rlm

To analyze a large codebase exceeding context limits, you can use recursive context processing to chunk inputs and orchestrate sub-agents. This approach treats massive files as external context, processing them modularly before synthesizing a final report.

Recursive context processing for multi-file analysis is a technique that handles massive prompts by breaking them into chunks. Sub-agents summarize each chunk, and a final synthesis step combines the results to maintain quality across unbounded contexts.

Yes, you can use chunking and sub-agent orchestration for cross-repo analysis. This workflow supports analyzing multiple repositories by processing them in deterministic, modular steps and synthesizing the findings into a unified output.

Handling massive log files is best achieved through recursive chunking and external-context processing. By dividing the logs into manageable segments and using sub-agents to summarize each part, you can synthesize a comprehensive final analysis.

Yes, recursive LLM workflows support Python REPL integration alongside tools like Task, Read, Grep, and Glob. This combination enables deterministic chunking patterns and maintains analysis quality when processing unbounded contexts.

You should avoid recursive context processing for codebase analysis when your files fit comfortably within standard model context limits. It is specifically designed for unbounded contexts, massive logs, and multi-file scenarios that exceed traditional processing capabilities.