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Agent R1

Agent R1 is a modular Python framework for training multi-step LLM agents with reinforcement learning. It represents each agent interaction as a step-level transition containing an observation, action, feedback or reward, termination state and next observation.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Agent R1 — OpenViST project profile in Agent Frameworks.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Agent R1 when developing or studying reinforcement-learning training for LLM agents whose actions lead to tool or environment feedback and further decisions.

Who it is for

  • Developers building LLM agents that interact with tools or task environments.
  • Researchers creating or comparing agentic reinforcement-learning workflows.

Documented uses

  • Train LLM agents through multiple environment or tool interactions.
  • Define task environments that return observations, rewards and termination information.
  • Choose between a standard environment loop and a more custom agent workflow.
  • Keep interaction steps and reward assignment explicit during policy training.
Before you choose

Requirements & limits

The project is a public GitHub repository identified as MIT-licensed. Its README says to clone the repository and use the verl environment setup, specifying verl==0.7.0 for the current version.

  • The framework is for building and training agents, not a hosted end-user assistant.
  • AgentEnvLoop suits tasks that fit a reset-and-step environment interface; AgentFlowBase is the lower-level option for custom control.
  • The README’s GSM8K single-step example is described as a setup check; the minimal tool-calling tutorial is a separate next stage.
  • The documented environment setup depends on verl, with version 0.7.0 specified.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Check the GSM8K training setup

Use the documented single-step example to check that the training environment, model path and dataset path are wired correctly.

Before you start

  • Clone the Agent R1 repository.
  • Use the same environment setup as verl; the README specifies verl==0.7.0.
  • Provide a model path and GSM8K dataset files, using processed data or a locally regenerated dataset.

Steps

  1. Prepare processed GSM8K data or regenerate a minimal dataset with the documented GSM8K preprocessing module.

  2. Set the model and dataset paths expected by the example.

  3. Run the documented single-step script, examples/gsm8k/run_steppo.sh, as a setup check.

Expected outcome

A check of whether the base training stack and its paths are wired correctly.

Guide limitations

  • The README describes this as a setup check, not a completed multi-step tool-calling run.
  • The guide does not establish hardware requirements or compatibility guarantees.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
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Sources checked
11 October 2026
Original website title
AgentR1/Agent-R1 · Official homepage title · retained in its source language
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