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AGBoss is an open-source TypeScript framework built on LangGraph. Define AI agent workflows declaratively, catch structural errors before execution with `agboss check`, and test components in isolation. Spring-style dependency injection included.
I've spent over 10 years building enterprise applications in Java, working extensively with Spring and dependency injection.
When I started building AI agents, something felt familiar: the ecosystem reminded me of enterprise Java before frameworks like Spring made application architecture more structured and manageable.
But there was another problem that bothered me even more.
A TypeScript agent workflow can compile successfully and still be structurally broken.
A required piece of data might not be available on a particular execution path. The types look fine, but the workflow fails when that path is actually executed.
That's why I built AGBoss.
It's an open-source TypeScript framework built on LangGraph, with:
Isolated deterministic testing — test components without running an entire multi-agent workflow.
My goal isn't to create yet another abstraction around LLM calls. I want complex agent systems to become easier to reason about, validate, test, and maintain.
AGBoss is still early (v0.1.0), and I'd particularly appreciate feedback from engineers building real agent workflows.
A few questions I'd love your thoughts on:
What structural bugs have you encountered in multi-agent workflows?
How are you currently testing individual agents and workflow branches?
Would static validation change how you develop AI applications?
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About AGBoss on Product Hunt
“Catch broken AI agent workflows before runtime”
AGBoss was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #142 on the daily leaderboard. AGBoss is an open-source TypeScript framework built on LangGraph. Define AI agent workflows declaratively, catch structural errors before execution with `agboss check`, and test components in isolation. Spring-style dependency injection included.
AGBoss was featured in Open Source (69k followers), Developer Tools (522.6k followers) and Artificial Intelligence (483.1k followers) on Product Hunt. Together, these topics include over 246.4k products, making this a competitive space to launch in.
Who hunted AGBoss?
AGBoss was hunted by Pavel Ravvich. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Pavel Ravvich
Hey Product Hunt! 👋
I'm Pavel, the creator of AGBoss.
I've spent over 10 years building enterprise applications in Java, working extensively with Spring and dependency injection.
When I started building AI agents, something felt familiar: the ecosystem reminded me of enterprise Java before frameworks like Spring made application architecture more structured and manageable.
But there was another problem that bothered me even more.
A TypeScript agent workflow can compile successfully and still be structurally broken.
A required piece of data might not be available on a particular execution path. The types look fine, but the workflow fails when that path is actually executed.
That's why I built AGBoss.
It's an open-source TypeScript framework built on LangGraph, with:
Static workflow verification — agboss check detects structural problems before execution.
Declarative composition — describe workflows and their connections explicitly.
Spring-style dependency injection — structure agent applications using familiar architectural patterns.
Isolated deterministic testing — test components without running an entire multi-agent workflow.
My goal isn't to create yet another abstraction around LLM calls. I want complex agent systems to become easier to reason about, validate, test, and maintain.
AGBoss is still early (v0.1.0), and I'd particularly appreciate feedback from engineers building real agent workflows.
A few questions I'd love your thoughts on:
What structural bugs have you encountered in multi-agent workflows?
How are you currently testing individual agents and workflow branches?
Would static validation change how you develop AI applications?
GitHub: https://github.com/agbossjs/agboss
Docs: https://agboss.dev/
Thanks for taking a look! I'm happy to discuss the architecture, implementation details, or trade-offs.