alexzhang13/rlm

Health v2 · maintenance only

Adoption not part of the score

5539 stars · 890 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A plug-and-play Python inference library for Recursive Language Models (RLMs), a paradigm where an LLM programmatically examines and decomposes arbitrarily long prompts in a sandboxed REPL and recursively calls itself over snippets. It supports multiple sandboxes, API-based and local LLMs, and includes a training environment for post-training RLMs.

Use cases

• process prompts longer than the model context window
• recursively decompose huge documents with sub-LLM calls
• run long-context inference with a CodeAct-style REPL harness
• swap llm.completion for rlm.completion in existing pipelines
• train custom models around the RLM paradigm
• evaluate long-context scaffolds against compaction and coding agents

When to choose

• your prompts exceed the model's context window by orders of magnitude
• you want a drop-in inference wrapper that handles near-infinite context
• you need sandboxed code execution with recursive sub-LLM calls
• you want to post-train a model around the RLM harness

When to avoid

• your prompts comfortably fit within the model context window
• you need low-latency single-shot completions without extra overhead
• you cannot allow code execution in your inference pipeline
• you need a battle-tested production system rather than a research tool

Facets

library · maturity active

llm-inference agent-framework sdk large-language-models artificial-intelligence python recursive-language-models long-context inference-scaling sandboxed-execution codeact repl sub-llm-calls training-environment natural-language-processing

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