Open-AgentRL

What does this agent do, and when should you use it?

Open-AgentRL brings together three agentic-RL research implementations: DemyAgent, RLAnything, and AutoTool. DemyAgent covers cold-start SFT and GRPO-TCR-based agentic RL, with evaluation workflows for math, science, and coding reasoning. RLAnything performs closed-loop optimization of policy, reward model, and environment for OSWorld computer control, AlfWorld text games, and coding tasks. AutoTool supplies a two-phase dynamic tool-selection framework that first stabilizes tool-integrated trajectories and then refines multi-step selection with KL-regularized Plackett–Luce ranking. It is a research training repository that requires users to supply models, data, configuration, and, for some scenarios, external execution infrastructure rather than a ready-to-deploy chat service.

For DemyAgent, the repository reads SFT, RL, and evaluation data in .parquet format, fine-tunes Qwen models through recipe/demystify/qwen3_4b_sft.sh or qwen2_7b_sft.sh, then runs GRPO-TCR with grpo_tcr_qwen2_7b.sh or grpo_tcr_qwen3_4b.sh. Code-execution tasks use a SandboxFusion /run_code endpoint, configured in sandbox_fusion_tool_config.yaml and verl/utils/reward_score/livecodebench/code_math.py. RLAnything launches training from scenario-specific YAML files through osworld_rl.py, alfworld_rl.py, and coding_rl.py, with matching *_eval.py evaluation programs. The AutoTool framework covers Phase I SFT-plus-RL trajectory stabilization and Phase II ranking-based tool-selection refinement; the repository says its full training data and toolset are still forthcoming.

1. A research team reproducing agentic-reasoning experiments from 3K SFT and 30K RL datasets, then evaluating on AIME, GPQA-Diamond, or LiveCodeBench-v6.
2. A team with a local or cloud SandboxFusion endpoint training Qwen-family models that execute code during reasoning.
3. A team that has configured Volcengine cloud VMs, security groups, and an OSWorld image for RL training or evaluation of GUI computer-control policies.
4. A researcher who has downloaded AlfWorld data and needs to train or evaluate RL policies in a text-based interactive game.
5. A group investigating dynamic selection over a large, evolving tool library and willing to work before AutoTool’s full data and toolset are released.

How do you install or deploy this agent?

For DemyAgent:

git clone https://github.com/Gen-Verse/Open-AgentRL.git
conda create -n OpenAgentRL python=3.11
conda activate OpenAgentRL
cd Open-AgentRL
bash scripts/install_vllm_sglang_mcore.sh
pip install -e .[vllm]

For RLAnything:

conda create --name rlanything python=3.10
source activate rlanything
pip install -r requirements_rlanything.txt

The supplied material does not state an API-key requirement. Code-execution RL additionally needs a SandboxFusion deployment or a Volcano Engine Cloud FaaS /run_code endpoint; the documented OSWorld setup uses Volcengine cloud VMs.

How do you use this agent?

Before DemyAgent cold-start SFT, set absolute TRAIN_DATA, EVAL_DATA, MODEL_PATH, and SAVE_PATH values in recipe/demystify/qwen3_4b_sft.sh or qwen2_7b_sft.sh, then run:

bash recipe/demystify/qwen3_4b_sft.sh

After configuring the SandboxFusion endpoint, RL data, model, benchmark paths, and default_local_dir, run:

bash recipe/demystify/grpo_tcr_qwen3_4b.sh

For RLAnything, the first training invocation depends on the scenario; for example:

python osworld_rl.py config=configs/osworld_rl.yaml

For a single-node run, set num_node to 1 in the corresponding YAML configuration.

What are this agent's strengths and limitations?

How does this agent compare with similar options?

Key facts side by side with the most closely related agents.

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