Master Mem0 with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Sign up at app.mem
ai and get your API key from the dashboard under Settings > API Keys Install the Python SDK: pip install mem0ai, then initialize with MemoryClient(api_key="your_key") Add your first memory: m.add([{"role": "user", "content": "I prefer dark mode"}], user_id="user1") Search memories in your app: results = m.search("user interface preferences", user_id="user1") View and manage all stored memories in the Mem0 dashboard at app.mem
ai/memories
💡 Quick Start: Follow these 3 steps in order to get up and running with Mem0 quickly.
Conversation history is raw text that grows linearly and contains noise. Mem0 extracts discrete facts, deduplicates them, resolves conflicts, and retrieves only what's relevant to the current query. It's the difference between carrying a filing cabinet and having a curated address book.
Mem0 supports any LLM provider. By default, it uses GPT-4o-mini for extraction as a balance of quality and cost. You can configure it to use any OpenAI, Anthropic, or local model. Higher-quality models produce better memory extraction but at higher cost per operation.
Each memory add operation requires one LLM call for extraction. With GPT-4o-mini, this is typically $0.001-0.005 per operation. Search operations use vector similarity and are cheaper. For high-volume applications, costs add up — budget approximately $0.01-0.02 per full conversation turn with memory.
Yes. Mem0 provides a LangChain-compatible memory class that drops into existing LangChain chains and agents. There are also integrations for LlamaIndex, CrewAI, and Autogen. The core Python SDK works with any framework.
Now that you know how to use Mem0, it's time to put this knowledge into practice.
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Follow our tutorial and master this powerful ai memory & search tool in minutes.
Tutorial updated March 2026