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."

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

# 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")

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%

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

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

# View memory statistics: total/distribution/confidence
stats = memory_stats()

mindcore-memory-mcp/
├── mindcore_memory/          # Python package (pip install entry)
│   ├── __init__.py
│   ├── memory_engine.py      # Core memory engine
│   ├── server.py             # MCP Server (stdio + HTTP dual transport)
│   ├── http_app.py           # HTTP endpoint (production deploy)
│   └── eval_framework.py     # Evaluation framework
├── tests/
│   └── test_memory.py        # Unit tests
├── examples/
│   └── basic_usage.py        # Usage examples
├── pyproject.toml
├── README.md
└── LICENSE

{
  "mcpServers": {
    "mindcore-memory": {
      "command": "pip",
      "args": ["install", "mindcore-memory"]
    }
  }
}

Search MindCore Memory in the extension marketplace.

curl -X POST http://localhost:8080/mcp \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -d '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"memory_store","arguments":{"content":"test"}},"id":1}'

This project is open source (MIT License). The code is completely free. Storage uses local JSON files with no cloud service dependency and no data collection.

MIT License - see LICENSE file for details.

Give AI memory. Make humans trust AI more.

I'm actively developing AI safety architecture projects including:

1. Cerebellum Evolution Engine - AI safety & evolution framework
2. MindCore - Cognitive memory architecture for AI systems
3. Border Guard - Self-evolving security operating system
4. Ternary Balance Boundary Algorithm - Novel equilibrium theory with 3 papers
5. MindCore Memory MCP - This project: production-grade long-term memory server

Interested in collaborating? Reach out:

• Email: 1410770089@qq.com
• GitHub: @woshilaohei

Author: Lao Hei