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AOP (Agent Orchestration Protocol)

The Agent Orchestration Protocol (AOP) is a powerful framework for deploying multiple Swarms agents as tools in an MCP (Model Context Protocol) server. This enables you to create a distributed system where agents can be accessed as individual tools, making them available for use by other systems, applications, or clients.

AOP provides two main classes:

AOP: Deploy agents as tools in a single MCP server
AOPCluster: Connect to and manage multiple MCP servers

Core Classes

AgentToolConfig

Configuration dataclass for converting an agent to an MCP tool.

Attribute	Type	Default	Description
`tool_name`	`str`	Required	The name of the tool in the MCP server
`tool_description`	`str`	Required	Description of what the tool does
`input_schema`	`Dict[str, Any]`	Required	JSON schema for the tool's input parameters
`output_schema`	`Dict[str, Any]`	Required	JSON schema for the tool's output
`timeout`	`int`	`30`	Maximum time to wait for agent execution (seconds)
`max_retries`	`int`	`3`	Number of retries if agent execution fails
`verbose`	`bool`	`False`	Enable verbose logging for this tool
`traceback_enabled`	`bool`	`True`	Enable traceback logging for errors

Task

Represents a task to be executed by an agent.

Attribute	Type	Default	Description
`task_id`	`str`	Auto-generated	Unique identifier for the task
`task`	`str`	`""`	The task or prompt to execute
`img`	`Optional[str]`	`None`	Optional image to be processed
`imgs`	`Optional[List[str]]`	`None`	Optional list of images to be processed
`correct_answer`	`Optional[str]`	`None`	Optional correct answer for validation
`priority`	`int`	`0`	Task priority (higher number = higher priority)
`created_at`	`float`	Auto-generated	Timestamp when task was created
`status`	`TaskStatus`	`PENDING`	Current status of the task
`result`	`Optional[str]`	`None`	Result of task execution
`error`	`Optional[str]`	`None`	Error message if task failed
`retry_count`	`int`	`0`	Number of times task has been retried
`max_retries`	`int`	`3`	Maximum number of retries allowed

TaskStatus

Enum representing the status of a task in the queue.

Value	Description
`PENDING`	Task is waiting to be processed
`PROCESSING`	Task is currently being processed
`COMPLETED`	Task has been completed successfully
`FAILED`	Task failed during execution
`CANCELLED`	Task was cancelled

QueueStatus

Enum representing the status of a task queue.

Value	Description
`RUNNING`	Queue is actively processing tasks
`PAUSED`	Queue is paused and not processing tasks
`STOPPED`	Queue is stopped

QueueStats

Statistics for a task queue.

Attribute	Type	Default	Description
`total_tasks`	`int`	`0`	Total number of tasks processed
`completed_tasks`	`int`	`0`	Number of successfully completed tasks
`failed_tasks`	`int`	`0`	Number of failed tasks
`pending_tasks`	`int`	`0`	Number of tasks currently pending
`processing_tasks`	`int`	`0`	Number of tasks currently being processed
`average_processing_time`	`float`	`0.0`	Average time to process a task
`queue_size`	`int`	`0`	Current size of the queue

TaskQueue

A thread-safe task queue for managing agent tasks.

Key Features:

Priority-based task ordering
Configurable worker threads
Automatic retry logic
Comprehensive statistics tracking
Thread-safe operations

Methods:

add_task() - Add a task to the queue
get_stats() - Get queue statistics
pause_workers() - Pause queue processing
resume_workers() - Resume queue processing
clear_queue() - Clear all pending tasks
get_status() - Get current queue status

AOP Class

Main class for deploying agents as tools in an MCP server.

