Reflection Agents

In this video, we will show you how to build three reflection style agents using LangGraph, an open-source framework for building stateful, multi-actor AI applications. Reflection is a prompting strategy used to improve the quality and success rate of agents and similar AI systems. It involves prompting an LLM to reflect on and critique its past actions, sometimes incorporating additional external information such as tool observations. ⏰ *Timestamps* ----------- 00:00 What is reflection? 00:48 Basic reflection Example 04:59 Reflexion 10:25 Language Agent Tree Search (LATS) 12:26 Choosing candidate node in LATS 15:42 Candidate Generation Node in LATS 17:15 Example run of LATS 17:34 Reviewing the run in LangSmith 19:34 Conclusion 🔗 * Links* ----------- 🤔 *Simple Reflection* - Python: https://github.com/langchain-ai/langgraph/blob/main/examples/reflection/reflection.ipynb 🧠 *Reflexion* - Python: https://github.com/langchain-ai/langgraph/blob/main/examples/reflexion/reflexion.ipynb - Paper: https://arxiv.org/abs/2303.11366 🌲 *Language Agents Tree Search* - Python: https://github.com/langchain-ai/langgraph/blob/main/examples/lats/lats.ipynb - Paper: https://arxiv.org/abs/2310.04406 Blog: https://blog.langchain.dev/reflection-agents/ Developing AI applications is easier with LangSmith. Create a free account at https://smith.langchain.com/ #ai #artificialintelligence #langchain #nlp #langgraph #agents #search