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To analyze an unfamiliar codebase, AI-assisted repo-analysis performs automated research and generates architectural insights. This approach connects to public repositories on GitHub, GitLab, or Bitbucket to understand project architecture and source code context.

The best way to answer questions about a specific open-source project is using RAG-based Q&A over its source code. This AI-assisted research method analyzes the repository to provide accurate, context-aware answers regarding its internal structure and features.

To perform repository analysis, you must first configure a DeepWiki MCP server within your environment. Once configured, you can run automated analysis and RAG-based Q&A directly against any accessible public repository to extract codebase insights.

Yes, DeepWiki works with GitLab and Bitbucket repositories. The codebase analysis functionality supports public repositories across GitHub, GitLab, and Bitbucket, allowing you to investigate features and understand code structure consistently across these platforms.

Yes, you can generate documentation for an unfamiliar project's architecture using AI-assisted research. By analyzing the repository context, the system provides code structure understanding and automated documentation generation for the analyzed source code.

Yes, you need a configured DeepWiki MCP server to explore code structure and features. This server acts as the centralized hub required to perform repository analysis and extract AI-powered architectural insights from public codebases.