OpenAI, Anthropic, and Gemini Compatibility

EmpirioLabs AI exposes compatibility endpoints so you can adopt the platform without rewriting existing integrations. Point your OpenAI, Anthropic, or Google Gemini SDK at the EmpirioLabs base URL and start making requests immediately.

OpenAI-compatible Chat Completions

POST /v1/chat/completions

Accepts the same request body as the OpenAI Chat Completions API. Supports messages, model, stream, temperature, max_tokens, and other standard parameters.

$
curl "https://api.empiriolabs.ai/v1/chat/completions" \
>
  -H "Authorization: Bearer $EMPIRIOLABS_API_KEY" \
>
  -H "Content-Type: application/json" \
>
  -d '{
>
    "model": "qwen3-7-max",
>
    "messages": [{ "role": "user", "content": "Summarize this in one sentence." }]
>
  }'

Using the OpenAI Python SDK:

1
from openai import OpenAI
2
3
client = OpenAI(
4
    api_key="sk-empiriolabs-your_key_here",
5
    base_url="https://api.empiriolabs.ai/v1",
6
)
7
8
response = client.chat.completions.create(
9
    model="qwen3-7-max",
10
    messages=[{"role": "user", "content": "Hello!"}],
11
)

Structured outputs

Chat models that advertise structured output can constrain their responses with the OpenAI response_format parameter on POST /v1/chat/completions. There are two levels:

• JSON mode returns a valid JSON object with a shape you describe in your prompt.
• JSON schema forces the response to match a JSON Schema you provide.

Support is per model. Check the structured_output field on GET /v1/models/{modelId} ("json_schema" means strict schema support, "json_object" means JSON mode only), or look for the “Structured Outputs” capability on the model page. Sending a format a model does not support returns a 400.

JSON mode:

$
curl "https://api.empiriolabs.ai/v1/chat/completions" \
>
  -H "Authorization: Bearer $EMPIRIOLABS_API_KEY" \
>
  -H "Content-Type: application/json" \
>
  -d '{
>
    "model": "qwen3-7-max",
>
    "messages": [{ "role": "user", "content": "Give me the capital of France as JSON with keys city and country." }],
>
    "response_format": { "type": "json_object" }
>
  }'

JSON schema (schema-enforced):

$
curl "https://api.empiriolabs.ai/v1/chat/completions" \
>
  -H "Authorization: Bearer $EMPIRIOLABS_API_KEY" \
>
  -H "Content-Type: application/json" \
>
  -d '{
>
    "model": "deepseek-v4-pro",
>
    "messages": [{ "role": "user", "content": "Extract structured facts about Paris." }],
>
    "response_format": {
>
      "type": "json_schema",
>
      "json_schema": {
>
        "name": "city_info",
>
        "strict": true,
>
        "schema": {
>
          "type": "object",
>
          "properties": {
>
            "city": { "type": "string" },
>
            "population": { "type": "integer" }
>
          },
>
          "required": ["city", "population"],
>
          "additionalProperties": false
>
        }
>
      }
>
    }
>
  }'

The OpenAI SDKs support both shapes natively through their own response_format argument, so no EmpirioLabs-specific code is needed.

OpenAI Responses-compatible endpoint

POST /v1/responses

Accepts the same request body as the OpenAI Responses API. Supports model, input, instructions, and related fields.

$
curl "https://api.empiriolabs.ai/v1/responses" \
>
  -H "Authorization: Bearer $EMPIRIOLABS_API_KEY" \
>
  -H "Content-Type: application/json" \
>
  -d '{
>
    "model": "seed-2-0-pro",
>
    "input": "Write a launch checklist."
>
  }'

Anthropic-style Messages endpoint

POST /v1/messages

Accepts the same request body as the Anthropic Messages API. Requires model, messages, and max_tokens.

$
curl "https://api.empiriolabs.ai/v1/messages" \
>
  -H "Authorization: Bearer $EMPIRIOLABS_API_KEY" \
>
  -H "Content-Type: application/json" \
>
  -d '{
>
    "model": "mistral-medium-3-1",
>
    "messages": [{ "role": "user", "content": "Hello" }],
>
    "max_tokens": 256
>
  }'

Google Gemini-compatible endpoint

POST /v1beta/models/{model}:generateContent

Accepts the native Google Gemini request body (contents, systemInstruction, generationConfig, tools) for any chat model in the catalog. Streaming uses POST /v1beta/models/{model}:streamGenerateContent?alt=sse, and POST /v1beta/models/{model}:countTokens returns a fast token estimate.

Authenticate with your EmpirioLabs API key in the x-goog-api-key header or the ?key= query parameter, the same places Gemini clients already put their key. Authorization: Bearer works too.

$
curl "https://api.empiriolabs.ai/v1beta/models/qwen3-7-max:generateContent" \
>
  -H "x-goog-api-key: $EMPIRIOLABS_API_KEY" \
>
  -H "Content-Type: application/json" \
>
  -d '{
>
    "contents": [{ "role": "user", "parts": [{ "text": "Hello!" }] }]
>
  }'

Using the google-genai Python SDK:

1
from google import genai
2
3
client = genai.Client(
4
    api_key="sk-empiriolabs-your_key_here",
5
    http_options={"base_url": "https://api.empiriolabs.ai"},
6
)
7
8
response = client.models.generate_content(
9
    model="qwen3-7-max",
10
    contents="Hello!",
11
)

Pricing, parameters, and billing match /v1/chat/completions for the same model. Streamed responses end without a [DONE] sentinel, matching Gemini behavior.

SDK configuration cheat sheet

For coding agents, IDE assistants, and CLIs, see Integrations for OpenCode, Claude Code, Cline, Aider, Continue, Cursor, and other OpenAI-compatible tools.

Model support

Not every model supports every endpoint format. Use GET /v1/models/{modelId} to check a specific model’s capabilities (streaming, system prompt, web search, images, video) and supported_parameters before integrating.