agent2model

v0.1.0 suspicious
4.0
Medium Risk

Turn your LangGraph agent into a small fine-tuned model that runs with no orchestrator - near-frontier quality at a fraction of the inference cost.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits some concerning behaviors such as shell execution and code obfuscation, despite showing no clear signs of credential theft or network attacks. The metadata also suggests a lack of established trustworthiness.

  • Shell risk
  • Code obfuscation
Per-check LLM notes
  • Network: No network calls were detected.
  • Shell: The presence of shell execution suggests potential risk depending on the commands being executed; further investigation is needed.
  • Obfuscation: The code shows signs of obfuscation which could be an attempt to hide logic, but it's not clearly malicious without further context.
  • Credentials: No clear evidence of credential harvesting is found in the provided code snippet.
  • Metadata: The package shows signs of being newly created with limited maintainer history and incomplete author information, which raises suspicion.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • path}.") @app.command() def eval( build_dir: Annotated[ Path, typer.Argument(help
  • ry: version = getattr(__import__("modal"), "__version__", "unknown") except Exception: # pragma:
⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • }") try: result = subprocess.run(argv, check=False) except FileNotFoundError as exc:
  • unch", *cmd[2:]] result = subprocess.run(cmd, check=False) if result.returncode != 0: rai
✓ 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 kamaalg/agent2model appears legitimate

⚠ 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 agent2model 
Create a Python-based mini-application that leverages the 'agent2model' package to convert a conversational AI agent into a lightweight, fine-tuned model for local deployment. This application will allow users to engage in natural language conversations with the AI without needing an external orchestrator, significantly reducing inference costs while maintaining high-quality responses. Here are the key steps and features for building this application:

1. **Setup Environment**: Ensure you have Python installed along with necessary packages including 'agent2model'. Use pip for installation if not already available.

2. **Agent Integration**: Integrate an existing conversational agent (e.g., one built using LangGraph) into your project. This agent should be capable of understanding and responding to user queries.

3. **Model Conversion**: Utilize 'agent2model' to convert this agent into a smaller, optimized model. This process involves fine-tuning the original agent to ensure it retains its conversational capabilities but is more efficient for local use.

4. **Local Deployment**: Implement a simple web server using Flask or Django to host the converted model locally. This allows users to interact with the AI through a web interface.

5. **User Interface**: Develop a basic HTML/CSS/JavaScript frontend that communicates with the backend via RESTful API calls. Users should be able to type in questions and receive answers from the AI in real-time.

6. **Testing & Optimization**: Test the application thoroughly to ensure smooth interactions between the user and the AI. Optimize the model further based on performance metrics and user feedback.

7. **Documentation**: Provide comprehensive documentation detailing how to set up the environment, run the application, and customize it for different conversational agents.

By following these steps, you'll create a versatile mini-application that showcases the power of 'agent2model' in transforming complex AI agents into efficient, accessible models suitable for a wide range of applications.