CodeCompass

CodeCompass helps developers tackle legacy or existing codebases by giving AI coding assistants the context they need to deliver spot-on suggestions. Legacy code is tough for AI—it’s often messy, outdated, and lacks clear documentation. CodeCompass solves this by analyzing your codebase with Qdrant Vector Store and powering AI with Ollama (local) or cloud agents like DeepSeek, using its Agentic RAG feature to make suggestions smarter and more relevant. It’s like giving your AI a roadmap to your code, so you can vibe code effortlessly.

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• Codebase Analysis: Maps your repository's structure and dependencies, now with support for indexing very large files through automated chunking.
• Smart AI Context with Agentic RAG: Utilizes a sophisticated Retrieval Augmented Generation (RAG) approach. The central agent_query tool intelligently orchestrates internal capabilities to gather comprehensive context. This includes analyzing git diff information (summarized if large), dynamically summarizing extensive file lists or code snippets, and more, ensuring AI suggestions are highly relevant.
• Intelligent Agent Orchestration: The core agent_query tool allows the AI to plan and execute multi-step tasks. It can proactively use a suite of internal capabilities to:
• Search code (capability_searchCodeSnippets)
• Retrieve full file content (capability_getFullFileContent), with summarization for large files.
• List directory contents (capability_listDirectory).
• Fetch adjacent code chunks (capability_getAdjacentFileChunks).
• Analyze repository overviews including diffs and relevant snippets (capability_getRepositoryOverview).
• Request more search results (capability_fetchMoreSearchResults) or more processing time if a query is complex.
• Flexible Setup: Runs locally with Ollama or connects to cloud AI