# Ability Acquisition: Enhancing Your Agent's Capabilities

## Introduction
- Understanding the Importance of Ability Acquisition
- The Concept of Abilities in AutoGPT

## Section 1: Identifying Necessary Abilities
- Analyzing the Requirements for Your Agent
- Categorizing Abilities: Core vs. Supplementary

## Section 2: Developing Abilities for Your Agent
- Integrating Existing Abilities from the Forge
- Developing Custom Abilities: A Step-by-step Guide

## Section 3: Implementing and Executing Abilities
- Utilizing the Agent Protocol for Ability Implementation
- Executing Abilities: Task and Step Execution
- Example: Developing and Executing an Ability using Task and Step Schemas

## Section 4: Encoding Abilities in Prompts for LLM Selection
- Understanding the Concept of Prompt Engineering
- Strategies for Effective Ability Encoding in Prompts
- Practical Examples: Encoding Various Abilities in Prompts

## Section 5: Testing and Debugging Abilities
- Employing Test-Driven Development for Ability Testing
- Debugging Common Issues in Ability Implementation

## Conclusion
- Recap of the Tutorial
- Preparing Your Agent for Ability Integration and Enhancement

## Additional Resources

From **The Rise and Potential of Large Language Model Based Agents: A Survey** *Zhiheng Xi (Fudan University) et al. arXiv.* [[paper](https://arxiv.org/abs/2305.14497)] [[code](https://github.com/woooodyy/llm-agent-paper-list)]
### Research Papers
- [2023/07] **ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs.** *Yujia Qin et al. arXiv.* [[paper](https://arxiv.org/abs/2307.16789)] [[code](https://github.com/openbmb/toolbench)] [[dataset](https://paperswithcode.com/dataset/toolbench)]
- [2023/05] **Large Language Models as Tool Makers.** *Tianle Cai et al. arXiv.* [[paper](https://arxiv.org/abs/2305.17126)] [[code](https://github.com/ctlllll/llm-toolmaker)]
- [2023/05] **CREATOR: Disentangling Abstract and Concrete Reasonings of Large Language Models through Tool Creation.** *Cheng Qian et al. arXiv.* [[paper](https://arxiv.org/abs/2305.14318)]
- [2023/04] **Tool Learning with Foundation Models.** *Yujia Qin et al. arXiv.* [[paper](https://arxiv.org/abs/2304.08354)] [[code](https://github.com/openbmb/bmtools)]
- [2023/04] **ChemCrow: Augmenting large-language models with chemistry tools.** *Andres M Bran (Laboratory of Artificial Chemical Intelligence, ISIC, EPFL) et al. arXiv.* [[paper](https://arxiv.org/abs/2304.05376)] [[code](https://github.com/ur-whitelab/chemcrow-public)]
- [2023/04] **GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Information.** *Qiao Jin, Yifan Yang, Qingyu Chen, Zhiyong Lu. arXiv.* [[paper](https://arxiv.org/abs/2304.09667)] [[code](https://github.com/ncbi/GeneGPT)]
- [2023/04] **OpenAGI: When LLM Meets Domain Experts.** *Yingqiang Ge et al. arXiv.* [[paper](https://arxiv.org/abs/2304.04370)] [[code](https://github.com/agiresearch/openagi)]
- [2023/03] **HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face.** *Yongliang Shen et al. arXiv.* [[paper](https://arxiv.org/abs/2303.17580)] [[code](https://github.com/microsoft/JARVIS)]
- [2023/03] **Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models.** *Chenfei Wu et al. arXiv.* [[paper](https://arxiv.org/abs/2303.04671)] [[code](https://github.com/microsoft/visual-chatgpt)]
- [2023/02] **Augmented Language Models: a Survey.** *Grégoire Mialon et al. arXiv.* [[paper](https://arxiv.org/abs/2302.07842)]
- [2023/02] **Toolformer: Language Models Can Teach Themselves to Use Tools.** *Timo Schick et al. arXiv.* [[paper](https://arxiv.org/abs/2302.04761)]
- [2022/05] **TALM: Tool Augmented Language Models.** *Aaron Parisi et al. arXiv.* [[paper](https://arxiv.org/abs/2205.12255)]
- [2022/05] **MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning.** *Ehud Karpas et al. arXiv.* [[paper](https://arxiv.org/abs/2205.00445)]
- [2022/04] **Do As I Can, Not As I Say: Grounding Language in Robotic Affordances.** *Michael Ahn et al. arXiv.* [[paper](https://arxiv.org/abs/2204.01691)] 
- [2021/12] **WebGPT: Browser-assisted question-answering with human feedback.** *Reiichiro Nakano et al. arXiv.* [[paper](https://arxiv.org/abs/2112.09332)]
- [2021/07] **Evaluating Large Language Models Trained on Code.** *Mark Chen et al. arXiv.* [[paper](https://arxiv.org/abs/2107.03374)] [[code](https://github.com/openai/human-eval)]



## Appendix
- Examples of Ability Implementations
- Glossary of Ability-Related Terms