agentql

v1.19.0 suspicious
4.0
Medium Risk

Tiny Fish AgentQL Python Client

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to its potential for executing shell commands and reliance on external services, despite showing no signs of malicious intent or obfuscation. However, the lack of repository information and sparse maintainer details warrant further scrutiny.

  • Shell risk due to execution of external commands
  • Metadata concerns with missing repository and sparse maintainer details
Per-check LLM notes
  • Network: The detected network patterns suggest the package makes HTTP requests which could be normal if it relies on external services.
  • Shell: The shell execution pattern indicates the package runs external commands, potentially installing dependencies, which could pose a risk if not properly sanitized or controlled.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The repository not being found and the maintainer having a short or missing author name raises some concerns, but there's no direct evidence of malice.

📦 Package Quality Overall: Low (4.2/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 5.0

Some documentation present

  • Documentation URL: "Documentation" -> https://docs.agentql.com
○ 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

  • 85 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)

  • " try: response = requests.get(url, timeout=DEFAULT_REQUEST_TIMEOUT_IN_SECONDS) res
  • : api_key} async with httpx.AsyncClient() as client: response = await client.post(url, d
  • "replace") async with httpx.AsyncClient() as client: response = await client.post(
  • } async with httpx.AsyncClient() as client: response = await client.post(
  • : api_key} async with httpx.AsyncClient() as client: response = await client.get(url, he
  • S_ENDPOINT async with httpx.AsyncClient() as client: response = await client.get(url, ti
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • dencies...") try: subprocess.run(["playwright", "install", "chromium"], check=True, capture_o
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: agentql.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 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 agentql 
Create a fully-functional mini-application called 'AgentQL Dashboard' using the Python package 'agentql'. This application will serve as a simple yet powerful tool for monitoring and managing a fleet of agents deployed across various environments. The primary goal of this application is to provide real-time status updates, performance metrics, and control capabilities over these agents. Here are the key steps and features you need to implement:

1. **Setup and Initialization**: Start by installing the 'agentql' package and setting up a basic Flask web server to host your dashboard.
2. **Agent Registration**: Allow users to register their agents with the dashboard. Each registration should include essential information such as agent name, environment type, and contact details.
3. **Real-Time Monitoring**: Implement a feature that fetches real-time status updates from each registered agent. Use the 'agentql' package to interact with the agents and retrieve their current state.
4. **Performance Metrics**: Display performance metrics for each agent, including CPU usage, memory usage, and network activity. These metrics should be updated periodically to reflect the latest data.
5. **Control Interface**: Provide a user interface that allows administrators to send commands to individual agents or groups of agents. Commands could range from simple pings to more complex tasks like restarting services.
6. **Notifications**: Set up a notification system that alerts users when critical issues arise with any of the agents. Notifications should be customizable based on severity levels.
7. **User Authentication**: Ensure that only authorized users can access the dashboard and perform actions. Implement a basic authentication mechanism using Flask-Login.
8. **Responsive Design**: Make sure the dashboard is responsive and works well on both desktop and mobile devices.

Throughout the development process, focus on leveraging the 'agentql' package's core functionalities to streamline interactions between the dashboard and the agents. Your final product should demonstrate a seamless integration of the 'agentql' client within a practical, real-world application.