memind is queued for analysis. Crawling candidates and ranking them takes a few minutes. This page updates itself when the list exists — you can also come back later.
What is known so far comes from the other direction:
These projects were analysed and named memind among their alternatives. The relationship is not symmetric — how memind rates them is a separate judgement, made when memind is analysed in its own right.
calls memind “if you are building Java agents and want a JVM-native self-evolving memory engine.”
TencentDB Agent Memory is a team-level memory hub for AI Agents — turning conversations, docs, and code into four reusable memory assets (Chat Memory, Skill, LLM-Wiki, Code-Graph) that are governed, shared, and equipped across agents and frameworks.
calls memind “Choose if you need a Java-native memory engine for JVM-based agent stacks.”
Memori is agent-native memory infrastructure. A LLM-agnostic layer that turns agent execution and conversation into structured, persistent state for production systems. Built for enterprise, Memori works with the data infrastructure you already run, no rip-and-replace, and deploys across managed cloud, single-tenant cloud, VPC, and on-premises.
calls memind “JVM-based cognitive memory engine if you need Java ecosystem integration.”
Self-evolving memory OS for LLM & AI Agents: ultra-persistent memory, hybrid-retrieval, and cross-task skill reuse, with 35.24% token savings and DeepSeek Harness support.
calls memind “choose when you need a JVM-native (Java) memory engine for agents.”
Self-evolving Context Database for AI Agents. Unify Agent Memory, Knowledge RAG and Skills.
calls memind “Choose it if you need a Java-based memory engine for JVM agent ecosystems, avoiding Python entirely.”
Cognee is the open-source AI memory platform for agents. Give your AI agents persistent long-term memory across sessions with a self-hosted knowledge graph engine.
calls memind “Use it if your backend is JVM/Java and you need a native self-evolving memory engine.”
Memory and context engine + app that is extremely fast, scalable, and can be run fully locally. The Memory API for the AI era.
calls memind “Use if you need a Java-based memory and context engine for JVM agents, not a TypeScript solution.”
#1 Persistent memory for AI coding agents based on real-world benchmarks
calls memind “Choose if you need a Java-native self-evolving memory engine for JVM-based agents instead of Hindsight's Python.”
Hindsight: Agent Memory That Learns
calls memind “choose it when you are building agents in Java and want a JVM-native memory engine.”
One portable memory layer for every AI agent: local-first, Markdown-native, user-owned, and self-evolving across apps, tools, and workflows.
calls memind “Choose when your agents run on the JVM and you need an in-process Java memory engine rather than calling a Python service.”
Memory library for building stateful agents