The full Foomstack whitepaper is currently being finalized. In the spirit of moving quickly, we're sharing our core principles and answers to key questions directly. This is a living document that outlines our vision.
Foomstack is a new kind of AI orchestration framework. You can think of it as the React moment for AI development. Just like React replaced fragile UI scripts with declarative components and a virtual DOM, Foomstack replaces brittle chains and agent scripts with declarative workflows and a static execution graph. This graph, a DAG, gives the system complete knowledge of the entire workflow structure before it runs. That unlocks capabilities no existing framework can offer: deterministic execution (no surprises mid-run), targeted retries (re-run just the failed step), evaluation and compensation logic, deep traceability, and the ability to model complex, real-world business processes. Developers don’t have to manage orchestration logic manually, they can compose workflows declaratively, using reusable components and clear boundaries. The result is both enterprise-grade reliability and a powerful developer model. It’s not just a framework, it’s a new way to build AI systems that scale.
AI models are improving at an exponential rate, but real-world deployment is lagging far behind. A recent MIT study found that 95% of enterprise AI pilots fail to produce measurable ROI. How is it possible that deployment is falling so far behind model capability?
Frontier models are now capable of performing a wide range of knowledge work tasks at or above human level, given ideal conditions. The new bottleneck for real-world AI systems isn’t model intelligence. It’s orchestration infrastructure: the frameworks we use to build, run, and manage AI workflows.
Right now, the market has converged around two dominant paradigms:
Neither of these frameworks can model the kinds of complex, stateful business workflows that enterprises actually need. They lack a shared source of truth, so they can’t offer features like compensation logic, controlled retries, execution transparency, or reproducibility at the workflow level. The orchestration lives in scattered Python scripts, buried control flow, and fragile LLM outputs, making these systems hard to reason about, test, or scale.
It’s exactly where web development was before React: scattered imperative code, inconsistent state handling, and no architectural foundation. What React did for UI, Foomstack does for AI workflows. It introduces a declarative component model and a static execution graph, allowing developers to build complex, composable workflows with clarity, and allowing the system to execute them safely and predictably at scale.
That’s why Foomstack needs to exist, not just to improve developer experience, but to unlock a new architectural era for deploying AI in the real world.
Most AI frameworks today fall into one of two categories: scripting frameworks and agentic runtimes. These were brilliant and necessary first steps, but these architectures weren’t designed to provide the enterprise-grade reliability that is now required to drive large-scale AI adoption.
Scripting frameworks, like LangChain, LangGraph, and Marvin, gave early developers a way to wire together prompt calls, tools, and branching logic. They were built in the era of chain-of-thought and few-shot demos, when the goal was just to get something working. But these systems don’t scale. They’re imperative, fragile, and deeply tied to the surface-level code that defines them. There is no shared, formal representation of the workflow, no single source of truth. That means: