GraphBit
Rust-core, Python-first Agentic AI framework
5.0•21 reviews•2.3K followers
Rust-core, Python-first Agentic AI framework
5.0•21 reviews•2.3K 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
Launching today
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
GraphBit
Thanks everyone, I'm Musa, founder of GraphBit.
We built PRFlow after getting frustrated with AI code reviewers that flood your PR with noise, miss the issues that actually matter, and feel different every time they run.
The market has options. We know that. We built PRFlow anyway because none of them solved the core problem: consistency and cross-file context in a single pass.
PRFlow is a deterministic baseline reviewer that lives inside GitHub. Open a PR and a structured review posts in minutes, every time, with the same output. It traces the exact function that changed across cross-file dependencies, not just the diff lines. That is how it caught 14 security issues on a PR where another tool found zero.
We benchmarked PRFlow on 10 real public pull requests. Rated 4.3/5 on average. Every review is live on GitHub and readable right now.
PRFlow handles the baseline so your team focuses on architecture, intent, and edge cases. Not repeated first-pass checks.
There are other tools. Try PRFlow on a real repo and see the difference yourself. We read every comment.
DiffSense
@musa_molla Yes. Very noizy. You need a master PR reviewer jsut to review the AI reviewers 😅.... Is graphbit free for public repos? id love to try it on one particular public repo thats launching soon on PH https://github.com/eoncode/runner-bar/
GraphBit
@conduit_design Haha exactly, reviewing the AI reviewer is a real problem 😄
Not free for public repos, but we have a launch offer running right now first 2,000 users get 200,000 tokens and 20 free tracings. That's more than enough to put it through its paces on runner-bar.
Sign up at platform.graphbit.ai and give it a proper test. Would love to hear what it catches
DiffSense
@musa_molla I do 300 commits a day. It would last me 45min 😅 this is the agentic age. we do 10x the work now. Quality PR Review is great. but in the agentic age. it would cost too much. Do you have some sort of plans to address this tension?
GraphBit
@conduit_design 300 commits a day! okay that's a different scale entirely 😄
You're pointing at something real. The coin model works for standard team velocity but agentic pipelines change the math completely.
We're thinking about smarter triggering, reviewing at meaningful checkpoints like pre-merge or when specific file types change, rather than every single commit. That's the direction that makes sense for this use case.
Would love to understand your workflow better. The agentic scale problem is one we want to solve properly
articuler.ai
"Learns your team's standards over time" , this is where I'd love to dig deeper 👀
The architectural choice that fascinates me: how do you handle the fact that team standards are themselves moving targets? A staff engineer ships a new pattern on Monday, the team adopts it by Friday, and your model has three months of "this is how we do it" in its weights. Does PRFlow have a way to detect when the team is intentionally drifting vs accidentally regressing? Feels like the hardest problem in this category. I personally need a product which can tackle this challenge.
Great launch — rooting for the team today 🚀
GraphBit
@jason_shen3 This is the hardest problem in the space and you've articulated it perfectly.
Honest answer: right now PRFlow learns from explicit corrections, when your team flags something as intentional, it stores and applies that. So a new pattern gets reinforced when engineers actively confirm it in review conversations.
The drift vs regression detection you're describing - knowing when the team is intentionally evolving vs accidentally breaking convention - that's a deeper layer we're working toward. The memory architecture is built to support it but we're not claiming to solve it fully yet.
Appreciate you pushing on this. The teams that think at this level are exactly who we're building for 🙏
And congrats on @articuler.ai, Matching on intent across 980M profiles is a genuinely hard problem, the playbook feature especially, turning a cold connection into a warm conversation before it even starts. Rooting for you on launch day..
articuler.ai
@musa_molla ❤️❤️
GraphBit
@jason_shen3 Thanks. Right now, PRFlow learns team standards from explicit review feedback and corrections, so when engineers mark a pattern as intentional, that gets stored and reused in future reviews.
Detecting intentional standard drift versus accidental regression is a harder unsolved layer; the memory architecture is designed for it, but we’re not claiming full automatic drift detection yet.
Triforce Todos
Love the baseline approach.
@musa_molla , Congrats on the launch! Can it run on every push or just on PR open?
GraphBit
@abod_rehman Yes, it can run on every push to an open PR, not just when the PR is first opened. Right now PRFlow triggers on PR open, on new commits pushed to the branch, and when a draft is moved to ready for review.
