Agent Registry & Metadata

{
  "name": "image-classifier",
  "display_name": "Image Classifier",
  "version": "1.2.0",
  "description": "Classifies images into categories using a pre-trained neural network",
  "type": "docker",
  "container": {
    "image": "registry.example.com/agents/image-classifier:1.2.0",
    "tag": "1.2.0"
  },
  "author": {
    "name": "AI Platform Team",
    "email": "ai-platform@example.com",
    "url": "https://github.com/example/image-classifier"
  },
  "license": "MIT",
  "tags": ["vision", "classification", "neural-network", "image-processing"],
  "categories": ["Computer Vision", "Machine Learning"],
  "visibility": "public",
  "input_schema": {
    "type": "object",
    "required": ["image"],
    "properties": {
      "image": {
        "type": "string",
        "format": "binary",
        "description": "Image to classify (JPEG or PNG)"
      },
      "top_k": {
        "type": "integer",
        "description": "Number of top categories to return",
        "default": 5,
        "minimum": 1,
        "maximum": 20
      },
      "threshold": {
        "type": "number",
        "description": "Confidence threshold for returned categories",
        "default": 0.2,
        "minimum": 0.0,
        "maximum": 1.0
      }
    }
  },
  "output_schema": {
    "type": "object",
    "properties": {
      "categories": {
        "type": "array",
        "items": {
          "type": "object",
          "properties": {
            "label": {
              "type": "string",
              "description": "Category label"
            },
            "confidence": {
              "type": "number",
              "description": "Confidence score between 0 and 1"
            }
          }
        }
      },
      "processing_time": {
        "type": "number",
        "description": "Processing time in milliseconds"
      }
    }
  },
  "modalities": ["image"],
  "resources": {
    "memory": "1Gi",
    "cpu": "0.5",
    "gpu": "0.0"
  },
  "documentation_url": "https://docs.example.com/agents/image-classifier",
  "repository_url": "https://github.com/example/image-classifier",
  "url": "https://agents.example.com/image-classifier/a2a", // NEW: A2A endpoint
  "provider": { // NEW: Structured provider info
    "name": "AI Platform Team",
    "contact": "ai-platform@example.com",
    "url": "https://github.com/example/image-classifier"
  },
  "capabilities": { // NEW: Capabilities block
    "streaming": true,
    "pushNotifications": false,
    "stateTransitionHistory": true
  },
  "authentication": { // NEW: Authentication block
    "type": "apiKey",
    "description": "API key required in Authorization header"
  },
  "default_input_modes": ["image"], // NEW: Default input modes
  "default_output_modes": ["categories"], // NEW: Default output modes
  "skills": [ // NEW: Skills block
    {
      "id": "classify-image",
      "name": "Classify Image",
      "description": "Classifies an input image and returns the top categories.",
      "tags": ["vision", "classification"],
      "examples": ["Classify a photo of a cat"],
      "inputModes": ["image"],
      "outputModes": ["categories"]
    }
  ],
  "changelog": [
    {
      "version": "1.2.0",
      "date": "2025-03-15",
      "changes": ["Added support for PNG images", "Improved classification accuracy", "Reduced memory usage"]
    },
    {
      "version": "1.1.0",
      "date": "2025-02-01",
      "changes": ["Added top_k parameter", "Added threshold parameter"]
    },
    {
      "version": "1.0.0",
      "date": "2025-01-10",
      "changes": ["Initial release"]
    }
  ],
  "quality": {
    "test_coverage": 0.87,
    "security_scan": "passed",
    "certification": "verified"
  }
}

# Example of registering an agent using the Python SDK
from meta_agent_platform import AgentRegistry

# Initialize the registry client
registry = AgentRegistry(api_key="your-api-key")

# Load metadata from file
with open("agent-metadata.json", "r") as f:
    metadata = json.load(f)

# Register the agent
try:
    agent_id = registry.register_agent(metadata)
    print(f"Agent registered successfully with ID: {agent_id}")
except ValidationError as e:
    print(f"Validation error: {e}")
except VersionConflictError as e:
    print(f"Version conflict: {e}")

# Example of searching for agents using the Python SDK
from meta_agent_platform import AgentRegistry

# Initialize the registry client
registry = AgentRegistry(api_key="your-api-key")

# Search for image classification agents
results = registry.search_agents(
    query="image classification",
    filters={
        "type": "docker",
        "modalities": ["image"],
        "visibility": ["public", "workspace"],
        "tags": ["classification"],
        "min_quality": 0.8
    },
    sort_by="relevance",
    limit=10
)

print(f"Found {len(results)} agents:")
for agent in results:
    print(f"- {agent.display_name} (v{agent.version}): {agent.description}")

query FindImageClassifiers {
  agents(
    filter: {
      type: "docker",
      modalities: ["image"],
      tags_include: ["classification"],
      visibility_in: [PUBLIC, WORKSPACE]
    },
    sort: { field: QUALITY, direction: DESC },
    first: 10
  ) {
    edges {
      node {
        id
        name
        displayName
        version
        description
        author {
          name
          url
        }
        quality {
          testCoverage
          securityScan
          certification
        }
        inputSchema
        outputSchema
        modalities
        tags
      }
    }
    pageInfo {
      hasNextPage
      endCursor
    }
  }
}

# Example of selecting a specific agent version
from meta_agent_platform import AgentRegistry

# Initialize the registry client
registry = AgentRegistry(api_key="your-api-key")

# Get the latest version
latest_agent = registry.get_agent("image-classifier")

# Get a specific version
specific_version = registry.get_agent("image-classifier", version="1.1.0")

# Get the latest patch of a minor version
minor_version = registry.get_agent("image-classifier", version="1.1")

# Get the latest minor of a major version
major_version = registry.get_agent("image-classifier", version="1")

# Example of using an agent from the registry in a workflow
from meta_agent_platform import AgentRegistry, Workflow

# Initialize the registry client
registry = AgentRegistry(api_key="your-api-key")

# Get agents for the workflow
image_classifier = registry.get_agent("image-classifier")
text_generator = registry.get_agent("text-generator")

# Create a workflow
workflow = Workflow("image-description-workflow")

# Add agents to the workflow
workflow.add_agent("classifier", image_classifier)
workflow.add_agent("generator", text_generator)

# Connect agents
workflow.connect(
    source="classifier.output.categories",
    target="generator.input.context"
)

# Set workflow input mapping
workflow.set_input_mapping({
    "image": "classifier.input.image"
})

# Set workflow output mapping
workflow.set_output_mapping({
    "description": "generator.output.text",
    "categories": "classifier.output.categories"
})

# Save the workflow
workflow_id = workflow.save()
print(f"Workflow created with ID: {workflow_id}")