Mistral Webhooks Events

Mistral AI Events, Agents & Async Patterns

Overview

Async and event-driven patterns for Mistral AI: the Agents API for stateful multi-turn workflows, Batch API for cost-effective bulk inference (50% cheaper), SSE streaming endpoints, background job queues, and Python async processing. Mistral does not have native webhooks — this skill covers the patterns that replace them.

Prerequisites

• @mistralai/mistralai SDK installed
• MISTRAL_API_KEY configured
• For agents: La Plateforme access to create agents
• For batch: JSONL file preparation

Instructions

Step 1: Mistral Agents API

Create stateful agents with instructions, tools, and model configuration:

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

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

// Create an agent on La Plateforme
const agent = await client.agents.create({
  name: 'Code Reviewer',
  model: 'mistral-large-latest',
  instructions: `You are an expert code reviewer. Analyze code for:
    - Security vulnerabilities
    - Performance issues
    - Best practice violations
    Provide actionable feedback with severity ratings.`,
  description: 'Reviews code for security, performance, and best practices',
  tools: [
    {
      type: 'function',
      function: {
        name: 'search_codebase',
        description: 'Search the codebase for patterns',
        parameters: {
          type: 'object',
          properties: { query: { type: 'string' } },
          required: ['query'],
        },
      },
    },
  ],
});

// Chat with the agent (stateful conversation)
const response = await client.agents.complete({
  agentId: agent.id,
  messages: [
    { role: 'user', content: 'Review this function:\n```\nfunction auth(pwd) { return pwd === "admin123"; }\n```' },
  ],
});

console.log(response.choices?.[0]?.message?.content);

Step 2: Batch API for Bulk Inference

50% cost reduction for non-time-sensitive workloads:

// 1. Prepare JSONL input file
const batchRequests = [
  {
    custom_id: 'req-1',
    body: {
      model: 'mistral-small-latest',
      messages: [{ role: 'user', content: 'Summarize: ...' }],
      max_tokens: 200,
    },
  },
  {
    custom_id: 'req-2',
    body: {
      model: 'mistral-small-latest',
      messages: [{ role: 'user', content: 'Classify: ...' }],
      max_tokens: 50,
    },
  },
];

// Write to JSONL
import { writeFileSync } from 'fs';
writeFileSync('batch-input.jsonl',
  batchRequests.map(r => JSON.stringify(r)).join('\n')
);

// 2. Upload file and create batch job
const file = await client.files.upload({
  file: { fileName: 'batch-input.jsonl', content: readFileSync('batch-input.jsonl') },
  purpose: 'batch',
});

const batch = await client.batch.jobs.create({
  inputFiles: [file.id],
  endpoint: '/v1/chat/completions',
  model: 'mistral-small-latest',
});

console.log(`Batch job: ${batch.id}, status: ${batch.status}`);

// 3. Poll for completion
async function waitForBatch(jobId: string): Promise<any> {
  while (true) {
    const status = await client.batch.jobs.get({ jobId });
    console.log(`Status: ${status.status}`);

    if (status.status === 'SUCCESS') return status;
    if (status.status === 'FAILED') throw new Error(`Batch failed: ${status.errors}`);

    await new Promise(r => setTimeout(r, 30_000)); // Check every 30s
  }
}

Step 3: Event-Driven Streaming Architecture

import { EventEmitter } from 'events';

interface MistralEvents {
  'chat:start': { requestId: string; model: string };
  'chat:chunk': { requestId: string; content: string; index: number };
  'chat:complete': { requestId: string; content: string; usage: any };
  'chat:error': { requestId: string; error: Error };
}

class MistralEventBus extends EventEmitter {
  private client: Mistral;

  constructor() {
    super();
    this.client = new Mistral({ apiKey: process.env.MISTRAL_API_KEY! });
  }

  async streamChat(requestId: string, messages: any[], model = 'mistral-small-latest') {
    this.emit('chat:start', { requestId, model });

