Fix broken link to removed expiration-date page

Generated-By: mintlify-agent

.claude-plugin/marketplace.json

@@ -12,7 +12,7 @@
       "name": "mem0",
       "source": "./mem0-plugin",
       "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search to Claude workflows.",
-      "version": "0.1.1"
+      "version": "0.1.0"
     }
   ]
 }

.cursor-plugin/marketplace.json

@@ -12,7 +12,7 @@
       "name": "mem0",
       "source": "./mem0-plugin",
       "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search.",
-      "version": "0.1.1"
+      "version": "0.1.0"
     }
   ]
 }

.github/workflows/ci.yml

@@ -14,48 +14,8 @@ on:
       - 'mem0/**'
       - 'tests/**'
       - 'embedchain/**'
-      - 'pyproject.toml'
 
 jobs:
-  changelog_check:
-    if: github.event_name == 'pull_request'
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Require CHANGELOG entry when Python SDK version changes
-        env:
-          BASE_SHA: ${{ github.event.pull_request.base.sha }}
-          HEAD_SHA: ${{ github.event.pull_request.head.sha }}
-        run: |
-          set -euo pipefail
-
-          extract_version() {
-            python3 -c "import sys, re; m = re.search(r'^\s*version\s*=\s*\"([^\"]+)\"', sys.stdin.read(), re.M); print(m.group(1) if m else '')"
-          }
-
-          base_version=$(git show "$BASE_SHA:pyproject.toml" 2>/dev/null | extract_version || echo "")
-          head_version=$(extract_version < pyproject.toml)
-
-          echo "Base version: ${base_version:-<unknown>}"
-          echo "Head version: $head_version"
-
-          if [ -z "$base_version" ] || [ "$base_version" = "$head_version" ]; then
-            echo "pyproject.toml version unchanged — no CHANGELOG entry required."
-            exit 0
-          fi
-
-          echo "Detected version bump ${base_version} -> ${head_version}. Checking docs/changelog/sdk.mdx…"
-
-          if git diff --name-only "$BASE_SHA" "$HEAD_SHA" -- docs/changelog/sdk.mdx | grep -q .; then
-            echo "Changelog update present in docs/changelog/sdk.mdx ✅"
-          else
-            echo "::error file=pyproject.toml::pyproject.toml version changed from ${base_version} to ${head_version} but docs/changelog/sdk.mdx was not updated in this PR. Add a new <Update> entry under the Python tab for v${head_version}."
-            exit 1
-          fi
-
   check_changes:
     runs-on: ubuntu-latest
     outputs:

.github/workflows/docs-llms-txt-check.yml

@@ -1,45 +0,0 @@
-name: docs - llms.txt check
-
-# Blocks PRs that introduce new .mdx pages without a matching entry in
-# docs/llms.txt, or that link to pages that no longer exist. Contributors
-# must update docs/llms.txt in the same PR. Run locally with:
-#   python scripts/check-llms-txt-coverage.py           # read-only
-#   python scripts/check-llms-txt-coverage.py --write   # scaffold placeholders
-
-on:
-  pull_request:
-    paths:
-      - 'docs/**/*.mdx'
-      - 'docs/llms.txt'
-      - 'scripts/check-llms-txt-coverage.py'
-      - 'scripts/llms-txt-ignore.txt'
-  workflow_dispatch: {}
-
-permissions:
-  contents: read
-
-jobs:
-  check-llms-txt:
-    runs-on: ubuntu-24.04-arm
-    timeout-minutes: 2
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Verify docs/llms.txt coverage
-        run: |
-          if ! python3 scripts/check-llms-txt-coverage.py; then
-            echo ""
-            echo "::error title=llms.txt out of sync::docs/llms.txt does not match docs/**/*.mdx."
-            echo ""
-            echo "To fix:"
-            echo "  1. Run locally:  python scripts/check-llms-txt-coverage.py --write"
-            echo "     This appends placeholder entries under '## Unclassified - needs triage'."
-            echo "  2. For each placeholder:"
-            echo "       - replace [TODO: Platform|OSS|Both] with the correct scope tag"
-            echo "       - rewrite the description as 'Use when ...'"
-            echo "       - move the entry into the appropriate section"
-            echo "       - delete the '## Unclassified - needs triage' heading once empty"
-            echo "  3. Resolve any stale URLs listed above by updating or removing the link."
-            echo "  4. Commit the updated docs/llms.txt to this PR."
-            exit 1
-          fi

.github/workflows/ts-sdk-ci.yml

@@ -24,42 +24,6 @@ jobs:
             ts_sdk:
               - 'mem0-ts/**'
 
-  changelog_check:
-    needs: check_changes
-    if: github.event_name == 'pull_request' && needs.check_changes.outputs.ts_sdk_changed == 'true'
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Require CHANGELOG entry when SDK version changes
-        env:
-          BASE_SHA: ${{ github.event.pull_request.base.sha }}
-          HEAD_SHA: ${{ github.event.pull_request.head.sha }}
-        run: |
-          set -euo pipefail
-
-          base_version=$(git show "$BASE_SHA:mem0-ts/package.json" 2>/dev/null | jq -r .version || echo "")
-          head_version=$(jq -r .version mem0-ts/package.json)
-
-          echo "Base version: ${base_version:-<unknown>}"
-          echo "Head version: $head_version"
-
-          if [ -z "$base_version" ] || [ "$base_version" = "$head_version" ]; then
-            echo "mem0-ts/package.json version unchanged — no CHANGELOG entry required."
-            exit 0
-          fi
-
-          echo "Detected version bump ${base_version} -> ${head_version}. Checking docs/changelog/sdk.mdx…"
-
-          if git diff --name-only "$BASE_SHA" "$HEAD_SHA" -- docs/changelog/sdk.mdx | grep -q .; then
-            echo "Changelog update present in docs/changelog/sdk.mdx ✅"
-          else
-            echo "::error file=mem0-ts/package.json::mem0-ts/package.json version changed from ${base_version} to ${head_version} but docs/changelog/sdk.mdx was not updated in this PR. Add a new <Update> entry under the TypeScript tab for v${head_version}."
-            exit 1
-          fi
-
   build_ts_sdk:
     needs: check_changes
     if: needs.check_changes.outputs.ts_sdk_changed == 'true'

