GraphBit
Rust-core, Python-first Agentic AI framework
5.0•21 reviews•2.4K followers
Rust-core, Python-first Agentic AI framework
5.0•21 reviews•2.4K followers
GraphBit is a high-performance AI agent framework with a Rust core and seamless Python bindings. It combines Rust’s speed and reliability with Python’s simplicity, empowering developers to build intelligent, enterprise-grade agents with ease.
This is the 2nd launch from GraphBit. View more
Graphbit PRFlow
Launched this week
Your AI teammate that reviews every pull request before it ships.
Tested on 10 real projects, PRFlow found 7 critical security issues where competitors found zero.
Learns your team's standards over time. Pay per review, not per seat.
Free Options
Launch Team / Built With
Rust core + Python bindings is the combo I've been waiting for — most frameworks pick one or compromise
GraphBit
@novamaker01 That's exactly the tradeoff we made. Rust owns the execution and routing layer, Python owns everything that needs flexibility and ecosystem access. Neither compromises the other
GraphBit
@novamaker01 Exactly the tradeoff we were aiming for. Rust for execution and routing, Python for flexibility and ecosystem access.
How long does the minutes promise take for a 500+ line PR, Musa?
GraphBit
@antonio_manuel1 Thanks. The exact time depends on file count, PR complexity, and how much cross-file context needs to be pulled in, but the architecture is built to keep even larger PRs in the minutes range(0-3 minutes) through single-pass review and token-budgeted context handling.
GraphBit
@antonio_manuel1 @rupak_chandra_bhowmick Rupak covered the technical side well. Short version, single pass architecture means we're not making multiple round trips, which is what keeps larger PRs in the same range
GraphBit
Hey Product Hunt! 👋 Thrilled to be here on launch day.
I'm Junaid Hossain, one of the makers behind PRFlow, and I want to share why we built this.
We kept hitting the same wall: AI code reviewers that catch nothing meaningful on the first pass, flood your PR with noise, and feel completely different run to run. Consistency was broken at the foundation.
PRFlow is our answer to that. It doesn't just scan diffs, it traces the exact function that changed and follows it across cross-file dependencies in a single pass. That's how it caught 7 critical security issues, including an XSS vulnerability spanning a Ruby model, an HTML template, and a JavaScript file, where competitors found zero.
What makes it different in practice:
Every PR gets a structured review, every time, not just when you're lucky
It learns your team's standards from feedback, so noise goes down over time automatically
Pay per review, not per seat. Therefore, no bloated contracts for a tool you're still evaluating
We benchmarked on 10 real public PRs. Some of the reviews are live on GitHub. You can read them right now.
Would love for you to install it on a real repo and tell us what you think. We read every single comment. 🙏
What caught my attention was the cross file dependency tracing part. Most AI code review tools only look at the changed lines on their own, so they miss problems where a small change in one file ends up breaking something somewhere else in the codebase. Tracing how a function change affects its actual dependencies feels way more useful, and honestly that’s probably why PRFlow was able to catch security issues that other tools completely missed.
I was wondering how well that scales though. If someone opens a really large PR with hundreds of modified files, does the cross file analysis start slowing down a lot, or does the Rust based core keep the performance fairly stable even at that size?
Cross-file dependency tracing seems really useful - a lot of review tools miss issues that only show up across multiple files. If PRFlow catches something like an XSS issue, how does it present that to developers? Does it leave separate inline comments in each file, or generate one explanation that shows the full data flow across the stack? I’d imagine the way it surfaces the issue matters a lot here.
Wion - Audio Dating
GraphBit
@tanjum That's exactly the balance we were going for, baseline handled automatically so engineers stay focused on what needs human judgment.
On larger PRs and multi-file changes: PRFlow traces the exact function that changed and follows its dependencies across files in the same PR. Token budget is managed so larger PRs don't get shallow reviews. The depth stays consistent regardless of PR size
GraphBit
@tanjum Thanks. For bigger PRs, PRFlow builds context in layers using our own context engine. It extracts structured context from each changed file, enriches that with cross-file dependencies, and then reviews the PR as a whole rather than one file at a time. For very large PRs, it also uses token budgeting and file prioritization so the review stays focused and useful.
Earth.fm
Really like the direction here. Most teams already have code review processes in place, but review fatigue and repetitive comments still slow things down a lot.
What stood out to me about PRFlow is that it seems focused on improving reviewer focus instead of trying to fully replace human reviews. That balance is important for engineering teams.
Curious to see how teams integrate this into their existing PR workflow over time. Congrats on the launch 👏
GraphBit
@1mirul Exactly the balance we were going for. PRFlow handles the repetitive stuff so the senior devs can focus on what actually needs their eyes. Thanks for getting it
GraphBit
Thanks, @1mirul. That’s very much the design philosophy behind PRFlow. We’re not trying to replace human review, but to automate the repetitive process so engineers can focus on architectural decisions, business logic, and edge cases. The goal is to make review workflows more consistent inside GitHub while keeping humans in control of the final judgment.