agentpact

v0.1.17 suspicious
4.0
Medium Risk

Python client for the AgentPact API

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risks in terms of network, shell, obfuscation, and credential activities. However, the metadata quality and maintainer activity are concerning, warranting further investigation.

  • Low maintainer activity
  • Poor metadata quality
Per-check LLM notes
  • Network: The presence of network calls is likely normal for 'agentpact', possibly indicating legitimate API interactions.
  • Shell: No shell execution patterns detected, suggesting low risk.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows low maintainer activity and poor metadata quality, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (2.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
○ Low Documentation 1.0

No documentation detected

  • No documentation URL, doc files, or meaningful description found
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 108 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • pi_key}" self._http = httpx.Client(base_url=self.base_url, headers=headers, timeout=timeout)
✓ 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

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with agentpact 
Create a Python-based mini-application that leverages the 'agentpact' package to manage and monitor interactions between different software agents in a distributed system. Your application will serve as a simple dashboard where users can see real-time status updates of these agents and manage their configurations.

### Features:
1. **Agent Registration**: Allow users to register new agents with basic information such as name, type, and description.
2. **Status Monitoring**: Implement a feature that periodically checks the status of registered agents and displays it on the dashboard.
3. **Configuration Management**: Provide functionality for users to update the configuration settings of registered agents.
4. **Alert System**: Set up an alert system that notifies users when an agent encounters issues or fails to respond within expected timeframes.
5. **Dashboard UI**: Develop a user-friendly web interface using Flask or Django to visualize the status and configurations of all registered agents.

### Steps to Build the Application:
1. **Setup Environment**: Ensure you have Python installed along with 'agentpact'. Use pip to install any other necessary packages like Flask or Django for the web interface.
2. **Define Data Models**: Create data models for Agents and Configurations. These models should capture all relevant details about each agent and its configurations.
3. **Implement Core Functionality**:
   - Register New Agents: Utilize 'agentpact' methods to create new agent instances and save them to your database.
   - Monitor Status: Write a script or use 'agentpact' hooks to periodically check the health and status of each agent. Store this information in your database.
   - Update Configurations: Allow users to modify agent configurations through the web interface. Use 'agentpact' to apply these changes.
4. **Build Web Interface**: Using Flask or Django, develop a web interface where users can view the status of all agents, register new ones, and manage configurations.
5. **Integrate Alert System**: Incorporate an alert system that sends notifications via email or SMS if an agent's status changes unexpectedly.
6. **Testing & Deployment**: Thoroughly test your application to ensure all features work as intended. Consider deploying it on a cloud platform like AWS or Heroku for accessibility.

### Utilization of 'agentpact':
- **Registration & Configuration**: Use 'agentpact' to create and manage agent instances, setting initial configurations.
- **Health Checks**: Leverage 'agentpact' functionalities to perform health checks on agents and retrieve status information.
- **Configuration Updates**: Apply 'agentpact' methods to update agent configurations based on user inputs from the web interface.