Atmosphere/atmosphere

• highreadme#1

  Update README H1 and opening sentence to clarify identity and purpose

  原因：

  当前

  <h1 align="center">Atmosphere</h1>
  
  <p align="center">
    A framework for building streaming AI agents on the JVM. Atmosphere owns the transport layer — tokens flow from the LLM runtime to the client through a broadcaster you can filter, gate, and observe.
  </p>

  复制粘贴的修复

  复制

  <h1 align="center">Atmosphere: Real-time Transport for Java AI Agents</h1>
  
  <p align="center">
    This is the *new* Atmosphere, a dedicated framework for building streaming AI agents on the JVM, distinct from the legacy Atmosphere web framework. It uniquely owns the transport layer — tokens flow from the LLM runtime to the client through a broadcaster you can filter, gate, and observe.
  </p>

  待办完成忽略
• mediumtopics#2

  Add more specific topics to reinforce AI agent transport focus

  原因：

  当前

  acp, agentic-ai, embabel, event-driven, java, koog, langchain4j, mcp, multiagent, quarkus, spring-ai, spring-boot, sse, websocket

  复制粘贴的修复

  复制

  acp, agentic-ai, embabel, event-driven, java, koog, langchain4j, mcp, multiagent, quarkus, spring-ai, spring-boot, sse, websocket, llm-agents, ai-transport, real-time-ai, agent-communication, http3, webtransport

  待办完成忽略
• lowreadme#3

  Add a 'Why Atmosphere for AI Agents?' section to the README

  原因：

  复制粘贴的修复

  复制

  Add a new section to your README, for example, under the heading `## Why Atmosphere for AI Agents?` with content similar to: 'Unlike general streaming platforms (e.g., Apache Kafka, Flink) or traditional multi-agent systems (e.g., JADE, Akka), Atmosphere provides a dedicated, opinionated transport layer specifically for modern LLM-based AI agents. It handles the unique complexities of AI agent communication, including tool calling, memory, and multi-protocol support, allowing developers to focus on agent logic rather than infrastructure.'

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

  How to build real-time streaming AI agents on JVM supporting multiple communication protocols?

  你：未被推荐
  AI 推荐顺序：

  1. Apache Flink (apache/flink)
  2. Apache Kafka (apache/kafka)
  3. Flink ML
  4. DeepLearning4J (DL4J) (eclipse/deeplearning4j)
  5. Kafka Connect
  6. Netty (netty/netty)
  7. Apache Camel (apache/camel)
  8. Akka Streams (akka/akka)
  9. Akka Actors (akka/akka)
  10. Apache Pulsar (apache/pulsar)
  11. Akka HTTP (akka/akka-http)
  12. Akka gRPC (akka/akka-grpc)
  13. Spring Cloud Stream (spring-cloud/spring-cloud-stream)
  14. RabbitMQ
  15. Spring AI (spring-projects/spring-ai)
  16. Spring WebFlux
  17. Spring Integration (spring-projects/spring-integration)
  18. Apache Samza (apache/samza)
  19. ONNX Runtime (microsoft/onnxruntime)
  20. TensorFlow Lite
  21. Quarkus (quarkusio/quarkus)
  22. Eclipse Paho MQTT client (eclipse/paho.mqtt.java)
  23. RESTEasy Reactive

  AI 推荐了 23 个替代方案，却始终没点名 Atmosphere/atmosphere。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  Looking for a Java framework to develop multi-agent systems with robust real-time communication.

  你：未被推荐
  AI 推荐顺序：

  1. JADE
  2. Akka
  3. Apache MINA
  4. Vert.x
  5. Spring Integration

  AI 推荐了 5 个替代方案，却始终没点名 Atmosphere/atmosphere。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  pass

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of Atmosphere/atmosphere?

  pass
  ✓AI 明确点名了 Atmosphere/atmosphere

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• If a team adopts Atmosphere/atmosphere in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI 明确点名了 Atmosphere/atmosphere

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• In one sentence, what problem does the repo Atmosphere/atmosphere solve, and who is the primary audience?

  pass
  ✓AI 明确点名了 Atmosphere/atmosphere

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？