Mind-Mem: A Persistent Memory System for AI Agents

Mind-Mem is a production-ready persistent memory system designed specifically for MCP-compatible agents such as Claude Code and OpenClaw. It aims to address the core pain point of current large language models (LLMs) lacking long-term memory. Its key features include: using BM25F + vector hybrid retrieval technology to balance precise matching and semantic understanding; built-in governance awareness mechanism to ensure data security and compliance; support for 84 MCP tools and 15 client ecosystems; and a reliable storage base built on PostgreSQL. Currently iterated to version v4.0.5, it has over 5155 test cases, providing continuous learning and long-term collaboration capabilities for AI agents.

Although current LLMs have strong reasoning and generation capabilities, they have the 'goldfish-like' memory limitation—forgetting historical content after the conversation ends, which severely restricts the application depth in complex task scenarios. For example, AI assistants cannot continuously remember user identities, task progress, and other information, leading to low collaboration efficiency. The Mind-Mem project was born to solve this pain point, focusing on providing a fully functional persistent memory solution for MCP-compatible agents.

Hybrid Retrieval Strategy

• BM25F: A classic term frequency weighting algorithm, good at precise matching of keywords/entities, with high computational efficiency and strong interpretability;
• Vector Retrieval: Captures semantic similarity, supports synonyms, fuzzy queries, and cross-language retrieval;
• Hybrid Fusion: Combines the advantages of both through weight allocation to balance precision and semantic understanding.

Governance Awareness Mechanism

Includes data classification and grading, audit tracking, lifecycle management, and privacy desensitization processing to meet enterprise-level compliance requirements.

MCP Ecosystem Integration

Provides standardized memory operation interfaces (store/retrieve/update/forget) through the MCP protocol, and integrates 84 MCP tools to achieve fine-grained control.

Storage Base

Built on PostgreSQL, leveraging its reliability, scalability (pgvector/full-text search extensions), transaction support, and mature operation and maintenance solutions.

Key Application Scenarios

• Long-term project collaboration: Maintain cross-session context continuity;
• Personalized services: Precipitate user preferences and feedback;
• Knowledge base construction: Organize valuable information from interactions;
• Multi-session task tracking: Record progress and to-do items;
• Team collaboration enhancement: Share consistent project status.

Open Source Ecosystem Collaboration

As part of the OpenClaw ecosystem, it forms a complementary toolchain with Claude Code (intelligent coding) and OpenClaw (agent framework), avoiding vendor lock-in and supporting flexible combinations.

Version Evolution

• v4.0.3: Fixed stability issues with the PG-backed recall pipeline;
• v4.0.5: Optimized document and badge alignment, and achieved idempotency of the release workflow.

Core Conclusion

Mind-Mem endows AI agents with long-term memory capabilities, upgrading them from one-time conversation tools to continuous learning collaboration partners. As AI becomes popular in complex scenarios, persistent memory will become an essential infrastructure. With its mature architecture, rich functions, and active ecosystem, Mind-Mem is a production-grade memory solution worth adopting by developers.