Xpersona

Crawler Summary

CrewAI-Laptop-Recommendation-System answer-first brief

AI-powered Laptop Recommendation System using CrewAI, Python, and Amazon Laptop Dataset. 🎯 Laptops Recommend - AI-Powered Laptop Recommendation System A web-based application that uses **CrewAI** and **FastAPI** to recommend laptops based on student profile, budget, and technical requirements. The system scrapes data from Amazon and provides intelligent recommendations powered by multiple LLM providers. --- 📋 Features - ✅ **AI-Powered Recommendations** - Uses CrewAI agents to analyze and recommend lapt Capability contract not published. No trust telemetry is available yet. 1 GitHub stars reported by the source. Last updated 10/9/2026.

Freshness

Last checked 10/9/2026

Best For

CrewAI-Laptop-Recommendation-System is best for crewai, multi-agent workflows where OpenClaw compatibility matters.

Not Ideal For

Contract metadata is missing or unavailable for deterministic execution.

Evidence Sources Checked

editorial-content, GITHUB REPOS, runtime-metrics, public facts pack

CardFactsSnapshotContractTrust
Agent DossierGITHUB REPOSSafety: 66/100

CrewAI-Laptop-Recommendation-System

AI-powered Laptop Recommendation System using CrewAI, Python, and Amazon Laptop Dataset. 🎯 Laptops Recommend - AI-Powered Laptop Recommendation System A web-based application that uses **CrewAI** and **FastAPI** to recommend laptops based on student profile, budget, and technical requirements. The system scrapes data from Amazon and provides intelligent recommendations powered by multiple LLM providers. --- 📋 Features - ✅ **AI-Powered Recommendations** - Uses CrewAI agents to analyze and recommend lapt

OpenClawself-declared

Public facts

5

Change events

1

Artifacts

0

Freshness

Oct 9, 2026

Verifiededitorial-contentNo verified compatibility signals1 GitHub stars

Capability contract not published. No trust telemetry is available yet. 1 GitHub stars reported by the source. Last updated 10/9/2026.

1 GitHub starsTrust evidence available

Trust score

Unknown

Compatibility

OpenClaw

Freshness

Oct 9, 2026

Vendor

Ajimmujawar44

Artifacts

0

Benchmarks

0

Last release

Unpublished

Executive Summary

Key links, install path, and a quick operational read before the deeper crawl record.

Verifiededitorial-content

Summary

Capability contract not published. No trust telemetry is available yet. 1 GitHub stars reported by the source. Last updated 10/9/2026.

View Source

Setup snapshot

  1. 1

    Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.

  2. 2

    Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Evidence Ledger

Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.

Verifiededitorial-content
Vendor (1)

Vendor

Ajimmujawar44

profilemedium
Observed Oct 9, 2026Source linkProvenance
Compatibility (1)

Protocol compatibility

OpenClaw

contractmedium
Observed Oct 9, 2026Source linkProvenance
Adoption (1)

Adoption signal

1 GitHub stars

profilemedium
Observed Oct 9, 2026Source linkProvenance
Security (1)

Handshake status

UNKNOWN

trustmedium
Observed unknownSource linkProvenance
Integration (1)

Crawlable docs

6 indexed pages on the official domain

search_documentmedium
Observed Apr 15, 2026Source linkProvenance

Release & Crawl Timeline

Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.

Self-declaredagent-index

Docs Update

Docs refreshed: Sign in to GitHub · GitHub

search_documentmedium

Fresh crawlable documentation was indexed for the official domain.

Observed Apr 15, 2026

Artifacts Archive

Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.

Self-declaredGITHUB REPOS

Extracted files

0

Examples

6

Snippets

0

Languages

python

Executable Examples

text

fastapi>=0.110.0           # Web framework
uvicorn>=0.28.0            # ASGI server
pandas>=2.0.0              # Data processing
crewai>=1.14.1             # AI agents framework
python-dotenv>=1.0.0       # Environment variable management
langchain-google-genai>=1.0.0  # Google Gemini integration
litellm>=1.88.0            # Multi-LLM provider support (Kimi, etc.)

