🤖 AI Analysis

Final verdict: SAFE

Based on the analysis notes, the package exhibits low risk indicators across all categories with no signs of malicious behavior or supply-chain attack vectors.

No network calls detected
Subprocess usage appears benign for git operations
No obfuscation or credential harvesting attempts observed

Per-check LLM notes

Network: No network calls detected, which is normal and expected.
Shell: The use of subprocess for git operations seems benign, likely for version control purposes.
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

Test runner config found: pyproject.toml

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (8189 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

91 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 4.0

Found 2 shell execution pattern(s)

h() -> str | None: return subprocess.check_output(["git", "rev-parse", "HEAD"]).decode().strip() or None
"B603", # subprocess without shell=True (needed for CLI) "B607", # partial executable path (ne

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "jalal" 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 agents-builder

Develop a knowledge management system (KMS) that leverages the 'agents-builder' package to create a retrieval-aware agent capable of answering questions based on a dynamically updated corpus of documents. This KMS will serve as a personal knowledge repository, allowing users to store, retrieve, and query information efficiently. The application should include the following features:

1. **Document Ingestion**: Users should be able to upload various types of documents (PDFs, Word Docs, etc.) into the system. The agent should automatically parse these documents and add their content to the underlying knowledge graph.
2. **Query Interface**: Provide a simple command-line interface where users can type queries about the information stored in the system. The agent should utilize the knowledge graph and LLMs to provide relevant answers or summaries.
3. **Graph Visualization**: Implement a feature that allows users to visualize the relationships between different pieces of information within the knowledge graph, enhancing understanding and navigation of the stored data.
4. **Dynamic Updates**: Ensure that the system can handle real-time updates to the knowledge graph as new documents are added or existing ones are modified.
5. **Customizable Configuration**: Allow users to customize the behavior of the agent through configuration files, including specifying preferred LLM models, adjusting the sensitivity of the search algorithm, and more.

To achieve these objectives, make extensive use of the 'agents-builder' package by utilizing its core components such as the base Agent, graph construction capabilities, LLM integration, and tool loading functionalities from MCP. Additionally, explore how you can extend the provided primitives to meet specific needs of your KMS, such as integrating additional data sources or refining the question-answering process.