by nextmoca · Agent Tool · ★ 102
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ADL — Agent Definition Language A vendor-neutral, open standard for defining AI agents. Developed and maintained by Next Moca Global, Inc. 🚀 Overview For questions or contributions, visit: https://github.com/nextmoca/adl/issues Getting Started ADL (Agent Definition Language) is an open, declarative, vendor-neutral specification for defining AI agents in a consistent, auditable, and interoperable way. It provides a shared language for describing: an agent’s identity and purpose its tools and capabilities its LLM configuration its access to knowledge (RAG) its permissions and sandbox its dependencies its governance metadata If OpenAPI defines APIs, ADL defines agents. 🧠 Why ADL Exists Enterprises adopting AI agents face several systemic challenges: Each vendor defines “agents” differently Tool contracts are inconsistent RAG pipelines are wired differently across apps Permissions are rarely explicit Governance teams have no centralized visibility Age
| Stars | 102 |
| Forks | 5 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 66.270778279782/100 |
| Open Issues | 3 |
| Last Updated | 2026-05-29 |
| Created | 2025-12-04 |
| Platforms | python |
| Est. Tokens | ~4k |
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adl is ADL (Agent Definition Language) is a vendor-neutral, declarative standard for defining AI agents, including their tools, LLM settings, RAG inputs, permissions, and dependencies. It brings consistency,. It is categorized as a Agent Tool with 102 GitHub stars.
adl is primarily written in Python.
You can find installation instructions and usage details in the adl GitHub repository at github.com/nextmoca/adl. The project has 102 stars and 5 forks, indicating an active community.
The top alternatives to adl on Agent Skills Hub include claude-skills-marketplace, tutor-skills, lenny-skills. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.