🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits significant obfuscation and metadata risks, suggesting potential hidden malicious activities despite no direct evidence of network, shell, or credential abuse.

High obfuscation risk
Suspicious metadata indicators

Per-check LLM notes

Network: No network calls suggest the package does not attempt to communicate externally without reason.
Shell: No shell execution suggests the package does not execute system commands, reducing risk of unauthorized access or data manipulation.
Obfuscation: The code appears to be using obfuscation techniques which could hide malicious activities, increasing suspicion.
Credentials: No clear evidence of credential harvesting is present, but the obfuscated code may mask such activities.
Metadata: The repository's recent creation, low activity, single contributor, and quick commit history suggest potential risk.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

ore.memories.find_one({"_id": __import__("bson").ObjectId(_id)}) assert doc["embedding"] == vec asse

✓ 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 10.0

Git history flags: Repository created very recently: 4 day(s) ago (2026-06-02T07:31:34Z)

Repository created very recently: 4 day(s) ago (2026-06-02T07:31:34Z)
Repository has zero stars and zero forks
Single contributor with only 4 commit(s) — possibly throwaway account
All 4 commits happened within 24 hours

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "MongoDB Developer Relations" 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 agency-swarm-mongodb

Create a Virtual Reality Social Network (VRSN) Experience Manager using the 'agency-swarm-mongodb' package. This application will serve as a backend manager for a VR social network platform, allowing users to save and recall their experiences within virtual environments. Here’s a step-by-step guide on what your application should achieve:

1. **User Authentication**: Implement user registration and login functionality. Users should be able to create accounts and log in securely.
2. **Experience Recording**: Enable users to record their experiences within the VR environment. This includes capturing moments such as locations visited, interactions made, and even emotional states.
3. **Memory Storage & Retrieval**: Use the 'agency-swarm-mongodb' package to store these experiences in a MongoDB database. The package supports both semantic (structured data) and episodic (unstructured, event-based) memories, which are ideal for storing VR experiences.
4. **Search & Filter**: Allow users to search and filter through their experiences based on various criteria such as date, location, emotions felt, etc.
5. **Sharing Capabilities**: Integrate sharing options so users can share their experiences with friends or the broader community.
6. **Analytics Dashboard**: Provide an analytics dashboard for administrators to monitor usage trends, popular locations, peak activity times, etc.

### Key Features to Utilize from 'agency-swarm-mongodb':
- **Thread/Session Persistence**: Ensure that user sessions are persisted across different VR sessions using MongoDB Atlas.
- **Vector Memory**: Leverage the semantic and episodic vector memory capabilities of the package to enhance the search and retrieval functionalities of the recorded experiences.
- **Scalability & Reliability**: Benefit from MongoDB Atlas's scalability and reliability features to ensure smooth operation even as the user base grows.

This project aims to bridge the gap between virtual reality experiences and real-world digital memory, providing a unique way for users to document and revisit their VR adventures.