Use FluentC anywhere you build AI.
From low-code automation to LLM agents, FluentC brings translation memory, batch scale and real-time speed into every workflow.
Four ways in. Same engine.
REST API
One endpoint for text, HTML and JSON, in real-time or batch mode. The base layer everything else sits on.
POST /ai_agent/translateREST API documentation →MCP server
Give any MCP-capable agent a translation tool. Point the client at the server and add your key.
mcp.fluentc.aiMCP server documentation →Drop FluentC into your Zaps and Make scenarios.
If a tool can send an HTTP request, it can call FluentC. That covers Zapier and Make automations: form submissions, CRM entries, support tickets. Batch mode handles big queues without per-translation charges, and translation memory keeps duplicates from being billed twice. Step-by-step settings are in the Make and Zapier guide.
- 01
Trigger
A Google Form is submitted, a CRM entry is created, a support ticket comes in.
- 02
Request
An HTTP module posts the text to /ai_agent/translate with your API key.
- 03
Wait
Real-time mode answers in the same call. For batch, poll /ai_agent/results until the job is complete.
- 04
Deliver
Write the translation to a Google Sheet, post it to Slack, or update the record.
Make your GPT agent multilingual in one call.
Register FluentC as a callable function. The model asks for a translation, your handler calls the API, and the answer goes back into the conversation. Real-time mode is fast enough for function calling, and common phrases come straight from memory. The full loop is in the function calling guide.
- Fast real-time results for function calling
- Plain JSON in and out, so it drops into any tool-calling setup
- Smart memory for repeated phrases
- 128+ languages out of the box
{
"type": "function",
"function": {
"name": "translate_text",
"description": "Translate text into a target language using FluentC",
"parameters": {
"type": "object",
"properties": {
"text": { "type": "string", "description": "Text to be translated" },
"target_language": { "type": "string", "description": "Language code, e.g. \"fr\"" }
},
"required": ["text", "target_language"]
}
}
}// Runs when the model calls translate_text.
export async function translate_text({ text, target_language }) {
const res = await fetch("https://api.fluentc.ai/ai_agent/translate", {
method: "POST",
headers: {
Authorization: process.env.FLUENTC_KEY,
"Content-Type": "application/json",
},
body: JSON.stringify({
input: text,
input_format: "text",
target_language,
mode: "real-time",
}),
});
const { translation } = await res.json();
return { translated_text: translation };
}{ "translated_text": "Bonjour, comment puis-je vous aider ?" }Building something that isn't on this list?
Tell us what you're wiring up and we'll tell you how it fits.
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