Monorepos and mixed-language codebases are hard to search, reason about, and safely refactor at scale. Code-Graph-RAG converts source trees into a single language-agnostic graph so you can ask precise, grounded questions about structure and changes, and apply surgical AST-based edits driven by models.
What Sets It Apart
- Unified graph schema across many languages: Tree-sitter parsers feed a single Memgraph schema that normalizes functions, classes, modules and their relationships — so you can run cross-language queries and analyses without juggling per-language tools.
- Natural language → Cypher pipeline: LLM-backed Cypher generation turns plain-English queries into graph queries, producing answers grounded in graph nodes and the actual source code rather than hallucinated summaries.
- Structural search & replace with AST tooling: ast-grep and AST-aware agents let you match and rewrite code by structure (not regex), enabling safer, large-scale refactors with previewed diffs.
- Flexible model and deployment choices: Works with cloud models (e.g., Gemini/OpenAI), local runtimes (Ollama), and runs as an MCP server for direct agent integrations — so teams can balance capability, latency, and data control.
Who it's for and tradeoffs
Great fit if you maintain large monorepos or polyglot services and need precise, auditable code discovery, cross-file data-flow tracing, or automated structural refactoring. Look elsewhere if you need a lightweight, zero-infrastructure editor plugin — the system expects a graph DB (Memgraph), optional vector services, and some orchestration (Docker/daemon) for full functionality. Also consider privacy needs: by default it can integrate cloud LLMs, so on-prem/local model options are recommended for sensitive codebases.
