What it does
Mem0 adds, searches, and updates application memories for users or agents. Its integrations support different vector stores and optional graph-backed memory.
Why it’s interesting
Persistent memory is a data lifecycle problem as much as a retrieval problem: useful systems need to reconcile new statements with stored facts and support deletion.
Real-world use case
Remember a user’s preferred programming language across sessions without resending the full conversation history.
What I can learn
- 01
Memory extraction
turn dialogue into candidate facts.
- 02
Reconciliation
update or remove stale memories.
- 03
Retrieval
combine semantic relevance with user and session filters.
One thing to explore
Memory add and search flow
Follow the mem0 implementation and connect it to the concepts above.
Explore the source (opens in a new tab)10-Minute Challenge
Add then retrieve a sample memory
Before you start: Python. The default Memory setup may need model-provider configuration; consult the current quickstart if initialization requests it.
- Install the Python package in an isolated environment.
- Run the script; local model and database setup may be needed on first use.
- Inspect the returned memory records and try searching for a different detail.
pip install mem0aifrom mem0 import Memory
memory = Memory()memory.add("I build apps with FastAPI and Next.js.", user_id="demo")print(memory.search("Which backend framework?", user_id="demo"))