Skip to main content Execute Mistral AI primary workflow: Chat Completions and Streaming.
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".
npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill mistral-core-workflow-a ai automation claude-code devops mcp ai-agents
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 setUnderstanding 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
interface Message {
role: 'system' | 'user' | 'assistant';
content: string;
}
class MistralConversation {
private messages: Message[] = [];
private client: Mistral;
private model: string;
constructor(systemPrompt: string, model = 'mistral-small-latest') {
this.client = new Mistral({ apiKey: process.env.MISTRAL_API_KEY });
this.model = model;
this.messages.push({ role: 'system', content: systemPrompt });
}
async send(userMessage: string): Promise<string> {
this.messages.push({ role: 'user', content: userMessage });
const response = await this.client.chat.complete({
model: this.model,
messages: this.messages,
});
const reply = response.choices?.[0]?.message?.content ?? '';
this.messages.push({ role: 'assistant', content: reply });
return reply;
}
// Prevent context window overflow
trimHistory(maxTurns = 20): void {
const system = this.messages[0];
const recent = this.messages.slice(1).slice(-maxTurns * 2);
this.messages = [system, ...recent];
}
}
// Usage
const conv = new MistralConversation('You are a coding tutor.');
await conv.send('How do I reverse a list in Python?');
await conv.send('What about in-place?');
Step 3: Streaming Responses async function streamChat(
messages: Message[],
onChunk: (text: string) => void,
): Promise<string> {
const stream = await client.chat.stream({
model: 'mistral-small-latest',
messages,
});
let full = '';
for await (const event of stream) {
const text = event.data?.choices?.[0]?.delta?.content;
if (text) {
full += text;
onChunk(text);
}
}
return full;
}
// Express.js SSE endpoint
app.post('/chat/stream', async (req, res) => {
res.setHeader('Content-Type', 'text/event-stream');
res.setHeader('Cache-Control', 'no-cache');
res.setHeader('Connection', 'keep-alive');
const stream = await client.chat.stream({
model: 'mistral-small-latest',
messages: req.body.messages,
});
for await (const event of stream) {
const content = event.data?.choices?.[0]?.delta?.content;
if (content) {
res.write(`data: ${JSON.stringify({ content })}\n\n`);
}
}
res.write('data: [DONE]\n\n');
res.end();
});
Step 4: JSON Mode and JSON Schema Mode // JSON mode — model returns valid JSON
const jsonResponse = await client.chat.complete({
model: 'mistral-small-latest',
messages: [
{ role: 'user', content: 'List 3 countries with capitals as JSON array.' },
],
responseFormat: { type: 'json_object' },
});
const data = JSON.parse(jsonResponse.choices?.[0]?.message?.content ?? '{}');
// JSON Schema mode — guarantees structure conformance
const schemaResponse = await client.chat.complete({
model: 'mistral-small-latest',
messages: [
{ role: 'user', content: 'Classify this ticket: "Login page crashes on mobile"' },
],
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'],
},
},
},
});
Step 5: Guardrails and Moderation // Built-in safe_prompt flag — injects safety system prompt
const safeResponse = await client.chat.complete({
model: 'mistral-small-latest',
messages: [{ role: 'user', content: userInput }],
safePrompt: true,
});
// Dedicated moderation API — classify text against policy categories
const moderation = await client.classifiers.moderate({
model: 'mistral-moderation-latest',
inputs: [userInput],
});
const flagged = moderation.results[0].categories;
// Check: flagged.sexual, flagged.hate_and_discrimination, flagged.violence, etc.
if (Object.values(flagged).some(Boolean)) {
throw new Error('Content flagged by moderation');
}
Step 6: Model Selection Guide type UseCase = 'realtime' | 'analysis' | 'code' | 'vision' | 'embedding';
const MODEL_MAP: Record<UseCase, { model: string; note: string }> = {
realtime: { model: 'mistral-small-latest', note: '256k ctx, fast, $0.1/M in' },
analysis: { model: 'mistral-large-latest', note: '256k ctx, reasoning, $0.5/M in' },
code: { model: 'codestral-latest', note: '256k ctx, code + FIM, $0.3/M in' },
vision: { model: 'pixtral-large-latest', note: '128k ctx, multimodal' },
embedding: { model: 'mistral-embed', note: '1024-dim vectors, $0.1/M in' },
};
function selectModel(use: UseCase): string {
return MODEL_MAP[use].model;
}
Output
Chat completions with configurable parameters
Multi-turn conversation management with history trimming
Real-time streaming responses
JSON and JSON Schema structured output
Content moderation via guardrails
Error Handling Error Cause Solution 401 UnauthorizedInvalid API key Verify MISTRAL_API_KEY 429 Rate LimitedRPM or TPM exceeded Implement backoff (see mistral-rate-limits) 400 Bad RequestInvalid model or params Check model ID and message format Context exceeded Too many tokens Trim conversation history Empty JSON response Missing instruction Tell model to respond in JSON in prompt
Resources
Next Steps For embeddings and function calling, see mistral-core-workflow-b.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).
