AReaL: Accelerate LLM Agent Development with Reinforcement Learning
From the repo · “The RL Bridge for LLM-based Agent Applications. Made Simple & Flexible.”
AReaL provides a flexible reinforcement learning bridge for large language model-based agents, allowing founders to build and train custom AI agents
- •When you need to integrate reinforcement learning with large language models for complex decision-making tasks
- •When you want to build custom AI agents that can learn from interactions and adapt to new situations
- •When you require a flexible and modular framework for building and training AI agents
python examples/train_llm_agent.py --env CartPole-v1 --model-size large --training-steps 10000
Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.
Installs with a command or two; your AI agent can do it for you.
mkdir -p ~/.claude/skills/areal && curl -fsSL https://workflowstacks.com/api/skills/areal/claude-skill -o ~/.claude/skills/areal/SKILL.mdOpens the app with this repo with the prompt ready to go — no copy-paste needed.
AReaL: Accelerate LLM Agent Development with Reinforcement Learning is a very large Python project (~159k lines across 624 code files, plus 387 test files). Setup is light: installs like a normal app. Reading the code is optional. Last commit this month, Apache-2.0 license, has a test suite.
- 1README.mdStart here — what it does and how to install it
- 2AGENTS.mdThe instructions the AI actually follows
- 3pyproject.tomlDependencies and the commands it exposes
- 4areal/__init__.pyInside areal/ — the main logic begins here
- 5examples/__init__.pyA worked example — copy this to get going
Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.
Installs with a command or two; your AI agent can do it for you.
mkdir -p ~/.claude/skills/areal && curl -fsSL https://workflowstacks.com/api/skills/areal/claude-skill -o ~/.claude/skills/areal/SKILL.mdOpens the app with this repo with the prompt ready to go — no copy-paste needed.
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