haive.agents.multi
==================

.. py:module:: haive.agents.multi

.. autoapi-nested-parse::

   MultiAgent Package - Advanced Multi-Agent Coordination and Orchestration.

   This package provides the MultiAgent system for coordinating multiple AI agents in
   sophisticated workflows, enabling complex task decomposition, parallel processing,
   conditional routing, and hierarchical agent management for enterprise-scale applications.

   The MultiAgent framework enables:
   - **Sequential Workflows**: Step-by-step agent coordination with state passing
   - **Parallel Processing**: Simultaneous agent execution with result aggregation
   - **Conditional Routing**: Dynamic agent selection based on state conditions
   - **Hierarchical Management**: Multi-level agent supervision and coordination
   - **State Management**: Sophisticated state sharing and transformation between agents

   Core Architecture:
       MultiAgent implements advanced coordination patterns through several key components:

       **Execution Engine**:
           - Sequential execution with state passing between agents
           - Parallel execution with concurrent agent processing
           - Conditional routing with dynamic decision making
           - Hybrid execution modes combining multiple patterns
           - Error handling and recovery across agent boundaries

       **State Management System**:
           - Unified state schema across all coordinated agents
           - State transformation and projection for agent compatibility
           - Shared context preservation throughout workflow execution
           - Agent-specific state isolation and protection
           - Cross-agent communication and data sharing

       **Agent Coordination**:
           - Dynamic agent addition and removal during execution
           - Agent capability discovery and matching
           - Load balancing and resource optimization
           - Agent health monitoring and failure recovery
           - Performance monitoring and optimization

       **Workflow Control**:
           - Conditional branching and decision points
           - Loop and iteration control structures
           - Error handling and exception propagation
           - Timeout and resource limit enforcement
           - Progress monitoring and reporting

   Agent Coordination Patterns:
       **Sequential Execution**:
           - Linear workflow with agent-to-agent handoffs
           - State accumulation and transformation through pipeline
           - Dependency management and ordering constraints
           - Error propagation and recovery strategies

       **Parallel Processing**:
           - Concurrent agent execution for independent tasks
           - Result aggregation and synthesis
           - Resource optimization and load distribution
           - Synchronization and coordination mechanisms

       **Conditional Routing**:
           - Dynamic agent selection based on runtime conditions
           - Complex decision trees and branching logic
           - State-dependent workflow adaptation
           - Multi-criteria agent selection

       **Hierarchical Coordination**:
           - Multi-level agent supervision and management
           - Supervisor agents coordinating worker agents
           - Nested MultiAgent workflows
           - Delegation and task distribution patterns

   .. rubric:: Examples

   Basic sequential workflow for content creation::

       from haive.agents.multi import MultiAgent
       from haive.agents.simple import SimpleAgent
       from haive.agents.react import ReactAgent
       from haive.core.engine.aug_llm import AugLLMConfig

       # Create specialized agents for content pipeline
       researcher = ReactAgent(
           name="researcher",
           engine=AugLLMConfig(
               tools=[web_search, fact_checker],
               temperature=0.4,
               system_message="You are a thorough researcher who gathers comprehensive information."
           )
       )

       writer = SimpleAgent(
           name="writer",
           engine=AugLLMConfig(
               temperature=0.8,
               system_message="You are a skilled writer who creates engaging, well-structured content."
           )
       )

       editor = SimpleAgent(
           name="editor",
           engine=AugLLMConfig(
               temperature=0.3,
               system_message="You are a meticulous editor who improves clarity and correctness."
           )
       )

       # Create sequential content creation workflow
       content_pipeline = MultiAgent(
           name="content_creation",
           agents=[researcher, writer, editor],
           execution_mode="sequential"
       )

       # Execute coordinated workflow
       result = await content_pipeline.arun(
           "Create a comprehensive article about renewable energy trends in 2024"
       )
       # Flow: researcher gathers data → writer creates content → editor refines

   Parallel processing for multi-perspective analysis::

       from haive.agents.multi import MultiAgent
       from haive.agents.simple import SimpleAgent
       from haive.core.engine.aug_llm import AugLLMConfig