bash

git clone https://github.com/sumitsartale4952/Crew_AI_Project-Web_scrap_data_from_amazon_and_laptops_Recommend.git
cd Laptop_Recomandation

bash

# Using venv
python -m venv venv

# Activate virtual environment
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

bash

pip install -r backend/requirements.txt

env

# OpenAI Configuration
OPENAI_API_KEY=your_openai_api_key_here

# Google Gemini Configuration
GOOGLE_API_KEY=your_google_api_key_here
GEMINI_API_KEY=your_gemini_api_key_here

# Kimi/NVIDIA Configuration (Optional)
NVIDIA_API_KEY=your_nvidia_api_key_here

text

Laptop_Recomandation/
├── backend/
│   ├── main.py              # FastAPI main application
│   ├── crew.py              # CrewAI configuration
│   ├── data_manager.py      # Data processing utilities
│   ├── tools.py             # Custom tools for agents
│   ├── requirements.txt      # Python dependencies
│   └── test_backend.py       # Backend tests
├── frontend/
│   ├── index.html           # Main HTML page
│   ├── app.js               # Frontend JavaScript
│   └── style.css            # Styling
├── Datasets/
│   ├── amazon_laptops.csv   # Raw laptop data
│   └── Clean Dataset.csv    # Cleaned data
├── Scraping_Data_From_Amazon.ipynb  # Data scraping notebook
└── README.md                # This file

Docs & README

Full documentation captured from public sources, including the complete README when available.

Self-declaredGITHUB REPOS

Docs source

GITHUB REPOS

Editorial quality

ready

AI-powered Laptop Recommendation System using CrewAI, Python, and Amazon Laptop Dataset. 🎯 Laptops Recommend - AI-Powered Laptop Recommendation System A web-based application that uses **CrewAI** and **FastAPI** to recommend laptops based on student profile, budget, and technical requirements. The system scrapes data from Amazon and provides intelligent recommendations powered by multiple LLM providers. --- 📋 Features - ✅ **AI-Powered Recommendations** - Uses CrewAI agents to analyze and recommend lapt

Full README

🎯 Laptops Recommend - AI-Powered Laptop Recommendation System

A web-based application that uses CrewAI and FastAPI to recommend laptops based on student profile, budget, and technical requirements. The system scrapes data from Amazon and provides intelligent recommendations powered by multiple LLM providers.

📋 Features

  • ✅ AI-Powered Recommendations - Uses CrewAI agents to analyze and recommend laptops
  • ✅ Multi-LLM Support - Works with OpenAI, Google Gemini, and Kimi (via LiteLLM)
  • ✅ Dataset Management - Data cleaning and statistical analysis tools
  • ✅ Interactive Dashboard - Beautiful frontend for browsing and filtering laptops
  • ✅ Real-time Processing - Fast API responses with streaming support
  • ✅ CORS Enabled - Ready for external integrations

🛠️ Requirements

System Requirements

  • Python 3.8 or higher
  • Node.js (optional, for frontend development)

Python Dependencies

All dependencies are listed in backend/requirements.txt:

fastapi>=0.110.0           # Web framework
uvicorn>=0.28.0            # ASGI server
pandas>=2.0.0              # Data processing
crewai>=1.14.1             # AI agents framework
python-dotenv>=1.0.0       # Environment variable management
langchain-google-genai>=1.0.0  # Google Gemini integration
litellm>=1.88.0            # Multi-LLM provider support (Kimi, etc.)

🚀 Installation & Setup

Step 1: Clone the Repository

git clone https://github.com/sumitsartale4952/Crew_AI_Project-Web_scrap_data_from_amazon_and_laptops_Recommend.git
cd Laptop_Recomandation

Step 2: Create a Virtual Environment (Recommended)

# Using venv
python -m venv venv

# Activate virtual environment
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

Step 3: Install Dependencies

pip install -r backend/requirements.txt

Step 4: Configure Environment Variables

Create a .env file in the root directory with your API keys:

# OpenAI Configuration
OPENAI_API_KEY=your_openai_api_key_here

# Google Gemini Configuration
GOOGLE_API_KEY=your_google_api_key_here
GEMINI_API_KEY=your_gemini_api_key_here

# Kimi/NVIDIA Configuration (Optional)
NVIDIA_API_KEY=your_nvidia_api_key_here

📦 Project Structure

Laptop_Recomandation/
├── backend/
│   ├── main.py              # FastAPI main application
│   ├── crew.py              # CrewAI configuration
│   ├── data_manager.py      # Data processing utilities
│   ├── tools.py             # Custom tools for agents
│   ├── requirements.txt      # Python dependencies
│   └── test_backend.py       # Backend tests
├── frontend/
│   ├── index.html           # Main HTML page
│   ├── app.js               # Frontend JavaScript
│   └── style.css            # Styling
├── Datasets/
│   ├── amazon_laptops.csv   # Raw laptop data
│   └── Clean Dataset.csv    # Cleaned data
├── Scraping_Data_From_Amazon.ipynb  # Data scraping notebook
└── README.md                # This file

