axonflow-litellm

v1.0.3 suspicious
4.0
Medium Risk

AxonFlow governance integration for LiteLLM

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low individual risks across network, shell, and obfuscation checks but has a notable metadata risk due to unusual commit activity and missing maintainer details.

  • Metadata risk due to unusual commit history
  • Lack of maintainer information
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 no suspicious system command executions.
  • Obfuscation: No obfuscation patterns detected, suggesting low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The sudden spike in commits and the lack of maintainer information suggest potential risks.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

  • Test runner config found: pyproject.toml
  • 1 test file(s) detected (e.g. test_logger.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://docs.getaxonflow.com/docs/integration/litellm
  • Detailed PyPI description (5697 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

  • 28 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 17 commits in getaxonflow/axonflow-litellm
  • Two distinct contributors found

🔬 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: getaxonflow.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: All 17 commits happened within 24 hours

  • All 17 commits happened within 24 hours
⚠ 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 axonflow-litellm 
Your task is to create a simple, yet powerful command-line utility using Python that integrates the 'axonflow-litellm' package to manage and govern AI models. This utility will serve as a bridge between developers and AI models, allowing them to easily query, monitor, and optimize their usage of LiteLLM services through AxonFlow's governance framework.

The utility should include the following features:
1. **Model Management**: Allow users to list, select, and switch between different AI models supported by LiteLLM.
2. **Query Interface**: Enable users to input queries or prompts to the selected model and receive responses directly from the command line.
3. **Governance Insights**: Provide real-time insights into the performance and usage metrics of the AI models, such as latency, throughput, and cost estimates.
4. **Optimization Suggestions**: Based on the usage patterns, suggest optimizations to improve the efficiency and cost-effectiveness of AI model usage.
5. **Logging and Reporting**: Automatically log all interactions and generate periodic reports on the usage trends and performance metrics.

To achieve these functionalities, you will need to utilize the 'axonflow-litellm' package effectively. Specifically, explore its capabilities in managing API keys, handling requests, and integrating governance rules. Your goal is to demonstrate how this package can streamline the process of working with AI models, making it accessible and efficient for developers.

This project not only showcases the power of 'axonflow-litellm' but also provides a practical tool for developers looking to enhance their interaction with AI models.

💬 Discussion Feed

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