agentmem-crewai

v0.1.1 suspicious
4.0
Medium Risk

AgentMem integration for CrewAI — drop-in memory tools for any Crew agent

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risk in terms of network usage, shell execution, and obfuscation. However, the metadata risk score is elevated due to incomplete author information, making it suspicious but not conclusive evidence of malicious intent.

  • Incomplete author information
  • No immediate signs of malicious activity
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access to function.
  • Shell: No shell execution patterns detected, indicating no immediate risk of command injection or similar attacks.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author's information is incomplete and they may be new or inactive, which raises some suspicion but not enough to conclusively identify it as malicious.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository rooney011/agentmem-sdk appears legitimate

⚠ 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 agentmem-crewai 
Create a conversational AI assistant named 'CrewMate' that leverages the 'agentmem-crewai' package to enhance user interactions. This mini-app should allow users to have persistent conversations with CrewMate, where CrewMate remembers past interactions and can recall them to provide more contextually relevant responses. Here’s a detailed breakdown of the steps and features you need to implement:

1. **Setup**: Install the 'agentmem-crewai' package and set up your environment with necessary dependencies like Python and Flask.
2. **Memory Integration**: Integrate 'agentmem-crewai' into your application to enable CrewMate to store and retrieve user interaction data. This includes setting up the memory system and ensuring it persists across sessions.
3. **User Interface**: Develop a simple web interface using Flask where users can input messages and receive responses from CrewMate. Ensure the UI is intuitive and user-friendly.
4. **Conversation Handling**: Implement logic within CrewMate to handle incoming messages, process them using natural language understanding (NLU), and generate appropriate responses. Utilize 'agentmem-crewai' to access previous conversation history when formulating responses.
5. **Contextual Responses**: Enhance CrewMate’s response generation by incorporating historical context from previous interactions. For example, if a user asks about their last query, CrewMate should be able to recall and summarize the previous conversation.
6. **Customization Options**: Allow users to customize their experience with CrewMate by providing options to adjust the level of detail in responses or specify preferences for certain topics or tones.
7. **Testing and Feedback**: Test the application thoroughly to ensure smooth operation and accurate memory recall. Incorporate a feedback mechanism where users can rate the quality of CrewMate’s responses and suggest improvements.

This project aims to demonstrate the power of 'agentmem-crewai' in building intelligent, context-aware conversational agents capable of maintaining meaningful interactions over time.