ai-provider-mistral-sdk

Mistral SDK Patterns

Quick Guide: Use @mistralai/mistralai (ESM-only) to interact with Mistral's API. Use client.chat.complete() for chat, client.chat.stream() for streaming (async iterable via for await), client.chat.parse() with a Zod schema for structured outputs, and client.fim.complete() for Codestral fill-in-middle code completion. The SDK uses responseFormat (camelCase) not response_format. Streaming events expose content via event.data.choices[0]?.delta?.content. Retries default to strategy: "none" -- you must configure them explicitly for production.

<critical_requirements>

CRITICAL: Before Using This Skill

All code must follow project conventions in CLAUDE.md (kebab-case, named exports, import ordering, import type, named constants)

(You MUST use responseFormat (camelCase) in SDK calls -- NOT response_format (snake_case). The SDK uses camelCase property names throughout.)

(You MUST configure retries explicitly -- the SDK defaults to strategy: "none" (no retries), unlike OpenAI's SDK which retries automatically)

(You MUST consume streaming results with for await (const event of result) and access content via event.data.choices[0]?.delta?.content -- the event shape differs from OpenAI)

(You MUST never hardcode API keys -- use process.env["MISTRAL_API_KEY"] with the bracket notation the SDK documents)

(You MUST use client.chat.parse() with a Zod schema for structured outputs -- NOT manual JSON.parse() on completion content)

</critical_requirements>

Auto-detection: Mistral, mistral, @mistralai/mistralai, client.chat.complete, client.chat.stream, client.chat.parse, client.fim.complete, client.embeddings.create, mistral-large, mistral-small, codestral, pixtral, ministral, magistral, devstral, MISTRAL_API_KEY, responseFormat, mistral-embed

When to use:

• Building applications that call Mistral models directly (Mistral Large, Small, Codestral, etc.)
• Implementing chat completions with SSE streaming
• Using Codestral for code generation and fill-in-middle (FIM) completion
• Extracting structured data with client.chat.parse() and Zod schemas
• Implementing function calling / tool use
• Creating embeddings for RAG pipelines or semantic search
• Processing images with vision-capable models (Mistral Small, Medium, Large, Ministral)
• Using Mistral Agents API for pre-configured agent completions

Key patterns covered:

• Client initialization and configuration (retries, timeouts, custom HTTP client)
• Chat completions (chat.complete) and streaming (chat.stream)
• Structured outputs with chat.parse() and Zod schemas
• Function calling / tool use with tool call loop
• Embeddings (embeddings.create) with mistral-embed
• Vision (image URL / base64 with vision-capable models)
• Codestral FIM (fim.complete) for code completion
• Error handling, retry configuration, and production patterns

When NOT to use:

• Multi-provider applications where you need to switch between Mistral, OpenAI, Anthropic, etc. -- use a unified provider SDK
• React-specific chat UI hooks (useChat) -- use a framework-integrated AI SDK
• When you need OpenAI-compatible endpoints -- use OpenAI SDK with Mistral's compatible endpoint instead

Examples Index

• [Core: Setup & Configuration](examples/core.md) -- Client init, production config, error handling, retries, custom HTTP client
• [Chat & Streaming](examples/chat.md) -- Chat completions, streaming with async iteration, multi-turn
• [Structured Output](examples/structured-output.md) -- chat.parse() with Zod, JSON mode, typed responses
• [Function Calling](examples/function-calling.md) -- Tool definitions, tool call loop, streaming tools
• [Embeddings & Vision](examples/embeddings-vision.md) -- Semantic search, image analysis with vision-capable models
• [Codestral FIM](examples/codestral.md) -- Fill-in-middle code completion, code generation
• [Quick API Reference](reference.md) -- Model IDs, method signatures, error types, configuration options

<philosophy>

Philosophy

The @mistralai/mistralai SDK is auto-generated from Mistral's OpenAPI spec using Speakeasy, giving you a thin, type-safe wrapper over the REST API. It is ESM-only and uses camelCase property names (not snake_case like the REST API).

Core principles:

1. ESM-only -- The package is published as ESM only. CommonJS projects must use await import(). This is a hard constraint, not optional.
2. camelCase API surface -- SDK properties use camelCase (responseFormat, maxTokens, toolChoice) even though the REST API uses snake_case. This catches OpenAI SDK migrants who write response_format.
3. No automatic retries -- Unlike OpenAI's SDK (2 retries by default), Mistral defaults to strategy: "none". You must configure retries explicitly for production.
4. Streaming via async iterables -- chat.stream() returns an EventStream consumed with for await...of. Events have a data wrapper: event.data.choices[0]?.delta?.content.
5. Structured outputs via chat.parse() -- Pass a Zod schema directly to responseFormat and access message.parsed for typed results. No manual JSON schema construction needed.
6. Codestral FIM -- Dedicated fim.complete() endpoint for fill-in-middle code completion, separate from chat.

When to use the Mistral SDK directly:

• You only use Mistral models and want the simplest, most direct integration
• You need Mistral-specific features (Codestral FIM, Mistral Agents, Voxtral audio)
• You want minimal dependencies and zero abstraction overhead
• You need the latest Mistral API features on day one

When NOT to use:

• You need to switch between providers (OpenAI, Anthropic, Mistral) -- use a unified provider SDK
• You want React-specific chat UI hooks -- use a framework-integrated AI SDK
• You want an OpenAI-compatible wrapper -- Mistral exposes an OpenAI-compatible endpoint, use the OpenAI SDK for that

</philosophy>

<patterns>

Core Patterns

Pattern 1: Client Setup

Initialize the Mistral client. It reads MISTRAL_API_KEY from the environment.

// lib/mistral.ts -- basic setup
import { Mistral } from "@mistralai/mistralai";

const client = new Mistral({
  apiKey: process.env["MISTRAL_API_KEY"] ?? "",
});

export { client };

// lib/mistral.ts -- production configuration
import { Mistral } from "@mistralai/mistralai";

const TIMEOUT_MS = 30_000;

const client = new Mistral({
  apiKey: process.env["MISTRAL_API_KEY"] ?? "",
  timeoutMs: TIMEOUT_MS,
  retryConfig: {
    strategy: "backoff",
    backoff: {
      initialInterval: 1_000,
      maxInterval: 30_000,
      exponent: 1.5,
      maxElapsedTime: 120_000,
    },
    retryConnectionErrors: true,
  },
});

export { client };

Why good: Explicit retry config (SDK defaults to no retries), named constants, env var with bracket notation

See: [examples/core.md](examples/core.md) for custom HTTP client, async API key provider, error handling

Pattern 2: Chat Completions

Basic chat using chat.complete().

const result = await client.chat.complete({
  model: "mistral-large-latest",
  messages: [
    { role: "system", content: "You are a helpful coding assistant." },
    { role: "user", content: "Explain TypeScript generics." },
  ],
});

const content = result?.choices?.[0]?.message?.content;
console.log(content);

Why good: Uses system role for instructions, safe optional chaining on nullable response

// BAD: Using snake_case properties (REST API style, not SDK style)
const result = await client.chat.complete({
  model: "mistral-large-latest",
  messages: [{ role: "user", content: "hello" }],
  response_format: { type: "json_object" }, // WRONG: use responseFormat
  max_tokens: 100, // WRONG: use maxTokens
});

Why bad: SDK uses camelCase properties -- response_format and max_tokens