Agents

Agents

Intro

Inner Monologue

以大型語言模型打造的AI Agent

LLM Agent Paper List

The Birth of An Agent: Construction of LLM-based Agents

---

AI Agents: Evolution, Architecture, and Real-World Applications

---

ADAS

Paper: Automated Design of Agentic Systems
Code: https://github.com/ShengranHu/ADAS

---

Gödel Agent

Paper: Gödel Agent: A Self-Referential Agent Framework for Recursive Self-Improvement
Code: https://github.com/Arvid-pku/Godel_Agent

---

Voyager

Paper: Voyager: An Open-Ended Embodied Agent with Large Language Models
Code: https://github.com/MineDojo/Voyager

---

NPCpy

Code: https://github.com/NPC-Worldwide/npcpy

from npcpy.npc_compiler import NPC
simon = NPC(
          name='Simon Bolivar',
          primary_directive='Liberate South America from the Spanish Royalists.',
          model='gemma3:4b',
          provider='ollama'
          )
response = simon.get_llm_response("What is the most important territory to retain in the Andes mountains?")
print(response['response'])

---

Frameworks

OpenAI Agent SDK

Code: https://github.com/openai/openai-agents-python
pip install openai-agents

---

LangChain

• [LangChain RAG]((https://python.langchain.com/docs/tutorials/rag/)
• Build a Question/Answering system over SQL data
• Build a Question Answering application over a Graph Database

Kaggle:

• LangChain RAG
• LangChain RAG Webpage

---

HuggingFace smolagents

Examples:

• Text-to-SQL
• Agentic RAG
• Orchestrate a multi-agent system
• Web Browser Automation with Agents

Kaggle:

• Smolagents simple
• Smolagents websearch

---

DSPy

DSPy: Programming—not prompting—Foundation Models

Kaggle:

• DSPy AirlineCustomerService
• DSPy Image Generation Prompt Iteration

Blog: Building and Optimizing AI Applications with DSPy and Gemini Flash 2.5

---

AG2

Kaggle:

• AG2 Agent coding
• AG2 Agent Tools and Run Method examples
• AG2 Conversable Agent
• AG2 Group Chat
• AG2 Groupchat with RAG

---

AutoGen

---

MCP (Model Context Protocol)

MCP (Model Context Protocol) is an open-source standard for connecting AI applications to external systems.

MCP SDK

---

AgentGym

Paper: AgentGym: Evolving Large Language Model-based Agents across Diverse Environments
Code: https://github.com/WooooDyy/AgentGym

---

AgentGym-RL

Paper: AgentGym-RL: Training LLM Agents for Long-Horizon Decision Making through Multi-Turn Reinforcement Learning
Code: https://github.com/WooooDyy/AgentGym-RL

Modular System Design of AgentGym-RL

Post-Training Strategies

AgentGym-RL supports a suite of mainstream online RL algorithms: PPO, GRPO, RLOO, REINFORCE++.
Beyond online RL, AgentGym-RL also supports a broad range of complementary training paradigms: SFT, DPO, AgentEvol.

ScalingInter-RL: Progressive Scaling Interaction for Agent RL

---

AgentScope

Paper: AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applications
Github: https://github.com/agentscope-ai/agentscope

---

Alpha Evolve

Blog: AlphaEvolve：Google DeepMind 開創 AI 演算法自主進化新紀元
Paper: AlphaEvolve: A coding agent for scientific and algorithmic discovery

This site was last updated October 26, 2025.