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External memory workflows solve large-data analysis bottlenecks by keeping contexts out of the prompt and operating on compact slices and derived results. This approach orchestrates data loading and computation efficiently to bypass local memory bottlenecks.

The best way to orchestrate external memory workflows for local data workstations is using a modular 5-phase loop: Load, Orient, Compute, Recurse, and Converge. This workflow manages complex analyses by loading contexts, searching, and computing efficiently across large datasets.

To start an external memory workflow for a large codebase or repository, you run the MCP launcher against your local workspace. This initializes the workflow, integrates the necessary agent, and verifies the environment with an install-check hook before loading contexts.

Yes, you can process large data without loading everything into the prompt by using an external memory workflow. This method keeps large contexts out of the prompt entirely, operating instead on compact data slices and derived computation results to conserve memory.

Yes, external memory workflows support recursive computation for complex data analysis through a dedicated 5-phase loop. The Recurse phase specifically allows you to iterate and compute efficiently on compact slices before converging the final derived results.