Recursive Language Models (RLM)

This is a paper from December 2025 that recently had a resurgence due to the improvement of LLMs and the proven successful implementation of sub-agents with Claude Code.

RLM are an agent architecture that overcome LLM context and reasoning limits by giving agents programmatic control over their own context via a REPL.

RLM works over a mutable context and can recursively spawn sub-agents to work on sub-tasks.

An RLM agent has:

• A context object: a mutable structure that can scale to very large contexts
• A recursive agent function: rlm_agent(query, context) → response, which can spawn child agents
• A Python execution environment: enabling search, filtering, and computation over context

The agent alternates between writing code, inspecting results, and delegating work recursively.

#RLM with Google's Agent Development Kit (ADK)

ADK adapts RLMs for production by providing low-level control over execution, memory, and orchestration via BaseAgent. Key features:

• Lazy context loading from files instead of massive in-memory prompts
• Parallel recursive delegation for scalable reasoning
• Tool-first reasoning over code before language
• Built-in observability for debugging recursive behavior

ADK preserves the core RLM idea—recursive, compute-over-context agents—while making it practical to deploy at scale.

Sources: Zhang Tweet, RLM with ADK Tweet, paper