# AutoManual: Constructing Instruction Manuals by LLM Agents via Interactive Environmental Learning

arXiv: https://arxiv.org/abs/2405.16247 · HTML: https://mlsanity.com/paper/2405.16247/

## Key facts

- Cited by 61 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 37 of its 45 references that are in the map.
- First posted on arXiv 25 May 2024.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 30 of 37)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023)
- [WebArena: A Realistic Web Environment for Building Autonomous Agents](https://mlsanity.com/paper/2307.13854/) (2023)
- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [WebGPT: Browser-assisted question-answering with human feedback](https://mlsanity.com/paper/2112.09332/) (2021)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [MemGPT: Towards LLMs as Operating Systems](https://mlsanity.com/paper/2310.08560/) (2023)
- [Code as Policies: Language Model Programs for Embodied Control](https://mlsanity.com/paper/2209.07753/) (2022)
- [ALFWorld: Aligning Text and Embodied Environments for Interactive Learning](https://mlsanity.com/paper/2010.03768/) (2020)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [ExpeL: LLM Agents Are Experiential Learners](https://mlsanity.com/paper/2308.10144/) (2023)
- [Large Language Models as Optimizers](https://mlsanity.com/paper/2309.03409/) (2023)
- [ProgPrompt: Generating Situated Robot Task Plans using Large Language Models](https://mlsanity.com/paper/2209.11302/) (2022)
- [Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models](https://mlsanity.com/paper/2310.04406/) (2023)
- [Executable Code Actions Elicit Better LLM Agents](https://mlsanity.com/paper/2402.01030/) (2024)
- [LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models](https://mlsanity.com/paper/2212.04088/) (2022)
- [Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents](https://mlsanity.com/paper/2302.01560/) (2023)
- [Language Models can Solve Computer Tasks](https://mlsanity.com/paper/2303.17491/) (2023)
- [ChatGPT for Robotics: Design Principles and Model Abilities](https://mlsanity.com/paper/2306.17582/) (2023)
- [Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memory](https://mlsanity.com/paper/2305.17144/) (2023)
- [Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration](https://mlsanity.com/paper/1802.08802/) (2018)
- [PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization](https://mlsanity.com/paper/2310.16427/) (2023)

