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Understand, search, and plan code changes with whole-repo semantic context.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "rpg-encoder" yet — see the docs or source repo.
Using the repository semantic graph, find the core modules related to user authentication and authorization, and explain how they relate to each other.
A list of relevant code modules plus an explanation of their semantic relationships in the repository.
From the whole-repo context, analyze the main files, classes, or function call paths likely involved in the “order creation” feature.
The key implementation path for the feature and the main code locations involved.
I want to add a new field to an existing API. Using whole-repo context, plan which files may need updates, the impact scope, and key considerations.
A cross-file change plan including affected modules and potential dependencies.
When working in an unfamiliar repository, developers can use graph-based whole-repo semantic understanding to quickly locate relevant code. It is useful for finding implementation clues spread across multiple modules.
Before changing a feature or API, teams can explore module relationships and plan the scope of updates. This helps reduce blind spots caused by looking at only local files.
When an AI agent needs whole-repo context in a single tool call, this tool provides graph-based code understanding. It is suitable for search, exploration, and change planning.
It provides whole-repo understanding through a semantic graph of the codebase, enabling AI agents to search, explore, and plan changes. Its focus is getting repository-wide context in a single tool call.
The description emphasizes semantic codebase understanding and a graph, not just keyword lookup. It is better suited to exploring code and planning modifications with whole-repo relationships in mind.
The provided material does not include installation steps, runtime requirements, or key information. For prerequisites, see the source repository.
Query, understand, and edit large multilingual codebases with AI knowledge graphs.
Gives AI coding agents semantic code graphs for faster, more precise answers.
Run local semantic search and call graph analysis across codebases.
Search codebases semantically and find relevant snippets with source locations.
Ask natural-language questions over codebases with citations and impact analysis.
Offline code retrieval and structural analysis tools for AI agents with fewer tokens.