A declarative language for defining AI agents, their capabilities, and tools. Think of ADL as "OpenAPI for AI Agents" - a standardized specification that enables consistent agent definition, documentation, and code generation across platforms. ADL simplifies agent development by generating production-ready code from a single manifest. Build faster. Deploy smarter. Stay consistent.
ADL (Agent Definition Language) is a unified, declarative specification language designed to address the growing complexity and fragmentation in the AI agent ecosystem. Just as OpenAPI revolutionized API development by providing a standard way to describe REST services, ADL does the same for AI agents.
Inspired by the pain points observed in Agent-to-Agent (A2A) communication and Model Context Protocol (MCP) implementations, ADL was created to solve the fundamental challenges of agent interoperability and standardization.
Following Domain Driven Design principles, ADL allows you to model your agent's domain logic, capabilities, and behaviors in a vendor-agnostic way. As the AI industry experiences explosive growth with new tools, frameworks, and vendors emerging daily, ADL provides a standardized approach to define, configure, and deploy AI agents across different platforms and environments while maintaining clear domain boundaries and consistent terminology.
Here's a simple ADL manifest that defines a customer support agent:
# customer-support-agent.adl
apiVersion: adl.dev/v1
kind: Agent
metadata:
name: customer-support
description: "AI agent for handling customer inquiries and support requests"
version: "1.0.0"
spec:
capabilities:
streaming: true
pushNotifications: true
stateTransitionHistory: true
agent:
provider: openai
model: gpt-4o
systemPrompt: |
You are a professional customer support agent specialized in helping customers with their inquiries and issues.
You provide empathetic, accurate, and helpful responses while maintaining a professional tone.
You can search knowledge bases, analyze customer sentiment, and escalate issues when necessary.
maxTokens: 4096
temperature: 0.3
tools:
- name: knowledge_search
description: "Search the company knowledge base for relevant information"
schema:
type: object
properties:
query:
type: string
description: "The search query"
category:
type: string
description: "Knowledge base category to search in"
enum: ["faq", "troubleshooting", "policies", "products"]
required: ["query"]
- name: sentiment_analysis
description: "Analyze customer message sentiment"
schema:
type: object
properties:
message:
type: string
description: "The customer message to analyze"
required: ["message"]
- name: create_ticket
description: "Create a support ticket for escalation"
schema:
type: object
properties:
title:
type: string
description: "Ticket title"
description:
type: string
description: "Detailed description of the issue"
priority:
type: string
description: "Ticket priority level"
enum: ["low", "medium", "high", "urgent"]
default: "medium"
customer_id:
type: string
description: "Customer identifier"
required: ["title", "description", "customer_id"]
server:
port: 8080
debug: false
auth:
enabled: true
language:
go:
module: "github.com/company/customer-support-agent"
version: "1.22"From this single manifest, ADL generates:
- Production-ready code for multiple platforms (Python, TypeScript, Go)
- API documentation with OpenAPI specs
- Configuration files for deployment (Docker, Kubernetes)
- Test suites with conversation flows
- Monitoring dashboards with key metrics
The AI industry is evolving at breakneck speed. Every day brings:
- New AI tools and platforms flooding the market
- Vendor-specific implementations that create lock-in
- Inconsistent interfaces across different AI services
- Complex integration challenges when combining multiple AI tools
- Rapid obsolescence of custom implementations
This rapid proliferation creates significant challenges for developers and organizations:
- Development Fragmentation: Each AI vendor requires different integration approaches
- Maintenance Overhead: Keeping up with constantly changing APIs and interfaces
- Vendor Lock-in: Tight coupling to specific platforms limits flexibility
- Inconsistent Quality: Ad-hoc implementations lead to unreliable agent behavior
- Knowledge Silos: Expertise becomes scattered across numerous specialized tools
ADL addresses these challenges by providing:
🎯 Standardization: A unified language that abstracts away vendor-specific implementations while maintaining the flexibility to leverage unique capabilities.
⚡ Rapid Development: Generate production-ready agent code from a single, readable manifest instead of writing boilerplate for each platform.
🔄 Portability: Define once, deploy anywhere. Switch between AI providers without rewriting your agent logic.
📈 Consistency: Ensure reliable, predictable agent behavior across different environments and deployments.
🛡️ Future-Proofing: Adapt to new AI tools and vendors by updating the ADL compiler, not your agent definitions.
🔧 Maintainability: Centralized configuration makes it easy to update, version, and manage complex agent ecosystems.
In an era where AI capabilities are advancing monthly and new vendors emerge weekly, ADL provides the stability and abstraction layer that organizations need to:
- Innovate Rapidly: Focus on agent logic and business value rather than integration complexity
- Reduce Risk: Avoid vendor lock-in while leveraging best-of-breed AI services
- Scale Efficiently: Deploy consistent agent behaviors across multiple platforms and environments
- Adapt Quickly: Respond to new AI capabilities without architectural rewrites
ADL transforms the chaotic landscape of AI tooling into a manageable, standardized development experience, enabling teams to build sophisticated AI agents that can evolve with the industry rather than being left behind by it.
Note: This project is independently developed and is not backed by any venture capital or corporate interests, ensuring that ADL remains focused on developer needs rather than investor demands.