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Codebase Architecture Analysis

Status: 🧪 In Testing
Version: v0.1.x
Last Updated: November 15, 2025

Overview

Multi-agent collaborative analysis of large codebases (FastAPI example) creating comprehensive architecture documentation by reading 30+ files through coordinated agent exploration and synthesis.

Feature Description

Goal

Enable multiple agents to collaboratively analyze large codebases, understand architecture, identify patterns, and generate comprehensive documentation without human guidance.

Key Components

  1. Coordinated File Discovery

    • Agents identify important files through README, imports, and structure analysis
    • Prioritize core components over utilities
    • Balance breadth (many files) vs. depth (thorough analysis)

  2. Distributed Reading Strategy

    • Assign file subsets to different agents
    • Use memory system to avoid re-reading
    • Share findings through agent communication

  3. Architecture Synthesis

    • Identify design patterns (MVC, dependency injection, etc.)
    • Map component interactions and data flows
    • Document request/response lifecycle
    • Extract key abstractions and interfaces

  4. Documentation Generation

    • Create architecture diagrams (text-based or Mermaid)
    • Write component descriptions
    • Document key patterns and conventions
    • Generate getting-started guide for contributors

Target: FastAPI Repository

  • Size: ~100 Python files
  • Reading Goal: 30+ core files
  • Output: Comprehensive architecture document

Test Strategy

File Selection Tests

  • Verify agents identify core files (not just tests/examples)
  • Test prioritization: core > utils > tests
  • Validate coverage of main architecture components

Reading Efficiency Tests

  • Measure file reads per agent
  • Check for redundant reads (should use memory)
  • Validate 30+ file coverage achieved
  • Test with various codebase sizes

Analysis Quality Tests

  • Verify design patterns correctly identified
  • Check completeness of component interaction map
  • Validate data flow documentation accuracy
  • Test against ground truth (FastAPI docs)

Documentation Tests

  • Review generated docs for completeness
  • Check technical accuracy
  • Validate usefulness for new contributors
  • Compare to human-written architecture docs

Validation Criteria

  • ✅ 30+ files analyzed from FastAPI
  • ✅ All major components documented
  • ✅ Key patterns identified (routing, dependencies, etc.)
  • ✅ <30 minutes total execution time
  • ✅ Documentation useful to new contributors

Implementation Notes

Configuration:

# tools/memory/gpt5mini_gemini_codebase_analysis_memory.yaml
agents:
  - name: explorer
    role: Identify and prioritize files
    backend: gpt-5-mini
  
  - name: analyzer_1
    role: Read and analyze core components
    backend: gemini-2.0-flash
    memory: persistent
  
  - name: analyzer_2
    role: Read and analyze utilities
    backend: gemini-2.0-flash
    memory: persistent
  
  - name: synthesizer
    role: Create architecture documentation
    backend: gpt-5-mini

coordination:
  pattern: sequential
  memory_sharing: enabled

Test Command:

git clone https://github.com/tiangolo/fastapi.git
cd fastapi
massgen --config tools/memory/gpt5mini_gemini_codebase_analysis_memory.yaml \
  --query "Analyze this codebase architecture"

Expected Output Structure:

  • Architecture Overview
  • Component Map
  • Design Patterns Used
  • Request Flow Diagram
  • Key Abstractions
  • Getting Started for Contributors

Related Work

  • Persistent Memory (v0.1.5) - Memory system foundation
  • Multi-Turn Filesystem (v0.0.25) - File access capabilities
  • Parallel File Operations (v0.1.15 planned) - Will improve read performance