by SALT-NLP · Agent Tool · ★ 128
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Latest News 🔥 [2025/12] 📢 New Release: Real User Trajectories Dataset We’ve released a subset of real user trajectories collected through our platform to support research on human-agent collaboration. Download the dataset here! [2025/02] 🎉 Co-Gym (real) is now publicly available! Visit our website to use collaborative agents for travel planning and tabular analysis. We've also open-sourced our UI components for developers to build upon. [2025/01] Initial release with arXiv preprint (check out our Twitter thread for walkthrough video). We are working on releasing Co-Gym (real) web preview for in-the-wild study, alongside tools for developing collaborative agents locally. What is Collaborative Gym? (Use Collaborative Agents Now!) Collaborative Gym (Co-Gym) is a framework for enabling and evaluating Human-Agent Collaboration. Unlike traditional chatbots, Co-Gym allows both human users and AI agents to not only exchange messages but also take actions in a shared workspace. Additionally, unlike autonomous agents that take
| Stars | 128 |
| Forks | 19 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 72.0550895991402/100 |
| Open Issues | 1 |
| Last Updated | 2026-04-30 |
| Created | 2024-12-18 |
| Platforms | python |
| Est. Tokens | ~1251k |
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collaborative-gym is Framework and toolkits for building and evaluating collaborative agents that can work together with humans.. It is categorized as a Agent Tool with 128 GitHub stars.
collaborative-gym is primarily written in Python. It covers topics such as agent, human-agent-collaboration, human-agent-interaction.
You can find installation instructions and usage details in the collaborative-gym GitHub repository at github.com/SALT-NLP/collaborative-gym. The project has 128 stars and 19 forks, indicating an active community.
collaborative-gym is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to collaborative-gym on Agent Skills Hub include Agent-HTML, LLM-Tool-Survey, CoAgents. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.