GraphBit
@abod_rehman Thank you! PRFlow triggers on PR open and every push to an open PR, so automated pull request review happens continuously throughout the lifecycle, not just at the start. Every new commit gets a fresh pass. No gaps in coverage
Quick question: does GraphBit support connecting to self-hosted or open-source LLMs (like Ollama or local Llama models), or is it limited to cloud API providers like OpenAI and Anthropic? Thinking about use cases where data can't leave the network.
GraphBit
@aanchal_dahiya GraphBit is model-agnostic by design and on-prem deployment is supported. For data-sensitive use cases, the architecture allows local tokenization before any LLM contact. Self-hosted models including local Llama setups can be connected. Happy to walk through the specifics if you want to share more about your setup.
GraphBit
@aanchal_dahiya Right now, it is limited to cloud API providers.
The current supported path is Anthropic, Azure OpenAI, and OpenAI, not Ollama or local/self-hosted Llama models today. But self-hosted models including local Llama setups can be connected. Would love to know your setup.
As a solo dev who reviews my own PRs (building FinTrackrr, a free personal finance tracker), I miss critical issues all the time. The idea of an AI teammate that learns your team's coding standards and catches security issues that humans miss is genuinely valuable. The pay-per-review pricing model is smart — especially for solo devs and small teams without enterprise budgets. Does it support Python codebases or is it primarily focused on JS/TS?
GraphBit
@asim_saeed1 Thanks. Yes, it supports Python. PRFlow is not limited to JS/TS, and Python is one of the main codebase types we’ve been building and testing it around. Also, just to clarify on pricing, our plans are currently token-based, so when you buy a plan you get a graphbit coin allocation rather than being charged separately per individual use.
GraphBit
@asim_saeed1 Solo devs reviewing their own code is actually one of the use cases we care most about, you're the ones with the least margin for error and the least backup.
Python is fully supported and one of the stacks we've tested most heavily. The auth bypass we caught in our benchmark was in a Python codebase.
Coin-based means you buy what you need and use it at your own pace. No monthly seat pressure
Congratulations on the launch. I am a solo dev building Badge, I will definitely try this for my repo as an AI reviewer. One question - you said you save cross file context in a single pass, one obvious questions comes - how do you deal with loss in the middle, because that directly translates to misses in review.
GraphBit
@lokesh_motwani1 Great question and glad you're going to try it on Badge.
The single pass doesn't mean one giant context window. PRFlow extracts only the relevant function scope and its cross-file dependencies before sending to the model, so the actual input is tight and focused, not a full repo dump. That's what keeps the middle from getting lost.
Token budgeting handles the rest, larger PRs get prioritized by semantic significance rather than being truncated blindly.
Important thing is, no system is perfect on very large PRs, but the extraction step before the model call is what keeps the signal-to-noise ratio high
GraphBit
@lokesh_motwani1 Good question. We try to reduce that risk before the model call, not after it. PRFlow does structured context extraction first, adds cross-file dependency context, then applies per-file token budgets and memory budgets so one large file does not crowd out the rest of the PR.
If the PR is too large, we prioritize the reviewable files instead of pretending nothing gets lost. So the approach is basically controlled compression plus prioritization, not “throw the full diff into one prompt and hope for the best.”
the cross-file context piece is what gets me - most reviewers treat a PR as a flat list of diffs and miss the connective tissue entirely. curious how you handle PRs where the meaningful change is in what didn't get updated, like a call site that should have changed but wasn't touched?
GraphBit
@liviu_chita hat's one of the sharpest questions we've gotten today.
Right now PRFlow follows what changed and traces its dependencies within the PR. A missing update at a call site that was never touched, that's a gap we don't fully close yet, because we work within the scope of what the diff includes.
What partially helps, cross-file dependency tracing sometimes surfaces the call site in context even if it wasn't edited, depending on how tightly the functions are linked. But I won't overstate it — detecting what should have changed but didn't is a harder problem and one we're thinking about seriously.
Good thing to keep pushing on
GraphBit
@liviu_chita That’s exactly one of the cases we try to catch. PRFlow enriches the changed code with cross-file references and can look up related functions or patterns outside the diff, so it can flag “this call site should have been updated too” even when the missed file was untouched.
The practical constraint is that the comment still has to anchor to a changed line in the PR, so the issue gets attached to the nearest relevant changed code rather than the untouched file itself.