apiforgepy

v3.0.0 safe
3.0
Low Risk

API observability & intelligence for FastAPI/Starlette — local-first, privacy-first

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators and does not exhibit any signs of malicious behavior or supply-chain attacks. The primary concern is the low activity in the repository, which could impact future maintenance.

  • Low network risk
  • No shell execution risk
  • No obfuscation risk
  • No credential risk
  • Repository has low activity
Per-check LLM notes
  • Network: The network patterns indicate legitimate HTTP requests being made, likely for API interaction. However, without context, there's a low risk but need to verify the URL destinations and payloads.
  • Shell: No shell execution patterns detected, suggesting no immediate risk associated with shell command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets and credentials.
  • Metadata: The repository's low activity and the maintainer's limited history suggest potential unreliability, but there are no clear signs of malicious intent.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 5 test file(s) found

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

Some documentation present

  • Documentation URL: "Documentation" -> https://apiforge-organisation.github.io/docs/
  • Detailed PyPI description (4530 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

  • 26 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 50 commits in APIForge-Organisation/sdk-python
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

  • }).encode() req = urllib.request.Request( self._url + "/routes", data
  • try: with urllib.request.urlopen(req, timeout=10): pass excep
  • rics}).encode() req = urllib.request.Request( self._url, data=payload,
  • try: with urllib.request.urlopen(req, timeout=10): with self._lock:
  • try: with urllib.request.urlopen(url, timeout=20) as resp: data =
✓ 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "APIForge" 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 apiforgepy 
Create a mini-application that monitors and analyzes the performance of a FastAPI service using the 'apiforgepy' package. This application will serve as a tool to enhance observability and provide insights into the health and efficiency of your FastAPI endpoints. Here’s a detailed guide on how to proceed:

1. **Setup Environment**: Begin by setting up a Python virtual environment and installing necessary packages including FastAPI, Starlette, and 'apiforgepy'.
2. **Create FastAPI Service**: Develop a simple FastAPI service with at least three different types of endpoints (GET, POST, DELETE). These endpoints should simulate typical CRUD operations.
3. **Integrate 'apiforgepy'**: Utilize 'apiforgepy' to monitor these endpoints. Implement logging and tracing functionalities provided by 'apiforgepy' to capture request/response times, error rates, and other relevant metrics.
4. **Dashboard Creation**: Create a basic dashboard within the FastAPI service itself or using a frontend framework like React or Vue.js. This dashboard should display real-time performance metrics of the monitored endpoints.
5. **Alert System**: Integrate an alert system that notifies you via email or SMS when certain thresholds are breached (e.g., response time exceeds a certain limit).
6. **Privacy Considerations**: Ensure all data captured by 'apiforgepy' respects user privacy and complies with GDPR or CCPA guidelines if applicable.
7. **Documentation**: Provide comprehensive documentation detailing how to set up the monitoring system, how to interpret the metrics, and any configuration options available.

By following these steps, you'll have a functional mini-app that not only showcases the capabilities of 'apiforgepy' but also serves as a practical tool for enhancing the observability of FastAPI services.

💬 Discussion Feed

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