🤖 Agentic-AI-Workflows
_{A Modular Library of Agentic Intelligence Pipelines}

A curated collection of modular Agentic AI workflows - from automation pipelines to multi-step reasoning agents, tools, planners, orchestrators, and intelligent task executors.

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🌟 Overview

Agentic-AI-Workflows is a centralized library of reusable, modular, plug-and-play AI workflows built around:

• ➰ Reasoning loops (ReAct, Reflexion, Tree-of-Thought, etc.)
• 🛠️ Tool-using agents & API-calling agents
• 🧠 Planning, orchestration, and delegation systems
• 🚀 Automation workflows for real tasks
• 🗃️ Domain-specific agent configurations (RAG, research agents, code agents, etc.)
• ♻️ Reusable workflow components for building custom agent architectures

This repository serves as a knowledge base + template library + implementation hub for rapidly building and deploying intelligent, autonomous systems.

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📌 Every workflow is self-contained.
Each subdirectory can include:

• README.md for the workflow
• main.py or run.py
• agent.json / config.yaml
• tools/, memory/, or components/

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🚀 Key Highlights

🧩 1. Modular & Composable

Build complex AI systems using small reusable components:

• Agents
• Tools
• Memory modules
• Controllers
• Workflow steps
• Planners

Combine them like LEGO blocks.

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🧬 2. Support for Multiple Agentic Patterns

This library includes workflows built using:

• ReAct (Reason + Act)
• Reflexion (self-critique loops)
• Tree of Thought (ToT)
• Graph-based agent routing
• Planner–executor architectures
• Multi-agent collaboration
• Long-running task automation

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✅ 3. Real-World Ready

Workflows can include:

• Web search
• Browser automations
• API calls
• Document parsing
• Code generation & execution
• Data transformations
• Task automation
• Research pipelines

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📦 4. Pre-configured Templates

Each workflow folder can act as a standalone template you can clone and customize:

• Research assistant
• Coding assistant
• Data analysis agent
• Workflow automation bot
• Task planner
• RAG or Graph RAG reasoning agent
• Multi-tool executor

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🧪 5. Transparent, Traceable, Debuggable

Workflows are built with traceability in mind:

• Step-by-step logs
• Chain-of-thought visualization (when allowed)
• Tool response logging
• Failure recovery hooks
• Simulation mode / dry run mode

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🧠 Philosophy

This repository promotes:

✔ Agentic Modularity
Each flow stands alone but shares common principles.

✔ Reusability
Copy → modify → deploy any workflow.

✔ Extensibility
Every directory can be extended with:
• more tools
• deeper pipelines
• multi-agent collaboration
• memory & knowledge retrieval

✔ Transparency
Human-readable configs & simple code structures.

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🏁 Getting Started

📥 Clone the Repo

git clone https://github.com/kalpthakkar/Agentic-AI-Workflows.git

cd Agentic-AI-Workflows

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🤝 Contributing

Welcome to contribute:

• New workflows
• Improvements to core components
• Bug fixes
• Documentation contributions

To contribute:

• Fork repository

  https://github.com/kalpthakkar/Agentic-AI-Workflows/fork

• Create a branch:

  git checkout -b feature-xyz

• Commit your changes

  # Stage changes
  git add .
  
  # Commit
  git commit -m "Your message"
  
  # Push the new branch
  git push -u origin feature-xyz

• Submit a Pull Request

  gh pr create --fill

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🌱 Future Roadmap

🧠 Add LLM-agnostic agent templates
🧩 Add GUI for configuring workflows
🌍 Add cloud-native deployment patterns
🔗 Integrate vector databases for long-term memory
🛠 Add advanced multi-agent orchestration modules
📚 Provide more real-world workflow recipes

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📞 Contact

For any inquiries or support, please contact:

• Kalp Thakkar - kalpthakkar2001@gmail.com
• GitHub: kalpthakkar
• LinkedIn: kalpthakkar

⚡ Let's Automate the Future. ⚡