azure-functions-langgraph

v0.7.2 safe
4.0
Medium Risk

LangGraph integration for Azure Functions Python v2 — deploy compiled graphs as HTTP endpoints

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal signs of potential risks, with no indications of malicious activities or supply-chain attacks. The incomplete metadata is a minor concern but does not significantly elevate the overall risk.

  • Minimal network, shell, obfuscation, and credential risks.
  • Incomplete author metadata.
Per-check LLM notes
  • Network: The observed network call is likely for testing purposes given the 'http://test' base URL and does not indicate malicious activity.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author's information is incomplete, which raises some concern but does not strongly indicate malicious intent.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 17 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 17 test file(s) detected (e.g. __init__.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://yeongseon.github.io/azure-functions-langgraph-python
  • Detailed PyPI description (29704 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 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 666 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 100 commits in yeongseon/azure-functions-langgraph-python
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • nsport(fa) httpx_client = httpx.Client(transport=transport, base_url="http://test") return Sync
✓ 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

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository yeongseon/azure-functions-langgraph-python appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 azure-functions-langgraph 
Create a mini-application that leverages the 'azure-functions-langgraph' package to serve as a simple yet powerful graph-based data processing service. This application will allow users to submit JSON data representing nodes and edges of a graph, and perform various operations on it via HTTP requests. Here’s a step-by-step guide to building this application:

1. **Setup Environment**: Begin by setting up your development environment. Ensure you have Python installed and create a virtual environment. Install the required packages including 'azure-functions', 'azure-functions-langgraph', and any other dependencies needed.

2. **Define Graph Operations**: Decide on the operations your app will support. For instance, finding shortest paths, detecting cycles, calculating centrality measures, etc. These operations will be implemented using the LangGraph framework provided by the 'azure-functions-langgraph' package.

3. **Design API Endpoints**: Design a set of RESTful API endpoints that will accept JSON data representing the graph structure (nodes and edges) and the operation to perform. Each endpoint will correspond to a specific function in Azure Functions.

4. **Implement Azure Function Handlers**: Use the 'azure-functions-langgraph' package to compile your graph operations into functions that can be deployed as HTTP triggers. For example, one function might handle adding new nodes or edges, another for executing a specific graph algorithm, and yet another for retrieving results.

5. **Testing & Validation**: After implementing the functions, thoroughly test them using sample data to ensure they work as expected. Validate the responses against known results or expected outcomes.

6. **Deployment**: Deploy your Azure Functions to Azure. Ensure all necessary configurations (such as authentication settings) are correctly set up.

7. **Documentation & User Guide**: Create documentation for your application, detailing how to use each API endpoint, the expected input formats, and sample use cases.

8. **Monitoring & Maintenance**: Set up monitoring tools to track the performance and health of your application once deployed. Regularly update and maintain the application based on user feedback and new requirements.

This project aims to demonstrate the power and flexibility of integrating graph-based data processing capabilities into cloud services using Azure Functions and the 'azure-functions-langgraph' package.

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