Constructor Parameters

Parameter	Type	Default	Description
`server_name`	`str`	`"AOP Cluster"`	Name for the MCP server
`description`	`str`	`"A cluster that enables you to deploy multiple agents as tools in an MCP server."`	Server description
`agents`	`any`	`None`	Optional list of agents to add initially
`port`	`int`	`8000`	Port for the MCP server
`transport`	`str`	`"streamable-http"`	Transport type for the MCP server
`verbose`	`bool`	`False`	Enable verbose logging
`traceback_enabled`	`bool`	`True`	Enable traceback logging for errors
`host`	`str`	`"localhost"`	Host to bind the server to
`queue_enabled`	`bool`	`True`	Enable queue-based task execution for improved performance
`max_workers_per_agent`	`int`	`1`	Maximum number of worker threads per agent
`max_queue_size_per_agent`	`int`	`1000`	Maximum queue size per agent
`processing_timeout`	`int`	`30`	Timeout for processing tasks in seconds
`retry_delay`	`float`	`1.0`	Delay between retries in seconds
`log_level`	`str`	`"INFO"`	Logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
`*args`	`Any`	-	Additional positional arguments passed to FastMCP
`**kwargs`	`Any`	-	Additional keyword arguments passed to FastMCP

Methods

add_agent()

Add an agent to the MCP server as a tool.

Parameter	Type	Default	Description
`agent`	`AgentType`	Required	The swarms Agent instance to deploy
`tool_name`	`str`	`None`	Name for the tool (defaults to agent.agent_name)
`tool_description`	`str`	`None`	Description of the tool (defaults to agent.agent_description)
`input_schema`	`Dict[str, Any]`	`None`	JSON schema for input parameters
`output_schema`	`Dict[str, Any]`	`None`	JSON schema for output
`timeout`	`int`	`30`	Maximum execution time in seconds
`max_retries`	`int`	`3`	Number of retries on failure
`verbose`	`bool`	`None`	Enable verbose logging for this tool
`traceback_enabled`	`bool`	`None`	Enable traceback logging for this tool

Returns: str - The tool name that was registered

add_agents_batch()

Add multiple agents to the MCP server as tools in batch.

Parameter	Type	Default	Description
`agents`	`List[Agent]`	Required	List of swarms Agent instances
`tool_names`	`List[str]`	`None`	Optional list of tool names
`tool_descriptions`	`List[str]`	`None`	Optional list of tool descriptions
`input_schemas`	`List[Dict[str, Any]]`	`None`	Optional list of input schemas
`output_schemas`	`List[Dict[str, Any]]`	`None`	Optional list of output schemas
`timeouts`	`List[int]`	`None`	Optional list of timeout values
`max_retries_list`	`List[int]`	`None`	Optional list of max retry values
`verbose_list`	`List[bool]`	`None`	Optional list of verbose settings
`traceback_enabled_list`	`List[bool]`	`None`	Optional list of traceback settings

Returns: List[str] - List of tool names that were registered

remove_agent()

Remove an agent from the MCP server.

Parameter	Type	Description
`tool_name`	`str`	Name of the tool to remove

Returns: bool - True if agent was removed, False if not found

list_agents()

Get a list of all registered agent tool names.

Returns: List[str] - List of tool names

get_agent_info()

Get information about a specific agent tool.

Parameter	Type	Description
`tool_name`	`str`	Name of the tool

Returns: Dict[str, Any] - Agent information, or None if not found

start_server()

Start the MCP server.

run()

Run the MCP server (alias for start_server).

get_server_info()

Get information about the MCP server and registered tools.

Returns: Dict[str, Any] - Server information

_register_tool()

Parameter	Type	Description
`tool_name`	`str`	Name of the tool to register
`agent`	`AgentType`	The agent instance to register

_execute_agent_with_timeout()

Execute an agent with a timeout and all run method parameters (internal method).

Parameter	Type	Default	Description
`agent`	`AgentType`	Required	The agent to execute
`task`	`str`	Required	The task to execute
`timeout`	`int`	Required	Maximum execution time in seconds
`img`	`str`	`None`	Optional image to be processed by the agent
`imgs`	`List[str]`	`None`	Optional list of images to be processed by the agent
`correct_answer`	`str`	`None`	Optional correct answer for validation or comparison

Returns: str - The agent's response

Raises: TimeoutError if execution exceeds timeout, Exception if agent execution fails

_get_agent_discovery_info()

Get discovery information for a specific agent (internal method).

