anchor-engine

v4.4.1 suspicious
5.0
Medium Risk

Deterministic LLM output rectification engine — model-agnostic, zero API cost, <10ms latency

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to its network activity and metadata indicating potential lack of maintenance or trustworthiness.

  • network risk of 4/10
  • metadata risk of 6/10
Per-check LLM notes
  • Network: The package makes network calls which could indicate legitimate functionality like API interaction, but further investigation is needed to ensure the host's legitimacy and data handling practices.
  • Shell: No shell execution patterns were detected, suggesting minimal risk of direct system command execution from the package.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows signs of being new and potentially not maintained, with a suspicious maintainer history.

📦 Package Quality Overall: Medium (6.0/10)

✦ High Test Suite 9.0

Test suite present — 9 test file(s) found

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

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/yunusgungor/anchor#readme
  • Detailed PyPI description (9208 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

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

Limited contributor diversity

  • 1 unique contributor(s) across 47 commits in yunusgungor/anchor
  • Single author but highly active (47 commits)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

  • try: response = requests.post(f"{self.host}/api/generate", json=payload, timeout=60)
  • try: response = requests.post(f"{self.host}/api/generate", json=payload, stream=True, time
  • return lambda p: requests.post( f"{host}/api/generate", jso
✓ 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: yunusgungor.com>

✓ 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 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • 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 anchor-engine 
Develop a mini-application named 'AnchorChat' that leverages the 'anchor-engine' package to ensure deterministic responses from a Language Model (LLM). AnchorChat should serve as a robust chatbot framework where users can engage in conversations without worrying about varying outputs from the LLM due to its inherent randomness. Here’s how you will build it:

1. **Setup**: Begin by installing the necessary packages including 'anchor-engine' and any other dependencies required for your project.
2. **Integration of LLM**: Integrate your chosen LLM into the application. Ensure it supports the functionalities needed for a conversational interface.
3. **Deterministic Responses**: Utilize 'anchor-engine' to process user inputs through the LLM and return deterministic outputs. This means configuring 'anchor-engine' to take in prompts, generate stable responses, and handle any variations in input interpretation consistently.
4. **User Interface**: Design a simple yet effective user interface (UI) for AnchorChat. It could be a command-line interface (CLI) or a web-based UI using frameworks like Flask or Django.
5. **Additional Features**:
   - **Context Management**: Implement a feature that allows AnchorChat to remember previous interactions in a conversation, enhancing the coherence of the dialogue.
   - **Custom Anchors**: Provide users with the ability to define custom anchors or keywords that trigger specific deterministic responses from the bot.
   - **Feedback Loop**: Incorporate a mechanism for users to rate the relevance and quality of responses, which can help improve the bot's performance over time.
6. **Testing and Optimization**: Test the application thoroughly under various scenarios to ensure reliability and efficiency. Use real-world data and user feedback to refine and optimize the application's performance.
7. **Documentation**: Finally, document the setup, configuration, and usage of AnchorChat. Include examples and best practices for integrating 'anchor-engine' within different types of applications.

By following these steps, you'll create a versatile and reliable chatbot application that showcases the power of 'anchor-engine' in delivering consistent and high-quality conversational experiences.

💬 Discussion Feed

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