.gitignore

@@ -4,10 +4,6 @@ __pycache__/
 *$py.class
 **/node_modules/
 
-# Self-hosted server local runtime state
-server/history/
-server/.env
-
 # C extensions
 *.so
 
@@ -19,8 +15,8 @@ dist/
 downloads/
 eggs/
 .eggs/
-/lib/
-/lib64/
+lib/
+lib64/
 parts/
 sdist/
 var/

AGENTS.md

@@ -27,7 +27,7 @@ This is a **polyglot monorepo** containing Python and TypeScript packages, CLIs,
 | `server/` | FastAPI REST server for self-hosted Mem0 (Docker: FastAPI + PostgreSQL/pgvector + Neo4j) |
 | `openmemory/` | Self-hosted memory platform — `api/` (FastAPI + Alembic + MCP server) and `ui/` (Next.js 15 + React 19) |
 | `mem0-plugin/` | AI editor plugins (Claude Code, Cursor, Codex) — MCP server connection, lifecycle hooks, skills |
-| `skills/` | Claude Code skill definitions. Reference skills (SDK knowledge, always-on): `mem0/`, `mem0-cli/`, `mem0-vercel-ai-sdk/`. Pipeline skills (run on demand): `mem0-integrate/`, `mem0-test-integration/` |
+| `skills/` | Claude Code skill definitions — `mem0/`, `mem0-cli/`, `mem0-vercel-ai-sdk/` |
 | `docs/` | Documentation site (Mintlify) |
 | `tests/` | Python SDK tests (pytest) |
 | `evaluation/` | Benchmarking framework — LOCOMO evals, experiment runner, score generation |
@@ -35,7 +35,6 @@ This is a **polyglot monorepo** containing Python and TypeScript packages, CLIs,
 | `cookbooks/` | Jupyter notebooks — customer support chatbot, AutoGen integration |
 | `embedchain/` | Legacy Embedchain RAG framework (maintained separately, Poetry-based) |
 | `pr-reviews/` | Pull request review materials |
-| `scripts/` | Repo-wide utility scripts (e.g., `check-llms-txt-coverage.py` for docs/llms.txt sync) |
 
 ### Core Package Dependencies
 
@@ -387,9 +386,7 @@ Model Context Protocol support in multiple places:
 ### Plugin & Skills System
 
 - `mem0-plugin/` provides integrations for Claude Code, Cursor, and Codex via MCP server connections and lifecycle hooks for automatic memory capture.
-- `skills/` contains structured skill definitions for AI agents, split into two categories:
-  - **Reference skills** (always-on SDK knowledge): `mem0` (Python + TS SDKs, framework integrations), `mem0-cli` (terminal workflows), `mem0-vercel-ai-sdk` (Vercel AI provider).
-  - **Pipeline skills** (run on demand): `mem0-integrate` wires Mem0 into an existing repo via a TDD pipeline; `mem0-test-integration` verifies what the integrator produced on the same branch. The two are loosely coupled via `.mem0-integration/` artifacts.
+- `skills/` contains structured skill definitions for AI agents, covering SDK usage, CLI workflows, and Vercel AI SDK patterns.
 
 ### Adding a New Provider
 
@@ -436,7 +433,6 @@ To add a new LLM, embedding, vector store, or reranker provider:
 |----------|------|---------|
 | Issue Labeler | `issue-labeler.yml` | Automatic issue labeling |
 | Stale Bot | `stale.yml` | Marks stale issues and PRs |
-| llms.txt Check | `docs-llms-txt-check.yml` | Blocks PRs touching `docs/**/*.mdx` when `docs/llms.txt` is out of sync. Fix locally with `python scripts/check-llms-txt-coverage.py --write`. |
 
 ## Task Completion Guidelines
 
@@ -455,7 +451,6 @@ These guidelines outline typical artifacts for different task types. Use judgmen
 2. **Unit tests**: Comprehensive test coverage for new functionality
 3. **Documentation**: Update relevant docs in `docs/` for public APIs
 4. **Examples**: Add usage examples if the feature introduces new user-facing behavior
-5. **llms.txt**: Any new `.mdx` page under `docs/` must be linked in `docs/llms.txt` with a scope tag (`[Platform]` / `[OSS]` / `[Both]`) and a `Use when ...` description. The `docs-llms-txt-check.yml` workflow runs on every PR that touches docs and **fails the check** if the index is out of sync. To fix: run `python scripts/check-llms-txt-coverage.py --write` locally to scaffold placeholders under `## Unclassified - needs triage`, then replace the `[TODO: ...]` tags, rewrite descriptions as `Use when ...`, move entries into the right section, and delete the triage heading when empty.
 
