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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Standardized environment infrastructure for Agentic AI development.
| Date | Stars |
|---|---|
| 2026-07-31 | 313 |
| 2026-08-06 | 313 |
Today
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15.0
growth rate 0.00%/day
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<h1 align="center">AEnvironment</h1>
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| <a href="https://inclusionai.github.io/AEnvironment/"><b>Documentation</b></a> |
<a href="./docs/images/wechat_qrcode.png" target="_blank"><img src="./docs/images/wechat_icon.png" alt="WeChat Group QR Code" width="20" style="vertical-align: middle;"> <b>WeChat (微信) Group</b></a> |
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<p align="center"><b>Everything as Environment</b> — A Production-Grade Environment Platform for Agentic RL and Agent</p>
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<p align="center">
<img src="./docs/images/cover.png" alt="AEnvironment Architecture" width="800"/>
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<a href="https://github.com/inclusionAI/AEnvironment/blob/main/LICENSE"><img src="https://img.shields.io/badge/License-Apache%202.0-blue.svg" alt="License"></a>
<a href="https://pypi.org/project/aenvironment/"><img src="https://img.shields.io/pypi/v/aenvironment.svg" alt="PyPI"></a>
<a href="https://python.org"><img src="https://img.shields.io/badge/python-3.12+-blue.svg" alt="Python"></a>
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---
## 📰 News
- **Deploy Skill** (Feb 2026) - 🎉 New **Claude Code Skill** for automated deployment! Deploy instances and services directly from Claude Code with support for three workflows: local build, existing image, and registered environments. [Get Started](#deploy-skill)
- **v0.1.4** (Jan 2026) - AEnv CLI now supports **instance** and **service** management! Deploy and manage your agents and applications with simple commands. See [CLI Guide](./docs/guide/cli.md) for details.
---
## About AEnvironment
AEnvironment is a unified environment platform for the **Agentic RL** era, built on the core philosophy of **"Everything as Environment"**. By extending standardized MCP protocol, AEnvironment provides out-of-the-box infrastructure for environment providers, algorithm developers, and agent developers, allowing them to focus on agent capabilities rather than the tedious details of environment setup.
Within Ant Group, AEnvironment serves as a key environment layer technology, deeply integrated with the AReaL reinforcement learning framework, supporting large-scale Agentic RL training and agent service deployment.
### Core Philosophy: Everything as Environment
AEnvironment abstracts everything as an environment—from simple tool functions to complex multi-agent systems, all accessible through a unified Environment interface. This unified abstraction enables capabilities to be registered, combined, and replaced like building blocks, seamlessly converging Benchmark integration, RL training, and agent deployment on the same infrastructure.
### Key Features
**🔧 Built-in Benchmarks, Zero-Cost Integration** - Ready-to-use benchmark environments with no complex configuration. Currently supported: TAU2-Bench, SWE-Bench, and Terminal-Bench.
**🚀 Seamless Agentic RL Training Integration** - With native MCP support and OpenAI Agent SDK compatibility, you can focus on agent logic and seamlessly integrate into RL training workflows.
**🤖 Agent as Environment** - Treat agents as environments, enabling multi-agent orchestration. Compatible with mainstream agent frameworks including OpenAI Agents SDK.
**⚡ Rapid Development to Production** - Define tools, build, and deploy in seconds. AEnvironment provides a unified, low-threshold environment API abstraction, making environments no longer a bottleneck in the training pipeline.
## Use Cases
### Mini Program IDE
Build AI-powered mini-program generation systems where agents leverage AEnvironment as the standard environment infrastructure. The [Mini Program example](./aenv/examples/mini-program/) demonstrates:
- **AEnvironment as Infrastructure**: Agents utilize AEnvironment as the standardized environment infrastructure, providing consistent tooling and runtime capabilities
- **AI Agent Integration**: Multi-turn conversations powered by OpenAI API
- **MCP Tools**: File operations, code execution, and validation tools
- **Live Preview**: Real-time preview oExcerpt of 12,388 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f5aa6e81a6d180f7, llm:Topics: agent, ant-asystem, asystem, benchmark, environment, mcp, reinforcement-learning, rl, sandbox; description: 'Standardized environment infrastructure for Agentic AI development.'
matched fp:f5aa6e81a6d180f7, llm:Topics: agent, ant-asystem, asystem, benchmark, environment, mcp, reinforcement-learning, rl, sandbox; description: 'Standardized environment infrastructure for Agentic AI development.'
matched fp:f5aa6e81a6d180f7, llm:Topics: agent, ant-asystem, asystem, benchmark, environment, mcp, reinforcement-learning, rl, sandbox; description: 'Standardized environment infrastructure for Agentic AI development.'