer sk-teamo-xxxxxx"

Currently available models (examples):

3. Anthropic native format /v1/messages

3.1 Basic request

curl https://api.teamorouter.com/v1/messages \
  -H "x-api-key: sk-teamo-xxxxxx" \
  -H "anthropic-version: 2023-06-01" \
  -H "content-type: application/json" \
  -d '{
    "model": "claude-fable-5",
    "max_tokens": 1024,
    "messages": [
      {"role": "user", "content": "Introduce yourself in one sentence"}
    ]
  }'

Example response:

{
  "id": "msg_01KxDE...",
  "type": "message",
  "role": "assistant",
  "model": "claude-fable-5",
  "content": [{"type": "text", "text": "I am Claude..."}],
  "stop_reason": "end_turn",
  "usage": {"input_tokens": 159, "output_tokens": 34}
}

3.2 Streaming with SSE

Add "stream": true. The response is text/event-stream:

curl https://api.teamorouter.com/v1/messages \
  -H "x-api-key: sk-teamo-xxxxxx" \
  -H "anthropic-version: 2023-06-01" \
  -H "content-type: application/json" \
  -d '{
    "model": "claude-fable-5",
    "max_tokens": 1024,
    "stream": true,
    "messages": [{"role": "user", "content": "Write a short poem"}]
  }'

Event sequence: message_start -> content_block_start -> multiple content_block_delta events -> content_block_stop -> message_delta -> message_stop.

3.3 Python SDK with anthropic

from anthropic import Anthropic

client = Anthropic(
    api_key="sk-teamo-xxxxxx",
    base_url="https://api.teamorouter.com",
)

resp = client.messages.create(
    model="claude-fable-5",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello"}],
)
print(resp.content[0].text)

4. OpenAI-compatible format /v1/chat/completions and /v1/responses

4.1 Basic Chat Completions request

curl https://api.teamorouter.com/v1/chat/completions \
  -H "Authorization: Bearer sk-teamo-xxxxxx" \
  -H "content-type: application/json" \
  -d '{
    "model": "gpt-5.6-sol",
    "max_tokens": 1024,
    "messages": [{"role": "user", "content": "Hello"}]
  }'

The response follows the standard OpenAI chat.completion structure:

{
  "object": "chat.completion",
  "model": "gpt-5.6-sol",
  "choices": [
    {"index": 0, "message": {"role": "assistant", "content": "Hello!"}, "finish_reason": "stop"}
  ],
  "usage": {"prompt_tokens": 214, "completion_tokens": 3, "total_tokens": 217}
}

4.2 Python SDK with openai

from openai import OpenAI

client = OpenAI(
    api_key="sk-teamo-xxxxxx",
    base_url="https://api.teamorouter.com/v1",
)

resp = client.chat.completions.create(
    model="gpt-5.6-sol",
    messages=[{"role": "user", "content": "Hello"}],
)
print(resp.choices[0].message.content)

4.3 Streaming

Add "stream": true. TeamoRouter returns standard OpenAI SSE chunks in the data: {...} format and ends with data: [DONE].

4.4 Basic Responses API request

If your application already uses the OpenAI Responses API, call /v1/responses directly:

curl https://api.teamorouter.com/v1/responses \
  -H "Authorization: Bearer sk-teamo-xxxxxx" \
  -H "content-type: application/json" \
  -d '{
    "model": "gpt-5.6-sol",
    "input": "Introduce TeamoRouter in one sentence"
  }'

4.5 Python SDK with openai Responses

from openai import OpenAI

client = OpenAI(
    api_key="sk-teamo-xxxxxx",
    base_url="https://api.teamorouter.com/v1",
)

resp = client.responses.create(
    model="gpt-5.6-sol",
    input="Introduce TeamoRouter in one sentence",
)
print(resp.output_text)

4.6 Responses API streaming

Add "stream": true to receive standard OpenAI Responses API streaming events.

