Source: LangChain (Sydney Runkle) — 2026-07-01 (approx.)
Summary
LangChain published a technical walkthrough on integrating Recursive Language Models (RLMs) — a technique originating from MIT CSAIL research — into its Deep Agents framework. Instead of stuffing all retrieved context into a single window, an RLM-equipped agent runs code in a REPL that recursively dispatches sub-queries over pieces of context, only pulling back what's relevant. The post frames this as a direct answer to "context rot," where agent performance degrades as context windows fill with marginally relevant material.
Key Takeaways
- RLMs treat context as something to be programmatically queried and decomposed, not simply concatenated into the prompt — a REPL loop recursively calls the model over sub-chunks of context.
- Positioned as a harness-level fix for context rot, distinct from just using a larger context window or a separate memory store.
- Ships as a pattern usable directly within LangChain's Deep Agents, making it accessible without custom agent-loop engineering.
- Relevant to teams hitting quality degradation in long-running or heavily-tool-augmented agent sessions.