Which package? This is EverOS Cloud — the managed SaaS client (
pip install everos-cloud).Want to self-host? Use the open-source
everoslibrary instead.
Give your AI agents memory that persists across sessions — managed, searchable, and typed. Add a conversation; EverOS turns it into structured, retrievable memory you can query in one call.
- Self-evolving memory — memory doesn't just pile up, it improves. Background consolidation merges related episodes and refines user profiles over time, so recall gets sharper the more your agent is used.
- Structured memory, not chat logs — extracts episodes, user profiles, and reusable agent cases & skills from raw conversations, so retrieval returns meaning, not transcripts.
- Retrieval that fits the query — keyword, vector, hybrid (default), or agentic multi-step search.
- Multimodal — attach images, audio, and documents to any message.
- Built for production — fully managed (no vector DB or extraction pipeline to run), with low-latency retrieval and high-concurrency throughput. The engineering guarantees you don't get from self-hosting.
- Fully typed (pydantic v2) — every request/response is a typed model with full hints, so you get editor autocomplete and validation instead of raw dicts.
pip install everos-cloudPre-releases need
--pre:pip install --pre everos-cloud.Upgrading from the 0.4.x client? 1.x is a rewrite with a new API surface — see the migration guide. Pin
everos-cloud<1to stay on the old client.
Get an API key from the EverOS Console, then:
from everos_cloud import EverOS
with EverOS(api_key="sk-...") as client:
client.add(session_id="session-1", messages=[
{"sender_id": "user-1", "role": "user", "content": "I love hiking in the mountains"},
])
results = client.search("outdoor hobbies", user_id="user-1")
print(results)Full usage — every memory operation, profile editing, and multimodal upload — is in
quickstart.md.
Prefer typed control? The generated low-level client is exposed as client.memory / client.storage.