       # Create specialized analysis agents
       technical_analyst = SimpleAgent(
           name="technical_analyst",
           engine=AugLLMConfig(
               temperature=0.2,
               system_message="You analyze technical feasibility and implementation details."
           )
       )

       business_analyst = SimpleAgent(
           name="business_analyst",
           engine=AugLLMConfig(
               temperature=0.3,
               system_message="You analyze business impact, ROI, and market considerations."
           )
       )

       risk_analyst = SimpleAgent(
           name="risk_analyst",
           engine=AugLLMConfig(
               temperature=0.2,
               system_message="You identify potential risks, challenges, and mitigation strategies."
           )
       )

       user_experience_analyst = SimpleAgent(
           name="ux_analyst",
           engine=AugLLMConfig(
               temperature=0.4,
               system_message="You analyze user experience and usability implications."
           )
       )

       # Create parallel analysis workflow
       analysis_team = MultiAgent(
           name="comprehensive_analysis",
           agents=[technical_analyst, business_analyst, risk_analyst, user_experience_analyst],
           execution_mode="parallel"
       )

       # Execute parallel analysis
       analysis_results = await analysis_team.arun(
           "Analyze the proposal to implement AI-powered customer service automation"
       )
       # All agents analyze simultaneously, results are aggregated

   Conditional routing based on complexity assessment::

       from haive.agents.multi import MultiAgent
       from haive.agents.simple import SimpleAgent
       from haive.agents.react import ReactAgent
       from haive.core.engine.aug_llm import AugLLMConfig

       # Create complexity classifier
       classifier = SimpleAgent(
           name="complexity_classifier",
           engine=AugLLMConfig(
               temperature=0.1,
               system_message="You classify task complexity as 'simple', 'moderate', or 'complex'."
           )
       )

       # Create agents for different complexity levels
       simple_processor = SimpleAgent(
           name="simple_processor",
           engine=AugLLMConfig(
               system_message="You handle straightforward tasks efficiently."
           )
       )

       moderate_processor = ReactAgent(
           name="moderate_processor",
           engine=AugLLMConfig(
               tools=[basic_tools],
               system_message="You handle moderately complex tasks with some tool usage."
           )
       )

       complex_processor = ReactAgent(
           name="complex_processor",
           engine=AugLLMConfig(
               tools=[advanced_tools],
               system_message="You handle complex tasks requiring extensive reasoning and tools."
           ),
           max_iterations=10
       )

       # Create conditional routing workflow
       adaptive_processor = MultiAgent(
           name="adaptive_task_processor",
           agents=[classifier, simple_processor, moderate_processor, complex_processor],
           execution_mode="conditional"
       )

       # Add routing logic
       adaptive_processor.add_conditional_edge(
           from_agent="classifier",
           condition=lambda state: state.get("complexity") == "simple",
           true_agent="simple_processor",
           false_agent="moderate_processor"
       )

       adaptive_processor.add_conditional_edge(
           from_agent="classifier",
           condition=lambda state: state.get("complexity") == "complex",
           true_agent="complex_processor",
           false_agent="moderate_processor"
       )

       # Execute with automatic routing
       result = await adaptive_processor.arun("Analyze this data and provide insights")

   Hierarchical multi-agent system with supervision::

       from haive.agents.multi import MultiAgent
       from haive.agents.simple import SimpleAgent
       from haive.agents.react import ReactAgent
       from haive.core.engine.aug_llm import AugLLMConfig

       # Create worker agents for specific domains
       frontend_team = MultiAgent(
           name="frontend_development",
           agents=[
               ReactAgent(name="ui_designer", engine=design_config, tools=[design_tools]),
               ReactAgent(name="frontend_dev", engine=dev_config, tools=[frontend_tools]),
               SimpleAgent(name="ux_reviewer", engine=review_config)
           ],
           execution_mode="sequential"
       )

       backend_team = MultiAgent(
           name="backend_development",
           agents=[
               ReactAgent(name="api_designer", engine=api_config, tools=[api_tools]),
               ReactAgent(name="backend_dev", engine=dev_config, tools=[backend_tools]),
               SimpleAgent(name="security_reviewer", engine=security_config)
           ],
           execution_mode="sequential"
       )

       qa_team = MultiAgent(
           name="quality_assurance",
           agents=[
               ReactAgent(name="test_designer", engine=test_config, tools=[testing_tools]),
               ReactAgent(name="automation_tester", engine=automation_config, tools=[automation_tools]),
               SimpleAgent(name="qa_reviewer", engine=qa_config)
           ],
           execution_mode="parallel"
       )

