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AI Workflow Engine

This project is an AI workflow engine that turns a natural-language intent into an executable workflow: a planner (LLM) produces a plan, which is compiled into a multi-step workflow, and an executor runs each step by calling tools.

Tools are pluggable—in-process (e.g. Notion), remote HTTP (e.g. MCP Tavily), and stdio subprocesses (e.g. a small prime-check MCP server)—and are registered in a central registry so the executor can invoke them in a uniform way.

About

The AI Workflow Engine bridges probabilistic AI (LLMs) and deterministic software execution:

  1. User Intent → Natural language query
  2. Planner (LLM) → Translates intent into a structured Plan
  3. Workflow Compilation → Plan compiled into immutable, executable Workflow
  4. Executor → Runs workflow step-by-step, calling tools

Key Features: Tool discovery from MCP servers, dynamic planner prompts, workflow persistence, transport-agnostic tools (in-process/HTTP/stdio).

Example Queries:

  • "Check if 101 is a prime number and write the result to Notion"
  • "Search for Arsenal FC latest match analysis and post it on Notion"

Note: The LLM is used only once to translate the query into a plan. The rest executes deterministically.

For detailed architecture documentation, see CASESTUDY.md.

Sequence Diagram

sequenceDiagram
    actor User
    participant Application
    participant MCPServers@{ "type": "collections"}
    participant ExternalWorld@{ "type": "collections" }
    participant LLM


    Note over Application: Application Started
    Note over Application: Initialize ToolRegistry

    Note over Application, MCPServers: Tool Discovery
    Application->>MCPServers: GET /tools/list
    MCPServers->>Application: Array<{ name: string, inputSchema: JsonSchema}>
    Note over Application: Load Tool Details in ToolRegistry

    User-->>Application: User Prompt/Query
    Note over Application: Generates a prompt from UserInput

    Application-->>LLM: Sends Prompt
    LLM-->>Application: Receives rawOutput from LLM

    Note over Application: Parses and validates the rawOutput to a Workflow
    Note over Application: Executor starts executing the Workflow.

    loop Required number of call for the Workflow
      Application->>MCPServers: POST /tools/call or JSON over stdio
      MCPServers->>ExternalWorld: Side Effect (eg writing to file) or fetching data
      ExternalWorld->>MCPServers: Data From ExternalWorld
      MCPServers->>Application: `{ result: JSON }`
    end

    Application-->>User: Final Response
Loading

Requirements

Prerequisites: Node.js v18+, npm/yarn, Docker & Docker Compose

Environment Variables (create .env file):

OPENAI_API_KEY=your_openai_api_key_here
NOTION_API_KEY=your_notion_api_key_here
NOTION_DATABASE_ID=your_notion_database_id_here
TAVILLY_API_KEY=your_tavily_api_key_here  # Optional
MCP_TAVILY_URL=http://localhost:13001      # Optional, defaults shown
DATABASE_URL=postgresql://postgres:postgres@localhost:5432/ai_workflow_dev

How To Run

# 1. Clone and install
git clone <repository-url>
cd ai-workflow-engine
npm install

# 2. Set up environment variables (create .env file)
# See Requirements section above

# 3. Start database and run migrations
npm run db:start
npx prisma migrate dev

# 4. (Optional) Start MCP Tavily server
npm run dev:mcp-tavily

# 5. Start application
npm run dev

The application runs on http://localhost:13099.

Test endpoints:

# Health check
curl http://localhost:13099/health

# Create and execute workflow
curl -X POST http://localhost:13099/run \
  -H "Content-Type: application/json" \
  -d '{"intent": "Check if 101 is a prime number"}'

# Create plan only
curl -X POST http://localhost:13099/plan \
  -H "Content-Type: application/json" \
  -d '{"intent": "Search for Arsenal FC match analysis"}'

API Endpoints

  • GET /health - Health check
  • POST /plan - Create plan from intent (no execution) 
    { "intent": "Your query here" }
  • POST /run - Create plan, compile workflow, save to DB, and execute 
    { "intent": "Your query here" }

Development

Database commands:

npm run db:start    # Start database
npm run db:stop     # Stop database
npm run db:logs     # View logs
npm run db:console  # Open console

Type checking: npm run build

Architecture

For detailed architecture documentation, see CASESTUDY.md.

License

ISC