Agent skill
mistral-hello-world
Create a minimal working Mistral AI chat completion example. Use when starting a new Mistral integration, testing your setup, or learning basic Mistral API patterns. Trigger with phrases like "mistral hello world", "mistral example", "mistral quick start", "simple mistral code", "mistral chat".
Install this agent skill to your Project
npx add-skill https://github.com/jeremylongshore/claude-code-plugins-plus-skills/tree/main/plugins/saas-packs/mistral-pack/skills/mistral-hello-world
SKILL.md
Mistral AI Hello World
Overview
Minimal working examples demonstrating Mistral AI chat completions, streaming, multi-turn conversation, and JSON mode. Uses the official @mistralai/mistralai TypeScript SDK and mistralai Python SDK.
Prerequisites
- Completed
mistral-install-authsetup - Valid
MISTRAL_API_KEYenvironment variable set - Node.js 18+ or Python 3.9+
Instructions
Step 1: Basic Chat Completion
TypeScript (hello-mistral.ts)
import { Mistral } from '@mistralai/mistralai';
const client = new Mistral({ apiKey: process.env.MISTRAL_API_KEY });
async function main() {
const response = await client.chat.complete({
model: 'mistral-small-latest',
messages: [
{ role: 'user', content: 'Say "Hello, World!" in a creative way.' },
],
});
console.log(response.choices?.[0]?.message?.content);
console.log('Tokens used:', response.usage);
}
main().catch(console.error);
Python (hello_mistral.py)
import os
from mistralai import Mistral
client = Mistral(api_key=os.environ["MISTRAL_API_KEY"])
response = client.chat.complete(
model="mistral-small-latest",
messages=[
{"role": "user", "content": "Say 'Hello, World!' in a creative way."}
],
)
print(response.choices[0].message.content)
print(f"Tokens: {response.usage}")
Step 2: Run the Example
# TypeScript
npx tsx hello-mistral.ts
# Python
python hello_mistral.py
Step 3: Streaming Response
Streaming delivers the first token in ~200ms instead of waiting 1-2s for the full response.
TypeScript
import { Mistral } from '@mistralai/mistralai';
const client = new Mistral({ apiKey: process.env.MISTRAL_API_KEY });
async function streamChat() {
const stream = await client.chat.stream({
model: 'mistral-small-latest',
messages: [
{ role: 'user', content: 'Tell me a short story about AI.' },
],
});
for await (const event of stream) {
const content = event.data?.choices?.[0]?.delta?.content;
if (content) process.stdout.write(content);
}
console.log(); // newline
}
streamChat().catch(console.error);
Python
stream = client.chat.stream(
model="mistral-small-latest",
messages=[{"role": "user", "content": "Tell me a short story about AI."}],
)
for event in stream:
content = event.data.choices[0].delta.content
if content:
print(content, end="", flush=True)
print()
Step 4: Multi-Turn Conversation
const messages: Array<{ role: 'system' | 'user' | 'assistant'; content: string }> = [
{ role: 'system', content: 'You are a helpful coding assistant.' },
{ role: 'user', content: 'What is the capital of France?' },
];
const r1 = await client.chat.complete({
model: 'mistral-small-latest', messages,
});
const answer = r1.choices?.[0]?.message?.content ?? '';
console.log('A1:', answer);
// Continue the conversation
messages.push({ role: 'assistant', content: answer });
messages.push({ role: 'user', content: 'What about Germany?' });
const r2 = await client.chat.complete({
model: 'mistral-small-latest', messages,
});
console.log('A2:', r2.choices?.[0]?.message?.content);
Step 5: JSON Mode (Structured Output)
const response = await client.chat.complete({
model: 'mistral-small-latest',
messages: [
{ role: 'user', content: 'List 3 programming languages with their year of creation as JSON.' },
],
responseFormat: { type: 'json_object' },
});
const data = JSON.parse(response.choices?.[0]?.message?.content ?? '{}');
console.log(data);
Step 6: With Temperature and Token Limits
const response = await client.chat.complete({
model: 'mistral-small-latest',
messages: [{ role: 'user', content: 'Write a haiku about coding.' }],
temperature: 0.7, // 0-1, higher = more creative
maxTokens: 100, // cap output length
topP: 0.9, // nucleus sampling
});
Output
- Working code file with Mistral client initialization
- Successful API response with generated text
- Console output showing response and token usage
Error Handling
| Error | Cause | Solution |
|---|---|---|
Import Error |
SDK not installed | Run npm install @mistralai/mistralai |
401 Unauthorized |
Invalid API key | Check MISTRAL_API_KEY is set |
ERR_REQUIRE_ESM |
CommonJS project | Use import syntax or dynamic await import() |
429 Rate Limited |
Too many requests | Wait and retry with backoff |
Model Quick Reference
| Model ID | Best For | Context |
|---|---|---|
mistral-small-latest |
Fast, cost-effective tasks | 256k |
mistral-large-latest |
Complex reasoning, analysis | 256k |
codestral-latest |
Code generation, FIM | 256k |
mistral-embed |
Text/code embeddings | 8k |
pixtral-large-latest |
Vision + text (multimodal) | 128k |
Resources
Next Steps
Proceed to mistral-core-workflow-a for production chat patterns or mistral-local-dev-loop for dev workflow setup.
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