agentscope
★ 34.4k
vs
LLM-Agents-Papers
★ 2.4k
agentscope vs LLM-Agents-Papers
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.; LLM-Agents-Papers: This repository provides a comprehensive list of papers focused on Large Language Model (LLM) based agents. It covers various aspects, including enhancement techniques, interaction methods, diverse applications, and underlying infrastructure for LLM agents.
01
TL;DR
agentscopeMulti-Agent · ★ 34.4k
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+.
LLM-Agents-PapersMulti-Agent · ★ 2.4k
For academic researchers seeking comprehensive paper collections on LLM agents.
02
Side-by-Side Comparison
Field
agentscopeLLM-Agents-PapersCategory
Multi-Agent
Multi-Agent
Stars
★ 34.4k
★ 2.4k
License
Apache-2.0
—
Install Command
uv pip install agentscope
git clone https://github.com/AGI-Edgerunners/LLM-Agents-Papers.git
Updated
1d ago
1y ago
Open Source
Yes
Yes
Tags
Agent Framework, Multi-Agent Systems, Large Language Models
LLM Agents, Paper List, AI Research
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.
LLM-Agents-Papers01Comprehensive survey of LLM agent papers
02Techniques for enhancing LLM agent capabilities
03Methods for agent interaction and collaboration
04Diverse applications of LLM agents in various fields
05Infrastructure and evaluation benchmarks for LLM agents
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.
LLM-Agents-Papers↳For academic researchers seeking comprehensive paper collections on LLM agents.
↳As a learning resource for students and newcomers to the field of AI agents.
↳To guide practitioners in understanding and implementing advanced LLM agent techniques.
05
Best For
agentscopeMost PopularEssentialTrending
LLM-Agents-Papers—
FAQ
FAQ
What is the difference between agentscope and LLM-Agents-Papers?
Both agentscope and LLM-Agents-Papers are in the Multi-Agent category. agentscope has 34.4k stars, while LLM-Agents-Papers has 2.4k stars.
Which is better, agentscope or LLM-Agents-Papers?
The best choice depends on your use case. Choose agentscope if Developing multi-agent collaborative applications and strategic games., and LLM-Agents-Papers if For academic researchers seeking comprehensive paper collections on LLM agents..
Is agentscope free or open source?
Yes, agentscope is open source on GitHub (Apache-2.0).
Is LLM-Agents-Papers free or open source?
Yes, LLM-Agents-Papers is open source on GitHub.
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