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Wontopos — long-term memory for AI agents. Pure semantic retrieval, identical recall in every language, ~100× lower LLM bill.

Project description

Wontopos — long-term memory for AI agents

pip install wontopos
from wontopos import Client

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

  • Pure semantic retrieval — no keyword/BM25. Identical recall in every language (한국어 · 日本語 · 中文 · English).
  • No LLM in the loopstore / search / recall never call a language model. You pay embeddings, not generation.
  • Bounded retrievalrecall() returns a small, fixed-size slice (~1,200 tokens) regardless of how much you've stored. 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) Semantic search
recall(query, user_id) One-call context (short + long + surrounding)
history(user_id) Recent turns (short-term)
stats(user_id) Counts
delete(user_id, memory_id) Delete one memory
delete_all(user_id) GDPR erase (delete every memory for the user)

All methods take a user_id — memories are isolated per user, then per account (your API key).

Errors

Any non-2xx response raises WosError(status, message):

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")
    else:
        print(e.status, e.message)

Self-host

Point at your own engine:

mem = Client(api_key="...", base_url="https://wos.your-host.com")

Links

License: MIT.

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