Parameter	Type	Description
`tool_name`	`str`	Name of the agent tool

Returns: Optional[Dict[str, Any]] - Agent discovery information, or None if not found

get_queue_stats()

Get queue statistics for agents including task counts, processing times, and queue status.

Parameter	Type	Default	Description
`tool_name`	`str`	`None`	Optional specific agent name. If None, returns stats for all agents.

Returns: Dict[str, Any] - Queue statistics

Response Format:

{
  "success": true,
  "agent_name": "agent_name",
  "stats": {
    "total_tasks": 10,
    "completed_tasks": 8,
    "failed_tasks": 1,
    "pending_tasks": 1,
    "processing_tasks": 0,
    "average_processing_time": 2.5,
    "queue_size": 1,
    "queue_status": "running"
  }
}

pause_agent_queue()

Pause the task queue for a specific agent.

Parameter	Type	Description
`tool_name`	`str`	Name of the agent tool

Returns: bool - True if paused successfully, False if not found

resume_agent_queue()

Resume the task queue for a specific agent.

Parameter	Type	Description
`tool_name`	`str`	Name of the agent tool

Returns: bool - True if resumed successfully, False if not found

clear_agent_queue()

Clear all pending tasks from an agent's queue.

Parameter	Type	Description
`tool_name`	`str`	Name of the agent tool

Returns: int - Number of tasks cleared, -1 if error

get_task_status()

Get the status of a specific task by task ID.

Parameter	Type	Description
`tool_name`	`str`	Name of the agent tool
`task_id`	`str`	ID of the task

Returns: Dict[str, Any] - Task status information

Response Format:

{
  "success": true,
  "task": {
    "task_id": "uuid",
    "task": "task description",
    "status": "completed",
    "result": "task result",
    "error": null,
    "retry_count": 0,
    "created_at": 1234567890.0
  }
}

Discovery Tools

AOP automatically registers several discovery tools that allow agents to learn about each other and enable dynamic agent discovery within the cluster.

Queue Management Tools

When queue-based execution is enabled, AOP automatically registers several queue management tools that allow monitoring and control of task queues.

get_queue_stats

Get queue statistics for agents including task counts, processing times, and queue status.

Parameter	Type	Default	Description
`agent_name`	`str`	`None`	Optional specific agent name. If None, returns stats for all agents.

Returns: Dict[str, Any] - Queue statistics

pause_agent_queue

Pause the task queue for a specific agent.

Parameter	Type	Description
`agent_name`	`str`	Name of the agent tool

Returns: Dict[str, Any] - Success status and message

resume_agent_queue

Resume the task queue for a specific agent.

Parameter	Type	Description
`agent_name`	`str`	Name of the agent tool

Returns: Dict[str, Any] - Success status and message

clear_agent_queue

Clear all pending tasks from an agent's queue.

Parameter	Type	Description
`agent_name`	`str`	Name of the agent tool

Returns: Dict[str, Any] - Number of tasks cleared and success status

get_task_status

Get the status of a specific task by task ID.

Parameter	Type	Description
`agent_name`	`str`	Name of the agent tool
`task_id`	`str`	ID of the task

Returns: Dict[str, Any] - Task status information

discover_agents

Discover information about agents in the cluster including their name, description, system prompt (truncated to 200 chars), and tags.

Parameter	Type	Default	Description
`agent_name`	`str`	`None`	Optional specific agent name to get info for. If None, returns info for all agents.

Returns: Dict[str, Any] - Agent information for discovery

Response Format:

{
  "success": true,
  "agents": [
    {
      "tool_name": "agent_name",
      "agent_name": "Agent Name",
      "description": "Agent description",
      "short_system_prompt": "Truncated system prompt...",
      "tags": ["tag1", "tag2"],
      "capabilities": ["capability1", "capability2"],
      "role": "worker",
      "model_name": "model_name",
      "max_loops": 1,
      "temperature": 0.5,
      "max_tokens": 4096
    }
  ]
}

get_agent_details

Get detailed information about a single agent by name including configuration, capabilities, and metadata.

