Multi-Agent Collaborative Architecture: How Litho Achieves Deep Semantic Understanding of Code

As the core technical architecture of the Litho project, the multi-agent collaborative system achieves deep semantic understanding of codebases through specialized division of labor and collaborative working mechanisms. This article provides a detailed analysis of how Litho decomposes complex code understanding tasks into specialized collaboration among multiple agents, and the technical advantages and practical value brought by this architectural design.
Project Open Source Address: https://github.com/sopaco/deepwiki-rs

1. Agent Architecture Design Philosophy

1.1 Evolution from Monolithic to Multi-Agent

Traditional AI code analysis tools typically use monolithic architecture, facing numerous challenges:

Architecture Type	Advantages	Disadvantages
Monolithic Architecture	Simple implementation, easy deployment	Functional coupling, difficult extension
Microservices Architecture	Independent modules, easy to extend	Large network overhead, complex operations
Multi-Agent Architecture	Specialized division of labor, collaborative intelligence	High implementation complexity, difficult debugging

Core considerations for Litho choosing multi-agent architecture:

graph TD
    A[Code Understanding Complexity] --> B[Task Decomposition Needs]
    C[Specialized Analysis Needs] --> D[Agent Division of Labor]
    E[Extensibility Needs] --> F[Plugin Architecture]
    B --> G[Multi-Agent Collaboration]
    D --> G
    F --> G

1.2 Core Principles of Agent Design

Litho's agent design follows four major principles:

1. Single Responsibility Principle: Each agent focuses on specific analysis tasks
2. Interface Segregation Principle: Agents communicate through standardized interfaces
3. Dependency Inversion Principle: Agents depend on abstract interfaces rather than concrete implementations
4. Open/Closed Principle: Supports agent extension without modifying existing logic

2. Core Agent System Analysis

2.1 Preprocessing Stage Agents

2.1.1 Structure Scanner Agent (StructureScanner)

Responsibilities: Recursively traverse project directories, identify core file structures

pub struct StructureScanner {
    importance_calculator: ImportanceCalculator,
    file_filter: FileFilter,
}

impl StructureScanner {
    pub async fn scan_project(&self, path: &Path) -> Result<ProjectStructure> {
        // Implement directory traversal and file importance scoring
    }
}

Key Technologies:

• Importance Scoring Algorithm: Calculate file importance based on file location, size, reference count
• Intelligent Filtering Mechanism: Automatically exclude test files, configuration files, and other non-core content

2.1.2 Language Processor Manager (LanguageProcessorManager)

Responsibilities: Dispatch to corresponding language processors based on file type

graph LR
    A[File Input] --> B[Extension Recognition]
    B --> C{Rust File?}
    C -->|Yes| D[RustProcessor]
    C -->|No| E{Python File?}
    E -->|Yes| F[PythonProcessor]
    E -->|No| G{TypeScript File?}
    G -->|Yes| H[TypeScriptProcessor]
    G -->|No| I[Generic Processor]

Supported Language Processors:

• RustProcessor: Parse mod declarations, trait implementations, macro expansion
• PythonProcessor: Analyze class inheritance, decorators, type annotations
• TypeScriptProcessor: Handle interfaces, generics, module imports

2.2 Research Stage Agent Cluster

2.2.1 System Context Researcher (SystemContextResearcher)

Core Task: Analyze system positioning and boundaries within the enterprise environment

sequenceDiagram
    participant R as System Context Researcher
    participant M as Memory System
    participant L as LLM Service
    participant F as File Tools

    R->>M: Read Project Structure Information
    R->>F: Explore README and Configuration Files
    F->>R: Return Project Description
    R->>L: Analyze System Goals and Users
    L->>R: Return Context Analysis
    R->>M: Store SystemContextReport

Analysis Dimensions:

• Business Goals: Core business problems the system solves
• User Roles: Target users and usage scenarios of the system
• External Dependencies: Integration relationships with external systems
• Technical Constraints: Technical limitations of architecture decisions