▶️ Running the Application

Start the Backend & Frontend Server

The frontend is served from the backend, so you only need to run one command:

cd backend
python -m uvicorn main:app --reload --host 0.0.0.0 --port 8000

Then open your browser and navigate to:

http://localhost:8000

API Endpoints

| Endpoint | Method | Description | |----------|--------|-------------| | / | GET | Serve frontend dashboard | | /api/stats | GET | Get dataset statistics | | /api/laptops | GET | Get all cleaned laptops | | /api/clean | POST | Trigger data cleaning | | /api/recommend | POST | Get AI recommendations |

💡 Usage Example

Sending a Recommendation Request

curl -X POST http://localhost:8000/api/recommend \
  -H "Content-Type: application/json" \
  -H "X-API-Key: your_api_key" \
  -d '{
    "provider": "openai",
    "major": "Computer Science",
    "budget": 1500,
    "ram": 16,
    "brand": "Dell",
    "os_name": "Windows",
    "details": "Need a laptop for programming and machine learning"
  }'

🔑 Supported LLM Providers

| Provider | Configuration | |----------|---| | OpenAI | Set OPENAI_API_KEY environment variable | | Google Gemini | Set GOOGLE_API_KEY or GEMINI_API_KEY | | Kimi (via LiteLLM) | Set NVIDIA_API_KEY or use header |

📊 Data Cleaning

The application includes data cleaning tools. Run:

curl -X POST http://localhost:8000/api/clean

This will:

  • Clean raw Amazon laptop data
  • Remove duplicates
  • Standardize formats
  • Generate statistics

🔧 Troubleshooting

Issue: "LiteLLM not installed"

Solution: Install LiteLLM:

pip install litellm

Issue: "API Key not found"

Solution: Ensure your .env file is configured with the correct API keys.

Issue: Frontend not loading

Solution: Make sure the frontend folder exists in the project root with index.html, app.js, and style.css.

Issue: Port 8000 already in use

Solution: Use a different port:

python -m uvicorn main:app --reload --host 0.0.0.0 --port 8080

📝 Development Notes

Adding New LLM Providers

Edit backend/crew.py to add support for additional LLM providers.

Customizing Agents

Modify agent configurations in backend/crew.py to change behavior.

Frontend Customization

Edit HTML/CSS/JS in the frontend/ directory. Changes will hot-reload thanks to uvicorn's --reload flag.

📄 License

This project is licensed under the MIT License. See LICENSE file for details.

👤 Author

Ajim Mujawar

GitHub: [https://github.com/ajimmujawar44]

🤝 Contributing

Contributions are welcome! Please:

  1. Fork the repository
  2. Create a feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'Add amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

📞 Support

For issues, questions, or suggestions, please open an issue on GitHub or contact the author.

Last Updated: July 2026

Contract & API

Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.

MissingGITHUB REPOS

Endpoints

Dossier APISnapshot APIContract APITrust API

Contract coverage

Status

missing

Auth

None

Streaming

No

Data region

Unspecified

Protocol support

OpenClaw: self-declared

Requires: none

Forbidden: none

Guardrails

Operational confidence: low

No positive guardrails captured.
Invocation examples
curl -s "https://www.xpersona.co/api/v1/agents/crewai-ajimmujawar44-crewai-laptop-recommendation-system/snapshot"
curl -s "https://www.xpersona.co/api/v1/agents/crewai-ajimmujawar44-crewai-laptop-recommendation-system/contract"
curl -s "https://www.xpersona.co/api/v1/agents/crewai-ajimmujawar44-crewai-laptop-recommendation-system/trust"

Reliability & Benchmarks

Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.

Missingruntime-metrics

Trust signals

Handshake

UNKNOWN

Confidence

unknown

Attempts 30d

unknown

Fallback rate

unknown

Runtime metrics

Observed P50

unknown

Observed P95

unknown

Rate limit

unknown

Estimated cost

unknown

Do not use if

Contract metadata is missing or unavailable for deterministic execution.
No benchmark suites or observed failure patterns are available.

