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agentenv-framework
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agentenv-framework

Active·★ 357·Apache-2.0·Updated 2026-10-22

AgentEnv Framework is a Python SDK and CLI tool designed for building, deploying, and running agentic environments. It facilitates the creation of containerized agent servers, manages their deployment, and provides mechanisms to grade agent performance within these environments.

agentenv-framework sits in the LLM Infra category, so it is usually better judged by workflow fit, deployment style, and team needs than by isolated features alone. Based on the available metadata, it leans most clearly toward Python SDK and CLI for comprehensive agent environment management.. A representative use case is Developing and testing new AI agent designs and behaviors.. The listed license is Apache-2.0. GitHub stars are currently 354. Relevant tags include Python, Agentic Environments.

This write-up is based on the project's README and public repository data. We have not hand-tested the tool.

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Guides
#Python#Agentic Environments#SDK#CLI#Agent Evaluation#Containerization#Plugin Architecture#Deployment
$ Install
$ pip install agentenv-framework
↗ Visit site★ GitHub
01

Features

01Python SDK and CLI for comprehensive agent environment management.
02Supports building and deploying containerized agentic environments.
03Facilitates agent evaluation through task-based grading.
04Provides a robust plugin architecture for extending functionality.
05Manages versioned images and deployment of agent environments.
02

Why choose it

+Python SDK and CLI for comprehensive agent environment management.
+Supports building and deploying containerized agentic environments.
+Developing and testing new AI agent designs and behaviors.
+Comes with a clearly listed Apache-2.0 license.
03

Trade-offs

!Compatibility coverage appears narrow, so your target environment should be validated before adoption.
04

Compatibility

Python
Runtime
Verified via docs
Docker
Containerization
Verified via docs
05

Quick start

1
$ pip install agentenv-framework
06

Use cases

↳Developing and testing new AI agent designs and behaviors.
↳Benchmarking and evaluating agent performance on standardized tasks.
↳Deploying custom agent environments for specific research or application needs.
↳Building and sharing a registry of reusable agent environments and evaluation tasks.
↳Extending agent capabilities via a flexible plugin system.
07

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Creating realistic RL environments requires collaboration between researchers, engineers, and domain experts across many dimensions: artifacts, environments tools, dynamism of the environment, reproducibility, and more. There is no open source framework for building these environments effectively. Until now.

357Apache-2.0
Last commit1d ago
StatusActive
CategoryLLM Infra
Links
Documentation↗Discussion↗Issues↗Releases↗
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