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@@ -487,6 +487,7 @@ The following notebooks implement agent architectures prototypical of the "plan-
 
 When output quality is a major concern, it's common to incorporate some combination of self-critique or reflection and external validation to refine your system's outputs. The following examples demonstrate research that implement this type of design.
 
+- [Basic Reflection](./examples/reflection/reflection.ipynb): add a simple "reflect" step in your graph to prompt your system to revise its outputs.
 - [Reflexion](./examples/reflexion/reflexion.ipynb): critique missing and superflous aspects of the agent's response to guide subsequent steps. Based on [Reflexion](https://arxiv.org/abs/2303.11366), by Shinn, et. al.
 - [Language Agent Tree Search](./examples/lats/lats.ipynb): execute multiple agents in parallel, using reflection and environmental rewards to drive a Monte Carlo Tree Search. Based on [LATS](https://arxiv.org/abs/2310.04406/LanguageAgentTreeSearch/), by Zhou, et. al.