Parameter	Type	Description
`agent_name`	`str`	Name of the agent to get information for.

Returns: Dict[str, Any] - Detailed agent information

Response Format:

{
  "success": true,
  "agent_info": {
    "tool_name": "agent_name",
    "agent_name": "Agent Name",
    "agent_description": "Agent description",
    "model_name": "model_name",
    "max_loops": 1,
    "tool_description": "Tool description",
    "timeout": 30,
    "max_retries": 3,
    "verbose": false,
    "traceback_enabled": true
  },
  "discovery_info": {
    "tool_name": "agent_name",
    "agent_name": "Agent Name",
    "description": "Agent description",
    "short_system_prompt": "Truncated system prompt...",
    "tags": ["tag1", "tag2"],
    "capabilities": ["capability1", "capability2"],
    "role": "worker",
    "model_name": "model_name",
    "max_loops": 1,
    "temperature": 0.5,
    "max_tokens": 4096
  }
}

get_agents_info

Get detailed information about multiple agents by providing a list of agent names.

Parameter	Type	Description
`agent_names`	`List[str]`	List of agent names to get information for.

Returns: Dict[str, Any] - Detailed information for all requested agents

Response Format:

{
  "success": true,
  "agents_info": [
    {
      "agent_name": "agent_name",
      "agent_info": { /* detailed agent info */ },
      "discovery_info": { /* discovery info */ }
    }
  ],
  "not_found": ["missing_agent"],
  "total_found": 1,
  "total_requested": 2
}

list_agents

Get a simple list of all available agent names in the cluster.

Returns: Dict[str, Any] - List of agent names

Response Format:

{
  "success": true,
  "agent_names": ["agent1", "agent2", "agent3"],
  "total_count": 3
}

search_agents

Search for agents by name, description, tags, or capabilities using keyword matching.

Parameter	Type	Default	Description
`query`	`str`	Required	Search query string
`search_fields`	`List[str]`	`["name", "description", "tags", "capabilities"]`	Optional list of fields to search in. If None, searches all fields.

Returns: Dict[str, Any] - Matching agents

Response Format:

{
  "success": true,
  "matching_agents": [
    {
      "tool_name": "agent_name",
      "agent_name": "Agent Name",
      "description": "Agent description",
      "short_system_prompt": "Truncated system prompt...",
      "tags": ["tag1", "tag2"],
      "capabilities": ["capability1", "capability2"],
      "role": "worker",
      "model_name": "model_name",
      "max_loops": 1,
      "temperature": 0.5,
      "max_tokens": 4096
    }
  ],
  "total_matches": 1,
  "query": "search_term",
  "search_fields": ["name", "description", "tags", "capabilities"]
}

AOPCluster Class

Class for connecting to and managing multiple MCP servers.

AOPCluster Constructor Parameters

Parameter	Type	Default	Description
`urls`	`List[str]`	Required	List of MCP server URLs to connect to
`transport`	`str`	`"streamable-http"`	Transport type for connections

AOPCluster Methods

get_tools()

Get tools from all connected MCP servers.

Parameter	Type	Default	Description
`output_type`	`Literal["json", "dict", "str"]`	`"dict"`	Format of the output

Returns: List[Dict[str, Any]] - List of available tools

find_tool_by_server_name()

Find a tool by its server name (function name).

