Turning Code into a Visual Graph: How We're Using Memgraph for Indexation

Understanding a large codebase can feel like navigating a maze. When our agents index a repository, they're building a comprehensive knowledge graph—a map of all the files, functions, dependencies, and connections within the code.

While the data itself is powerful, the real "wow factor" comes from being able to see that structure. That’s why we’ve bundled Memgraph Lab, a fantastic graph visualization UI, right into our agent instance.

Bringing the Codebase to Life

Right now, if you're running the agent locally, all the visualization capabilities of Memgraph Lab are available. It’s a simple matter of exposing port 3000 inside the container, and suddenly, you have a visual, interactive index of your entire codebase.

This isn't just a gimmick; it's a powerful tool for both our internal teams and, eventually, our customers.

Key Use Cases for the Code Graph

Here are the immediate benefits we've identified for having this visual layer integrated:

1. Debugging & Sharing State: When we're running into issues or seeing unexpected results in a trace, we can export the exact state of the graph at that time. This is huge. We can attach that exported state to a ticket or a Slack message, and any other developer can import it into their local Memgraph Lab instance to see precisely what the agent indexed and where the potential issue lies. This, combined with our tracing capabilities, should significantly speed up our debugging cycles.
2. Guided Code Exploration: We know that trying to write complex Cypher (Memgraph’s query language) just to explore a codebase is too much work. That’s why we’re pre-configuring useful queries that are a single button click away. Imagine a customer clicking a button in our plugin that runs a query like:
   • "Find all my endpoints and their connected security schemes."
   • "Show the dependencies of the service I currently have open."
   • "Locate all instances of Maxis connected to Pepos (a joke for the team, but you get the idea!)." This makes the graph immediately useful, even for non-graph experts.
3. Visual Clarity Through Custom Styling: A standard graph can quickly become a mess of dots and lines. To make the visualization truly useful, we are applying custom styling. We can assign standing-out icons for different node types—a specific icon for a function, another for a class, and distinct icons for files or folders. This transforms a generic visualization into a highly intuitive map of the codebase architecture.

Looking Ahead: Diffing the Graph

One of the most exciting future possibilities came up in our discussion: diffing the graph.

Right now, we typically delete and rebuild the knowledge graph on every major code change because it's fast. However, for true analytical power and to ensure the index is correct, we plan to implement incremental builds.

If we can do incremental builds, we can calculate the difference between the graph before a code change and the graph after. This means a user could get a visual display of exactly what was affected by their change:

• "Did I actually have everything dependent on what I expected it to be?"
• "Is everything affected tied to what I expected to tie it to?"

This is where the graph analytics side of things moves from a 'nice-to-have' to a critical quality assurance tool.

We are very excited about the potential of bringing this level of visibility to code indexing. Would you be interested in a deeper dive into the specific Cypher queries we are developing to explore the code graph?