2.2.2 Domain Modules Detector (DomainModulesDetector)

Core Algorithm: Domain discovery algorithm based on dependency graphs

pub struct DomainDetectionAlgorithm {
    dependency_graph: DependencyGraph,
    clustering_algorithm: HierarchicalClustering,
}

impl DomainDetectionAlgorithm {
    pub fn detect_domains(&self) -> Vec<DomainModule> {
        // 1. Build module dependency graph
        // 2. Apply clustering algorithm to identify functional domains
        // 3. Validate domain boundary rationality
    }
}

Clustering Strategy Comparison:

Clustering Algorithm	Applicable Scenarios	Application in Litho
K-means	Uniform data distribution	Preliminary domain division
Hierarchical Clustering	Clear hierarchical structure	Sub-domain discovery
DBSCAN	More noisy data	Abnormal module identification

2.2.3 Workflow Analyzer (WorkflowAnalyzer)

Analysis Method: Reconstruct business processes from code execution paths

graph TD
    A[Entry Point Identification] --> B[Call Chain Tracking]
    B --> C[Control Flow Analysis]
    C --> D[Data Flow Analysis]
    D --> E[Business Process Reconstruction]
    E --> F[Mermaid Flowchart Generation]

    style A fill:#E3F2FD
    style F fill:#C8E6C9

Key Technology Breakthroughs:

• Dynamic Execution Path Inference: Infer runtime behavior through static analysis
• Exception Handling Flow Identification: Analyze business meaning of error handling logic
• Concurrency Pattern Analysis: Identify asynchronous tasks and parallel processing patterns

2.2.4 Key Modules Insighter (KeyModulesInsighter)

Insight Dimensions:

• Technical Complexity: Code metrics like cyclomatic complexity, nesting depth
• Business Importance: Core position of modules in business processes
• Technical Debt: Code quality issues and improvement suggestions
• Design Patterns: Identified and applied design patterns

2.3 Composition Stage Agents

2.3.1 Documentation Composition Hub (DocumentationComposer)

Composition Logic: Coordinate multiple editors to generate standardized documentation

pub struct DocumentationComposer {
    editors: Vec<Box<dyn DocumentEditor>>,
    template_engine: TemplateEngine,
}

impl DocumentationComposer {
    pub async fn compose_documentation(&self, context: &GeneratorContext) -> Result<DocTree> {
        let mut doc_tree = DocTree::new();

        for editor in &self.editors {
            let document = editor.generate(context).await?;
            doc_tree.add_document(document);
        }

        Ok(doc_tree)
    }
}

2.3.2 Specialized Editor Cluster

Editor Type	Input Source	Output Document	Special Features
Overview Editor	SystemContextReport	Project Overview.md	Business value description
Architecture Editor	DomainModuleReport	Architecture Overview.md	C4 model diagram generation
Process Editor	WorkflowReport	Workflow.md	Sequence diagram generation
Insight Editor	KeyModulesReport	Module Insights/	Technical depth analysis

3. Agent Collaborative Working Mechanism

3.1 Memory Bus Communication Pattern

All agents exchange data through unified memory context:

graph TB
    subgraph "Agent Cluster"
        A[Preprocessing Agent]
        B[Research Agent]
        C[Composition Agent]
    end

    subgraph "Memory Storage Domain"
        D[Memory Context]
    end

    subgraph "Support Services"
        E[LLM Client]
        F[Cache Manager]
        G[Tool Services]
    end

    A --> D
    B --> D
    C --> D
    D --> E
    D --> F
    D --> G

Communication Protocol Design:

// Memory key name specification
pub struct MemoryKey {
    scope: String,    // Scope: preprocess, research, compose
    module: String,   // Module name
    key: String,      // Specific data key
}

// Data serialization format
pub enum MemoryValue {
    CodeInsight(CodeInsight),
    DomainReport(DomainModuleReport),
    WorkflowReport(WorkflowReport),
    // ... Other data types
}

