anamne

v1.1.0 suspicious
5.0
Medium Risk

Brain-inspired personal memory layer for AI tools

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risks, particularly due to potential credential harvesting and the use of shell commands without proper sanitization.

  • Potential credential harvesting
  • Use of subprocess.run with shell=True
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package relies on external services.
  • Shell: Use of subprocess.run with shell=True and Popen suggests potential execution of external commands, which could be risky if not properly sanitized or controlled.
  • Obfuscation: No obfuscation patterns detected in the code.
  • Credentials: Potential risk of credential harvesting observed with suspicious HTTP/FTP scheme usage.
  • Metadata: The maintainer has only one package, and the repository is not popular, which could indicate a lower level of scrutiny and potential risk.

📦 Package Quality Overall: Medium (5.6/10)

✦ High Test Suite 9.0

Test suite present — 6 test file(s) found

  • Test runner config found: pyproject.toml
  • 6 test file(s) detected (e.g. test_cluster.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (8859 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

  • 83 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 89 commits in venumittapalli576/anamne
  • Single author but highly active (89 commits)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 8.0

Found 4 shell execution pattern(s)

  • wd: Path = REPO) -> None: subprocess.run(cmd, shell=True, cwd=cwd, check=True, std
  • open, list[dict]]: proc = subprocess.Popen( ["anamne", "mcp-server"], cwd=cwd or str(PR
  • file:// """ result = subprocess.run( ["anamne", "import-web", url, "--dry-run"],
  • None: subprocess.run(cmd, shell=True, cwd=cwd, check=True, stdout=subprocess.
⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • n-http(s) schemes "file:///etc/passwd", "ftp://example.com/", ] @pytest.mark.skipif(not _ha
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Venu Mittapalli" 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 anamne 
Create a personalized AI-driven diary application named 'MemoMind' using the 'anamne' Python package. This application will serve as a brain-inspired personal memory layer, enhancing the way users interact with their daily experiences through AI. Here’s a detailed guide on how to build it:

1. **Setup Project Environment**: Start by setting up your Python environment. Ensure you have the latest version of Python installed along with the necessary libraries such as Flask for the backend and Bootstrap for a sleek frontend.
2. **Integrate 'anamne' Package**: Use the 'anamne' package to implement a memory layer that stores user inputs in a manner inspired by human memory processes. This includes encoding, storing, and retrieving memories efficiently.
3. **Design User Interface**: Design a simple yet elegant user interface where users can log their daily activities, thoughts, and feelings. Each entry should be date-stamped and categorized.
4. **Memory Encoding & Storage**: Utilize 'anamne' to encode each entry into a format that mimics neural patterns. Store these encoded memories in a database, ensuring they are retrievable based on various parameters like date, category, or emotional tone.
5. **Search & Retrieve Memories**: Implement a search feature that allows users to retrieve specific memories based on keywords, dates, or even emotional states. Use 'anamne' to enhance the retrieval process, making it more intuitive and faster.
6. **AI-Driven Insights**: Integrate AI algorithms to provide insights into the user’s mood trends over time, identify recurring themes in their entries, and suggest actions based on these insights.
7. **Security & Privacy**: Ensure all user data is stored securely. Provide options for users to control who can access their entries and under what conditions.
8. **Testing & Deployment**: Thoroughly test the application for usability, security, and performance. Deploy it on a cloud platform like Heroku or AWS once it meets all requirements.

**Suggested Features**:
- Mood Tracking: Allow users to tag their entries with emotions (happy, sad, anxious, etc.).
- Timeline View: Provide a visual timeline of all entries, highlighting key events.
- AI Suggestions: Offer personalized tips or reflections based on past entries.
- Sharing Options: Enable users to share specific entries or summaries with trusted contacts.
- Backup & Sync: Ensure seamless synchronization across devices and offer backup options.

By following these steps and incorporating the unique capabilities of the 'anamne' package, MemoMind will not only serve as a diary but also as an intelligent companion that learns from and enhances the user’s life experiences.

💬 Discussion Feed

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