From Code to Graph: A Novel Approach to Codebase Understanding for AI Agents

[vitali87]

Dear Code-Graph-RAG aficionados,

I will be speaking at Big Data LDN conference at 16:00 - 16:30 on Wednesday, 23rd Sep, 2026. Please join me to discuss the current status and the future of CGR.

Whoever does not know: Big Data LDN is the UK's leading data, analytics and AI conference & exhibition.

Modern coding agents navigate large codebases by grepping, reading files, and following references. It works, but every structural question like "what calls this function?" becomes multiple rounds of search-and-read, burning tool calls, tokens, and latency, and the answer is only as complete as the search terms the agent happened to pick.

Code is a graph, and these are graph questions. Code-Graph-RAG parses multi-language codebases with Tree-sitter into a unified knowledge graph in Memgraph, where calls, inheritance, imports, and definitions are already materialized. "What calls UserService.create_user?" becomes a single traversal returning an exact, complete answer, not one stitched together from text matches. The same structure powers AST-targeted surgical edits, and the toolset is exposed to Claude Code and other clients over MCP.

This session covers the architecture: Tree-sitter parsing, the language-agnostic schema, and natural-language-to-Cypher querying. It makes the case for when precomputed structure beats search-and-read, and for when the two worlds combine: graph traversal and grep working together so agents reach for the right tool at the right time.

Here is the link to register:
https://www.bigdataldn.com/en-gb/conference/session-details.5119.270402.from-code-to-graph-a-novel-approach-to-codebase-understanding-for-ai-agents.html