aaep-langchain-producer

v1.0.0 suspicious
4.0
Medium Risk

AAEP integration for LangChain agents via callback handler

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in direct execution and network activities but raises concerns due to the recent creation of the repository and the limited history of the maintainer.

  • Recent repository creation
  • Limited maintainer history
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution patterns detected, indicating no immediate risk from command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of credential theft.
  • Metadata: The repository was created recently and the maintainer has limited history, raising suspicion.

🔬 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: izusoft.tech>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository created very recently: 4 day(s) ago (2026-06-01T19:52:23Z)

  • Repository created very recently: 4 day(s) ago (2026-06-01T19:52:23Z)
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • 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 aaep-langchain-producer 
Your task is to develop a mini-application called 'LangChainTalk' which leverages the 'aaep-langchain-producer' package to facilitate communication between LangChain agents and an external system through AAEP (Assistive AI Event Protocol). This application will serve as a bridge, allowing users to interact with LangChain agents in real-time while logging these interactions for future analysis. Here’s how you’ll build it:

1. **Setup Project**: Begin by setting up a new Python environment and installing necessary packages including 'aaep-langchain-producer', 'langchain', and any other dependencies required.

2. **Define Interaction Mechanism**: Implement a user-friendly interface (console-based for simplicity) where users can input queries or commands. These inputs will be directed to a LangChain agent via the 'aaep-langchain-producer'.

3. **Callback Handler Integration**: Utilize the 'aaep-langchain-producer' package to integrate a callback handler that listens for responses from the LangChain agent. Ensure that this handler not only captures the response but also logs it into a local database for later review.

4. **Real-Time Feedback**: Design the application to provide real-time feedback to users. Once a query is submitted, display a loading message until the LangChain agent responds. Upon receiving the response, show it back to the user immediately.

5. **Logging System**: Implement a robust logging system that stores all interactions (both user inputs and LangChain responses) in a structured format. Consider using SQLite for simplicity.

6. **Enhanced Features**: To make your application more versatile, consider adding features like command history (displaying previous queries and responses), the ability to switch between different LangChain agents, and options to save sessions for later retrieval.

7. **Testing and Documentation**: Thoroughly test your application to ensure it works as expected under various conditions. Document your code well, providing instructions on how to set up and use the application, along with examples of how to extend its functionality.

By following these steps, you'll create a functional and user-friendly tool that showcases the power of integrating LangChain agents with external systems using the 'aaep-langchain-producer' package.