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# Deep Agents

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  1. langchain.com

    Deep Agents makes subagents async: the orchestrator answers whenever its own loop is free while long tasks run in parallel

    Deep Agents adds async subagents: an orchestrator stays in the foreground talking to the user and organizing context, while subagents run long tasks as background workers and report back on completion. Previously, in a standard ReAct loop, each long tool call blocked the loop and every other task queued behind it; now subagents no longer hold the orchestrator's loop, the user can reach it whenever its own loop isn't busy, and the work an agent can take on grows with the number of background workers rather than the length of one loop.

  2. langchain.com

    Long-running agents stop drowning in context: LangChain moves memory into files that survive a model swap

    LangChain's open source Deep Agents harness applies five context engineering techniques against context rot in long tasks: offloading oversized tool results and user messages to the filesystem, summarizing history near the limit, loading skills on demand, isolating noisy work in subagents, and keeping long-term memory as a plain file. Information is moved out of the active window rather than deleted and can be read back, so memory lives in storage the user controls and survives a model or provider switch.

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