AI Analysis
Final verdict: SAFE
The package shows minimal signs of potential malicious activity, with no detected risks for shell execution, obfuscation, or credential harvesting. The metadata risk is slightly elevated due to the maintainer's single package history.
- Low network risk but unexplained network calls
- Single package by maintainer
Per-check LLM notes
- Network: The presence of network calls suggests the package may communicate with external services, which could be legitimate if documented and used for functionality like updates or logging.
- Shell: No shell execution patterns were detected, indicating a low risk of direct command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
- Credentials: No credential harvesting patterns detected, indicating low risk of secret or sensitive information theft.
- Metadata: The maintainer has only one package, indicating potential inexperience or a new account.
Heuristic Checks
Outbound Network Calls
score 1.5
Found 1 network call pattern(s)
y: response = requests.post( url, data=json.dumps(payload), headers=
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: memtensor.cn
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository MemTensor/MemOS appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "MemTensor" 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 MemoryOS
Create a personal knowledge management tool named 'MemoMaster' using the Python package 'MemoryOS'. This tool should allow users to store, organize, and retrieve information efficiently, leveraging the advanced memory management capabilities of 'MemoryOS'. Here are the steps and features to implement: 1. **Setup and Initialization**: Begin by setting up a basic Flask web application as the backend. Integrate 'MemoryOS' into your project following its official documentation. 2. **User Authentication**: Implement user registration and login functionalities to ensure data privacy. Store user-specific data in 'MemoryOS' sessions or databases. 3. **Note Creation**: Allow users to create notes with titles, descriptions, and tags. Use 'MemoryOS' to efficiently manage and persist these notes. 4. **Search Functionality**: Develop a search feature that allows users to find their notes based on keywords, tags, or dates. Utilize 'MemoryOS' indexing capabilities for faster searches. 5. **Organization Tools**: Enable users to categorize their notes into different folders or categories. Use 'MemoryOS' to handle folder hierarchies and file management. 6. **Sharing and Collaboration**: Implement a feature that lets users share specific notes or folders with others. 'MemoryOS' should support real-time updates and synchronization across multiple devices. 7. **Backup and Restore**: Provide an option for users to backup their notes and restore them if needed. 'MemoryOS' should facilitate seamless backup and recovery processes. 8. **Analytics Dashboard**: Create a dashboard that provides insights into user activity, such as most frequently accessed notes, popular tags, etc. Use 'MemoryOS' analytics tools to generate these reports. 9. **Integration with External Services**: Consider integrating MemoMaster with external services like Google Drive or Dropbox for additional storage options. 'MemoryOS' should support these integrations through API calls or SDKs. Throughout the development process, focus on utilizing 'MemoryOS' to enhance the performance, scalability, and user experience of MemoMaster.