       # Create project supervisor
       project_supervisor = MultiAgent(
           name="software_project",
           agents=[frontend_team, backend_team, qa_team],
           execution_mode="parallel_then_sequential"  # Teams work in parallel, then integration
       )

       # Execute hierarchical project
       project_result = await project_supervisor.arun(
           "Develop a complete e-commerce web application with payment integration"
       )

   Dynamic agent composition with runtime adaptation::

       from haive.agents.multi import MultiAgent
       from haive.agents.simple import SimpleAgent
       from haive.agents.react import ReactAgent

       class AdaptiveMultiAgent(MultiAgent):
           """MultiAgent that adapts its composition based on task requirements."""

           async def analyze_requirements(self, task: str) -> List[str]:
               """Analyze task to determine required agent capabilities."""
               # Use LLM to analyze task and determine needed capabilities
               analyzer = SimpleAgent(name="requirement_analyzer", engine=analysis_config)
               requirements = await analyzer.arun(f"What agent capabilities are needed for: {task}")
               return self.parse_requirements(requirements)

           async def compose_team(self, capabilities: List[str]) -> List[Agent]:
               """Dynamically compose team based on required capabilities."""
               team = []

               if "research" in capabilities:
                   team.append(ReactAgent(name="researcher", tools=[search_tools]))

               if "analysis" in capabilities:
                   team.append(ReactAgent(name="analyst", tools=[analysis_tools]))

               if "creative" in capabilities:
                   team.append(SimpleAgent(name="creative_writer", engine=creative_config))

               if "technical" in capabilities:
                   team.append(ReactAgent(name="technical_expert", tools=[technical_tools]))

               return team

           async def arun(self, task: str):
               """Execute with dynamic team composition."""
               # Analyze requirements
               capabilities = await self.analyze_requirements(task)

               # Compose optimal team
               optimal_team = await self.compose_team(capabilities)

               # Update agents
               self.agents = optimal_team

               # Execute with optimal team
               return await super().arun(task)

       # Use adaptive multi-agent
       adaptive_system = AdaptiveMultiAgent(name="adaptive_solver")
       result = await adaptive_system.arun("Complex multifaceted problem requiring various expertise")

   Structured output coordination across agents::

       from haive.agents.multi import MultiAgent
       from haive.agents.simple import SimpleAgent
       from haive.core.engine.aug_llm import AugLLMConfig
       from pydantic import BaseModel, Field
       from typing import List

       # Define structured output schemas
       class ResearchFindings(BaseModel):
           key_facts: List[str] = Field(description="Important facts discovered")
           sources: List[str] = Field(description="Information sources")
           confidence: float = Field(ge=0.0, le=1.0, description="Confidence in findings")

       class ContentDraft(BaseModel):
           title: str = Field(description="Article title")
           outline: List[str] = Field(description="Article structure")
           content: str = Field(description="Full article content")
           word_count: int = Field(description="Total word count")

       class EditorialReview(BaseModel):
           clarity_score: float = Field(ge=0.0, le=10.0, description="Clarity rating")
           engagement_score: float = Field(ge=0.0, le=10.0, description="Engagement rating")
           improvements: List[str] = Field(description="Suggested improvements")
           final_content: str = Field(description="Edited content")

       # Create agents with structured outputs
       structured_researcher = SimpleAgent(
           name="structured_researcher",
           engine=AugLLMConfig(
               structured_output_model=ResearchFindings,
               temperature=0.3
           )
       )

       structured_writer = SimpleAgent(
           name="structured_writer",
           engine=AugLLMConfig(
               structured_output_model=ContentDraft,
               temperature=0.7
           )
       )

       structured_editor = SimpleAgent(
           name="structured_editor",
           engine=AugLLMConfig(
               structured_output_model=EditorialReview,
               temperature=0.2
           )
       )

       # Create structured workflow
       structured_pipeline = MultiAgent(
           name="structured_content_creation",
           agents=[structured_researcher, structured_writer, structured_editor],
           execution_mode="sequential"
       )

