| # Memory Integration: Enabling Your Agent to Remember and Learn | |
| ## Introduction | |
| - Importance of Memory Integration in AI Agents | |
| - Overview of Memory Mechanisms in AutoGPT | |
| ## Section 1: Understanding Memory Integration | |
| - Concept of Memory in AI Agents | |
| - Types of Memory: Short-term vs. Long-term | |
| ## Section 2: Implementing Memory in Your Agent | |
| - Setting up Memory Structures in the Forge Environment | |
| - Utilizing Agent Protocol for Memory Integration | |
| ## Section 3: Developing Learning Mechanisms | |
| - Creating Learning Algorithms for Your Agent | |
| - Implementing Learning Mechanisms using Task and Artifact Schemas | |
| ## Section 4: Testing and Optimizing Memory Integration | |
| - Employing AGBenchmark for Memory Testing | |
| - Optimizing Memory for Enhanced Performance and Efficiency | |
| ## Section 5: Best Practices in Memory Integration | |
| - Tips and Strategies for Effective Memory Integration | |
| - Avoiding Common Pitfalls in Memory Development | |
| ## Conclusion | |
| - Recap of the Tutorial | |
| - Future Directions in Memory Integration | |
| ## 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)] | |
| ##### Memory capability | |
| ###### Raising the length limit of Transformers | |
| - [2023/05] **Randomized Positional Encodings Boost Length Generalization of Transformers.** *Anian Ruoss (DeepMind) et al. arXiv.* [[paper](https://arxiv.org/abs/2305.16843)] [[code](https://github.com/google-deepmind/randomized_positional_encodings)] | |
| - [2023-03] **CoLT5: Faster Long-Range Transformers with Conditional Computation.** *Joshua Ainslie (Google Research) et al. arXiv.* [[paper](https://arxiv.org/abs/2303.09752)] | |
| - [2022/03] **Efficient Classification of Long Documents Using Transformers.** *Hyunji Hayley Park (Illinois University) et al. arXiv.* [[paper](https://arxiv.org/abs/2203.11258)] [[code](https://github.com/amazon-science/efficient-longdoc-classification)] | |
| - [2021/12] **LongT5: Efficient Text-To-Text Transformer for Long Sequences.** *Mandy Guo (Google Research) et al. arXiv.* [[paper](https://arxiv.org/abs/2112.07916)] [[code](https://github.com/google-research/longt5)] | |
| - [2019/10] **BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension.** *Michael Lewis(Facebook AI) et al. arXiv.* [[paper](https://arxiv.org/abs/1910.13461)] [[code](https://github.com/huggingface/transformers/tree/main/src/transformers/models/bart)] | |
| ###### Summarizing memory | |
| - [2023/08] **ExpeL: LLM Agents Are Experiential Learners.** *Andrew Zhao (Tsinghua University) et al. arXiv.* [[paper](https://arxiv.org/abs/2308.10144)] [[code]([https://github.com/thunlp/ChatEval](https://github.com/Andrewzh112/ExpeL))] | |
| - [2023/08] **ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate.** *Chi-Min Chan (Tsinghua University) et al. arXiv.* [[paper](https://arxiv.org/abs/2308.07201)] [[code](https://github.com/thunlp/ChatEval)] | |
| - [2023/05] **MemoryBank: Enhancing Large Language Models with Long-Term Memory.** *Wanjun Zhong (Harbin Institute of Technology) et al. arXiv.* [[paper](https://arxiv.org/abs/2305.10250)] [[code](https://github.com/zhongwanjun/memorybank-siliconfriend)] | |
| - [2023/04] **Generative Agents: Interactive Simulacra of Human Behavior.** *Joon Sung Park (Stanford University) et al. arXiv.* [[paper](https://arxiv.org/abs/2304.03442)] [[code](https://github.com/joonspk-research/generative_agents)] | |
