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3 months ago
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CodeBoarding is an open-source codebase analysis tool that generates high-level diagram representations of codebases using static analysis and LLM agents, that humans and agents can interact with.
It’s designed to support onboarding, documentation, and comprehension for large, complex systems.
- Extract modules and their relationships based on the control flow graph of the project.
- Builds different levels of abstraction with an LLM agent (multi-provider support)
- Outputs interactive diagrams (Mermaid.js) for integration into docs, IDEs, CI/CD.
📄 Existing visual generations: GeneratedOnBoardings
🌐 Try for your open-source project: www.codeboarding.org/demo
For detailed architecture information, see our diagram documentation.
graph LR
Orchestration_Workflow["Orchestration & Workflow"]
Static_Code_Analyzer["Static Code Analyzer"]
AI_Analysis_Engine["AI Analysis Engine"]
Analysis_Persistence["Analysis Persistence"]
Output_Generator["Output Generator"]
Orchestration_Workflow -- "invokes analysis on" --> Static_Code_Analyzer
Static_Code_Analyzer -- "returns raw graph data to" --> Orchestration_Workflow
Orchestration_Workflow -- "consults and saves analysis to" --> Analysis_Persistence
Analysis_Persistence -- "provides cached analysis to" --> Orchestration_Workflow
Orchestration_Workflow -- "invokes with graph data" --> AI_Analysis_Engine
AI_Analysis_Engine -- "returns high-level model to" --> Orchestration_Workflow
Orchestration_Workflow -- "sends model for rendering to" --> Output_Generator
click Orchestration_Workflow href "https://github.com/CodeBoarding/CodeBoarding/tree/main/.codeboarding/Orchestration_Workflow.md" "Details"
click Static_Code_Analyzer href "https://github.com/CodeBoarding/CodeBoarding/tree/main/.codeboarding/Static_Code_Analyzer.md" "Details"
click AI_Analysis_Engine href "https://github.com/CodeBoarding/CodeBoarding/tree/main/.codeboarding/AI_Analysis_Engine.md" "Details"
Setup the environment:
uv venv --python 3.11
uv pip sync
# LLM Provider (choose one)
OPENAI_API_KEY=
ANTHROPIC_API_KEY=
GOOGLE_API_KEY=
AWS_BEARER_TOKEN_BEDROCK=
# Core Configuration
CACHING_DOCUMENTATION=false # Enable/disable documentation caching
REPO_ROOT=./repos # Directory for downloaded repositories
ROOT_RESULT=./results # Directory for generated outputs
PROJECT_ROOT=/path/to/CodeBoarding # Source project root (must end with /CodeBoarding)
DIAGRAM_DEPTH_LEVEL=1 # Max depth level for diagram generation
# Optional
GITHUB_TOKEN= # For accessing private repositories
LANGSMITH_TRACING=false # Optional: Enable LangSmith tracing
LANGSMITH_ENDPOINT= # Optional: LangSmith endpoint
LANGSMITH_PROJECT= # Optional: LangSmith project name
LANGCHAIN_API_KEY= # Optional: LangChain API key
💡 Tip: Our experience has shown that using Google Gemini‑2.5‑Pro yields the best results for complex diagram generation tasks.
python demo.py <github_repo_url> --output-dir <output_path>
We have visualized over 300+ popular open-source projects. See examples:
graph LR
ChatTTS_Core_Orchestrator["ChatTTS Core Orchestrator"]
Text_Processing_Module["Text Processing Module"]
Speech_Synthesis_Models["Speech Synthesis Models"]
Velocity_Inference_Engine["Velocity Inference Engine"]
System_Utilities_Configuration["System Utilities & Configuration"]
ChatTTS_Core_Orchestrator -- "Orchestrates Text Flow" --> Text_Processing_Module
ChatTTS_Core_Orchestrator -- "Receives Processed Text" --> Text_Processing_Module
ChatTTS_Core_Orchestrator -- "Orchestrates Synthesis Flow" --> Speech_Synthesis_Models
ChatTTS_Core_Orchestrator -- "Receives Audio Output" --> Speech_Synthesis_Models
ChatTTS_Core_Orchestrator -- "Initializes & Configures" --> System_Utilities_Configuration
ChatTTS_Core_Orchestrator -- "Loads Assets" --> System_Utilities_Configuration
Text_Processing_Module -- "Receives Raw Text" --> ChatTTS_Core_Orchestrator
Text_Processing_Module -- "Provides Processed Text" --> ChatTTS_Core_Orchestrator
Speech_Synthesis_Models -- "Receives Processed Data" --> ChatTTS_Core_Orchestrator
Speech_Synthesis_Models -- "Generates Audio Output" --> ChatTTS_Core_Orchestrator
Speech_Synthesis_Models -- "Delegates Inference To" --> Velocity_Inference_Engine
Speech_Synthesis_Models -- "Receives Inference Results" --> Velocity_Inference_Engine
Speech_Synthesis_Models -- "Utilizes GPU Resources" --> System_Utilities_Configuration
Speech_Synthesis_Models -- "Accesses Model Config" --> System_Utilities_Configuration
Velocity_Inference_Engine -- "Executes Model Inference" --> Speech_Synthesis_Models
Velocity_Inference_Engine -- "Returns Inference Output" --> Speech_Synthesis_Models
Velocity_Inference_Engine -- "Receives Engine Configuration" --> System_Utilities_Configuration
System_Utilities_Configuration -- "Provides Assets & Config" --> ChatTTS_Core_Orchestrator
System_Utilities_Configuration -- "Provides GPU & Config" --> Speech_Synthesis_Models
System_Utilities_Configuration -- "Provides Engine Config" --> Velocity_Inference_Engine
click ChatTTS_Core_Orchestrator href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main//ChatTTS/ChatTTS_Core_Orchestrator.md" "Details"
click Text_Processing_Module href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main//ChatTTS/Text_Processing_Module.md" "Details"
click Speech_Synthesis_Models href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main//ChatTTS/Speech_Synthesis_Models.md" "Details"
