agentscope-otel

v0.2.1 suspicious
4.0
Medium Risk

Python SDK for AgentScope — AI Agent Observability with OpenTelemetry

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a medium level of suspicion due to its metadata risk, which includes a lack of repository and maintainer history. However, other risk factors such as network, shell, obfuscation, and credential risks are relatively low.

  • Metadata risk is high due to insufficient repository and maintainer history.
  • Other specific risk factors (network, shell, obfuscation, credential) are low.
Per-check LLM notes
  • Network: The use of network calls is expected if the package interacts with external services or APIs.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package is suspicious due to the lack of repository and maintainer history, indicating potential malicious intent.

📦 Package Quality Overall: Low (4.8/10)

✦ High Test Suite 9.0

Test suite present — 3 test file(s) found

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

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/moklabs/agentscope/tree/main/packages/sdk
  • Detailed PyPI description (3004 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 5.0

Partial type annotation coverage

  • 35 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)

  • """ async with httpx.AsyncClient(timeout=self._timeout) as client: response = awa
✓ 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

No author email provided

✓ 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 agentscope-otel 
Your task is to create a simple yet powerful AI agent monitoring tool using the 'agentscope-otel' Python package. This tool will help developers understand the behavior and performance of their AI agents in real-time. Here’s how you’ll build it:

1. **Project Setup**: Start by setting up your Python environment and installing the necessary packages including 'agentscope-otel'.
2. **AI Agent Integration**: Integrate a basic AI agent into your application. This could be a simple chatbot or any other AI-driven application.
3. **OpenTelemetry Configuration**: Configure OpenTelemetry to collect traces from your AI agent. Use 'agentscope-otel' to facilitate this process, ensuring observability is seamless.
4. **Real-Time Monitoring Dashboard**: Develop a dashboard that displays real-time metrics about the AI agent’s performance. Include key metrics such as response times, error rates, and usage statistics.
5. **Alerting System**: Implement an alerting system that notifies users when certain thresholds are exceeded (e.g., high error rate, slow response time).
6. **User Interface**: Create a user-friendly interface where users can interact with the AI agent and monitor its performance.
7. **Documentation**: Write comprehensive documentation explaining how to set up and use the monitoring tool, including setup instructions, configuration options, and best practices.

Suggested Features:
- Interactive querying capabilities to allow users to ask questions about the agent’s performance.
- Historical data storage and analysis to track trends over time.
- Customizable alert rules based on specific performance metrics.
- Support for multiple AI agents within a single monitoring instance.

The goal is to create a robust, scalable solution that enhances the development and deployment of AI applications by providing deep insights into their operational behavior.