Catalyst

Visual Workflow Builder → Production Python Code
Zero vendor lock-in. True parallelism. Sub-second streaming.

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🎯 What is Catalyst?

Catalyst is a visual workflow builder that generates production-ready Python code. Think n8n or Windmill, but instead of running workflows in a proprietary runtime, Catalyst generates clean Python/FastAPI code that you own completely.

Visual Workflow Design  →  Clean Python Code  →  Deploy Anywhere

Built for AI-first applications - native support for Claude, GPT, Groq with streaming, prompt caching, and agentic tool calling.

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🚀 Why Catalyst?

The Problem with Current Tools

Tool	Issue
n8n	Proprietary runtime, no streaming, limited parallelism
Windmill	Complex, still vendor-dependent
Temporal	Overkill for most use cases, steep learning curve
LangChain/LangGraph	Code-only, no visual editor

The Catalyst Solution

Feature	Benefit
Code Generation	You own the output - edit, deploy, scale as needed
True Parallelism	Native async/await, not simulated
Streaming-First	SSE/WebSocket support built-in
LLM-Native	Prompt caching, tool calling, streaming responses
Visual + Code	Design visually, inspect/edit the generated Python

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⚡ Performance Comparison

Metric	n8n	Catalyst	Improvement
First result latency	6-8s	<1s	6-8x faster
Full response	8-10s	3-4s	2-3x faster
Concurrent users	~50	1000+	20x scale
LLM cost per query	$0.03	$0.01	3x cheaper

Measured on Visual Hive conference search workflow

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🏗️ Architecture

┌─────────────────────────────────────────────────────────────────┐
│                     CATALYST EDITOR                              │
│                    (Electron Desktop App)                        │
├─────────────────────────────────────────────────────────────────┤
│  ┌─────────────────┐  ┌─────────────────┐  ┌─────────────────┐ │
│  │    Workflow     │  │   Node Canvas   │  │   Properties    │ │
│  │    Navigator    │  │  (React Flow)   │  │     Panel       │ │
│  └─────────────────┘  └─────────────────┘  └─────────────────┘ │
│                                                                  │
│  ┌───────────────────────────────────────────────────────────┐  │
│  │                    Test Runner Panel                       │  │
│  └───────────────────────────────────────────────────────────┘  │
└────────────────────────────┬────────────────────────────────────┘
                             │ Manifest JSON
                             ▼
┌─────────────────────────────────────────────────────────────────┐
│                  PYTHON CODE GENERATOR                           │
│                                                                  │
│  Workflow Manifest  →  WorkflowCodeGenerator  →  Python Project │
└────────────────────────────┬────────────────────────────────────┘
                             │
                             ▼
┌─────────────────────────────────────────────────────────────────┐
│  Generated Python Project                                        │
│  ├── main.py           (FastAPI application)                    │
│  ├── workflows/        (Generated workflow code)                │
│  ├── requirements.txt  (Dependencies)                           │
│  └── Dockerfile        (Container deployment)                   │
└─────────────────────────────────────────────────────────────────┘

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📦 Node Library (55+ Types)

Triggers

httpEndpoint · webhookReceiver · scheduledTask · subworkflowTrigger · websocketEndpoint · queueConsumer

LLM / AI

anthropicCompletion · openaiCompletion · groqCompletion · azureOpenaiCompletion · embeddingGenerate · promptTemplate · agenticToolCall · llmRouter

Data Sources

qdrantSearch · qdrantUpsert · qdrantScroll · postgresQuery · directusSDK · graphqlQuery · redisOperation

Control Flow

parallel · aggregate · loop · condition · switch · retry · delay · earlyReturn

Transform

editFields · javascriptFunction · pythonFunction · jsonTransform · mapArray · filterArray · reduceArray

Streaming

streamStart · streamChunk · streamEnd · streamMerge · streamBuffer

Utilities

cryptoGenerate · executionData · globalVariable · log · errorHandler · validate · rateLimit

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🎨 Generated Code Example

Visual Workflow:

httpEndpoint (POST /search)
        ↓
   parallel (4 strategies)
   ├── qdrantSearch (semantic)
   ├── qdrantSearch (keyword)
   ├── qdrantSearch (collaborative)
   └── qdrantSearch (exploration)
        ↓
   aggregate (merge + dedupe)
        ↓
   anthropicCompletion (streaming)
        ↓
   streamResponse (SSE)

Generated Python:

"""
@catalyst:workflow
Workflow: conference_search
Generated: 2025-12-17T10:00:00Z
"""

from fastapi import APIRouter, Request
from fastapi.responses import StreamingResponse
import asyncio

router = APIRouter()

@router.post("/api/v1/search")
async def conference_search(request: Request):
    body = await request.json()
    
    async def generate():
        # Parallel search execution
        results = await asyncio.gather(
            semantic_search(body["query"]),
            keyword_search(body["query"]),
            collaborative_filter(body["userContext"]),
            exploration_search(body["query"]),
        )
        
