agentscope
★ 33.7k
vs
GPA-LM
★ 161
agentscope vs GPA-LM
agentscope: AgentScope is a production-ready, easy-to-use agent framework designed for increasingly agentic LLMs. It offers essential abstractions, built-in support for finetuning, and flexible multi-agent orchestration for various deployment environments.; GPA-LM: This repository serves as a comprehensive collection of research papers focused on the intersection of game-playing agents and large language models. It covers various methods, applications, and challenges, providing a dynamic timeline of recent advancements in the field of LLM-based agents for games.
01
TL;DR
agentscopeMulti-Agent · ★ 33.7k
When to choose:
- You want composable Python 3.11+ building blocks (ReAct loop, toolkit, pipelines, A2A, realtime voice) with bilingual documentation.
- You use Chinese model providers (DashScope, DeepSeek, Volcengine) alongside global APIs under an Apache-2.0 license.
When to skip:
- You rely on v1.x AgentScope code and do not want to migrate to the redesigned v2.0 architecture.
- You need Python 3.10 or .NET support; AgentScope 2.0 requires Python 3.11+.
GPA-LMVision / Multimodal · ★ 161
Staying updated with the latest research in LLM-based game agents.
02
Side-by-Side Comparison
Field
agentscopeGPA-LMCategory
Multi-Agent
Vision / Multimodal
Stars
★ 33.7k
★ 161
License
Apache-2.0
—
Install Command
uv pip install agentscope
git clone https://github.com/BAAI-Agents/GPA-LM.git
Updated
1d ago
2y ago
Open Source
Yes
Yes
Tags
Agent Framework, Multi-Agent Systems, Large Language Models
Large Language Models, Game AI, Multi-Agent Systems
03
Features
agentscope01Easy-to-use with built-in ReAct agents, tools, and human-in-the-loop steering.
02Extensible with broad ecosystem integrations, MCP, and A2A protocol support.
03Production-ready for local, serverless, or Kubernetes deployments with OTel support.
04Voice-enabled agents capable of speech understanding and response.
05Seamless integration with Reinforcement Learning for agent tuning.
GPA-LM01Curated list of research papers on Game Playing Agents and Large Models.
02Regular updates with the latest papers and associated code/projects.
03Categorized by publication month for easy chronological tracking.
04Provides direct links to arXiv PDFs and project pages.
05Covers diverse topics within LLM-based game agents, including multi-agent systems and embodied AI.
04
Use Cases
agentscope↳Developing multi-agent collaborative applications and strategic games.
↳Building agents with advanced tool-use capabilities and multi-step reasoning.
↳Tuning agent performance using Reinforcement Learning for continuous improvement.
GPA-LM↳Staying updated with the latest research in LLM-based game agents.
↳Discovering relevant papers and codebases for academic research or project development.
↳Understanding the evolution and trends of Large Language Models in game playing.
05
Best For
agentscopeMost PopularEssentialTrending
GPA-LMHidden Gem
FAQ
FAQ
What is the difference between agentscope and GPA-LM?
Both agentscope and GPA-LM are in the Multi-Agent category. agentscope has 33.7k stars, while GPA-LM has 161 stars.
Which is better, agentscope or GPA-LM?
The best choice depends on your use case. Choose agentscope if Developing multi-agent collaborative applications and strategic games., and GPA-LM if Staying updated with the latest research in LLM-based game agents..
Is agentscope free or open source?
Yes, agentscope is open source on GitHub (Apache-2.0).
Is GPA-LM free or open source?
Yes, GPA-LM is open source on GitHub.
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