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.

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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

Need	What Atmosphere provides
Stream to real clients	WebSocket, SSE, long-polling, and gRPC run through one broadcaster pipeline as always-on defaults; WebTransport over HTTP/3 is optional (needs jetty-http3-server or reactor-netty-http on the classpath plus a dev cert)
Swap AI integrations	One AgentRuntime SPI with twelve runtime adapters and contract-tested capability flags
Ship a deep agent	A plain @Agent is batteries-included — memory, a plan (write_todos), a virtual filesystem, and sub-agent delegation (task), on by default via the harness
Govern execution	Policy admission, @AgentScope, human approval, plan-and-verify, cost ceilings, PII rewriting, and admin kill switches
Pause for humans	Durable HITL approvals hibernate without holding a thread, persist workflow state, and resume through REST approval surfaces
Resume long runs	Durable sessions, run IDs, replay buffers, checkpoints, and reconnect-safe continuation
Observe and replay	An opt-in session tape records session-boundary AI events (SQLite-durable) — reconstruct a recorded run, or a multi-agent coordination tree, from the tape with no model call, then distill a smaller model from it
Expose the same agent everywhere	Browser endpoints plus MCP (stateless RC on the MCP 2026-07-28 spec, sessions back to 2024-11-05), A2A, AG-UI, Slack, Telegram, Discord, WhatsApp, and Messenger modules

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:

Layer	What Atmosphere ships	Provided by your stack
Streaming transport	atmosphere-runtime over WebSocket, SSE, long-polling (always-on defaults), gRPC, plus optional WebTransport/HTTP-3 (needs jetty-http3-server or reactor-netty-http on the classpath plus a dev cert)	—
Runtime dispatch	atmosphere-ai AgentRuntime SPI + 12 adapters with contract-tested capability flags	Model hosting (we call providers; we do not host weights)
Orchestration	@Coordinator, AgentFleet, handoffs, conditional routing, an event-sourced coordination journal with causal lineage (append-only NDJSON persistence, DAG projection, what-if forking), result evaluation, and durable hibernating Workflow<S> over CheckpointStore (per-step retry, resume across JVM restart, no thread held while hibernated; add atmosphere-checkpoint-temporal and the same workflow runs on a Temporal service) — durable step execution, not wall-clock triggering	Cron / wall-clock scheduling (your container scheduler or a dedicated scheduler fires the workflow)
Memory	AiConversationMemory per-conversation history (in-memory, plus durable SQLite/Redis through the ConversationPersistence SPI in atmosphere-durable-sessions{-sqlite,-redis}), LongTermMemory per-user facts (InMemoryLongTermMemory, SqliteLongTermMemory, RedisLongTermMemory), SemanticRecallInterceptor for BYO vector-store recall	Managed vector stores (use Spring AI's VectorStore, LangChain4j embeddings, or your own)
Governance	Policy admission, @AgentScope, plan-and-verify, PII redaction, cost ceilings, durable HITL approvals, admin kill switches	—
Protocol surface	MCP, A2A, AG-UI, Slack/Telegram/Discord/WhatsApp/Messenger channel adapters	—
Code execution	atmosphere-sandbox SandboxProvider SPI + DockerSandboxProvider default	Browser automation, headless Chromium
SDK	atmosphere.js (React, Vue, Svelte, React Native, vanilla TS), wasync (JVM client)	—

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

Terminology

Term	Meaning in Atmosphere	Examples
Model provider	The model/API vendor or endpoint that serves tokens	OpenAI, Gemini compatibility endpoint, Ollama, DashScope, local OpenAI-compatible proxies
Runtime adapter	The Atmosphere integration that implements AgentRuntime	Built-in, Spring AI, LangChain4j, Google ADK, Embabel, Koog, Semantic Kernel, AgentScope, Spring AI Alibaba, Anthropic, Cohere, CrewAI
Capability	A feature advertised by a runtime adapter and pinned by contract tests	tool calling, embeddings, streaming, structured output, prompt caching

Use provider for model vendors and runtime adapter for Atmosphere integrations. Not every runtime adapter exposes every capability.

