Large Language Model Agents have taken over LLM and Artificial Intelligence application design by storm, so this time we check out and simplify six main concepts and five popular papers documenting ways to set up language model based agents, as well as directly testing examples.

Resources -
@LangChain Agent Tutorials & Code -    • LangGraph (Python)  
Overview on LLM Agents -    • AI Agents! Giving Reasoning and Tools...  

Link to Miro Board PDF: https://drive.google.com/file/d/1ESnr...

Papers:
Reflexion Paper: https://arxiv.org/abs/2303.11366
LATs Paper: https://arxiv.org/abs/2310.04406
Plan-And-Execute Paper: https://arxiv.org/abs/2305.04091
ReWOO Paper: https://arxiv.org/abs/2305.18323
LLMCompiler Paper: https://arxiv.org/abs/2312.04511

LangSmith Traces:
Basic Reflection: https://smith.langchain.com/public/60...
Reflexion: https://smith.langchain.com/public/83...
LATs: https://smith.langchain.com/public/d4...
Plan-And-Execute: https://smith.langchain.com/public/c1...
ReWOO: https://smith.langchain.com/public/ee...
LLMCompiler: https://smith.langchain.com/public/28...

Chapters:
00:00 - Intro
01:08 - Basic Reflection
02:44 - Basic Reflection Testing
06:32 - Reflexion Actor
09:57 - Reflexion Action Testing
12:25 - Language Agent Tree Search (LATs)
17:04 - LATs Testing
20:54 - Plan And Execute
23:38 - Plan And Execute Testing
26:28 - Reasoning Without Observation (ReWOO)
29:26 - ReWOO Testing
31:11 - LLMCompiler
35:19 - LLMCompiler Testing
36:05 - Outro