KhoomeiK/LlamaGym

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1254 stars · 65 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

LlamaGym is a Python library that simplifies fine-tuning LLM-based agents with online reinforcement learning in Gym-style environments. It provides a single Agent abstract class that handles conversation context, episode batching, reward assignment, and PPO setup.

Use cases

• fine-tune an LLM agent with reinforcement learning in a Gym environment
• train an LLM to play blackjack via RL
• experiment with agent prompts and hyperparameters across RL environments
• simplify PPO setup for LLM agents
• build agents that learn online from reward signals

When to choose

• you want to fine-tune an LLM agent with RL in a Gymnasium environment without writing boilerplate
• you want a minimal abstract class to iterate on agent prompting and hyperparameters
• you already have a Gym environment and an LLM you want to train

When to avoid

• you need a production-ready, actively maintained RL training framework
• you want offline fine-tuning without reinforcement learning
• you need multi-agent or non-Gym training setups

Facets

library · maturity experimental

reinforcement-learning llm-training agent-framework machine-learning reinforcement-learning large-language-models machine-learning python gym-environments fine-tuning ppo abstract-class openai-gym ai-agents

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markdown · JSON · MCP: product_card(name="KhoomeiK/LlamaGym")