getmnemo 0.1.0

Python SDK for Mnemo Memory — long-term memory infrastructure for AI agents.

ledgermem

Python SDK for LedgerMem Memory — long-term memory infrastructure for AI agents.

pip install ledgermem

Quickstart

from ledgermem import LedgerMem

memory = LedgerMem(api_key="lk_live_...", workspace_id="ws_...")

# Store an atomic fact
memory.add("User prefers Japanese short-grain rice for onigiri.")

# Retrieve relevant facts
hits = memory.search("what kind of rice does the user like?")
for hit in hits.hits:
    print(f"{hit.score:.2f}  {hit.content}")

Async variant:

import asyncio
from ledgermem import AsyncLedgerMem

async def main() -> None:
    async with AsyncLedgerMem(api_key="...", workspace_id="...") as m:
        await m.add("Trip to Costa Rica was 5 days, brought 7 shirts.")
        res = await m.search("how many shirts did I pack?")
        print(res.hits[0].content)

asyncio.run(main())

Configuration

The client reads from env vars when arguments are not passed explicitly:

Env var	Default	Notes
LEDGERMEM_API_KEY	(required)	from https://app.proofly.dev/settings/api-keys
LEDGERMEM_WORKSPACE_ID	(required)	from the dashboard URL
LEDGERMEM_ACTOR_ID	none	optional — scopes calls to a single user
LEDGERMEM_API_URL	https://api.proofly.dev	override for self-hosted

API surface

Method	Purpose
search(query, *, limit=8, actor_id=None)	Hybrid 7-strategy retrieval. Returns SearchResponse.
add(content, *, metadata=None, actor_id=None)	Store an atomic fact. Returns Memory.
update(memory_id, *, content=None, metadata=None)	Patch existing memory.
delete(memory_id)	Remove a memory.
list(*, limit=20, cursor=None, actor_id=None)	Cursor-paginated list.

All methods exist on both LedgerMem (sync) and AsyncLedgerMem (async).

Development

pip install -e ".[dev]"
pytest
ruff check .
mypy src

License

MIT