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mem0ai/mem0: The memory layer for Personalized AI

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Mem0 provides a smart, self-improving memory layer for Large Language Models, enabling personalized AI experiences across applications. Note: The Mem0 repository now also includes the Embedchain project. We continue to maintain and support Embedchain ❤️. You can find the Embedchain codebase in the embedchain directory. For detailed usage instructions and API reference, visit our documentation at docs.mem0.ai. For production environments, you can use Qdrant as a vector store: Join our Slack or Discord community for support and discussions. If you have any questions, feel free to reach out to us using one of the following methods: The memory layer for Personalized AI

Learn more · Join Discord · Demo 📄 Benchmarking Mem0's token-efficient memory algorithm → New Memory Algorithm (April 2026) Benchmark Old New Tokens Latency p50 LoCoMo 71.4 92.5 7.0K 0.88s LongMemEval 67.8 94.4 6.8K 1.09s BEAM (1M) — 64.1 6.7K 1.00s BEAM (10M) — 48.6 6.9K 1.05s All benchmarks run on the same production-representative model stack. Single-pass retrieval (one call, no agentic loops) at a top_200 retrieval budget. Scores reflect Mem0's managed platform, which includes proprietary optimizations not available in the open-source SDK; open-source users should expect…

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