Read the full article on DataCamp: Mistral Agents API – A Guide With Demo Project

Learn how to build intelligent assistants using Mistral’s Agents API and explore agent creation, tool usage, memory retention, and orchestration. A hands-on Nutrition Coach demo ties it all together.

Overview

The Mistral Agents API introduces an agentic layer over large language models (LLMs), enabling multi-step tasks, web access, tool usage, and persistent memory across conversations. This tutorial walks through building a multi-agent AI Nutrition Coach that:

  • Estimates calories using web search
  • Logs meals with timestamp
  • Suggests the next healthy meal
  • Generates a food image
  • Offers a Gradio frontend for interaction

What Is Mistral’s Agents API?

Unlike simple LLM chat APIs, the Agents API allows:

  • Tool use like web search, image generation, and custom APIs
  • Conversation memory to track long-term context
  • Agent handoffs and multi-agent orchestration

Agents consist of:

  • Instructions (e.g., “You are a meal-logging coach”)
  • Tools (e.g., web_search, log_meal)
  • Memory (conversation history and tool responses)

Core Concepts Explained

  • Connectors: Built-in tools like web_search, image_generation
  • MCP Tools: Custom developer tools exposed as APIs
  • Entries & Handoffs: Structured message/tool logs and agent delegations
  • Supported Models: mistral-medium-latest, mistral-large-latest

Mistral Agents API Workflow

Project Overview: Nutrition Coach With Mistral Agents

The app receives a user’s meal and responds with:

  1. Calorie estimate via web search
  2. Meal log with timestamp
  3. Next meal suggestion
  4. Image generation of that meal

Users interact with this system through a friendly Gradio UI.

Step 1: Prerequisites

Install dependencies:

pip install -r requirements.txt

Dependencies:

  • mistralai==0.0.7
  • gradio==4.34.0
  • python-dotenv==1.1.0

Create a .env file:

MISTRAL_API_KEY=your_key_here

Step 2: Setting Up Mistral Tools

Tool registration and configuration

Define schemas, instantiate the Mistral client, and load environment variables in configs.py.

Calorie search agent

web_search.py uses Mistral’s web_search connector to estimate calories from meal descriptions.

Prompt-based fallback tools

In next.py:

  • estimate_calories() → fallback calorie estimate
  • log_meal() → logs user meals
  • suggest_next_meal() → suggests healthy alternatives

Image generation

image_gen.py uses the image_generation connector to render a realistic visual of the suggested meal.

Step 3: Full Nutritionist Pipeline

All tools come together in agent.py to:

  1. Search or estimate calories
  2. Log the meal
  3. Suggest a next meal
  4. Generate an image
{
  "logged": "Meal logged...",
  "next_meal_suggestion": "...",
  "meal_image": "generated_images/meal_x.png",
  "tools_used": [...]
}

Step 4: Creating a User Interface With Gradio

app.py connects the backend pipeline with a sleek Gradio interface. The UI allows users to:

  • Enter a username
  • Describe their meal
  • Choose a dietary preference
  • View calorie estimate, meal log, next meal suggestion, and image
python app.py

Folder Structure

Mistral_Agent_API/
├── tools/
│   ├── configs.py
│   ├── web_search.py
│   ├── next.py
│   ├── image_gen.py
├── agent.py
├── app.py
├── .env
├── requirements.txt
├── generated_images/

Conclusion

You now have a working Nutritionist AI assistant powered by Mistral’s Agents API. This agent demonstrates:

  • Multi-agent orchestration
  • Tool usage (connectors + chat completions)
  • Persistent memory
  • Image generation
  • Real-world usability with Gradio

Explore agent-based systems with similar tutorials:

Learn more by reading the full article on DataCamp:
https://www.datacamp.com/tutorial/mistral-agents-api