Codeboarding – Interactive architecture diagrams for codebases

See what your AI is building before it breaks.

CodeBoarding gives developers and coding agents a visual map of a codebase. It combines static analysis with LLM reasoning to generate architecture diagrams, component-level documentation, and navigable outputs you can use in your IDE, CI, and docs.

Website · Open VSX extension · Explore examples · VS Code extension · GitHub Action ·Discord

Install the extension from Open VSX.

• Keep architecture visible while agents code.

• Review AI-generated changes with system context before they turn into hidden debt.

• Understand large repositories faster with layered diagrams and component breakdowns.

• Share the same visual model across local workflows, IDEs, pull requests, and docs.

• High-level system architecture diagrams.

• Deeper component diagrams for important subsystems.

• Markdown documentation in .codeboarding/

. - Mermaid output that is easy to embed in docs and PRs.

• Incremental updates when only part of the codebase changes.

graph LR
    Application_Orchestrator_Repository_Manager["Application Orchestrator & Repository Manager"]
    LLM_Agent_Core["LLM Agent Core"]
    Static_Code_Analyzer["Static Code Analyzer"]
    Agent_Tooling_Interface["Agent Tooling Interface"]
    Incremental_Analysis_Engine["Incremental Analysis Engine"]
    Documentation_Diagram_Generator["Documentation & Diagram Generator"]
    Application_Orchestrator_Repository_Manager -- "Orchestrator initiates analysis workflow, leveraging incremental updates based on detected code changes." --> Incremental_Analysis_Engine
    Application_Orchestrator_Repository_Manager -- "Orchestrator passes project context and triggers the main analysis workflow for the LLM Agent." --> LLM_Agent_Core
    Incremental_Analysis_Engine -- "Incremental engine requests static analysis for specific code segments (new or changed)." --> Static_Code_Analyzer
    Static_Code_Analyzer -- "Static analyzer provides analysis results to the incremental engine for caching." --> Incremental_Analysis_Engine
    LLM_Agent_Core -- "LLM Agent invokes specialized tools to interact with the codebase and analysis data." --> Agent_Tooling_Interface
    Agent_Tooling_Interface -- "Agent tools query the static analysis engine for detailed code insights." --> Static_Code_Analyzer
    Static_Code_Analyzer -- "Static analysis engine provides requested data to the agent tools." --> Agent_Tooling_Interface
    LLM_Agent_Core -- "LLM Agent delivers structured analysis insights for documentation and diagram generation." --> Documentation_Diagram_Generator
    click Application_Orchestrator_Repository_Manager href "https://github.com/CodeBoarding/CodeBoarding/blob/main/.codeboarding/Application_Orchestrator_Repository_Manager.md" "Details"
    click LLM_Agent_Core href "https://github.com/CodeBoarding/CodeBoarding/blob/main/.codeboarding/LLM_Agent_Core.md" "Details"
    click Static_Code_Analyzer href "https://github.com/CodeBoarding/CodeBoarding/blob/main/.codeboarding/Static_Code_Analyzer.md" "Details"
    click Agent_Tooling_Interface href "https://github.com/CodeBoarding/CodeBoarding/blob/main/.codeboarding/Agent_Tooling_Interface.md" "Details"
    click Incremental_Analysis_Engine href "https://github.com/CodeBoarding/CodeBoarding/blob/main/.codeboarding/Incremental_Analysis_Engine.md" "Details"
    click Documentation_Diagram_Generator href "https://github.com/CodeBoarding/CodeBoarding/blob/main/.codeboarding/Documentation_Diagram_Generator.md" "Details"

For a deeper architecture walkthrough, see .codeboarding/overview.md.

uv sync --frozen
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
python install.py
python main.py full --local /path/to/repo

Requires Python 3.12 or 3.13. The recommended install method is pipx, which keeps the CLI in its own isolated environment:

pipx install codeboarding --python python3.12
codeboarding-setup
codeboarding full --local /path/to/repo

Or, if you prefer pip, install into a virtual environment (not the global Python):

pip install codeboarding
codeboarding-setup
codeboarding full --local /path/to/repo

Output is written to /path/to/repo/.codeboarding/

.

python install.py

and codeboarding-setup

download language server binaries to ~/.codeboarding/servers/

, shared across projects. Node.js (and its bundled npm

) is required for the Python, TypeScript, JavaScript, and PHP language servers; if neither node

nor CODEBOARDING_NODE_PATH

is set, setup downloads a pinned Node.js runtime into ~/.codeboarding/servers/nodeenv/

automatically.

On first run, CodeBoarding creates ~/.codeboarding/config.toml

. Set one provider there or use environment variables.

[provider]

[llm]

Shell environment variables such as OPENAI_API_KEY

, ANTHROPIC_API_KEY

, GOOGLE_API_KEY

, and OLLAMA_BASE_URL

take precedence over the config file. For private repositories, set GITHUB_TOKEN

in your environment.

python main.py full --local ./my-project

python main.py full --local ./my-project --depth-level 2

python main.py incremental --local ./my-project

python main.py partial --local ./my-project --component-id "1.2"

python main.py full https://github.com/pytorch/pytorch

CLIfor local analysis, automation, and CI workflows.VS Code extensionfor in-editor visual architecture.GitHub Actionto keep diagrams updated in CI.

• Languages: Python, TypeScript, JavaScript, Java, Go, PHP, Rust, C#.

• LLM providers: OpenAI, Anthropic, Google, Vercel AI Gateway, AWS Bedrock, Ollama, OpenRouter, and more.

• Visualized 800+ open-source repositories.

• Browse generated examples in GeneratedOnBoardings. - Try the hosted explorer at codeboarding.org/diagrams.

If you want to improve CodeBoarding, open an issue or send a pull request. We welcome improvements to analysis quality, output generators, integrations, and developer experience.

CodeBoarding is building an open standard for code understanding: a visual, accurate, high-level representation of a codebase that both humans and agents can use.