mindcore-memory 0.1.6

MCP Server for AI long-term memory and context management

MindCore Memory MCP

mcp-name: io.github.woshilaohei/mindcore-memory

AI Long-Term Memory Server — Production-grade persistent memory for AI agents.

"The best AI agent isn't the smartest — it's the one that remembers."

Value Proposition

MindCore Memory solves AI Agent's biggest pain point: limited context windows, lost information in long conversations, and broken cross-session memory continuity.

What Problem It Solves

Pain Point	Status Quo	MindCore Memory
AI forgets everything	Conversation ends, all lost	Persistent long-term memory
No cross-session recall	Re-teach every session	Cross-session knowledge reuse
Memory chaos, no priority	All memories weighted equally	Importance grading + confidence
RAG brute-force injection	Context overload, quality drops	Precise context window

Quick Start (3 lines)

# 1. Install
pip install mindcore-memory

# 2. Launch MCP Server
mindcore-memory

# 3. Call from your AI Agent
memory_id = memory_store("User says his name is Zhang San, free on Wednesday")
context = memory_recall("User's schedule")

Eval Framework Results

Storage Integrity:     100% (data persistence correct)
Recall Relevance:      100% (relevant memories recalled first)
Confidence Calibration: 100% (confidence correctly calibrated)
Importance Weighting:   100% (high-priority memories ranked higher)
Context Efficiency:    100% (context window not overloaded)

Overall Score: 100%

Core Tools

memory_store - Store memory

memory_store(
    content="Python was created by Guido van Rossum from Netherlands",
    importance=3,        # 1-4 importance level
    tags=["python", "history"],
    confidence=0.95,      # confidence score
    source="agent"       # agent/user/tool
)

memory_recall - Recall memory

memory_recall(
    query="Who created Python",
    tags=["python"],      # optional tag filter
    limit=10             # return count
)

memory_context - Build context window

# Build optimal context for current task (auto-dedup + priority sort)
context = memory_context(
    query="Current project status",
    max_tokens=2000      # auto-truncate
)

MCP Server Setup

Add to your MCP client configuration:

{
  "mcpServers": {
    "mindcore-memory": {
      "command": "python",
      "args": ["-m", "mindcore_memory.server"]
    }
  }
}

License

MIT License - Copyright (c) 2025 Lao Hei

Links

• GitHub: https://github.com/woshilaohei/mindcore-memory-mcp
• PyPI: https://pypi.org/project/mindcore-memory/
• MCP Registry: https://registry.modelcontextprotocol.io/servers/io.github.woshilaohei/mindcore-memory