Parameter	Type	Description
`server_name`	`str`	The name of the tool/function to find

Returns: Dict[str, Any] - Tool information, or None if not found

Tool Parameters

All agent tools accept the following parameters:

Parameter	Type	Required	Description
`task`	`str`	Yes	The main task or prompt to execute
`img`	`str`	No	Single image to be processed by the agent
`imgs`	`List[str]`	No	Multiple images to be processed by the agent
`correct_answer`	`str`	No	Correct answer for validation or comparison

Output Format

All agent tools return a standardized response format:

{
  "result": "string",     // The agent's response to the task
  "success": "boolean",   // Whether the task was executed successfully
  "error": "string"       // Error message if execution failed (null if successful)
}

Complete Examples

Basic Server Setup

from swarms import Agent
from swarms.structs.aop import AOP

# Create specialized agents
research_agent = Agent(
    agent_name="Research-Agent",
    agent_description="Expert in research, data collection, and information gathering",
    model_name="anthropic/claude-sonnet-4-5",
    max_loops=1,
    top_p=None,
    dynamic_temperature_enabled=True,
    system_prompt="""You are a research specialist. Your role is to:
    1. Gather comprehensive information on any given topic
    2. Analyze data from multiple sources
    3. Provide well-structured research findings
    4. Cite sources and maintain accuracy
    5. Present findings in a clear, organized manner
    
    Always provide detailed, factual information with proper context.""",
)

analysis_agent = Agent(
    agent_name="Analysis-Agent",
    agent_description="Expert in data analysis, pattern recognition, and generating insights",
    model_name="anthropic/claude-sonnet-4-5",
    max_loops=1,
    top_p=None,
    dynamic_temperature_enabled=True,
    system_prompt="""You are an analysis specialist. Your role is to:
    1. Analyze data and identify patterns
    2. Generate actionable insights
    3. Create visualizations and summaries
    4. Provide statistical analysis
    5. Make data-driven recommendations
    
    Focus on extracting meaningful insights from information.""",
)

# Create AOP instance
deployer = AOP(
    server_name="MyAgentServer",
    port=8000,
    verbose=True,
    log_level="INFO"
)

# Add agents individually
deployer.add_agent(research_agent)
deployer.add_agent(analysis_agent)

# Start the server
deployer.run()

Batch Agent Addition

from swarms import Agent
from swarms.structs.aop import AOP

# Create multiple agents
agents = [
    Agent(
        agent_name="Research-Agent",
        agent_description="Expert in research and data collection",
        model_name="anthropic/claude-sonnet-4-5",
        max_loops=1,
    ),
    Agent(
        agent_name="Writing-Agent", 
        agent_description="Expert in content creation and editing",
        model_name="anthropic/claude-sonnet-4-5",
        max_loops=1,
    ),
    Agent(
        agent_name="Code-Agent",
        agent_description="Expert in programming and code review", 
        model_name="anthropic/claude-sonnet-4-5",
        max_loops=1,
    ),
]

# Create AOP instance
deployer = AOP("MyAgentServer", verbose=True)

# Add all agents at once
tool_names = deployer.add_agents_batch(agents)

print(f"Added {len(tool_names)} agents: {tool_names}")

# Start the server
deployer.run()

Advanced Configuration with Tags and Capabilities

from swarms import Agent
from swarms.structs.aop import AOP

# Create agent with custom configuration, tags, and capabilities
research_agent = Agent(
    agent_name="Research-Agent",
    agent_description="Expert in research and data collection",
    model_name="anthropic/claude-sonnet-4-5",
    max_loops=1,
    # Add tags and capabilities for better discovery
    tags=["research", "data-collection", "analysis"],
    capabilities=["web-search", "data-gathering", "report-generation"],
    role="researcher"
)

# Create AOP with custom settings
deployer = AOP(
    server_name="AdvancedAgentServer",
    port=8001,
    host="0.0.0.0",  # Allow external connections
    verbose=True,
    traceback_enabled=True,
    log_level="DEBUG"
)

# Add agent with custom tool configuration
deployer.add_agent(
    agent=research_agent,
    tool_name="custom_research_tool",
    tool_description="Custom research tool with extended capabilities",
    timeout=60,  # 60 second timeout
    max_retries=5,  # 5 retries
    verbose=True,
    traceback_enabled=True
)

