docs: fix the stale benchmark table and finish the one-liner sweep

Addresses review feedback on #6944. The Performance Improvements table in platform-v2-to-v3.mdx still read 91.6 (+20.2) / 93.4 (+25.6) while the Overview prose in the same file already read 92.5 / 94.4, so the page stated both sets of numbers. Reconciled against the research team's current figures in README.md: 71.4 -> 92.5 (+21.1) and 67.8 -> 94.4 (+26.6). Delivered the three one-liner sweep edits the PR description claimed but never shipped: the README banner alt text, the mem0-ts README title and lede, and the docs/introduction.mdx frontmatter description. The dated changelog entry at 91.6 / 93.4 stays as-is; it records what was announced on 2026-04-14.
Merge branch 'main' into docs/readme-benchmarks-refresh
2026-08-14 18:38:06 +05:30 · 2026-08-14 17:13:37 +05:30 · 2026-08-13 19:27:20 +05:30 · 2026-08-13 18:06:40 +05:30
16 changed files with 19 additions and 19 deletions
@@ -11,7 +11,7 @@
    {
      "name": "mem0",
      "source": "./integrations/mem0-plugin",
-      "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search to Claude workflows.",
+      "description": "Mem0, the memory layer for AI agents. Add persistent memory, personalization, and semantic search to Claude workflows.",
      "version": "0.2.14"
    }
  ]
@@ -11,7 +11,7 @@
    {
      "name": "mem0",
      "source": "./integrations/mem0-plugin",
-      "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search.",
+      "description": "Mem0, the memory layer for AI agents. Add persistent memory, personalization, and semantic search.",
      "version": "0.2.14"
    }
  ]
@@ -1,4 +1,4 @@
-# Mem0 - The Memory Layer for Personalized AI
+# Mem0 - The Memory Layer for AI Agents

 ## Overview

@@ -1,6 +1,6 @@
 <p align="center">
  <a href="https://github.com/mem0ai/mem0">
-    <img src="docs/images/banner-sm.png" width="800px" alt="Mem0 - The Memory Layer for Personalized AI">
+    <img src="docs/images/banner-sm.png" width="800px" alt="Mem0 - The Memory Layer for AI Agents">
  </a>
 </p>
 <p align="center" style="display: flex; justify-content: center; gap: 20px; align-items: center;">
@@ -1,7 +1,7 @@
 {
  "$schema": "https://mintlify.com/docs.json",
  "name": "Mem0",
-  "description": "Mem0 is a self-improving memory layer for LLM applications, enabling personalized AI experiences that save costs and delight users.",
+  "description": "Mem0 is the memory layer for AI agents, giving them persistent, personalized context across sessions.",
  "theme": "aspen",
  "colors": {
    "primary": "#8F74E0",
@@ -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

@@ -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
@@ -1,6 +1,6 @@
 ---
 title: "Build AI apps that remember"
-description: "Add persistent, self-improving memory to your AI app with Mem0 Platform or self-hosted Open Source."
+description: "Give your AI agents persistent memory with Mem0 Platform or self-hosted Open Source."
 mode: "custom"
 ---

@@ -20,7 +20,7 @@ The new Mem0 release redesigns both extraction and retrieval, and cleans up the
 - **SDK cleanup**: Deprecated parameters removed, naming conventions standardized
 - **API surface aligned with Platform**: Entity IDs now follow the same convention across OSS and Platform: top-level kwargs for `add()` / `delete_all()`, inside `filters` for `search()` / `get_all()`

-These changes produce a **+20 point improvement on LoCoMo** (71.4 → 91.6) and **+26 point improvement on LongMemEval** (67.8 → 93.4), while cutting extraction latency roughly in half.
+These changes produce a **+21 point improvement on LoCoMo** (71.4 → 92.5) and **+27 point improvement on LongMemEval** (67.8 → 94.4), while cutting extraction latency roughly in half.

 ## Breaking Changes

@@ -11,7 +11,7 @@ iconType: "solid"

 ## Overview

-The new Mem0 memory algorithm is a ground-up redesign of how memories are extracted, stored, and retrieved. It scores **91.6 on LoCoMo** and **93.4 on LongMemEval**: a +20 and +26 point improvement over the previous algorithm: while cutting extraction latency roughly in half.
+The new Mem0 memory algorithm is a ground-up redesign of how memories are extracted, stored, and retrieved. It scores **92.5 on LoCoMo** and **94.4 on LongMemEval**, a +21 and +27 point improvement over the previous algorithm, while cutting extraction latency roughly in half.

 | What Changed | Before | After |
 |---|---|---|
@@ -293,8 +293,8 @@ If your application previously read graph relations from the API response (`rela

 | Metric | Previous Algorithm | New Algorithm |
 |---|---|---|
-| **LoCoMo Overall** | 71.4 | **91.6** (+20.2) |
-| **LongMemEval Overall** | 67.8 | **93.4** (+25.6) |
+| **LoCoMo Overall** | 71.4 | **92.5** (+21.1) |
+| **LongMemEval Overall** | 67.8 | **94.4** (+26.6) |
 | **Extraction latency (p50)** | ~2.0s | **~1.0s** |
 | **Mean tokens per query** | N/A | 6.8-7.0K (top200) |

@@ -4,7 +4,7 @@ description: "Managed memory layer for AI agents, production-ready in minutes"
 icon: "cloud"
 ---

-Mem0 Platform is the fully managed memory layer for your AI apps and agents. Your users stop repeating themselves and your agents keep context across sessions, with no vector store, reranker, or infrastructure to run.
+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.

