Korely Memory

Korely Memory

Memory for AI agents that knows what is still true.

Korely stores what your agent learns as typed facts with a validity window. When something changes, the old fact is superseded instead of overwritten. Your agent reads the current truth, and you can still ask what was true on any past date.

import time

from korely_memory import Korely

korely = Korely() # reads KORELY_API_KEY from the environment

korely.add("Maria is on the Pro plan, billed yearly.", user_id="maria") korely.add("Maria downgraded to Free.", user_id="maria")

time.sleep(10) # facts are extracted server-side; see "Writes settle asynchronously"

ctx = korely.get_context(query="what plan is Maria on?", user_id="maria") print(ctx.context)

## Known facts

- Maria downgraded_to Free (since 2026-09-08)

- Maria billing_frequency yearly (since 2026-09-08)

#

Pro is gone from the current facts. It was superseded, not deleted.

The exact predicate is chosen by the extractor, so downgraded_to here might be subscribes_to or plan on your run. What is guaranteed is the behaviour: the old value leaves the current set, and stays retrievable with its invalid_at.

Nothing was deleted. The Pro fact is still there, carrying invalid_at and a pointer to what replaced it:

korely.get_facts(user_id="maria", include_invalidated=True)

Maria downgraded_to Free valid_from=... invalid_at=None

Maria plan Pro valid_from=... invalid_at=2026-09-08T08:31:09Z

Time travel over real dates

Pass timestamp when the event happened in the past, and facts inherit it as valid_from. Then as_of answers over the world's timeline, not your ingestion order:

korely.add("Franco signed up on the Pro plan.", user_id="franco", timestamp="2026-01-15")

korely.add("Franco downgraded to Free.", user_id="franco", timestamp="2026-06-20")

korely.get_facts(user_id="franco", as_of="2026-03-01") # subscribes_to -> Pro plan korely.get_facts(user_id="franco", as_of="2026-08-01") # subscribes_to -> Free korely.get_facts(user_id="franco") # subscribes_to -> Free

Without timestamp every fact starts being true the moment you write it, so as_of on an earlier date returns nothing. That is correct, and usually not what you want when you are importing history.

as_of answers what was true on a date, as the store knows it today. known_as_of (Korely Agent 0.1.26) answers the other question: what the store knew on that date. They differ when a change is told late. An agent learns on 5 October that a customer moved on 1 September:

korely.get_facts(user_id="maria", as_of="2026-09-20")        # lives_in -> Turin

korely.get_facts(user_id="maria", known_as_of="2026-09-20") # lives_in -> Milan

On 20 September the move had happened, and the store did not know it yet. known_as_of is on get_facts, get_profile and get_context, which rebuilds the block as the store could have built it then: what an agent could have known when it decided. Each fact carries closed_known_at, when the store learned it had ended.

Writes settle asynchronously

add() returns as soon as the memory is stored, then extraction runs server-side. That means:

The time.sleep(10) above exists only so the snippet works when you paste it. You do not need it in production: an agent writes at the end of one turn and reads at the start of the next, and by then the facts are there.

If you do need to know exactly when, ask instead of guessing. Every memory carries a status of processing, ready, or error, and events() reports what is still in flight:

page = korely.events()

page.processing # 0 page.events[0] # MemoryEvent(memory_id='mem_...', status='ready', ...)

processing == 0 means none of your project's writes (in the user_id scope, if you pass one) is still being extracted, however many there are, so a script can wait on that one number instead of walking every id. After a batch() import, wait for batch_status() to finish first: memories the job has not stored yet are not counted.

Polling is the only signal: no webhook fires when extraction finishes. The webhook events are memory.created, fact.invalidated and quota.warning.

Install

pip install korely-memory      # Python, plus the `korely` CLI

npm install korely-memory # Node / TypeScript

Both clients have zero runtime dependencies.

For LangGraph, the optional pip install 'korely-memory[langgraph]' adds a store, memory tools and a context helper: see python/README.md.

Get a key

The hobby tier is free and needs no signup form:

curl -X POST https://api.korely.ai/v1/agents/init \

-H 'Content-Type: application/json' \ -d '{"agent_caller": "your-name-here"}'

The response carries a kor_live_ key. Set it as KORELY_API_KEY and the SDK, the CLI, and the REST API all authenticate with it. From code, Korely.init_agent() (Python) and Korely.initAgent() (JS) make the same call.

The account behind that key has no login: the key is the account. Keep it, because it is also the only way to delete the account (korely delete-account --yes, or delete_account(confirm=True) in the SDK).

Or let the CLI do it for you. korely init saves the key to ~/.korely/config.json, and the SDK reads it from there when KORELY_API_KEY is not set, so this is enough to get going:

pip install korely-memory

korely init --agent --agent-caller your-name python -c "from korely_memory import Korely; print(Korely().get_context(query='hi').tokens)"

TypeScript

import { Korely } from "korely-memory";

const korely = new Korely();

await korely.add("Maria downgraded to Free.", { user_id: "maria" }); const ctx = await korely.getContext({ query: "what plan is Maria on?", user_id: "maria" });

With the Vercel AI SDK, withKorelyMemory(model, { userId }) from korely-memory/ai-sdk gives any model this memory in one line: see js/README.md.

CLI

korely add "Maria downgraded to Free." --user-id maria

korely context "what plan is Maria on?" --user-id maria korely facts --as-of 2026-03-01 --user-id maria korely audit --user-id maria --all --json > maria.json # everything that touched Maria

Every command is listed in python/README.md.

What Korely does

• Typed facts. Subject, predicate, object, extracted server-side. No prompt engineering on your side.
• Bi-temporal validity. Every fact carries valid_from and invalid_at, so the store separates when something was true from when it was recorded.
• Contradiction resolution. A new fact that conflicts with an old one supersedes it and records which fact replaced it. Nothing is silently dropped.
• Point-in-time queries. as_of answers what was true on any past date, and known_as_of what the store knew on it.
• Entity graph. Entities and relations are extracted automatically and available on every tier, including free.
• Semantic retrieval. Memories and facts are ranked by meaning (vector similarity), and facts also by how recently a memory confirmed them.
• Prompt-ready context. get_context() returns a block you can paste straight into a system prompt, with the token count.
• EU-hosted. Runs in Helsinki. End users can see, correct, and erase what agents remember about them.

Async

An agent in production does not make one call at a time. AsyncKorely mirrors every method of Korely, so everything you learned carries over:

import asyncio

from korely_memory import AsyncKorely

async def main(): korely = AsyncKorely() contexts = await asyncio.gather( korely.get_context(query="what plan?", user_id="a"), korely.get_context(query="what plan?", user_id="b"), korely.get_context(query="what plan?", user_id="c"), )

asyncio.run(main())

Six calls against the live API: 5.6s sequential, 1.7s concurrent.

Calls run on a thread pool rather than an async HTTP library, because keeping this package at zero runtime dependencies is worth more than the last drop of efficiency. Your event loop is never blocked and requests really do overlap.

Examples

examples/audit_trail.py answers the question this store exists for: what did your agent know on the day it answered?

A support agent tells a customer in March that they have priority support. In June the customer moves to a cheaper plan. In September they complain, quoting your bot back at you. Was the bot wrong, or right at the time?

  What the store believed in March, when the agent answered:

customer-4821 · subscribes_to · Business plan Business plan · includes · priority support

What is true today: customer-4821 · subscribes_to · Standard plan customer-4821 · lacks · priority support

Right in March, right today, and both provable. Run it yourself in about twenty seconds:

pip install korely-memory

korely init --agent --agent-caller audit-example python examples/audit_trail.py

It checks its own claims rather than making them, erasure included.

Repository layout

Coding assistants

korely init --tool claude-code (or codex, or cursor) in a repository connects that assistant to one memory per repository: it is handed the memory when a session starts, and its sessions are saved to it. Every assistant you connect shares it. See the Python README.

MCP

Korely runs a hosted MCP server, so a coding agent can read and write memory without any package:

claude mcp add --transport http korely https://api.korely.ai/agent/mcp \

--header "Authorization: Bearer kor_live_..."

Documentation

Full REST contract, concepts, and integration guides: korely.ai/agents/docs

License

MIT. These clients are open source; the hosted service they talk to is not.