Waku Memory provides portable, persistent memory infrastructure for AI agents, enabling them to retain and recall context across different applications and harnesses. The platform offers a unified store that decouples agent memory from any single vendor or tool, ensuring data portability and interoperability. It is designed for developers building AI systems that require long-term, cross-application memory capabilities.
Funding
Funding not disclosed
Founders
Product
Problem
AI agents often operate with limited context windows and lack persistent memory, forcing them to lose information between sessions and preventing them from building on past interactions. This fragmentation limits their ability to provide personalized, continuous assistance and requires developers to build complex, bespoke memory solutions for each application.
Solution
Waku Memory provides a portable, unified memory layer for AI agents, allowing them to store, retrieve, and manage information across different sessions and applications. The platform acts as a single, vendor-neutral store that decouples agent memory from any specific AI model or harness, ensuring data remains accessible and controllable. By offering a standardized API, Waku Memory enables developers to integrate persistent memory into their agents with minimal effort, allowing for more coherent and context-aware interactions. This approach simplifies the development of AI systems that can learn from past interactions and maintain a continuous understanding of user preferences and tasks.
Target Audience
Primary customers are developers and engineering teams building AI agents, chatbots, and autonomous systems that require persistent, cross-session memory to deliver personalized and contextually aware user experiences.
Features
- Unified memory store that consolidates agent data from multiple sources into a single, queryable interface
- Vendor-neutral architecture that prevents lock-in and ensures compatibility with various AI models and frameworks
- Standardized API for simple integration, allowing developers to add persistent memory capabilities with minimal code
- Support for cross-application memory, enabling agents to share context and learnings across different tools and platforms
- Designed for scalability to handle growing volumes of agent-generated data without performance degradation