agentic-ai-sdk

v1.0.0 suspicious
5.0
Medium Risk

SDK for building AI agent 2.0.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risks due to insecure links and a less experienced maintainer, but lacks clear indicators of malicious intent.

  • Metadata risk due to non-secure links
  • Maintainer has limited experience with only one published package
Per-check LLM notes
  • Network: Network calls indicate the package is likely intended to interact with an external API, which is common for SDKs.
  • Shell: No shell execution patterns detected, indicating no immediate risk from command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package contains non-secure links and the maintainer has only one published package, which may indicate a less experienced or potentially suspicious account.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

  • httpx.AsyncClient: return httpx.AsyncClient( base_url=cfg["base_url"], headers={"X-API-K
  • m_name) http_client = httpx.AsyncClient(transport=logging_transport, event_hooks=event_hooks) el
  • m_name) http_client = httpx.AsyncClient(transport=logging_transport) if llm_config.provider ==
  • self._client = httpx.Client(headers=headers, timeout=timeout, verify=verify_ssl) de
  • "http": http_client = httpx.AsyncClient( headers=server.headers or {}, timeo
✓ 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

No author email provided

⚠ Suspicious Page Links score 4.0

Found 2 suspicious link(s) on the package page

  • Non-HTTPS external link: http://host.docker.internal:8000/
  • Non-HTTPS external link: http://backend:8000/
✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Frank Lin" 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 agentic-ai-sdk 
Create a fully functional mini-application named 'TaskMaster' using the 'agentic-ai-sdk' Python package. TaskMaster will serve as an advanced task management system where users can create, assign, and track tasks across different projects. This application aims to demonstrate the capabilities of AI agents in automating and optimizing task management processes.

### Project Overview:
- **Application Name:** TaskMaster
- **Primary Functionality:** Create, Assign, Track Tasks
- **Secondary Functionality:** Intelligent Task Scheduling, Priority Adjustment, and Progress Reporting
- **Target Audience:** Individuals and teams who need efficient task management solutions

### Features:
1. **User Management:** Users can register, log in, and manage their profiles.
2. **Project Creation:** Users can create multiple projects and categorize tasks within these projects.
3. **Task Management:** Users can create tasks, set deadlines, assign them to team members, and mark them as completed.
4. **AI Agent Assistance:** Utilize the 'agentic-ai-sdk' to implement an AI agent that assists in task scheduling, prioritization, and progress monitoring.
5. **Progress Tracking:** A dashboard showing the status of all tasks and projects.
6. **Notifications:** Real-time notifications for task updates and deadlines.
7. **Analytics:** Provide analytics on task completion rates and project timelines.

### Implementation Steps:
1. **Setup Environment:** Install necessary packages including 'agentic-ai-sdk'.
2. **Design Database Schema:** Define models for User, Project, Task, and AI Agent states.
3. **Develop Backend Logic:** Implement CRUD operations for tasks and projects, along with user authentication.
4. **Integrate AI Agent:** Use 'agentic-ai-sdk' to develop an AI agent capable of analyzing task data and suggesting optimal schedules and priorities.
5. **Frontend Development:** Build a simple web interface for users to interact with the application.
6. **Testing & Debugging:** Test the application thoroughly, ensuring all features work as expected.
7. **Deployment:** Deploy the application on a cloud service provider like AWS or Heroku.

### Utilizing 'agentic-ai-sdk':
- **Task Scheduling:** The AI agent will analyze task dependencies and deadlines to suggest the best order and timing for completing tasks.
- **Priority Adjustment:** Based on urgency and importance, the AI agent will adjust task priorities dynamically.
- **Progress Monitoring:** The AI agent will monitor task progress and alert users if there are any delays or potential issues.
- **Analytics Generation:** The AI agent will generate reports on task performance and project timelines, providing insights for future planning.

This project aims to showcase the power of AI in enhancing productivity and efficiency in daily task management activities.