## Built on by (most cited first, 30 of 61)

- [Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems](https://mlsanity.com/paper/2504.01990/) (2025)
- [Memp: Exploring Agent Procedural Memory](https://mlsanity.com/paper/2508.06433/) (2025)
- [A Survey on the Optimization of Large Language Model-based Agents](https://mlsanity.com/paper/2503.12434/) (2025)
- [Learning Hierarchical Procedural Memory for LLM Agents through Bayesian Selection and Contrastive Refinement](https://mlsanity.com/paper/2512.18950/) (2025)
- [Coarse-to-Fine Grounded Memory for LLM Agent Planning](https://mlsanity.com/paper/2508.15305/) (2025)
- [Evo-Harness: Context-to-Harness Skill Compilation for Self-Evolving Agents](https://mlsanity.com/paper/2608.15071/) (2026)
- [RoboMemory: A Brain-inspired Multi-memory Agentic Framework for Interactive Environmental Learning in Physical Embodied Systems](https://mlsanity.com/paper/2508.01415/) (2025)
- [On Safety Risks in Experience-Driven Self-Evolving Agents](https://mlsanity.com/paper/2604.16968/) (2026)
- [ContractSkill: Repairable Contract-Based Skills for Multimodal Web Agents](https://mlsanity.com/paper/2603.20340/) (2026)
- [Self-Evolving World Models for LLM Agent Planning](https://mlsanity.com/paper/2606.30639/) (2026)
- [OpenApps: Simulating Environment Variations to Measure UI-Agent Reliability](https://mlsanity.com/paper/2511.20766/) (2025)
- [Policy Guided Tree Search for Enhanced LLM Reasoning](https://mlsanity.com/paper/2502.06813/) (2025)
- [AgentDebugX: An Open-Source Toolkit for Failure Observability, Attribution, and Recovery in LLM Agents](https://mlsanity.com/paper/2607.18754/) (2026)
- [H2R: Hierarchical Hindsight Reflection for Multi-Task LLM Agents](https://mlsanity.com/paper/2509.12810/) (2025)
- [RSIAgent: Autonomous Exploration for Recursive Self-improvement in New Environments](https://mlsanity.com/paper/2609.15364/) (2026)
- [PG-Agent: An Agent Powered by Page Graph](https://mlsanity.com/paper/2509.03536/) (2025)
- [ABSTRAL: Automatic Design of Multi-Agent Systems Through Iterative Refinement and Topology Optimization](https://mlsanity.com/paper/2603.22791/) (2026)
- [SkillLens: Adaptive Multi-Granularity Skill Reuse for Cost-Efficient LLM Agents](https://mlsanity.com/paper/2605.08386/) (2026)
- [Multi-level Value Alignment in Agentic AI Systems: Survey and Perspectives](https://mlsanity.com/paper/2506.09656/) (2025)
- [EvoEmpirBench: Dynamic Spatial Reasoning with Agent-ExpVer](https://mlsanity.com/paper/2509.12718/) (2025)
- [Fine-tuning with RAG for Improving LLM Learning of New Skills](https://mlsanity.com/paper/2510.01375/) (2025)
- [JEF-Hinter: Leveraging Offline Knowledge for Improving Web Agents Adaptation](https://mlsanity.com/paper/2510.04373/) (2025)
- [Large Language Model Agents Are Not Always Faithful Self-Evolvers](https://mlsanity.com/paper/2601.22436/) (2026)
- [Skill-as-Pseudocode: Refactoring Skill Libraries to Pseudocode for LLM Agents](https://mlsanity.com/paper/2605.27955/) (2026)
- [AutoRPA: Efficient GUI Automation through LLM-Driven Code Synthesis from Interactions](https://mlsanity.com/paper/2605.21082/) (2026)
- [Skill is Not One-Size-Fits-All: Model-Aware Skill Alignment for LLM Agents](https://mlsanity.com/paper/2605.30723/) (2026)
- [VASO: Formally Verifiable Self-Evolving Skills for Physical AI Agents](https://mlsanity.com/paper/2606.05395/) (2026)
- [Environment-Grounded Automated Prompt Optimization for LLM Game Agents](https://mlsanity.com/paper/2606.17838/) (2026)
- [SkillAdam: Stable and Efficient Skill Evolution for Agents](https://mlsanity.com/paper/2609.08944/) (2026)
- [AlignEvoSkill: Towards Knowledge-Aware and Task-Aligned Agent Skill Evolution](https://mlsanity.com/paper/2506.23149/) (2025)

## Abstract (arXiv)

Large Language Models (LLM) based agents have shown promise in autonomously completing tasks across various domains, e.g., robotics, games, and web navigation. However, these agents typically require elaborate design and expert prompts to solve tasks in specific domains, which limits their adaptability. We introduce AutoManual, a framework enabling LLM agents to autonomously build their understanding through interaction and adapt to new environments. AutoManual categorizes environmental knowledge into diverse rules and optimizes them in an online fashion by two agents: 1) The Planner codes actionable plans based on current rules for interacting with the environment. 2) The Builder updates the rules through a well-structured rule system that facilitates online rule management and essential detail retention. To mitigate hallucinations in managing rules, we introduce a *case-conditioned prompting* strategy for the Builder. Finally, the Formulator agent compiles these rules into a comprehensive manual. The self-generated manual can not only improve the adaptability but also guide the planning of smaller LLMs while being human-readable. Given only one simple demonstration, AutoManual significantly improves task success rates, achieving 97.4% with GPT-4-turbo and 86.2% with GPT-3.5-turbo on ALFWorld benchmark tasks. The code is available at https://github.com/minghchen/automanual.