- 《Building LLM Powered Applications》 —— 实用的 LLM 应用开发指南
- 《Generative AI with LangChain》 —— LangChain 框架深入指南
- 《Designing Autonomous AI Agent Systems》 —— Agent 系统设计原理
- DeepLearning.AI 的 "Building Agentic RAG with LlamaIndex" 系列
- LangChain Academy —— LangChain 官方课程（免费）
- Andrew Ng 的 "AI Agentic Design Patterns" 系列
- LangChain Discord —— 活跃的开发者社区
- Reddit r/LangChain —— 讨论和分享
- GitHub Discussions —— 各框架的官方讨论区
- Hugging Face —— 开源模型社区
- 掘金 / 稀土掘金 —— 搜索 "Agent 开发"、"LangChain 实战" 等话题，有大量中文实践文章
- 知乎 —— 关注 "AI Agent"、"LLM 应用开发" 话题，跟踪行业讨论和技术分析
- CSDN —— Agent 开发教程和踩坑记录
- B 站 / YouTube 中文频道 —— 搜索 "Agent 开发教程"，有不少优质视频教程
- 微信公众号 —— 推荐关注：机器之心、量子位、AI科技大本营等（跟踪最新 Agent 技术动态）
- 通义千问社区 —— 阿里云的 LLM 开发者社区，适合使用国内模型的开发者
以下是本书涉及的核心学术论文，按技术主题分类。每个主题在书中都有对应的独立论文解读章节，建议按照学习进度有选择地阅读。
💡 深度解读章节索引：
| 论文 | 作者 | 年份 | 本书章节 | 链接 |
|---|
| Chain-of-Thought Prompting Elicits Reasoning in Large Language Models | Wei et al. (Google Brain) | 2022 | 3.3 | arXiv:2201.11903 |
| Large Language Models are Zero-Shot Reasoners | Kojima et al. | 2022 | 3.3 | arXiv:2205.11916 |
| Self-Consistency Improves Chain of Thought Reasoning | Wang et al. (Google Brain) | 2023 | 3.3, 17.2 | arXiv:2203.11171 |
| Tree of Thoughts: Deliberate Problem Solving with LLMs | Yao et al. (Princeton) | 2023 | 3.3 | arXiv:2305.10601 |
| ReAct: Synergizing Reasoning and Acting in Language Models | Yao et al. (Princeton) | 2022 | 3.3, 6.2 | arXiv:2210.03629 |
| Plan-and-Solve Prompting | Wang et al. | 2023 | 6.3 | arXiv:2305.04091 |
| 论文 | 作者 | 年份 | 本书章节 | 链接 |
|---|
| Toolformer: Language Models Can Teach Themselves to Use Tools | Schick et al. (Meta) | 2023 | 4.1 | arXiv:2302.04761 |
| Gorilla: Large Language Model Connected with Massive APIs | Patil et al. (UC Berkeley) | 2023 | 4.1 | arXiv:2305.15334 |
| ToolLLM: Facilitating LLMs to Master 16000+ Real-world APIs | Qin et al. | 2023 | 4.1 | arXiv:2307.16789 |
| ToolACE: Winning the Points of LLM Function Calling | Liu et al. (华为诺亚方舟 & 中科大) | 2024 | 4.6 | arXiv:2409.00920 |
| RAG-MCP: Mitigating Prompt Bloat in LLM Tool Selection | Gan et al. | 2025 | 4.6 | arXiv:2505.03275 |
| 论文 | 作者 | 年份 | 本书章节 | 链接 |
|---|
| Voyager: An Open-Ended Embodied Agent with LLMs | Wang et al. (NVIDIA & Caltech) | 2023 | 5.6 | arXiv:2305.16291 |
| CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets | Yuan et al. (北京大学) | 2024 | 5.6 | arXiv:2309.17428 |
| 论文 | 作者 | 年份 | 本书章节 | 链接 |
|---|
| Generative Agents: Interactive Simulacra of Human Behavior | Park et al. (Stanford) | 2023 | 5.1 | arXiv:2304.03442 |
| MemGPT: Towards LLMs as Operating Systems | Packer et al. (UC Berkeley) | 2023 | 5.1 | arXiv:2310.08560 |
| MemoryBank: Enhancing LLMs with Long-Term Memory | Zhong et al. | 2023 | 5.1 | arXiv:2305.10250 |
| Cognitive Architectures for Language Agents (CoALA) | Sumers et al. | 2023 | 5.1 | arXiv:2309.02427 |
| HippoRAG: Neurobiologically Inspired Long-Term Memory for LLMs | Gutiérrez et al. (OSU) | 2024 | 5.6 | arXiv:2405.14831 |
| Zep: A Temporal Knowledge Graph Architecture for Agent Memory | Rasmussen et al. | 2025 | 5.6 | arXiv:2501.13956 |
| 论文 | 作者 | 年份 | 本书章节 | 链接 |
|---|
| Reflexion: Language Agents with Verbal Reinforcement Learning | Shinn et al. | 2023 | 6.4 | arXiv:2303.11366 |
| Self-Refine: Iterative Refinement with Self-Feedback | Madaan et al. (CMU) | 2023 | 6.4 | arXiv:2303.17651 |
| CRITIC: LLMs Can Self-Correct with Tool-Interactive Critiquing | Gou et al. | 2023 | 6.4 | arXiv:2305.11738 |
| Large Language Models Cannot Self-Correct Reasoning Yet | Huang et al. | 2023 | 6.4 | arXiv:2310.01798 |
| 论文 | 作者 | 年份 | 本书章节 | 链接 |
