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What you can do with Agentenv Framework

AgentEnv Framework is a Python SDK and CLI for building, deploying and running containerized agentic environments and the tasks that grade agents inside them.

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scaleapi/agentenv-framework on GitHub

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What do you want to do?

4 use cases

  • Run the hello task to check your install

    Confirm agent-env works on your machine with the built-in hello task.

    View prompt
    In the scaleapi/agentenv-framework repo, run `agent-env run hello` from <project folder>, then run `agent-env --version`. Report the output of both commands and whether the hello task completed. Verify that the version printed matches the version you installed.

  • Run local environments in the explorer

    Start the explorer on your own Docker daemon using the all-local config.

    View prompt
    In the scaleapi/agentenv-framework repo, prepare <project folder> with an .agentenv/config.toml copied from .agentenv/config.example.toml, then run `agent-env up`. Report the address or message the explorer prints and whether it started. Verify that the Docker daemon is reachable before you start.

  • Build a custom environment with the SDK

    Add the SDK to a Python project and build an environment server for your tasks.

    View prompt
    In <project folder>, add agentenv-framework with `uv add agentenv-framework`, then follow the environment server SDK section in packages/agentenv-protocol/README.md of scaleapi/agentenv-framework to create a minimal environment server. Report the files you created and the commands you ran. Verify that `python -c "import agent_env"` runs without error inside the project environment.

  • Set up a clone for a contribution with a coding agent

    Clone the repo, sync dependencies and follow AGENTS.md before changing code.

    View prompt
    Clone https://github.com/scaleapi/agentenv-framework, run `uv sync --extra dev`, then read AGENTS.md and CONTRIBUTING.md. Report the test tiers and pull request title format you found, and the change you plan for <target bug or feature>. Verify that the virtual environment activates before you edit any code.

How to use Agentenv Framework

  1. Check you have what it needs: Python 3.11.4 or newer; uv (https://docs.astral.sh/uv/) or pip; A running Docker daemon for running environments locally.
  2. Open scaleapi/agentenv-framework on GitHub and read the README and license.
  3. Install uv from https://docs.astral.sh/uv/ if it is not already on your machine.
  4. Run uv tool install agentenv-framework.
  5. Run agent-env run hello.
  6. Run agent-env --version to confirm the installed version.

These steps come from our top use case, Run the hello task to check your install, which also has a copy-paste agent prompt.

Agentenv Framework alternatives · Best AI repos for Software and DevOps

About this repo

Requirements

  • Python 3.11.4 or newer
  • uv (https://docs.astral.sh/uv/) or pip
  • A running Docker daemon for running environments locally
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Questions about Agentenv Framework

What is Agentenv Framework?

AgentEnv Framework is a Python SDK and CLI for building, deploying and running containerized agentic environments and the tasks that grade agents inside them. It is an open-source project on GitHub: https://github.com/scaleapi/agentenv-framework.

Who is Agentenv Framework for?

Written for ML engineers, RL researchers and platform engineers who need to build, run or score agent environments, and for coding agents working on the repo.

What can I do with Agentenv Framework?

4 use cases so far, for example: Run the hello task to check your install; Run local environments in the explorer; Build a custom environment with the SDK. Each has steps and a copy-paste agent prompt.

What do I need to run Agentenv Framework?

Python 3.11.4 or newer; uv (https://docs.astral.sh/uv/) or pip; A running Docker daemon for running environments locally.

What are alternatives to Agentenv Framework?

Other hot AI repos in Software and DevOps: Harness2, iFixAi, Orca, Diagram Design, AI Memory.

Is Agentenv Framework safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.