    try {
      const stream = await this.client.chat.stream({ model, messages });
      let full = '';
      let index = 0;

      for await (const event of stream) {
        const content = event.data?.choices?.[0]?.delta?.content;
        if (content) {
          full += content;
          this.emit('chat:chunk', { requestId, content, index: index++ });
        }
      }

      this.emit('chat:complete', { requestId, content: full, usage: { estimatedTokens: Math.ceil(full.length / 4) } });
      return full;
    } catch (error) {
      this.emit('chat:error', { requestId, error: error as Error });
      throw error;
    }
  }
}

// Wire up listeners
const bus = new MistralEventBus();
bus.on('chat:start', ({ requestId, model }) => console.log(`[${requestId}] Starting ${model}`));
bus.on('chat:chunk', ({ content }) => process.stdout.write(content));
bus.on('chat:complete', ({ requestId, usage }) => console.log(`\n[${requestId}] Done`));
bus.on('chat:error', ({ requestId, error }) => console.error(`[${requestId}] Error: ${error.message}`));

Step 4: Background Job Queue with BullMQ

import { Queue, Worker } from 'bullmq';
import { Mistral } from '@mistralai/mistralai';

const connection = { host: 'localhost', port: 6379 };
const chatQueue = new Queue('mistral-chat', { connection });

// Worker processes jobs
const worker = new Worker('mistral-chat', async (job) => {
  const client = new Mistral({ apiKey: process.env.MISTRAL_API_KEY! });

  const response = await client.chat.complete({
    model: job.data.model ?? 'mistral-small-latest',
    messages: job.data.messages,
  });

  const result = {
    content: response.choices?.[0]?.message?.content,
    usage: response.usage,
  };

  // Optional: call webhook on completion
  if (job.data.callbackUrl) {
    await fetch(job.data.callbackUrl, {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify({ jobId: job.id, ...result }),
    });
  }

  return result;
}, {
  connection,
  concurrency: 5,
  limiter: { max: 10, duration: 1000 }, // 10 jobs/sec max
});

// Enqueue from API
async function enqueueChat(messages: any[], callbackUrl?: string) {
  const job = await chatQueue.add('chat', {
    messages,
    model: 'mistral-small-latest',
    callbackUrl,
  }, {
    attempts: 3,
    backoff: { type: 'exponential', delay: 2000 },
  });

  return { jobId: job.id, status: 'queued' };
}

Step 5: Python Async Batch Processing

import asyncio
import os
from mistralai import Mistral

async def process_batch(prompts: list[str], concurrency: int = 5):
    """Process prompts concurrently with rate limiting."""
    client = Mistral(api_key=os.environ["MISTRAL_API_KEY"])
    semaphore = asyncio.Semaphore(concurrency)
    results = []

    async def process_one(prompt: str, idx: int):
        async with semaphore:
            response = await client.chat.complete_async(
                model="mistral-small-latest",
                messages=[{"role": "user", "content": prompt}],
            )
            return {"index": idx, "content": response.choices[0].message.content}

    tasks = [process_one(p, i) for i, p in enumerate(prompts)]
    results = await asyncio.gather(*tasks, return_exceptions=True)
    return results

# Usage
results = asyncio.run(process_batch([
    "Summarize quantum computing",
    "Explain neural networks",
    "What is reinforcement learning",
]))

Error Handling

Examples

Queue a resumable bulk summarization job

Put each document ID—not its sensitive content—into the queue payload, enforce a bounded concurrency limit, and persist the provider request ID with the job state. On worker retry, reuse the idempotency key and publish the result only once the callback signature and output schema pass validation.

Resources

• Agents API
• Batch Inference
• Streaming
• BullMQ Docs

Output

• Agents API integration for stateful workflows
• Batch API for 50%-cheaper bulk processing
• Event-driven streaming architecture
• Background job queue with retry/callback
• Python async concurrent processing