🤖 AI Analysis

Final verdict: SAFE

The ai-agent-gateway package appears safe based on the analysis. While there are some minor concerns regarding network and shell execution risks, these do not strongly indicate malicious intent.

Low obfuscation and credential risks
Some signs of low maintainer activity and poor metadata quality

Per-check LLM notes

Network: The network calls detected are typical for an AI agent that might need to communicate with external services or APIs.
Shell: The shell execution pattern suggests the package interacts with Docker, possibly for container management. This is not inherently malicious but requires scrutiny if unexpected.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows some signs of low maintainer activity and poor metadata quality, but there's no direct evidence of malicious intent.

📦 Package Quality Overall: Low (2.8/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (9606 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

◈ Medium Type Annotations 5.0

Partial type annotation coverage

741 type-annotated function signatures detected in source

○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

view": True, } async with httpx.AsyncClient(timeout=10.0) as client: response = await client.post(ur
None, ) -> None: async with httpx.AsyncClient(timeout=10.0) as client: response = await client.post(ur
config) http_client = httpx.AsyncClient( headers=headers, timeout=httpx.Timeout(time
t, connect=10.0) client = httpx.AsyncClient(**client_kwargs) self._client_state[client] = { "t

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

return False try: subprocess.run( [docker_bin, "image", "inspect", self._image],

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

⚠ Suspicious Page Links score 4.0

Found 2 suspicious link(s) on the package page

Non-HTTPS external link: http://127.0.0.1:8000/api/chat/init
Non-HTTPS external link: http://127.0.0.1:8000/api/chat

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with ai-agent-gateway

Create a real-time sentiment analysis mini-app using the 'ai-agent-gateway' package. This app will allow users to input text and receive instant sentiment analysis results, leveraging the capabilities of the 'ai-agent-gateway' for its streaming and generic AI agent functionalities. Here’s a detailed plan on how to build it:

1. **Project Setup**: Start by setting up a new Python environment. Install the 'ai-agent-gateway' package along with any other necessary dependencies like Flask for web serving.

2. **API Integration**: Use 'ai-agent-gateway' to integrate with a sentiment analysis API. This involves configuring the gateway to connect to your chosen AI service provider and setting up the necessary authentication.

3. **Real-Time Processing**: Implement real-time processing by utilizing the streaming capabilities provided by 'ai-agent-gateway'. Ensure that the app can handle continuous data streams efficiently.

4. **User Interface**: Develop a simple web interface using Flask where users can enter text and see sentiment analysis results in real-time. Design the UI to be user-friendly and visually appealing.

5. **Feature Enhancements**: Consider adding additional features such as language detection, emotion classification beyond just positive/negative sentiment, and even sentiment strength indicators.

6. **Testing & Optimization**: Rigorously test the app under various conditions to ensure reliability and performance. Optimize the code for better efficiency, especially in handling high volumes of data.

7. **Documentation**: Finally, document all steps taken during development, including configuration settings, code snippets, and deployment instructions. This will help others understand and possibly contribute to the project.