LangChain explains how Deep Agents manage context with offloading, summarization, and memory

LangChain explains how its open-source Deep Agents harness keeps an agent's context window manageable during long tasks.

It describes offloading large tool results to files, summarizing older messages near the context limit, loading skills on demand, isolating noisy work in subagents, and storing long-term memory in a file.

Each technique is implemented as middleware that runs at specific points in the agent loop.