Mistral AI Agents

Mistral AI Agents are autonomous systems powered by large language models (LLMs) that can understand high-level instructions, plan tasks, use tools, execute processing steps, and take actions to achieve specific goals. They support complex workflows, multi-agent collaboration, and persistent conversation state.

Mistral AI Agents use several foundation models including Mistral 7B, which is lightweight and efficient for basic tasks; Mixtral, a general-purpose model with broad knowledge; Mistral Large, offering advanced reasoning and instruction following; and Codestral, which is specialized for programming tasks. Developers can choose models based on performance, speed, and specialization needs.

Mistral AI Agents offer several features including task planning and decomposition, where agents break down complex tasks into manageable steps. They also provide memory management, with persistent memory maintaining conversation context across sessions, and tool integration through built-in connectors for web search, code execution, image generation, document library access, and external API integration. Other features include self-reflection and error correction, where agents evaluate outcomes and adjust actions dynamically; multi-agent orchestration for coordination between specialized agents to solve complex workflows; model customization through fine-tuning with base prompts, few-shot learning, and custom datasets; and a multi-language SDK with Python and TypeScript client libraries for flexible integration.

Agents maintain persistent conversation states, allowing coherent and context-aware interactions over time. Conversations can be started or continued at any point, and multiple agents can participate in a conversation. The API supports flexible interaction objects such as Agents, Conversations, and Entries to represent user and assistant actions.

Mistral AI Agents provide several tools and connectors, including web search for real-time internet information lookup to enhance response accuracy, and code execution for secure sandboxed Python code execution for data analysis and scientific computing. They also offer image generation for visual content creation using specialized models, a document library for Retrieval-Augmented Generation (RAG) to access stored documents for informed responses, and MCP (Model Context Protocol) for external integrations and context management. Additionally, agents support function calling and handoffs, allowing them to call other agents or functions to delegate tasks within workflows.

Creating and chatting with agents may require an active subscription depending on the platform and usage tier. Specific details can be found in Mistral’s help center or documentation.

The system prompt is critical in controlling agent behavior in production. High-quality, detailed system prompts, sometimes thousands of words, define how the agent responds, what tools it can use, and how it uses them. Writing the system prompt often takes more time than coding itself.

Currently, mistral-medium-latest and mistral-large-latest are supported for building agents.

To get started with Mistral AI Agents, you should choose the appropriate foundation model based on your task, define a detailed system prompt to guide agent behavior, and use the SDK (Python or TypeScript) to create and deploy agents. You will also need to integrate built-in connectors as needed for your workflow, manage conversations and persistent memory for coherent interactions, and optionally, orchestrate multiple agents for complex tasks.