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Instead of a single LLM handling support end-to-end, two specialised agents collaborate:\n\n- **Support Agent** — researches the inquiry by scraping official documentation and drafts a thorough response\n- **QA Agent** — reviews the draft for accuracy, completeness, and tone before delivery\n\nThe pattern mirrors how real support teams operate: a first responder handles the inquiry, a senior reviewer ensures quality before the response reaches the customer.\n\n---\n\n## System Architecture\n\n```\nCustomer Inquiry\n      │\n      ▼\n┌─────────────────────────┐\n│   Support Agent          │  role: Senior Support Representative\n│   ─────────────────────  │  tools: ScrapeWebsiteTool (docs)\n│   • Reads inquiry        │  allow_delegation: False\n│   • Scrapes docs         │  memory: shared via Crew\n│   • Drafts response      │\n└───────────┬─────────────┘\n            │  passes draft\n            ▼\n┌─────────────────────────┐\n│   QA Agent               │  role: Support QA Specialist\n│   ─────────────────────  │  tools: none (review only)\n│   • Reviews draft        │  allow_delegation: True\n│   • Checks accuracy      │  memory: shared via Crew\n│   • Finalises tone       │\n└───────────┬─────────────┘\n            │\n            ▼\n    Final Customer Response\n```\n\n---\n\n## Key Concepts Demonstrated\n\n| Concept | Implementation |\n|---|---|\n| **Role Playing** | Each agent has a distinct `role`, `goal`, and `backstory` |\n| **Focus** | Agents are prompted to stay in character and avoid assumptions |\n| **Tool Use** | Support Agent uses `ScrapeWebsiteTool` to ground answers in official docs |\n| **Cooperation** | QA Agent can delegate tasks back to the Support Agent if needed |\n| **Guardrails** | Task `expected_output` constrains response scope and format |\n| **Memory** | `memory=True` on the Crew enables agents to share context across tasks |\n\n---\n\n## Tech Stack\n\n- [CrewAI](https://github.com/joaomdmoura/crewAI) — multi-agent orchestration\n- [crewai-tools](https://github.com/joaomdmoura/crewAI-tools) — `ScrapeWebsiteTool`\n- [OpenAI GPT-3.5-turbo](https://platform.openai.com/docs) — LLM backbone\n- [python-dotenv](https://pypi.org/project/python-dotenv/) — secure API key loading\n\n---\n\n## How to Run\n\n### 1. Clone the repo\n```bash\ngit clone https://github.com/Pradeep-Kumar25th/multi-agent-customer-support.git\n```\n\n### 2. Install dependencies\n```bash\npip install -r requirements.txt\n```\n\n### 3. Set up your API key\n```bash\ncp .env.example .env\n# Edit .env and add your OPENAI_API_KEY\n```\n\n### 4. Launch the notebook\n```bash\njupyter notebook multi_agent_customer_support.ipynb\n```\n\nRun all cells. 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