Swarm AI

Build & Test

./mvnw clean test       # 3004 tests, all passing
./mvnw clean install    # install to local Maven repo

Quick Start

Available on Maven Central. Recommended approach is to import the BOM and let it manage versions for all SwarmAI modules:

<dependencyManagement>
    <dependencies>
        <dependency>
            <groupId>ai.intelliswarm</groupId>
            <artifactId>swarmai-bom</artifactId>
            <version>1.0.9</version>
            <type>pom</type>
            <scope>import</scope>
        </dependency>
    </dependencies>
</dependencyManagement>

<dependencies>
    
    <dependency>
        <groupId>ai.intelliswarm</groupId>
        <artifactId>swarmai-core</artifactId>
    </dependency>

    
    <dependency>
        <groupId>ai.intelliswarm</groupId>
        <artifactId>swarmai-tools</artifactId>
    </dependency>

    
    <dependency>
        <groupId>ai.intelliswarm</groupId>
        <artifactId>swarmai-dsl</artifactId>
    </dependency>
</dependencies>

Enterprise + Studio modules (swarmai-enterprise, swarmai-studio) are licensed under BSL 1.1 and distributed via GitHub Packages instead of Maven Central. Configure a <server> block in your ~/.m2/settings.xml with a Personal Access Token having read:packages scope to consume them.

Agent researcher = Agent.builder()
    .role("Research Analyst")
    .goal("Find accurate, up-to-date information")
    .backstory("Experienced researcher who verifies facts.")
    .chatClient(chatClient)
    .tool(webSearchTool)
    .permissionMode(PermissionLevel.READ_ONLY)
    .build();

Agent writer = Agent.builder()
    .role("Content Writer")
    .goal("Write clear, engaging reports")
    .backstory("Turns research into well-structured articles.")
    .chatClient(chatClient)
    .build();

Task research = Task.builder()
    .id("research").description("Research: {topic}")
    .agent(researcher).build();

Task report = Task.builder()
    .id("report").description("Write a report from findings")
    .agent(writer).dependsOn("research")
    .outputFormat(OutputFormat.MARKDOWN).build();

SwarmOutput result = Swarm.builder()
    .agents(List.of(researcher, writer))
    .tasks(List.of(research, report))
    .process(ProcessType.SEQUENTIAL)
    .build()
    .kickoff(Map.of("topic", "AI agents in enterprise"));

How SwarmAI compares

If you don't need multi-agent orchestration with governance, use Spring AI directly. If you're on Python, use LangGraph. SwarmAI's wedge is JVM teams that need an opinionated agent layer with budget, RBAC, and approval gates baked in.