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AI long-term memory MCP server with importance scoring and confidence calibration
AI long-term memory MCP server with importance scoring and confidence calibration
Valid MCP server (2 strong, 1 medium validity signals). No known CVEs in dependencies. Imported from the Official MCP Registry. 1 finding(s) downgraded by scanner intelligence.
8 files analyzed · 1 issue found
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Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-woshilaohei-mindcore-memory": {
"args": [
"mindcore-memory"
],
"command": "uvx"
}
}
}From the project's GitHub README.
让 AI 记住一切,不再遗忘。生产级长期记忆 MCP Server。
"The best AI agent isn't the smartest — it's the one that remembers."
MindCore Memory 解决 AI Agent 最大痛点:上下文窗口有限、长对话信息丢失、跨session记忆断裂。
| 痛点 | 现状 | MindCore Memory |
|---|---|---|
| AI 上下文忘性大 | 对话结束什么都忘 | ✅ 持久化长期记忆 |
| 跨session无法回忆 | 每次都重新教 | ✅ 跨会话知识复用 |
| 记忆混乱无优先级 | 所有记忆权重一样 | ✅ 重要性分级+置信度 |
| RAG暴力灌入 | 上下文过载质量下降 | ✅ 精准上下文窗口 |
# 1. 安装
pip install mindcore-memory
# 2. 启动 MCP Server
mindcore-memory
# 3. 在你的 AI Agent 中调用
memory_id = memory_store("用户说他叫张三,周三有空")
context = memory_recall("用户的时间安排")
✅ Storage Integrity: 100% (存储持久化正确)
✅ Recall Relevance: 100% (相关记忆优先召回)
✅ Confidence Calibration: 100% (置信度正确校准)
✅ Importance Weighting: 100% (高优先级记忆排名靠前)
✅ Context Efficiency: 100% (上下文窗口不过载)
Overall Score: 100%
memory_store - 存储记忆memory_store(
content="Python是荷兰人Guido van Rossum创建的",
importance=3, # 1-4级重要性
tags=["python", "history"],
confidence=0.95, # 置信度
source="agent" # agent/user/tool
)
memory_recall - 召回记忆memory_recall(
query="Python创始人是谁",
tags=["python"], # 可选标签过滤
limit=10 # 返回数量
)
memory_context - 构建上下文窗口# 为当前任务构建最优上下文(自动去重+优先级排序)
context = memory_context(
query="当前项目状态",
max_tokens=2000 # 自动截断
)
memory_stats - 系统状态# 查看记忆统计:总数/分布/置信度
stats = memory_stats()
本项目为开源项目(MIT License),代码完全免费。存储层使用本地 JSON 文件,无云服务依赖,无数据收集。如需商业合作或定制开发,欢迎联系作者。
如果 MindCore Memory 对你有帮助:
mindcore-memory-mcp/
├── mindcore_memory/ # Python 包(pip install 入口)
│ ├── __init__.py
│ ├── memory_engine.py # 核心记忆引擎
│ ├── server.py # MCP Server(stdio+HTTP双传输)
│ ├── http_app.py # HTTP端点(生产部署)
│ └── eval_framework.py # 评测框架
├── tests/
│ └── test_memory.py # 单元测试
├── examples/
│ └── basic_usage.py # 使用示例
├── .github/workflows/
│ └── ci.yml # CI/CD
├── pyproject.toml
├── README.md
└── LICENSE
{
"mcpServers": {
"mindcore-memory": {
"command": "pip",
"args": ["install", "mindcore-memory"]
}
}
}
直接在扩展市场搜索 MindCore Memory。
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}'
| 标准 | 实现 |
|---|---|
| JSON-RPC 2.0 | ✅ stdio + HTTP 双传输 |
| Bearer Token认证 | ✅ HTTP端点可选认证 |
| 输入验证 | ✅ Pydantic schemas |
| CI/CD | ✅ GitHub Actions |
| 单元测试 | ✅ pytest + 覆盖率 |
| Eval Framework | ✅ 5项核心指标 |
| 可观测性 | ✅ structlog完整日志 |
| 用户数据主权 | ✅ JSONL本地文件,无vendor lock-in |
欢迎提交 Issue 和 PR!
MIT License - 详见 LICENSE
/---
本仓库不只是 MCP Server - 它是 MindCore 心智系统的代码实现。
核心公式:轨迹 = 边界 = 进化 = 认知 = 边界(闭环)
| 文档 | 内容 |
|---|---|
| 设计蓝图总览 | MindCore 完整设计文档索引 |
| MindCore 四层记忆 | L0-L3 四层渐进式记忆完整设计 |
| 小脑进化引擎 | 五锚点并行 + 写垄断 + 出口审计 |
| 三元平衡理论与DEA算法 | 正反演化算法框架与学术理论 |
| VSOS 安全系统 | 24维安全坐标系 |
| 硬件专利 | 算力熔断与内存隔离专利 |
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