Compare the Top AI Memory Layers that integrate with Cursor as of September 2025

This a list of AI Memory Layers that integrate with Cursor. Use the filters on the left to add additional filters for products that have integrations with Cursor. View the products that work with Cursor in the table below.

What are AI Memory Layers for Cursor?

AI memory layers refer to specialized components within artificial intelligence architectures that store and retrieve contextual information to improve decision-making and learning. These layers enable models to remember past interactions, patterns, or data points, enhancing continuity and relevance in tasks like natural language processing or reinforcement learning. By incorporating memory layers, AI systems can better handle complex sequences, adapt to new inputs, and maintain state over longer durations. Memory layers can be implemented using techniques such as attention mechanisms, recurrent networks, or external memory modules. This capability is crucial for building more sophisticated, human-like AI that can learn from experience and context over time. Compare and read user reviews of the best AI Memory Layers for Cursor currently available using the table below. This list is updated regularly.

  • 1
    Cognee

    Cognee

    Cognee

    ​Cognee is an open source AI memory engine that transforms raw data into structured knowledge graphs, enhancing the accuracy and contextual understanding of AI agents. It supports various data types, including unstructured text, media files, PDFs, and tables, and integrates seamlessly with several data sources. Cognee employs modular ECL pipelines to process and organize data, enabling AI agents to retrieve relevant information efficiently. It is compatible with vector and graph databases and supports LLM frameworks like OpenAI, LlamaIndex, and LangChain. Key features include customizable storage options, RDF-based ontologies for smart data structuring, and the ability to run on-premises, ensuring data privacy and compliance. Cognee's distributed system is scalable, capable of handling large volumes of data, and is designed to reduce AI hallucinations by providing AI agents with a coherent and interconnected data landscape.
    Starting Price: $25 per month
  • 2
    ByteRover

    ByteRover

    ByteRover

    ByteRover is a self-improving memory layer for AI coding agents that unifies the creation, retrieval, and sharing of “vibe-coding” memories across projects and teams. Designed for dynamic AI-assisted development, it integrates into any AI IDE via the Memory Compatibility Protocol (MCP) extension, enabling agents to automatically save and recall context without altering existing workflows. It provides instant IDE integration, automated memory auto-save and recall, intuitive memory management (create, edit, delete, and prioritize memories), and team-wide intelligence sharing to enforce consistent coding standards. These capabilities let developer teams of all sizes maximize AI coding efficiency, eliminate repetitive training, and maintain a centralized, searchable memory store. Install ByteRover’s extension in your IDE to start capturing and leveraging agent memory across projects in seconds.
    Starting Price: $19.99 per month
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