Imported from jeremylongshore/tons-of-skills-marketplace (skills/.curated/mistral-sdk-patterns/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill mistral-sdk-patterns. Copyright stays with the author (MIT).

Mistral SDK Patterns

Overview

Production-ready patterns for the Mistral AI SDK. Covers singleton client, retry/backoff, structured output, streaming, function calling, batch embeddings, and async Python — all with proper error handling. SDK is ESM-only for TypeScript (@mistralai/mistralai), sync+async for Python (mistralai).

Prerequisites

• @mistralai/mistralai (TypeScript) or mistralai (Python) installed
• MISTRAL_API_KEY environment variable set

Instructions

Step 1: Singleton Client with Configuration

TypeScript

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

let _client: Mistral | null = null;

export function getMistralClient(): Mistral {
  if (!_client) {
    const apiKey = process.env.MISTRAL_API_KEY;
    if (!apiKey) throw new Error('MISTRAL_API_KEY not set');

    _client = new Mistral({
      apiKey,
      timeoutMs: 30_000,
      maxRetries: 3,
    });
  }
  return _client;
}

// Reset for testing
export function resetClient(): void {
  _client = null;
}

Python

import os
from mistralai import Mistral

_client = None

def get_client() -> Mistral:
    global _client
    if _client is None:
        api_key = os.environ.get("MISTRAL_API_KEY")
        if not api_key:
            raise RuntimeError("MISTRAL_API_KEY not set")
        _client = Mistral(api_key=api_key, timeout_ms=30_000, max_retries=3)
    return _client

Step 2: Structured Output with JSON Schema

import { z } from 'zod';

// Define schema with Zod, then convert to JSON Schema for Mistral
const TicketSchema = z.object({
  category: z.enum(['bug', 'feature', 'question']),
  severity: z.enum(['low', 'medium', 'high', 'critical']),
  summary: z.string(),
});

type Ticket = z.infer<typeof TicketSchema>;

async function classifyTicket(text: string): Promise<Ticket> {
  const client = getMistralClient();

  const response = await client.chat.complete({
    model: 'mistral-small-latest',
    messages: [
      { role: 'system', content: 'Classify the support ticket.' },
      { role: 'user', content: text },
    ],
    responseFormat: {
      type: 'json_schema',
      jsonSchema: {
        name: 'ticket_classification',
        schema: {
          type: 'object',
          properties: {
            category: { type: 'string', enum: ['bug', 'feature', 'question'] },
            severity: { type: 'string', enum: ['low', 'medium', 'high', 'critical'] },
            summary: { type: 'string' },
          },
          required: ['category', 'severity', 'summary'],
        },
      },
    },
  });

  const raw = JSON.parse(response.choices?.[0]?.message?.content ?? '{}');
  return TicketSchema.parse(raw); // Validate at runtime
}

Step 3: Streaming with Accumulated Result

interface StreamResult {
  content: string;
  finishReason: string;
}

async function streamWithAccumulation(
  messages: Array<{ role: string; content: string }>,
  onChunk: (text: string) => void,
): Promise<StreamResult> {
  const client = getMistralClient();
  const stream = await client.chat.stream({
    model: 'mistral-small-latest',
    messages,
  });

  let content = '';
  let finishReason = '';

  for await (const event of stream) {
    const delta = event.data?.choices?.[0];
    if (delta?.delta?.content) {
      content += delta.delta.content;
      onChunk(delta.delta.content);
    }
    if (delta?.finishReason) {
      finishReason = delta.finishReason;
    }
  }

  return { content, finishReason };
}

Step 4: Python Async Pattern

import asyncio
from mistralai import Mistral

async def process_batch(prompts: list[str], model: str = "mistral-small-latest"):
    """Process multiple prompts concurrently with semaphore for rate limiting."""
    client = Mistral(api_key=os.environ["MISTRAL_API_KEY"])
    semaphore = asyncio.Semaphore(5)  # Max 5 concurrent requests

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

    results = await asyncio.gather(*[process_one(p) for p in prompts])
    return results

Step 5: Retry with Exponential Backoff

async function withRetry<T>(
  fn: () => Promise<T>,
  maxRetries = 3,
): Promise<T> {
  for (let attempt = 0; attempt <= maxRetries; attempt++) {
    try {
      return await fn();
    } catch (error: any) {
      const status = error.status ?? error.statusCode;
      const retryable = status === 429 || status >= 500;

      if (!retryable || attempt === maxRetries) throw error;

      // Respect Retry-After header if present
      const retryAfter = error.headers?.get?.('retry-after');
      const delay = retryAfter
        ? parseInt(retryAfter) * 1000
        : Math.min(1000 * 2 ** attempt, 30_000);

      console.warn(`Attempt ${attempt + 1} failed (${status}), retrying in ${delay}ms`);
      await new Promise(r => setTimeout(r, delay));
    }
  }
  throw new Error('Unreachable');
}

// Usage
const response = await withRetry(() =>
  client.chat.complete({
    model: 'mistral-large-latest',
    messages: [{ role: 'user', content: 'Hello' }],
  })
);

Step 6: Token Usage Tracking

interface UsageStats {
  totalPromptTokens: number;
  totalCompletionTokens: number;
  totalRequests: number;
  costUsd: number;
}

const PRICING: Record<string, { input: number; output: number }> = {
  'mistral-small-latest': { input: 0.1, output: 0.3 },
  'mistral-large-latest': { input: 0.5, output: 1.5 },
  'mistral-embed':        { input: 0.1, output: 0 },
  'codestral-latest':     { input: 0.3, output: 0.9 },
};

class UsageTracker {
  private stats: UsageStats = { totalPromptTokens: 0, totalCompletionTokens: 0, totalRequests: 0, costUsd: 0 };

  record(model: string, usage: { promptTokens?: number; completionTokens?: number }): void {
    const pt = usage.promptTokens ?? 0;
    const ct = usage.completionTokens ?? 0;
    this.stats.totalPromptTokens += pt;
    this.stats.totalCompletionTokens += ct;
    this.stats.totalRequests++;

    const p = PRICING[model] ?? PRICING['mistral-small-latest'];
    this.stats.costUsd += (pt / 1e6) * p.input + (ct / 1e6) * p.output;
  }

  report(): UsageStats { return { ...this.stats }; }
}

Error Handling

Examples

Retry a transient completion safely

Wrap a deterministic completion in withRetry, preserve the provider’s Retry-After value when present, and record usage only after a successful final response. Do not retry a 401 or schema-validation failure: those require a credential or request correction, not more traffic.

Resources

• TypeScript SDK (client-ts)
• Python SDK (client-python)
• API Reference
• Pricing

Output

• Singleton client pattern for TypeScript and Python
• Structured output with JSON Schema validation
• Streaming with accumulation
• Retry/backoff for resilient API calls
• Token usage tracking with cost estimation