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mem0

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Basic Information

Mem0 provides a modular memory layer designed to be embedded into AI assistants and autonomous agents to enable long-term, personalized interactions. The repository supplies SDKs, APIs, and a managed platform as well as a self-hosted option so developers can persist and retrieve user, session, and agent state over time. Its primary purpose is to deliver a production-ready memory infrastructure that reduces token costs and latency while improving response accuracy, and to support use cases such as personalized chatbots, customer support systems, healthcare assistants, and adaptive gaming or productivity environments. The project includes research-backed performance claims, a reference implementation, installation packages for Python and JavaScript, and integrations and demos to help teams add context-aware memory to their LLM-powered applications.

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App URL
https://github.com/mem0ai/mem0
Github Repository
https://github.com/mem0ai/mem0/blob/main/README.md

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App Details

Features
Mem0 implements multi-level memory that stores User, Session, and Agent state and offers adaptive personalization. It exposes a developer-friendly API and cross-platform SDKs with both hosted and self-hosted deployment models. The repo documents basic operations such as searching relevant memories and adding new memories, and it lists supported LLMs with gpt-4o-mini as the default example. It advertises a local OpenMemory MCP option for private memory management. Integrations and demos include ChatGPT with Memory, a browser extension, and connectors for Langgraph and CrewAI. Research highlights claim higher accuracy, lower token usage, and faster responses compared to full-context approaches. Packages are installable via pip and npm and the project is released under Apache 2.0.
Use Cases
Mem0 helps developers and teams add persistent, context-rich memory to LLM applications so assistants behave consistently and personalize over time. By retaining relevant past interactions it enables more contextual answers, recall of prior tickets or preferences, and continuity across sessions which is useful for customer support, healthcare personalization, and adaptive user experiences. The memory layer is designed to cut token costs and latency compared to sending full conversation history, and the project provides both managed hosting and self-hosting options to meet privacy and operational requirements. Developer tooling, quickstart examples, and integrations make it easier to integrate memory into existing LLM pipelines and production systems.

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