mem0
VerifiedMem0 adds persistent memory to AI agents for personalized responses.
What is mem0?
Mem0 is an open-source memory layer that gives AI assistants and agents the ability to retain context over time. It supports user-level, session-level, and agent-level memory so conversations stay consistent without manual state management.
The latest algorithm performs single-pass fact extraction, links entities across entries, and combines semantic, keyword, and temporal signals for retrieval. This produces higher accuracy on long-context benchmarks while keeping token usage low.
It targets builders of customer-support bots, personal assistants, healthcare tools, and productivity agents who need reliable recall of past interactions without rebuilding memory logic from scratch.
Capabilities
What you can build with mem0
Customer Support
Recall prior tickets and user preferences to deliver consistent, context-aware replies across multiple sessions.
AI Assistants
Maintain long-term user facts so conversations feel continuous and personalized rather than stateless.
Healthcare Applications
Track patient history and stated preferences to support more tailored care recommendations.
Install mem0
npm install -g @mem0/cli# 1. Install
npm install -g @mem0/cli # or: pip install mem0-cli
# 2. Sign up as an agent (replace `claude-code` with your name)
mem0 init --agent --agent-caller claude-code
# 3. Add a memory
mem0 add "I am using mem0"
# 4. Search
mem0 search "am I using mem0"- 1Install the CLI globally with npm or pip.
- 2Run mem0 init with the --agent flag and your agent name to obtain an API key.
- 3Use the add command to store a memory string.
- 4Query stored memories with the search command.
- 5Later claim the account with an email if needed; existing memories remain intact.
mem0: pros & cons
Pros
- +Strong benchmark gains on long-context memory tasks with modest token cost.
- +Agent-first signup flow removes email friction for quick testing.
- +Entity linking and temporal scoring improve retrieval relevance.
- +Open-source evaluation framework lets users reproduce results.
Cons
- –New algorithm requires migration steps from earlier versions.
- –Self-hosted setup still needs separate infrastructure for production scale.
- –Performance claims tied to specific model stacks used in benchmarks.
Frequently asked questions
No, the core library is open source and can be self-hosted; a managed option also exists.
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