8 Best ART Alternatives in 2026 (Open Source)

ART — Agent Reinforcement Trainer: train multi-step agents for real-world tasks using GRPO. Give your agents on-the-job training. Reinforcement learning for Qwen3.6, . Provides serverless reinforcement learning infrastructure to train agents on real-world tasks with managed GPU resources.

These 8 open-source tools do the same job. They are ordered by how closely they match ART, with live GitHub data so you can see which projects are actively maintained.

1. 1. Agent Lightning

   The absolute trainer to light up AI agents.

   What sets it apart: Enables RL training of agents using their real harnesses with zero code changes through a proxy architecture.

   Best for: Researchers training AI agents with RL; Teams needing lightweight agent training frameworks; Coding agent performance improvement

   ⭐ 18.5kART vs Agent Lightning →Full profile →

2. 2. LlamaGym

   Fine-tune LLM agents with online reinforcement learning

   What sets it apart: vs raw Gym + LLM integration: simplified abstraction handling RL-specific challenges (context management, batching, reward assignment) — bridges the gap between Gymnasium environments and LLM fine-tuning

   Best for: Training LLM agents for interactive game/simulation environments; Experimenting with agent prompting strategies via RL; Research into reinforcement learning for language models

   ⭐ 1.3kART vs LlamaGym →Full profile →

3. 3. ColossalAI

   Making large AI models cheaper, faster and more accessible

   What sets it apart: vs DeepSpeed / Megatron-LM: unified system combining 7+ parallelism strategies with auto-parallelism selection — train LLaMA-70B 195% faster with built-in RLHF pipeline and application-specific acceleration (Open-Sora, Stable Diffusion)

   Best for: Training 7B-70B+ parameter language models on multi-GPU clusters; Fine-tuning domain-specific LLMs on limited budgets ($300-$5000); RLHF-based conversational AI training pipelines

   ⭐ 41.4kART vs ColossalAI →Full profile →

4. 4. Axolotl

   Go ahead and axolotl questions

   What sets it apart: vs LLaMA-Factory: broader training method support (GRPO/QAT/ScatterMoE) with faster new model adoption; vs HuggingFace TRL: more production-ready with multi-GPU optimization and single YAML config

   Best for: Fine-tuning latest open-source LLMs; LoRA/QLoRA training on consumer GPUs; Research teams exploring preference tuning methods

   ⭐ 12.5kART vs Axolotl →Full profile →

5. 5. Ray

   Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

   What sets it apart: vs Spark: Python-native with actor model and ML-specific libraries (Train/Tune/Serve); vs Dask: broader AI/ML ecosystem with RLlib, serving, and managed Anyscale platform

   Best for: Scaling ML training and serving across clusters; Distributed hyperparameter tuning; Building scalable AI inference pipelines

   ⭐ 44.0kART vs Ray →Full profile →

6. 6. AgentScope

   Build and run agents you can see, understand and trust.

   What sets it apart: Unlike LangGraph (stateful graph orchestration) and CrewAI (role-based crews), AgentScope uniquely combines realtime voice agents, A2A protocol, agentic RL fine-tuning, and Kubernetes-native deployment — designed for the rising capability of agentic LLMs

   Best for: Teams building production multi-agent systems with realtime voice and A2A interoperability; Chinese-market developers wanting first-class DashScope/Qwen integration

   ⭐ 32.6kART vs AgentScope →Full profile →

7. 7. Lumos

   Code and data for "Lumos: Learning Agents with Unified Data, Modular Design, and Open-Source LLMs"

   What sets it apart: vs GPT-4 agents: unified modular framework achieving competitive performance with 7B-13B models — planning + grounding + execution separation enables task-agnostic agent architecture from Allen AI

   Best for: Multi-step reasoning: web navigation, QA, math problem-solving; Research into efficient agent architectures with small models; Building agents competitive with GPT-4 at lower cost

   ⭐ 477ART vs Lumos →Full profile →

8. 8. AutoAct

   [ACL 2024] AutoAct: Automatic Agent Learning from Scratch for QA via Self-Planning

   What sets it apart: vs ReAct/Reflexion/BOLAA: division-of-labor strategy automatically creates specialized Plan/Tool/Reflect sub-agents from self-synthesized trajectories — zero dependency on closed-source model data or human annotations

   Best for: Research on automatic agent learning without GPT-4 dependency; Multi-hop QA requiring complex question decomposition; Teams wanting to train specialized sub-agents from self-generated data

   ⭐ 239ART vs AutoAct →Full profile →