Enzu: Multi-Agent Framework for AI Systems

Enzu is a declarative Go framework designed for building sophisticated multi-agent AI systems. It enables LLMs and AI agents to collaborate, execute parallel tasks, and leverage extensible tools while maintaining clear hierarchies and communication patterns.

🎯 Framework Capabilities

Agent System Architecture

• Hierarchical Agent Organization: Define agent roles, responsibilities, and relationships
• Dynamic Task Distribution: Automatically route tasks to specialized agents
• Parallel Processing: Execute multiple agent tasks concurrently
• State Management: Track and maintain agent states across interactions

Tool Integration System

• Declarative Tool Registry: Register and manage tools with clear interfaces
• Inheritance Patterns: Tools can be inherited and shared across agent hierarchies
• Thread-Safe Operations: Concurrent tool access with built-in safety mechanisms
• Custom Tool Creation: Extend functionality through a standardized tool interface

Execution Patterns

• Synergy-Based Collaboration: Group agents into task-focused collaborative units
• Context Propagation: Share context and state across agent boundaries
• Parallel Task Execution: Optimize performance through concurrent processing
• Error Recovery: Built-in retry mechanisms and error handling patterns

Communication Infrastructure

• HTTP Server Integration: Built-in REST API capabilities
• Structured Message Passing: Type-safe communication between agents
• Event System: Publish-subscribe patterns for agent coordination
• Logging System: Comprehensive tracing and debugging capabilities

🔧 Core Integration Patterns

1. Research and Analysis Pattern

// Pattern: Distributed Research System
type ResearchRequest struct {
    Topic       string   `json:"topic"`
    Subtopics   []string `json:"subtopics,omitempty"`
    MaxResults  int      `json:"max_results,omitempty"`
    TimeoutSecs int      `json:"timeout_secs,omitempty"`
}

// Create specialized research agents
researcher := enzu.NewAgent("Primary Researcher",
    "Deep research and fact verification",
    llm,
    enzu.WithToolLists("ResearchTool"),
    enzu.WithParallelExecution(true),
)

analyst := enzu.NewAgent("Data Analyst",
    "Process and analyze research results",
    llm,
    enzu.WithToolLists("AnalysisTool"),
    enzu.WithParallelExecution(true),
)

2. Self-Aware System Pattern

// Pattern: Self-Aware Interactive System
manager := enzu.NewSynergyManager("Self-Aware System", llm, logger)

// Define capability domains
researchSynergy := createDomainSynergy("Research", llm, logger)
analysisSynergy := createDomainSynergy("Analysis", llm, logger)
creativeSynergy := createDomainSynergy("Creative", llm, logger)

// Register domains
manager.AddSynergy(researchSynergy)
manager.AddSynergy(analysisSynergy)
manager.AddSynergy(creativeSynergy)

3. Tool Integration Pattern

// Pattern: Extensible Tool System
exaSearchOptions := tools.ExaSearchOptions{
    NumResults: 5,
    Type:      "neural",
    Contents: tools.Contents{
        Text: true,
    },
    UseAutoprompt:      true,
    StartPublishedDate: "2023-01-01T00:00:00.000Z",
}
tools.RegisterTool("ResearchTool", exaSearchOptions)

4. API Integration Pattern

// Pattern: Multi-Agent API Server
type Server struct {
    synergy *enzu.Synergy
    logger  *enzu.Logger
}

// Initialize server with parallel processing capabilities
func NewServer() (*Server, error) {
    // Create research agents with specific roles
    researchAgent1 := enzu.NewAgent("Research Agent 1",
        "Agent specialized in AI research",
        llm,
        enzu.WithToolLists("ResearchTool"),
        enzu.WithParallelExecution(true),
    )
    researchAgent2 := enzu.NewAgent("Research Agent 2",
        "Agent specialized in startup research",
        llm,
        enzu.WithToolLists("ResearchTool"),
        enzu.WithParallelExecution(true),
    )

    // Create parallel processing synergy
    synergy := enzu.NewSynergy(
        "Parallel AI Research",
        llm,
        enzu.WithAgents(researchAgent1, researchAgent2),
        enzu.WithLogger(logger),
    )

    return &Server{synergy: synergy, logger: logger}, nil
}

// Handle parallel task execution
func (s *Server) handleExecute(w http.ResponseWriter, r *http.Request) {
    var request struct {
        Tasks []string `json:"tasks"`
    }
    
    // Distribute tasks among agents
    agents := s.synergy.GetAgents()
    for i, taskDescription := range request.Tasks {
        agent := agents[i%len(agents)] // Round-robin distribution
        tasks = append(tasks, enzu.NewTask(taskDescription, agent))
    }
}

🚀 Capability Domains

1. Research & Information Retrieval

• Neural search integration (ExaSearch tool)
• Multi-agent research coordination
• Parallel information gathering
• Research result synthesis

2. Task Management & Execution

• Multi-agent task distribution
• Parallel task execution
• Progress tracking
• Result aggregation

3. Web Content Processing

• URL content fetching (FetchURL tool)
• HTML parsing and extraction
• CSS selector-based targeting
• Structured data collection

4. Synergy Management

• Multi-synergy orchestration
• Result synthesis across synergies
• Team-based agent organization
• Cross-team coordination
• Hierarchical task execution

5. Team Organization

• Role-specialized agents
• Team-based synergies
• Domain-specific agent groups
• Task-team alignment

6. API Integration & Scaling

• Parallel Task Distribution

  • Round-robin task assignment
  • Load-balanced processing
  • Concurrent execution
  • Real-time response handling

• HTTP Service Integration

  • RESTful endpoints
  • JSON request/response
  • Error handling patterns
  • Status monitoring

• Multi-Agent Coordination

  • Role-based agent assignment
  • Task synchronization
  • Result aggregation
  • State management

📦 Installation

go get github.com/teilomillet/enzu

📚 Integration Resources

Core Documentation

• /docs: Architecture and integration guides
• /docs/tutorials: Step-by-step implementation patterns
• /examples: Reference implementations and use cases

Example Implementations

1. Research Assistant System (examples/8_research_assistant_example.go)
2. Self-Aware System (examples/7_manager_mode_example.go)
3. Parallel Processing System (examples/4_parallel_example.go)
4. Tool Integration System (examples/3_tools_example.go)
5. API Integration System (examples/6_api_example.go)

Integration Patterns

1. HTTP API Integration

   • REST endpoint creation
   • Request/Response handling
   • Timeout management
   • Error recovery
   • Round-robin task distribution
   • Load balancing strategies

2. Tool Registry Integration

   • Tool registration
   • Capability inheritance
   • Access control
   • Resource management

3. Agent Collaboration

   • Task distribution
   • Result synthesis
   • Context sharing
   • Error handling