 ### New Provider (LLM / Embedding / Vector Store / Reranker)
 

LLM.md

@@ -1313,7 +1313,7 @@ async def delete_memory(memory_id: str):
 - **Documentation**: https://docs.mem0.ai
 - **GitHub Repository**: https://github.com/mem0ai/mem0
 - **Discord Community**: https://mem0.dev/DiG
-- **Platform**: https://app.mem0.ai?utm_source=oss&utm_medium=llm
+- **Platform**: https://app.mem0.ai
 - **Research Paper**: https://mem0.ai/research
 - **Examples**: https://github.com/mem0ai/mem0/tree/main/examples
 

Makefile

@@ -42,6 +42,9 @@ clean:
 test:
 	hatch run test
 
+test-py-3.9:
+	hatch run dev_py_3_9:test
+
 test-py-3.10:
 	hatch run dev_py_3_10:test
 

README.md

@@ -39,7 +39,7 @@
 </p>
 
 <p align="center">
-  <a href="https://mem0.ai/research"><strong>📄 Benchmarking Mem0's token-efficient memory algorithm →</strong></a>
+  <a href="https://mem0.ai/research"><strong>📄 Building Production-Ready AI Agents with Scalable Long-Term Memory →</strong></a>
 </p>
 
 ## New Memory Algorithm (April 2026)
@@ -85,17 +85,18 @@ See the [migration guide](https://docs.mem0.ai/migration/oss-v2-to-v3) for upgra
 
 ## 🚀 Quickstart Guide <a name="quickstart"></a>
 
-| | Library | Self-Hosted Server | Cloud Platform |
-|---|---------|-------------------|----------------|
-| **Best for** | Testing, prototyping | Teams running on their own infrastructure | Zero-ops production use |
-| **Setup** | `pip install mem0ai` | `docker compose up` | Sign up at [app.mem0.ai](https://app.mem0.ai?utm_source=oss&utm_medium=readme) |
-| **Dashboard** | -- | [Yes](https://docs.mem0.ai/open-source/setup) | Yes |
-| **Auth & API Keys** | -- | Yes | Yes |
-| **Advanced Features** | -- | Teasers | All included |
+Choose between our hosted platform or self-hosted package:
 
-Just testing? Use the library. Building for a team? Self-hosted. Want zero ops? Cloud.
+### Hosted Platform
 
-### Library (pip / npm)
+Get up and running in minutes with automatic updates, analytics, and enterprise security.
+
+1. Sign up on [Mem0 Platform](https://app.mem0.ai)
+2. Embed the memory layer via SDK or API keys
+
+### Self-Hosted (Open Source)
+
+Install the sdk via pip:
 
 ```bash
 pip install mem0ai
@@ -109,30 +110,10 @@ python -m spacy download en_core_web_sm
 ```
 
 Install sdk via npm:
-
 ```bash
 npm install mem0ai
 ```
 
-### Self-Hosted Server
-
-> **Note:** Self-hosted auth is on by default. Upgrading from a pre-auth build? Set `ADMIN_API_KEY`, register an admin through the wizard, or `AUTH_DISABLED=true` for local dev only. See [upgrade notes](https://docs.mem0.ai/open-source/setup#upgrade-notes).
-
-```bash
-# Recommended: one command — start the stack, create an admin, issue the first API key.
-cd server && make bootstrap
-
-# Manual: start the stack and finish setup via the browser wizard.
-cd server && docker compose up -d    # http://localhost:3000
-```
-
-See the [self-hosted docs](https://docs.mem0.ai/open-source/overview) for configuration.
-
-### Cloud Platform
-
-1. Sign up on [Mem0 Platform](https://app.mem0.ai?utm_source=oss&utm_medium=readme)
-2. Embed the memory layer via SDK or API keys
-
 ### CLI
 
 Manage memories from your terminal:
@@ -147,27 +128,6 @@ mem0 search "What does Alice prefer?" --user-id alice
 
 See the [CLI documentation](https://docs.mem0.ai/platform/cli) for the full command reference.
 
-### Agent Skills
-
-Teach your AI coding assistant (Claude Code, Codex, Cursor, Windsurf, OpenCode, OpenClaw, and any tool that supports the skills standard) how to build with Mem0. Two categories:
-
-**Reference skills — always on** (SDK knowledge loaded into the assistant's context):
-
-```bash
-npx skills add https://github.com/mem0ai/mem0 --skill mem0
-npx skills add https://github.com/mem0ai/mem0 --skill mem0-cli
-npx skills add https://github.com/mem0ai/mem0 --skill mem0-vercel-ai-sdk
-```
-
-**Pipeline skills — run on demand** (execute an end-to-end workflow in an existing repo):
-
-```bash
-npx skills add https://github.com/mem0ai/mem0 --skill mem0-integrate
-npx skills add https://github.com/mem0ai/mem0 --skill mem0-test-integration
-```
-
-Use `/mem0-integrate` to wire Mem0 into an existing repo via a test-first pipeline, then `/mem0-test-integration` to verify. See the [skills catalog](./skills/) or [Vibecoding with Mem0](https://docs.mem0.ai/vibecoding) for the full picture.
-
 ### Basic Usage
 
 Mem0 requires an LLM to function, with `gpt-5-mini` from OpenAI as the default. However, it supports a variety of LLMs; for details, refer to our [Supported LLMs documentation](https://docs.mem0.ai/components/llms/overview).

