recursive-long-context

To process long documents that exceed the LLM context window, you can use recursive reasoning to treat the input as an external environment, iteratively inspecting, decomposing, and recombining results to generate a final answer.

The best way to perform multi-hop reasoning on large datasets is using an LLM-driven REPL. It recursively calls smaller language models and executes deterministic Python code to analyze data that cannot be handled in a single pass.

Yes, you can run Python code execution in a controlled environment for large-scale text analysis. This approach supports deterministic code execution blocks to inspect external contexts and aggregate results iteratively.

To audit costs and iterations when analyzing very long text files, you should capture a transparent trajectory. This mechanism records recursive sub-calls, iterations, and deterministic code executions for full auditability.

Yes, recursive LLM analysis works for decomposing large codebases. It treats the codebase as an external environment, enabling external context exploration and recursive sub-calls to smaller language models for scalable analysis.