Mif Tools

io.github.varun29ankuS由 GitHub 账号 varun29ankuS 发布v0.2.1更新于 Oct 24, 2026

Convert, validate, and inspect AI agent memories across formats

已验证STDIO仅桌面知识与记忆AI 与机器学习

概览

AI 生成的概览

本地 MCP 服务器,让助手以厂商中立的 MIF 格式导出、导入、校验和检查 AI 智能体记忆。

可以做什么
将记忆导出为 MIF JSON 封装格式,并可再导入,自动识别 mem0、CrewAI、LangChain、通用 JSON 和 Markdown 格式。校验记忆文档,包括对 UUID、引用、时间戳和嵌入维度的深度检查。检查记忆文件并列出支持的格式。往返转换时保留记忆类型、标签、实体、嵌入、知识图谱和厂商扩展等可选数据。
适用场景
适合在不同系统或服务商之间迁移智能体记忆,或在迁移前让助手检查和审阅记忆导出文件。
运行要求
以本地进程通过 stdio 运行;需安装带 MCP 扩展的 Python 包,并用 mif mcp 命令启动。未声明需要账号、API 密钥或环境变量。
安装前请注意
记忆文件可能包含个人或敏感内容,该格式提到 PII 检测与脱敏标记。导入操作会向目标存储写入记忆,迁移前应先审阅导出内容。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Mif Tools,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。

其他 MCP 客户端

参照 仓库 中的启动说明。

README

Memory Interchange Format (MIF)

[PyPI] [npm] [License] [Tests] [Docs]

Your AI agent has 6 months of memories in System A. You want to try System B. Without MIF, you lose everything. With MIF:

bash
pip install mif-toolsmif convert mem0_export.json --to shodh -o memories.mif.json

Done. Your memories are portable.

What is MIF?

A vendor-neutral JSON envelope for AI agent memories. Like vCard for contacts or iCalendar for events — a minimal schema so memories move between providers without data loss.

3 required fields. That's it.

json
{  "mif_version": "2.0",  "memories": [    {      "id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",      "content": "User prefers dark mode across all applications",      "created_at": "2026-01-15T10:30:00Z"    }  ]}

Everything else — memory types, tags, entities, embeddings, knowledge graph, vendor extensions — is optional. Add what you have, ignore what you don't.

Install

bash
# Pythonpip install mif-tools              # core (zero dependencies)pip install mif-tools[validate]    # with JSON Schema validationpip install mif-tools[mcp]         # with MCP server
# Node.js / TypeScriptnpm install @varunshodh/mif-tools

Convert Between Formats

bash
# mem0 → MIFmif convert mem0_export.json --from mem0 -o memories.mif.json
# MIF → Markdown (Obsidian/Letta style)mif convert memories.mif.json --to markdown -o memories.md
# Auto-detect source formatmif convert any_memory_file.json -o output.mif.json
# Inspect any memory filemif inspect memories.json
# Validate MIF documentmif validate memories.mif.json

Python API

python
from mif import load, dump, convert, MifDocument, Memory
# Load from any format (auto-detects mem0, markdown, generic JSON, MIF)doc = load(open("mem0_export.json").read())print(f"{len(doc.memories)} memories loaded")
# Convert between formats in one linemarkdown = convert(data, from_format="mem0", to_format="markdown")
# Create memories from scratchdoc = MifDocument(memories=[    Memory(        id="123e4567-e89b-12d3-a456-426614174000",        content="User prefers dark mode",        created_at="2026-01-15T10:30:00Z",        memory_type="observation",        tags=["preferences", "ui"],    )])print(dump(doc))  # MIF v2 JSON
# Deep validation (UUIDs, references, timestamps, embedding dimensions)from mif import validate_deepok, warnings = validate_deep(open("export.mif.json").read())

Add MIF to Your MCP Server (10 lines)

python
from mif import load, dump
# Export handlerdef export_memories(user_id: str) -> str:    memories = my_storage.get_all(user_id)    return dump(memories)
# Import handler — auto-detects mem0, markdown, generic JSON, MIFdef import_memories(data: str) -> dict:    doc = load(data)    for mem in doc.memories:        my_storage.save(mem.id, mem.content, mem.created_at)    return {"memories_imported": len(doc.memories)}

Supported Formats

FormatIDAuto-detectDescription
MIF v2shodh"mif_version" in JSONNative format, lossless round-trip
mem0mem0JSON array with "memory" fieldmem0 memory exports
CrewAIcrewaiJSON array with "task_description"CrewAI LTMSQLiteStorage exports
LangChainlangchainJSON array with "namespace" + "value"LangChain/LangMem Item format
Generic JSONgenericJSON array with "content" fieldAny JSON memory array
MarkdownmarkdownStarts with ---YAML frontmatter (Letta/Obsidian style)

Full Spec

MIF supports optional fields for rich memory data:

  • Memory types — observation, decision, learning, error, context, conversation, and custom types
  • Entity references — named entities with type and confidence
  • Embeddings — model name, dimensions, vector (reuse or regenerate)
  • Knowledge graph — entities and relationships with confidence scores
  • Vendor extensions — system-specific metadata preserved on round-trip
  • Privacy — PII detection and redaction markers

Full specification: spec/mif-v2.md | JSON Schema: schema/mif-v2.schema.json

MCP Server

Expose MIF tools to any MCP-compatible AI client:

bash
pip install mif-tools[mcp]mif mcp

Tools: export_memories, import_memories, validate_memories, inspect_memories, list_formats

Adapters & Implementations

SystemStatusType
shodh-memoryProductionBuilt-in HTTP API (/api/export/mif, /api/import/mif)
mif-tools (PyPI)ProductionPython package with CLI + MCP server
@varunshodh/mif-tools (npm)ProductionTypeScript/Node.js package with CLI
mem0Adapter readyPython + npm
CrewAIAdapter readyPython + npm
LangChainAdapter readyPython + npm
Generic JSONAdapter readyPython + npm
Markdown (YAML frontmatter)Adapter readyPython + npm

Design Principles

  1. Minimal — 3 required fields. Everything else is optional.
  2. Extensible — Unknown fields and vendor extensions MUST be preserved on round-trip.
  3. Vendor-neutral — The schema doesn't favor any implementation.
  4. Forward-compatible — Importers MUST ignore unknown fields.

Contributing

We welcome adapter implementations for any memory system. See CONTRIBUTING.md.

Related

License

Apache 2.0

来源:README.md,提交 2162e8e

工具

0
SourceWeft 尚未在沙箱中启动过该软件包,因此其工具未知。

版本历史

1
  1. v0.2.1最新Sep 16, 2026

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