Does Mistral Agents API Remember Between Runs?

Yes, but only inside one conversation. The Mistral Agents API keeps a stateful conversation: every turn is stored as a conversation entry, and you can continue that same conversation later or branch a new path from any point in it. Start a fresh conversation, though, and the agent has no memory of the earlier one. There is no automatic, cross-conversation user memory built in.

That distinction matters if you are shipping an agent rather than chatting with one. A support agent that handles the same customer on Tuesday and again on Friday needs more than a conversation it can resume. It needs to remember who that customer is, what was decided, and what is still open, no matter which conversation the new message lands in.

What the Mistral Agents API actually stores

Mistral's own documentation describes the system as stateful conversations, not as a memory layer. Here is what that gives you:

1. Conversation history that persists. Each conversation keeps its entries in order, so the agent can reference earlier turns in the same conversation without you re-sending them.
2. Resume and branching. You can continue a previous conversation at any time, or start a new branch from an earlier point while keeping the original intact.
3. Built-in connectors. Code execution, web search, image generation, a document library, and MCP tools all plug into the agent, so it can act on more than text.
4. No cross-conversation recall by default. Nothing in the default setup carries facts from conversation A into conversation B. If your code does not pass the old context in, the agent does not have it.

In short: Mistral remembers the conversation you are in. It does not remember the user across conversations unless you build that yourself.

Where builders usually feel the gap

The gap shows up in three familiar places:

• Returning users. A customer opens a new chat and has to re-explain the problem, the account, and the decision from last week.
• Long-running work. A research or operations agent runs daily, but each run starts from a blank slate and re-does work it already finished.
• Multiple agents. Two agents serving the same user cannot see each other's conversations, so the user repeats themselves to each one.

The usual fixes are to store conversation IDs and reload them, to keep your own database of facts, or to add a memory tool the agent can query. All three work, and all three are infrastructure you now own: schema, retrieval, cleanup, and keeping it in sync as the product grows.

What a shared memory layer changes

This is where a hosted memory layer fits. A built agent connects to Vilix AI with an API key as a Bearer header to https://api.vilix.ai/mcp and gets full memory with zero memory infrastructure to run. The memory lives outside any single conversation, so the agent can pull relevant context from earlier conversations and other tools before it answers, and save what it learned for next time.

The practical difference for a Mistral agent is simple. Conversation state still handles the current thread. The memory layer handles everything around it: who the user is, what was decided last week, which approach already failed, and what is still open. The agent stops re-asking questions the user already answered somewhere else.

How to set it up

1. Keep using Mistral conversations for the live thread. They are the right place for turn-by-turn history.
2. Give the agent a memory tool it can call through MCP, pointed at one shared memory account.
3. At the start of a run, have the agent pull relevant memories for the user or task before it plans anything.
4. At the end of a run, save decisions, outcomes, and open items, not just the raw transcript.
5. When the same user returns in a new conversation, the agent recalls the saved context instead of starting over.

You do not need to replace Mistral's conversation system to do this. The two layers do different jobs, and they work best together: conversations hold the thread, memory holds the user.

Frequently asked questions

Does the Mistral Agents API have persistent memory?

Yes, in the form of stateful conversations. A conversation's entries persist and can be continued or branched later. It does not automatically remember facts across separate conversations.

Do I need to store conversation IDs myself?

If you want to resume a conversation, yes. Your application holds the conversation ID and passes it back when the user returns. Without it, the next message starts a new conversation with no history.

Can a Mistral agent use MCP tools for memory?

Yes. The Agents API supports MCP tools, which is how an external memory layer connects. The agent calls the memory tools like any other tool, and the memory lives outside Mistral's conversation store.

What is the difference between conversation history and agent memory?

Conversation history is the transcript of one thread. Agent memory is the distilled, retrievable knowledge across threads: facts, decisions, preferences, and open work. History answers "what did we say here." Memory answers "what do we know about this user."

Is conversation state enough for a production agent?

For a single-thread demo, yes. For an agent serving returning users, running on a schedule, or working alongside other agents, you will want a memory layer on top, so context survives beyond one conversation.

The bottom line

The Mistral Agents API remembers the conversation you are in, and it does that well. It does not remember the user across conversations on its own. If your agent serves people who come back, runs repeatedly, or shares users with other agents, add a memory layer underneath it. Your agent keeps its Mistral conversations, and gains a memory that follows the user everywhere else too.

Learn more about shared agent memory at https://vilix.ai/?utm_source=vilix-blog&utm_medium=article&utm_campaign=icp1-visibility