Foomstack - declarative AI orchestration framework

Orchestration is the new AI bottleneck

Frontier LLMs are already smart enough to complete most knowledge work tasks. Yet, a recent MIT study found that 95% of enterprise AI pilots fail to deliver ROI. The bottleneck isn't model intelligence; it's orchestration. What's missing is a robust framework and a declarative philosophy to turn AI's potential into reliable, production-grade systems.

Today's frameworks represent two architectural dead ends:

Scripting frameworks - precise but brittle

These imperative tools (e.g. LangChain) force developers to manually code every edge case. For real-world processes, this leads to a combinatorial explosion of complexity and unmanageable technical debt.

Architectural Limits

Brittle, hand-coded error handling for every path via imperative scripting.
Exponentially growing complexity when modeling real-world enterprise workflows.
Architecturally incapable of providing the enterprise-grade reliability that is required to drive large-scale AI adoption.

Chatbot frameworks - flexible but unreliable

These agentic runtimes (e.g. CrewAI) delegate control to the LLM, creating a non-deterministic 'black box' that sacrifices the predictability, auditability, and safety required for production systems.

Architectural Limits

Compounding Error: 95% step accuracy yields less than 60% workflow reliability.
Opaque, un-auditable execution paths that are impossible to debug.
Fundamentally misapplies LLMs as system controllers instead of components.

The Foomstack Solution: Structured Flexibility

A Static Graph as Your Single Source of Truth.

Foomstack compiles workflows into a declarative DAG, creating a single source of truth for your entire process. This enables deterministic execution, targeted retries, and powerful recovery patterns like compensation logic. The graph becomes a versioned, auditable asset that provides complete transparency into how your system operates.

Declarative Components, Not Brittle Scripts.

Describe your desired outcome with reusable, testable components. Foomstack handles the complex orchestration, freeing you to focus on business logic instead of tangled control flow. This mirrors the component model of modern web development, allowing teams to build libraries of trusted logic that compounds in value over time.

Structured Outputs & Guaranteed Reproducibility.

Every step returns a typed, validated schema, eliminating data-mismatch errors between steps. This contract-driven approach makes debugging trivial and guarantees that data flows cleanly through your system. The entire workflow is traceable and reproducible by default: same inputs, same results, every time.

Illustrative code example

@step # steps are leaf components in the execution DAG
def extract_user_stories():
    transcript = input("transcript")
    return Prompt( # Prompts are auto-compiled into XML
        system="You're a senior Business Analyst.",
        instructions="""
        Carefully read the following meeting transcript:
        {transcript}

        Extract all user stories described or implied.
        """,
        output_format="As a user, I want to..."
    )


@step
def clean_user_stories(stories):
    prior_call = use_context()  # use a hook to get context
    return Prompt(
        system="You're a senior QA engineer.",
        instructions="""
        Review the extracted user stories:
        {stories}

        Organize the stories into a numberd list.
        """,
        context=prior_call
    )


@component # Components are nestable and composable. 
def user_story_pipeline():
    # Example pipeline using two steps
    raw = extract_user_stories()
    return clean_user_stories(raw)


@app # app is the entry point
def main():
    return user_story_pipeline()

The React moment for AI development