8 Best Agent Lightning Alternatives in 2026 (Open Source)

Agent Lightning — The absolute trainer to light up AI agents. Enables RL training of agents using their real harnesses with zero code changes through a proxy architecture.

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

1. 1. 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,

   What sets it apart: Provides serverless reinforcement learning infrastructure to train agents on real-world tasks with managed GPU resources.

   Best for: Developing agents that learn from experience; Real-world multi-step task training; Researchers and developers using RL for agent improvement

   ⭐ 10.8kAgent Lightning vs ART →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

   ⭐ 1.3kAgent Lightning vs LlamaGym →Full profile →

3. 3. 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

   ⭐ 32.6kAgent Lightning vs AgentScope →Full profile →

4. 4. 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

   ⭐ 41.4kAgent Lightning vs ColossalAI →Full profile →

5. 5. 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

   ⭐ 12.5kAgent Lightning vs Axolotl →Full profile →

6. 6. SFighterAI

   This is an AI agent for Street Fighter II Champion Edition.

   What sets it apart: Trains an RL agent to beat Street Fighter II using only raw RGB pixels, demonstrating deep RL applied to classic gaming

   Best for: reinforcement-learning-research; game-ai-experimentation; learning-ppo-in-practice

   ⭐ 6.5kAgent Lightning vs SFighterAI →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

   ⭐ 477Agent Lightning vs Lumos →Full profile →

8. 8. SWE-agent

   SWE-agent takes a GitHub issue and tries to automatically fix it, using your LM of choice. It can also be employed for offensive cybersecurity or competitive coding challenges. [NeurIPS 2024]

   What sets it apart: Princeton/Stanford research project achieving SoTA on SWE-bench — the most rigorous benchmark for automated software engineering — with a simple, hackable design that leaves maximal agency to the LLM

   Best for: Automated bug fixing and issue resolution in GitHub repos; Research on AI-driven software engineering capabilities

   ⭐ 20.4kAgent Lightning vs SWE-agent →Full profile →