5. Gemini native format /v1beta/models/{model}:generateContent

5.1 Basic request

curl https://api.teamorouter.com/v1beta/models/gemini-3.5-flash:generateContent \
  -H "Authorization: Bearer sk-teamo-xxxxxx" \
  -H "content-type: application/json" \
  -d '{
    "contents": [
      {"role": "user", "parts": [{"text": "Introduce yourself in one sentence"}]}
    ]
  }'

Example response:

{
  "candidates": [
    {
      "content": {"role": "model", "parts": [{"text": "I am Gemini..."}]},
      "finishReason": "STOP"
    }
  ],
  "usageMetadata": {"promptTokenCount": 12, "candidatesTokenCount": 22, "totalTokenCount": 34}
}

5.2 Streaming with SSE

Use streamGenerateContent and add alt=sse:

curl "https://api.teamorouter.com/v1beta/models/gemini-3.5-flash:streamGenerateContent?alt=sse" \
  -H "Authorization: Bearer sk-teamo-xxxxxx" \
  -H "content-type: application/json" \
  -d '{
    "contents": [
      {"role": "user", "parts": [{"text": "Write a short poem"}]}
    ]
  }'

5.3 Environment variables

If your tool or SDK supports a custom Gemini Base URL, configure it like this:

export GOOGLE_GEMINI_BASE_URL="https://api.teamorouter.com"
export GEMINI_API_KEY="sk-teamo-xxxxxx"
export GEMINI_API_KEY_AUTH_MECHANISM="bearer"

Different Gemini SDKs may use different field names for custom endpoints. Common names include base_url, baseURL, apiEndpoint, or environment variables. The core rule is: point the Base URL to https://api.teamorouter.com and use your TeamoRouter API Key.

6. Image generation /v1/images

The image model gpt-image-2 uses a separate image endpoint and authenticates with Authorization: Bearer.

6.1 Generate images POST /v1/images/generations

curl https://api.teamorouter.com/v1/images/generations \
  -H "Authorization: Bearer sk-teamo-xxxxxx" \
  -H "content-type: application/json" \
  -d '{
    "model": "gpt-image-2",
    "prompt": "An orange cat typing on a keyboard, illustration style"
  }'

Example response. data[0].b64_json is the Base64-encoded image:

{
  "created": 1752345600,
  "data": [
    {"b64_json": "iVBORw0KGgo..."}
  ]
}

Set the timeout to 300 seconds. Image generation models can take longer to respond, and shorter timeouts may fail.

6.2 Edit images POST /v1/images/edits

Upload the source image as multipart/form-data and provide an edit instruction:

curl https://api.teamorouter.com/v1/images/edits \
  -H "Authorization: Bearer sk-teamo-xxxxxx" \
  -F model="gpt-image-2" \
  -F image="@photo.png" \
  -F prompt="Replace the background with a starry sky"

This endpoint is compatible with the OpenAI Images API.

7. Connect Claude Code / Codex / Gemini CLI and other agent tools

Claude Code uses the Anthropic protocol, Codex uses the OpenAI protocol, and Gemini CLI uses the Gemini protocol.

Just point the Base URL and API Key environment variables to TeamoRouter. You do not need to modify the tools themselves.

Claude Code (Anthropic protocol):

export ANTHROPIC_BASE_URL="https://api.teamorouter.com"
export ANTHROPIC_API_KEY="sk-teamo-xxxxxx"

OpenAI protocol tools (Chat Completions / Responses API):

export OPENAI_BASE_URL="https://api.teamorouter.com/v1"
export OPENAI_API_KEY="sk-teamo-xxxxxx"

Gemini CLI:

export GOOGLE_GEMINI_BASE_URL="https://api.teamorouter.com"
export GEMINI_API_KEY="sk-teamo-xxxxxx"
export GEMINI_API_KEY_AUTH_MECHANISM="bearer"

8. FAQ

First verify the protocol-to-header mapping: Anthropic uses x-api-key; OpenAI / Gemini use Authorization: Bearer; Gemini native endpoints also accept x-goog-api-key. Then confirm the key is complete, starts with sk-teamo-, has no extra spaces, and has not been deleted in the dashboard. Also check the base_url format: OpenAI SDKs need /v1; Anthropic SDKs do not.

Use the Anthropic native protocol (/v1/messages) for Claude models whenever possible. Claude Code and other agent tools must be configured with the Anthropic protocol. Calling Claude through the OpenAI-compatible format can lose prompt cache, thinking, and other capabilities, increasing cost and reducing quality. It is only suitable for simple chat scenarios. /v1/responses does not support Claude / Gemini and returns 400.

Use GET /v1/models to fetch the real-time model list first. Check the model ID spelling, use lowercase, and pay attention to - versus .. Also confirm the endpoint matches the model.

Large models such as Opus / Fable can take several seconds to tens of seconds for the first token during reasoning. This does not mean the request failed. In production, set "stream": true for better perceived latency and increase the client read timeout. The server supports responses up to 600 seconds.

Store keys in environment variables or a secret manager. Do not hardcode them, commit them to Git, or bundle them into a frontend / public client. If you suspect a key has leaked, delete it in the dashboard immediately and create a replacement.