🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in most categories but raises some concerns due to network communication and metadata analysis, suggesting potential legitimacy issues that require further investigation.

moderate network risk
single package from author

Per-check LLM notes

Network: Network calls are likely legitimate for SDKs to communicate with their services, but the exact endpoints and purposes need verification.
Shell: No shell execution patterns detected, indicating low risk for direct system command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The author has only one package, which may indicate a new or less active account, raising some suspicion but not conclusive evidence of malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

n self._index_id with httpx.Client(timeout=self.timeout) as c: r = c.get(f"{self.ba
lf._resolve_id() with httpx.Client(timeout=self.timeout) as c: r = c.post(
_k": int(top_k)} with httpx.Client(timeout=self.timeout) as c: r = c.post(

✓ 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

Repository agentbreeder/agentbreeder appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "AgentBreeder 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 agentbreeder-sdk

Create a mini-application called 'AgentBot' using the 'agentbreeder-sdk' Python package. This application will serve as a personal AI assistant for managing tasks and providing information based on user queries. Here are the steps and features to include:

1. **Setup and Configuration**: Initialize the project with a virtual environment. Install the 'agentbreeder-sdk' and other necessary Python packages such as requests for HTTP requests.
2. **User Authentication**: Implement a simple authentication system where users can sign up and log in. Store user credentials securely.
3. **AI Agent Definition**: Define an AI agent within the 'agentbreeder-sdk' that can understand natural language queries related to task management and general information retrieval.
4. **Task Management**: Allow users to create, view, update, and delete tasks through the AI agent. Users should be able to interact with the AI using simple commands like 'add task', 'show tasks', 'delete task', etc.
5. **Information Retrieval**: Enable the AI agent to answer general knowledge questions by integrating with an API service (e.g., Wikipedia API).
6. **Feedback Mechanism**: Implement a feature where users can provide feedback on the AI's performance, which can then be used to improve the agent over time.
7. **Logging and Analytics**: Track user interactions and AI responses for analytics purposes. Use this data to continuously improve the AI's performance.
8. **Deployment**: Deploy the application locally or on a cloud platform of your choice.

The 'agentbreeder-sdk' package will be primarily used for defining the AI agent's capabilities, validating its inputs, and deploying it for real-time interaction. Additionally, explore how you can leverage the SDK to make your AI more adaptive and efficient over time.