LlamaGym
Tool for fine-tuning LLM agents using reinforcement learning
🛡️ AgentReady threat assessment
MAESTRO 7-layer threat model + OWASP AIVSS risk score for LlamaGym, derived from its capabilities.
These scores are auto-generated from public information (the agent's own listing, docs, and repository) using the canonical OWASP AIVSS formula and the MAESTRO framework — an estimate for guidance, not a penetration test, audit, or certification. See the scoring methodology — every score is re-derived by the same automated method as an agent's public evidence changes.
Overview
LlamaGym is an innovative tool designed to simplify the process of fine-tuning large language model (LLM) agents through reinforcement learning. It provides a standardized environment for LLM agents, similar to how OpenAI's Gym standardized reinforcement learning environments. The platform allows users to easily experiment with and iterate on agent prompts and hyperparameters.
Key features and capabilities
- AGENT ABSTRACTION CLASS,
- REINFORCEMENT LEARNING LOOP,
- HYPERPARAMETER TUNING,
- MULTI-ENVIRONMENT SUPPORT,
- EASY EXPERIMENTATION,
- OPENAI GYM COMPATIBILITY,
- SIMPLIFIED RL IMPLEMENTATION
Use cases
- LLM AGENT FINE-TUNING,
- REINFORCEMENT LEARNING RESEARCH,
- AI MODEL OPTIMIZATION,
- CHATBOT ENHANCEMENT,
- CUSTOM AI AGENT DEVELOPMENT