agentenv-framework MCP Server
scaleapi/agentenv-framework
[](https://agentrank-ai.com/tool/scaleapi--agentenv-framework/?utm_source=badge&utm_medium=readme&utm_campaign=agentrank_badge) 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.
claude mcp add agentrank -- npx -y agentrank-mcp-server Overview
scaleapi/agentenv-framework is a Python tool licensed under Apache-2.0. 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. Topics: a2a, agent-evaluation, agentic-ai, ai-agents, llm-agents, llm-evaluation, mcp, model-context-protocol, python, reinforcement-learning, reinforcement-learning-environments, sandbox.
Ranked #5970 out of 24846 indexed tools.
Actively maintained with commits in the last week.
Ecosystem
Score Breakdown
255 stars → growing community
Last commit 7d ago → actively maintained
0/24 issues closed → many open issues
5 contributors → small team
No dependents → no downstream usage
Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →
How to Improve
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From the README
<h1></h1> AgentEnv Framework is a Python SDK and CLI for building, deploying and running agentic environments and the tasks that grade agents inside them. Environments are containerized servers that speak the open `agentenv-framework-protocol`; AgentEnv Framework builds them into versioned images, deploys them behind a gateway, points an agent at them, and scores what the agent did. **Documentation: [www.agentenvframework.com/docs](https://www.agentenvframework.com/docs)** covers environments, artifacts, agents, tasks, the registry and plugins. ## Install Both packages are on PyPI. You need Python 3.11 or newer and, to run environments locally, a running Docker daemon. With [uv](https://docs.astral.sh/uv/), install the `agent-env` command as a tool: ```bash uv tool install agentenv-framework agent-env run hello ``` `agent-env run hello` runs the built-in hello task, which needs no Docker, model or configuration. To try it without installing anything, run it with uvx: ```bash uvxRead full README on GitHub →
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