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Index repositories so LLMs can understand, search, and analyze code faster.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "VectorCode" yet — see the docs or source repo.
Use VectorCode to index this repository and summarize the overall architecture, core modules, major dependencies, and relationships between modules.
A structured repository overview describing the project layout, key files, and module responsibilities.
Use VectorCode to search this repository for code related to user authentication and login flow, then list the key files, entry points, and call chain.
A list of relevant implementation locations with function paths and brief notes for further changes.
First build an index with VectorCode, then analyze coupling in the payment module and identify refactoring opportunities, risks, and affected files.
A refactoring analysis with dependencies, risk areas, and the recommended scope of changes.
Search and navigate multiple code repositories with natural language understanding.
Enable AI agents and IDEs to search codebases with hybrid retrieval and precise line-level citations.
Index codebases into Qdrant for semantic code search and faster AI retrieval.
Add local semantic code search and persistent memory to AI code editors.
Search indexed codebases semantically with natural language across MCP clients.
Index repositories into a persistent graph for fast code search and understanding.