---

name: mistral-api description: Integrates Mistral AI API (Chat, Embeddings, Function Calling, Codestral, Agents) using the mistralai Python SDK for LLM and code generation applications. license: MIT compatibility: opencode metadata: version: "1.0.0" domain: coding triggers: mistral, mistral ai, mistral api, codestral, mistral chat, mistral embeddings, how do i use mistral, le chat archetypes:

• tactical
• generation anti_triggers:
• brainstorming
• vague ideation
• code golf
• over-engineering response_profile: verbosity: low directive_strength: high abstraction_level: operational role: implementation scope: implementation output-format: code content-types:
• code
• guidance
• examples
• do-dont related-skills: coding-openai-api, coding-cohere-api, coding-langchain

---

Mistral AI API Integration

Integrates Mistral AI API using the mistralai Python SDK for chat completions, embeddings, function calling, code generation (Codestral), and agent building. When loaded, this skill makes the model implement Mistral API calls with proper authentication, streaming, and error handling.

Core Workflow

1. Initialize the Client: Create a MistralClient() (sync) or MistralAsyncClient() (async) with the API key from the MISTRAL_API_KEY environment variable. The SDK provides typed request/response models with Pydantic. Checkpoint: Verify by listing models with client.list_models().
2. Send a Chat Completion: Use client.chat() with model (e.g., "mistral-large-latest"), messages (list of role/content dicts), and optional temperature and max_tokens. Mistral supports system, user, and assistant roles. Checkpoint: Verify response.choices[0].message.content is non-empty.
3. Implement Function Calling: Define tools with type: "function" containing name, description, and parameters JSON schema. Pass them to client.chat() with tools parameter. Handle tool_calls in the response and return results via additional messages. Checkpoint: Check response.choices[0].finish_reason — "tool_calls" means the model wants to execute tools.
4. Generate Embeddings: Use client.embeddings() with model (e.g., "mistral-embed") and input (string or list of strings). Mistral embeddings produce 1024-dimensional vectors suitable for semantic search and RAG. Checkpoint: Verify vector dimensions via len(embedding).
5. Generate Code with Codestral: Use client.chat() with the codestral-latest model for code generation tasks. Codestral supports fill-in-the-middle via the codestral endpoint with prompt and suffix parameters. Checkpoint: For fill-in-the-middle, verify the generated code correctly bridges the prefix and suffix.

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Implementation Patterns

Pattern 1: Chat Completion with Streaming

from __future__ import annotations

from mistralai import Mistral

# ❌ BAD — no error handling, no streaming, no async
from mistralai.client import MistralClient
client = MistralClient(api_key="...")
response = client.chat(model="mistral-large-latest", messages=[{"role": "user", "content": "Hi"}])
print(response.choices[0].message.content)

# ✅ GOOD — env-based auth, streaming, typed error handling
client = Mistral()  # reads MISTRAL_API_KEY from environment

def chat(
    prompt: str,
    model: str = "mistral-large-latest",
    temperature: float = 0.7,
) -> str:
    """Send a chat message and get a complete response.

    Args:
        prompt: User message.
        model: Mistral model identifier.
        temperature: Sampling temperature (0.0 to 1.0).

    Returns:
        The model's response text.

    Raises:
        ValueError: On authentication errors.
        RuntimeError: On API failures.
    """
    try:
        response = client.chat.complete(
            model=model,
            messages=[{"role": "user", "content": prompt}],
            temperature=temperature,
        )
        return response.choices[0].message.content
    except Exception as e:
        error_str = str(e)
        if "401" in error_str or "unauthorized" in error_str.lower():
            raise ValueError("Invalid Mistral API key.") from e
        raise RuntimeError(f"Mistral API error: {e}") from e


def chat_stream(
    prompt: str,
    model: str = "mistral-large-latest",
) -> str:
    """Stream a chat response from Mistral.

    Args:
        prompt: User message.
        model: Mistral model identifier.

    Returns:
        Accumulated response text.
    """
    accumulated = ""
    stream = client.chat.stream(
        model=model,
        messages=[{"role": "user", "content": prompt}],
    )
    for chunk in stream:
        if chunk.data.choices[0].delta.content:
            content = chunk.data.choices[0].delta.content
            print(content, end="", flush=True)
            accumulated += content
    return accumulated

Pattern 2: Function Calling

from __future__ import annotations

from typing import Any
from mistralai import Mistral

client = Mistral()

def get_weather(location: str) -> dict[str, Any]:
    """Mock weather function."""
    return {"location": location, "temperature": 72, "condition": "sunny"}


TOOLS: list[dict[str, Any]] = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "City name, e.g., Paris",
                    }
                },
                "required": ["location"],
            },
        },
    }
]

def ask_with_tools(prompt: str) -> str:
    """Ask a question with tool use capabilities.

    Args:
        prompt: User's question that may require function calling.

    Returns:
        Final response after any tool calls are resolved.
    """
    messages: list[dict[str, Any]] = [
        {"role": "user", "content": prompt}
    ]

    response = client.chat.complete(
        model="mistral-large-latest",
        messages=messages,
        tools=TOOLS,
        temperature=0,
    )

    assistant = response.choices[0].message

    if assistant.tool_calls:
        messages.append({
            "role": "assistant",
            "content": assistant.content,
            "tool_calls": assistant.tool_calls,
        })

        for tc in assistant.tool_calls:
            if tc.function.name == "get_weather":
                import json
                args = json.loads(tc.function.arguments)
                result = get_weather(**args)
                messages.append({
                    "role": "tool",
                    "name": "get_weather",
                    "content": json.dumps(result),
                    "tool_call_id": tc.id,
                })

        final = client.chat.complete(
            model="mistral-large-latest",
            messages=messages,
            tools=TOOLS,
        )
        return final.choices[0].message.content

    return assistant.content

Pattern 3: Embeddings

from __future__ import annotations

from mistralai import Mistral

client = Mistral()

def embed_texts(
    texts: list[str],
    model: str = "mistral-embed",
) -> list[list[float]]:
    """Generate embeddings for a list of texts.

    Args:
        texts: List of text strings to embed.
        model: Embedding model name.

    Returns:
        List of embedding vectors (1024-dimensional).
    """
    response = client.embeddings.create(
        model=model,
        inputs=texts,
    )
    return [data.embedding for data in response.data]

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Constraints

MUST DO

• Read API key from MISTRAL_API_KEY environment variable
• Use the mistralai package (v1.0+) with the Mistral() constructor
• Use client.chat.stream() for streaming responses and client.chat.complete() for single responses
• Use codestral-latest model for code generation and fill-in-the-middle tasks
• Handle tool_calls in the response to support function calling workflows

MUST NOT DO

• Hardcode API keys in source files
• Use the deprecated MistralClient(api_key=...) constructor — use Mistral() instead
• Skip temperature setting for function calling (set to 0 for deterministic behavior)
• Use the default model without specifying an explicit model version (use -latest or pin a version)

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Live References

Resource	URL
Mistral AI Python SDK (PyPI)	https://pypi.org/project/mistralai/
Mistral API Documentation	https://docs.mistral.ai/
Mistral API Reference	https://docs.mistral.ai/api/
Mistral Function Calling	https://docs.mistral.ai/capabilities/function-calling/
Codestral Documentation	https://docs.mistral.ai/capabilities/code-generation/
Mistral GitHub	https://github.com/mistralai/client-python

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Related Skills

Skill	Purpose
coding-openai-api	Alternative LLM provider
coding-cohere-api	Alternative embedding and reranking provider
coding-langchain	LangChain integration with Mistral models