# Add custom input/output schemas
custom_input_schema = {
    "type": "object",
    "properties": {
        "task": {
            "type": "string",
            "description": "The research task to execute"
        },
        "sources": {
            "type": "array",
            "items": {"type": "string"},
            "description": "Specific sources to research"
        },
        "depth": {
            "type": "string",
            "enum": ["shallow", "medium", "deep"],
            "description": "Research depth level"
        }
    },
    "required": ["task"]
}

custom_output_schema = {
    "type": "object", 
    "properties": {
        "result": {"type": "string"},
        "sources": {"type": "array", "items": {"type": "string"}},
        "confidence": {"type": "number", "minimum": 0, "maximum": 1},
        "success": {"type": "boolean"},
        "error": {"type": "string"}
    },
    "required": ["result", "success"]
}

# Add another agent with custom schemas
analysis_agent = Agent(
    agent_name="Analysis-Agent",
    agent_description="Expert in data analysis",
    model_name="anthropic/claude-sonnet-4-5",
    max_loops=1,
)

deployer.add_agent(
    agent=analysis_agent,
    tool_name="custom_analysis_tool",
    tool_description="Custom analysis tool",
    input_schema=custom_input_schema,
    output_schema=custom_output_schema,
    timeout=45,
    max_retries=3
)

# List all registered agents
print("Registered agents:", deployer.list_agents())

# Get server information
server_info = deployer.get_server_info()
print("Server info:", server_info)

# Start the server
deployer.run()

Queue Management Configuration

from swarms import Agent
from swarms.structs.aop import AOP

# Create agents
research_agent = Agent(
    agent_name="Research-Agent",
    agent_description="Expert in research and data collection",
    model_name="anthropic/claude-sonnet-4-5",
    max_loops=1,
)

analysis_agent = Agent(
    agent_name="Analysis-Agent",
    agent_description="Expert in data analysis",
    model_name="anthropic/claude-sonnet-4-5",
    max_loops=1,
)

# Create AOP with queue management enabled
deployer = AOP(
    server_name="QueueManagedServer",
    port=8000,
    queue_enabled=True,  # Enable queue-based execution
    max_workers_per_agent=2,  # 2 worker threads per agent
    max_queue_size_per_agent=500,  # Max 500 tasks per agent queue
    processing_timeout=60,  # 60 second timeout for processing
    retry_delay=2.0,  # 2 second delay between retries
    verbose=True
)

# Add agents
deployer.add_agent(research_agent)
deployer.add_agent(analysis_agent)

# Get queue statistics
stats = deployer.get_queue_stats()
print("Queue stats:", stats)

# Get stats for specific agent
research_stats = deployer.get_queue_stats("Research-Agent")
print("Research agent stats:", research_stats)

# Pause an agent's queue
deployer.pause_agent_queue("Research-Agent")
print("Research agent queue paused")

# Resume the queue
deployer.resume_agent_queue("Research-Agent")
print("Research agent queue resumed")

# Clear all pending tasks for an agent
cleared_count = deployer.clear_agent_queue("Analysis-Agent")
print(f"Cleared {cleared_count} tasks from Analysis-Agent queue")

# Start the server
deployer.run()

AOPCluster Usage

import json
from swarms.structs.aop import AOPCluster

# Connect to multiple MCP servers
cluster = AOPCluster(
    urls=[
        "http://localhost:8000/mcp",
        "http://localhost:8001/mcp", 
        "http://localhost:8002/mcp"
    ],
    transport="streamable-http"
)

# Get all available tools from all servers
all_tools = cluster.get_tools(output_type="dict")
print(f"Found {len(all_tools)} tools across all servers")

# Pretty print all tools
print(json.dumps(all_tools, indent=2))

# Find a specific tool by name
research_tool = cluster.find_tool_by_server_name("Research-Agent")
if research_tool:
    print("Found Research-Agent tool:")
    print(json.dumps(research_tool, indent=2))
else:
    print("Research-Agent tool not found")

Discovery Tools Examples

The AOP server automatically provides discovery tools that allow agents to learn about each other. Here are examples of how to use these tools:

# Example discovery tool calls (these would be made by MCP clients)

# Discover all agents in the cluster
all_agents = discover_agents()
print(f"Found {len(all_agents['agents'])} agents in the cluster")

