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Pricing sourced from Mem0 · Last verified March 2026
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View Full Features →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.
Open-source Python framework that orchestrates autonomous AI agents collaborating as teams to accomplish complex workflows. Define agents with specific roles and goals, then organize them into crews that execute sequential or parallel tasks. Agents delegate work, share context, and complete multi-step processes like market research, content creation, and data analysis. Supports 100+ LLM providers through LiteLLM integration and includes memory systems for agent learning. Features 48K+ GitHub stars with active community.
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