https://github.com/andrewsky-labs/zentrun
Prompt-driven automation platform - Transform natural language into executable workflows
https://github.com/andrewsky-labs/zentrun
agents ai-agents ai-automation business-automation gpt make-alternative mcp mcp-client n8n-alternative ollama openai palantir-alternative productivity vibe-coding workflow zapier-alternative
Last synced: 2 months ago
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Prompt-driven automation platform - Transform natural language into executable workflows
- Host: GitHub
- URL: https://github.com/andrewsky-labs/zentrun
- Owner: andrewsky-labs
- License: other
- Created: 2025-06-25T07:23:50.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-07-12T15:34:50.000Z (3 months ago)
- Last Synced: 2025-07-12T17:45:54.676Z (3 months ago)
- Topics: agents, ai-agents, ai-automation, business-automation, gpt, make-alternative, mcp, mcp-client, n8n-alternative, ollama, openai, palantir-alternative, productivity, vibe-coding, workflow, zapier-alternative
- Language: Vue
- Homepage: https://zentrun.com
- Size: 32.7 MB
- Stars: 22
- Watchers: 2
- Forks: 6
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
# Zentrun — One Prompt, Complete Automation
**"Send yesterday's sales to Slack every morning at 9 AM"**
This single sentence becomes working automation.
No setup wizards. No workflow builders. No API configurations.
**Just describe what you want.**
---
## 🎯 What You Can Build
### **Business Intelligence**
```
"Collect daily sales data, compare to targets, identify trends, send executive summary"
→ Complete BI pipeline with data collection, analysis, and reporting
```
### **Data Analysis & ML**
```
"Analyze customer churn patterns and build prediction models"
→ Data exploration + ML training + predictive insights
```
### **Competitive Monitoring**
```
"Track competitor websites for pricing changes, analyze impact, suggest responses"
→ Web monitoring + analysis + strategic recommendations
```
### **Customer Intelligence**
```
"Monitor support tickets, classify by urgency and topic, predict escalation needs"
→ Ticket analysis + ML classification + predictive insights
```
### **Content Operations**
```
"Find trending topics, generate content ideas, schedule posts, track performance"
→ Research + content creation + scheduling + analytics
```
Everything that you want can be automated.
---
## 🎬 See It Working
**Real demo: Complete marketing automation from prompts**
[](https://youtu.be/HcqcrWb2jxA)
Watch a marketing workflow get built with just natural language:
1. Collects AI news from multiple sources
2. Summarizes content with AI analysis
3. Posts automatically to Twitter
4. Analyzes engagement patterns
5. Visualizes results in dashboard
**Each step generated from language. No manual configuration.**
---
## 🚀 Installation
| Platform | Download |
|------------------|---------------------------------------------------------------------------------------------------------|
| Windows | [Download](https://download.zentrun.com/Zentrun%20Setup%200.0.1.exe) |
| macOS | [Download](https://download.zentrun.com/Zentrun-0.0.1-mac-x64.dmg) |
| Linux | [Download](https://download.zentrun.com/Zentrun-0.0.1-linux-x64.tar.gz) |
```bash
# Or build from source
git clone https://github.com/andrewsky-labs/zentrun
cd zentrun
yarn && yarn dev
```
---
## 💭 Why Automation Still Feels Like Work
### **The "AI-Assisted" Reality**
**Zapier with AI**
- ❌ "AI helps you build workflows" → Still need to configure triggers, actions, connections
- ❌ "Natural language setup" → AI generates workflow, you still need to understand their interface
**Make.com with AI Agents**
- ❌ "AI Agents automate tasks" → Agents work within workflows you build with drag-and-drop
- ❌ "Conversational automation" → Conversation helps configure, doesn't replace configuration
**Power Automate with Copilot**
- ❌ "Describe your workflow" → Generates Power Platform flows, still need to set up connections
- ❌ "Natural language flows" → You still debug in their visual interface
### **Zentrun's Approach**
```
💬 "Monitor competitor prices and alert me when they change significantly"
→ Running automation. No setup. No configuration. No workflow building.
💬 "Analyze customer feedback, identify trends, create weekly summary"
→ Complete pipeline from data collection to analysis to reporting.
💬 "Track our mentions on social media, classify sentiment, escalate negatives"
→ Full monitoring and response system with ML classification.
```
**The difference**: Others help you build automation. Zentrun just automates.
---
## 🔧 How This Is Possible
### **Browser-Native Execution**
- Uses your existing browser sessions (no API keys needed)
- Inherits all your login credentials automatically
- Handles complex web interactions like a human
- Bypasses API limitations and rate limits
### **AI-Powered Code Generation**
- Converts natural language to executable Python/SQL/JavaScript
- Generates browser automation scripts
- Creates data analysis workflows
- Builds ML training and inference pipelines
### **Local Processing Power**
- Fast local database (SQLite/DuckDB) for data operations
- Built-in ML libraries for custom model training
- Real-time analysis without cloud dependencies
- Complete workflow execution on your machine
### **Intelligent Adaptation**
- Self-healing when websites change
- Learning from successful automation patterns
- Context-aware decision making
- Automatic error recovery and retry logic
---
---
## 🛠️ Technical Architecture
### **Natural Language Processing**
- Advanced prompt parsing and intent recognition
- Context-aware code generation for automation logic
- Multi-step workflow planning from single descriptions
- Error recovery and script refinement
### **Browser Integration**
- Chrome DevTools Protocol for seamless browser control
- Session inheritance for instant access to logged-in services
- JavaScript execution environment for dynamic interactions
- Anti-detection techniques for reliable web automation
### **Data & ML Pipeline**
- SQLite/DuckDB for high-performance local analytics
- Vector databases for embedding storage and retrieval
- scikit-learn, TensorFlow, PyTorch for ML workflows
- Real-time inference and model serving capabilities
### **Execution Engine**
- Parallel processing for complex multi-step automations
- Intelligent scheduling and resource management
- Fault tolerance with automatic retry mechanisms
- Version control and rollback for automation logic
---
## 📋 Getting Started
### **Start Simple**
```
1. Install Zentrun
2. Try: "Summarize my unread emails from today"
3. Watch it work in your browser
4. Add more complex automations gradually
```
### **Common Use Cases**
- Daily email and calendar summaries
- Social media monitoring and analysis
- Competitive intelligence gathering
- Customer feedback analysis
- Financial reporting automation
### **Advanced Workflows**
- Multi-source data integration pipelines
- Custom ML model training on your data
- Complex decision-making automations
- Real-time monitoring and alerting systems
- Reusable MCP workflows (Save as Zent)
## 🤖 Supported Model Providers
Ollama
Deepseek
Silicon
QwenLM
Doubao
MiniMax
Fireworks
PPIO
OpenAI
Gemini
GitHub Models
Moonshot
OpenRouter
Azure OpenAI
Qiniu
Grok
** Compatible with any model provider in OpenAI/Gemini/Anthropic API format
## ✨ System Requirements
Minimum specs for a decent experience:
macOS: 13.6+ (8GB RAM for 3B models, 16GB for 7B, 32GB for 13B)
Windows: 10+ with GPU support for NVIDIA/AMD/Intel Arc
Linux: Most distributions work, GPU acceleration available
## ✨ Contributing
Please refer to [Contribution Guide](https://github.com/andrewsky-labs/zentrun/CONTRIBUTING.md).
## 📃 License
[LICENSE](./LICENSE)
---
**Stop building workflows. Start describing what you want.**
**Automation that actually understands natural language.**
⭐ **Star if you want automation to work like you always thought it should!**