rlm

Large documents exceeding chat context limits are processed using a persistent local Python REPL and a subagent to iteratively chunk, search, and extract information across multiple passes. This approach bypasses memory constraints by maintaining state locally and analyzing inputs systematically.

A persistent REPL loop for long-context analysis is a local Python environment that maintains state across invocations, allowing a subagent to progressively process document chunks and synthesize deterministic, auditable results beyond standard chat memory capacity.

To extract structured insights from large log files, initialize a persistent REPL with the context path, then run an execution loop that delegates LLM queries to a subagent on individual chunks, assembling final reports from the extracted data.

Yes, you can run deterministic LLM queries across multiple document chunks by using an integrated subagent interface and orchestration utilities that ensure auditable, consistent results when scanning large inputs beyond chat limits.

Yes, Python is required because the long-context loop relies on a persistent local Python REPL (rlm_repl.py) to maintain state, execute chunking utilities, and coordinate subagent queries for large document analysis.

Limitations of using a persistent REPL for long-context tasks include dependency on local Python execution environments and the need for iterative chunking, meaning tasks require multiple passes rather than instant single-shot context processing.