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Once you have an API key (from the Quickstart), you can see Synap’s memory in action in a few lines. This page ingests a single message, shows what Synap pulls out of it, and retrieves it back.
This is a B2C example: one user, identified by user_id alone. On a B2B instance you also pass a customer_id. See Identifiers & Scopes.

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1. Ingest one message

You hand Synap raw text; it runs the ingestion pipeline and stores structured memory. The call returns immediately with an ingestion_id you can wait on.
first_memory.py
import asyncio
from maximem_synap import MaximemSynapSDK

async def main():
    sdk = MaximemSynapSDK()          # reads SYNAP_API_KEY from the environment
    await sdk.initialize()
    try:
        result = await sdk.memories.create(
            document=(
                "User: I'm Alex, I run a small coffee roastery in Portland. "
                "I always prefer email over phone, and I'm planning to expand to a "
                "second location next spring."
            ),
            document_type="ai-chat-conversation",
            user_id="user_alex",
        )

        # Block until the pipeline finishes (good for scripts and tests).
        await sdk.memories.wait_for_completion(result.ingestion_id)
        print("Ingested:", result.ingestion_id)
    finally:
        await sdk.shutdown()

asyncio.run(main())

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2. See what Synap extracted

From that one message, Synap extracts structured memory types, not just a blob of text:
TypeWhat it found
FactAlex runs a coffee roastery in Portland.
PreferencePrefers email over phone.
Temporal eventPlanning a second location next spring.
EntitiesAlex, Portland, coffee roastery (resolved and linked in the graph).
You did not tag any of this by hand. Extraction and entity resolution happen automatically.

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3. Read it back

On the next turn, fetch context for the same scope you ingested at, before you call your LLM: 
context = await sdk.user.context.fetch(
    user_id="user_alex",
    search_query=["communication preferences", "business"],
)

for fact in context.facts:
    print("fact:", fact.content)
for pref in context.preferences:
    print("preference:", pref.content)
Inject context.facts and context.preferences into your system prompt, and your agent now “remembers” Alex on every future conversation.

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Where to go next

Quickstart

The full setup: create a Client, an Instance, and an API key, then run the loop.

Memory Model Cheat Sheet

The 5 identifiers, 2 write paths, and 4 fetch interfaces on one page.

Playground

Try it in the browser, no install or API key needed.

First Integration

Wire Synap into a real FastAPI + LLM app, end to end.