@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);
    }
}

Module on classpath	What gets registered
atmosphere-agent	Browser endpoint at /atmosphere/agent/my-agent, streaming AI dispatch, memory, commands, /help
atmosphere-mcp	MCP endpoint at /atmosphere/agent/my-agent/mcp — session protocol plus the stateless RC on the MCP 2026-07-28 spec (Tasks, MCP Apps, OAuth resource server)
atmosphere-a2a	A2A endpoint at /atmosphere/agent/my-agent/a2a with Agent Card discovery
atmosphere-agui	AG-UI endpoint at /atmosphere/agent/my-agent/agui
atmosphere-channels	Slack, Telegram, Discord, WhatsApp, and Messenger dispatch
atmosphere-admin	Admin dashboard and /api/admin/* control surfaces
built-in console	Console UI at /atmosphere/console/

Deep agents, batteries-included

A plain @Agent is a deep agent out of the box. The harness defaults to the full set, so a bare annotation already carries:

• Memory — long-term facts recalled across sessions, plus conversation history with a compaction seam.
• A plan it maintains — the built-in write_todos tool (or a runtime's native plan surface), streamed to the client as it changes.
• A virtual filesystem — six bounded, conversation-scoped tools (ls, read_file, write_file, edit_file, glob, grep).
• Sub-agent delegation — on a @Coordinator, delegate_task for pre-declared fleet members and task to spawn an ephemeral, isolated sub-agent on demand.
• Prompt caching, skills, and large-tool-output disk offload — on by default, tuned by config.

Narrow the set per agent (@Agent(harness = {Harness.MEMORY})) or opt down to a bare loop (harness = {}); @AiEndpoint is opt-in. Every primitive reports its actual attached state at /api/console/info — runtime truth, never configuration intent. This is the same deep-agent capability set as LangChain deepagents, hosted by a JVM framework — see the comparison.

AI Runtime Adapters

atmosphere-ai ships the AgentRuntime SPI plus the Built-in OpenAI-compatible adapter. Eleven additional adapters live in separate modules — nine wrap a third-party framework, and two (atmosphere-anthropic, atmosphere-cohere) are native HTTP+SSE clients for the Anthropic Messages API and the Cohere v2 chat API respectively. Drop one runtime adapter on the classpath and the same @Agent code dispatches through it.

Atmosphere does not replace these frameworks. You keep the native framework for what it does best — its models, tools, planners, memory, and vector stores — and Atmosphere adds the service layer around it: real-time transports, governance, HITL approval, durable sessions, replay, and MCP/A2A/AG-UI exposure. The adapter is a bridge, not a replacement; the Notes column below says which native framework each one is the best fit for.

Capabilities are intentionally not identical. The authoritative matrix is pinned by AbstractAgentRuntimeContractTest.expectedCapabilities(), so a runtime cannot drift from its declared feature set without breaking tests.

Runtime adapter	Backing framework	Spring Boot	Capability highlights	Notes
atmosphere-ai (Built-in)	OpenAI-compatible HTTP client	3.5 / 4.0	tool calling, JSON mode, vision, audio, prompt caching, token usage, native retry, tool-call deltas	Default. Works with OpenAI-compatible endpoints such as OpenAI, Gemini compatibility endpoints, Ollama, and local proxies.
atmosphere-spring-ai	Spring AI 2.0.0	4.0	tool calling, structured output, vision, audio, prompt caching, token usage	Best fit for Spring Boot applications already using Spring AI.
atmosphere-langchain4j	LangChain4j 1.17.0	4.0	tool calling, structured output, vision, audio, prompt caching, token usage	Best fit for LangChain4j tool ecosystems and non-Spring services.
atmosphere-adk	Google ADK 1.5.0	4.0	agent orchestration, tool calling, multi-modal, prompt caching	Multi-agent runtime with AGENT_ORCHESTRATION.
atmosphere-koog	JetBrains Koog 1.0.0	4.0	agent orchestration, tool calling, multi-modal, prompt caching, cancellation	Multi-agent runtime.
atmosphere-semantic-kernel	Microsoft Semantic Kernel 1.5.0	4.0	tool calling, structured output, vision, token usage	No audio path through the SK Java SDK today.
atmosphere-agentscope	Alibaba AgentScope 1.0.12	4.0	tool calling, structured output, vision, audio, conversation memory, token usage, cancellation	AgentScopeToolBridge routes every @AiTool invocation through ToolExecutionHelper.executeWithApproval.
atmosphere-embabel	Embabel 0.5.0	3.5 only	agent orchestration, tool calling, vision, conversation memory	Requires atmosphere-spring-boot3-starter and the -Pspring-boot3 profile.
atmosphere-spring-ai-alibaba	Spring AI Alibaba 1.1.2.3	3.5 only	tool calling, structured output, vision, audio, conversation memory, token usage	Buffered streaming (the upstream ReactAgent.call() returns one AssistantMessage); UsageCapturingChatModel decorator threads token usage. For token-by-token streaming, use another adapter until Alibaba ships a Spring AI 2.x-aligned agent framework.
atmosphere-anthropic	Anthropic Messages API (no third-party SDK)	3.5 / 4.0	tool calling, structured output, conversation memory, token usage, per-request retry	Native HTTP+SSE client; tool loop capped at five rounds, cancellation-aware. Configure via anthropic.api.key (system property or AiConfig.LlmSettings); custom headers (Helicone-Auth, tenant IDs, tracing) are passthrough with reserved-header filtering.
atmosphere-cohere	Cohere v2 chat API (no third-party SDK)	3.5 / 4.0	tool calling, structured output, vision, multi-modal, conversation memory, token usage, per-request retry	Native HTTP+SSE client against POST /v2/chat; tool dispatch routes through ToolExecutionHelper.executeWithApproval. Configure via cohere.api.key (system property or AiConfig.LlmSettings).
atmosphere-crewai	CrewAI 1.14+ sidecar bridge	3.5 / 4.0	agent orchestration, tool calling, structured output, token usage, cancellation	Java HTTP+SSE bridge to a Python CrewAI sidecar. Availability is gated by a live /health probe, not classpath presence.

