Your First Memory in 5 Minutes

Get EverOS working in under 5 minutes. By the end you’ll have stored a short conversation, extracted it into a memory, and searched it back: the full memory loop, in one script.

These guides use EverOS Cloud and the everos-cloud Python SDK.The underlying v2 Memory API is the same on Cloud and self-hosted, so the patterns here apply to both:

Cloud: use the everos-cloud SDK, as shown in these guides.
Self-hosted (OSS): call the same v2 endpoints on your own host. See Open Source.

Prerequisites

pip install everos-cloud
export EVEROS_API_KEY="your_api_key"

Get a key from the EverOS Console. The EverOS client reads EVEROS_API_KEY automatically.

Step 1: Store a conversation

Send a few messages into a session. Each message carries a sender_id: the participant the memory is attributed to.

from everos_cloud import EverOS

client = EverOS()  # reads EVEROS_API_KEY

client.add(
    session_id="quickstart_001",
    messages=[
        {"sender_id": "user_alice", "role": "user",
         "content": "I prefer working in the morning, usually 6am to 10am — that's when I'm most productive."},
        {"sender_id": "assistant", "role": "assistant",
         "content": "Got it — I'll keep mornings free for your deep work."},
        {"sender_id": "user_alice", "role": "user",
         "content": "Afternoons are for meetings, never before noon."},
    ],
    async_mode=False,  # wait for the write to land so we can extract immediately
)

Why async_mode=False?By default writes are asynchronous. Add returns immediately and EverOS ingests in the background.This demo sets async_mode=False so the messages are stored before the next step runs.In production, leave it on the default and let extraction run in the background.

Step 2: Extract the memory

Extraction normally waits for a conversation boundary, such as a topic change or a time gap. Call flush to run boundary detection now and extract what’s ready.

client.flush("quickstart_001")

In production you don’t need to flush every turn. Let EverOS batch and extract on its own.Flush when you need a memory available now: the end of a session, or right before a search that depends on the latest turn.

Step 3: Search your memory

Search within a user’s memories. Search requires a user_id (or agent_id) and returns ranked episodes, each with a full narrative and a relevance score.

results = client.search(
    "when does alice prefer to work",
    user_id="user_alice",
    method="hybrid",
    top_k=5,
)

episodes = results.episodes or []
print(f"Found {len(episodes)} relevant memories:\n")
for ep in episodes:
    print(f"Summary: {ep.summary}")
    print(f"Score:   {ep.score:.3f}")
    print("-" * 40)

Example output:

Found 1 relevant memories:

Summary: Alice told the assistant she prefers working in the morning from 6 AM
to 10 AM because that is when she is most productive. She stated that afternoons
are for meetings, never before noon.
Score:   0.352
----------------------------------------

Nothing came back?Episodes only exist once extraction has run, and a very short exchange may not form one at all.To make extraction happen now, write with async_mode=False and then call flush, as Steps 1 and 2 do.To read the turns that haven’t been extracted yet, pin the session in your search:

results = client.search(
    "when does alice prefer to work",
    user_id="user_alice",
    filters={"session_id": "quickstart_001"},
)

for msg in (results.unprocessed_messages or []):
    print(msg.role, msg.content)

That returns the session’s raw buffered messages alongside any episodes, so a live agent sees the latest turns instead of a gap. It only works when you pin one session this way. Searching by user_id alone never returns them.

Complete script

Copy, set your key, and run:

# pip install everos-cloud -U
# export EVEROS_API_KEY="your_api_key"

from everos_cloud import EverOS

client = EverOS()

# 1. Store a conversation (async_mode=False so it lands before we extract)
print("1. Storing conversation...")
client.add(
    session_id="quickstart_bob",
    messages=[
        {"sender_id": "user_bob", "role": "user",
         "content": "I love hiking on weekends, especially in the mountains."},
        {"sender_id": "assistant", "role": "assistant",
         "content": "Nice — I'll suggest mountain trails for your weekend plans."},
        {"sender_id": "user_bob", "role": "user",
         "content": "Please do. I try to get out every Saturday."},
    ],
    async_mode=False,
)

# 2. Force extraction
print("2. Extracting memory...")
client.flush("quickstart_bob")

# 3. Search
print("3. Searching memories...\n")
results = client.search(
    "outdoor activities bob enjoys",
    user_id="user_bob",
    method="hybrid",
    top_k=5,
)

episodes = results.episodes or []
print(f"Found {len(episodes)} memories:")
for ep in episodes:
    print(f"Summary: {ep.summary}")
    print(f"Score:   {ep.score:.3f}")
    print("-" * 40)

What’s next?

Personal AI Assistant

Build a 1:1 assistant that remembers preferences and context across sessions.

Multi-Party Conversations

Attribute memory per participant in group chats and meetings.

Python Integration

Production patterns: error handling, retries, and clean client setup.

API Reference

Full v2 endpoint docs with request/response schemas.

Getting Started

Use Cases

Integration & Patterns

Your First Memory in 5 Minutes

Prerequisites

Step 1: Store a conversation

Step 2: Extract the memory

Step 3: Search your memory

Complete script

What’s next?

Personal AI Assistant

Multi-Party Conversations

Python Integration

API Reference

​Prerequisites

​Step 1: Store a conversation

​Step 2: Extract the memory

​Step 3: Search your memory

​Complete script

​What’s next?

Personal AI Assistant

Multi-Party Conversations

Python Integration

API Reference

Prerequisites

Step 1: Store a conversation

Step 2: Extract the memory

Step 3: Search your memory

Complete script

What’s next?