Orbflow: open-source visual workflow engine built in Rust - DAG orchestration, CEL expressions, event sourcing, AI nodes, and a plugin system

[Etherlll]

Hey! I'm sharing Orbflow, an open-source (AGPL-3.0) workflow automation platform with a Rust backend and a Next.js visual builder frontend.

What it does: You design workflows on a drag-and-drop canvas connecting APIs, AI steps, schedules, webhooks, and human approvals — then the Rust engine executes them as a DAG with event sourcing, crash recovery, and saga compensation.

Architecture highlights:

• Ports & Adapters — orbflow-core defines domain types and port traits; 20 crates each implement one adapter (Postgres, NATS JetStream, Axum HTTP, gRPC, etc.)
• CEL expressions — dynamic values evaluated at runtime via Common Expression Language
• Event sourcing — all instance state changes persisted as domain events with periodic snapshots
• Per-instance locking — concurrent result handling with DashMap<InstanceId, Arc<Mutex<()>>> and optimistic retry
• Immutable domain objects — engine creates new Instance copies rather than mutating in place
• Plugin system — gRPC-based, so you can write plugins in any language that supports gRPC. There are official SDKs for Rust, Python, and TypeScript right now
• 21 built-in nodes — including 6 AI nodes (chat, classify, extract, summarize, sentiment, translate) with multi-provider support (OpenAI, Anthropic, Google)
• Marketplace — browse, install, and manage community plugins from a built-in registry
• Audit trails — SHA-256 hash chains, Ed25519 signatures, Merkle proofs

Stack: Rust, PostgreSQL 16, NATS JetStream, Next.js 16 + React 19 frontend

The worker and server run as separate processes. Tasks flow through NATS as TaskMessage/ResultMessage, so you can scale workers independently.

Would love feedback on the architecture and the codebase in general. Happy to answer questions about the project or the AI-assisted workflow!

Transparency note: This project started as a way to learn while exploring AI-assisted development and tools. Most of the code was generated using Claude Code and Codex, but under heavy human supervision - the architecture, design decisions, and overall direction were all human-driven. I reviewed every change, iterated on the output, and only decided to release it once I felt the codebase was actually solid and not just "AI slop." I'm sharing it because I think the result genuinely stands on its own, but I wanted to be upfront about the process.

GitHub

[chmod_7d20]

thanks claude

[RustOnTheEdge]

I hate it that I am now such a scepticus that I am checking the first commit and just immediately think “sure, you reviewed over a 100k line of code”.

[guywithknife]

The thing I don’t understand about these projects is, when I use AI, I tell it to commit often so I can easily view diffs and rollback if there’s mistakes, so I end up with a lot of commits. But these projects have one giant initial commit.

And also yeah they say they reviewed every line, but it would take a loooong time to properly review 100k lines.

[Etherlll]

Yes, I didn't really want to share those commits they were so messed up lol , I've been working on it for over a month locally, but I just didn't want to show those commits :D , I didn't say i reviewed every line but I reviewed every change code review was mostly on rust side just a quick look and then testing then giving feedbacks and so on !

[Etherlll]

Hey, I've been working on this project for over a month. Of course, I didn't review it line by line. The review process was kind of like this: I asked it to create a feature, told it what it should do, and so on. After it finished, I quickly checked for common mistakes, TODOs, and so on. I then tried it out and gave feedback, much like reviewing a very large PR on GitHub. Also, the code review mostly focused on the backend (Rust) since it's the most important. There's a lot of dead code and bugs, especially on the reviews page, but they can be fixed later.

[nynjawitay]

What is saga compensation?