aigie

v0.2.43 suspicious
6.0
Medium Risk

Enterprise-grade AI agent reliability monitoring and autonomous remediation

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits high obfuscation risk due to the presence of eval and dynamic imports, suggesting potential for malicious code injection. While there's no clear evidence of credential harvesting or shell execution, the network calls raise concerns about unexpected behavior or data exfiltration.

  • High obfuscation risk
  • Potential network-related risks
Per-check LLM notes
  • Network: Network calls to external URLs suggest potential data transmission which could be benign but might indicate unexpected behavior or data exfiltration.
  • Shell: No shell execution patterns detected.
  • Obfuscation: The use of eval and dynamic import patterns suggests potential for code injection and obfuscation, indicating a higher risk.
  • Credentials: No direct evidence of credential harvesting is present, but the dynamic import could be used indirectly to load sensitive information.

📦 Package Quality Overall: Medium (5.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://docs.aigie.io
  • Detailed PyPI description (9239 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 605 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 9.0

Found 6 network call pattern(s)

  • tion/json") req = urllib.request.Request( f"{self._sync_fallback_url}/v1/trac
  • T", ) urllib.request.urlopen(req, timeout=5.0).close() # noqa: S310 # last-reso
  • tion/json") req = urllib.request.Request( f"{api_url}/v1/traces/{trace_id}",
  • T", ) urllib.request.urlopen(req, timeout=5.0).close() logger.debug(
  • Y", "") self.client = httpx.AsyncClient(timeout=60.0) async def get_dashboard_stats( se
  • self._client = http_client or httpx.Client(timeout=10.0) self._resolved: ResolvedConfig = _SENT
⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

  • on.''' return eval(expression) ``` """ def decorat
  • try: module = __import__(module_path, fromlist=[class_name]) llm_class = getattr(module, class_name, None)
  • try: __import__(name) frameworks.append(name)
✓ 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: aigie.io

✓ 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 2.0

1 maintainer concern(s) found

  • Author "Aigie" 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 aigie 
Create a fully-functional mini-application named 'ReliabilityGuard' using the Python package 'aigie'. This application aims to monitor the reliability of various AI agents within an enterprise environment and autonomously initiate remediation actions when issues are detected. The application should have the following core functionalities:

1. **Agent Monitoring**: Implement a feature that allows ReliabilityGuard to continuously monitor multiple AI agents for performance degradation, anomalies, or failures. This could include metrics such as response time, success rate, error rates, etc.
2. **Alerting System**: Develop an alerting system that notifies the appropriate team or individual via email, SMS, or another preferred method when an AI agent is not performing optimally.
3. **Autonomous Remediation**: Utilize the 'aigie' package to enable ReliabilityGuard to automatically take corrective actions when it detects issues. These actions might include restarting the agent, scaling resources up or down, or even rolling back to a previous version if necessary.
4. **Reporting & Analytics**: Provide detailed reports on the health and performance of monitored AI agents over time. This includes graphical representations of trends, peak usage times, and other insights that can help optimize AI operations.
5. **Configuration Management**: Allow users to configure which agents to monitor, set thresholds for alerts, specify remediation actions, and customize notification methods directly through ReliabilityGuard's interface.
6. **User Interface**: Design a simple yet intuitive user interface that enables easy setup, configuration, and monitoring of all connected AI agents.

To achieve these goals, you will need to leverage the 'aigie' package's capabilities for monitoring and autonomous remediation. Start by importing and setting up the package to connect to your AI agents. Then, develop the logic for monitoring and alerting based on the data collected from the agents. Finally, integrate the package's autonomous remediation features to ensure that any identified issues are addressed without manual intervention. Remember to document each step of the process and provide clear instructions on how to install and use ReliabilityGuard.