| - [2023/04] **Unleashing Infinite-Length Input Capacity for Large-scale Language Models with Self-Controlled Memory System.** *Xinnian Liang(Beihang University) et al. arXiv.* [[paper](https://arxiv.org/abs/2304.13343)] [[code](https://github.com/wbbeyourself/scm4llms)] | |
| - [2023/03] **Reflexion: Language Agents with Verbal Reinforcement Learning.** *Noah Shinn (Northeastern University) et al. arXiv.* [[paper](https://arxiv.org/abs/2303.11366)] [[code](https://github.com/noahshinn024/reflexion)] | |
| - [2023/05] **RecurrentGPT: Interactive Generation of (Arbitrarily) Long Text.** Wangchunshu Zhou (AIWaves) et al. arXiv.* [[paper](https://arxiv.org/pdf/2305.13304.pdf)] [[code](https://github.com/aiwaves-cn/RecurrentGPT)] | |
| ###### Compressing memories with vectors or data structures | |
| - [2023/07] **Communicative Agents for Software Development.** *Chen Qian (Tsinghua University) et al. arXiv.* [[paper](https://arxiv.org/abs/2307.07924)] [[code](https://github.com/openbmb/chatdev)] | |
| - [2023/06] **ChatDB: Augmenting LLMs with Databases as Their Symbolic Memory.** *Chenxu Hu(Tsinghua University) et al. arXiv.* [[paper](https://arxiv.org/abs/2306.03901)] [[code](https://github.com/huchenxucs/ChatDB)] | |
| - [2023/05] **Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memory.** *Xizhou Zhu (Tsinghua University) et al. arXiv.* [[paper](https://arxiv.org/abs/2305.17144)] [[code](https://github.com/OpenGVLab/GITM)] | |
| - [2023/05] **RET-LLM: Towards a General Read-Write Memory for Large Language Models.** *Ali Modarressi (LMU Munich) et al. arXiv.* [[paper](https://arxiv.org/abs/2305.14322)] [[code](https://github.com/tloen/alpaca-lora)] | |
| - [2023/05] **RecurrentGPT: Interactive Generation of (Arbitrarily) Long Text.** Wangchunshu Zhou (AIWaves) et al. arXiv.* [[paper](https://arxiv.org/pdf/2305.13304.pdf)] [[code](https://github.com/aiwaves-cn/RecurrentGPT)] | |
| ##### Memory retrieval | |
| - [2023/08] **Memory Sandbox: Transparent and Interactive Memory Management for Conversational Agents.** *Ziheng Huang(University of California—San Diego) et al. arXiv.* [[paper](https://arxiv.org/abs/2308.01542)] | |
| - [2023/08] **AgentSims: An Open-Source Sandbox for Large Language Model Evaluation.** *Jiaju Lin (PTA Studio) et al. arXiv.* [[paper](https://arxiv.org/abs/2308.04026)] [[project page](https://www.agentsims.com/)] [[code](https://github.com/py499372727/AgentSims/)] | |
| - [2023/06] **ChatDB: Augmenting LLMs with Databases as Their Symbolic Memory.** *Chenxu Hu(Tsinghua University) et al. arXiv.* [[paper](https://arxiv.org/abs/2306.03901)] [[code](https://github.com/huchenxucs/ChatDB)] | |
| - [2023/05] **MemoryBank: Enhancing Large Language Models with Long-Term Memory.** *Wanjun Zhong (Harbin Institute of Technology) et al. arXiv.* [[paper](https://arxiv.org/abs/2305.10250)] [[code](https://github.com/zhongwanjun/memorybank-siliconfriend)] | |
| - [2023/04] **Generative Agents: Interactive Simulacra of Human Behavior.** *Joon Sung Park (Stanford) et al. arXiv.* [[paper](https://arxiv.org/abs/2304.03442)] [[code](https://github.com/joonspk-research/generative_agents)] | |
| - [2023/05] **RecurrentGPT: Interactive Generation of (Arbitrarily) Long Text.** Wangchunshu Zhou (AIWaves) et al. arXiv.* [[paper](https://arxiv.org/pdf/2305.13304.pdf)] [[code](https://github.com/aiwaves-cn/RecurrentGPT)] | |
| ## Appendix | |
| - Examples of Memory Integration Implementations | |
| - Glossary of Memory-Related Terms | |