Give Your AI a Long-Term Memory: A Plain-English Guide to Memobase

For global developers who want their apps to remember users—without the hype.

Three Opening Questions

1. Why does my chatbot greet me like a stranger every single time?
2. Can an AI remember that I speak Korean, love Mexican food, and hate ALL-CAPS typing?
3. Will the memory system still work if my user base jumps from 10 to 100 000 overnight?

If any of these sound familiar, you have just found the answer: 「Memobase」.
It is a user-profile–centric memory layer that turns scattered conversations into a structured, time-aware snapshot of each human. The rest of this post walks through the official docs, line-by-line, in everyday English.

What Is Memobase in One Sentence?

❝

Memobase = a database of user profiles + a timeline of user events + a plug-and-play memory module for any LLM stack.

❞

Real Problems It Solves

Architecture at a Glance

User chat
   │
   ├─① ChatBlob (raw messages)
   │    └─Buffer (1024 tokens or 1 h idle)
   │
   ├─② Flush (async or manual)
   │    └─LLM extracts → structured fields
   │
   ├─③ Profile & Event
   │    ├─basic_info, interest, work …
   │    └─timestamped events (e.g., “2025-07-21 bought lantern”)
   │
   └─④ Context API
        └─Packs everything into a short string for your prompt

Local vs Cloud: A 30-Second Comparison

Five-Step Quick Start

1. Two Things You Need

• 「Project URL」

  • Local: http://localhost:8019
  • Cloud: https://api.memobase.dev
• 「Project Token」

  • Local: secret
  • Cloud: sk-proj-xxxxxx

2. Install the Python SDK

pip install memobase

3. Check the Connection

from memobase import MemoBaseClient, ChatBlob

client = MemoBaseClient(
    project_url="http://localhost:8019",
    api_key="secret"
)
assert client.ping()  # Should return True

4. Create a User and Feed Data

uid = client.add_user({"source": "blog_demo"})
user = client.get_user(uid)

dialogue = [
    {"role": "user", "content": "Hi, I'm Gus, 25, studied Korean"},
    {"role": "assistant", "content": "Nice to meet you, Gus!"}
]
blob_id = user.insert(ChatBlob(messages=dialogue))

❝

By default the raw messages are discarded after processing. To keep them, flip one flag in the config.

❞

5. Trigger Memory Generation

user.flush(sync=True)  # Wait for processing
print(user.profile(need_json=True))

Example output (trimmed):

{
  "basic_info": {
    "name": {"content": "Gus"},
    "age": {"content": "25"}
  },
  "education": {
    "major": {"content": "Korean"}
  }
}

Plug Memory into Your Prompt—One Line

memory_snippet = user.context(max_token_size=300, prefer_topics=["basic_info"])

Result:

# Memory
Unless the user asks, do not mention these details.
## User Background:
- basic_info:name: Gus
- basic_info:age: 25

Paste that snippet into your system prompt and the model instantly knows the user.

Frequently Asked Questions

client.delete_user(uid)

Beyond the Basics

1. Design Your Own Profile Fields

Only need work and interest? Edit the config and the generated profile will skip demographics, saving tokens.

2. Time-Travel Search

Looking for “the lantern the user mentioned last week”?

events = user.search_events(query="lantern", after="2025-07-14")

3. Batch Ad-Matching Example

profiles = user.profile()
for p in profiles:
    if p.topic == "work" and "Engineer" in p.content:
        ad = "JetBrains 30 % off"

Production Deployment Checklist

• [ ] Docker 20+
• [ ] Port 8019 free
• [ ] Postgres + Redis running
• [ ] .env copied from .env.example
• [ ] docker compose up -d
• [ ] curl localhost:8019/health returns {"status":"ok"}

What to Read Next

1. Interactive Playground – explore memory in the browser, no install.
2. Memobase-Playground on GitHub – full Next.js + FastAPI chat template.
3. Memobase-Inspector – open-source admin UI with charts and test bench.

Closing Thoughts

Turning AI from a one-off tool into a long-term companion boils down to memory. Memobase solves the three problems that matter:

• It 「remembers」 the user.
• It 「respects」 your control over what to remember.
• It 「scales」 without breaking the bank.

Run the five steps above, and your AI will finally have a past—and a much better future.