agent-assembly

v0.0.1a5 suspicious
5.0
Medium Risk

Python SDK for AI Agent Assembly - A governance-native runtime for AI agents

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits concerning shell execution capabilities and lacks critical metadata such as an author's name, raising suspicion about its true intentions.

  • Shell risk is high due to potential for executing arbitrary commands.
  • Missing author information and single associated package increase suspicion.
Per-check LLM notes
  • Network: The network patterns detected may be for legitimate communication but could also indicate potential unauthorized external calls.
  • Shell: The shell execution patterns are concerning as they suggest the package can execute arbitrary commands, which could be exploited for malicious purposes.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, suggesting no risk of secret theft.
  • Metadata: The package has a missing author and a single associated package, raising concerns about its legitimacy and intent.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

  • """ try: with socket.create_connection((host, port), timeout=0.1): return True exce
  • }" self._client = httpx.Client( base_url=self.gateway_url,
  • H try: response = httpx.get(url, timeout=timeout) except httpx.HTTPError: re
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • assembly[cli]" ) subprocess.Popen( [aasm_path, *AASM_AUTO_START_ARGV], stdout=
  • og_path.open("ab") return subprocess.Popen( [str(binary), "serve", "--port", str(port)],
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: agent-assembly.dev>

✓ 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 agent-assembly 
Create a fully functional mini-application named 'AI Task Orchestrator' that leverages the 'agent-assembly' Python package to manage and govern AI agents within a microservices architecture. This application will serve as a platform where users can define tasks, assign them to specific AI agents, monitor their progress, and receive notifications upon completion. Here’s a detailed step-by-step guide on how to implement this application:

1. **Setup Project Environment**: Initialize a new Python project and install the 'agent-assembly' package along with other necessary libraries such as Flask for web development.
2. **Define Tasks API**: Develop REST APIs using Flask that allow users to create, update, delete, and retrieve tasks. Each task should have details like name, description, priority level, and assigned AI agent.
3. **Task Assignment Logic**: Implement logic within the 'AI Task Orchestrator' to automatically assign tasks to available AI agents based on their capabilities and current workload. Use the 'agent-assembly' package to facilitate communication and coordination between the orchestrator and agents.
4. **Monitoring and Notifications**: Enable real-time monitoring of task execution status through a dashboard integrated into the application. Additionally, set up a notification system that alerts users via email or SMS when tasks are completed or if any issues arise during execution.
5. **Governance Features**: Utilize the governance-native runtime provided by 'agent-assembly' to enforce policies and ensure compliance with organizational standards throughout the task lifecycle.
6. **Testing and Documentation**: Thoroughly test all components of the application to ensure reliability and efficiency. Document your implementation process, including setup instructions, API documentation, and usage examples.

Suggested Features:
- Integration with popular AI services for seamless task execution.
- Support for multi-tenant environments to cater to diverse user groups.
- Advanced analytics dashboard for performance tracking and optimization.
- Flexible configuration options to tailor the application to different business needs.

By following these steps and incorporating the above features, you will develop a robust and versatile tool that simplifies the management of AI-driven workflows.