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RLMX

RLMX is a CLI research tool that implements the RLM (REPL-LM) algorithm. It lets LLMs navigate large codebases and document collections programmatically through a persistent Python REPL — instead of stuffing everything into context.
Research Preview — RLMX is experimental. The RLM algorithm is bleeding-edge research. Expect sharp edges. Tell us on Discord.

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How it works

Traditional RAG retrieves chunks and hopes for the best. RLMX takes a different approach:
  1. Prompt externalization — Your context (files, directories) is loaded into a Python REPL as the context variable. Only metadata appears in the LLM message history. The LLM never sees raw context in its messages.
  2. Iterative REPL loop — The LLM writes Python code in ```repl``` blocks. RLMX executes each block in a persistent subprocess, feeds results back, and the LLM iterates until it has the answer.
  3. Recursive sub-calls — Inside REPL code, the LLM can spawn child queries:
    • llm_query(prompt) — single LLM completion
    • llm_query_batched(prompts) — concurrent LLM calls
    • rlm_query(prompt) — spawn a full child RLM session
    • rlm_query_batched(prompts) — parallel child RLM sessions
  4. Termination — The loop ends when the LLM calls FINAL("answer") or FINAL_VAR("variable_name"), or when max iterations is reached.

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Why use RLMX?

ApproachContext handlingBest for
RAGRetrieve chunks, stuff into promptSimple Q&A over small docs
Full contextDump everything into system promptSmall codebases, high cost
RLMX (RLM)LLM navigates programmaticallyLarge codebases, complex analysis
RLMX (CAG)Cache full context at provider levelRepeated queries, batch Q&A
RLMX handles codebases and document collections that are too large for a single context window, while keeping costs low through programmatic navigation and provider-level caching.

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Part of the Automagik ecosystem

RLMX works standalone or as part of a Genie workflow. Use it as a research tool inside agent sessions, as a batch processor for document interrogation, or as a library in your own tools.

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Requirements

  • Node.js >= 18
  • Python 3.10+ (for the REPL subprocess)
  • An LLM API key (Google Gemini, Anthropic, OpenAI, or any pi/ai provider)

Quickstart

Install, configure, and run your first query in under five minutes.

CLI Reference

Every command, flag, and output mode documented.

Configuration

rlmx.yaml format, config commands, and fallback files.

Batch Mode

Bulk interrogation, caching, and cost estimation.