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Clone & Create Environment\n\n```bash\ngit clone https://github.com/your-username/crewai-agentic-demo.git\ncd crewai-agentic-demo\n\npython -m venv .venv\nsource .venv/bin/activate  # On Windows: .venv\\Scripts\\activate\n```\n\n### 2. Install Dependencies\n\n```bash\npip install -r requirements.txt\n```\n\nMinimal `requirements.txt`:\n\n```text\nfastapi\nuvicorn[standard]\ncrewai\nlangchain\nlangchain-community\nlangchain-core\npydantic\n```\n\n> You can pin exact versions later if needed.\n\n### 3. Install and Run Ollama\n\nDownload Ollama from their website, then:\n\n```bash\nollama pull llama3\nollama serve\n```\n\nBy default, the demo assumes Ollama is available at `http://localhost:11434`.\n\n### 4. Run the API\n\n```bash\nuvicorn app:app --reload\n```\n\nOpen the docs at: `http://localhost:8000/docs`\n\n---\n\n## 📡 Example Request\n\n### cURL\n\n```bash\ncurl -X POST \"http://localhost:8000/explain\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n        \"topic\": \"What is Retrieval-Augmented Generation (RAG)?\",\n        \"level\": \"intermediate\"\n      }'\n```\n\n### Expected JSON Response (simplified)\n\n```json\n{\n  \"topic\": \"What is Retrieval-Augmented Generation (RAG)?\",\n  \"level\": \"intermediate\",\n  \"summary\": \"...short summary here...\",\n  \"detailed_explanation\": \"...multi-agent explanation...\",\n  \"suggested_learning_path\": \"...step-by-step study plan...\",\n  \"raw_output\": \"...full CrewAI run output...\"\n}\n```\n\n---\n\n## 🧩 How This Helps Your Portfolio\n\nThis repo shows recruiters and clients that you can:\n\n- Design **agentic AI workflows** using CrewAI\n- Integrate a **local LLM (Ollama)** for cost-efficient experimentation\n- Expose AI workflows via a clean **FastAPI** interface\n- Write **modular, readable Python code** suitable for productionization\n\n---\n\n## ✅ Next Extensions (If You Want to Improve Later)\n\n- Add logging per agent and step\n- Support multiple LLM backends (OpenAI, Anthropic, etc.)\n- Add a small frontend (React / Streamlit) to interact with `/explain`\n- Implement structured evaluation for explanation quality\n\n---\n\n**Author:Subrata Roy — Cloud & Edge AI Solution Architect (Gen, Agentic & IoT)  \nUse this repo as a **public GitHub project** \n","readmeExcerpt":"CrewAI Agentic Explainer Demo (Ollama + FastAPI) It demonstrates a **multi-agent AI workflow** using **CrewAI** and a **local LLM via Ollama** to explain technical AI topics at different levels (beginner / intermediate / advanced). 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