Memory Interchange Format (MIF)

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

pip install mif-tools
mif 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.

{
  "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

# Python
pip install mif-tools              # core (zero dependencies)
pip install mif-tools[validate]    # with JSON Schema validation
pip install mif-tools[mcp]         # with MCP server

# Node.js / TypeScript
npm install @varunshodh/mif-tools

Convert Between Formats

# mem0 → MIF
mif 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 format
mif convert any_memory_file.json -o output.mif.json

# Inspect any memory file
mif inspect memories.json

# Validate MIF document
mif validate memories.mif.json

Python API

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 line
markdown = convert(data, from_format="mem0", to_format="markdown")

# Create memories from scratch
doc = 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_deep
ok, warnings = validate_deep(open("export.mif.json").read())

Add MIF to Your MCP Server (10 lines)

from mif import load, dump

# Export handler
def export_memories(user_id: str) -> str:
    memories = my_storage.get_all(user_id)
    return dump(memories)

# Import handler — auto-detects mem0, markdown, generic JSON, MIF
def 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

| Format | ID | Auto-detect | Description | |--------|----|-------------|-------------| | MIF v2 | shodh | "mif_version" in JSON | Native format, lossless round-trip | | mem0 | mem0 | JSON array with "memory" field | mem0 memory exports | | CrewAI | crewai | JSON array with "task_description" | CrewAI LTMSQLiteStorage exports | | LangChain | langchain | JSON array with "namespace" + "value" | LangChain/LangMem Item format | | Generic JSON | generic | JSON array with "content" field | Any JSON memory array | | Markdown | markdown | Starts 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:

pip install mif-tools[mcp]
mif mcp

Tools: export_memories, import_memories, validate_memories, inspect_memories, list_formats

Adapters & Implementations

| System | Status | Type | |--------|--------|------| | shodh-memory | Production | Built-in HTTP API (/api/export/mif, /api/import/mif) | | mif-tools (PyPI) | Production | Python package with CLI + MCP server | | @varunshodh/mif-tools (npm) | Production | TypeScript/Node.js package with CLI | | mem0 | Adapter ready | Python + npm | | CrewAI | Adapter ready | Python + npm | | LangChain | Adapter ready | Python + npm | | Generic JSON | Adapter ready | Python + npm | | Markdown (YAML frontmatter) | Adapter ready | Python + 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

• Documentation — Full docs site
• MCP SEP #2342 — Original proposal to the Model Context Protocol
• tower-mcp #531 — Tracking issue in tower-mcp
• shodh-memory — Reference implementation (Rust)
• mif-tools on PyPI — Python package
• @varunshodh/mif-tools on npm — npm package

License

Apache 2.0