cli/node/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mem0/cli",
-  "version": "0.2.4",
+  "version": "0.2.3",
   "description": "The official CLI for mem0 — the memory layer for AI agents",
   "type": "module",
   "bin": {

cli/node/src/backend/base.ts

@@ -15,6 +15,7 @@ export interface AddOptions {
 	infer?: boolean;
 	expires?: string;
 	categories?: string[];
+	enableGraph?: boolean;
 }
 
 export interface SearchOptions {
@@ -28,6 +29,7 @@ export interface SearchOptions {
 	keyword?: boolean;
 	filters?: Record<string, unknown>;
 	fields?: string[];
+	enableGraph?: boolean;
 }
 
 export interface ListOptions {
@@ -40,6 +42,7 @@ export interface ListOptions {
 	category?: string;
 	after?: string;
 	before?: string;
+	enableGraph?: boolean;
 }
 
 export interface DeleteOptions {

cli/node/src/backend/platform.ts

@@ -115,9 +115,10 @@ export class PlatformBackend implements Backend {
 		if (opts.infer === false) payload.infer = false;
 		if (opts.expires) payload.expiration_date = opts.expires;
 		if (opts.categories) payload.categories = opts.categories;
+		if (opts.enableGraph) payload.enable_graph = true;
 		payload.source = "CLI";
 
-		return (await this._request("POST", "/v3/memories/add/", {
+		return (await this._request("POST", "/v1/memories/", {
 			json: payload,
 		})) as Record<string, unknown>;
 	}
@@ -175,9 +176,10 @@ export class PlatformBackend implements Backend {
 		if (opts.rerank) payload.rerank = true;
 		if (opts.keyword) payload.keyword_search = true;
 		if (opts.fields) payload.fields = opts.fields;
+		if (opts.enableGraph) payload.enable_graph = true;
 		payload.source = "CLI";
 
-		const result = (await this._request("POST", "/v3/memories/search/", {
+		const result = (await this._request("POST", "/v2/memories/search/", {
 			json: payload,
 		})) as unknown;
 		if (Array.isArray(result)) return result;
@@ -225,9 +227,10 @@ export class PlatformBackend implements Backend {
 			extraFilters: Object.keys(extra).length > 0 ? extra : undefined,
 		});
 		if (apiFilters) payload.filters = apiFilters;
+		if (opts.enableGraph) payload.enable_graph = true;
 		payload.source = "CLI";
 
-		const result = (await this._request("POST", "/v3/memories/", {
+		const result = (await this._request("POST", "/v2/memories/", {
 			json: payload,
 			params,
 		})) as unknown;

cli/node/src/branding.ts

@@ -96,7 +96,7 @@ export function printError(message: string, hint?: string): void {
 	const resolvedHint =
 		hint ??
 		(message.includes("Authentication failed")
-			? `Run ${brand("mem0 init")} to reconfigure your API key · https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cli-node`
+			? `Run ${brand("mem0 init")} to reconfigure your API key · https://app.mem0.ai/dashboard/api-keys`
 			: undefined);
 	if (resolvedHint) {
 		console.error(`  ${dim(resolvedHint)}`);

cli/node/src/commands/config.ts

@@ -29,6 +29,7 @@ export function cmdConfigShow(opts: { output?: string } = {}): void {
 					agent_id: config.defaults.agentId || null,
 					app_id: config.defaults.appId || null,
 					run_id: config.defaults.runId || null,
+					enable_graph: config.defaults.enableGraph,
 				},
 				platform: {
 					api_key: redactKey(config.platform.apiKey),
@@ -55,6 +56,7 @@ export function cmdConfigShow(opts: { output?: string } = {}): void {
 	]);
 	table.push(["defaults.app_id", config.defaults.appId || dim("(not set)")]);
 	table.push(["defaults.run_id", config.defaults.runId || dim("(not set)")]);
+	table.push(["defaults.enable_graph", String(config.defaults.enableGraph)]);
 	table.push(["", ""]);
 
 	// Platform

cli/node/src/commands/init.ts

@@ -185,7 +185,7 @@ function promptLine(label: string, defaultValue?: string): Promise<string> {
 async function setupPlatform(config: Mem0Config): Promise<void> {
 	console.log();
 	console.log(
-		`  ${dim("Get your API key at https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cli-node")}`,
+		`  ${dim("Get your API key at https://app.mem0.ai/dashboard/api-keys")}`,
 	);
 	console.log();
 
@@ -234,7 +234,7 @@ async function validatePlatform(config: Mem0Config): Promise<void> {
 		} else {
 			printError(
 				`Could not connect: ${status.error ?? "Unknown error"}`,
-				"Visit https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cli-node to get a new key, or run mem0 init again.",
+				"Visit https://app.mem0.ai/dashboard/api-keys to get a new key, or run mem0 init again.",
 			);
 		}
 	} catch (e) {

cli/node/src/commands/memory.ts

@@ -49,6 +49,7 @@ export async function cmdAdd(
 		noInfer: boolean;
 		expires?: string;
 		categories?: string;
+		enableGraph: boolean;
 		output: string;
 	},
 ): Promise<void> {
@@ -139,6 +140,7 @@ export async function cmdAdd(
 				infer: !opts.noInfer,
 				expires: opts.expires,
 				categories: cats,
+				enableGraph: opts.enableGraph,
 			});
 		});
 	} catch (e) {
@@ -223,6 +225,7 @@ export async function cmdSearch(
 		keyword: boolean;
 		filterJson?: string;
 		fields?: string;
+		enableGraph: boolean;
 		output: string;
 	},
 ): Promise<void> {
@@ -271,6 +274,7 @@ export async function cmdSearch(
 				keyword: opts.keyword,
 				filters,
 				fields: fieldList,
+				enableGraph: opts.enableGraph,
 			});
 		});
 	} catch (e) {
@@ -364,6 +368,7 @@ export async function cmdList(
 		category?: string;
 		after?: string;
 		before?: string;
+		enableGraph: boolean;
 		output: string;
 	},
 ): Promise<void> {
@@ -391,6 +396,7 @@ export async function cmdList(
 				category: opts.category,
 				after: opts.after,
 				before: opts.before,
+				enableGraph: opts.enableGraph,
 			});
 		});
 	} catch (e) {

