kartik-mem0
c8b93de8e9
README first fold: - banner 800px -> 520px - badges condensed from two paragraphs into one row (Trendshift kept, inline) - lead with Introduction instead of benchmarks - new How it works section explaining the add/search loop - benchmarks moved below the intro and retitled from the date-stamped New Memory Algorithm (April 2026) - dropped Research Highlights, which restated the benchmark table verbatim Benchmarks: the README was already correct at 92.5 / 94.4, matching mem0.ai/research and docs/core-concepts/memory-evaluation.mdx. The stale copies were in the migration guides, which quote the numbers as a live reason to upgrade. Updated both to 92.5 / 94.4 (+21 / +27). docs/changelog/highlights.mdx keeps 91.6 / 93.4 inside its dated 2026-04-14 entry, which records what was announced at the time. One-liner: standardized on 'the memory layer for AI agents', already the canonical form in cli-spec.json, cli/python/pyproject.toml and the CLI specification. Swept the remaining variants.
63 lines
2.5 KiB
Plaintext
63 lines
2.5 KiB
Plaintext
---
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title: "Overview"
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description: "Managed memory layer for AI agents, production-ready in minutes"
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icon: "cloud"
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---
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Mem0 Platform is the fully managed memory layer for AI agents. Your users stop repeating themselves and your agents keep context across sessions, with no vector store, reranker, or infrastructure to run.
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## Why teams pick the Platform
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- **Personalized replies.** Memories persist across users and agents, cutting prompt bloat and repeat questions.
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- **Zero infrastructure.** Mem0 runs the vector store and rerankers, so there is nothing to provision, tune, or maintain.
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- **Enterprise-ready.** Audit logs and workspace governance ship by default.
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## How it works
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<Steps>
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<Step title="Add">
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Send Mem0 your messages and conversations.
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</Step>
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<Step title="Extract and store">
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Mem0 distills them into facts and links entities across memories.
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</Step>
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<Step title="Recall">
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At query time, Mem0 returns only the most relevant memories.
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</Step>
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</Steps>
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For the full pipeline, see [How Mem0 works](/core-concepts/how-it-works).
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<AccordionGroup>
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<Accordion title="What you get with Mem0 Platform" icon="sparkles">
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| Feature | Why it helps |
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| --- | --- |
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| Fast setup | Add a few lines of code and you're production-ready, with no vector database or LLM configuration required. |
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| Production scale | Automatic scaling, high availability, and managed infrastructure so you focus on product work. |
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| Advanced features | Webhooks, multimodal support, and custom categories are ready to enable. |
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| Enterprise ready | Audit logs, workspace governance, and dedicated support keep security and governance covered. |
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</Accordion>
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</AccordionGroup>
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## Explore the Platform
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<CardGroup cols={2}>
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<Card title="How Mem0 works" icon="diagram-project" href="/core-concepts/how-it-works">
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The extraction and retrieval pipeline, end to end.
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</Card>
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<Card title="Run the quickstart" icon="rocket" href="/platform/quickstart">
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Get an API key and save your first memory.
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</Card>
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<Card title="Understand memory types" icon="brain" href="/core-concepts/memory-types">
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How user, agent, app, and run memory differ.
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</Card>
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<Card title="Add, search, and update" icon="layer-group" href="/core-concepts/memory-operations/add">
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The core memory operations, end to end.
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</Card>
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</CardGroup>
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<Tip>
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Deciding whether to self-host? Compare hosting models in the <Link href="/platform/platform-vs-oss">Platform vs OSS guide</Link>, or switch tabs to the Open Source docs to run Mem0 yourself.
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</Tip>
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