🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to potential shell execution risks, which need further verification to ensure they are legitimate for the package's intended functionality.

Shell execution risk observed
No other significant risks identified

Per-check LLM notes

Network: No network calls detected, which is normal and does not indicate any risk.
Shell: Shell execution is observed but seems to be related to Git commands and subprocess management, which might be legitimate for version control operations or other utilities, but requires further investigation to confirm legitimacy.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.

📦 Package Quality Overall: Low (3.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

1 test file(s) detected (e.g. test_provider_setup.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (13565 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

501 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

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

intable}") return subprocess.run(command, cwd=cwd, check=True) def git_command(*args: str)
t: str) -> bool: result = subprocess.run( git_command("-C", str(repo_root), "cat-file", "-e",
r, ): completed = subprocess.run( command, input=prompt_text,
> StepResult: completed = subprocess.run( command, cwd=cwd, env=dict(env), stdin=subprocess.D
letedProcess[str]: return subprocess.run( ["git", "-c", f"safe.directory={repo_root.as_posix(
return git result = subprocess.run( ["git", "init"], cwd=memory_root, t

✓ 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 agents-remember-mcp

Create a fully-functional mini-application that leverages the 'agents-remember-mcp' package to manage a personalized knowledge base for users. This application will serve as a tool for users to store, retrieve, and organize their personal notes, memories, and data efficiently. The app should allow users to create, edit, delete, and search through their entries using natural language queries. Additionally, it should have the capability to categorize entries into different topics or tags for better organization.

Step 1: Set up the development environment with Python and install the 'agents-remember-mcp' package.
Step 2: Design a simple user interface (command-line or web-based) for interacting with the application.
Step 3: Implement functions to connect to the 'agents-remember-mcp' server and utilize its capabilities for storing and retrieving context-related information.
Step 4: Develop features that enable users to input new entries, including text and metadata like date and tags.
Step 5: Implement a search function that allows users to query their entries using keywords or natural language questions.
Step 6: Add functionality for editing and deleting entries.
Step 7: Integrate a tagging system to help users categorize their entries.
Step 8: Ensure the application can handle multiple users and maintain data privacy.
Step 9: Test the application thoroughly to ensure all features work as expected.

The 'agents-remember-mcp' package will be crucial in managing the context of each entry, allowing the application to understand and retrieve information more accurately based on the user's queries. Users should be able to interact with the application in a way that feels intuitive and seamless.