floop Feedback Loop

Example Use Cases

• 1

  Learn and retain corrections across sessions to improve behavior.

• 2

  Context-aware activation of behaviors based on file type, task, and semantic relevance.

• 3

  Scalable, vector-accelerated retrieval of relevant behaviors using a graph-based memory model.

Description

floop captures corrections given to AI agents and converts them into durable behaviors stored in a graph structure. It uses spreading activation and vector-accelerated retrieval to contextually activate relevant behaviors, allowing the agent to improve over time rather than starting fresh each session. The tool integrates with any AI supporting the Model Context Protocol and offers CLI commands for managing the behavior store and server.