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docs: refresh README first fold, reconcile stale benchmark numbers, unify the one-liner

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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.
This commit is contained in:
kartik-mem0
2026-08-13 18:06:40 +05:30
parent 96d45b78c7
commit c8b93de8e9
15 changed files with 57 additions and 72 deletions
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docs/integrations/raycast.mdx
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@@ -3,7 +3,7 @@ title: "Raycast Extension"
description: "Mem0 Raycast extension for intelligent memory management"
---
Mem0 is a self-improving memory layer for LLM applications, enabling personalized AI experiences that save costs and delight users. This extension lets you store and retrieve text snippets using Mem0's intelligent memory system. Find Mem0 in [Raycast Store](https://www.raycast.com/dev_khant/mem0) for using it.
Mem0 is the memory layer for AI agents, giving them persistent, personalized context across sessions. This extension lets you store and retrieve text snippets using Mem0's intelligent memory system. Find Mem0 in [Raycast Store](https://www.raycast.com/dev_khant/mem0) for using it.
## Getting Started
docs/integrations/respan.mdx
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@@ -7,7 +7,7 @@ Build AI applications with persistent memory and comprehensive LLM observability
## Overview
Mem0 is a self-improving memory layer for LLM applications, enabling personalized AI experiences that save costs and delight users. Respan (formerly Keywords AI) provides complete LLM observability.
Mem0 is the memory layer for AI agents, giving them persistent, personalized context across sessions. Respan (formerly Keywords AI) provides complete LLM observability.
Combining Mem0 with Respan allows you to:
1. Add persistent memory to your AI applications