Sqry – semantic code search using AST and call graphs
I built sqry, a local code search tool that works at the semantic level rather than the text level. The motivation: ripgrep is great for finding strings, but it can't tell you "who calls this function", "what does this function call", or "find all public async functions that return Result". Those questions require understanding code structure, not just matching patterns. sqry parses your code into an AST using tree-sitter, builds a unified call/ import/dependency graph, and lets you query it: sqry query "callers:authenticate" sqry query "kind:function AND visibility:public AND…
What it does
In the maker’s words, at launch
I built sqry, a local code search tool that works at the semantic level rather than the text level. The motivation: ripgrep is great for finding strings, but it can't tell you "who calls this function", "what does this function call", or "find all public async functions that return Result". Those questions require understanding code structure, not just matching patterns. sqry parses your code into an AST using tree-sitter, builds a unified call/ import/dependency graph, and lets you query it: sqry query "callers:authenticate" sqry query "kind:function AND visibility:public AND lang:rust" sqry graph trace-path main handle_request sqry cycles sqry ask "find all error handling functions" The `sqry ask` command translates natural language into sqry query syntax locally, using a compact 22M-parameter model with no network calls. Some things that might be interesting to HN: - 35 language plugins via tree-sitter (C, Rust, Go, Python, TypeScript, Java, SQL, Terraform, and more) - Cross-language edge detection: FFI linking (Rust↔C/C++), HTTP route matching (JS/TS↔Python/Java/Go) - 33-tool MCP server so AI assistants get exact call graph data instead of relying on embedding similarity - Arena-based graph with CSR storage; indexed queries run ~4ms warm - Cycle detection, dead code analysis, semantic diff between git refs It's MIT-licensed and builds from source with Rust 1.90+. Fair warning: full build takes ~20 GB disk because 35 tree-sitter grammars compile from source. Repo: https://github.com/verivusai-labs/sqry Docs: https://sqry.dev Happy to answer questions about the architecture, the NL translation approach, or the cross-language detection.
Does the same job
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- ACAdvanced Code Search with Tree-sitter AST and Qdrant Vector DB2025 · app.repogram.com · ▲5
For the past couple of months, I’ve been building a tool that enables natural language search over large codebases using Tree-Sitter for syntax parsing and Qdrant for vector-based retrieval. https://app.repogram.com ### How It Works - Tree-Sitter is used to parse syntax trees and extract high-quality vector embeddings of code. - These embeddings are stored in Qdrant, enabling fast similarity search across your entire repo. - A combination of re-ranking processes refine search results, producing highly relevant answers to code-related questions. The results have been incredibly…
- SMSourcerer – MCP for semantic code search that reduces token waste2025 · github.com · ▲9
I built this because I got tired of watching Claude Code read through massive files just to find a few functions. Sourcerer lets AI agents search code semantically and grab exactly the code chunks they need instead of burning tokens on whole files. It uses tree-sitter to parse your codebase and creates a searchable index. So instead of "read auth.py (538 lines)", an agent can search for "user authentication logic" and get back just the relevant functions. Demo: https://asciinema.org/a/736638 GitHub: https://github.com/st3v3nmw/sourcerer-mcp
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