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Mistral Provider

Mistral Provider explains the cloud layer of MTPX with practical guidance for building inspectable, tool-using agents.

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Mistral Provider
Chapter 035ProvidersCloudIndex
01Orientation

Mistral Provider belongs to the providers track. The page breaks the idea into responsibilities, implementation rules, failure modes, and the signals you should expose in a product UI.

Use this when

Use this when you need to understand how mistral provider affects a real MTPX agent before you wire it into an application.

  • What mistral provider owns in the runtime.
  • How it connects to planning, tool execution, events, providers, or storage.
  • What to log, test, and expose to users when this layer is active.
  • Common mistakes that make agent systems hard to inspect.
02 / syntax starter
from mtp import Agent
from mtp.providers import Groq

agent = Agent.MTPAgent(
    provider=Groq(model="llama-3.3-70b-versatile"),
    tools=tools,
)
03 / manual

Read the system

The complete source manual—syntax, examples, linked references, edge cases, and implementation notes.

Documentation index ↗

Mistral AI provides fast, capable models with native tool calling support.

Install

bashpip install "mtpx[mistral]"

Or install the SDK directly:

bashpip install mistralai

API Key Setup

  1. Install dotenv support:
bash   pip install python-dotenv

Or with the MTP extra:

bash   pip install "mtpx[dotenv]"
  1. Create a .env file in your project root:
text   MISTRAL_API_KEY=your_key_here
  1. Load it in your code before creating the provider:
python   from mtp import Agent

   Agent.load_dotenv_if_available()  # reads .env file

Get an API key at console.mistral.ai.

Option 2: System environment variable

bash# Linux/macOS
export MISTRAL_API_KEY="..."

# Windows PowerShell
$env:MISTRAL_API_KEY="..."

Quick Start

pythonfrom mtp import Agent
from mtp.providers import Mistral

Agent.load_dotenv_if_available()  # loads MISTRAL_API_KEY from .env

provider = Mistral(model="mistral-large-latest")
tools = Agent.ToolRegistry()
agent = Agent(provider=provider, tools=tools)

reply = agent.run_loop("What is 25 * 4 + 10?")
print(reply)

Parameters

ParameterTypeDefaultDescription
modelstr"mistral-large-latest"Mistral model ID
api_keystr | NoneNoneAPI key (falls back to MISTRAL_API_KEY env var)
temperaturefloat0.0Sampling temperature
tool_choicestr"auto"Tool selection: "auto", "none", "any", or specific tool name
parallel_tool_callsboolTrueAllow parallel tool calls
clientAny | NoneNonePre-configured Mistral client instance

Capabilities

CapabilityValue
Tool callingYes
Parallel tool callsNo
Input modalitiestext
StreamingFallback
Usage metricsBasic
Reasoning metadataNo
Native asyncNo (uses thread fallback)
  • mistral-large-latest — Best tool calling (default)
  • mistral-small-latest — Fast, cheaper
  • codestral-latest — Code-focused

Full Example

pythonfrom mtp import Agent
from mtp.providers import Mistral

Agent.load_dotenv_if_available()

provider = Mistral(
    model="mistral-large-latest",
    temperature=0.0,
    tool_choice="auto",
)

tools = Agent.ToolRegistry()
agent = Agent(provider=provider, tools=tools, debug_mode=True)

reply = agent.run_loop(
    "Calculate (25 * 4) + 10",
    max_rounds=3,
)
print(reply)

Notes

  • Mistral uses the mistralai SDK with client.chat.complete() API.
  • Text-only input (no image/audio/video/file support).
  • Usage metrics extraction is basic (prompt/completion/total tokens).

Source

src/mtp/providers/mistral_provider.py

01

Read

Understand where Mistral Provider sits in the agent loop before adding abstractions.

02

Wire

Connect the smallest useful provider, registry, store, or event stream first.

03

Observe

Expose events, logs, results, and failure states while the runtime is still moving.

04

Harden

Add policy, tests, retries, and audit traces after the behavior is visible.

Next docMock / Simple Planner Provider