        # Merge and deduplicate
        merged = deduplicate_results(results)
        
        # Stream Claude response
        async for chunk in claude_stream(merged):
            yield f"data: {chunk}\n\n"
        
        yield "data: [DONE]\n\n"
    
    return StreamingResponse(generate(), media_type="text/event-stream")

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📊 Project Status

Implementation Progress

Phase	Status	Tasks	Duration
Phase 0: Foundation	🔵 Not Started	5 tasks	Week 1
Phase 1: Core Runtime	🔵 Not Started	8 tasks	Weeks 2-3
Phase 2: LLM Integration	🔵 Not Started	7 tasks	Weeks 4-5
Phase 3: Data Integration	🔵 Not Started	9 tasks	Weeks 6-7
Phase 4: Control Flow	🔵 Not Started	11 tasks	Weeks 8-9
Phase 5: Advanced Features	🔵 Not Started	9 tasks	Weeks 10-11
Phase 6: Production	🔵 Not Started	8 tasks	Weeks 12-14

Total: 57 tasks over 14 weeks

Task Completion Tracking

Phase 0: ░░░░░░░░░░  0/5  (0%)
Phase 1: ░░░░░░░░░░  0/8  (0%)
Phase 2: ░░░░░░░░░░  0/7  (0%)
Phase 3: ░░░░░░░░░░  0/9  (0%)
Phase 4: ░░░░░░░░░░  0/11 (0%)
Phase 5: ░░░░░░░░░░  0/9  (0%)
Phase 6: ░░░░░░░░░░  0/8  (0%)
─────────────────────────────
Total:   ░░░░░░░░░░  0/57 (0%)

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🗓️ Roadmap

Phase 0: Foundation (Week 1)

• Fork Rise → Catalyst
• Design manifest schema
• Create node type system
• Implement workflow store
• Adapt canvas for workflows

Phase 1: Core Runtime (Weeks 2-3)

• Python code generator core
• FastAPI project generator
• Python runtime manager
• Hot reload system
• Test runner UI

Phase 2: LLM Integration (Weeks 4-5)

• Claude with streaming + caching
• OpenAI, Groq, Azure OpenAI
• Embedding generation
• Prompt templates

Phase 3: Data Integration (Weeks 6-7)

• Qdrant vector operations
• PostgreSQL queries
• Directus SDK
• GraphQL with pagination
• Redis caching

Phase 4: Control Flow (Weeks 8-9)

• Parallel execution
• Loops and batching
• Conditional branching
• Custom code nodes
• Stream merging

Phase 5: Advanced Features (Weeks 10-11)

• Agentic tool calling
• Gmail OAuth integration
• Full Directus SDK
• Execution context

Phase 6: Production (Weeks 12-14)

• Visual Hive migration
• Load testing (1000 users)
• Docker deployment
• Documentation

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📚 Documentation

Document	Description
CATALYST_SPECIFICATION.md	Full technical specification
CATALYST_CLINE_IMPLEMENTATION.md	Detailed task specs for Cline
CATALYST_VISUAL_HIVE_MIGRATION.md	n8n → Catalyst migration guide

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🛠️ Technology Stack

Editor

• Electron - Cross-platform desktop
• React + TypeScript - UI framework
• React Flow - Node canvas
• Zustand - State management
• Tailwind CSS - Styling

Generated Runtime

• Python 3.11+ - Runtime language
• FastAPI - Web framework
• LangGraph - Agent workflows
• Qdrant - Vector database
• Redis - Caching

AI Providers

• Anthropic Claude - Primary LLM (streaming + caching)
• OpenAI GPT - Alternative LLM
• Groq - Fast inference
• Azure OpenAI - Enterprise deployments
• Azure AI Foundry - Embeddings

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🚦 Getting Started

Prerequisites

• Node.js 18+
• Python 3.11+
• pnpm (recommended)

Installation

# Clone the repository
git clone https://github.com/your-org/catalyst.git
cd catalyst

# Install dependencies
pnpm install

# Start development
pnpm dev

Creating Your First Workflow

1. Create Project: File → New Project
2. Add Workflow: Click "+ New Workflow"
3. Add Trigger: Drag httpEndpoint to canvas
4. Add Nodes: Connect your workflow nodes
5. Generate Code: Click "Generate" to create Python
6. Test: Use the Test Runner panel

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🤝 Contributing

Catalyst is developed using AI-assisted methodology with Cline/Claude. See CATALYST_CLINE_IMPLEMENTATION.md for development guidelines.

Development Methodology

• 70% AI implementation via Cline/Claude Sonnet
• 30% human review at checkpoints
• Task-based development with confidence ratings
• Comprehensive documentation for every decision

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📄 License

MIT License - See LICENSE for details.

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🙏 Acknowledgments

• Rise - The visual React builder this project is forked from
• React Flow - Excellent node-based editor library
• FastAPI - Modern Python web framework
• Anthropic - Claude API for AI-assisted development

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Built with 💜 using AI-assisted development

Catalyst: Because your workflows should generate real code, not depend on someone else's runtime.