See the full capability matrix for text streaming, tool calling, structured output, system prompts, agent orchestration, conversation memory, tool approval, vision, audio, multi-modal, prompt caching, token usage, retry, passivation, and tool-call deltas.

Enterprise Controls

Atmosphere keeps governance on the critical path rather than as an afterthought.

Control	Module	What it does
Policy admission	atmosphere-ai	GovernancePolicy, PolicyRing, allow/deny lists, rate limits, time windows, metadata requirements
Scope enforcement	atmosphere-ai	@AgentScope blocks out-of-purpose prompts before runtime dispatch
Human approval	atmosphere-agent, atmosphere-ai	command confirmations, permission modes, tool approval policies
Durable HITL workflows	atmosphere-checkpoint, atmosphere-durable-sessions	checkpointed approval gates, REST approve/reject/resume endpoints, and reconnect-safe replay for long-running agent work
Stateful interactions	atmosphere-interactions	stateful agent turns with a durable steps[] log, background runs retrievable after disconnect, conversation chaining via previous_interaction_id, and live step streaming over WebSocket; REST surface at /api/interactions (mutations default-deny)
Plan-and-verify	atmosphere-verifier	verifies LLM-emitted tool workflows before execution; six structural verifiers run today (control-flow gate, allowlist, well-formedness, capability, taint, automaton); a seventh SMT backend ships as atmosphere-verifier-smt (SMTInterpol pure-JVM default, Z3 opt-in via native libs) proving numeric invariants over symbolic tool-call data flow; the no-op default applies only when that module is absent. It implements Erik Meijer's Guardians of the Agents (CACM, Jan 2026) pattern in native Java
PII and cost controls	atmosphere-ai	stream-level PII redaction, token usage, per-tenant cost ceilings
Admin control plane	atmosphere-admin	dashboard, REST/MCP control surfaces, kill switches, journal flow viewer, governance decisions, workflow authoring UI (authoring + persistence; manifest execution runtime not yet wired), eval dashboard
Enterprise console bundle	atmosphere-admin-bundle	single Maven dep aggregating spring-boot-starter + admin + ai + coordinator + agent + rag + checkpoint + durable-sessions
Compliance evidence	atmosphere-ai, atmosphere-admin	OWASP Agentic Top 10, EU AI Act, HIPAA, SOC 2 matrices, admin-console evidence views, and AGT-compatible verification output
Sandbox execution	atmosphere-sandbox	DockerSandboxProvider default and SandboxProvider SPI for isolated code execution

Governance policy can be YAML-driven:

version: "1.0"
policies:
  - name: deny-destructive-sql
    type: deny-list
    config:
      phrases: ["DROP TABLE", "TRUNCATE", "DELETE FROM"]
  - name: tenant-cost-ceiling
    type: cost-ceiling
    config:
      ceilingDollars: 50.0

Or annotation-driven:

@AiEndpoint(path = "/support")
@AgentScope(
    purpose = "Customer support: orders, billing, store hours",
    forbiddenTopics = {"code", "programming", "medical advice"},
    onBreach = AgentScope.Breach.POLITE_REDIRECT)
public class SupportAgent { /* @Prompt method */ }

See the governance policy plane reference, governance docs, and spring-boot-ms-governance-chat sample.

