What it does
SocratiCode indexes local codebases for AI-native semantic search and code understanding. It chunks code using AST awareness, builds hybrid search indices combining BM25 and embeddings, and automatically constructs dependency graphs and symbol-level call flows. The server runs as a Docker container, keeps indices up-to-date via file watchers, and supports multi-branch and multi-repo indexing at scale—tested on repositories exceeding 40 million lines. All indexing and searching is local by default; cloud embeddings (OpenAI, Google Gemini) are optional.
Who it's for
Backend teams indexing proprietary codebases where sending code off-machine is prohibited. Organizations running multiple AI agents simultaneously on the same project who need a shared, automatically-coordinated knowledge layer. Developers on large monorepos using Claude Code, Cursor, or other AI IDEs who want instant architectural context and semantic search without manual setup.
Common use cases
- Search a codebase semantically to find where a feature or concept is implemented across thousands of files.
- Analyze code blast radius: understand all dependencies on a changed module before refactoring.
- Prime Claude or another AI assistant with architectural context before code generation tasks.
- Run multiple independent agents on the same codebase with automatic index coordination.
- Navigate code structure visually through the interactive HTML dependency graph explorer.
Setup pitfalls
- Requires
DockerandNode.js18+. Large codebases need significant disk space and initial indexing time; resumable checkpoints prevent data loss on interruption. - Reads and writes the entire filesystem to build and maintain indices; isolate the container or carefully review file access permissions.
- Classified as high risk due to filesystem and network capabilities—evaluate before running on sensitive systems.
- Optional cloud embeddings require API keys (OpenAI, Google Gemini); no CI currently enabled, so verify stability on non-critical codebases first.