cli/node/src/commands/utils.ts

@@ -63,7 +63,7 @@ export async function cmdStatus(
 				`  ${dim("Run")} ${brand("mem0 init")} ${dim("to reconfigure your API key")}`,
 			);
 			lines.push(
-				`  ${dim("Get a key at")} ${brand("https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cli-node")}`,
+				`  ${dim("Get a key at")} ${brand("https://app.mem0.ai/dashboard/api-keys")}`,
 			);
 		}
 	}

cli/node/src/config.ts

@@ -28,6 +28,7 @@ export interface DefaultsConfig {
 	agentId: string;
 	appId: string;
 	runId: string;
+	enableGraph: boolean;
 }
 
 export interface TelemetryConfig {
@@ -49,6 +50,7 @@ export function createDefaultConfig(): Mem0Config {
 			agentId: "",
 			appId: "",
 			runId: "",
+			enableGraph: false,
 		},
 		platform: {
 			apiKey: "",
@@ -85,6 +87,8 @@ export function loadConfig(): Mem0Config {
 		config.defaults.agentId = defaults.agent_id ?? "";
 		config.defaults.appId = defaults.app_id ?? "";
 		config.defaults.runId = defaults.run_id ?? "";
+		config.defaults.enableGraph = defaults.enable_graph ?? false;
+
 		const telemetry = data.telemetry ?? {};
 		config.telemetry.anonymousId = telemetry.anonymous_id ?? "";
 	}
@@ -100,6 +104,12 @@ export function loadConfig(): Mem0Config {
 		config.defaults.agentId = process.env.MEM0_AGENT_ID;
 	if (process.env.MEM0_APP_ID) config.defaults.appId = process.env.MEM0_APP_ID;
 	if (process.env.MEM0_RUN_ID) config.defaults.runId = process.env.MEM0_RUN_ID;
+	if (process.env.MEM0_ENABLE_GRAPH) {
+		config.defaults.enableGraph = ["true", "1", "yes"].includes(
+			process.env.MEM0_ENABLE_GRAPH.toLowerCase(),
+		);
+	}
+
 	return config;
 }
 
@@ -113,6 +123,7 @@ export function saveConfig(config: Mem0Config): void {
 			agent_id: config.defaults.agentId,
 			app_id: config.defaults.appId,
 			run_id: config.defaults.runId,
+			enable_graph: config.defaults.enableGraph,
 		},
 		platform: {
 			api_key: config.platform.apiKey,
@@ -143,6 +154,7 @@ const KEY_MAP: Record<string, [keyof Mem0Config, string]> = {
 	"defaults.agent_id": ["defaults", "agentId"],
 	"defaults.app_id": ["defaults", "appId"],
 	"defaults.run_id": ["defaults", "runId"],
+	"defaults.enable_graph": ["defaults", "enableGraph"],
 	// Short-form aliases
 	api_key: ["platform", "apiKey"],
 	base_url: ["platform", "baseUrl"],
@@ -151,6 +163,7 @@ const KEY_MAP: Record<string, [keyof Mem0Config, string]> = {
 	agent_id: ["defaults", "agentId"],
 	app_id: ["defaults", "appId"],
 	run_id: ["defaults", "runId"],
+	enable_graph: ["defaults", "enableGraph"],
 };
 
 export function getNestedValue(config: Mem0Config, dottedKey: string): unknown {

cli/node/src/index.ts

@@ -134,6 +134,18 @@ function resolveIds(
 	};
 }
 
+/**
+ * Resolve graph tri-state: --no-graph > --graph > config default.
+ */
+function resolveGraph(
+	config: Mem0Config,
+	opts: { graph?: boolean; noGraph?: boolean },
+): boolean {
+	if (opts.noGraph) return false;
+	if (opts.graph) return true;
+	return config.defaults.enableGraph;
+}
+
 // ── Main program ──────────────────────────────────────────────────────────
 
 program
@@ -224,6 +236,8 @@ program
 	.option("--no-infer", "Skip inference, store raw.")
 	.option("--expires <date>", "Expiration date (YYYY-MM-DD).")
 	.option("--categories <value>", "Categories (JSON array or comma-separated).")
+	.option("--graph", "Enable graph memory extraction.", false)
+	.option("--no-graph", "Disable graph memory extraction.")
 	.option("-o, --output <format>", "Output format: text, json, quiet.", "text")
 	.option("--api-key <key>", "Override API key.")
 	.option("--base-url <url>", "Override API base URL.")
@@ -239,8 +253,9 @@ program
 			opts.baseUrl,
 		);
 		const ids = resolveIds(config, opts);
+		const enableGraph = resolveGraph(config, opts);
 		const output = isAgent ? "agent" : opts.output;
-		await cmdAdd(backend, text, { ...ids, ...opts, output });
+		await cmdAdd(backend, text, { ...ids, ...opts, enableGraph, output });
 	});
 
