aiagentnotes

v0.1.0 suspicious
4.0
Medium Risk

SparkNotes for your AI agents — log runs to AgentNotes with one pip install and env vars.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to unverified maintainer details and missing repository, despite showing no signs of immediate malicious intent.

  • Metadata risk with sparse maintainer information
  • Repository not found
Per-check LLM notes
  • Network: The presence of network calls using requests.Session is common and not necessarily indicative of malicious activity, but could be used for data exfiltration if misused.
  • Shell: No shell execution patterns were detected, which is normal and indicates no immediate risk from this aspect.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The repository is not found and the maintainer information is sparse, raising suspicion.

📦 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/mattmerrick/agentnotes/tree/main/sdk
  • Detailed PyPI description (1463 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

  • 22 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • ock() self._session = requests.Session() self._session.headers.update( {
✓ 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: gmail.com>

✓ 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 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • 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 aiagentnotes 
Create a mini-application called 'AI Agent Tracker' using the Python package 'aiagentnotes'. This application will serve as a personal dashboard for tracking and managing multiple AI agent experiments. The primary goal is to provide users with a simple yet powerful tool to log, review, and analyze their AI agent runs effortlessly. Here’s a detailed breakdown of the project requirements:

1. **Setup**: Install 'aiagentnotes' via pip and configure environment variables for logging.
2. **User Interface**: Develop a basic command-line interface (CLI) for user interaction. Users should be able to run commands such as `start`, `stop`, `list`, and `view` to manage their AI agent logs.
3. **Logging Mechanism**: Utilize 'aiagentnotes' to automatically log each AI agent run details. Each log entry should include timestamp, experiment name, duration, and any custom metrics provided by the user.
4. **Experiment Management**: Allow users to create and manage multiple experiments. They should be able to start a new experiment, stop it when done, and view its logs.
5. **Analytics**: Implement basic analytics features such as viewing average duration per experiment, most active times, and top-performing experiments based on user-defined metrics.
6. **Customization**: Enable users to customize which metrics they want to track during each experiment. For example, accuracy, loss, training time, etc.
7. **Persistence**: Ensure all data is stored persistently so that users can review past experiments even after closing the application.
8. **Security**: Use environment variables securely to store sensitive information like API keys if needed for advanced features.
9. **Documentation**: Provide clear documentation on how to set up the application, use the CLI, and interpret the analytics data.

This project aims to simplify the process of managing AI experiments by leveraging 'aiagentnotes' for seamless logging and analysis, making it easier for developers and researchers to keep track of their work.