aviaraeye

v0.3.0 suspicious
4.0
Medium Risk

Ergonomic LLM observability wrapper around Langfuse

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risk in terms of network, shell, obfuscation, and credential handling. However, it has moderate metadata risk due to low maintainer activity and poor metadata quality.

  • Low maintainer activity
  • Poor metadata quality
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communications.
  • Shell: No shell execution patterns detected, indicating the package likely does not execute system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows signs of low maintainer activity and poor metadata quality, raising suspicion but without clear indicators of malicious intent.

📦 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://aviaraeye.aviaralabs.com/docs
  • Detailed PyPI description (3049 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 45 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

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: aviaralabs.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

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with aviaraeye 
Create a fully-functional mini-application called 'ErgoLang' that leverages the 'aviaraeye' package to provide ergonomic observability over language models like ChatGPT or Claude. Your application should serve as a user-friendly interface for developers and data scientists who want to monitor and analyze the performance of their LLMs. Here’s a step-by-step guide on what your application should include:

1. **Setup Environment**: Begin by setting up a Python environment and installing necessary packages including 'aviaraeye', 'requests', and any other dependencies required.
2. **User Interface Design**: Design a simple yet effective command-line interface (CLI) or web-based UI where users can input their API keys for the LLM they wish to monitor.
3. **Integration with LLMs**: Utilize 'aviaraeye' to wrap around the chosen LLM APIs (such as OpenAI’s ChatGPT or Anthropic’s Claude). This will enable you to capture detailed logs of interactions, including queries, responses, latency, and more.
4. **Monitoring Dashboard**: Implement a dashboard within the application that displays real-time metrics about the LLM's performance. Use 'aviaraeye' to filter and present key insights such as response time distributions, error rates, and usage patterns.
5. **Alert System**: Incorporate an alert system that notifies users via email or SMS when certain thresholds are breached, such as high error rates or unexpected delays.
6. **Data Export**: Allow users to export the collected data in CSV or JSON format for further analysis outside the application.
7. **Security Measures**: Ensure that all API keys and sensitive information are handled securely. Implement encryption for storing and transmitting these details.
8. **Documentation**: Provide comprehensive documentation detailing how to install and use 'ErgoLang', including setup instructions, examples of how to interpret the monitoring data, and best practices for maintaining secure access to LLMs.

In utilizing the 'aviaraeye' package, focus on its ability to streamline the process of logging and analyzing LLM interactions, making it easier for users to gain deep insights into their model's behavior without needing to write complex code for observability.

💬 Discussion Feed

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