8 Best LlamaGym Alternatives in 2026 (Open Source)
LlamaGym — Fine-tune LLM agents with online reinforcement learning. 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
These 8 open-source tools do the same job. They are ordered by how closely they match LlamaGym, with live GitHub data so you can see which projects are actively maintained.
| Tool | GitHub stars | Stars / 30d | Last commit |
|---|---|---|---|
| LlamaGym(original) | 1.3k | +1 | 2024-03-10 |
| LLM Agents | 1.1k | +2 | 2025-06-23 |
| AgentScope | 32.6k | +1,846 | 2026-09-30 |
| Lagent | 2.3k | +7 | 2026-04-20 |
| BondAI | 226 | +1 | 2024-01-14 |
| llama-cpp-agent | 659 | +6 | 2026-03-09 |
| Flappy | 304 | +-0 | 2024-04-11 |
| RestGPT | 1.4k | +2 | 2023-09-28 |
| gptrpg | 992 | +0 | 2023-05-02 |
1. LLM Agents
Build agents which are controlled by LLMs
What sets it apart: Minimal educational agent implementation in very few lines of code, making LLM agent architecture transparent and easy to understand
Best for: understanding-agent-architecture; learning-tool-augmented-llms; building-simple-agents
2. 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
3. Lagent
A lightweight framework for building LLM-based agents
What sets it apart: vs LangChain/CrewAI: PyTorch-inspired design with intuitive layer composition, dual sync/async interfaces, and built-in session-isolated memory for concurrent agent workloads
Best for: Multi-agent workflows with iterative self-refinement; Research with InternLM/Qwen models and custom agents
4. BondAI
BondAI is an open-source tool for developing AI Agent Systems. BondAI handles the implementation complexities including memory/context management, error handling, vector/semantic search and includes a
What sets it apart: vs LangChain agents: extensive pre-built tool ecosystem (search, email, trading, phone calls, databases) with minimal setup — CLI access makes agent interaction accessible without coding
Best for: Multi-agent research automation with diverse tool integration; Document generation combining web scraping and analysis; Task automation across multiple data sources and services
5. llama-cpp-agent
The llama-cpp-agent framework is a tool designed for easy interaction with Large Language Models (LLMs). Allowing users to chat with LLM models, execute structured function calls and get structured ou
What sets it apart: Enabled function calling and structured output from any local LLM through grammar-based guided sampling, making capabilities previously exclusive to fine-tuned models available to all llama.cpp-compatible models — now deprecated
Best for: Getting structured output from local LLMs without fine-tuning; Building function-calling agents with open-source models locally
6. Flappy
Production-Ready LLM Agent SDK for Every Developer
What sets it apart: vs Python-centric frameworks (LangChain, etc.): language-agnostic agent framework supporting Node.js, Java/Kotlin, C# — production-ready with sandbox security and cost-efficiency balancing
Best for: Multi-language AI agent development beyond Python; Production applications needing sandboxed code execution; ETL data processing and external API orchestration
7. RestGPT
An LLM-based autonomous agent controlling real-world applications via RESTful APIs
What sets it apart: vs basic API wrappers: iterative coarse-to-fine planning combining high-level task decomposition with fine-grained API selection — addresses practical challenges of multi-step API orchestration
Best for: Automating complex multi-step REST API workflows; Research into LLM planning for API orchestration; Testing LLM capabilities against realistic API integration tasks
8. gptrpg
A demo of an GPT-based agent existing in an RPG-like environment
What sets it apart: vs Generative Agents (Stanford) / AI Town: minimal browser-based RPG with real-time Phaser rendering — proof of concept connecting GPT-3.5 decisions to a visual 2D game environment via WebSocket
Best for: Researchers exploring LLM-driven agent behavior in simulated 2D environments; Developers interested in game AI with natural language decision making; Educational demonstrations of AI agents in interactive worlds