 // ── Memory: search ────────────────────────────────────────────────────────
@@ -270,6 +285,8 @@ program
 	.option("--keyword", "Use keyword search.", false)
 	.option("--filter <json>", "Advanced filter expression (JSON).")
 	.option("--fields <list>", "Specific fields to return (comma-separated).")
+	.option("--graph", "Enable graph in search.", false)
+	.option("--no-graph", "Disable graph in search.")
 	.option("-o, --output <format>", "Output: text, json, table.", "text")
 	.option("--api-key <key>", "Override API key.")
 	.option("--base-url <url>", "Override API base URL.")
@@ -293,6 +310,7 @@ program
 			opts.baseUrl,
 		);
 		const ids = resolveIds(config, opts);
+		const enableGraph = resolveGraph(config, opts);
 		const output = isAgent ? "agent" : opts.output;
 		await cmdSearch(backend, resolvedQuery, {
 			...ids,
@@ -302,6 +320,7 @@ program
 			keyword: opts.keyword,
 			filterJson: opts.filter,
 			fields: opts.fields,
+			enableGraph,
 			output,
 		});
 	});
@@ -345,6 +364,8 @@ program
 	.option("--category <name>", "Filter by category.")
 	.option("--after <date>", "Created after (YYYY-MM-DD).")
 	.option("--before <date>", "Created before (YYYY-MM-DD).")
+	.option("--graph", "Enable graph in listing.", false)
+	.option("--no-graph", "Disable graph in listing.")
 	.option("-o, --output <format>", "Output: text, json, table.", "table")
 	.option("--api-key <key>", "Override API key.")
 	.option("--base-url <url>", "Override API base URL.")
@@ -360,6 +381,7 @@ program
 			opts.baseUrl,
 		);
 		const ids = resolveIds(config, opts);
+		const enableGraph = resolveGraph(config, opts);
 		const output = isAgent ? "agent" : opts.output;
 		await cmdList(backend, {
 			...ids,
@@ -368,6 +390,7 @@ program
 			category: opts.category,
 			after: opts.after,
 			before: opts.before,
+			enableGraph,
 			output,
 		});
 	});

cli/node/tests/cli-integration.test.ts

@@ -107,22 +107,22 @@ describe("CLI Integration — help and version", () => {
     expect(result.exitCode).toBe(0);
   });
 
-  it("add help has --output flag", () => {
+  it("add help has --graph flag", () => {
     const result = run(["add", "--help"]);
     expect(result.exitCode).toBe(0);
-    expect(result.stdout).toContain("--output");
+    expect(result.stdout).toContain("--graph");
   });
 
-  it("search help has --rerank flag", () => {
+  it("search help has --graph flag", () => {
     const result = run(["search", "--help"]);
     expect(result.exitCode).toBe(0);
-    expect(result.stdout).toContain("--rerank");
+    expect(result.stdout).toContain("--graph");
   });
 
-  it("list help has --category flag", () => {
+  it("list help has --graph flag", () => {
     const result = run(["list", "--help"]);
     expect(result.exitCode).toBe(0);
-    expect(result.stdout).toContain("--category");
+    expect(result.stdout).toContain("--graph");
   });
 });
 

cli/node/tests/commands.test.ts

@@ -42,7 +42,7 @@ describe("cmdAdd", () => {
       userId: "alice",
       immutable: false,
       noInfer: false,
-
+      enableGraph: false,
       output: "text",
     });
     expect(mockBackend.add).toHaveBeenCalledOnce();
@@ -55,7 +55,7 @@ describe("cmdAdd", () => {
       messages: JSON.stringify([{ role: "user", content: "I love Python" }]),
       immutable: false,
       noInfer: false,
-
+      enableGraph: false,
       output: "text",
     });
     expect(mockBackend.add).toHaveBeenCalledOnce();
@@ -67,7 +67,7 @@ describe("cmdAdd", () => {
       userId: "alice",
       immutable: false,
       noInfer: false,
-
+      enableGraph: false,
       output: "json",
     });
     expect(output).toContain("results");
@@ -79,7 +79,7 @@ describe("cmdAdd", () => {
       userId: "alice",
       immutable: false,
       noInfer: false,
-
+      enableGraph: false,
       output: "quiet",
     });
     expect(output).not.toContain("dark mode");
@@ -101,7 +101,7 @@ describe("cmdAdd deduplicates PENDING", () => {
       userId: "alice",
       immutable: false,
       noInfer: false,
-
+      enableGraph: false,
       output: "text",
     });
     expect(output.match(/Queued/g)?.length).toBe(1);
@@ -114,7 +114,7 @@ describe("cmdAdd deduplicates PENDING", () => {
       userId: "alice",
       immutable: false,
       noInfer: false,
-
+      enableGraph: false,
       output: "json",
     });
     const data = JSON.parse(output);
@@ -130,7 +130,7 @@ describe("cmdAdd deduplicates PENDING", () => {
       userId: "alice",
       immutable: false,
       noInfer: false,
-
+      enableGraph: false,
       output: "agent",
     });
     const data = JSON.parse(output);
@@ -148,7 +148,7 @@ describe("cmdSearch", () => {
       threshold: 0.3,
       rerank: false,
       keyword: false,
-
+      enableGraph: false,
       output: "text",
     });
     expect(output).toContain("Found 2");
@@ -162,7 +162,7 @@ describe("cmdSearch", () => {
       threshold: 0.3,
       rerank: false,
       keyword: false,
-
+      enableGraph: false,
       output: "json",
     });
     expect(output).toContain("memory");
@@ -177,7 +177,7 @@ describe("cmdSearch", () => {
       threshold: 0.3,
       rerank: false,
       keyword: false,
-
+      enableGraph: false,
       output: "text",
     });
     expect(errOutput).toContain("No memories found");
@@ -205,7 +205,7 @@ describe("cmdList", () => {
       userId: "alice",
       page: 1,
       pageSize: 100,
-
+      enableGraph: false,
       output: "table",
     });
     expect(output).toContain("dark mode");
@@ -218,7 +218,7 @@ describe("cmdList", () => {
       userId: "alice",
       page: 1,
       pageSize: 100,
-
+      enableGraph: false,
       output: "text",
     });
     expect(errOutput).toContain("No memories found");
@@ -316,7 +316,7 @@ describe("agent mode", () => {
       userId: "alice",
       immutable: false,
       noInfer: false,
-
+      enableGraph: false,
       output: "agent",
     });
     const parsed = JSON.parse(output.trim());
@@ -336,7 +336,7 @@ describe("agent mode", () => {
       threshold: 0.3,
       rerank: false,
       keyword: false,
-
+      enableGraph: false,
       output: "agent",
     });
     const parsed = JSON.parse(output.trim());
@@ -361,7 +361,7 @@ describe("agent mode", () => {
       userId: "alice",
       page: 1,
       pageSize: 100,
-
+      enableGraph: false,
       output: "agent",
     });
     const parsed = JSON.parse(output.trim());