Media & Demo

Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.

Missingno-media
No screenshots, media assets, or demo links are available.

Related Agents

Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.

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Machine Appendix

Contract JSON

{
  "contractStatus": "missing",
  "authModes": [],
  "requires": [],
  "forbidden": [],
  "supportsMcp": false,
  "supportsA2a": false,
  "supportsStreaming": false,
  "inputSchemaRef": null,
  "outputSchemaRef": null,
  "dataRegion": null,
  "contractUpdatedAt": null,
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Invocation Guide

{
  "preferredApi": {
    "snapshotUrl": "https://www.xpersona.co/api/v1/agents/crewai-ajimmujawar44-crewai-laptop-recommendation-system/snapshot",
    "contractUrl": "https://www.xpersona.co/api/v1/agents/crewai-ajimmujawar44-crewai-laptop-recommendation-system/contract",
    "trustUrl": "https://www.xpersona.co/api/v1/agents/crewai-ajimmujawar44-crewai-laptop-recommendation-system/trust"
  },
  "curlExamples": [
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-ajimmujawar44-crewai-laptop-recommendation-system/snapshot\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-ajimmujawar44-crewai-laptop-recommendation-system/contract\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-ajimmujawar44-crewai-laptop-recommendation-system/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_REPOS",
      "generatedAt": "2026-10-09T23:31:20.558Z"
    }
  },
  "retryPolicy": {
    "maxAttempts": 3,
    "backoffMs": [
      500,
      1500,
      3500
    ],
    "retryableConditions": [
      "HTTP_429",
      "HTTP_503",
      "NETWORK_TIMEOUT"
    ]
  }
}

Trust JSON

{
  "status": "unavailable",
  "handshakeStatus": "UNKNOWN",
  "verificationFreshnessHours": null,
  "reputationScore": null,
  "p95LatencyMs": null,
  "successRate30d": null,
  "fallbackRate": null,
  "attempts30d": null,
  "trustUpdatedAt": null,
  "trustConfidence": "unknown",
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Capability Matrix

{
  "rows": [
    {
      "key": "OPENCLEW",
      "type": "protocol",
      "support": "unknown",
      "confidenceSource": "profile",
      "notes": "Listed on profile"
    },
    {
      "key": "crewai",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    },
    {
      "key": "multi-agent",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    }
  ],
  "flattenedTokens": "protocol:OPENCLEW|unknown|profile capability:crewai|supported|profile capability:multi-agent|supported|profile"
}

Facts JSON

[
  {
    "factKey": "vendor",
    "category": "vendor",
    "label": "Vendor",
    "value": "Ajimmujawar44",
    "href": "https://github.com/ajimmujawar44/CrewAI-Laptop-Recommendation-System",
    "sourceUrl": "https://github.com/ajimmujawar44/CrewAI-Laptop-Recommendation-System",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-10-09T17:02:03.794Z",
    "isPublic": true
  },
  {
    "factKey": "protocols",
    "category": "compatibility",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-ajimmujawar44-crewai-laptop-recommendation-system/contract",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-ajimmujawar44-crewai-laptop-recommendation-system/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-10-09T17:02:03.794Z",
    "isPublic": true
  },
  {
    "factKey": "traction",
    "category": "adoption",
    "label": "Adoption signal",
    "value": "1 GitHub stars",
    "href": "https://github.com/ajimmujawar44/CrewAI-Laptop-Recommendation-System",
    "sourceUrl": "https://github.com/ajimmujawar44/CrewAI-Laptop-Recommendation-System",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-10-09T17:02:03.794Z",
    "isPublic": true
  },
  {
    "factKey": "docs_crawl",
    "category": "integration",
    "label": "Crawlable docs",
    "value": "6 indexed pages on the official domain",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  },
  {
    "factKey": "handshake_status",
    "category": "security",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-ajimmujawar44-crewai-laptop-recommendation-system/trust",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-ajimmujawar44-crewai-laptop-recommendation-system/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true
  }
]

Change Events JSON

[
  {
    "eventType": "docs_update",
    "title": "Docs refreshed: Sign in to GitHub · GitHub",
    "description": "Fresh crawlable documentation was indexed for the official domain.",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  }
]

Sponsored

Ads related to CrewAI-Laptop-Recommendation-System and adjacent AI workflows.