> ## Documentation Index > Fetch the complete documentation index at: https://docs.meliai.app/llms.txt > Use this file to discover all available pages before exploring further. # POST /v1/messages — Anthropic-Compatible Chat API > Use the Anthropic SDK with Meliai via POST /v1/messages. Point any Anthropic-compatible client at Meliai's base URL with your sk-mel- API key. The `/v1/messages` endpoint mirrors the Anthropic Messages API, letting you use the official `anthropic` Python or TypeScript SDK — or any Anthropic-compatible client — without code changes. Point the client at `https://api.meliai.ai` with your `sk-mel-` API key and every request is routed across Meliai's European provider network. Claude model identifiers such as `claude-sonnet-4` are accepted as aliases and mapped to equivalent open-weight models running entirely on EU infrastructure. Claude model names (e.g. `claude-sonnet-4`) are logical aliases mapped to capable open-weight equivalents. No Anthropic infrastructure is involved — your data stays within the EU at all times. ## Endpoint ``` POST https://api.meliai.ai/v1/messages ``` **Authorization:** `Bearer sk-mel-` via `Authorization` header, or `x-api-key: sk-mel-`. *** ## Parameters Model ID to use. You may pass an Anthropic-style alias (e.g. `"claude-sonnet-4"`) or a direct open-weight model ID. Append a [routing flavor suffix](/concepts/routing) such as `:speed` or `:eco` to control provider selection. Conversation turns in Anthropic format. Each object requires: * `role` — `"user"` or `"assistant"` * `content` — a string, or an array of content blocks (`text`, `image`, `tool_use`, `tool_result`) Maximum number of tokens to generate. Anthropic's API treats this field as required; include it on every request. System prompt placed before the conversation. Equivalent to a `{"role": "system", ...}` message in the OpenAI format. Sampling temperature in `[0, 1]`. Higher values increase output diversity. Defaults to the model's built-in default. When `true`, the response streams as SSE events using the Anthropic streaming format. Default: `false`. Tool definitions following the Anthropic tool-calling schema. Each tool object contains `name`, `description`, and `input_schema` (a JSON Schema object). Controls tool invocation. Anthropic-format object with a `type` field: `"auto"`, `"any"`, or `"tool"` (plus `name` when specifying a particular tool). *** ## Examples ### Basic chat ```python Python (anthropic SDK) theme={null} import os from anthropic import Anthropic client = Anthropic( api_key=os.environ["MELIAI_API_KEY"], base_url="https://api.meliai.ai", ) response = client.messages.create( model="claude-sonnet-4", max_tokens=512, system="You are a helpful assistant.", messages=[{"role": "user", "content": "Name three Hanseatic cities."}], ) print(response.content[0].text) ``` ```javascript Node.js (anthropic SDK) theme={null} import Anthropic from "@anthropic-ai/sdk"; const client = new Anthropic({ apiKey: process.env.MELIAI_API_KEY, baseURL: "https://api.meliai.ai", }); const response = await client.messages.create({ model: "claude-sonnet-4", max_tokens: 512, system: "You are a helpful assistant.", messages: [{ role: "user", content: "Name three Hanseatic cities." }], }); console.log(response.content[0].text); ``` ```bash curl theme={null} curl https://api.meliai.ai/v1/messages \ -H "x-api-key: $MELIAI_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "claude-sonnet-4", "max_tokens": 512, "system": "You are a helpful assistant.", "messages": [ {"role": "user", "content": "Name three Hanseatic cities."} ] }' ``` ### Streaming ```python Python theme={null} with client.messages.stream( model="claude-sonnet-4", max_tokens=512, messages=[{"role": "user", "content": "Explain the EU AI Act briefly."}], ) as stream: for text in stream.text_stream: print(text, end="", flush=True) ``` ```javascript Node.js theme={null} const stream = client.messages.stream({ model: "claude-sonnet-4", max_tokens: 512, messages: [{ role: "user", content: "Explain the EU AI Act briefly." }], }); for await (const event of stream) { if ( event.type === "content_block_delta" && event.delta.type === "text_delta" ) { process.stdout.write(event.delta.text); } } ``` *** ## Token Count Preflight Use `POST /v1/messages/count_tokens` to estimate the token count for a request before sending it. The endpoint accepts the same body as `/v1/messages` and returns a count without consuming any generation credits. ``` POST https://api.meliai.ai/v1/messages/count_tokens ``` ```python Python theme={null} token_response = client.messages.count_tokens( model="claude-sonnet-4", system="You are a helpful assistant.", messages=[{"role": "user", "content": "Name three Hanseatic cities."}], ) print(f"Input tokens: {token_response.input_tokens}") ``` ```bash curl theme={null} curl https://api.meliai.ai/v1/messages/count_tokens \ -H "x-api-key: $MELIAI_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "claude-sonnet-4", "system": "You are a helpful assistant.", "messages": [ {"role": "user", "content": "Name three Hanseatic cities."} ] }' ``` **Response:** ```json theme={null} { "input_tokens": 28 } ``` *** ## Response A successful non-streaming response returns HTTP `200` with the Anthropic Messages response shape, extended with Meliai's `environment_impact` and `billing_cost` fields. ```json theme={null} { "id": "msg_01XyzAbcDef", "type": "message", "role": "assistant", "content": [ { "type": "text", "text": "Three notable Hanseatic cities are Hamburg, Lübeck, and Bremen." } ], "model": "claude-sonnet-4", "stop_reason": "end_turn", "usage": { "input_tokens": 28, "output_tokens": 18 }, "environment_impact": { "energy_kwh": 0.000038, "carbon_g_co2": 0.0027, "water_liters": 0.00015, "renewable_percent": 96, "pue": 1.15, "provider_id": "provider-eu-north", "location": "Amsterdam, NL" }, "billing_cost": { "energy": 0.000019, "credits": 0.00035, "paid_with": "credits" } } ``` All inference runs on GDPR-compliant European infrastructure. Your prompts and completions are never used to train any model.