mistral-core-workflow-a

name	mistral-core-workflow-a
description	Execute Mistral AI chat completions with streaming, multi-turn, and guardrails. Use when implementing chat interfaces, building conversational AI, or integrating Mistral for text generation. Trigger with phrases like "mistral chat", "mistral completion", "mistral streaming", "mistral conversation", "mistral guardrails".
allowed-tools	Read, Write, Edit, Bash(npm:*), Grep
version	1.12.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","mistral","workflow"]
compatibility	Designed for Claude Code, also compatible with Codex and OpenClaw

Mistral AI Core Workflow A: Chat Completions

Overview

Production chat completion patterns for Mistral AI: multi-turn conversations, streaming responses, JSON mode structured output, guardrails/moderation, and model selection. Uses the @mistralai/mistralai SDK.

Prerequisites

• Completed mistral-install-auth setup
• MISTRAL_API_KEY environment variable set
• Understanding of Mistral model tiers

Instructions

Step 1: Basic Chat Completion

import { Mistral } from '@mistralai/mistralai';

const client = new Mistral({ apiKey: process.env.MISTRAL_API_KEY });

async function chat(userMessage: string): Promise<string> {
  const response = await client.chat.complete({
    model: 'mistral-small-latest',
    messages: [
      { role: 'system', content: 'You are a helpful assistant.' },
      { role: 'user', content: userMessage },
    ],
  });
  return response.choices?.[0]?.message?.content ?? '';
}

Step 2: Multi-Turn Conversation Manager

  {
  :  |  | ;
  : ;
}

  {
   : [] = [];
   : ;
   : ;

  () {
    . =  ({ : process.. });
    . = model;
    ..({ : , : systemPrompt });
  }

   (: ): <> {
    ..({ : , : userMessage });

     response =  ...({
      : .,
      : .,
    });

     reply = response.?.[]?.?. ?? ;
    ..({ : , : reply });
     reply;
  }

  
  (maxTurns = ):  {
     system = .[];
     recent = ..().(-maxTurns * );
    . = [system, ...recent];
  }
}


 conv =  ();
 conv.();
 conv.();