Code-Graph-RAG Use Cases: 9 Practical Applications for AI-Powered Code Understanding

Code-Graph-RAG transforms any multi-language codebase into a queryable knowledge graph, enabling natural-language exploration, automated refactoring, dead-code elimination, and real-time AI-assisted editing.

Code-Graph-RAG (CGR) is an open-source tool that parses source code with Tree-sitter and stores entities and relationships in Memgraph as a unified, language-agnostic knowledge graph. This architecture unlocks capabilities impossible with traditional text search or AST tools alone. Below are the primary use cases for code-graph-rag demonstrated in the vitali87/code-graph-rag implementation.

Use Case 1: Cross-Language Codebase Navigation

Modern repositories mix Python, TypeScript, Rust, Go, and more. CGR parses 15+ languages into a single graph schema where you can trace relationships across language boundaries.

The parser in codebase_rag/utils/dependencies.py resolves language-specific Tree-sitter grammars, while codebase_rag/graph_loader.py streams the unified schema to Memgraph. This lets you ask questions like "Which Go service calls the Python data-processing module?" without knowing either codebase's structure.

# Parse a polyglot monorepo

cgr start --repo-path ./my-monorepo --update-graph

# Cross-language query

cgr query "Show me all Java services that import Python modules"

Use Case 2: Natural-Language Code Queries

The RAG pipeline converts plain English into executable Cypher queries. In codebase_rag/main.py, the query command uses an AI model to generate graph traversals, then returns concrete source locations rather than hallucinated answers.

cgr query "Which functions call process_data and have cyclomatic complexity above 10?"

The system translates this to a Cypher query against the graph schema (documented in docs/architecture/graph-schema.md), executes it, and formats results with source snippets and file paths.

Use Case 3: AI-Assisted Refactoring with AST Guarantees

Traditional find-and-replace risks breaking code. CGR's editing flow uses AST-based "surgical" patches with mandatory diff previews.

When you invoke a rewrite, codebase_rag/main.py orchestrates the transformation through ast-grep patterns, generates the patch, and requires user confirmation before any file modification. This prevents the fragility of regex-based replacements.

# Structural replacement: convert print() to logger.info()

cgr replace --pattern 'print($ARGS)' --replacement 'logger.info($ARGS)'

The preview shows exact line changes before you confirm, as detailed in the Code Optimisation Guide.

Use Case 4: Dead Code Detection via Call Graph Traversal

Unused code accumulates in large repositories. CGR's dead-code detection uses call-graph edges to identify unreachable definitions.

In codebase_rag/dead_code.py, the algorithm walks CALLS and REFERENCES edges from specified entry points, marking any unvisited nodes as potentially dead. The FLOWS_TO edges (handled in codebase_rag/flow_verdict.py) add data-flow awareness to reduce false positives.

# Find unreachable code starting from main.py

cgr dead-code --entry-points main.py,cli.py

This surfaces functions, classes, and modules with no incoming calls from your production entry points.

Use Case 5: Data-Flow Analysis and Security Tracing

The FLOWS_TO relationship traces how values propagate through assignments, function calls, and I/O sinks. Added in recent releases for 10 languages, this enables taint analysis without external security tools.

You can query: "Which user inputs flow to database queries without sanitization?" The graph stores this as path traversals through FLOWS_TO edges connecting variables, parameters, and sink nodes.

See docs/architecture/data-flow-edges.md for the edge semantics and supported language coverage.

Use Case 6: Real-Time Incremental Graph Updates

Long-running development sessions require synchronized knowledge graphs. The realtime_updater in codebase_rag/graph_updater.py watches filesystem changes and incrementally updates the graph without full re-parsing.

# Start watching for changes

cgr daemon up --watch

# Or enable watch mode during start

cgr start --repo-path ./project --watch

File modifications trigger targeted updates to affected nodes and edges, keeping AI queries accurate as you edit.

Use Case 7: MCP Server Integration for External Tools

CGR exposes its capabilities via the Model Context Protocol (MCP), allowing external AI assistants to query and modify codebases programmatically.

The MCP server implementation lets tools like Claude Code execute cgr query, cgr replace, and cgr dead-code without shell escapes. This bridges CGR's graph intelligence with IDE-integrated AI workflows.

Documentation in docs/guide/mcp-server.md covers configuration and available endpoints.

Use Case 8: Semantic Search with Vector Embeddings

Beyond structural queries, CGR indexes code semantics using Qdrant vector storage. The codebase_rag/vector_store.py module embeds function bodies and docstrings for similarity search.

Combined with graph traversal, this enables queries like "Find functions similar to authenticate_user that also call the database layer." The vector results seed graph exploration for precision.

Use Case 9: Custom Language and Enterprise Deployment

Organizations with proprietary languages or strict data requirements benefit from extensible architecture and air-gapped deployment.

Adding a language requires only a YAML pattern file for ast-grep or a Tree-sitter grammar, as documented in docs/advanced/adding-languages.md. For enterprise use, CGR supports managed cloud or on-premise Docker deployments with the Memgraph+Qdrant stack, including air-gapped installations for data sovereignty.

# Enterprise deployment template

docker-compose -f docker-compose.enterprise.yml up

Summary

The use cases for code-graph-rag span the entire software maintenance lifecycle:

• Unified navigation across Python, TypeScript, Rust, Go, Java, C, C++, C#, PHP, Lua, Dart, and more
• Natural-language exploration powered by AI-generated Cypher queries against Memgraph
• Safe automated refactoring with AST-based patches and mandatory preview diffs
• Dead-code elimination using call-graph and data-flow reachability analysis
• Security and data-flow tracing via FLOWS_TO edge traversal
• Real-time synchronization through filesystem watching in codebase_rag/graph_updater.py
• External tool integration via MCP server protocol
• Semantic similarity search with Qdrant vector embeddings
• Custom extensibility for new languages and enterprise deployment constraints

These capabilities derive from core files including codebase_rag/main.py (CLI), codebase_rag/graph_loader.py (schema management), codebase_rag/cypher_queries.py (query generation), and codebase_rag/dead_code.py (reachability analysis).

Frequently Asked Questions

What databases does Code-Graph-RAG require?

Code-Graph-RAG requires Memgraph for the graph database and Qdrant for vector storage. The cgr daemon up command launches both as Docker containers. You can also use existing Memgraph or Qdrant instances by configuring connection URLs via environment variables.

How does Code-Graph-RAG handle large codebases?

CGR uses incremental parsing and streaming graph loads. The --update-graph flag forces a fresh parse, while omitting it applies only changed files. The realtime_updater watches files continuously to keep the graph synchronized without full re-indexing. Memory usage scales with graph complexity, not source code size.

Can I add support for a language not in the default list?

Yes. Adding a language requires either a Tree-sitter grammar for full AST parsing or an ast-grep YAML pattern file for structural search. The guide in docs/advanced/adding-languages.md covers both approaches. The codebase_rag/utils/dependencies.py module dynamically resolves available parsers at runtime.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}