agno-ejentum

v0.2.0 safe
4.0
Medium Risk

Agno Toolkit for the Ejentum Reasoning Harness. Eight agent-callable methods: 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: SAFE

The package appears to be generally safe with moderate concerns regarding network interactions and potential lack of community support.

  • Moderate network risk due to external API calls
  • Potential inactivity or lack of community support
Per-check LLM notes
  • Network: The presence of network calls suggests the package interacts with an external API, which could be legitimate but requires further investigation into the purpose and destination of these calls.
  • Shell: No shell execution patterns were detected, indicating low risk in this aspect.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows signs of potential inactivity or lack of community support, which raises some concerns but does not definitively indicate malice.

📦 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_tool.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://ejentum.com/docs/api_reference
  • Detailed PyPI description (4961 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

  • 11 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/agno-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: response = requests.post( self.api_url, headers={
✓ 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 agno-ejentum 
Create a mini-application named 'CognitiveTaskMaster' which leverages the 'agno-ejentum' package to manage complex cognitive tasks through a series of interactive steps. This application will serve as a personal assistant for managing daily tasks, learning new skills, and enhancing problem-solving abilities by integrating advanced reasoning capabilities provided by the package.

Step 1: Define the Application Scope
- The application should allow users to input their tasks or problems, ranging from simple reminders to complex problem-solving scenarios.
- Users should be able to specify the type of assistance needed (e.g., learning a new skill, solving a math problem).

Step 2: Implement Task Management Features
- Develop a user-friendly interface where users can add, edit, and delete tasks.
- Integrate a feature that allows users to prioritize their tasks based on urgency or importance.

Step 3: Utilize 'agno-ejentum' Package for Cognitive Assistance
- Use the 'reasoning' method to provide logical solutions to problems entered by the user.
- Employ the 'code' method to generate code snippets for programming-related tasks.
- Leverage the 'memory' method to recall relevant information or previous solutions related to the current task.
- Apply the 'anti_deception' method to verify the authenticity and reliability of the information or solutions provided.

Step 4: Enhance with Adaptive Methods
- Implement adaptive methods ('adaptive_reasoning', 'adaptive_code', 'adaptive_anti_deception', 'adaptive_memory') to dynamically adjust the approach based on the complexity and nature of the task.
- Ensure these adaptive methods use an adapter LLM to fit the operation to the specific task at hand.

Step 5: Provide Structured Outputs
- After processing a task, present the output in a structured format that includes both a natural-language explanation and an executable reasoning topology.
- Allow users to save these outputs for future reference.

Suggested Features:
- Integration with calendar applications for scheduling tasks.
- A feedback loop where users can rate the effectiveness of the provided solutions, which can be used to improve future responses.
- A learning mode where the application learns from the user's interactions to better tailor its responses over time.

The goal is to create a versatile tool that not only assists with daily tasks but also enhances the user's ability to solve problems and learn new skills using advanced cognitive techniques.