# Discover a specific agent
research_agent_info = discover_agents(agent_name="Research-Agent")
if research_agent_info['success']:
    agent = research_agent_info['agents'][0]
    print(f"Agent: {agent['agent_name']}")
    print(f"Description: {agent['description']}")
    print(f"Tags: {agent['tags']}")
    print(f"Capabilities: {agent['capabilities']}")

# Get detailed information about a specific agent
agent_details = get_agent_details(agent_name="Research-Agent")
if agent_details['success']:
    print("Agent Info:", agent_details['agent_info'])
    print("Discovery Info:", agent_details['discovery_info'])

# Get information about multiple agents
multiple_agents = get_agents_info(agent_names=["Research-Agent", "Analysis-Agent"])
print(f"Found {multiple_agents['total_found']} out of {multiple_agents['total_requested']} agents")
print("Not found:", multiple_agents['not_found'])

# List all available agents
agent_list = list_agents()
print(f"Available agents: {agent_list['agent_names']}")

# Search for agents by keyword
search_results = search_agents(query="research")
print(f"Found {search_results['total_matches']} agents matching 'research'")

# Search in specific fields only
tag_search = search_agents(
    query="data", 
    search_fields=["tags", "capabilities"]
)
print(f"Found {tag_search['total_matches']} agents with 'data' in tags or capabilities")

Dynamic Agent Discovery Example

Here's a practical example of how agents can use discovery tools to find and collaborate with other agents:

from swarms import Agent
from swarms.structs.aop import AOP

# Create a coordinator agent that can discover and use other agents
coordinator = Agent(
    agent_name="Coordinator-Agent",
    agent_description="Coordinates tasks between different specialized agents",
    model_name="anthropic/claude-sonnet-4-5",
    max_loops=1,
    tags=["coordination", "orchestration", "management"],
    capabilities=["agent-discovery", "task-distribution", "workflow-management"],
    role="coordinator"
)

# Create specialized agents
research_agent = Agent(
    agent_name="Research-Agent",
    agent_description="Expert in research and data collection",
    model_name="anthropic/claude-sonnet-4-5",
    max_loops=1,
    tags=["research", "data-collection", "analysis"],
    capabilities=["web-search", "data-gathering", "report-generation"],
    role="researcher"
)

analysis_agent = Agent(
    agent_name="Analysis-Agent",
    agent_description="Expert in data analysis and insights",
    model_name="anthropic/claude-sonnet-4-5",
    max_loops=1,
    tags=["analysis", "data-processing", "insights"],
    capabilities=["statistical-analysis", "pattern-recognition", "visualization"],
    role="analyst"
)

# Create AOP server
deployer = AOP(
    server_name="DynamicAgentCluster",
    port=8000,
    verbose=True
)

# Add all agents
deployer.add_agent(coordinator)
deployer.add_agent(research_agent)
deployer.add_agent(analysis_agent)

# The coordinator can now discover other agents and use them
# This would be done through MCP tool calls in practice
def coordinate_research_task(task_description):
    """
    Example of how the coordinator might use discovery tools
    """
    # 1. Discover available research agents
    research_agents = discover_agents()
    research_agents = [a for a in research_agents['agents'] if 'research' in a['tags']]
    
    # 2. Get detailed info about the best research agent
    if research_agents:
        best_agent = research_agents[0]
        agent_details = get_agent_details(agent_name=best_agent['agent_name'])
        
        # 3. Use the research agent for the task
        research_result = research_agent.run(task=task_description)
        
        # 4. Find analysis agents for processing the research
        analysis_agents = search_agents(query="analysis", search_fields=["tags"])
        if analysis_agents['matching_agents']:
            analysis_agent_name = analysis_agents['matching_agents'][0]['agent_name']
            analysis_result = analysis_agent.run(task=f"Analyze this research: {research_result}")
            
            return {
                "research_result": research_result,
                "analysis_result": analysis_result,
                "agents_used": [best_agent['agent_name'], analysis_agent_name]
            }
    
    return {"error": "No suitable agents found"}

# Start the server
deployer.run()

Tool Execution Examples

Once your AOP server is running, you can call the tools using MCP clients. Here are examples of how the tools would be called:

