Which package? This is EverOS Cloud — the managed SaaS client (pip install everos-cloud).

Want to self-host? Use the open-source everos library instead.

EverOS Cloud — Python SDK

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.

Why EverOS Cloud

• 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.
• Knowledge bases — ingest documents into a searchable topic library alongside conversational memory; ingest is async and reports progress through the task API.
• 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.

Install

pip install everos-cloud

Pre-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<1 to stay on the old client.

Quickstart

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)

Knowledge bases work the same way — ingest is asynchronous, so wait on the task:

kb   = client.kb_create("Employee Handbook")
ack  = client.doc_ingest(kb.id, "Leave policy", "Employees accrue 20 days...")
task = client.task_wait(ack.task_id)          # polls until the document is queryable

hits = client.kb_search(kb.id, "how much leave do I get")

Full usage — every memory operation, knowledge base, async task, profile editing, and multimodal upload — is in quickstart.md.

Two ways to call the API

EverOS covers the common calls with plain kwargs in and the response's .data out. New methods are named <resource>_<verb> (kb_create, doc_ingest, task_wait, tag_bind), so typing client.kb lists the knowledge-base surface; the methods 1.0.0 shipped are bare verbs (add, search, get, flush, edit, delete, upload).

Everything the API offers — all 31 operations, including knowledge-base categories and document topics — is on the generated typed clients, reachable as client.memory, client.storage, client.knowledge, client.tasks. Those take and return the full typed models, so responses arrive as an envelope you read .data from. The per-endpoint reference for them is under docs/.

client.kb_create("Handbook")                          # facade   -> KbData
client.knowledge.create_knowledge_base({"name": "…"})  # generated -> envelope, .data
client.knowledge.list_topics(kb_id, doc_id)            # generated only

Documentation

• Full SDK usage
• Quickstart
• API Reference
• Core Concepts

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EverMind Ecosystem

EverMind connects memory research, production-ready products, and practical integrations into one open-source ecosystem.

Together, these projects form EverMind's research-to-runtime stack: methods and benchmarks become reusable memory infrastructure, products, and agent integrations.