lm
★ 91
vs
on-policy
★ 2.0k
lm vs on-policy
lm: Houtini LM connects Claude Code to a local LLM, offloading bounded tasks like boilerplate generation, code review, and commit messages to a free, private local model, while Claude handles complex reasoning. It tracks token savings and supports various local LLM backends.; on-policy: This repository implements MAPPO, a multi-agent variant of PPO, widely used in cooperative multi-agent games and research. It provides robust implementations for various multi-agent environments like StarCraft II, Hanabi, and Google Research Football, along with detailed training scripts and hyperparameter guidance.
01
TL;DR
Choose lm if…Generate boilerplate, test stubs, and documentation
Choose on-policy if…Research and experimentation in cooperative multi-agent reinforcement learning
02
Side-by-Side Comparison
03
Features
lm01Offload bounded tasks to a local LLM via Claude Code
02Token tracking with session footer for cost visibility
03Compatible with multiple local LLM backends (LM Studio, Ollama, vLLM, etc.)
04Streaming responses with 55-second soft timeout to avoid client-side timeouts
05Multiple specialized tools (chat, custom_prompt, code_task, discover, list_models)
on-policy01Implementation of MAPPO (Multi-Agent PPO)
02Support for diverse multi-agent environments (e.g., StarCraft II, Hanabi)
03Ready-to-use training scripts for various scenarios
04Detailed hyperparameter guidance and updated results
05Default support for shared policy among agents
04
Use Cases
lm↳Generate boilerplate, test stubs, and documentation
↳Perform code review, explanation, and format conversion
↳Draft commit messages and brainstorm approaches
on-policy↳Research and experimentation in cooperative multi-agent reinforcement learning
↳Benchmarking and evaluating PPO's effectiveness in MARL scenarios
↳Training AI agents for popular multi-agent games like StarCraft II and Hanabi
05
Best For
lmTrendingAPI IntegrationDev Tooling
on-policyTrendingReinforcement LearningMulti-Agent AI
FAQ
FAQ
What is the difference between lm and on-policy?
Both lm and on-policy are in the LLM Infra category. lm has 91 stars, while on-policy has 2.0k stars.
Which is better, lm or on-policy?
The best choice depends on your use case. Choose lm if Generate boilerplate, test stubs, and documentation, and on-policy if Research and experimentation in cooperative multi-agent reinforcement learning.
Is lm free or open source?
Yes, lm is open source on GitHub (MIT).
Is on-policy free or open source?
Yes, on-policy is open source on GitHub (MIT).
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