|---|
| Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks | Lewis et al. (Meta AI) | 2020 | 7.1 | arXiv:2005.11401 |
| Self-RAG: Learning to Retrieve, Generate, and Critique | Asai et al. | 2023 | 7.1 | arXiv:2310.11511 |
| Corrective Retrieval Augmented Generation (CRAG) | Yan et al. | 2024 | 7.1 | arXiv:2401.15884 |
| From Local to Global: A Graph RAG Approach | Edge et al. (Microsoft) | 2024 | 7.1 | arXiv:2404.16130 |
| LightRAG: Simple and Fast Retrieval-Augmented Generation | Guo et al. (香港大学) | 2024 | 7.6 | arXiv:2410.05779 |
| 论文 | 作者 | 年份 | 本书章节 | 链接 |
|---|
| ReAct: Synergizing Reasoning and Acting in Language Models | Yao et al. (Princeton) | 2022 | 6.2 | arXiv:2210.03629 |
| Plan-and-Solve Prompting | Wang et al. | 2023 | 6.3 | arXiv:2305.04091 |
| Reflexion: Language Agents with Verbal Reinforcement Learning | Shinn et al. | 2023 | 6.4 | arXiv:2303.11366 |
| Learning to Reason with LLMs (OpenAI o1) | OpenAI | 2024 | 6.6 | openai.com |
| DeepSeek-R1: Incentivizing Reasoning Capability via RL | DeepSeek-AI | 2025 | 6.6 | arXiv:2501.12948 |
| 论文 | 作者 | 年份 | 本书章节 | 链接 |
|---|
| MetaGPT: Meta Programming for Multi-Agent Collaboration | Hong et al. | 2023 | 14.1 | arXiv:2308.00352 |
| Communicative Agents for Software Development (ChatDev) | Qian et al. | 2023 | 14.1 | arXiv:2307.07924 |
| AutoGen: Enabling Next-Gen LLM Applications | Wu et al. (Microsoft) | 2023 | 14.1 | arXiv:2308.08155 |
| AgentVerse: Facilitating Multi-Agent Collaboration | Chen et al. | 2023 | 14.1 | arXiv:2308.10848 |
| Magentic-One: A Generalist Multi-Agent System | Fourney et al. (Microsoft) | 2024 | 14.6 | arXiv:2411.04468 |
| Multi-Agent Collaboration Mechanisms: A Survey of LLMs | Nguyen et al. | 2025 | 14.6 | arXiv:2501.06322 |
| 论文 | 作者 | 年份 | 本书章节 | 链接 |
|---|
| Not What You've Signed Up For: Indirect Prompt Injection | Greshake et al. | 2023 | 17.1 | arXiv:2302.12173 |
| HackAPrompt: Exposing Systemic Weaknesses of LLMs | Schulhoff et al. | 2023 | 17.1 | arXiv:2311.16119 |
| FActScore: Fine-grained Atomic Evaluation of Factual Precision | Min et al. (UW) | 2023 | 17.2 | arXiv:2305.14251 |
| A Survey on Hallucination in Large Language Models | Huang et al. | 2023 | 17.2 | arXiv:2311.05232 |
| InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Agents | Zhan et al. | 2024 | 17.6 | arXiv:2403.02691 |
| AgentDojo: Dynamic Environment for Agent Attack/Defense | Debenedetti et al. (ETH Zurich) | 2024 | 17.6 | arXiv:2406.13352 |
| Agent Security Bench (ASB): Attacks and Defenses in LLM Agents | Zhang et al. | 2025 | 17.6 | arXiv:2410.02644 |
| 论文 | 作者 | 年份 | 说明 | 链接 |
|---|
| A Survey on Large Language Model based Autonomous Agents | Wang et al. (人大) | 2023 | 最全面的 LLM Agent 综述 | arXiv:2308.11432 |
| The Rise and Potential of Large Language Model Based Agents: A Survey | Xi et al. | 2023 | Agent 的崛起与潜力综述 | arXiv:2309.07864 |
| LLM Powered Autonomous Agents | Lilian Weng (OpenAI) | 2023 | 优秀的技术博客，适合入门 | lilianweng.github.io |
| Multi-Agent Collaboration Mechanisms: A Survey of LLMs | Nguyen et al. | 2025 | 多 Agent 协作机制综述 | arXiv:2501.06322 |
💡 阅读建议：如果时间有限，优先阅读以下 7 篇"必读"论文：① ReAct（Agent 基本范式）② Generative Agents（记忆系统设计）③ RAG 原始论文（知识增强）④ Reflexion（自我改进）⑤ DeepSeek-R1（推理模型，2025）⑥ Magentic-One（通用多 Agent 系统，2024）⑦ A Survey on LLM based Autonomous Agents（全景综述）。
Agent 领域发展很快，建议关注：
- LangChain Blog —— 框架更新和最佳实践
- OpenAI Blog —— 模型能力更新和 Agents SDK 发展
- Anthropic Blog —— Claude 模型和 MCP 协议更新
- Google AI Blog —— Gemini 模型和 A2A 协议动态
- The Batch (by Andrew Ng) —— AI 行业周报
- arXiv —— 最新研究论文（搜索 "LLM Agent"）
- DeepSeek Blog —— 开源推理模型的最新进展