3.2 Asynchronous Parallel Execution Strategy

Research stage agents support parallel execution:

pub async fn execute_research_pipeline(context: &GeneratorContext) -> Result<()> {
    let research_tasks = vec![
        tokio::spawn(research_system_context(context.clone())),
        tokio::spawn(research_domain_modules(context.clone())),
        tokio::spawn(research_workflows(context.clone())),
        tokio::spawn(research_key_modules(context.clone())),
    ];

    let results = futures::future::join_all(research_tasks).await;

    // Process results and store in memory
    for result in results {
        if let Ok(report) = result {
            context.memory.store("research", &report.key(), report)?;
        }
    }

    Ok(())
}

3.3 Error Handling and Fault Tolerance Mechanism

Hierarchical Error Handling Strategy:

Error Level	Handling Strategy	Impact Scope
Agent-Level Error	Retry mechanism, maximum 3 retries	Single agent task
Stage-Level Error	Degraded processing, generate placeholder documentation	Current processing stage
System-Level Error	Graceful termination, save intermediate results	Entire documentation generation process

4. ReAct Reasoning Engine Deep Analysis

4.1 Application of ReAct Pattern in Code Understanding

The ReAct (Reasoning + Acting) pattern enables agents to think like human developers:

sequenceDiagram
    participant A as Agent
    participant T as Thinking Phase
    participant AC as Action Phase
    participant O as Observation Phase

    A->>T: Analyze Current Problem
    T->>AC: Decide Needed Information
    AC->>O: Execute Tool Calls
    O->>T: Return Observation Results
    T->>AC: Continue Reasoning Based on Results
    AC->>O: May Need More Information
    O->>T: Update Understanding
    T->>A: Generate Final Conclusion

4.2 Tool Calling Mechanism

Litho provides rich tool sets for agents:

Tool Type	Function Description	Usage Scenarios
File Exploration Tools	List directory contents, search files	Project structure analysis
File Reading Tools	Read file content, support encoding detection	Code content analysis
Code Parsing Tools	Extract AST information, analyze syntax structure	Language-specific analysis
Dependency Analysis Tools	Build module dependency graphs, identify call relationships	Architecture understanding

4.3 Multi-round Reasoning Optimization

Reasoning Depth Control:

pub struct ReActExecutor {
    max_turns: usize,      // Maximum reasoning rounds
    timeout: Duration,     // Timeout control
    fallback_strategy: FallbackStrategy, // Fallback strategy
}

impl ReActExecutor {
    pub async fn execute(&self, initial_prompt: &str) -> Result<String> {
        for turn in 0..self.max_turns {
            let response = self.llm.chat(¤t_prompt).await?;

            if self.is_final_answer(&response) {
                return self.extract_final_answer(&response);
            }

            let action = self.extract_action(&response);
            let observation = self.execute_tool(action).await?;
            current_prompt = self.update_prompt(¤t_prompt, &observation);
        }

        self.fallback_strategy.execute(initial_prompt)
    }
}

5. Performance Optimization Strategies

5.1 Agent Execution Optimization

5.1.1 Concurrency Control Strategy

graph LR
    A[Task Queue] --> B[Concurrency Controller]
    B --> C[Agent 1]
    B --> D[Agent 2]
    B --> E[Agent 3]
    C --> F[Result Aggregation]
    D --> F
    E --> F

Concurrency Parameter Tuning:

[performance]
max_concurrent_agents = 5
llm_request_timeout = "30s"
file_scan_batch_size = 100

5.1.2 Cache Optimization Strategy

Multi-level Cache Architecture:

• LLM Result Cache: Response cache based on prompt hash
• Code Insight Cache: Static analysis result cache
• Document Structure Cache: Generation template and layout cache

5.2 Memory Management Optimization

Memory Usage Monitoring:

pub struct MemoryMonitor {
    peak_usage: AtomicUsize,
    current_usage: AtomicUsize,
    leak_detector: LeakDetector,
}

impl MemoryMonitor {
    pub fn check_memory_health(&self) -> MemoryHealth {
        if self.current_usage.load(Ordering::Relaxed) > WARNING_THRESHOLD {
            MemoryHealth::Warning
        } else if self.current_usage.load(Ordering::Relaxed) > CRITICAL_THRESHOLD {
            MemoryHealth::Critical
        } else {
            MemoryHealth::Healthy
        }
    }
}

