# RLM: Recursive Language Models for Large Codebases - Shashi, Superagentic AI

Source: [YouTube](https://www.youtube.com/watch?v=8oyalrfwgjw)  
Feed7 permalink: https://feed7.dev/p/rlm-recursive-language-models-for-large-codebases-shashi-superagentic-ai-1w21nex  
Published: Unknown  
Trust: Source Linked (source_linked)

## Why Included

RLMs treat a large repository as external data that an agent inspects with code, returning bounded evidence to the main context instead of loading or summarizing everything upfront.

## Source Summary

A recursive language model keeps the repository outside the primary prompt and gives the model a programmable environment for inspecting files, dependencies, tests, and configuration. Each pass returns a **bounded observation** rather than the whole codebase.

## Practical Implication

For monorepo analysis, let the agent write targeted inspection code, preserve its evidence, and make recursive model calls only when the current evidence is insufficient. Capture the **plan, code, observations, subcalls, budget, and final output** for debugging.

## Agent-Ready Context

A recursive language model keeps the repository outside the primary prompt and gives the model a programmable environment for inspecting files, dependencies, tests, and configuration. Each pass returns a **bounded observation** rather than the whole codebase.

For monorepo analysis, let the agent write targeted inspection code, preserve its evidence, and make recursive model calls only when the current evidence is insufficient. Capture the **plan, code, observations, subcalls, budget, and final output** for debugging.

RLM is a pattern, not a single implementation, and the talk demonstrates a research playground rather than comparative results on large-codebase tasks. Its claims about proprietary agents using related ideas are partly observational and should not be treated as verified architecture.

## Context Map

- Layer: context
- Domains: coding
- Topics: context-engineering, retrieval, harness-engineering

## Uncertainty

- RLM is a pattern, not a single implementation, and the talk demonstrates a research playground rather than comparative results on large-codebase tasks. Its claims about proprietary agents using related ideas are partly observational and should not be treated as verified architecture.

## Agent Instruction

Use this item as source-backed context. Do not invent claims beyond the linked source. If this item conflicts with another source, call out the conflict.