AEnvironment

AEnvironment#

Everything as Environment — A production-grade environment platform for Agentic Reinforcement Learning.

What is AEnvironment?#

AEnvironment (AEnv) is a unified environment platform designed for the Agentic RL era. It provides a complete solution for:

  • 🎯 Model Benchmarking: Built-in support for popular benchmarks (TAU, SWE-bench, etc.)

  • 🚀 RL Training at Scale: Seamless integration with RL frameworks (AReaL, VERL, SLIME)

  • 🤖 Agent Development: Rich tooling for building and testing AI agents

  • 🔧 Custom Environments: Easy-to-use SDK for creating new environments

Design Philosophy#

Everything as Environment#

AEnvironment treats everything as an environment — from simple tools to complex multi-agent systems. This unified abstraction enables:

        graph LR
    A[Agent] --> B[Environment]
    B --> C[Tools]
    B --> D[Other Agents]
    B --> E[Benchmarks]
    B --> F[Real Services]

  • Composability: Environments can be nested and composed

  • Interoperability: Standard MCP protocol for tool communication

  • Scalability: From local development to distributed clusters

Agent as Environment#

A unique feature of AEnvironment is treating agents themselves as environments. This enables:

  • A2A (Agent-to-Agent): Agents can interact with other agents as environments

  • Multi-Agent Systems: Build complex agent ecosystems

  • Agent Testing: Use agents to test other agents

Key Features#

🎯 Zero-Cost Benchmark Integration

Built-in support for mainstream benchmarks. Get started with SWE-bench, TAU, and more in minutes.

⚡ High Performance

Optimized runtime delivers fast environment creation compared to traditional container solutions.

🔌 MCP Protocol Support

Full compatibility with Model Context Protocol for seamless tool integration.

🛠️ 30-Second Development

From code to running environment in 30 seconds with our SDK and CLI tools.

🤖 Agent Framework Integration

Native support for OpenAI Agents, CAMEL, and other popular agent frameworks.

📦 Production Ready

Battle-tested infrastructure powering Ant Group’s AI applications.

Quick Example#

from aenv import Environment, register_tool

# Define a tool
@register_tool
def search_code(query: str, max_results: int = 10) -> list:
    """Search code in the repository."""
    return [{"file": "main.py", "line": 42, "content": "..."}]

# Use the environment
async with Environment("swe-env") as env:
    # List available tools
    tools = await env.list_tools()

    # Call a tool
    result = await env.call_tool("search_code", {"query": "bug fix"})
    print(result.content)

Architecture Overview#

AEnvironment adopts a layered architecture design, dividing the system into two core domains: the Development Side and the Traffic Side, achieving decoupling between environment development and runtime execution.

Core Characteristics#

  • Architecture Design: Development side defines environments, traffic side executes runtime, providing unified interfaces based on MCP protocol

  • Extensibility: Supports extensible sandbox engines such as Kubernetes

  • Metadata-Driven: Environment configurations are stored in EnvHub, queried dynamically at runtime, supporting environment version management and rapid iteration

AEnvironment Architecture

Development Side#

Responsible for environment definition and metadata management:

  • Flow: AEnv CLI → EnvHub → Redis

  • Developers push environment configurations to EnvHub through CLI, with metadata stored in Redis

Traffic Side#

Responsible for creating and managing runtime environment instances:

  • Flow: AEnv SDK → API Service → Controller/Other Sandbox Engine → Environment Sandbox

  • Users create environment instances through SDK, API Service queries EnvHub for metadata, and creates instances through sandbox engines (e.g., Kubernetes)

  • Tool invocations are proxied by API Service to SDK within the sandbox, executing MCP tools and returning results

Getting Started#

Ready to dive in? Check out our Quick Start Guide guide to create your first environment in minutes.

Getting Started

User Guide

Architecture

Examples

Roadmap

Development

Contents