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
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Curious what enterprise agent workload made you reach for Rust over staying in Python. Most agent frameworks bottleneck on LLM inference, not framework speed.
GraphBit
@ebazan33 The bottleneck for us wasn't inference speed, you're right that LLM latency dominates. The reason we reached for Rust was deterministic routing. We needed the orchestration layer to be predictable and auditable, not probabilistic. When PRFlow makes a decision about what context to include or how to route a review, that decision has to be the same every time. Python frameworks gave us flexibility but not that guarantee
@musa_molla That makes sense. "Deterministic and auditable" is actually a bigger enterprise sell than "fast." Auditors care more about repeatability than throughput. Worth leaning into that in positioning. Good luck with the launch.
GraphBit
@ebazan33 Good question. It was not about making the LLM itself faster. The reason for Rust was the orchestration layer around enterprise agent workloads, things like concurrency, runtime stability, lower overhead in tool and memory flows, and more predictable performance under load, while keeping Python as the developer-facing layer.
@rupak_chandra_bhowmick The "Python facing the developer, Rust handling the load" architecture is the right call. Same playbook polars and pydantic used. Solid choice for a long-term framework. Good luck.
The 'deterministic baseline' angle is what caught my attention. Most AI reviewers feel like a black box that gives different results run to run. How do you handle PRs that touch generated code or vendored files? Those often create a lot of noise in reviews.
GraphBit
@christian_knaut Generated files and vendored dependencies are detected and skipped automatically before the review even starts. Lockfiles, protobufs, minified code, migration files; PRFlow classifies them and excludes them from the review scope. The model only sees code your team actually wrote
GraphBit
@christian_knaut Thanks. We filter a lot of that noise up front.
Generated or vendored paths can be excluded through file filters and repo-level ignore rules, so the review stays focused on real code changes.
Congrats on the launch! How do you define noise vs a real issue in your rule engine?
GraphBit
@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.
GraphBit
@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
Congrats, Musa! Does PRFlow handle cross file refactors where a function signature changes across 10 files?
GraphBit
@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.
GraphBit
@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
Does your benchmark include PRs with generated code or vendored dependencies?
GraphBit
@alexis_rodriguez7 Mostly no. PRFlow filters out a lot of low-value review surface by default, including dependencies, build artifacts, binary/non-code files, and it also supports repo-level ignore rules for auto-generated or vendored paths. So our benchmark focus was on reviewable PR code, not noise from vendored or generated files.
GraphBit
@alexis_rodriguez7 Good question. Our benchmark used real open-source PRs, generated files and vendored dependencies are automatically detected and skipped. PRFlow only reviews code your team actually wrote
Does the single pass analysis catch issues that span three or more dependent files?
GraphBit
@imogen_wallace Yes. PRFlow analyzes the PR holistically, not file by file, and adds cross-file dependency context during review. That makes it better at finding issues that span several dependent files.
GraphBit
@imogen_wallace Yes, that is the core of how PRFlow works. Most tools do diff-level scanning and miss issues that live in connected files. PRFlow does cross-file bug detection in GitHub PRs by tracing the actual function that changed and following its dependencies. In our benchmark we caught an XSS vulnerability spanning a Ruby model, an HTML template, and a JavaScript file - classic case of automated XSS detection that a diff-only tool would never reach. Reducing technical debt with AI code review only works if the review actually sees the full picture. Happy to share the GitHub link to that specific finding if useful.
Congrats on the launch! How do you define noise vs a real issue in your rule engine?
GraphBit
@boyuan_deng1 Great question. We don't use a rule engine, that's actually a key part of how PRFlow avoids noise in pull request security auditing.
Instead of predefined rules, PRFlow uses context-aware pull request analysis. It extracts the exact function that changed, pulls in cross-file dependencies, and retrieves past feedback from your team's correction history. The AI then evaluates against that full picture, not a checklist.
What reduces noise in practice: if your team has previously flagged something as intentional, PRFlow stores that and stops raising it. Over time the signal-to-noise ratio improves automatically without you writing a single rule.
The honest answer is no system is perfect on day one, but the memory layer is what separates it from tools that feel like a coin toss every PR. Happy to dig into specifics if you have a particular case in mind.
GraphBit
@boyuan_deng1 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.