portable-ai-memory 1.0.0

Python SDK for the Portable AI Memory (PAM) interchange format

Portable AI Memory (PAM) — Python SDK

A Python SDK for the Portable AI Memory (PAM) interchange format — a universal way to store, validate, and convert AI user memories across providers.

What is PAM?

AI assistants learn about you over time — your preferences, facts about your life, project context. But these memories are locked inside each provider. If you switch from ChatGPT to Claude, or use both, your context doesn't follow you.

PAM solves this with an open interchange format. It defines three document types:

• MemoryStore — your memories (preferences, facts, context) with integrity checksums and semantic relations
• Conversation — full chat history with messages, tool calls, citations, and attachments
• EmbeddingsFile — vector embeddings linked to memories for semantic search

This SDK lets you:

1. Convert exports from ChatGPT, Claude, Gemini, Grok, and Copilot into PAM format
2. Validate PAM documents with deep integrity checks (cross-references, temporal ordering, content hashes)
3. Build PAM documents programmatically with type-safe Pydantic models

Installation

pip install portable-ai-memory        # core SDK (models, I/O, validation, converters)
pip install 'portable-ai-memory[cli]' # + CLI tool: typer, rich (pam command)
pip install 'portable-ai-memory[dev]' # + dev tools: pytest, ruff, mypy
pip install 'portable-ai-memory[all]' # cli + dev combined

Quick Start

Load and validate a PAM file

from portable_ai_memory import load, validate_memory_store

store = load("memory-store.json")
result = validate_memory_store(store)

if result.is_valid:
    print(f"Valid — {len(store.memories)} memories")
else:
    for issue in result.errors:
        print(issue)

Convert a provider export

import json
from pathlib import Path

from portable_ai_memory.converters import detect_provider
from portable_ai_memory import ProviderNotDetectedError

try:
    converter = detect_provider("conversations.json")
    data = json.loads(Path("conversations.json").read_text())
    conversations = converter.convert_conversations(
        data, owner_id="user-123",
    )
except ProviderNotDetectedError as e:
    print(f"Unknown format: {e}")

Build a memory store from scratch

from portable_ai_memory import MemoryStore, MemoryObject, Owner, save

# MemoryObject.create() auto-fills content_hash, temporal, provenance
store = MemoryStore(
    schema_version="1.0",
    owner=Owner(id="user-123"),
    memories=[
        MemoryObject.create(
            id="mem-001",
            type="preference",
            content="User prefers dark mode.",
            platform="my-app",
        )
    ],
)
save(store, "memory-store.json")

# Convenience lookups
mem = store.get_memory_by_id("mem-001")
prefs = store.get_memories_by_type("preference")

CLI

# Validate a PAM file or bundle directory
pam validate memory-store.json
pam validate ./my-pam-bundle/

# Convert a provider export to a PAM bundle
pam convert ~/chatgpt-export/ -o ./pam-bundle/ --owner-id user-123

# Inspect a PAM file
pam inspect memory-store.json

Supported Providers

Provider	Format
OpenAI (ChatGPT)	conversations.json
Anthropic (Claude)	conversations.json + memories.json
Google (Gemini)	Takeout JSON or HTML
xAI (Grok)	prod-grok-backend.json
Microsoft (Copilot)	CSV exports

To list registered converters programmatically:

from portable_ai_memory.converters import list_converters

print(list_converters())  # ['chatgpt', 'claude', 'gemini', 'grok', 'copilot']

Development

git clone --recurse-submodules [e-mail]:portable-ai-memory/python-sdk.git
cd python-sdk
uv sync --all-extras
uv run pytest

Note: The PAM JSON Schemas live in the main portable-ai-memory repo and are included here as a git submodule under vendor/portable-ai-memory. If you cloned without --recurse-submodules, run: git submodule update --init --recursive

Links

• PAM Specification
• GitHub Repository

License

Apache License 2.0