agentify-core

v0.5.0 suspicious
6.0
Medium Risk

Framework-agnostic AI agent library for building single and multi-agent systems

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits multiple concerning behaviors including high credential risk and subprocess execution, suggesting potential misuse or vulnerability exploitation.

  • High credential risk (attempting to read '/etc/passwd')
  • Subprocess execution without shell=True
Per-check LLM notes
  • Network: The network call appears to be for weather data from OpenWeatherMap, which seems legitimate but should be confirmed with the package's documentation.
  • Shell: Subprocess execution without 'shell=True' is less risky but still warrants caution; ensure the input is sanitized and understand its purpose within the package.
  • Obfuscation: No obfuscation patterns detected in the provided code snippet.
  • Credentials: The code attempts to read the system's '/etc/passwd' file and checks for 'Access denied' or 'Error', which may indicate an attempt to harvest credentials or system access information.
  • Metadata: The author details are sparse, indicating potential lack of transparency or a new/untested maintainer.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • try: response = requests.get( "https://api.openweathermap.org/data/2.5/we
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • try: completed = subprocess.run( tokens, capture_output=True
  • onment using argv parsing (no shell=True). " "Use this when the user asks to run ter
⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • esult = tool._read_file("../../etc/passwd") assert "Access denied" in result or "Error" i
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: hotmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository fa8i/Agentify appears legitimate

⚠ 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 agentify-core 
Create a fully-functional mini-application called 'Agentified Task Manager' using the 'agentify-core' Python package. This application will simulate a simple task management system where multiple AI agents can be assigned different tasks and work collaboratively to manage them. The primary goal is to demonstrate the capabilities of 'agentify-core' in handling both single and multi-agent scenarios within a real-world context.

Step-by-step instructions:
1. Define the main components of the application including the TaskManager class, which will oversee all tasks and agents.
2. Implement two types of agents: TaskAssignerAgent and TaskExecutorAgent. The TaskAssignerAgent will be responsible for assigning tasks to available TaskExecutorAgents based on their current workload.
3. Utilize 'agentify-core' to create these agents in a framework-agnostic manner, ensuring that the application can easily integrate into different environments without needing to rewrite agent logic.
4. Each TaskExecutorAgent should have the ability to receive tasks from the TaskAssignerAgent, execute the tasks, and report back to the TaskManager upon completion.
5. Integrate a simple user interface (CLI or GUI) through which users can add new tasks to the system and view the status of ongoing and completed tasks.
6. Ensure the application supports logging of all task assignments, executions, and completions for audit purposes.
7. Test the application by simulating various scenarios such as adding new tasks, assigning them to agents, executing tasks, and handling errors gracefully when agents fail to complete tasks.

Suggested Features:
- Support for dynamic agent addition/removal during runtime to simulate a flexible workforce.
- Prioritization of tasks based on urgency and importance, allowing TaskAssignerAgent to make informed decisions.
- Error handling mechanisms to deal with unexpected failures in task execution.
- Detailed logging and reporting to track the performance of each agent over time.
- Integration with external task data sources, such as databases or APIs, to fetch tasks programmatically.

The 'agentify-core' package is utilized throughout the application to define agent behaviors, manage communication between agents, and handle coordination in a multi-agent environment. By leveraging 'agentify-core', the application showcases its flexibility and robustness in managing complex workflows involving multiple AI agents.