cli/node/tests/config.test.ts

@@ -64,6 +64,7 @@ describe("createDefaultConfig", () => {
     expect(config.platform.baseUrl).toBe("https://api.mem0.ai");
     expect(config.platform.apiKey).toBe("");
     expect(config.defaults.userId).toBe("");
+    expect(config.defaults.enableGraph).toBe(false);
   });
 });
 
@@ -104,4 +105,9 @@ describe("setNestedValue", () => {
     expect(config.defaults.userId).toBe("bob");
   });
 
+  it("coerces boolean for enable_graph", () => {
+    const config = createDefaultConfig();
+    expect(setNestedValue(config, "defaults.enable_graph", "true")).toBe(true);
+    expect(config.defaults.enableGraph).toBe(true);
+  });
 });

cli/python/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "mem0-cli"
-version = "0.2.4"
+version = "0.2.3"
 description = "The official CLI for mem0 — the memory layer for AI agents"
 readme = "README.md"
 license = "Apache-2.0"

cli/python/src/mem0_cli/__init__.py

@@ -1,3 +1,3 @@
 """mem0 CLI — the command-line interface for the mem0 memory layer."""
 
-__version__ = "0.2.4"
+__version__ = "0.2.3"

cli/python/src/mem0_cli/app.py

@@ -267,6 +267,8 @@ def add(
     categories: str | None = typer.Option(
         None, "--categories", help="Categories (JSON array or comma-separated)."
     ),
+    graph: bool = typer.Option(False, "--graph", help="Enable graph memory extraction."),
+    no_graph: bool = typer.Option(False, "--no-graph", help="Disable graph memory extraction."),
     output: str = typer.Option(
         "text", "--output", "-o", help="Output format: text, json, quiet.", rich_help_panel="Output"
     ),
@@ -293,6 +295,13 @@ def add(
     backend, config = _get_backend_and_config(api_key, base_url)
     ids = _resolve_ids(config, user_id=user_id, agent_id=agent_id, app_id=app_id, run_id=run_id)
 
+    if no_graph:
+        graph_enabled = False
+    elif graph:
+        graph_enabled = True
+    else:
+        graph_enabled = config.defaults.enable_graph
+
     cmd_add(
         backend,
         text,
@@ -304,6 +313,7 @@ def add(
         no_infer=no_infer,
         expires=expires,
         categories=categories,
+        enable_graph=graph_enabled,
         output=output,
     )
 
@@ -347,6 +357,12 @@ def search(
         help="Specific fields to return (comma-separated).",
         rich_help_panel="Search",
     ),
+    graph: bool = typer.Option(
+        False, "--graph", help="Enable graph in search.", rich_help_panel="Search"
+    ),
+    no_graph: bool = typer.Option(
+        False, "--no-graph", help="Disable graph in search.", rich_help_panel="Search"
+    ),
     output: str = typer.Option(
         "text", "--output", "-o", help="Output: text, json, table.", rich_help_panel="Output"
     ),
@@ -380,6 +396,13 @@ def search(
     backend, config = _get_backend_and_config(api_key, base_url)
     ids = _resolve_ids(config, user_id=user_id, agent_id=agent_id, app_id=app_id, run_id=run_id)
 
+    if no_graph:
+        graph_enabled = False
+    elif graph:
+        graph_enabled = True
+    else:
+        graph_enabled = config.defaults.enable_graph
+
     cmd_search(
         backend,
         query,
@@ -390,6 +413,7 @@ def search(
         keyword=keyword,
         filter_json=filter_json,
         fields=fields,
+        enable_graph=graph_enabled,
         output=output,
     )
 
@@ -456,6 +480,12 @@ def list_cmd(
     before: str | None = typer.Option(
         None, "--before", help="Created before (YYYY-MM-DD).", rich_help_panel="Filters"
     ),
+    graph: bool = typer.Option(
+        False, "--graph", help="Enable graph in listing.", rich_help_panel="Filters"
+    ),
+    no_graph: bool = typer.Option(
+        False, "--no-graph", help="Disable graph in listing.", rich_help_panel="Filters"
+    ),
     output: str = typer.Option(
         "table", "--output", "-o", help="Output: text, json, table.", rich_help_panel="Output"
     ),
@@ -481,6 +511,13 @@ def list_cmd(
     backend, config = _get_backend_and_config(api_key, base_url)
     ids = _resolve_ids(config, user_id=user_id, agent_id=agent_id, app_id=app_id, run_id=run_id)
 
+    if no_graph:
+        graph_enabled = False
+    elif graph:
+        graph_enabled = True
+    else:
+        graph_enabled = config.defaults.enable_graph
+
     cmd_list(
         backend,
         **ids,
@@ -489,6 +526,7 @@ def list_cmd(
         category=category,
         after=after,
         before=before,
+        enable_graph=graph_enabled,
         output=output,
     )
 

cli/python/src/mem0_cli/backend/base.py

@@ -26,6 +26,7 @@ class Backend(ABC):
         infer: bool = True,
         expires: str | None = None,
         categories: list[str] | None = None,
+        enable_graph: bool = False,
     ) -> dict: ...
 
