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Wontopos — long-term memory for AI agents

pip install wontopos

Get an API key in the console. Keys look like wos-live-...; the client also reads WONTOPOS_API_KEY from the environment.

from wontopos import Client

mem = Client(api_key="wos-live-...")

# Each end-user / agent / topic gets its own store — create it once.
# (A "default" store already exists, so you can skip this and omit the id.)
mem.create_store("alice")
mem.add("she prefers tea over coffee", user_id="alice")

# one call → short-term + long-term + context, ready for your LLM prompt
ctx = mem.recall("what does alice drink?", user_id="alice")

Why

  • The same in every language — identical recall whichever language a memory was written in (Korean · Japanese · Chinese · English).
  • No LLM in the loop — store / search / recall never call a language model. You pay retrieval, not generation.
  • Bounded retrieval — recall() returns a small, fixed-size slice regardless of how much you've stored (~1,000 tokens on tablet-2, the default engine). Your LLM bill stops growing with history.

Methods

Method Purpose
add(content, user_id, **metadata) Store one memory
add_turn(user_msg, assistant_msg, user_id?) Store a conversation exchange
add_bulk(content, user_id, category=, timestamp=) Backfill a long history
update(old_memory_id, new_content, user_id?) Supersede an old fact
search(query, user_id, limit=10, **opts) Search stored memories
search_full(query, user_id, limit=10, **opts) The same search with every field kept — images and verify_used included
recall(query, user_id) One-call context (short + long + surrounding)
history(user_id) Recent turns (short-term)
stats(user_id) Counts
get(user_id, memory_id) Fetch one memory by id (the text you stored, and its metadata)
list_memories(user_id, limit=100, cursor=) Browse/export a store's raw memories, paged
delete(user_id, memory_id) Delete one memory
delete_all(user_id) GDPR erase (delete every memory for the user)
add_speaker(speaker, user_id?) Register a person (explicit, up to 50 to start)
list_speakers(user_id?) Registered people + per-person memory counts
remove_speaker(speaker, user_id?) Unregister; memories stay, the tag goes

All methods take a user_id — it names the store: one isolated memory space per end-user, agent, or topic, then per account (your API key). WHO said each memory inside a store is the speaker tag below — storing the assistant's own words never needs a separate id.

Who said it (speakers)

Every memory can carry a speaker: "me" for the assistant's own words, or a person's name. Speakers are explicit, like stores: register a person once, then store under their name — a typo can never silently become a new person. Search accepts a speaker too, so you can recall one person's words only.

mem.add_speaker("Bob", user_id="alice")      # once per person
mem.add("I promised to send the report on Friday", user_id="alice", speaker="me")
mem.add("Bob said the deadline moved to Tuesday", user_id="alice", speaker="Bob")
mem.search("what did Bob say about deadlines?", user_id="alice", speaker="Bob")

Results arrive best-first — take the list in the order given. similarity on each memory is a raw closeness score, not the ranking key: what produces the order is internal and is not returned, so sorting by it makes results worse. There is no score field.

A store registers up to 50 people to start (a limit we plan to raise); "me" never needs registration and never counts against it.

Async

Same surface, awaitable — needs the extra:

pip install "wontopos[async]"
from wontopos import AsyncClient

async with AsyncClient(api_key="wos-live-...", user_id="alice") as mem:
    await mem.add("she prefers tea over coffee")
    hits = await mem.search("what does alice drink?")

Every Client method exists on AsyncClient with identical arguments and semantics (retries, redirect refusal, guards). Close with async with or await mem.aclose().

Recall caching

Opt in per search and repeated or extended queries reuse the previous result at 10% of the normal rate (Tablet and Scroll models).

It is not free to turn on: the FIRST call writes the cache and bills the query tokens at 2x for a 5m TTL, 3x for 1h. Only hits inside the TTL bill at 0.1x. So it pays for a query you repeat or extend, and costs more for one you issue once — do not switch it on globally. Any write to the store invalidates its cache at once, so a hit can never predate a new memory.

hits = mem.search("...the conversation so far...", user_id="alice",
                  cache_control={"ttl": "5m"})   # or "1h"

Reliability

Built in, no configuration needed:

  • Automatic retries — 429 always, and 408 / 502 / 503 / 504 or a connection error only when a retry cannot double-process a write. The writes and the searches are POSTs, and any of those four may have been returned after the write already landed, so those get 429 and connect-level failures only. The reads and the deletes that address a whole store — list_stores, list_speakers, delete_store, remove_speaker, forget_image — are GET or DELETE and do retry all four. Twice, with exponential backoff + jitter, honoring the server's Retry-After. Tune with Client(retries=...); retries=0 disables.
  • Redirects refused — the API key never follows a 3xx to another host.
  • Timeouts — 30s per attempt by default (Client(timeout=...)), and a total budget for the whole call across every retry with Client(deadline=...) / with_deadline(secs). At the defaults one call can hold for 30s + backoff + 30s
    • backoff + 30s, which a request handler with five seconds cannot use.
  • Key never in logs — repr(client) masks the API key.
  • Wipe guard — delete() without a memory_id raises instead of silently meaning "delete everything"; wiping a store is only ever the explicit delete_all(user_id) / delete_store(user_id).

Security

Built in, none of it configurable off:

  • TLS 1.2 floor and certificate verification that cannot be disabled.
  • Redirects refused — a 3xx is an error, so the key never follows one to another host.
  • Response size cap — anything over 64MB is refused instead of buffered.
  • Key hygiene — keys are trimmed (a stray newline from a file otherwise becomes a mystery 401) and inner whitespace is rejected; model names are validated before they reach a header.
  • Client.from_env() reads WONTOPOS_API_KEY (or WOS_API_KEY) — keep keys out of source code.
  • Plain-HTTP base URLs on non-local hosts warn. One dependency (requests, floor >=2.32 for its certificate-verification fix).

Errors

Any non-2xx response raises WosError(status, message). When the server sent a request id it's on e.request_id — include it when contacting support.

from wontopos import Client, WosError

try:
    mem.search("...", user_id="alice")
except WosError as e:
    if e.status == 401:
        print("API key invalid or revoked")
    elif e.status == 429:
        print("Rate limited — back off")   # already retried twice by then
    else:
        print(e.status, e.message, e.request_id)

A different API host

Point the client somewhere other than the default endpoint - a dedicated region, a proxy of your own, or a local test server:

mem = Client(api_key="...", base_url="https://api.example.com")

Reporting a bug

Found something wrong, or something that looks unsafe? Tell us — every report gets read.

Include the SDK version (wontopos.__version__) and the language. If it involves a store id or a memory, describe the shape rather than pasting the contents — we do not need your data to fix it.

Changelog

The three clients release in lockstep — same version, same surface, same day. Patch releases are additive. Five inside 2.2 were not, deliberately and each with its reason; the changelog lists them.

See CHANGELOG.md.

Metadata

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