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multi-agent system that automates supplier sourcing and generates structured comparison reports using LLM-powered agents.\n\nThis project demonstrates agent specialization, controlled hand-offs, schema validation, state management, and workflow orchestration using **Python + CrewAI + LLM APIs**.\n\n---\n\n## Problem Statement\n\nDesign and implement a collaborative Manufacturing Agent architecture featuring specialization and structured hand-off protocols.\n\nThe system consists of:\n\n* **Researcher Agent** → Collects supplier information\n* **Writer Agent** → Synthesizes and formats structured comparison reports\n* **Orchestrator** → Controls execution flow and state management\n\nThe goal is to produce high-quality, structured outputs from complex sourcing queries.\n\n---\n\n## System Overview\n\nThe system follows a layered architecture:\n\n```\nUser → Streamlit UI → Orchestrator\n                   → Researcher Agent → LLM API\n                   → Writer Agent     → LLM API\n                   → Storage + Schema Validation\n```\n\n### Agent Roles\n\n#### Researcher Agent\n\n* Interprets supplier sourcing queries\n* Identifies potential suppliers\n* Extracts supplier attributes\n* Outputs structured raw JSON\n\n#### Writer Agent\n\n* Cleans and normalizes supplier data\n* Generates structured comparison tables\n* Ranks suppliers\n* Produces executive summary report\n\n#### Orchestrator\n\n* Creates session/run ID\n* Executes agents sequentially\n* Validates schema between stages\n* Stores artifacts\n* Returns final results to UI\n\n---\n\n## User Interface Flow\n\nThe application follows a gated interaction flow to ensure secure API usage.\n\n### Step 1 – Enter API Key\n\nWhen the application loads:\n\n* User selects LLM Provider (Gemini)\n* User enters API Key\n* User selects Model (default: `gemini/gemini-2.5-flash`)\n* Optional: Enable Verbose Logs\n\nUntil a valid API key is entered, the sourcing form remains hidden.\n\n---\n\n### Step 2 – Manufacturing Sourcing Query\n\nAfter entering the API key, the **Manufacturing Sourcing Query** panel becomes visible.\n\nThe form includes pre-filled default values which can be modified:\n\n| Field                | Default Value     |\n| -------------------- | ----------------- |\n| Process              | Injection Molding |\n| Materials            | ABS               |\n| Location Preference  | India             |\n| Monthly Capacity Min | 50000             |\n| Certifications       | ISO 9001          |\n\nUsers can modify these values before clicking **Run Agents**.\n\nThe system converts these structured inputs into a sourcing query for the Researcher Agent.\n\n---\n\n## Architecture Diagram\n\n![Architecture Diagram](images/Architecture_Diagram_1.png)\n\n---\n\n## Workflow\n\n1. User opens the application\n2. User enters LLM API Key in the sidebar\n3. Manufacturing Sourcing Query form becomes visible\n4. User reviews or edits pre-filled sourcing parameters\n5. User clicks \"Run Agents\"\n6. Researcher Agent generates raw supplier dataset\n7. Schema validation occurs\n8. Writer Agent generates structured comparison report\n9. Artifacts stored\n10. UI displays final output\n\n---\n\n## Project Structure\n\n```\nMulti-agent-system/\n│\n├── agents.py          # Agent definitions\n├── orchestrator.py    # Workflow controller\n├── tasks.py           # Task definitions\n├── schemas.py         # JSON validation models\n├── storage.py         # File-based persistence\n├── frontend.py        # Streamlit UI\n├── requirements.txt\n└── artifacts/         # Generated run outputs\n```\n\n---\n\n## Sample Output Artifacts\n\nEach run generates:\n\n```\nartifacts/\n  run_YYYYMMDD_HHMMSS/\n    raw_suppliers.json        # Researcher output\n    structured_suppliers.json # Writer normalized output\n    report.md                 # Final comparison report\n    state.json                # Workflow state tracking\n\n```\n\n---\n\n## Tech Stack\n\n* Python 3.10+\n* CrewAI\n* Gemini / LLM API\n* Streamlit\n* Pydantic (Schema Validation)\n\n---\n\n## 🛠 Installation\n\n### 1️⃣ Clone the Repository\n\n```bash\ngit clone https://github.com/omtiwari17/Multi-agent-system.git\ncd Multi-agent-system\n```\n\n### 2️⃣ Create Virtual Environment\n\n```bash\npython -m venv venv\nvenv\\Scripts\\activate   # Windows\n```\n\n### 3️⃣ Install Dependencies\n\n```bash\npip install -r requirements.txt\n```\n\n---\n\n## Environment Setup\n\nCreate a `.env` file:\n\n```\nCREWAI_DISABLE_TELEMETRY=true\n```\n# API key is entered through the Streamlit UI (not stored in .env)\n\n---\n\n## Running the Application\n\n```bash\nstreamlit run frontend/app.py\n```\n\nOpen the browser at:\n\n```\nhttp://localhost:8501\n```\n\n---\n\n## Example Query\n\nDefault UI Configuration Example:\n\n* Process: Injection Molding\n* Materials: ABS\n* Location: India\n* Minimum Monthly Capacity: 50,000 units\n* Certifications: ISO 9001\n\nOutput:\n\n* Ranked supplier list\n* Comparison table\n* Risk and gap analysis\n* Executive summary\n\n---\n\n## Design Highlights\n\n✔ Role-based agent specialization\n✔ JSON-based structured handoffs\n✔ Schema validation between stages\n✔ Run-based artifact storage\n✔ Clear separation of concerns\n✔ Extensible architecture for additional agents\n\n---\n\n## Future Enhancements\n\n* Add Compliance Agent\n* Add RFQ Generator Agent\n* Add Negotiation Strategy Agent\n* Integrate vector database (RAG)\n* Deploy backend as REST API\n* Add Docker containerization\n* Add caching layer\n* Add rate-limit fallback logic\n\n---\n\n## Non-Functional Considerations\n\n* Session isolation\n* Controlled retries for LLM errors\n* No API key persistence\n* Deterministic workflow states\n* Structured artifact logging\n* API key is session-based and not stored on disk\n* Query form remains hidden until valid API key is provided\n\n---\n\n## Development Goals\n\nThis project demonstrates:\n\n* Multi-agent orchestration\n* LLM workflow engineering\n* State management\n* Structured output enforcement\n* Practical AI system design\n\n---\n\n## Authors\n\n* Om Tiwari \n* Paridhi Shirwalkar \n* Nitesh Chourasiya \n* Mradul Jain \n","readmeExcerpt":"Multi-Agent Manufacturing System A role-based multi-agent system that automates supplier sourcing and generates structured comparison reports using LLM-powered agents. 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