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
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
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
- readme: https://github.com/Atmosphere/atmosphere · fetched 2026-08-28 · a624ce372c4c
- homepage: https://async-io.live · fetched 2026-08-29 · ddd3faf484b2
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| Atmosphere/atmosphere | main | 95 |
For agents
markdown · JSON · MCP: product_card(name="Atmosphere/atmosphere")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem