ant-ai

v1.4.0 suspicious
4.0
Medium Risk

A lightweight Python framework for building multi-agent AI systems

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to potential shell execution and credential access patterns, though these may be justified by its intended functionality. The lack of network calls and obfuscation slightly mitigates the risk.

  • Shell risk detected
  • Potential credential risk
Per-check LLM notes
  • Network: No network calls detected, which is low risk.
  • Shell: Detection of shell execution suggests potential for executing arbitrary commands, which could be risky if not properly controlled.
  • Obfuscation: No obfuscation patterns detected in the package.
  • Credentials: The pattern detected may indicate an attempt to access sensitive files, but could also be part of legitimate functionality such as file path manipulation within the application.
  • Metadata: The maintainer has only one package on PyPI, which may indicate a new or less active account.

📦 Package Quality Overall: Medium (5.4/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://idea-idsia.github.io/ant-ai/
  • Detailed PyPI description (4665 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 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 158 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 5 unique contributor(s) across 100 commits in idea-idsia/ant-ai
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • sult: CompletedProcess[str] = subprocess.run( command, cwd=self.cwd,
  • self.timeout, shell=True, ) return { "stdout
⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • escape the workspace (e.g. "../etc/passwd", "/absolute/path") are caught by `_resolve` and re
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: supsi.ch>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository idea-idsia/ant-ai appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Cezar Sas, Vincenzo Giuffrida, Sandra Mitrovic, Matteo Salani" 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 ant-ai 
Your task is to develop a simple yet engaging simulation game using the 'ant-ai' package, which allows you to create a multi-agent system where each agent represents an ant in an ant colony. This game will simulate the daily activities of ants, including foraging for food, caring for larvae, and defending against intruders. The goal is to demonstrate the emergent behavior of the colony as a whole while highlighting the individual behaviors of each ant.

1. **Game Setup**: Create a virtual environment with a grid-based world where each cell can contain food, larvae, or be empty. Each ant has specific roles such as worker, soldier, or queen. The environment should be randomly initialized with a certain number of food sources and larvae.

2. **Agent Behavior**: Implement different behaviors for each type of ant:
   - Workers: Forage for food and return it to the nest. They also care for larvae by bringing them food.
   - Soldiers: Protect the nest from intruders (e.g., simulated predators). They have higher strength but lower speed compared to workers.
   - Queen: Focuses on laying eggs to increase the colony size. She doesn't move around much but is crucial for colony growth.

3. **Interaction Rules**: Define rules for how ants interact with their environment and each other. For example, when a worker finds food, it should return to the nest and drop the food there. If a soldier encounters a predator, it should engage in combat.

4. **Emergent Behavior**: Observe how the colony behaves over time. Ants should collectively gather more food than they need, allowing some to be stored for future use. The colony should also grow larger over time as the queen lays more eggs, leading to more workers and soldiers.

5. **User Interface**: Develop a basic command-line interface (CLI) or a simple graphical user interface (GUI) using a library like Pygame to visualize the game. Users should be able to see the ants moving around, interacting with the environment, and performing their tasks.

6. **Extensibility**: Consider adding additional features such as new types of ants, more complex environments, or even the ability to introduce human players who can control certain ants or manipulate the environment.

By utilizing the 'ant-ai' package, you'll be able to define the agents (ants), set their behaviors, and manage interactions between them efficiently. Remember to document your code well and provide instructions on how to run the simulation.