agent-gantry

v0.4.0 safe
4.0
Medium Risk

Universal Tool Orchestration Platform - Intelligent, secure tool orchestration for LLM-based agent systems

🤖 AI Analysis

Final verdict: SAFE

The package shows low risk indicators across multiple categories, with no clear evidence of malicious intent. While some elements like obfuscation and network usage warrant further attention, they do not conclusively point towards a supply-chain attack.

  • Moderate obfuscation risk
  • Potential network security concerns
Per-check LLM notes
  • Network: The use of HTTP requests is common in packages that require network communication, but should be reviewed to ensure proper authorization and data handling.
  • Shell: No shell execution patterns were detected, which is normal and indicates no immediate risk from this aspect.
  • Obfuscation: The obfuscation pattern seems unusual but not definitively malicious without more context.
  • Credentials: No clear signs of credential harvesting observed.
  • Metadata: The maintainer has only one package, which may indicate a new or less active account.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • httpx async with httpx.AsyncClient() as client: response = await client.get(
  • equest async with httpx.AsyncClient() as client: response = await client.post(
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • ctorStore"): module = __import__("agent_gantry.adapters.vector_stores.remote", fromlist=[name]) return getattr(module, name) raise AttributeErr
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository CodeHalwell/Agent-Gantry appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Agent-Gantry Contributors" 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-gantry 
Create a mini-application called 'AgentScheduler' that leverages the 'agent-gantry' package to manage and orchestrate various AI agents performing different tasks in a secure and intelligent manner. This application will serve as a platform where users can define tasks, assign them to specific agents based on their capabilities, and monitor their progress. Here’s a detailed breakdown of the project steps and features:

1. **Setup Project Environment**: Initialize your Python environment and install the necessary packages including 'agent-gantry'. Ensure all dependencies are up-to-date.
2. **Define Agent Types**: Create different types of agents within 'AgentScheduler', each capable of handling distinct tasks such as data processing, security audits, or content generation. Each agent type should have a unique set of capabilities and limitations defined using 'agent-gantry'.
3. **Task Management System**: Implement a system where users can submit tasks to 'AgentScheduler'. These tasks should be categorized based on their nature (e.g., data analysis, report generation). Users should also be able to specify any parameters required for task execution.
4. **Intelligent Task Assignment**: Utilize 'agent-gantry' to intelligently assign tasks to the most suitable agent based on current load, availability, and the task requirements. For example, if a high-priority task requires immediate attention, 'AgentScheduler' should prioritize assigning it to an available agent.
5. **Monitoring and Reporting**: Develop a feature within 'AgentScheduler' that allows users to track the status of their tasks in real-time. This includes visual representations of task progress, estimated completion times, and alerts for any issues encountered during execution.
6. **Security Enhancements**: Since 'agent-gantry' emphasizes security, implement robust authentication and authorization mechanisms within 'AgentScheduler'. This ensures only authorized users can access and modify tasks and agent configurations.
7. **User Interface**: Although not mandatory, consider building a simple web interface using Flask or Django to interact with 'AgentScheduler'. This UI should allow users to easily submit tasks, view task statuses, and manage agent configurations.
8. **Documentation and Testing**: Write comprehensive documentation detailing how to use 'AgentScheduler' and its integration with 'agent-gantry'. Conduct thorough testing to ensure all components work seamlessly together.

This project aims to demonstrate the versatility and efficiency of 'agent-gantry' in orchestrating complex AI workflows while ensuring security and intelligence in task management.