agent-memory Tools
5 best agent-memory tools and apps, curated and ranked by community upvotes on ProductCool. Updated daily as new agent-memory products launch.
Supermemory is a fast, scalable memory and context engine designed for the AI era. It solves the problem of providing AI agents with persistent, low-latency access to user data, conversation history, and application state. It's built for developers and teams who need a reliable, locally-runnable memory infrastructure for their AI-powered tools and agents.
This is an open-source book titled 'Deep Understanding of AI Agent: Design Principles and Engineering Practice' by Li Bojie. It provides a systematic guide to the core concepts, design principles, and practical engineering techniques for creating effective AI agents. The repository contains the full book text, compiled PDFs, and chapter-by-chapter example code. It is designed for developers, engineers, and researchers who want to move beyond simple AI models and learn how to architect and implement sophisticated, autonomous agent systems.
OpenViking is a self-evolving context database that unifies agent memory, knowledge retrieval (RAG), and executable skills into a single system. It solves the problem of fragmented and stateless AI agents by providing persistent, learnable context that grows with use. It's built for developers creating advanced autonomous agents, AI assistants, and long-running AI applications that require coherent memory and adaptive capabilities.
MiroFish is a simple yet powerful swarm intelligence engine designed to model complex systems and predict outcomes. It solves the problem of making accurate predictions in dynamic, multi-agent environments by simulating collective behaviors. This tool is ideal for researchers, data scientists, and developers working on optimization, forecasting, and simulation projects where traditional models fall short.
Semantica is an open-source, graph-native infrastructure that provides AI systems with structured context, causal reasoning, and full decision provenance. It solves critical production AI failures like data silos and black-box decisions by making AI explainable, traceable, and accountable by design. It's built for developers and enterprises using AI agents, LangGraph, CrewAI, LlamaIndex, and other frameworks who need trust and auditability in production.