🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of potential obfuscation techniques and has a newly created profile with limited maintainer history, raising concerns about its legitimacy.

High obfuscation risk due to the use of eval
Limited maintainer history and non-existent git repository

Per-check LLM notes

Network: Network calls are likely for legitimate purposes such as API interactions or updates.
Shell: No shell execution patterns detected.
Obfuscation: The use of eval with restricted builtins and environment variable checks suggests an attempt to obfuscate code execution, which could be used for malicious purposes.
Credentials: No clear evidence of credential harvesting was found.
Metadata: The package is newly created with limited maintainer history and a non-existent git repository, raising some suspicion but not conclusive evidence of malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

i_key}" async with httpx.AsyncClient(timeout=self.timeout) as client: response = awa
ings.") async with httpx.AsyncClient(timeout=60.0) as client: response = await clien

⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

try: result = eval(expression, {"__builtins__": {}}, {}) # noqa: S307 — demo o
mode == "auto" and not __import__("os").environ.get("OPENAI_API_KEY") ) print("=" * 50)
tool(): registry_tools = __import__("agentloop.tools", fromlist=["ToolRegistry"]).ToolRegistry() @registry_tools.register(description=

✓ 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 score 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Only one version has ever been released — brand new package
Author "agentloop 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 agentloop-framework

Create a chatbot application using the 'agentloop-framework' package that acts as a personal financial advisor. This application should be able to interact with users through text-based conversations, providing advice on budgeting, savings, and investment strategies based on user inputs. The app should also be capable of integrating with external financial data sources to provide real-time financial insights.

Step-by-step guide:
1. Setup the environment by installing the 'agentloop-framework' package and any necessary dependencies.
2. Define the agent's capabilities and tools it will use to interact with the user and external data sources. These tools could include a budget calculator, a savings estimator, and an investment advisor module.
3. Implement an observe-act loop within the agent using the 'agentloop-framework'. This loop should allow the agent to process user inputs, retrieve relevant financial data from external sources, and generate responses based on the analysis.
4. Develop a user interface where users can input their financial questions or situations, and receive personalized advice from the agent.
5. Test the application thoroughly to ensure accurate financial advice and smooth interaction between the user and the agent.
6. Deploy the application so it can be accessed via a web interface or command-line tool.

Suggested Features:
- Ability to input monthly income and expenses for budget planning.
- Estimation of savings goals based on current spending habits.
- Analysis of potential investment options based on risk tolerance and financial goals.
- Integration with external APIs for real-time stock market data.
- Historical financial data analysis for better financial planning.
- User-friendly interface for easy interaction with the financial advisor agent.

Utilization of 'agentloop-framework':
- Use the 'agentloop-framework' to define the observe-act cycle of the financial advisor agent. This includes observing user inputs, fetching financial data, analyzing the data, and acting by providing personalized advice back to the user. The framework simplifies the creation of complex interactions and ensures that the agent can dynamically adapt its responses based on the latest financial data available.