sqry review
A focused open-source choice for structural search, code relationships, and navigation.
Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
sqry is an open-source semantic code search engine for developers working with local repositories and codebases. It parses source code into abstract syntax trees, builds symbol and relationship graphs, and supports searches for callers, callees, imports, exports, implementations, cycles, unused symbols, and duplicates. The product runs locally on Windows, macOS, and Linux, supports 37 language plugins, and includes CLI, MCP, and LSP components. It is a strong fit for developers who need more than regex or literal symbol-name search and want code relationships available in a local workflow.
Its main strength is the depth of its structural model. Boolean queries can operate over symbols and relationships, while call hierarchy and code navigation are available through LSP. Dependency analysis adds cycle and unused-symbol checks, giving sqry a broader role than a basic code finder. The published integrations include VS Code, Neovim, Helix, GitHub Actions, Claude, Codex, Gemini, Cursor, and Windsurf. That combination suits teams and individuals moving between editors, automation, and AI-assisted development, especially when local code handling is important.
sqry follows an open-source, self-hosted model and is MIT-licensed. The free plan and local deployment make it accessible without a paid tier, while also placing setup and environment management with the user. Developers who need structural queries, relationship-aware navigation, or dependency analysis should consider sqry. Those who only need straightforward text search, or who prefer a hosted product with managed operations and a broader editor ecosystem, may be better served by an alternative. Its focused scope is an advantage for code intelligence workflows but may be unnecessary for simpler repositories.
sqry pros and cons
- Where it wins
- AST indexing enables structural searches across a codebase
- Caller, callee, dependency, cycle, and unused-symbol analysis
- MCP, LSP, and integrations for editors and AI assistants
- Where it doesn't
- Self-hosted deployment requires managing the local environment
- Structural search may be more than needed for simple text lookup
- Editor integration is centered on VS Code, Neovim, and Helix
sqry fact sheet, pricing and score →
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