rlm-pattern

To analyze large files exceeding context limits, use a multi-agent orchestration pattern that partitions content into manageable chunks, processes them with specialized agents, and synthesizes a final report. This prevents context overload while enabling deep multi-file analysis.

Multi-agent team orchestration for cross-file analysis is a pattern where specialized agents handle distinct content types like code, data, JSON, logs, and prose. It uses partitioning strategies and two-phase synthesis to aggregate chunk results into a unified report.

Yes, you can use multi-agent partitioning to process JSON and log files. The pattern includes content-type detection and assigns specialized agents to analyze specific formats, ensuring safe context management and concise reporting across diverse data structures.

To start a multi-file analysis session, initiate an orchestration flow that detects content types, partitions data into chunks, assigns them to specialized analyst agents, and synthesizes the aggregated chunk reports into a final comprehensive output.

The best way to synthesize results from partitioned content chunks is using a two-phase synthesis approach. This method aggregates concise analyst reports from specialized agents to prevent context overload while delivering deep insights across large datasets.

Context overload happens during large directory analysis when content exceeds the model's context window. Prevent it by using safe context management with specialized agents that enforce concise reporting and partitioning strategies before two-phase synthesis.