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aieval-py

PyPIInfrastructure

No liveness check has reached it yet; it is registered on GitHub and PyPI and has been in the index since 11 Oct 2026. How this is checked

aieval-py is an MIT-licensed Python toolkit for deterministic runtime validation of LLM outputs and agent actions. It provides evaluation, guardrail, and reliability mechanisms for developers building AI agents and LLM-powered applications.

Inferred · not functionally tested

Open SourceMITCLISelf-hosted
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Overview

6 features

Purpose: Preventing unsafe, invalid, or unreliable LLM outputs and agent actions at runtime.

Inferred · not functionally tested

Audience: AI developers building LLM applications and agents

Inferred · not functionally tested

Functions: agents, data_extraction

Inferred · not functionally tested

Interfaces: API: unknown · MCP: unknown · CLI: indicated (inferred, not tested) · Self-hosting: indicated (inferred, not tested)

Recorded constraints: pricing: open_source · license: MIT · platforms: CLI · deployment: cli, self_hosted

Constraint provenance is unknown; confirm requirements with the publisher.

Record sources: pypi.org · github.com. These links do not verify the individual claims.

aieval-py is an AI security & guardrails project. Inferred · not functionally tested: It focuses on preventing unsafe, invalid, or unreliable LLM outputs and agent actions at runtime. Inferred · not functionally tested: aieval-py is an open-source project aimed at AI developers building LLM applications and agents. Basis unknown · not verified: The project is open source (MIT). Basis unknown · not verified: aieval-py is available on the command line, and it can be self-hosted.

It is developed by Hamza1331, and it first shipped in 2026. Development happens publicly on GitHub with 4 commits in the last 90 days. Inferred · not functionally tested: Among its 6 catalogued features are Output Validation, Agent Action Checks, and Runtime Guardrails.

Summary written by a language model from the project’s public pages.

Tasks: Inferred · not functionally tested

  • Output Validation
  • Agent Action Checks
  • Runtime Guardrails
  • LLM Evaluation
  • Pydantic Integration
  • Deterministic Checks

Topics: Inferred · not functionally tested

Tags
llm-validationagent-safetyruntime-guardrailspydantic-validation
AI capabilities
TextStructured
Inference: Local

JSON profile · Text profile · Access guide

Built with & integrations

Runs on
CLISelf-hosted

Trust & compliance

License
MIT
Public signals
HTTPSOpen SourceFree tierGitHubActive maintenance

Indexing history

1

What PulseGate has recorded for this listing

  1. Indexed11 Oct · 14:01 UTC
    aieval-py seen via PyPI Bulk Enumerator
    Source: PyPI Bulk Enumerator · Open

Frequently asked questions about aieval-py

What does aieval-py do?
Inferred · not functionally tested: Aieval-py focuses on preventing unsafe, invalid, or unreliable LLM outputs and agent actions at runtime. It is catalogued under AI security & guardrails on PulseGate.
Who is aieval-py for?
Inferred · not functionally tested: aieval-py is an open-source project built for AI developers building LLM applications and agents.
Does aieval-py have a free plan?
Basis unknown · not verified: Yes — aieval-py is open source under the MIT license and free to use.
What platforms does aieval-py run on?
Basis unknown · not verified: aieval-py runs on the command line. It can also be self-hosted.
Is aieval-py still maintained?
The GitHub repository shows 4 commits in the last 90 days.
What are alternatives to aieval-py?
Similar projects tracked by PulseGate include RENKER, revathi, and VITNA.RENKERrevathiVITNA
Who makes aieval-py?
aieval-py is developed by Hamza1331.
When did aieval-py launch?
aieval-py first shipped in 2026.

Also in AI security & guardrails

Same category — not a similarity match