Ross ROSS = Recommend OSS · open-source software intelligence for agents

Atmosphere/atmosphere

Portable AI agent runtime for the JVM. One @Agent class runs on Spring AI, LangChain4j, Anthropic, or 9 more behind one SPI. Token streaming, tool calls, human approvals, and governance over WebSocket, SSE, gRPC, or WebTransport/HTTP3. Speaks MCP, A2A, and AG-UI. observed · 2026-08-28

github.com/Atmosphere/atmosphere · homepage · Java · Apache-2.0 (permissive) observed · 2026-08-28

Health v2 · maintenance only

95/100

  • Activity 99
  • Release rhythm 87
  • Longevity 100
How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.

  • gap_med: 1.0
  • age_days: 5908
  • days_rel: 10
  • days_push: 8
  • n_releases_24m: 63

Full methodology

Adoption not part of the score

3796 stars · 761 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Atmosphere is a portable AI agent runtime for the JVM that lets a single @Agent class run across twelve AI runtimes (Spring AI, LangChain4j, Anthropic, and more) behind one SPI. It provides token streaming, tool calls, human approvals, and governance over WebSocket, SSE, long-polling, gRPC, or WebTransport/HTTP3, and speaks MCP, A2A, and AG-UI protocols.

Use cases

  • stream LLM tokens to browsers over WebSocket or SSE from a Java backend
  • build production AI agents on the JVM with tool calls and human approval gates
  • swap AI frameworks like Spring AI or LangChain4j without rewriting agent endpoints
  • add governance, policy admission, and cost ceilings to AI agent execution
  • run durable, resumable agent sessions that survive JVM restarts
  • orchestrate multi-agent fleets with live observability
  • connect Java agents to MCP, A2A, or AG-UI clients

When to choose

  • you are building AI agents in Java/Kotlin on the JVM and need production-grade transports
  • you need framework portability across multiple AI runtimes behind one API
  • you require human-in-the-loop approvals, governance policies, or durable resumable runs
  • you want real-time streaming (WebSocket/SSE/gRPC) plus MCP/A2A/AG-UI protocol support out of the box

When to avoid

  • you are building agents in Python, JavaScript, or another non-JVM ecosystem
  • you need a simple prototype where a direct SDK call to one LLM provider suffices
  • you want a fully managed cloud agent platform rather than a self-hosted framework
  • your stack has no JVM dependency and adding one is not justified

Facets

framework · maturity active

agent-framework websocket rpc middleware llm-inference rag mcp chatbot authorization streaming large-language-models backend developer-tools web-development jvm cross-platform ai-agents agent-runtime mcp a2a ag-ui human-in-the-loop token-streaming sse grpc webtransport governance durable-sessions multi-agent spring-ai langchain4j real-time spring quarkus

2 sources

Member repositories

RepositoryRoleHealth v2
Atmosphere/atmospheremain95

For agents

markdown · JSON · MCP: product_card(name="Atmosphere/atmosphere")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem