korely-memory

The Python SDK for Korely Agents: memory for AI agents, with bi-temporal typed facts and contradiction checking built in.

A typed, zero-dependency client over the Korely REST API. Every method maps 1:1 onto an endpoint, so anything you can do with curl you can do here, and the JSON shapes in the API reference are the attribute shapes you get back. All the intelligence (embeddings, entity and typed-fact extraction, contradiction checking, bi-temporal validity) runs server-side, so your install stays small and your process stays light.

Install

pip install korely-memory

Python 3.9 or later, for the SDK, the korely CLI and the korely-mcp stdio server alike: the MCP server needs no extra package since 0.1.16 (pip install 'korely-memory[mcp]' still works, the extra is empty).

Quickstart

from korely_memory import Korely

korely = Korely(api_key="kor_live_...", region="eu")
# or read the key from the environment (KORELY_API_KEY)
korely = Korely(region="eu")

# Remember: the write path extracts facts and resolves contradictions
korely.add("Maria lives in Rome", user_id="maria")
korely.add("Maria moved to Milan", user_id="maria")

# Recall the raw memories, ranked by meaning. Both come back: memories are
# kept as written, it is the facts extracted from them that get superseded.
for hit in korely.search("where does Maria live", user_id="maria", limit=5):
    print(hit.id, hit.score, hit.snippet)

# One-call, prompt-ready context for your LLM. Its "Known facts" are the
# current ones, so the Rome fact, once superseded, is not among them.
# (Facts are extracted a few seconds after each write on the hosted service.)
ctx = korely.get_context("where should I send the package?", user_id="maria",
                         token_budget=800)
messages = [{"role": "system", "content": f"You are helpful.\n\n{ctx.context}"}]

Methods

Every method wraps exactly one REST endpoint.

AsyncKorely has the same methods, awaitable.

add(..., timestamp="2026-01-15") backfills the past: facts extracted inherit the timestamp as their valid_from, so as_of point-in-time queries reflect when things were true, not when they were ingested. Each item of batch() takes the same timestamp key, so a migration keeps its real dates:

korely.batch([
    {"content": "Franco signed up on the Pro plan.", "user_id": "franco", "timestamp": "2026-01-15"},
    {"content": "Franco downgraded to Free.", "user_id": "franco", "timestamp": "2026-06-20"},
])

A timestamp that is not an ISO 8601 date or datetime refuses the whole batch with a 422 naming the item (memories[1].timestamp), before anything is queued.

list_agents() / delete_agent(agent_id) manage your agent namespaces: call list_agents() after an agent_cap_exceeded error to reuse an existing agent_id, or delete_agent() to purge a throwaway one. The page's total counts the namespaces of this key's project; used counts the cap slots taken across the account, which is what the 403 compares with cap. delete_agent() raises NotFoundError for a name this project does not use, and its receipt's slot_freed says whether the slot is free now (it is not while another project of the account still uses the name).

delete_all(user_id=) answers with memories_deleted and facts_deleted, the rows physically erased. memories_forgotten and facts_invalidated carry the same numbers under their old names and are deprecated.

correct_fact() returns the new fact, whose invalidated lists every fact the correction superseded (the corrected one, plus any the contradiction check closed). A correction that restates the fact as it already stands supersedes nothing: the same fact comes back, reconfirmed, with invalidated == [].

get_facts() returns a list of Fact that also carries .total, the number of facts matching the filters across all pages, so offset knows when to stop. get_all(), get_facts(), users(), list_agents() and events() take a limit up to 200.

Bi-temporal facts

The differentiator: typed (subject, predicate, object) facts with validity over time. Ask what was true on any date.

# Current state
facts = korely.get_facts(entity="Northwind Hosting")
print(facts[0].object)      # 50 euro per month
print(facts[0].invalid_at)  # None: active

# Point-in-time: what did we believe on June 1?
facts = korely.get_facts(entity="Northwind Hosting", as_of="2026-06-01")
print(facts[0].object)      # 40 euro per month

Scoping

Three identifiers, three levels of scope, the same everywhere (SDK, REST, MCP):

• agent_id: your application or agent (one namespace per product surface)
• user_id: your end user (free-form string; unlimited on every tier)
• run_id: one session or run (sub-scope inside a user)

korely.add("Asked to be contacted on Slack", agent_id="support-bot", user_id="customer-4812")
results = korely.search("contact preference", user_id="customer-4812")

Always pass user_id on reads in multi-tenant products. Filters are additive (AND); a search without user_id spans every end user in the namespace.

Error handling

Every error the server answers with is an APIError carrying the stable code and the message of the REST error envelope ({"code", "message"}), so you can branch on err.code; a self-hosted install that answers FastAPI's detail is read the same way, and err.body keeps the response as it came. The common statuses also have their own subclass. Everything subclasses KorelyError, which is also what a client-side problem raises (no key, a connection error, a timeout).

import time
from korely_memory import Korely, AuthenticationError, NotFoundError, QuotaExceededError

korely = Korely(api_key="kor_live_...")
try:
    memory = korely.get("mem_8f2c1a")
except AuthenticationError:
    raise                           # 401: check or rotate the key
except NotFoundError:
    memory = None                   # 404: forgotten or never existed
except QuotaExceededError as err:   # 429
    if err.retry_after is None:
        raise                       # monthly quota used up: nothing to wait for
    time.sleep(err.retry_after)     # rate limit: wait as long as the server said
    memory = korely.get("mem_8f2c1a")

The SDK does not retry on its own.

LangGraph

pip install 'korely-memory[langgraph]'   # Python 3.10+, as LangGraph itself

korely_memory.integrations.langgraph gives a graph three ways to use Korely. Take the ones you need: import korely_memory loads none of them, so the core package keeps zero dependencies.

Context before the model answers

korely_context(client, user_id, query) makes one GET /v1/context call and returns the text for a SystemMessage: the user's current facts and the memories relevant to the question, within token_budget, under a Current date: YYYY-MM-DD line. In our measurements the model answers better when its prompt carries the date; include_date=False leaves it out and today= sets it. Write each turn back with add(), and the next turn finds it, on any thread.

from dataclasses import dataclass

from langchain.chat_models import init_chat_model
from langchain_core.messages import SystemMessage
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.graph import START, MessagesState, StateGraph
from langgraph.runtime import Runtime

from korely_memory import Korely
from korely_memory.integrations.langgraph import korely_context

korely = Korely()                          # reads KORELY_API_KEY
model = init_chat_model("provider:model")  # any chat model LangChain supports


@dataclass
class Context:
    user_id: str


def call_model(state: MessagesState, runtime: Runtime[Context]):
    user_id = runtime.context.user_id
    question = state["messages"][-1].text
    memory = korely_context(korely, user_id, question, token_budget=800)
    reply = model.invoke([SystemMessage(memory), *state["messages"]])
    korely.add([{"role": "user", "content": question},
                {"role": "assistant", "content": reply.text}], user_id=user_id)
    return {"messages": [reply]}


builder = StateGraph(MessagesState, context_schema=Context)
builder.add_node(call_model)
builder.add_edge(START, "call_model")
graph = builder.compile(checkpointer=InMemorySaver())

graph.invoke(
    {"messages": [{"role": "user", "content": "Where should I send the package?"}]},
    {"configurable": {"thread_id": "1"}},
    context=Context(user_id="maria"),
)

The checkpointer keeps one thread's messages; Korely keeps what the user said across all of them. akorely_context() is the same call for an async node.

Tools

from korely_memory.integrations.langgraph import create_korely_tools

tools = create_korely_tools(korely, user_id="maria")   # [search_memory, save_memory]
model_with_tools = model.bind_tools(tools)

search_memory(query) answers with the same block as korely_context(); save_memory(content) stores one memory. The app binds the user (and agent_id=) when it creates the tools: neither is in the tools' schema, so the model can neither see nor change them. Create the tools per user, for instance in the node that calls the model. A Korely error reaches the model as the tool's answer instead of ending the run.

Store

from korely_memory.integrations.langgraph import KorelyStore

graph = builder.compile(checkpointer=InMemorySaver(), store=KorelyStore(korely))
# in a node: runtime.store.put(("memories", user_id), key, {"content": "..."})

KorelyStore is a LangGraph BaseStore, for code that expects one: runtime.store, LangMem's memory tools. Each item is a Korely memory of the user, so Korely extracts facts from it and korely_context() and the tools find it. The memory's text is the value's content, text, memory or data string, else one key: value line per field (index=[...] on a put, or index_fields=, picks other fields). The value itself travels in the memory's metadata and comes back exactly. Each put is a write like any other: it is digested into facts and counts in your plan's writes, so the store suits what a user said or decided, not caches or scratch state.

The namespace ("memories", user_id) is that Korely end user; KorelyStore(korely, agent_id="support-bot") adds the agent, and namespace_to_scope= maps other shapes (a function returning the user id or (user_id, agent_id)). A search covers exactly the namespace it names, never the ones below it. Each namespace is also a Korely run, run_id="langgraph:memories.maria": that is how the store reads its own items and nothing else of the user's, while the rest of Korely reads them as the user's memories. A value must fit in a memory's metadata, 8 KB of JSON on the current server. delete forgets as delete() does; erasure is delete_all(user_id=). Two writers on one key at the same instant can leave two memories: reads take the newest, and the next put or delete removes the other.

MCP server

pip install 'korely-memory[mcp]'   # Python 3.9+ since 0.1.16

korely-mcp is a stdio MCP server with four tools (korely_get_context, korely_add, korely_search, korely_get_facts), the same four the hosted server at https://api.korely.ai/agent/mcp offers, with the same arguments (korely_add takes timestamp) and the same fact lines: a fact whose end date is still to come reads [until 2027-01-01], not [superseded ...], and dates are UTC days. It reads the key from KORELY_API_KEY or from the file korely init saved.

Links

• SDK docs
• API reference
• Cookbook: a chatbot that remembers

MIT licensed.