Get a flexible RL bridge for LLM-based agents with AReaL, designed for founders, with 5.5k+ GitHub stars
5,537 stars557 forksPythonQuality 9/10Updated 7/14/2026100% free ยท open source
What it does
AReaL provides a flexible reinforcement learning bridge for large language model-based agents, allowing founders to build and train custom AI agents
Install / run
pip install -r requirements.txt
When to use it
โขWhen you need to integrate reinforcement learning with large language models for complex decision-making tasks
โขWhen you want to build custom AI agents that can learn from interactions and adapt to new situations
โขWhen you require a flexible and modular framework for building and training AI agents
Quick start
1Clone the AReaL repository using `git clone https://github.com/areal-project/AReaL.git`
2Navigate to the AReaL directory using `cd AReaL`
3Install the required dependencies using `pip install -r requirements.txt`
4Run the example script using `python examples/train_llm_agent.py` to train an LLM-based agent
5Modify the `config.yaml` file to customize the agent's architecture and training parameters
Ready-to-paste prompt
python examples/train_llm_agent.py --env CartPole-v1 --model-size large --training-steps 10000
Heads up: Make sure you have the necessary GPU resources and a compatible Python version (Python 3.8 or later) to run the AReaL framework, as specified in the README
Saves to your device
Topics
agent
llm
llm-agent
llm-reasoning
machine-learning-systems
mlsys
reinforcement-learning
rl
What's inside โ free to inspect
No purchase needed
Read the entire source before you build โ unlike paid marketplaces that hide it behind a buy button.