🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risks with no signs of malicious intent. The network and shell execution risks are relatively low and typical for its intended functionality.

Low obfuscation and credential risk
Incomplete maintainer profile

Per-check LLM notes

Network: Network calls are typical for packages that require internet access for functionality.
Shell: Shell execution can be risky if not properly sanitized or controlled, potentially allowing for arbitrary command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
Credentials: No credential harvesting patterns detected, indicating low risk of credential theft.
Metadata: The maintainer has an incomplete profile and may be new or inactive, raising some suspicion but not conclusive evidence of malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

y: async with httpx.AsyncClient(timeout=config.proxy_timeout_seconds) as client:
s, ) client = httpx.AsyncClient(timeout=stream_timeout) stream_context = client.stre
} try: async with httpx.AsyncClient(timeout=30.0) as client: async with client.strea

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 8.0

Found 4 shell execution pattern(s)

ings_arg}") result = subprocess.run(cmd, capture_output=True, text=True, shell=is_windows)
essfully.""" try: subprocess.run(command, capture_output=True, check=True, shell=shell)
try: completed = subprocess.run( [binary_path, "--version"], capture
: self._process = subprocess.Popen( command, cwd=str(self.confi

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: vrsen.ai>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository VRSEN/agency-swarm 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 agency-swarm

Create a fully functional mini-app that simulates a swarm of autonomous agents interacting within a defined environment using the 'agency-swarm' Python package. This app will serve as a simple yet engaging tool to demonstrate the capabilities of the package in managing complex agent-based systems. The project should include the following steps and features:

1. **Setup**: Begin by installing the 'agency-swarm' package in your Python environment. Ensure you have a virtual environment set up for this project.
2. **Environment Creation**: Design a basic 2D environment where the agents can move around. This environment should have obstacles and goals that the agents aim to reach.
3. **Agent Design**: Define different types of agents that can interact within the environment. Each type of agent should have unique behaviors, such as seeking food, avoiding predators, or exploring new territories.
4. **Interaction Rules**: Implement rules for how these agents interact with each other and their environment. For example, agents could communicate with each other to warn about dangers or share information about resources.
5. **Visualization**: Use a library like Matplotlib or Pygame to visualize the simulation in real-time. This visualization should clearly show the movement of agents, their interactions, and any changes in the environment.
6. **User Interaction**: Allow users to control certain aspects of the simulation, such as adding or removing agents, changing environmental conditions, or modifying interaction rules.
7. **Logging and Analysis**: Include a feature to log data from the simulation, such as agent positions, interactions, and environmental states over time. Provide tools for analyzing this data to understand the dynamics of the swarm.
8. **Documentation and Testing**: Write comprehensive documentation explaining how to install and run the app, along with examples of different scenarios and configurations. Ensure the app is thoroughly tested to handle various edge cases and user inputs.

The 'agency-swarm' package is utilized throughout the project to manage the agents and their interactions, providing a robust framework for building and scaling the simulation. By the end of this project, you should have a fully functional mini-app that not only demonstrates the power of 'agency-swarm' but also serves as a valuable tool for studying swarm behavior and complex systems.