GraphBit

Rust-core, Python-first Agentic AI framework

5.0•21 reviews•

2K followers

Rust-core, Python-first Agentic AI framework

5.0•21 reviews•

2K followers

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AI Infrastructure Tools

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LLM Developer Tools

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

AI code reviewer that catches what others miss

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 tags:Productivity•Developer Tools•GitHub

Launch Team / Built With

Wispr Flow: Dictation That Works Everywhere — Stop typing. Start speaking. 4x faster.

Stop typing. Start speaking. 4x faster.

Promoted

GraphBit

Maker

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.

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4d ago

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/

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5h ago

GraphBit

Maker

@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.

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4h ago

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?

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2h ago

GraphBit

Maker

@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

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1h ago

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 🚀

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10h ago

GraphBit

Maker

@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..

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10h ago

articuler.ai

@musa_molla ❤️❤️

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10h ago

Triforce Todos

Love the baseline approach.
@musa_molla , Congrats on the launch! Can it run on every push or just on PR open?

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13h ago

GraphBit

Maker

@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.

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13h ago

GraphBit

Maker

@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

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13h ago

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?

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10h ago

GraphBit

Maker

@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.

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10h ago

GraphBit

Maker

@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

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8h ago

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.

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5h ago

GraphBit

Maker

@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.

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5h ago

Congrats on the launch! How do you define noise vs a real issue in your rule engine?

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12h ago

GraphBit

Maker

@barnaby_lloyd Thanks. In PRFlow, noise means low-value feedback like trivial nits, duplicate comments, or findings below the repo’s configured threshold. A real issue is something actionable that affects correctness, security, performance, maintainability, or cross-file behavior.

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12h ago

GraphBit

Maker

@barnaby_lloyd @rupak_chandra_bhowmick Rupak nailed it. The only thing I'd add is that the threshold shifts over time based on your team's feedback. If your team consistently dismisses a certain type of comment, PRFlow stops raising it. The definition of noise becomes specific to your repo, not a generic preset

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12h ago

Congrats, Musa! Does PRFlow handle cross file refactors where a function signature changes across 10 files?

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12h ago

GraphBit

Maker

@emily_carter18 , Thanks. Yes, within a single PR that’s exactly the kind of cross-file change PRFlow is meant to handle. It analyzes the PR holistically, so a function signature change across 10 files is reviewed as one connected refactor rather than 10 unrelated edits, subject to the PR’s file-size limits.

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11h ago

GraphBit

Maker

@emily_carter18 @rupak_chandra_bhowmick Rupak covered it well. The key word is "connected" PRFlow treats the whole PR as one unit, not file by file. That's what makes refactors like this reviewable in a meaningful way rather than generating 10 isolated comments that miss the bigger picture

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11h ago

1 2 3 4

Previous GraphBit Launches

GraphBitRust-core, Python-first Agentic AI framework

Launched on September 14th, 2025

Forum Threads

p/graphbit

•

5mo ago

Featured

The most underrated trend in AI is how humans are redesigning their work with AI, not around it.

Teams aren t just adding AI into existing workflows.
They re reshaping the workflows themselves with the AI agent in the loop.

Steps get removed.
Objectives become clearer.
Old constraints disappear.
Processes reorganize around what the system can now do natively.

The real productivity gain isn t automation.
It s rethinking the architecture entirely.

Curious to hear from this crowd:
What s one workflow you rebuilt because AI made the old version irrelevant?

p/graphbit

•

7mo ago

Featured

What’s the biggest hidden cost you’ve faced when running AI in production?

It s easy to measure latency or accuracy.

But the real costs often hide in the background- compute burn, idle tokens, redundant calls, or that temporary caching fix that quietly eats your budget.

p/graphbit

•

6mo ago

Featured

The most dangerous failure in AI is the one you don’t measure

Here s something uncomfortable I ve learned building AI agent systems:

AI rarely fails at the step we re watching.

It fails somewhere quieter
a retry that hides a timeout,
a queue that grows by every hour,
a memory leak that only matters at scale,
a slow drift that looks like variation until it s too late.

Most teams measure accuracy.
Some measure latency.

View all

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.

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.

GraphBit

Rust-core, Python-first Agentic AI framework

Rust-core, Python-first Agentic AI framework

Graphbit PRFlow

Previous GraphBit Launches

Forum Threads

The most underrated trend in AI is how humans are redesigning their work with AI, not around it.

What’s the biggest hidden cost you’ve faced when running AI in production?

The most dangerous failure in AI is the one you don’t measure

Previous GraphBit Launches

Forum Threads

The most underrated trend in AI is how humans are redesigning their work with AI, not around it.

What’s the biggest hidden cost you’ve faced when running AI in production?

The most dangerous failure in AI is the one you don’t measure

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