Give your AI memory in one parameter

By default, an LLM forgets you the moment a conversation ends. Start a new chat and it has no idea who you are, what you told it last week, or what you prefer. For a real product, that is a dealbreaker. Users expect the app to remember.

The standard fix is a memory pipeline you build yourself. Extract the important facts from each conversation. Turn them into embeddings. Store the vectors in a database. On every new message, run a similarity search, pull the relevant facts, and inject them into the prompt. That is a meaningful chunk of engineering, and you maintain it forever.

Backboard collapses that into one parameter: memory

. Set it to "Auto"

and your assistant remembers.

Memory is stored on the assistant, so pass the same assistant_id

and memory="Auto"

. Facts the user shares in one conversation are recalled in the next.

pip install backboard-sdk
python
import asyncio
from backboard import BackboardClient

async def main():
    client = BackboardClient(api_key="YOUR_API_KEY")

    await client.send_message(
        "My name is Sarah. I work at Google as a software engineer.",
        assistant_id="your-assistant-id",
        memory="Auto",
    )

    reply = await client.send_message(
        "What do you remember about me?",
        assistant_id="your-assistant-id",
        memory="Auto",
    )
    print(reply.content)  # name, employer, and role

asyncio.run(main())
js
const send = (body) =>
  fetch("https://app.backboard.io/api/threads/messages", {
    method: "POST",
    headers: {
      "X-API-Key": "YOUR_API_KEY",
      "Content-Type": "application/json",
    },
    body: JSON.stringify(body),
  }).then((r) => r.json());

await send({
  content: "My name is Sarah. I work at Google as a software engineer.",
  assistant_id: "your-assistant-id",
  memory: "Auto",
});

const reply = await send({
  content: "What do you remember about me?",
  assistant_id: "your-assistant-id",
  memory: "Auto",
});

console.log(reply.content);
curl -X POST "https://app.backboard.io/api/threads/messages" \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"content": "My name is Sarah. I work at Google as a software engineer.", "assistant_id": "your-assistant-id", "memory": "Auto"}'

curl -X POST "https://app.backboard.io/api/threads/messages" \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"content": "What do you remember about me?", "assistant_id": "your-assistant-id", "memory": "Auto"}'

No embedding step. No vector database. No retrieval code. One parameter, and the assistant extracts the facts, stores them, and recalls them when they are relevant.

"Auto"

actually does Behind that single value, Backboard runs the full loop:

It works across every thread under the same assistant, which is exactly the behavior you want: the user is remembered no matter which conversation they are in.

memory

is a per-turn parameter. Pass it on each call where you want memory active. Pick one value:

memory

and memory_pro

cannot be used together in the same message. Use memory

for everyday recall and memory_pro

when accuracy matters more than cost.

response = await client.send_message(
    "What were my project deadlines?",
    assistant_id="your-assistant-id",
    memory_pro="Auto",
)

"Auto"

covers most apps. When you need to manage memory directly, the assistant exposes full CRUD: list, add, search, update, and delete. You own the data and can export it whenever you want.

await client.add_memory(
    assistant_id,
    content="User prefers dark mode in all applications",
)

results = await client.search_memories(
    assistant_id,
    query="user interface preferences",
    limit=5,
)
for m in results["memories"]:
    print(m["content"])

Persistent memory is usually a project: an extraction pipeline, a vector store, retrieval code, and ongoing upkeep. Backboard makes it a parameter. Set memory="Auto"

, reuse the assistant, and your AI remembers your users across every conversation. When you need precision or control, switch to memory_pro

or manage memories directly. No database required.

Grab a key and try it: app.backboard.io

Memory docs: docs.backboard.io/concepts/memory