agentprdiff

v0.3.1 safe
3.0
Low Risk

Guard your LLM agents in CI. Snapshot tests that catch behavioral regressions when models, prompts, or vendors change.

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risks across all categories with no detected network calls, shell executions, obfuscations, or credential issues. The metadata risk slightly increases due to the maintainer's single package history.

  • No network calls or shell executions detected.
  • Maintainer has only one package, slightly increasing metadata risk.
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require internet access.
  • Shell: No shell execution detected, indicating the package does not execute external commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, indicating safe handling of secrets and credentials.
  • Metadata: The maintainer has only one package, which could indicate a new or less active account, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (5.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://agentprdiff.dev
  • Detailed PyPI description (17666 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

  • 129 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 44 commits in vnageshwaran-de/agentprdiff
  • Single author but highly active (44 commits)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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 vnageshwaran-de/agentprdiff appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Vinoth Nageshwaran" 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 agentprdiff 
Develop a mini-application called 'PromptGuard' using Python and the 'agentprdiff' package. This tool will help developers and researchers ensure that their language model-based applications remain consistent even when underlying models, prompts, or vendors change. PromptGuard should perform snapshot testing on responses from different versions of language models to detect any behavioral changes.

Step-by-Step Instructions:
1. Set up a Python virtual environment and install necessary packages including 'agentprdiff'.
2. Define a set of test cases (prompts) that your application will use to compare responses from different versions of the same language model.
3. Integrate 'agentprdiff' into your application to automatically generate snapshots of responses from the current version of the language model.
4. Implement functionality to periodically (e.g., daily) fetch new responses from updated versions of the language model and compare them against existing snapshots.
5. If significant differences are detected between the new response and the snapshot, notify the user via email or logging mechanism about the potential regression.
6. Provide a command-line interface for users to manually trigger a comparison between a specific version of the language model and the latest snapshot.

Suggested Features:
- Support for multiple language models (from different vendors).
- Configuration options for specifying which models and prompts to include in snapshot testing.
- A user-friendly report summarizing all detected regressions over time.
- Optional integration with continuous integration (CI) systems like GitHub Actions to automate the process of running snapshot tests.