Memind
Self-evolving cognitive memory and context engine for AI agents in Java. Empowering 24/7 proactive agents like OpenClaw with understanding and SOTA performance.
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What is Memind?
Memind is the first Java-native state-of-the-art memory and context engine for AI agents. It provides self-evolving cognitive memory that enables AI agents to remember, learn, and get smarter with each interaction. Built natively in Java, it serves as a context engine empowering 24/7 proactive agents like OpenClaw with understanding capabilities.
The tool offers progressive learning capabilities where agents achieve 25-60% memory reuse compared to 0% in traditional RAG systems. It provides session persistence that remembers across conversations, domain expertise building over time, and intelligent research with proactive information gathering. The system includes auto-compacting 3-tier memory with hybrid search and supports cross-session cognitive memory.
Memind is designed for developers building AI agents in Java who need persistent memory that survives across sessions, projects, and machines. It is ideal for teams building terminal agents, browser agents, MCP clients, and custom workflows. The tool is particularly valuable for enterprise applications requiring agents that maintain context and learn progressively.
Key integrations include support for OpenClaw, PI, Hermes, and NemoClaw agents. It works with Claude Code through plugins that persist memory across sessions using auto-memory bridging and context-compaction detection. The memory runtime is universal and cross-session, remembering decisions, patterns, and context.
Memind pricing
Pricing model: Freemium
Open source and free to use. Available on GitHub under the openmemind organization. No paid tiers mentioned - entirely community-driven open source project.
Memind pros
- First Java-native SOTA memory engine for AI agents
- Self-evolving cognitive memory that improves over time
- 25-60% memory reuse vs 0% traditional RAG
- 25x faster response speed with memory reuse
- Persistent memory across sessions, projects, and machines
- Auto-compacting 3-tier memory architecture
- Hybrid search for efficient memory retrieval
- Cross-session cognitive memory support
- Works with OpenClaw, PI, Hermes, and NemoClaw
- Claude Code plugin integration available
- Progressive learning with each interaction
- Session persistence remembers across conversations
- Domain expertise builds over time
- Proactive information gathering with tools
- Open source on GitHub
- Universal memory runtime
- Time-based enrichment via hooks
- Context-compaction detection
Memind cons
- Java-only implementation limits language flexibility
- Requires ChromaDB server setup
- Open source with limited enterprise support
- Documentation may be incomplete
- Best suited for Java developers only
- MCP score of 5.3/10 indicates moderate safety concerns
- Requires AWS Bedrock access for some features
- Configuration complexity for production deployment
Frequently asked questions about Memind
What is Memind?
Memind is the first Java-native state-of-the-art memory and context engine for AI agents. It provides self-evolving cognitive memory that enables AI agents to remember, learn, and get smarter with each interaction.
What programming language is Memind built in?
Memind is built natively in Java, making it the first Java-native SOTA memory and context engine for AI agents.
How does Memind compare to traditional RAG?
Memind achieves 25-60% memory reuse compared to 0% in traditional RAG systems. It also provides 25x faster response speed with memory reuse and progressive improvement versus no learning in traditional RAG.
Does Memind persist memory across sessions?
Yes, Memind provides persistent memory that survives across sessions, projects, and machines. It remembers decisions, patterns, and context across coding sessions.
What agents does Memind support?
Memind works with OpenClaw, PI, Hermes, and NemoClaw. It connects to terminal agents, browser agents, MCP clients, and custom workflows.
Is Memind open source?
Yes, Memind is an open source project available on GitHub under the openmemind organization.
What memory architecture does Memind use?
Memind uses auto-compacting 3-tier memory with hybrid search for efficient memory retrieval and management.
Can I use Memind with Claude Code?
Yes, there is a memind-memory plugin available through Claude Code Hooks Plugin that persists memory across Claude Code sessions using auto-memory bridging and context-compaction detection.
What prerequisites are needed to run Memind?
Requirements include Python 3.8+, ChromaDB Server for cognitive-memory setup, and optionally AWS Bedrock access configured for Claude and Titan models.
How does Memind enable progressive learning?
Memind enables progressive learning by allowing agents to remember, learn, and get smarter with each interaction. It achieves 25-60% memory reuse and builds domain expertise over time through persistent session memory.