       # Execute with structured data flow
       final_result = await structured_pipeline.arun("Create article about space exploration")
       # Each agent produces validated structured output passed to next agent

   Performance Characteristics:
       **Execution Performance**:
           - Sequential workflows: Sum of individual agent execution times + 10-50ms coordination overhead
           - Parallel workflows: Max individual agent time + 20-100ms aggregation overhead
           - Conditional routing: Classification time + selected agent execution + 5-20ms routing overhead
           - State management: <10ms per agent transition for typical state sizes

       **Scalability Metrics**:
           - Agent count: 100+ agents per MultiAgent workflow supported
           - Parallel execution: Limited by system resources and LLM provider rate limits
           - State size: Handles MB-scale state objects with efficient serialization
           - Nested workflows: 10+ levels of MultiAgent nesting supported

       **Resource Management**:
           - Memory efficiency: Optimized state sharing and agent lifecycle management
           - Error recovery: Configurable retry strategies and fallback mechanisms
           - Timeout handling: Per-agent and workflow-level timeout controls
           - Resource limits: CPU and memory usage monitoring and limits

   Integration Patterns:
       **Workflow Orchestration**:
           - Business process automation with agent-based steps
           - Data processing pipelines with specialized agent components
           - Content creation workflows with review and approval stages
           - Complex analysis workflows with multiple perspectives

       **Microservice Architecture**:
           - Agent-based microservices with MultiAgent coordination
           - Service mesh integration with agent discovery and routing
           - Load balancing and scaling across agent instances
           - Cross-service communication through agent interfaces

       **Enterprise Integration**:
           - ERP system integration with agent-based business logic
           - Customer service automation with escalation workflows
           - Document processing with multi-stage validation
           - Compliance and audit workflows with approval chains

   Advanced Features:
       **Dynamic Reconfiguration**:
           - Runtime agent addition and removal
           - Workflow pattern switching based on conditions
           - Performance-based agent selection and optimization
           - Adaptive resource allocation

       **State Management**:
           - Sophisticated state transformation between agents
           - Agent-specific state views and projections
           - State versioning and rollback capabilities
           - Cross-workflow state sharing and persistence

       **Monitoring and Observability**:
           - Comprehensive execution tracing and logging
           - Performance metrics and bottleneck identification
           - Agent health monitoring and failure detection
           - Workflow visualization and debugging tools

   Best Practices:
       **Workflow Design**:
           - Design agents with clear, focused responsibilities
           - Use appropriate execution modes for task characteristics
           - Implement proper error handling and recovery strategies
           - Design state schemas for forward compatibility

       **Performance Optimization**:
           - Minimize state transfer between agents
           - Use parallel execution for independent tasks
           - Implement caching for frequently used agent results
           - Monitor and optimize agent execution times

       **Error Handling**:
           - Implement comprehensive error recovery strategies
           - Use conditional routing for error handling workflows
           - Design fallback agents for critical workflow paths
           - Monitor and alert on workflow failures

   Version History:
       **v4.0** (Current):
           - Enhanced state management with projection support
           - Improved parallel execution and synchronization
           - Advanced conditional routing with complex decision logic
           - Performance optimizations and monitoring improvements

       **v3.0**:
           - Hierarchical MultiAgent support
           - Dynamic agent composition capabilities
           - Enhanced error handling and recovery
           - Structured output coordination

       **v2.0**:
           - Parallel execution support
           - Conditional routing implementation
           - State management improvements
           - Performance monitoring integration

       **v1.0**:
           - Initial MultiAgent implementation
           - Sequential workflow execution
           - Basic agent coordination patterns

   .. seealso::

      :mod:`haive.agents.multi.enhanced_multi_agent_v4`: Latest MultiAgent implementation
      :mod:`haive.agents.simple`: SimpleAgent for basic agent building blocks
      :mod:`haive.agents.react`: ReactAgent for tool-based reasoning
      :mod:`haive.agents.base`: Base Agent class and coordination patterns



Submodules
----------

.. toctree::
   :maxdepth: 1

   /autoapi/haive/agents/multi/agent/index
   /autoapi/haive/agents/multi/base/index
   /autoapi/haive/agents/multi/core/index
   /autoapi/haive/agents/multi/simple/index
   /autoapi/haive/agents/multi/utils/index