Introduction

MC-AI is an agentic orchestration engine built on a Cyclic State Graph architecture. It empowers you to build complex, fault-tolerant, multi-step AI workflows reliably in production.

Why use MC-AI?

Most AI orchestration frameworks model workflows as linear chains or strict DAGs (Directed Acyclic Graphs) — agent A calls agent B, which calls agent C. This works well for simple pipelines, but falls apart when you need:

• An agent to loop back and try again based on validation feedback.
• A supervisor to dynamically route work based on real-time findings.
• Multiple candidates to evolve in parallel across generations to find the absolute best solution.
• A workflow to pause for human review, and resume safely hours later without context loss.

MC-AI solves this by using a Cyclic State Graph. Nodes in the graph can loop, revisit previous nodes, and make runtime routing decisions by examining a shared state blackboard.

Core mental model

Everything in MC-AI revolves around four core concepts:

Concept	What it is
Graph	Your workflow definition — a set of nodes connected by edges.
Node	A unit of work: an Agent, an MCP Tool, a Supervisor, or a Subgraph.
State	A shared blackboard. All nodes read from and write to this state.
Reducer	A pure function that takes the current state and an action, and returns the new state.

Agents never talk directly to each other. They read from the shared state, do their work, and emit actions. Reducers apply those actions to produce a new state. This guarantees that every state transition is auditable, eliminates race conditions in swarms, and enables features like time-travel debugging and workflow rollbacks.

Architecture overview

Here is how the underlying GraphRunner executes your workflows:

flowchart TB
    Start(["run() / stream()"]) --> EvalStart["Evaluate Start Node"]
    EvalStart --> ExecNode{"Execute Node"}
    
    ExecNode -->|Agent Node| LLM["LLM + Tools"]
    ExecNode -->|Supervisor| Route["Route Decision"]
    ExecNode -->|Map Node| Parallel["Parallel Fan-Out"]
    ExecNode -->|Subgraph| Nested["Nested Graph"]
    
    LLM --> Reduce["Apply Reducer → Update State"]
    Route --> Reduce
    Parallel --> Reduce
    Nested --> Reduce
    
    Reduce --> Persist["Persist Event/Checkpoint"]
    Persist --> EvalEdges["Evaluate Edge Conditions"]
    
    EvalEdges --> Loop{"Is End Node?"}
    
    Loop -->|"No"| ExecNode
    Loop -->|"Yes"| Done(["__done__"])

Because the GraphRunner is a lightweight TypeScript library, there’s no heavy control plane to spin up. You can embed it directly in your Fastify/Express server, execute it in a background queue like BullMQ, or run it in a serverless function.

What MC-AI is not

• Not a chatbot UI builder — MC-AI is a backend workflow engine.
• Not a low-code tool — Workflows are defined in TypeScript with full type safety, not a drag-and-drop builder.
• Not tied to a specific model — We use the Vercel AI SDK under the hood, so you can use Anthropic, OpenAI, Groq, local models, or any other compatible provider.

Next steps

• Quick Start — install the library and run a workflow in under 5 minutes.
• Core Concepts — dive deeper into the graph model.