Memobase is a user profile-based memory system designed to bring long-term user memory to your LLM applications.
What does that mean?

An issue with LLms is that although within a single ongoing conversation they keep the context so they can follow along and build on what they’re discussing with the user, they don’t automatically remember past conversations once the session ends. By default, each interaction in a new chat starts from scratch.

This is an issue especially when developing chatbots which should be able to remember past context for offering the user an enriched experience. Frameworks like Mem0 do just that; they have memory that allows recalling past conversations or tasks.

But isn't RAG an answer to this problem? The answer is that RAG and memory aren't competing — they're complementary.
RAG handles organizational knowledge with precision, while memory specializes in user-level personalization and context retention.

Memory is best when dealing with long-term, user-focused interactions. If your application needs to track and summarize ongoing conversations, store personalized preferences, or manage low-density data from chats, memory excels at capturing and using that context without needing exact document searches.

For user-facing AI like assistants, tutors, or customer service, the real challenge is understanding who the user is: their evolving preferences and context. Here's where Memobase steps in. It's a lightweight Python based open-source memory backend that models structured user profiles with under 80ms retrieving latency—no RAG or Knowlege Graph involved so far. This means your AI can maintain a rich, up-to-date understanding of the user, enabling truly personalized and engaging interactions.

It's core features are:

• Memory for User, not Agent
  Understand who the user is. Define and control exactly what user information your AI captures.
• Time-aware Memory
  Track what has happened in the user’s life. Memobase has more than user profiles, it also records user event. User event is essential to answer time-related question, see how we can improve temporal memory much better than other memory solutions.
• Controllable Memory
  Among all types of memory, only some may enhance your product experience. Memobase offers a flexible configuration for you to design the profile.
• Easy Integration
  Minimal code changes to integrate with your existing LLM stack with API, Python/Node/Go SDK.
• Batch-Process
  Memobase offers every user a buffer to batch processing the chats after the conversation. Fast & Cheap.
• Production Ready
  Memobase is building with FastAPI, Postgres and Redis, supporting request caching, authing, telemetry, fully dockerized.

To set up:

1. Start your Memobase server locally
2. Connect client
3. Manage Users
4. Insert Data
5. Get your Memory
6. Integrate memory into your prompt
   print(u.context(max_token_size=500, prefer_topics=["basic_info"]))

If you don't want to use the native Memobase solution then you can opt for the OpenAI or Ollama SDKs.

There's a Playground where you can experience the framework by starting a conversation for Memobase to build its memory as you talk, and see how profiles, events logs, and AI remembers you.

The benchmarks are positive. Running it through the LOCOMO benchmark, Memobase scored 85% on temporal reasoning which is higher than Mem0 or Zep.

Having user profiles and customized memory enables much better UX and advanced capabilities, a perfect fit whether you're building virtual companions, educational tools, or personalized assistants. Memobase facilitates all those.

More Information

Memobase Github
Memobase Main
Memobase Playground 

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