alexzhang13/rlm-minimal

• highreadme#1

  Reposition the README's opening to clarify RLM focus

  Why:

  CURRENT

  # Recursive Language Models (minimal version) 
  
  Link to the official RLM codebase
  
  Link to the paper
  
  Link to the original blogpost 📝
  
  I received a lot of requests to put out a notebook or gist version of the codebase I've been using. Sadly it's a bit entangled with a bunch of random state, cost, and code execution tracking logic that I want to clean up while I run other experiments. In the meantime, I've re-written a simpler version of what I'm using so people can get started building on top and writing their own RLM implementations. Happy hacking!
  
  I've provided a basic, minimal implementation of a recursive language model (RLM) with a REPL environment for OpenAI clients. Like the blogpost, we only implement recursive sub-calls with `depth=1` inside the RLM environment. Enabling further depths is as simple as replacing the `Sub_RLM` class with the `RLM_REPL` class, but you may need to finagle the `exec`-based REPL environments to work better here (because now your sub-RLMs have their own REPL environments!).
  
  In this stripped implementation, we exclude a lot of the logging, cost tracking, prompting, and REPL execution details of the experiments run in the blogpost. It's relatively easy to modify and build on top of this code to reproduce those results, but it's currently harder to go from my full codebase to supporting any new functionality.

  COPY-PASTE FIX

  Copy

  # Recursive Language Models (minimal version)
  
  This repository provides a super basic, minimal implementation of Recursive Language Models (RLMs) with REPL environments, specifically designed for experimentation and learning about recursive LLM agents. It's a stripped-down, gist-like version of a more complex codebase, focusing on the core concepts of recursive sub-calls with `depth=1` for OpenAI clients. This project is ideal for developers and researchers looking to quickly get started building and understanding their own RLM implementations without the overhead of logging, cost tracking, or complex prompting.
  
  Link to the official RLM codebase
  
  Link to the paper
  
  Link to the original blogpost 📝

  OpenDoneDismiss
• mediumhomepage#2

  Add a homepage link to the official RLM resources

  Why:

  COPY-PASTE FIX

  Copy

  https://your-official-rlm-blogpost-or-project-page.com

  OpenDoneDismiss

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

  How to implement a basic recursive language model for experimentation and learning?

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  1. PyTorch (pytorch/pytorch)
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  3. Keras (keras-team/keras)
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  Looking for a minimal Python example of recursive LLM agents with REPL capabilities.

  you: not recommended
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  1. LangChain (langchain-ai/langchain)
  2. AutoGPT (Significant-Gravitas/AutoGPT)
  3. BabyAGI (yoheinakajima/babyagi)
  4. CrewAI (joaomdmoura/crewai)
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