Sourcify
Effortlessly find and manage open-source dependencies for your projects.

Automate technical documentation and architecture visualization directly from your codebase.

CodeDriven is a specialized AI-orchestration platform designed to solve the chronic problem of 'documentation drift' in agile software development. By leveraging Advanced Syntax Tree (AST) analysis combined with Large Language Models (LLMs), CodeDriven treats the codebase as the single source of truth to automatically generate and maintain READMEs, API specifications, and architectural diagrams. In the 2026 market landscape, CodeDriven has evolved into a Knowledge Graph engine that doesn't just describe code but maps the logical dependencies across polyglot microservices. It integrates natively with Git workflows to trigger documentation updates during Pull Requests, ensuring that any logic change is reflected in the technical wiki immediately. Its architecture supports RAG (Retrieval-Augmented Generation) patterns, allowing engineering teams to query their own repositories in natural language to understand legacy systems or plan refactoring paths. The platform bridges the gap between high-level architectural intent and low-level implementation details, significantly reducing developer onboarding time and technical debt visibility.
CodeDriven is a specialized AI-orchestration platform designed to solve the chronic problem of 'documentation drift' in agile software development.
Explore all tools that specialize in diagram generation. This domain focus ensures CodeDriven delivers optimized results for this specific requirement.
Explore all tools that specialize in generate technical documentation. This domain focus ensures CodeDriven delivers optimized results for this specific requirement.
Explore all tools that specialize in visualize software architecture. This domain focus ensures CodeDriven delivers optimized results for this specific requirement.
Uses Abstract Syntax Tree parsing to understand code logic before feeding snippets to the LLM, reducing hallucinations.
Automatically translates code execution paths into visual sequence diagrams that update in real-time.
Monitors changes in logic and alerts when the existing documentation no longer matches the implementation.
Indexes dependencies across different languages (e.g., a Python backend calling a Go microservice).
Vectorizes the codebase to allow developers to ask 'Where is the auth logic handled?' and receive cited answers.
Analyzes code against SOC2 and GDPR requirements to auto-generate compliance reports.
Allows editing of documentation in a web UI and pushes the changes back to the repository as Markdown.
Sign up via GitHub or GitLab OAuth to establish repository access.
Install the CodeDriven GitHub App or configure the GitLab integration.
Select the primary repositories and branches (e.g., main/master) for initial indexing.
Configure the 'codedriven.yaml' file in the root directory to define documentation scope.
Run the initial 'Deep Scan' to generate the project's semantic knowledge graph.
Review the automatically generated Mermaid.js sequence and flow diagrams.
Customize the AI prompting templates for specific documentation styles (e.g., JSDoc vs. Google style).
Enable the 'Auto-PR' feature to allow the AI to suggest doc updates alongside code changes.
Invite team members and sync with internal wikis like Notion or Confluence via API.
Set up periodic security and technical debt audits within the dashboard.
All Set
Ready to go
Verified feedback from other users.
"Highly praised for its diagramming capabilities and ease of integration into existing CI/CD pipelines. Some users note initial setup complexity for very large monorepos."
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