autogen-ejentum

v0.2.0 suspicious
4.0
Medium Risk

AutoGen tools for the Ejentum Reasoning Harness. Eight agent-callable async functions returned by ejentum_tools(api_key=...): four dynamic (reasoning, code, anti_deception, memory) and four adaptive (adaptive_reasoning, adaptive_code, adaptive_anti_deception, adaptive_memory) that pre-fit the operation to the task via an adapter LLM. Each call retrieves a structured cognitive injection: a natural-language procedure plus an executable reasoning topology.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits some suspicious metadata indicators but does not show significant risks in terms of network, shell execution, obfuscation, or credential harvesting.

  • author with no name or history
  • unstarred, unforked repository
Per-check LLM notes
  • Network: The presence of network calls is common for packages that need to fetch data from external sources or APIs.
  • 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 package shows some red flags such as an author with no name or history, and an unstarred, unforked repository, suggesting potential risk.

📦 Package Quality Overall: Medium (5.4/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

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

Some documentation present

  • Documentation URL: "Documentation" -> https://ejentum.com/docs/api_reference
  • Detailed PyPI description (5983 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

  • 10 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 10 commits in ejentum/autogen-ejentum
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • try: async with httpx.AsyncClient(timeout=timeout_seconds) as client: response =
✓ 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: ejentum.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 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 autogen-ejentum 
Create a mini-application named 'EJentumTaskMaster' that leverages the 'autogen-ejentum' package to automate complex tasks based on user input. This app will serve as a personal assistant that can reason about tasks, write code snippets, detect deception, and manage memory. Here’s how you can structure your project:

1. **Setup**: Begin by installing the 'autogen-ejentum' package and setting up an API key from Ejentum.
2. **User Interface**: Design a simple command-line interface where users can interact with the app. They should be able to input tasks they need assistance with.
3. **Task Analysis**: Use the 'reasoning' function to analyze the task and understand its requirements. This could involve breaking down the task into smaller steps.
4. **Code Generation**: If the task involves coding, use the 'code' function to generate relevant code snippets. Ensure these snippets are executable and well-documented.
5. **Deception Detection**: Implement the 'anti_deception' function to ensure the generated solutions are not misleading or incorrect.
6. **Memory Management**: Utilize the 'memory' function to store and retrieve information related to previous tasks. This helps in providing contextually relevant responses.
7. **Adaptive Features**: Integrate the adaptive versions of each function ('adaptive_reasoning', 'adaptive_code', 'adaptive_anti_deception', 'adaptive_memory') to improve the app’s performance over time. These functions should adapt their behavior based on the specific nature of the task.
8. **Feedback Loop**: Incorporate a feedback mechanism where users can rate the quality of the solutions provided. Use this feedback to further refine the app's capabilities.
9. **Documentation**: Write comprehensive documentation explaining how to install and use the app, including examples of tasks it can handle.

The goal is to create an intelligent assistant that can handle a wide range of tasks efficiently and accurately, leveraging the advanced capabilities of 'autogen-ejentum'.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!