atendentepro

v0.57.0 suspicious
4.0
Medium Risk

Framework de orquestração de agentes IA com tom e estilo customizáveis. Integra documentos (RAG), APIs e bancos de dados em uma plataforma inteligente multi-agente.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in terms of network calls, shell executions, obfuscation, and credential harvesting. However, the metadata risk score is elevated due to insufficient author details and the unavailability of the git repository.

  • Elevated metadata risk
  • Sparse author information
  • Git repository unavailable
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communication.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author's information is sparse and the git repository is not available, raising some suspicion but not definitive evidence of malice.

📦 Package Quality Overall: Low (3.6/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/BeMonkAI/atendentepro#readme
  • Detailed PyPI description (69090 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: monkai.com.br>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with atendentepro 
Create a customer service chatbot using the 'atendentepro' package that integrates natural language processing, document retrieval, and database queries into a seamless conversational experience. This chatbot will serve as a virtual assistant for a fictional online store, handling customer inquiries about products, order status, and returns.

Steps:
1. Set up the environment and install the 'atendentepro' package.
2. Define the personality and style of the chatbot to ensure it matches the brand's tone and voice.
3. Configure the chatbot to interact with a local database containing product information and order details.
4. Implement a feature where the chatbot can retrieve relevant documents from a knowledge base using RAG techniques when answering questions about policies or procedures.
5. Enable the chatbot to perform real-time API calls to external services, such as weather updates or social media feeds, to enhance its responses.
6. Design a user-friendly interface where customers can type their questions and receive answers in a conversational format.
7. Test the chatbot thoroughly to ensure it provides accurate and helpful responses to common customer inquiries.
8. Deploy the chatbot on a web server or integrate it into an existing website.

Features:
- Customizable tone and style to match the brand image.
- Integration with a local database for real-time product and order information.
- RAG capabilities to provide contextually relevant information from a knowledge base.
- Real-time API integration for additional data sources.
- User-friendly interface for easy interaction.
- Comprehensive testing to ensure reliability and accuracy.

💬 Discussion Feed

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