ai-agentswarm

v0.6.0 suspicious
4.0
Medium Risk

A recursive, functional, and state-isolated Multi-Agent Framework.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some red flags, particularly concerning the metadata risk due to the maintainer's new or inactive account and lack of a proper author name. This, combined with the moderate network risk from incomplete code snippets, suggests a cautious approach is warranted.

  • metadata risk due to new/inactive maintainer account
  • incomplete code raising network interaction concerns
Per-check LLM notes
  • Network: The network calls seem to be legitimate HTTP requests, possibly for API interactions or updates, but the incomplete code snippets raise some uncertainty.
  • Shell: No shell execution patterns detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
  • Metadata: The maintainer has a new or inactive account and lacks a proper author name, which could indicate potential risk.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 5 test file(s) found

  • 5 test file(s) detected (e.g. test_agents.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/ai-agentswarm/agentswarm#readme
  • Detailed PyPI description (6888 chars)
○ 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

  • 96 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 74 commits in ai-agentswarm/agentswarm
  • Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

  • ) -> dict: async with httpx.AsyncClient(timeout=self.timeout) as client: response = awai
  • onHandler: async with httpx.AsyncClient(timeout=self.timeout) as client: response = awai
  • 1 minute async with httpx.AsyncClient(timeout=self.timeout) as client: for _ in range(
✓ Code Obfuscation

No obfuscation patterns detected

✓ 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: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository ai-agentswarm/agentswarm 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 ai-agentswarm 
Develop a simulation of a small city where multiple autonomous agents (such as pedestrians, vehicles, and service robots) interact within a defined environment. This project will utilize the 'ai-agentswarm' Python package to manage the interactions and behaviors of these agents in a functional and isolated manner. Each agent will have its own set of rules and goals, contributing to a dynamic and evolving cityscape.

### Project Scope:
- **Environment Setup:** Create a grid-based map representing a city block, including streets, sidewalks, buildings, parks, and parking lots.
- **Agent Types:** Define at least three types of agents: Pedestrians, Vehicles, and Service Robots.
- **Agent Behaviors:** Implement different behaviors for each type of agent based on their roles and objectives. For example, pedestrians might aim to reach specific destinations, vehicles may follow traffic rules, and service robots could perform tasks like delivering packages.
- **Interaction Rules:** Establish interaction rules between different agent types. For instance, vehicles must yield to pedestrians at crosswalks, and service robots might need to avoid obstacles and navigate safely around other agents.
- **Dynamic Scenarios:** Introduce dynamic scenarios such as traffic lights changing, pedestrians crossing streets, and service robots completing tasks.
- **Visualization:** Use a simple graphical interface or console output to visualize the movement and interactions of the agents.

### Utilization of 'ai-agentswarm':
- **Agent Initialization:** Use the 'ai-agentswarm' package to initialize each agent with its unique state and behavior function.
- **Behavior Execution:** Leverage the package's functional capabilities to execute the behavior of each agent in a recursive manner, allowing for continuous updates based on the environment and interactions with other agents.
- **State Isolation:** Ensure that each agent maintains its state independently using the state-isolation feature of 'ai-agentswarm', preventing unintended side effects from one agent's actions affecting another.
- **Recursive Interaction:** Implement recursive interaction logic where agents can dynamically adjust their behavior based on real-time data and feedback from the environment and other agents.

### Deliverables:
- A Python script that initializes the city environment and agents.
- A set of functions defining the behaviors of each agent type.
- Code demonstrating the use of 'ai-agentswarm' for managing agent interactions.
- A visualization tool showing the simulation results.

This project not only showcases the power of 'ai-agentswarm' in handling complex multi-agent systems but also provides a practical application in simulating urban environments.