Transform raw codebases into production-ready technical documentation with AI-driven AST analysis.
CodeDocs is a sophisticated AI-native documentation engine designed to solve the chronic problem of stale technical debt and outdated wikis. Built on a hybrid architecture that combines Abstract Syntax Tree (AST) parsing with Large Language Models (LLMs), CodeDocs performs deep semantic analysis of repositories to generate structured, human-readable documentation. In the 2026 landscape, it distinguishes itself by moving beyond simple docstring generation to 'Context-Aware Mapping,' where it identifies cross-service dependencies and architectural patterns within microservice environments. The platform integrates directly into CI/CD pipelines, ensuring that documentation is updated automatically with every commit. For Lead Architects, it provides a centralized 'Source of Truth' that reduces developer onboarding time by up to 60% and ensures that API specifications, internal logic flow, and deployment procedures are always in sync with the production code. Its 2026 roadmap emphasizes RAG-based (Retrieval-Augmented Generation) internal chat interfaces, allowing developers to query their own codebase in natural language to find specific logic implementations or historical context.
CodeDocs is a sophisticated AI-native documentation engine designed to solve the chronic problem of stale technical debt and outdated wikis.
Explore all tools that specialize in generate technical documentation. This domain focus ensures CodeDocs delivers optimized results for this specific requirement.
Explore all tools that specialize in ast analysis. This domain focus ensures CodeDocs delivers optimized results for this specific requirement.
Uses AST analysis to create a visual graph of how data moves through functions and modules.
Only re-indexes and updates documentation for modified files, preserving manual edits in unchanged sections.
Maintains context across different languages within the same mono-repo (e.g., Python backend to TS frontend).
Identifies Architectural Decision Records (ADRs) within commit messages and updates the project log.
Creates bi-directional links between documentation paragraphs and the specific lines of code they describe.
A natural language interface for developers to ask 'Where is the auth logic?' or 'How do I add a new endpoint?'
Allows users to switch between local LLMs (Ollama/Llama 3) for security or cloud LLMs for high-quality synthesis.
Authenticate with GitHub, GitLab, or Bitbucket via OAuth2.
Select the specific repositories for indexing and analysis.
Configure the .codedocs.yaml file to define exclusion patterns and output directories.
Initialize the AST parser to map function-level dependencies.
Select the LLM model (e.g., GPT-4o or Claude 3.5 Sonnet) for the synthesis layer.
Run the initial 'Deep Scan' to generate the baseline documentation.
Review and manually override AI-generated summaries where necessary.
Integrate the CodeDocs CLI into the local pre-commit hooks.
Set up the automated CI/CD trigger to rebuild documentation on PR merges.
Publish the live documentation portal to a custom domain or internal hosting.
All Set
Ready to go
Verified feedback from other users.
"Highly praised for its ability to understand complex Go and Rust logic where other tools fail. Some users find the initial setup of exclusion rules tedious."
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The intelligent workflow engine that captures, manages, and connects your developer fragments with on-device AI.
Auto-evolving technical documentation that lives in your codebase, not your backlog.
The AI-native workspace for high-velocity engineering and product teams.
Transform source code into comprehensive technical documentation with agentic AI analysis.
The AI-driven code assistant for streamlined development and instant technical documentation.
Automate technical documentation and architecture visualization directly from your codebase.