 ## Why teams pick the Platform

@@ -1,7 +1,7 @@
 {
  "name": "mem0",
  "version": "0.2.13",
-  "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search using the Mem0 Platform MCP server.",
+  "description": "Mem0, the memory layer for AI agents. Add persistent memory, personalization, and semantic search using the Mem0 Platform MCP server.",
  "author": {
    "name": "Mem0",
    "email": "support@mem0.ai"
@@ -16,7 +16,7 @@ compatibility: Requires Python 3.10+ or Node.js 18+, pip install mem0ai or npm i
 > - **mem0** (this skill) -- Platform Client SDK + OSS (Python + TypeScript)
 > - **[mem0-vercel-ai-sdk](https://github.com/mem0ai/mem0/tree/main/skills/mem0-vercel-ai-sdk)** -- Vercel AI SDK provider

-Mem0 is a managed memory layer for AI applications. It stores, retrieves, and manages user memories via API — no infrastructure to deploy. For self-hosted usage, see the OSS section in the client references below.
+Mem0 is the managed memory layer for AI agents. It stores, retrieves, and manages user memories via API, no infrastructure to deploy. For self-hosted usage, see the OSS section in the client references below.

 ## Step 1: Install and authenticate

@@ -1,6 +1,6 @@
-# Mem0 - The Memory Layer for Your AI Apps
+# Mem0 - The Memory Layer for AI Agents

-Mem0 is a self-improving memory layer for LLM applications, enabling personalized AI experiences that save costs and delight users. We offer both cloud and open-source solutions to cater to different needs.
+Mem0 is the memory layer for AI agents. We offer both cloud and open-source solutions to cater to different needs.

 See the complete [OSS Docs](https://docs.mem0.ai/open-source/node-quickstart).
 See the complete [Platform API Reference](https://docs.mem0.ai/api-reference).
@@ -1,7 +1,7 @@
 {
  "name": "mem0ai",
  "version": "3.1.6",
-  "description": "The Memory Layer For Your AI Apps",
+  "description": "The memory layer for AI agents",
  "main": "./dist/index.js",
  "module": "./dist/index.mjs",
  "types": "./dist/index.d.ts",
@@ -28,7 +28,7 @@ compatibility: Requires Python 3.10+ or Node.js 18+, pip install mem0ai or npm i
 > - **[mem0-cli](../mem0-cli/SKILL.md)** ([GitHub](https://github.com/mem0ai/mem0/tree/main/skills/mem0-cli)) -- Command-line interface
 > - **[mem0-vercel-ai-sdk](../mem0-vercel-ai-sdk/SKILL.md)** ([GitHub](https://github.com/mem0ai/mem0/tree/main/skills/mem0-vercel-ai-sdk)) -- Vercel AI SDK provider

-Mem0 is a managed memory layer for AI applications. It stores, retrieves, and manages user memories via API — no infrastructure to deploy. For self-hosted usage, see the OSS section in the client references below.
+Mem0 is the managed memory layer for AI agents. It stores, retrieves, and manages user memories via API, no infrastructure to deploy. For self-hosted usage, see the OSS section in the client references below.

 ## Step 1: Install and authenticate
Author	SHA1	Message	Date
kartik-mem0	08a5a931e0	docs: fix the stale benchmark table and finish the one-liner sweep Addresses review feedback on #6944. The Performance Improvements table in platform-v2-to-v3.mdx still read 91.6 (+20.2) / 93.4 (+25.6) while the Overview prose in the same file already read 92.5 / 94.4, so the page stated both sets of numbers. Reconciled against the research team's current figures in README.md: 71.4 -> 92.5 (+21.1) and 67.8 -> 94.4 (+26.6). Delivered the three one-liner sweep edits the PR description claimed but never shipped: the README banner alt text, the mem0-ts README title and lede, and the docs/introduction.mdx frontmatter description. The dated changelog entry at 91.6 / 93.4 stays as-is; it records what was announced on 2026-04-14.	2026-08-14 18:38:06 +05:30
kartik-mem0	bc5a7d763d	Merge branch 'main' into docs/readme-benchmarks-refresh	2026-08-14 17:13:37 +05:30
kartik-mem0	937fb01a0c	Revert the README rewrite, keep the benchmark and one-liner fixes The README first-fold restructure is being handled separately, so drop it from this PR rather than block the two changes that do not depend on it. README.md and mem0-ts/README.md go back to main verbatim. What stays: - docs/migration/oss-v2-to-v3.mdx and platform-v2-to-v3.mdx still said 91.6 LoCoMo / 93.4 LongMemEval. main's README already publishes 92.5 and 94.4, so the migration guides were the stale side of that disagreement, not the README. - the product one-liner is unified across the 11 other places that each worded it differently.	2026-08-13 19:27:20 +05:30
kartik-mem0	c8b93de8e9	docs: refresh README first fold, reconcile stale benchmark numbers, unify the one-liner 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.	2026-08-13 18:06:40 +05:30