# Example tool calls (these would be made by MCP clients)

# Basic task execution
result = research_tool(task="Research the latest AI trends in 2024")

# Task with single image
result = analysis_tool(
    task="Analyze this chart and provide insights",
    img="path/to/chart.png"
)

# Task with multiple images
result = writing_tool(
    task="Write a comprehensive report based on these images",
    imgs=["image1.jpg", "image2.jpg", "image3.jpg"]
)

# Task with validation
result = code_tool(
    task="Debug this Python function",
    correct_answer="Expected output: Hello World"
)

# The response format for all calls:
# {
#   "result": "The agent's response...",
#   "success": true,
#   "error": null
# }

Error Handling

AOP provides comprehensive error handling:

Timeout Protection: Each agent has configurable timeout limits
Retry Logic: Automatic retries on failure with configurable retry counts
Detailed Logging: Verbose logging with traceback information
Graceful Degradation: Failed agents don't crash the entire server

Best Practices

Best Practice	Description
Use Descriptive Names	Choose clear, descriptive tool names
Set Appropriate Timeouts	Configure timeouts based on expected task complexity
Enable Logging	Use verbose logging for debugging and monitoring
Handle Errors	Always check the `success` field in tool responses
Resource Management	Monitor server resources when running multiple agents
Security	Use appropriate host/port settings for your deployment environment
Use Tags and Capabilities	Add meaningful tags and capabilities to agents for better discovery
Define Agent Roles	Use the `role` attribute to categorize agents (coordinator, worker, etc.)
Leverage Discovery Tools	Use built-in discovery tools for dynamic agent collaboration
Design for Scalability	Plan for adding/removing agents dynamically using discovery tools
Enable Queue Management	Use queue-based execution for better performance and reliability
Monitor Queue Stats	Regularly check queue statistics to identify bottlenecks
Configure Workers	Set appropriate `max_workers_per_agent` based on your workload
Set Queue Limits	Configure `max_queue_size_per_agent` to prevent memory issues
Use Priority Tasks	Leverage task priority for important tasks that need faster processing
Monitor Task Status	Use `get_task_status()` to track individual task progress

Integration with Other Systems

AOP servers can be integrated with:

Integration Target	Description
MCP Clients	Any MCP-compatible client
Web Applications	Via HTTP transport
Other Swarms	As part of larger multi-agent systems
External APIs	Through MCP protocol

This makes AOP a powerful tool for creating distributed, scalable agent systems that can be easily integrated into existing workflows and applications.

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AOP (Agent Orchestration Protocol)

Core Classes

AgentToolConfig

Task

TaskStatus

QueueStatus

QueueStats

TaskQueue

AOP Class

Constructor Parameters

Methods

add_agent()

add_agents_batch()

remove_agent()

list_agents()

get_agent_info()

start_server()

run()

get_server_info()

_register_tool()

_execute_agent_with_timeout()

_get_agent_discovery_info()

get_queue_stats()

pause_agent_queue()

resume_agent_queue()

clear_agent_queue()

get_task_status()

Discovery Tools

Queue Management Tools

get_queue_stats

pause_agent_queue

resume_agent_queue

clear_agent_queue

get_task_status

discover_agents

get_agent_details

get_agents_info

list_agents

search_agents

AOPCluster Class

AOPCluster Constructor Parameters

AOPCluster Methods

get_tools()

find_tool_by_server_name()

Tool Parameters

Output Format

Complete Examples

Basic Server Setup

Batch Agent Addition

Advanced Configuration with Tags and Capabilities

Queue Management Configuration

AOPCluster Usage

Discovery Tools Examples

Dynamic Agent Discovery Example

Tool Execution Examples

Error Handling

Best Practices

Integration with Other Systems

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