Adl

<p align="center"> <img src="https://img.shields.io/badge/License-Apache_2.0-blue.svg" alt="License"/> <a href="https://github.com/nextmoca/adl"> <img src="https://img.shields.io/badge/ADL-GitHub-black?logo=github" alt="GitHub Repo"/> </a> <img src="https://img.shields.io/badge/Schema-Validated-brightgreen" alt="Schema Status"/> </p> <h1 align="center">ADL — Agent Definition Language</h1> <p align="center"><strong>A vendor-neutral, open standard for defining AI agents.</strong></p>

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🚀 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.

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🧠 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
• Agents are not portable across platforms
• Vendor lock-in slows enterprise adoption
• Compliance and auditability are fragile or impossible

ADL solves these problems by introducing a single, declarative, versioned artifact that describes what an agent is and what it is allowed to do.

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🧩 What ADL Is

ADL defines:

• Identity — name, description, role, owner, version
• LLM Settings — provider, model, temperature, max tokens
• Tools & Actions — typed parameters, descriptions, return schemas
• RAG Inputs — indices, types, metadata, paths
• Permissions — file I/O, network, env vars
• Dependencies — Python packages with optional version pins
• Governance — created_by, approved_by, timestamps, version notes

This makes agents:

• portable
• predictable
• auditable
• reproducible
• interoperable across vendors

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🚫 What ADL Is Not

To avoid confusion, ADL explicitly does not define:

• ❌ A2A (agent-to-agent) communication protocols
• ❌ runtime tool invocation semantics (e.g., MCP)
• ❌ prompt templating or formatting
• ❌ workflow orchestration (Airflow, Temporal, Dagster)
• ❌ API schemas (OpenAPI already solves that)
• ❌ message transport (HTTP, gRPC, JSON-RPC)

ADL is laser-focused on definition — not execution.

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🔍 ADL vs AI App Definition

ADL is an Agent Definition Language - not a general AI App definition format.

AI apps are broad and may include UI, API layers, deployments, data stores, or business logic.

Agents are specific:

• they reason
• they call tools
• they retrieve knowledge
• they act autonomously
• they require permission boundaries

ADL models agent competencies, not app-level infrastructure.

This is a key strategic distinction.

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🔄 Comparing ADL to Other Standards

ADL vs A2A

• A2A: defines how agents communicate
• ADL: defines what an agent is

ADL vs MCP

• MCP: runtime tool protocol
• ADL: declarative definition of tools and capabilities

ADL vs OpenAPI

• OpenAPI: describes HTTP services
• ADL: describes agent behavior, boundaries, and capabilities

ADL vs Workflow Engines

• Workflows = when & how tasks execute
• ADL = which agent executes them

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🌐 Why Next Moca Open Sourced ADL

Next Moca open-sourced ADL under Apache 2.0 to enable:

✔ Ecosystem-wide interoperability

✔ Enterprise trust and transparency

✔ Neutral governance

✔ Community-driven evolution

✔ Vendor adoption without lock-in

✔ Safe, compliant, standards-based agent deployment

Open sourcing ensures ADL becomes a true standard, not a proprietary configuration format.

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📘 Documentation

• 📄 ADL Spec (JSON Schema) — /schema/adl.schema.json
• 📚 Examples — /examples/
• 📐 Roadmap — /ROADMAP.md
• 🏛 Governance — /GOVERNANCE.md
• 🤝 Contributing — /CONTRIBUTING.md

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🛠 Getting Started

git clone https://github.com/nextmoca/adl.git
cd adl

Validate an ADL Agent File

pip install jsonschema
python tools/validate.py examples/minimal_agent.json

Or using Node:

npm install ajv
node tools/validate.js examples/minimal_agent.json

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Minimal Example

{
  "name": "campaign_image_generator",
  "description": "Generate a 1024x1024 marketing image from a creative brief.",
  "role": "Creative Producer",
  "llm": "openai",
  "llm_settings": {
    "temperature": 0,
    "max_tokens": 4096
  },
  "tools": [
    {
      "name": "generate_campaign_image",
      "description": "Generate a high-quality image from a prompt.",
      "parameters": [
        {
          "name": "prompt",
          "type": "string",
          "description": "Image prompt",
          "required": true
        }
      ],
      "invocation": { "type": "python_function" }
    }
  ],
  "rag": []
}

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Contributing

We welcome contributions!
Please see CONTRIBUTING.md for guidelines on RFCs, schema updates, and tooling improvements.

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License

Licensed under the Apache License, Version 2.0.

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About Next Moca

Next Moca is the enterprise platform for multi-agent workflows, agent orchestration, RAG pipelines, governance, and AI system observability. ADL is the foundation for how agents are defined consistently across the ecosystem.