agentic-ai-agents-factory-orchestrator
🥈SilverAgentic AI Agents Factory Orchestrator modular, and asynchronous AI agent factory designed for AI-made dynamic workflow orchestration using LLM integration. Build scalable, agentic automation pipelines with strong error handling, plugin-based capabilities, a concurrent operations or outsource workflow building to Operator Agent.
720Updated 2mo ago
Intermediate30min to implementautomation
Saves ~480 min per use
Quick InstallView Source
git clone https://github.com/InCoB/agentic-ai-agents-factory-orchestrator.gitWorks with:
Claude
Overview
About This Skill
The Agentic AI Agents Factory Orchestrator is a modular framework for building dynamic, scalable automation pipelines using LLM-powered agents. It provides plugin-based architecture, robust error handling, and asynchronous concurrent operations to orchestrate complex workflows. The factory supports multiple execution modes: agents can run workflows independently, or an Operator Agent can dynamically build and delegate tasks. Available domains include financial analysis, news processing, iterative multi-agent analysis, and local file processing. Setup involves cloning the repository, installing dependencies, configuring environment variables, then running via FastAPI REST API or interactive Gradio web interface.
How to Use
Clone the repository and set up a Python virtual environment. Install dependencies via pip install -r requirements.txt and configure API keys in .env. Launch the FastAPI server with uvicorn main:app --reload --port 8000 or the Gradio interface with python gradio_app.py. Run example workflows from the Examples directory to understand agent orchestration patterns.
Use Cases
Financial data analysis with multi-agent collaboration
News content processing and summarization pipelines
Iterative multi-agent problem solving and analysis
Local file processing with domain-specific workflows
Tags
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/InCoB/agentic-ai-agents-factory-orchestratorRequirements
- Claude Code or compatible AI agent
- Works with: Claude
Quick Start Guide
1
Install the Skill
Copy the install command above and run it in your terminal.
2
Open Your AI Agent
Launch Claude Code, Cursor, or your preferred AI coding agent.
3
Try It Out
Use the prompt template or examples below to test the skill.
4
Customize
Adapt the skill to your specific use case and workflow.
Usage Examples
Prompt Template
Create an agentic AI agent factory orchestrator for [COMPANY] in the [INDUSTRY] sector. The factory should integrate with [DATA] sources and be capable of dynamic workflow orchestration. Include robust error handling, plugin-based capabilities, and concurrent operations. Outline the architecture and key components needed to build this system.
Example Output
# Agentic AI Agent Factory Orchestrator for GreenTech Solutions ## Architecture Overview - **Core Orchestrator**: Manages the lifecycle of AI agents and workflows. - **Plugin System**: Modular components for extending functionality. - **Error Handling**: Robust mechanisms for fault tolerance. - **Concurrent Operations**: Parallel processing capabilities. ## Key Components 1. **Agent Factory**: Generates and configures AI agents based on specific tasks. 2. **Workflow Engine**: Orchestrates the execution of workflows across multiple agents. 3. **Data Integration Layer**: Connects to [DATA] sources for real-time data processing. 4. **Monitoring and Logging**: Tracks performance and logs operations for analysis. ## Implementation Steps 1. **Define Requirements**: Identify the specific needs and use cases for GreenTech Solutions. 2. **Design Architecture**: Create a detailed architecture diagram and component specifications. 3. **Develop Core Components**: Implement the core orchestrator, plugin system, and error handling mechanisms. 4. **Integrate Data Sources**: Connect to [DATA] sources and ensure seamless data flow. 5. **Test and Deploy**: Conduct thorough testing and deploy the system in a production environment.
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