Atmosphere

The real-time engine for AI agents on the JVM. Tokens flow from the LLM runtime to the client through a broadcaster you can filter, gate, and observe — over WebSocket, SSE, long-polling, or gRPC, and out through MCP, A2A, and AG-UI. A plain @Agent is a full deep agent, batteries-included, and Atmosphere handles reconnect, authorization, and governance.

Atmosphere is built for teams that need AI agents to behave like production services: streaming over real transports, guarded before every tool call, observable by tenant and run, and portable across AI frameworks without rewriting the endpoint.

Why Atmosphere

Scope

Atmosphere is a real-time, event-driven framework, not an agent-hosting platform. We ship the primitives your application uses at runtime; the host you choose (Tomcat, Jetty, Netty, Undertow, Quarkus, Spring Boot, or any servlet container) owns compute and scheduling. Payment rails and commerce primitives are out of scope. Compared to the agent-platform stack vocabulary that has emerged around offerings like Cloudflare Agents, AWS Bedrock Agents, and Vertex AI Agents:

The differentiator is the streaming + JVM + governance combination: long-lived stateful agent sessions over real-time transports, with policy admission on the critical path. For stateless, bursty, autonomous agents that should hibernate when idle, a serverless agent platform is usually the better fit. For human-in-the-loop, multi-channel, governed agents inside an existing JVM stack, Atmosphere is the right fit.

Quick Start

Run a sample

brew install Atmosphere/tap/atmosphere

# Or:
curl -fsSL https://raw.githubusercontent.com/Atmosphere/atmosphere/main/cli/install.sh | sh

atmosphere run spring-boot-multi-agent-startup-team

Create an app

atmosphere new my-agent --template ai-chat
cd my-agent
LLM_API_KEY=your-key ./mvnw spring-boot:run

Swap the runtime adapter

# Built-in is the default. --runtime spring-ai scaffolds the app against the
# Spring AI adapter instead (a CLI overlay injects its Maven dependencies).
atmosphere new my-agent --template ai-chat --runtime spring-ai

# Use --force when a sample already pins a runtime adapter.
atmosphere new my-agent --template ai-tools --runtime langchain4j --force

Import a skill

atmosphere import https://github.com/anthropics/skills/blob/main/skills/frontend-design/SKILL.md
cd frontend-design
LLM_API_KEY=your-key ./mvnw spring-boot:run

@Agent

One annotation declares the agent. Modules on the classpath decide which endpoints and integrations are registered.

@Agent(name = "my-agent", description = "What this agent does")
public class MyAgent {

    @Prompt
    public void onMessage(String message, StreamingSession session) {
        session.stream(message);
    }

    @Command(value = "/status", description = "Show status")
    public String status() {
        return "All systems operational";
    }

    @Command(value = "/reset", description = "Reset data",
             confirm = "This will delete all data. Are you sure?")
    public String reset() {
        return dataService.resetAll();
    }

    @AiTool(name = "lookup", description = "Look up data")
    public String lookup(@Param("query") String query) {
        return dataService.find(query);
    }
}