A unified and easy-to-extend tool-agent training framework based on verl, supporting diverse tool use scenarios and enabling AI agents to be trained via reinforcement learning for tool-calling capabilities.
One-Minute Overview#
VerlTool is a specialized reinforcement learning framework for training AI agents capable of using tools. It provides developers with a modular platform to easily integrate various tools and train agents to solve problems using these tools. If you're an AI researcher or engineer looking to build intelligent systems that can interact with their environment, VerlTool enables you to achieve this without building complex training pipelines from scratch.
Core Value: Provides a complete tool-environment interaction paradigm, supporting the full pipeline of reinforcement learning for tool-calling agents.
Quick Start#
Installation Difficulty: Medium - Requires installing multiple dependencies including verl, vllm, and SGLang, with reinforcement learning knowledge recommended
# Clone repository
git clone https://github.com/TIGER-AI-Lab/verl-tool.git
cd verl-tool
# Install dependencies
pip install -e .
Is this suitable for me?
- ✅ Researching tool-agent reinforcement learning: Need to train AI models that can use tools
- ✅ Building agents that interact with external systems: Need agents that can modify environment states and act based on feedback
- ❌ Simple LLM application development: No reinforcement learning training needed, just direct LLM API usage
- ❌ Beginner projects: Requires prior knowledge of reinforcement learning and LLMs
Core Capabilities#
1. Tool-Environment Decoupled Architecture - Simplified Tool Integration#
- Complete decoupling of actor rollout and environment interaction, with all tool integration via a unified API Actual Value: Adding new tools only requires creating a Python file, no core code modifications needed, significantly improving development efficiency
2. Tool-as-Environment Paradigm - State Management Support#
- Each tool interaction can modify the environment state, with system storing and reloading environment states for each trajectory Actual Value: Agents can perform multi-round interactions, remembering environmental changes, suitable for complex task scenarios
3. Native RL Framework - Optimized Training Pipeline#
- Natively supports multi-turn interactive loops between agents and their tool environments Actual Value: Training algorithms specifically optimized for tool-calling scenarios, improving training efficiency and effectiveness
4. Asynchronous RL Training - Accelerated Training Process#
- Supports trajectory-level asynchronous training, speeding up rollout generation with tool calling by at least 2x Actual Value: Significantly reduces training time, making large-scale tool-agent training feasible
Tech Stack & Integration#
Development Language: Python (87.1%), Shell (11.5%) Main Dependencies: verl (submodule), vllm, SGLang Integration Method: API / SDK / Library
Maintenance Status#
- Development Activity: Very active, with multiple commits per week
- Recent Updates: Continuously updated through 2025, including multiple new feature additions
- Community Response: Actively responds to issues and contributions, with clear contribution guidelines
Commercial & Licensing#
License: MIT
- ✅ Commercial: Allowed
- ✅ Modification: Allowed
- ⚠️ Restrictions: Must include original license and copyright notices
Documentation & Learning Resources#
- Documentation Quality: Comprehensive
- Official Documentation: GitHub Repository
- Example Code: Includes multiple training recipes and examples