🤖 AI Analysis

Final verdict: SUSPICIOUS

The package is considered suspicious due to its novelty, lack of maintainer history, and missing author information, despite showing no direct signs of malicious activity.

Low network, shell, obfuscation, and credential risks.
High metadata risk due to new package, unknown maintainer, and missing author details.

Per-check LLM notes

Network: The presence of network calls is expected if the package relies on external services or APIs. Further investigation into the purpose of these calls is recommended.
Shell: No shell execution patterns were detected, which is normal and does not indicate any immediate risk.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows signs of being potentially malicious due to its newness, lack of maintainer history, and missing author information.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

Test runner config found: pyproject.toml
1 test file(s) detected (e.g. test_search.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (2301 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

6 type-annotated function signatures (partial)

○ 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 1.5

Found 1 network call pattern(s)

async with httpx.AsyncClient() as http: r = await http.get(f"http

✓ 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 10.0

5 maintainer concern(s) found

Only one version has ever been released — brand new package
Package is very new: uploaded 2 day(s) ago
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 agora-moss

Create a real-time conversational search engine mini-app using the Python package 'agora-moss'. This app will integrate Moss semantic search capabilities into a conversational interface managed by Agora Conversational AI via MCP. The app should allow users to input questions or topics, and it will return relevant information from a predefined set of documents or web pages in real-time. The user experience should feel like chatting with a knowledgeable assistant who can provide detailed answers based on the available data.

Step 1: Set up your development environment by installing Python and the necessary libraries including 'agora-moss', Flask for the web framework, and requests for making HTTP calls.

Step 2: Configure Agora Conversational AI and Moss API endpoints to ensure seamless communication between the front-end and back-end services.

Step 3: Develop the backend logic using 'agora-moss' to handle incoming queries, process them through Moss for semantic search, and then send the results back to the user via the Agora Conversational AI service.

Step 4: Implement a simple yet effective user interface using HTML/CSS/JavaScript that allows users to type their queries and displays the responses in a conversational format.

Suggested Features:
- Real-time query processing and response generation.
- Integration of FAQ documents or knowledge base articles for context-aware responses.
- Ability to save and review past conversations for reference.
- Customizable response templates for a more personalized experience.

The 'agora-moss' package is utilized throughout the backend logic to facilitate the semantic search functionality, enabling the app to understand and interpret user queries accurately, and retrieve relevant information from the indexed content.