aegis-idempotency

v0.1.2 safe
1.0
Low Risk

Reliability layer for production AI agents: exactly-once execution, HITL approval gates, per-event billing

🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious behavior with minimal risk signals. It appears to be a legitimate tool for implementing idempotency checks.

  • Low network risk with legitimate-looking HTTP calls
  • No shell execution, obfuscation, or credential harvesting detected
Per-check LLM notes
  • Network: The observed network call patterns are likely legitimate, possibly for making HTTP requests to an external service for idempotency checks.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.

🔬 Heuristic Checks

Outbound Network Calls score 4.5

Found 3 network call pattern(s)

  • nt = self._external_client or httpx.AsyncClient( base_url=self.base_url, timeout=self.timeout
  • rt(handler) mock_client = httpx.AsyncClient(transport=transport, base_url="http://test") async with
  • ue_error(): mock_client = httpx.AsyncClient( transport=_make_transport(), base_url="http://test"
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 score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://test
Git Repository History score 10.0

Git history flags: Repository created very recently: 2 day(s) ago (2026-06-04T06:12:52Z)

  • Repository created very recently: 2 day(s) ago (2026-06-04T06:12:52Z)
  • Repository has zero stars and zero forks
  • Single contributor with only 4 commit(s) — possibly throwaway account
  • All 4 commits happened within 24 hours
Maintainer History score 6.0

3 maintainer concern(s) found

  • Package is very new: uploaded 2 day(s) ago
  • 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 aegis-idempotency
Develop a mini-application called 'AI Workflow Manager' that leverages the 'aegis-idempotency' package to manage and execute AI workflows reliably. This application will allow users to define and run AI tasks with guarantees of exactly-once execution, ensuring that each task is processed only once even in the presence of failures or retries. Additionally, the application will include human-in-the-loop (HITL) approval gates for critical tasks, allowing human operators to approve or reject task execution based on predefined criteria. Lastly, it will support per-event billing, enabling precise tracking and cost management for each event processed through the system.

Steps to develop the application:
1. Set up a Flask web server to handle HTTP requests for task definitions and execution.
2. Define a data model for storing task metadata, including task ID, status, and associated costs.
3. Implement a task execution service that uses 'aegis-idempotency' to ensure exactly-once execution of each task.
4. Integrate HITL approval gates into the task execution flow, where certain tasks require human approval before they can proceed.
5. Develop a billing system that tracks and calculates costs based on the number of events processed by each task.
6. Create a user interface for defining new tasks, monitoring task statuses, and viewing billing information.
7. Test the application thoroughly to ensure reliability, accuracy, and usability.

Suggested Features:
- Support for various types of AI tasks (e.g., image processing, natural language processing).
- Task prioritization and scheduling based on urgency and resource availability.
- Detailed logging and error reporting for troubleshooting and auditing purposes.
- Integration with popular cloud services for AI task execution and resource allocation.
- Customizable approval criteria for HITL gates to accommodate different use cases.
- Real-time notifications for task completion and billing updates.