airas-sdk

v0.2.1 safe
2.0
Low Risk

Adaptive Immune Runtime for Agent Systems — population-level failure prevention for AI agents

🤖 AI Analysis

Final verdict: SAFE

The package appears to be designed for legitimate use with minimal risk indicators. It has no signs of malicious activity or improper handling of sensitive data.

  • Low network, shell, obfuscation, and credential risks.
  • No evidence of supply-chain attack.
Per-check LLM notes
  • Network: The observed network call patterns suggest the package is designed to make HTTP requests, likely for API interactions, which is common for SDKs.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets and credentials.

📦 Package Quality Overall: Low (4.2/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (9525 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

  • 79 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 19 commits in yash1511-bogam/airas
  • Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • try: async with httpx.AsyncClient(timeout=30) as client: resp = await client.post(
  • agent self._client = httpx.AsyncClient( base_url=self.base_url, timeout=tim
✓ 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 score 5.0

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
  • All 19 commits happened within 24 hours
⚠ 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 airas-sdk 
Create a mini-application called 'AI Guardian' using the Python package 'airas-sdk'. This application will serve as a monitoring tool for a fleet of AI agents, ensuring their robust operation by implementing population-level failure prevention mechanisms. The primary goal is to detect and mitigate failures before they impact the performance of these agents.

### Features:
- **Agent Registration**: Allow users to register new AI agents into the system. Each agent should have unique identifiers and operational parameters.
- **Health Monitoring**: Continuously monitor the health status of each registered agent. Health status includes operational metrics such as response time, error rates, and resource consumption.
- **Adaptive Failure Prevention**: Implement adaptive mechanisms to prevent failures based on the health status of individual agents and the overall fleet. Use the 'airas-sdk' package to manage these adaptive strategies.
- **Alert System**: Notify users via email or SMS when critical failures are detected or when preventive actions are taken.
- **Dashboard Interface**: Develop a simple web interface to display real-time health statuses and historical data of the AI agents.

### Utilizing 'airas-sdk':
- **Population-Level Management**: Use 'airas-sdk' to manage a population of AI agents rather than individual instances. This involves setting up policies that apply to the entire group, adjusting according to the collective health of the agents.
- **Failure Prediction Models**: Leverage 'airas-sdk' to develop models that predict potential failures based on historical data and current trends.
- **Automated Recovery Mechanisms**: Integrate 'airas-sdk' to automatically initiate recovery procedures for agents showing signs of failing, ensuring minimal downtime.
- **Performance Optimization**: Apply 'airas-sdk' techniques to optimize the performance of the AI agents over time, adapting to changes in workload and environment.

This project aims to demonstrate the power of 'airas-sdk' in maintaining the reliability and efficiency of AI systems, making it a valuable tool for developers and operators managing complex AI fleets.

💬 Discussion Feed

Leave a comment

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