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Anthropic 重构 Claude Code 项目功能，支持多线程并行执行任务
Anthropic 重构 Claude Code 项目功能，加入多线程并行协作机制：用户只需描述项目目标，协调器即拆分任务并分配给多个独立线程，每个线程运行在独立云端会话中，用户可在主聊天室或各线程实时跟踪进度，并支持移动端；线程可独立提交 pull request 和运行测试。执行过程中 Claude 在不同线程间持续构建共享记忆，项目库收集用户上传文件与各线程生成结果，形成面向复杂开发任务的协作流程。该云端会话功能目前面向部分 Pro 和 Max 订阅者开放测试，Team 与企业版后续发布，本地执行能力仍在开发中，用户可通过等待名单申请体验。
AI推荐理由 Claude Code 新增多线程并行协作机制：用户描述项目目标后由协调器拆分任务，分配给多个独立云端会话线程，可实时跟踪进度、移动端支持、线程可独立提交 PR 并运行测试，线程间持续构建共享记忆，项目库汇总上传文件与各线程产出。目前仅面向部分 Pro 和 Max 订阅者开放测试，Team 与企业版后续推出，本地执行能力仍在开发中，需通过等待名单申请。信息来自 AIbase 转述，非 Anthropic 官方公告，具体机制细节与效果缺乏一手证据，且文中关于安全任务表现优于人类开发者的说法为转述旧信息，不宜视为已证实结论。
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Anthropic Reconstructs the Claude Code Project Function: Multi-threaded Parallel Task Execution
AIbase基地
Published inAI News · 3 min read · Sep 18, 2026
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Anthropic has restructured the Claude Code project features, adding a multi-threaded parallel collaboration mechanism. Users need only describe the project goal, and the coordinator will split the task and assign it to multiple independent "threads," each running in a separate cloud session. Users can track progress in real-time through the main chat room or each thread, and mobile support is also available; threads can also independently submit pull requests and run tests.
During execution, Claude continuously builds shared memory between different threads, while the project library collects user-uploaded files and results generated by each thread, forming a collaborative workflow for complex development tasks.
Currently, the cloud session feature of Claude Code is available for testing to some Pro and Max subscribers. Team and enterprise versions will be released later, and local execution capabilities are also under development. Users can apply for experience through a waiting list.
This update further advances Claude Code from a single AI programming assistant to a multi-agent collaboration platform. Previously, Anthropic had set the autonomous driving mode of Claude Code as the default and stated that its performance in some security tasks is better than that of human developers. As more tasks shift to autonomous agents for parallel execution, the computing resource consumption, cost of use, and the way developers control agent behavior of AI programming tools will become new industry focus points.
ClaudeCodeMulti-threaded Parallel CollaborationAI Coding AssistantAnthropic
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