Resources & Reading List

Curated entry points into the LLM agents literature

Getting Started

If you’re new to LLM agents, start with these:

  1. Lilian Weng — LLM Powered Autonomous Agents (June 2023)
    The most widely cited blog post introduction to the field. Covers planning, memory, tool use with great diagrams.

  2. Agentic Large Language Models: A Survey (Plaat et al., JAIR 2025)
    Peer-reviewed survey organizing the field around Reason–Act–Interact. Introduces the virtuous cycle framing, covers theory of mind and emergent social behavior, and provides a research agenda. Also at alphaxiv with annotations. Companion site: askeplaat.github.io/agentic-llm-survey-site

  3. ReAct paper (Yao et al., 2022)
    The canonical agent loop: Reason + Act interleaved. Essential reading.

  4. A Survey on Large Language Model based Autonomous Agents (Wang et al., 2023)
    Comprehensive academic survey covering construction, application, and evaluation.


Key Survey Papers

Title Authors Year Link
A Survey on Large Language Model based Autonomous Agents Wang et al. 2023 arXiv
The Rise and Potential of Large Language Model Based Agents Xi et al. 2023 arXiv
Agentic Large Language Models, a survey (JAIR 2025) Plaat et al. (Leiden) 2025 arXiv · site
Agent AI: Surveying the Horizons of Multimodal Interaction Wang et al. 2024 arXiv
Large Language Models as Tool Makers Cai et al. 2023 arXiv

Essential Papers by Category

Foundations

Reasoning & Planning

Multi-Agent

Memory

Tools & Actions

Coding Agents


Key Frameworks (GitHub)

Framework GitHub Description
LangChain github.com/langchain-ai/langchain Full-stack agent framework
LangGraph github.com/langchain-ai/langgraph Graph-based agent workflows
AutoGen github.com/microsoft/autogen Multi-agent conversation
CrewAI github.com/crewAIInc/crewAI Role-based agent crews
SWE-agent github.com/princeton-nlp/SWE-agent GitHub issue resolution
OpenDevin github.com/All-Hands-AI/OpenHands Open software dev agent
DSPy github.com/stanfordnlp/dspy Programming language models
MemGPT github.com/cpacker/MemGPT Agents with long-term memory

Influential Blog Posts

Introductions & Overviews

Title Author Date
LLM Powered Autonomous Agents Lilian Weng Jun 2023
Emerging Architectures for LLM Applications a16z Jun 2023
A Hitchhiker’s Guide to Building AI Agents Saurabh Alone 2025
Making Sense of Memory in AI Agents Leonie Monigatti 2025

Production & Practitioner Guides

Title Author Date
Building effective agents Anthropic (Schluntz & Zhang) Dec 2024
How we built our multi-agent research system Anthropic Engineering 2025
What We Learned from a Year of Building with LLMs Eugene Yan et al. 2024
Agents 101: The Art of Actually Getting Things Done Cognition AI (Devin) 2025
Building Your Own Coding Agent Martin Fowler 2025
AI in Production: What Actually Works in 2026 47 Billion 2026
Google Cloud: Lessons from 2025 on Agents and Trust Google Cloud CTO Office Dec 2025

Builder Diaries & Case Studies

Title Author Date
Strix: Meet the Stateful Agent Tim Kellogg Dec 2025
Memory Architecture for a Synthetic Being Tim Kellogg Dec 2025
Viable Systems: How to Build a Fully Autonomous Agent Tim Kellogg Jan 2026
One Human + One Agent + One Browser emsh.cat 2026
OpenAI Harness Engineering OpenAI 2025

Benchmarks & Evaluation

Benchmark Focus Link
SWE-bench Software engineering tasks swebench.com
WebArena Web navigation arxiv.org/abs/2307.13854
AgentBench Multi-domain agent eval arxiv.org/abs/2308.03688
GAIA General AI assistant tasks arxiv.org/abs/2311.12983
OSWorld Desktop GUI tasks arxiv.org/abs/2404.07972

Courses & Tutorials


References

Survey Papers

  • Agentic Large Language Models: A Survey (Plaat et al., 2025) — arXiv:2503.23037 / Site
  • A Survey on Large Language Model based Autonomous Agents (Wang et al., 2023) — arXiv:2308.11432
  • The Rise and Potential of Large Language Model Based Agents (Xi et al., 2023) — arXiv:2309.07864
  • Agent AI: Surveying the Horizons of Multimodal Interaction (Wang et al., 2024) — arXiv:2401.03568
  • Large Language Models as Tool Makers (Cai et al., 2023) — arXiv:2305.17126

Foundation Papers

  • ReAct: Synergizing Reasoning and Acting in Language Models (Yao et al., 2022) — arXiv:2210.03629
  • Toolformer: Language Models Can Teach Themselves to Use Tools (Schick et al., 2023) — arXiv:2302.04761
  • Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Wei et al., 2022) — arXiv:2201.11903
  • MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning (Karpukhin et al., 2022) — arXiv:2205.00445
  • WebGPT: Browser-assisted Question-Answering with Large Language Models (Nakano et al., 2021) — arXiv:2112.09332

Reasoning & Planning Papers

  • Tree of Thoughts: Deliberate Problem Solving with Large Language Models (Yao et al., 2023) — arXiv:2305.10601
  • Reflexion: Language Agents with Verbal Reinforcement Learning (Shinn et al., 2023) — arXiv:2303.11366
  • Self-Refine: Iterative Refinement with Self-Feedback (Madaan et al., 2023) — arXiv:2303.17651

Multi-Agent Papers

  • CAMEL: Communicative Agents for “Mind” Exploration of Large Scale Language Model Society (Li et al., 2023) — arXiv:2303.17760
  • MetaGPT: The Multi-Agent Framework (Hong et al., 2023) — arXiv:2308.00352
  • AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework (Wu et al., 2023) — arXiv:2308.08155
  • Generative Agents: Interactive Simulacra of Human Behavior (Park et al., 2023) — arXiv:2304.03442
  • ChatDev: Communicative Agents for Software Development (Qian et al., 2023) — arXiv:2307.07924

Memory Papers

  • MemGPT: Towards LLMs as Operating Systems (Packer et al., 2023) — arXiv:2310.08560

Tools & Actions Papers

  • HuggingGPT: Solving AI Tasks with Chatbot (Sap et al., 2023) — arXiv:2303.17580
  • Gorilla: Large Language Model Connected with Massive APIs (Patil et al., 2023) — arXiv:2305.15334
  • ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs (Qin et al., 2023) — arXiv:2307.16789

Coding Agent Papers

  • SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering (Yang et al., 2024) — arXiv:2405.15793
  • CodeAct: Executable Code Actions for Agentic Language Models (Wang et al., 2024) — arXiv:2402.01030
  • SWE-bench: Can Language Models Resolve Real-World GitHub Issues? (Jimenez et al., 2023) — arXiv:2310.06770

Blog Posts & Articles

Courses & Educational Resources

Benchmarks & Evaluation


About This Survey

This survey was compiled in March 2026 by Claude Sonnet. It covers the LLM agent literature from 2022 through early 2026.

View source on GitHub · Report an issue