click Velocity_Inference_Engine href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main//ChatTTS/Velocity_Inference_Engine.md" "Details"
click System_Utilities_Configuration href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main//ChatTTS/System_Utilities_Configuration.md" "Details"
graph LR
Core_Tensor_Operations["Core Tensor Operations"]
Neural_Network_Construction["Neural Network Construction"]
Automatic_Differentiation_Engine["Automatic Differentiation Engine"]
Optimization_Algorithms["Optimization Algorithms"]
Performance_Optimization["Performance Optimization"]
Distributed_Training_Infrastructure["Distributed Training Infrastructure"]
Model_Deployment_Optimization["Model Deployment & Optimization"]
Meta_Programming_Code_Generation["Meta-Programming & Code Generation"]
Functional_Programming_Transforms["Functional Programming Transforms"]
Neural_Network_Construction -- "relies on" --> Core_Tensor_Operations
Automatic_Differentiation_Engine -- "uses" --> Core_Tensor_Operations
Automatic_Differentiation_Engine -- "uses" --> Neural_Network_Construction
Optimization_Algorithms -- "optimizes" --> Neural_Network_Construction
Optimization_Algorithms -- "uses" --> Automatic_Differentiation_Engine
Performance_Optimization -- "optimizes" --> Core_Tensor_Operations
Performance_Optimization -- "optimizes" --> Neural_Network_Construction
Distributed_Training_Infrastructure -- "uses" --> Core_Tensor_Operations
Distributed_Training_Infrastructure -- "uses" --> Automatic_Differentiation_Engine
Distributed_Training_Infrastructure -- "uses" --> Neural_Network_Construction
Model_Deployment_Optimization -- "optimizes" --> Neural_Network_Construction
Model_Deployment_Optimization -- "optimizes" --> Core_Tensor_Operations
Meta_Programming_Code_Generation -- "generates code for" --> Core_Tensor_Operations
Meta_Programming_Code_Generation -- "generates code for" --> Neural_Network_Construction
Functional_Programming_Transforms -- "uses" --> Core_Tensor_Operations
Functional_Programming_Transforms -- "uses" --> Automatic_Differentiation_Engine
click Core_Tensor_Operations href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/pytorch/Core Tensor Operations.md" "Details"
click Neural_Network_Construction href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/pytorch/Neural Network Construction.md" "Details"
click Automatic_Differentiation_Engine href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/pytorch/Automatic Differentiation Engine.md" "Details"
click Optimization_Algorithms href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/pytorch/Optimization Algorithms.md" "Details"
click Performance_Optimization href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/pytorch/Performance Optimization.md" "Details"
click Distributed_Training_Infrastructure href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/pytorch/Distributed Training Infrastructure.md" "Details"
click Model_Deployment_Optimization href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/pytorch/Model Deployment & Optimization.md" "Details"
click Meta_Programming_Code_Generation href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/pytorch/Meta-Programming & Code Generation.md" "Details"
click Functional_Programming_Transforms href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/pytorch/Functional Programming Transforms.md" "Details"
graph LR
Application_Core["Application Core"]
Middleware["Middleware"]
Routing["Routing"]
Request_Handling_Validation["Request Handling & Validation"]
Dependency_Injection["Dependency Injection"]
Security["Security"]
Response_Handling["Response Handling"]
API_Documentation["API Documentation"]
Application_Core -- "sends request to" --> Middleware
Middleware -- "forwards request to" --> Routing
Routing -- "uses" --> Request_Handling_Validation
Routing -- "uses" --> Dependency_Injection
Routing -- "provides data for" --> Response_Handling
Dependency_Injection -- "enables" --> Security
Response_Handling -- "sends response to" --> Middleware
API_Documentation -- "inspects" --> Routing
API_Documentation -- "inspects" --> Request_Handling_Validation
click Application_Core href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/fastapi/Application_Core.md" "Details"
click Middleware href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/fastapi/Middleware.md" "Details"
click Routing href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/fastapi/Routing.md" "Details"
click Request_Handling_Validation href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/fastapi/Request_Handling_Validation.md" "Details"
click Dependency_Injection href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/fastapi/Dependency_Injection.md" "Details"
click Security href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/fastapi/Security.md" "Details"
click API_Documentation href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/fastapi/API_Documentation.md" "Details"
Browse more examples: GeneratedOnBoardings Repository
Codeboarding is integrated with everything we use:
- 📦 VS Code Extension: Interact with the diagram direclty in your IDE.
- ⚙️ GitHub Action: Automate diagram generation in CI/CD.
- 🔗 MCP Server: Serves the consize documentation to your AI Agent assistant (ClaudeCode, VSCode, Cursor, etc.)
Unified high-level representation for codebases that is accurate (hence static analysis). This representation is used by both people and agents → fully integrated in IDEs, MCP servers, and development workflows.
