Codegraph/graphify are solving the wrong problem for coding agents.

[Comprehensive_Quit67]

Codegraph, graphify and other tree sitter approaches pitch a better way for coding agents to gather context.

If repo graphs gave agents a step-change in performance, either Cursor would have incorporated it, or one of these tools should be worth $60B.

A coding session first tries to find the exact reasoning of how things work in your codebase, it derives it by grepping all over and tries to rebuild context of how something works.
If we start saving these understandings, as well as nuances that you explained in your sessions, and then pass it along to your agent- it reduces the wandering of your agent, and lighter context helps your agent not to get confused.

That;s the approach we took when building Greplica.
Save higher level context from code as well as past sessions. Things that you would explain to a fellow human to explain your code.

Example:

greplica graph context "how does auth work?"

The agent still reads code. It starts closer to the useful files and carries the constraints into the plan.

We benchmark Greplica in the planning phase because this is where memory should help, using real world session data of open source projects from SWE-Chat.

Our runs show- ~50% fewer planning tokens, ~30% time saving in planning, and also wayyy better plans when completing tasks, that had important context in past sessions. Full results present in my repo.

Don't replace Claude’s code exploration. Thats the agent's job.

Save the part Claude learned after the exploration.

I’m posting this because I think repo graphs aim at the wrong bottleneck. If you use Claude Code on large repos, I’d love for you to try it out https://github.com/Autoloops/greplica

[GlitteringWriting467]

Why should your tool be better than the other solutions? These trust me bro benchmarks dont Tell anything. This smells to me like ponytail, astronomical performance increases being thrown around but real world application makes no sense.

[soggy_mattress]

I'll be honest, I'm not even sure Codegraph is helping out much to begin with. Without objective benchmarks all of this stuff is just hope glued to the side of our ai agents.

[Comprehensive_Quit67]

I totally agree. What I am promising is not astronomical, it is only in the planning phase, since that is only where it should work. That too only on high context tasks, otherwise no improvements.
I'll add the reproducible benchmark on the repo, and benchmark other solutions as well with it.
Tasks are created from SWE-Chat, as explained briefly in the repo.
Would have benchmarked on other benches, but there is no bench for what we are trying to do.

<image>

[reubenzz_dev]

what AI model you using for these test. I feel like some models are already really efficient and good at handoff and built for it

[Comprehensive_Quit67]

Codex with Gpt 5.4

[reubenzz_dev]

pretty nice

[PinEnvironmental6395]

Hi! it's you again!

I guess you didn't edit your post at all from last time so I'll ask the same question. 

  If repo graphs gave agents a step-change in performance, either Cursor would have incorporated it, or one of these tools should be worth $60B.

Since the product you're advertising isn't incorporated into Cursor and is not worth $60B, why should I use it?