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Query, understand, and edit large multilingual codebases with AI knowledge graphs.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "code-graph-rag" yet — see the docs or source repo.
Analyze the login flow call chain in this monorepo, from the frontend entry point to the backend auth service. List the files, functions, and dependencies involved, and explain the flow step by step.
A cross-language, cross-module call path with key code locations and dependency relationships.
Help me quickly understand the payments service: summarize its core modules, external dependencies, main data flow, and which components I should read first.
A structured overview of the service, key module explanations, and a recommended reading order.
Add a new “refunded” value to the order status enum, then identify which APIs, database models, frontend displays, and test files are affected, and provide suggested changes or patches.
A list of impacted files, reasons for the changes, and actionable edits or patch suggestions.
Gives AI coding agents semantic code graphs for faster, more precise answers.
Query and understand large codebases with a fast knowledge graph for AI agents.
Provide structured code context and dependency graphs for efficient codebase understanding.
Ask natural-language questions over codebases with citations and impact analysis.
Build a knowledge graph from repos for Q&A and implementation planning.
Understand, search, and plan code changes with whole-repo semantic context.