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LangGraph + CrewAI + AutoGen\n\n### Build autonomous AI agent systems using three powerful frameworks\n*LangGraph for workflow control + CrewAI for agent teams + AutoGen for agent conversations*\n\n![Python](https://img.shields.io/badge/Python-3.10+-blue?style=for-the-badge&logo=python&logoColor=white)\n![LangChain](https://img.shields.io/badge/LangChain-0.2+-green?style=for-the-badge&logo=chainlink&logoColor=white)\n![LangGraph](https://img.shields.io/badge/LangGraph-Latest-purple?style=for-the-badge)\n![CrewAI](https://img.shields.io/badge/CrewAI-Latest-red?style=for-the-badge)\n![AutoGen](https://img.shields.io/badge/AutoGen-Microsoft-blue?style=for-the-badge&logo=microsoft&logoColor=white)\n![Gemini](https://img.shields.io/badge/Google_Gemini-API-orange?style=for-the-badge&logo=google&logoColor=white)\n\n<br/>\n\n**Built by [Manpreet Kaur](https://github.com/manuu231)**\nMS Data Science @ Clarkson University | AI/ML Engineer\n\n</div>\n\n---\n\n## 📌 What Does This Project Do?\n\nThree different multi-agent AI frameworks implemented and compared side by side — all powered by Google Gemini!\n\n```\nSingle Agent (basic):\nInput → Agent → Output\n\nMulti Agent (this project):\nInput → Agent1 → Agent2 → Agent3 → Better Output ✅\n```\n\n---\n\n## 🧠 Three Frameworks Explained\n\n### 1️⃣ LangGraph — Workflow Control\n```\nControls the FLOW of agents\nNodes connected by edges\nSupports loops and decisions\n\nSTART → Research → Writing → Review → END\n```\n\n### 2️⃣ CrewAI — Agent Teams\n```\nTeam of specialized agents\nEach agent has role + goal + backstory\nSequential or parallel execution\n\nResearcher → Writer → Editor → Final Output\n```\n\n### 3️⃣ AutoGen — Agent Conversations\n```\nAgents TALK to each other\nBack and forth until problem solved\nMicrosoft's framework\n\nUser: \"Explain RAG\"\nAssistant: \"RAG is... TERMINATE\"\n```\n\n---\n\n## 📊 Framework Comparison\n\n| Feature | LangGraph | CrewAI | AutoGen |\n|---------|-----------|--------|---------|\n| **Style** | Flow control | Task based | Conversation |\n| **Best for** | Complex workflows | Specialist teams | Agent dialogue |\n| **Made by** | LangChain | CrewAI team | Microsoft |\n| **Loops?** | ✅ Yes | ❌ No | ✅ Yes |\n| **Roles?** | ❌ No | ✅ Yes | ✅ Yes |\n\n---\n\n## 🛠️ Tech Stack\n\n| Tool | Purpose |\n|------|---------|\n| 🐍 Python | Programming language |\n| 🔗 LangChain | Base AI framework |\n| 📊 LangGraph | Stateful workflow graphs |\n| 👥 CrewAI | Multi agent orchestration |\n| 💬 AutoGen | Conversational agents |\n| 🤖 Google Gemini | LLM powering all agents |\n\n---\n\n## 🔄 How Each Framework Works\n\n### LangGraph Flow:\n```\nAgentState (shared memory)\n      │\n      ▼\nResearch Node → finds information\n      │\n      ▼\nWriting Node → creates content\n      │\n      ▼\nReview Node → improves quality\n      │\n      ▼\nFinal Output ✅\n```\n\n### CrewAI Flow:\n```\nResearcher Agent (role: Senior Analyst)\n      │ passes research to\n      ▼\nWriter Agent (role: Content Writer)\n      │ passes draft to\n      ▼\nEditor Agent (role: QA Editor)\n      │\n      ▼\nFinal Polished Output ✅\n```\n\n### AutoGen Flow:\n```\nUserProxy: sends question\n      │\n      ▼\nAssistantAgent: thinks + answers\n      │\n      ▼\nContains TERMINATE? \n   Yes → Stop ✅\n   No  → Continue conversation\n```\n\n---\n\n## ⚙️ Setup and Installation\n\n### Step 1 — Clone Repository\n```bash\ngit clone https://github.com/manuu231/Multi_Agent_AI_LangGraph_CrewAI_AutoGen.git\ncd Multi_Agent_AI_LangGraph_CrewAI_AutoGen\n```\n\n### Step 2 — Install Dependencies\n```bash\npip install -r requirements.txt\n```\n\n### Step 3 — Set Up API Key\nAdd to Colab Secrets or create `.env` file:\n```\nGOOGLE_API_KEY=your_gemini_key_here\n```\n\n### Step 4 — Run in Google Colab\nOpen `day11_langgraph_crewai_autogen.ipynb` in Google Colab and run all cells!\n\n---\n\n## 🔑 Key Concepts Learned\n\n| Concept | Meaning |\n|---------|---------|\n| `StateGraph` | Graph connecting all nodes in LangGraph |\n| `AgentState` | Shared memory between all nodes |\n| `add_node` | Add a step to LangGraph |\n| `add_edge` | Connect steps together |\n| `Agent` | CrewAI specialist with role + goal |\n| `Task` | What each CrewAI agent must do |\n| `Crew` | Team of CrewAI agents + tasks |\n| `AssistantAgent` | AutoGen agent that answers |\n| `UserProxyAgent` | AutoGen agent that asks |\n| `TERMINATE` | Signal to stop AutoGen conversation |\n| `verbose=True` | Show agent thinking process |\n\n---\n\n## 🚀 Example Outputs\n\n### LangGraph Output:\n```\n🔍 Research Node: Found key facts about ML\n✍️ Writing Node: Created clear explanation\n👀 Review Node: Improved and polished\n✅ Final: Professional ML explanation\n```\n\n### CrewAI Output:\n```\n🔍 Researcher: 5 key facts about RAG\n✍️ Writer: 100 word interview answer\n👀 Editor: Polished professional response\n✅ Final: Interview-ready RAG explanation\n```\n\n### AutoGen Output:\n```\n👤 User: Explain RAG, LangGraph, CrewAI\n🤖 Assistant: RAG is... 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