Agentor enables rapid development and deployment of AI agents and MCP tools. Operations teams benefit from serverless production environments. Connects to Python-based workflows and supports Claude agents.
git clone https://github.com/CelestoAI/agentor.gitAgentor enables rapid development and deployment of AI agents and MCP tools. Operations teams benefit from serverless production environments. Connects to Python-based workflows and supports Claude agents.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/CelestoAI/agentorCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Create an AI agent for [COMPANY] in the [INDUSTRY] sector that automates [SPECIFIC TASK]. The agent should use [DATA SOURCES] and integrate with [TOOLS/PLATFORMS]. Ensure it's production-ready and can be deployed in a serverless environment. Provide the code and deployment instructions.
# AI Agent for Customer Support Automation
## Overview
This AI agent automates customer support for a retail company, handling FAQs, order tracking, and basic troubleshooting. It integrates with the company's CRM and e-commerce platform.
## Features
- **24/7 Availability**: Handles customer queries round the clock.
- **Multi-Channel Support**: Works via web chat, email, and SMS.
- **Order Tracking**: Provides real-time order status updates.
- **FAQ Automation**: Answers common customer questions instantly.
## Deployment
The agent is deployed in a serverless environment using AWS Lambda and API Gateway. It's scalable, reliable, and cost-effective.
## Code Snippet
```python
# Example code for the AI agent
class CustomerSupportAgent:
def __init__(self, crm_api, ecommerce_api):
self.crm_api = crm_api
self.ecommerce_api = ecommerce_api
def handle_query(self, query):
# Logic to handle customer queries
pass
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