agentor

🥈Silver

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.

162280Updated 1w ago

Intermediate30min to implementautomation

Saves ~180 min per use

Quick InstallView Source

git clone https://github.com/CelestoAI/agentor.git

Works with:

Claude

Overview

About This Skill

How to Use

[{"step":"Define the agent's purpose and requirements.","details":"Start by clearly outlining the task the agent will perform (e.g., 'automate data entry from emails') and any specific requirements like error handling or logging needs. Use the prompt template to structure your requirements."},{"step":"Develop the agent using Agentor's Python SDK.","details":"Write the agent's logic using Agentor's Python library. Define MCP tools for each function the agent will perform (e.g., data extraction, routing, summarization). Include error handling and logging as specified in your requirements."},{"step":"Configure the agent for deployment.","details":"Set up the agent's deployment settings (e.g., memory, timeout) and MCP tool configurations. Test the agent locally using Agentor's CLI or Python environment before deploying."},{"step":"Deploy to Agentor's serverless environment.","details":"Use Agentor's deployment command (e.g., `agent.deploy(serverless=True)`) to push the agent to production. Monitor the deployment status and initial performance metrics in the Agentor dashboard."},{"step":"Integrate with your workflow.","details":"Connect the deployed agent to your existing systems (e.g., Claude, Python scripts, or web dashboards) using the provided MCP tool configurations. Test the integration with real data to ensure it meets your requirements."}]

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/CelestoAI/agentor

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Use Agentor to create and deploy an AI agent that [TASK_DESCRIPTION]. The agent should [SPECIFIC_REQUIREMENTS, e.g., 'process customer support tickets in real-time', 'generate weekly reports from database queries', or 'automate data entry from emails']. Deploy it to Agentor's serverless environment and provide the MCP tool configuration code for integration with [TARGET_PLATFORM, e.g., Claude, Python scripts, or a web dashboard]. Include error handling for [COMMON_ISSUES, e.g., API rate limits, missing data fields] and logging for [MONITORING_NEEDS, e.g., performance metrics, user activity].

Example Output

{"agent_specification":{"name":"CustomerSupportTicketProcessor","description":"An AI agent that automatically triages and routes customer support tickets from email and web forms to the appropriate team based on content analysis and priority scoring.","deployment":{"platform":"Agentor Serverless","runtime":"Python 3.11","memory":"1GB","timeout":"300s"},"mcp_tools":[{"name":"ticket_router","description":"Routes tickets to the correct team based on keyword analysis and priority.","parameters":{"ticket_id":"str","content":"str","source":"str (email/web_form)","attachments":"list[str]"},"response":{"team":"str (e.g., 'billing', 'technical_support', 'general_inquiries')","priority":"int (1-5)","assigned_agent":"str (agent name or 'unassigned')","next_steps":"list[str]"}},{"name":"ticket_summarizer","description":"Generates a concise summary of the ticket for the assigned agent.","parameters":{"ticket_id":"str"},"response":{"summary":"str","key_issues":"list[str]","sentiment_score":"float (-1 to 1)"}}],"error_handling":{"rate_limiting":"Retry with exponential backoff for API calls to external services (e.g., CRM, email provider).","missing_data":"Flag tickets with incomplete fields and route to 'general_inquiries' with a note for manual review.","logging":"Log all actions to Agentor's built-in monitoring dashboard with timestamps and agent_id for traceability."}},"deployment_code":{"python":"```python\nfrom agentor import Agent\nimport json\n\n# Initialize the agent\nagent = Agent(\n    name=\"CustomerSupportTicketProcessor\",\n    description=\"Processes and routes customer support tickets.\",\n    mcp_tools=[\"ticket_router\", \"ticket_summarizer\"]\n)\n\n# Define the workflow\n@agent.task\ndef process_ticket(ticket_data: dict):\n    try:\n        # Route the ticket\n        router_response = agent.call_tool(\n            \"ticket_router\",\n            ticket_id=ticket_data['id'],\n            content=ticket_data['content'],\n            source=ticket_data['source'],\n            attachments=ticket_data.get('attachments', [])\n        )\n        \n        # Summarize the ticket\n        summary_response = agent.call_tool(\n            \"ticket_summarizer\",\n            ticket_id=ticket_data['id']\n        )\n        \n        # Log the action\n        agent.log_action(\n            action=\"ticket_processed\",\n            ticket_id=ticket_data['id'],\n            team=router_response['team'],\n            priority=router_response['priority']\n        )\n        \n        return {\n            \"status\": \"success\",\n            \"team\": router_response['team'],\n            \"priority\": router_response['priority'],\n            \"summary\": summary_response['summary']\n        }\n    except Exception as e:\n        agent.log_error(\n            error=str(e),\n            ticket_id=ticket_data['id']\n        )\n        return {\"status\": \"error\", \"message\": str(e)}\n\n# Deploy the agent\nagent.deploy(serverless=True)\n```","claude_agent_config":"```json\n{\n  \"name\": \"CustomerSupportTicketProcessor\",\n  \"description\": \"Processes and routes customer support tickets in real-time.\",\n  \"tools\": [\n    {\n      \"type\": \"function\",\n      \"name\": \"ticket_router\",\n      \"description\": \"Routes tickets to the correct team based on content analysis.\",\n      \"parameters\": {\n        \"type\": \"object\",\n        \"properties\": {\n          \"ticket_id\": {\"type\": \"string\"},\n          \"content\": {\"type\": \"string\"},\n          \"source\": {\"type\": \"string\"},\n          \"attachments\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}\n        },\n        \"required\": [\"ticket_id\", \"content\", \"source\"]\n      }\n    },\n    {\n      \"type\": \"function\",\n      \"name\": \"ticket_summarizer\",\n      \"description\": \"Generates a summary of the ticket for the assigned agent.\",\n      \"parameters\": {\n        \"type\": \"object\",\n        \"properties\": {\n          \"ticket_id\": {\"type\": \"string\"}\n        },\n        \"required\": [\"ticket_id\"]\n      }\n    }\n  ],\n  \"serverless\": true\n}\n```"},"monitoring_output":{"deployed_at":"2024-05-20T14:30:00Z","status":"active","metrics":{"tickets_processed_last_24h":427,"avg_processing_time":"12.4s","teams_routed_to":{"technical_support":215,"billing":112,"general_inquiries":100},"errors":3,"error_types":{"rate_limiting":1,"missing_data":2}}}}