Mem0 is described as a "universal memory layer" for LLM applications that compresses and stores conversational memories so applications can preserve context, reduce token usage and latency, and personalize interactions across sessions, users, and agents. Core claims include memory compression to lower tokens and latency, hybrid retrieval combining vector search with an optional graph memory, production observability, and broad framework compatibility via a hosted managed platform and a self-hosted open-source distribution. Key capabilities described in the visited pages include multiple memory storage types (user, agent, session) that are timestamped, versioned, and exportable; memory compression and reranking to reduce prompt size and token costs; retrieval options including vector search (with optional reranker), graph memory (entity/relationship extraction mirrored to graph DBs such as Neo4j, Memgraph, Neptune, Kuzu) to enrich results while keeping vector ranking as primary, and advanced metadata/criteria filters. Mem0 supports multimodal inputs (e.g., images), async memory operations for high-throughput non-blocking writes, custom fact extraction and per-request toggles (for example, toggling graph writes), OpenAI-compatible endpoints for migration/self-hosting, and REST API plus SDKs (Python and JavaScript) with integration cookbooks and demos. Deployment options include a fully managed hosted Mem0 Platform and a self-hosted Mem0 OSS distribution suitable for on-prem, private cloud, Kubernetes, or air-gapped deployments; graph and vector stores can be self-managed or hosted (docs reference Neo4j Aura in a quickstart). Security and governance items called out in the docs and marketing include SOC 2, GDPR compliance, audit logs, workspace governance, HIPAA and BYOK mentioned as enterprise options, and traceability features (timestamped/versioned/exportable memories, audit logs, tracing for TTL/size/access). Pricing tiers listed on the pricing page include a Hobby free tier, Starter ($19/month), Pro ($249/month), and Enterprise flexible pricing; an alternative usage-based option is referenced for some customers. The site also advertises a Startup Program offering qualifying startups 3 months of Pro free. Benchmark claims on the homepage assert improvements versus OpenAI memory (the site cites statements such as "~26% higher response quality" and "~90% fewer tokens" in benchmarking claims); these are vendor claims and the docs note that such benchmark statements should be independently verified if needed. Integrations and ecosystem items referenced include LangGraph, CrewAI, LlamaIndex, Vercel AI SDK, Python and JavaScript SDKs, and multiple demos/cookbooks with demo code published to GitHub (docs reference a Mem0 demo GitHub repo). The docs include quickstarts, cookbooks, an open-source repo and contribution docs, and API reference and platform overview pages for CRUD and search endpoints. Known notes/caveats from the extraction: pricing page content is duplicated in multiple places on the site (an automated extraction encountered a validation error due to many features listed in a single plan), and homepage benchmark claims are vendor statements that should be verified externally.