🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks related to network and shell command execution, which could indicate potential security vulnerabilities. While there is no direct evidence of malicious activity, these signals warrant caution.

network risk due to external connections
high risk associated with executing shell commands

Per-check LLM notes

Network: The network call pattern suggests the package may attempt to connect to an external host, which could be benign if for legitimate purposes like updates or logging but requires further investigation.
Shell: Executing shell commands can be high risk as it allows the package to run arbitrary code on the user's system, potentially leading to security vulnerabilities.
Obfuscation: No obfuscation patterns detected in the code snippet.
Credentials: The usage of getpass indicates handling of sensitive information, likely for user authentication purposes, which is common but requires secure handling practices.
Metadata: The package has some concerning metadata issues but no clear signs of typosquatting or malicious intent.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

> bool: try: with socket.create_connection((host, port), timeout=2): return True except

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

reinstall") try: subprocess.run(install_cmd, check=True) except subprocess.CalledProcess

⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

pass value = getpass.getpass("Enter memgraph password: ") else:

✓ 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 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-context-graph

Create a mini-application that acts as a personal assistant manager, utilizing the 'agent-context-graph' Python package to integrate various AI agents into a cohesive system. This application will allow users to manage their AI assistants by connecting them to different actions and skills through a context graph. Users can define tasks that require multiple steps and delegate these tasks to specific agents based on their capabilities. Here’s a step-by-step guide on how to build this application:

1. **Setup Environment**: Begin by setting up a Python virtual environment and installing necessary packages including 'agent-context-graph'. Ensure all dependencies required for the package are also installed.

2. **Define Agents**: Create a list of available AI agents that your application will support. Each agent should have a set of predefined skills and actions it can perform. Use 'agent-context-graph' to define these actions and skills within a context graph structure.

3. **User Interface**: Develop a simple user interface where users can input their tasks. The UI should allow users to select which agents they want to use for each task and specify any parameters needed for the task execution.

4. **Task Delegation**: Implement logic to parse the user-defined tasks into smaller, manageable chunks. Based on the defined context graph, determine which agents should handle each chunk of the task. Use 'agent-context-graph' to navigate the graph and find the best path for task completion.

5. **Execution and Monitoring**: Once the tasks are delegated, monitor the progress of each task. Provide feedback to the user about the status of their tasks, such as whether they are being processed, completed, or if there are any issues.

6. **Feedback Loop**: Allow users to provide feedback on the performance of the agents. Use this feedback to improve the efficiency and accuracy of task delegation in future iterations.

7. **Security and Privacy**: Ensure that the application complies with security standards and respects user privacy. Implement measures to protect sensitive information and ensure secure communication between the application and the agents.

**Suggested Features**:
- A dashboard to view the status of all active tasks.
- Customizable agent profiles allowing users to tailor the behavior of their agents.
- An analytics module to track performance metrics of agents over time.
- Integration with popular calendar and task management tools for seamless task synchronization.