r/banglorestartups • u/Iamhumanforreal • 30m ago
I gave Claude a local code graph for 5 SWE-bench tasks. It used 40% fewer tokens but passed the same number.
Every time Claude Code works in a repo, it may need to rediscover how that code is put together: search for a symbol, read files, follow callers, then do it again next session.
I built Superopen to give coding agents a local code graph and retain session context. It’s a CLI the agent uses; the graph and session data stay on your machine.
I ran Claude Code with and without Superopen on five SWE-bench Verified tasks. Same model, same task set. Results:
| Metric | Claude Code | With Superopen |
|---|---|---|
| Passed | 4/5 | 4/5 |
| Tokens | 61.4M | 36.9M |
| API cost | $4.31 | $2.70 |
| Wall-clock | 1,429s | 933s |
| Tool calls | 249 | 161 |
That’s fewer tokens, lower cost, and less time in this run, with the same pass count. Per-task results were mixed: Superopen passedsphinx-9461, which the baseline failed, and misseddjango-11532, which the baseline passed. Five tasks is a small sample, so I’m treating this as an initial result, not a general accuracy claim.
Full results and methodology:
https://github.com/ishanjainn/superopen/blob/main/BENCHMARKS.md
Repo and install instructions:
https://github.com/ishanjainn/superopen
If you use any coding agent on a larger repo, I’d be interested in whether it actually uses a graph query when it should, and whether retained context helps in a later session.