Client Libraries

Install the TypeScript client:

npm install atmosphere.js

Use React, Vue, Svelte, React Native, or the low-level client directly:

import { useStreaming } from 'atmosphere.js/react';

function Chat() {
  const { fullText, isStreaming, send } = useStreaming({
    request: {
      url: '/atmosphere/agent/my-agent',
      transport: 'webtransport',
      fallbackTransport: 'websocket',
    },
  });

  return <p>{fullText}</p>;
}

For Java/Kotlin clients, use wAsync for async WebSocket, SSE, long-polling, and gRPC clients.

Flagship Samples

Sample	Shows
startup team	@Coordinator with A2A specialists, governance, checkpoints, skills, admin control plane
ai-chat	Streaming AI chat with auth, caching, and runtime adapter portability
ai-tools	Framework-agnostic @AiTool methods and approval gates
checkpoint-agent	Checkpointed @Coordinator workflow with REST approval/resume
ai-classroom	Multi-room collaborative AI with React Native / Expo client
guarded-email-agent	Plan-and-verify taint protection before any email tool fires
ms-governance-chat	Microsoft Agent Governance Toolkit-compatible YAML and decision endpoint
mcp-server	MCP tools, resources, prompts, and an MCP App (SEP-1865) with a bidirectional bridge, rendered in the console
rag-chat	Retrieval-augmented generation with ContextProvider
channels-chat	Slack, Telegram, Discord, WhatsApp, and Messenger channel dispatch
coding-agent	Docker sandbox provider for clone/read/stream coding workflows

All samples · cli/samples.json · atmosphere install for the interactive picker.

Add Atmosphere to an App

<!-- Spring Boot 4.0 starter -->
<dependency>
    <groupId>org.atmosphere</groupId>
    <artifactId>atmosphere-spring-boot-starter</artifactId>
    <version>${atmosphere.version}</version>
</dependency>

<!-- Agent module: @Agent, @Prompt, @Command, @AiTool -->
<dependency>
    <groupId>org.atmosphere</groupId>
    <artifactId>atmosphere-agent</artifactId>
    <version>${atmosphere.version}</version>
</dependency>

Add only what you need:

• AI runtime: atmosphere-ai or one runtime adapter from the runtime table
• Protocols: atmosphere-mcp, atmosphere-a2a, atmosphere-agui
• Channels: atmosphere-channels
• Multi-agent: atmosphere-coordinator
• Admin/control plane: atmosphere-admin
• Plan-and-verify: atmosphere-verifier
• Sandbox: atmosphere-sandbox
• Durable sessions: atmosphere-durable-sessions plus atmosphere-durable-sessions-sqlite or atmosphere-durable-sessions-redis
• Checkpoints: atmosphere-checkpoint
• Audit sinks: atmosphere-ai-audit-kafka, atmosphere-ai-audit-postgres
• Policy engines: atmosphere-ai-policy-rego (OPA), atmosphere-ai-policy-cedar (AWS Cedar)

For Spring Boot 3.5 deployments, including Embabel or Spring AI Alibaba, use atmosphere-spring-boot3-starter and build with -Pspring-boot3.

Requirements: Java 21+ · Spring Boot 4.0.7 or Spring Boot 3.5 via -Pspring-boot3 · Quarkus 3.36.0+ · current release from the Maven Central badge above.

Documentation

Tutorial · Full docs · CLI · Javadoc · Samples

Topic	Reference docs
Architecture & transports	Architecture · Transports · Broadcaster · WebTransport · Clustering
AI runtimes	AI adapters · Runtime selection · AI reference
Agents & protocols	@Agent · MCP · A2A · AG-UI · Coordinator · Channels
Governance & verification	Governance · Agent scope · Plan-and-verify (Meijer) · Tool approval · OWASP Agentic matrix
Durability & state	Durable sessions · Durable HITL · Checkpoints · Interactions
Operations	Admin & control plane · Observability · Benchmarks
Clients	JavaScript · React Native · Java / wAsync

Commercial Support

Production support tiers, compliance attestation, Microsoft Agent Governance Toolkit interop, plan-and-verify adoption, A2A v1.0.0 alignment, and legacy Atmosphere 2.x / 3.x long-term support are available from Async-IO. Book a 30-minute architecture call: async-io.live/contact.

Companion Projects

Project	Description
atmosphere-skills	Curated agent skill files: personality, tools, guardrails
homebrew-tap	Homebrew formulae for the Atmosphere CLI

License

Apache 2.0 - Copyright 2008-2026 Async-IO.org