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Run your first Java AI agent for free (with Mistral)

Most "build an AI agent" tutorials assume Python and a paid OpenAI key. This one is Java/Spring and free: you'll create a Mistral account, get a free API key, and run a real LLM-backed agent with AgentFlow4J — start to finish in about ten minutes.

Mistral offers a free tier on mistral-small — enough to build and test agents without spending anything.

Step 1 — Create a Mistral account

  1. Go to https://console.mistral.ai/
  2. Sign up (email, Google, or GitHub).
  3. You land on La Plateforme, Mistral's developer console.

Step 2 — Get a free API key

  1. In the console, open API Keys → https://console.mistral.ai/api-keys/
  2. Click Create new key, give it a name (e.g. agentflow4j-dev), and copy it. It looks like sk-....
  3. Keep it somewhere safe — you only see it once.

Treat the key like a password

Never paste it into a commit, screenshot, or chat tool. AgentFlow4J reads it from an environment variable, never from a file in your repo. If a key leaks, rotate it in the console immediately.

Export it in your shell:

export MISTRAL_API_KEY="sk-...your-key..."

Step 3 — Add AgentFlow4J + the Mistral starter

In a Spring Boot project, add the AgentFlow4J starter (via JitPack) and Spring AI's Mistral starter:

<repositories>
    <repository>
        <id>jitpack.io</id>
        <url>https://jitpack.io</url>
    </repository>
</repositories>

<!-- AgentFlow4J -->
<dependency>
    <groupId>com.github.datallmhub.agentflow4j</groupId>
    <artifactId>agentflow4j-starter</artifactId>
    <version>v0.6.0</version>
</dependency>

<!-- Spring AI — Mistral -->
<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-starter-model-mistral-ai</artifactId>
</dependency>

repositories { maven { url 'https://jitpack.io' } }

dependencies {
    implementation 'com.github.datallmhub.agentflow4j:agentflow4j-starter:v0.6.0'
    implementation 'org.springframework.ai:spring-ai-starter-model-mistral-ai'
}

Step 4 — Point Spring AI at Mistral

In src/main/resources/application.yml:

spring:
  ai:
    mistralai:
      api-key: ${MISTRAL_API_KEY}      # resolved from your shell at startup
      chat:
        options:
          model: mistral-small-latest  # the free-tier model
          temperature: 0.3

Spring AI now auto-configures a ChatClient backed by Mistral. AgentFlow4J uses that ChatClient as the brain of any agent.

Step 5 — Write and run an agent

import io.github.datallmhub.agentflow4j.core.Agent;
import io.github.datallmhub.agentflow4j.core.AgentContext;
import io.github.datallmhub.agentflow4j.core.AgentResult;
import io.github.datallmhub.agentflow4j.squad.ExecutorAgent;
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.boot.CommandLineRunner;
import org.springframework.context.annotation.Bean;

@Bean
CommandLineRunner demo(ChatClient.Builder chatClientBuilder) {
    Agent assistant = ExecutorAgent.builder()
            .name("assistant")
            .chatClient(chatClientBuilder.build())
            .systemPrompt("You are a concise, helpful assistant. Answer in one sentence.")
            .build();

    return args -> {
        AgentResult result = assistant.execute(
                AgentContext.of("What is backpressure in reactive streams?"));
        System.out.println("\nMistral says: " + result.text() + "\n");
    };
}

Run your app (mvn spring-boot:run) with MISTRAL_API_KEY set, and you'll see a real answer from Mistral — your first governed-ready Java agent, for free.

Where to go next

You now have a working LLM agent in Java. To turn it into a real multi-agent workflow:

  • Two API levels — Squad vs Graph, and how to compose agents into a stateful flow.
  • Cookbook — five runnable recipes (RAG, ticket triage, web research, Slack bot, batch processing).
  • Switch providers — move from Mistral to OpenAI, Anthropic, Gemini or local Ollama by changing one dependency.
  • Stop your agent burning $1000 overnight — once you're past the free tier, cap spend with budget, tool and approval gates.

Staying on the free tier

mistral-small-latest is covered by Mistral's free tier and is plenty for building and testing agents. When you move to heavier models or production traffic, AgentFlow4J's budget policy caps what an agent can spend — so a runaway loop can't surprise you on the invoice.