🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a moderate level of risk due to its high obfuscation risk and potential lack of maintenance, which raises concerns about its legitimacy and security.

High obfuscation risk due to base64 decoding for cryptographic operations.
Potential low maintenance efforts indicated by metadata.

Per-check LLM notes

Network: The presence of network calls might be legitimate if the package is designed to interact with external services or APIs.
Shell: No shell execution patterns detected.
Obfuscation: The presence of base64 decoding for cryptographic operations suggests potential obfuscation but could also be legitimate use of cryptography.
Credentials: No direct evidence of credential harvesting patterns detected.
Metadata: The package shows signs of low maintenance and effort, but no clear malicious indicators.

📦 Package Quality Overall: Low (4.8/10)

✦ High Test Suite 9.0

Test suite present — 6 test file(s) found

6 test file(s) detected (e.g. test_020_surface_unchanged.py)

◈ Medium Documentation 7.0

Some documentation present

Documentation URL: "Documentation" -> https://aigentsy.com/quickstart
Detailed PyPI description (1815 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

47 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 1.5

Found 1 network call pattern(s)

pi_key self._client = httpx.AsyncClient(base_url=self._base, timeout=30.0) def _headers(self, a

⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

s_b64"] canonical_bytes = base64.b64decode(canonical_b64) # 2. Sign locally — the private key is d
this function. priv_raw = base64.b64decode(keypair.private_key_base64) try: priv = Ed25519P
t_notice=False) pub_raw = base64.b64decode(kp.public_key_base64) sent_bodies = {} def handler
signature"] sig = base64.b64decode(sig_b64) Ed25519PublicKey.from_public_bytes(pub_
orithm == "Ed25519" pub = base64.b64decode(kp.public_key_base64) priv = base64.b64decode(kp.private
public_key_base64) priv = base64.b64decode(kp.private_key_base64) assert len(pub) == 32, "Ed25519 p

✓ 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 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 aigentsy-langgraph

Develop a mini-application that leverages the 'aigentsy-langgraph' Python package to create a decentralized social network focused on knowledge sharing. This application will enable users to connect with each other based on shared interests and expertise, forming a community where knowledge is exchanged through a peer-to-peer network rather than a centralized server. Here’s a step-by-step guide on how to approach this project:

1. **Setup**: Begin by installing the 'aigentsy-langgraph' package using pip. Understand its role in facilitating communication between nodes within a decentralized network.
2. **Node Creation**: Create a node that represents each user. Each node should contain information about the user such as name, interests, expertise, and connections.
3. **Graph Construction**: Use 'aigentsy-langgraph' to construct a graph where nodes represent users and edges represent relationships between them. Relationships can be based on shared interests, collaborations, or mutual connections.
4. **Knowledge Exchange**: Implement functionality that allows users to share information, articles, or resources directly with their connections. This exchange should occur peer-to-peer without needing a central server.
5. **Interest-Based Matching**: Develop a feature that matches users based on their interests and expertise, suggesting potential new connections for them to make.
6. **Security and Privacy**: Ensure that all data shared between nodes is secure and private. Utilize encryption methods supported by 'aigentsy-langgraph' to protect user information.
7. **User Interface**: Create a simple web interface using Flask or Django where users can log in, view their connections, share knowledge, and discover new connections.
8. **Testing and Deployment**: Test your application thoroughly to ensure it functions correctly. Deploy it using a cloud service like AWS or Google Cloud Platform.

This project aims to demonstrate the power of decentralized networks for social applications, focusing on the unique capabilities provided by 'aigentsy-langgraph' to manage and facilitate interactions within a distributed system.