6. Extensibility Design

6.1 Agent Plugin System

Plugin Interface Design:

pub trait AgentPlugin: Send + Sync {
    fn name(&self) -> &str;
    fn version(&self) -> &str;
    fn execute(&self, context: &GeneratorContext) -> Result<PluginResult>;
    fn dependencies(&self) -> Vec<&str>;
}

6.2 Configuration-Driven Agent Behavior

Dynamic Configuration Support:

[agents.system_context_researcher]
enabled = true
llm_model = "moonshot-v1-8k"
max_retries = 3
timeout = "60s"

[agents.domain_detector]
clustering_algorithm = "hierarchical"
min_domain_size = 3
similarity_threshold = 0.7

7. Actual Application Effect Analysis

7.1 Agent Collaboration Efficiency Evaluation

Performance on typical projects:

Project Scale	Number of Agents	Execution Time	Memory Usage	Documentation Quality Score
Small Project (10k lines)	4 core agents	2.1 minutes	128MB	8.7/10
Medium Project (100k lines)	6 agents	8.5 minutes	512MB	9.2/10
Large Project (500k lines)	8 agents	25.3 minutes	2GB	8.9/10

7.2 Comparison Advantages with Traditional Methods

Comparison Dimension	Traditional Method	Litho Multi-Agent	Advantage Analysis
Analysis Depth	Syntax level	Semantic level	Improves understanding accuracy
Extensibility	Difficult to modify	Plugin-based extension	Reduces maintenance costs
Performance	Serial processing	Parallel collaboration	Improves processing efficiency
Accuracy	Rule-dependent	Intelligent reasoning	Adapts to complex scenarios

8. Technical Challenges and Solutions

8.1 Inter-Agent Coordination Challenges

Challenge: Multiple agents analyzing the same code module may produce conflicts

Solution:

• Conflict Detection Mechanism: Identify inconsistencies in analysis results
• Priority Scheduling: Set execution priorities for different agents
• Result Fusion Algorithm: Intelligently merge multiple analysis perspectives

8.2 Resource Competition Management

Challenge: Concurrent agents competing for LLM services and file system resources

Solution:

• Resource Pool Management: Limit concurrent access numbers
• Priority Queue: Important tasks execute first
• Timeout Retry Mechanism: Handle temporary resource unavailability

9. Future Evolution Directions

9.1 Agent Capability Enhancement

Planned Enhancement Features:

• Cross-language Analysis: Support unified analysis of multi-language mixed projects
• Architecture Pattern Recognition: Automatically identify and apply architecture patterns
• Refactoring Suggestions Generation: Provide code refactoring suggestions based on analysis results

9.2 Collaboration Mechanism Optimization

Optimization Directions:

• Distributed Agents: Support cross-machine agent collaboration
• Federated Learning: Knowledge sharing and learning between multiple projects
• Adaptive Scheduling: Dynamically adjust agent configuration based on project characteristics

10. Summary

Litho's multi-agent collaborative architecture represents the technological frontier of AI-driven code understanding. Through specialized division of labor and collaborative working mechanisms, this system achieves deep semantic understanding of codebases and generates high-quality technical documentation. This architecture not only solves the limitations of traditional methods but also lays a solid foundation for future functional extensions and technological evolution.

Core Value Summary:

1. Specialized Analysis: Each agent focuses on specific analysis tasks, improving analysis depth
2. Collaborative Intelligence: Agents collaborate through memory bus, producing 1+1>2 effects
3. Extensible Architecture: Plugin design supports rapid functional extension and customization
4. Engineering Practice: Complete error handling, performance monitoring, and resource management mechanisms

The multi-agent architecture is the technical core of the Litho project and a key factor in maintaining its leading position in the automated documentation generation field. As AI technology continues to develop, this architectural pattern will be applied and promoted in more software development tools.