     @abstractmethod
@@ -43,6 +44,7 @@ class Backend(ABC):
         keyword: bool = False,
         filters: dict | None = None,
         fields: list[str] | None = None,
+        enable_graph: bool = False,
     ) -> list[dict]: ...
 
     @abstractmethod
@@ -61,6 +63,7 @@ class Backend(ABC):
         category: str | None = None,
         after: str | None = None,
         before: str | None = None,
+        enable_graph: bool = False,
     ) -> list[dict]: ...
 
     @abstractmethod

cli/python/src/mem0_cli/backend/platform.py

@@ -64,6 +64,7 @@ class PlatformBackend(Backend):
         infer: bool = True,
         expires: str | None = None,
         categories: list[str] | None = None,
+        enable_graph: bool = False,
     ) -> dict:
         payload: dict[str, Any] = {}
 
@@ -90,9 +91,11 @@ class PlatformBackend(Backend):
             payload["expiration_date"] = expires
         if categories:
             payload["categories"] = categories
+        if enable_graph:
+            payload["enable_graph"] = True
         payload["source"] = "CLI"
 
-        return self._request("POST", "/v3/memories/add/", json=payload)
+        return self._request("POST", "/v1/memories/", json=payload)
 
     def _build_filters(
         self,
@@ -103,7 +106,7 @@ class PlatformBackend(Backend):
         run_id: str | None = None,
         extra_filters: dict | None = None,
     ) -> dict | None:
-        """Build a filters dict for v3 API endpoints.
+        """Build a filters dict for v2 API endpoints.
 
         Entity IDs are ANDed (all provided IDs must match).
         Extra filters (date ranges, categories) are also ANDed.
@@ -149,6 +152,7 @@ class PlatformBackend(Backend):
         keyword: bool = False,
         filters: dict | None = None,
         fields: list[str] | None = None,
+        enable_graph: bool = False,
     ) -> list[dict]:
         payload: dict[str, Any] = {"query": query, "top_k": top_k, "threshold": threshold}
 
@@ -167,9 +171,11 @@ class PlatformBackend(Backend):
             payload["keyword_search"] = True
         if fields:
             payload["fields"] = fields
+        if enable_graph:
+            payload["enable_graph"] = True
         payload["source"] = "CLI"
 
-        result = self._request("POST", "/v3/memories/search/", json=payload)
+        result = self._request("POST", "/v2/memories/search/", json=payload)
         return (
             result
             if isinstance(result, list)
@@ -191,11 +197,12 @@ class PlatformBackend(Backend):
         category: str | None = None,
         after: str | None = None,
         before: str | None = None,
+        enable_graph: bool = False,
     ) -> list[dict]:
         payload: dict[str, Any] = {}
         params = {"page": str(page), "page_size": str(page_size)}
 
-        # Build filters — entity IDs and date filters go inside "filters"
+        # Build filters for v2 API — entity IDs and date filters go inside "filters"
         extra: dict[str, Any] = {}
         if category:
             extra["categories"] = {"contains": category}
@@ -213,9 +220,11 @@ class PlatformBackend(Backend):
         )
         if api_filters:
             payload["filters"] = api_filters
+        if enable_graph:
+            payload["enable_graph"] = True
         payload["source"] = "CLI"
 
-        result = self._request("POST", "/v3/memories/", json=payload, params=params)
+        result = self._request("POST", "/v2/memories/", json=payload, params=params)
         return (
             result
             if isinstance(result, list)

cli/python/src/mem0_cli/branding.py

@@ -146,7 +146,7 @@ def timed_status(console: Console, message: str):
             if "Authentication failed" in ctx.error_msg:
                 _err.print(
                     f"  [{DIM_COLOR}]Run [bold]mem0 init[/bold] to reconfigure your API key"
-                    f" · [bold]https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cli-python[/bold][/]"
+                    f" · [bold]https://app.mem0.ai/dashboard/api-keys[/bold][/]"
                 )
         raise
     else:

cli/python/src/mem0_cli/commands/config_cmd.py

@@ -39,6 +39,7 @@ def cmd_config_show(*, output: str = "text") -> None:
                     "agent_id": config.defaults.agent_id or None,
                     "app_id": config.defaults.app_id or None,
                     "run_id": config.defaults.run_id or None,
+                    "enable_graph": config.defaults.enable_graph,
                 },
                 "platform": {
                     "api_key": redact_key(config.platform.api_key),
@@ -72,6 +73,10 @@ def cmd_config_show(*, output: str = "text") -> None:
         "defaults.run_id",
         config.defaults.run_id or f"[{DIM_COLOR}](not set)[/]",
     )
+    table.add_row(
+        "defaults.enable_graph",
+        str(config.defaults.enable_graph).lower(),
+    )
     table.add_row("", "")
 
     # Platform

cli/python/src/mem0_cli/commands/init_cmd.py

@@ -19,13 +19,7 @@ from mem0_cli.branding import (
     print_info,
     print_success,
 )
-from mem0_cli.config import (
-    CONFIG_FILE,
-    DEFAULT_BASE_URL,
-    Mem0Config,
-    load_config,
-    save_config,
-)
+from mem0_cli.config import CONFIG_FILE, DEFAULT_BASE_URL, Mem0Config, load_config, save_config
 
 console = Console()
 err_console = Console(stderr=True)
@@ -358,9 +352,7 @@ def run_init(
 def _setup_platform(config: Mem0Config) -> None:
     """Platform setup flow."""
     console.print()
-    console.print(
-        f"  [{DIM_COLOR}]Get your API key at https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cli-python[/]"
-    )
+    console.print(f"  [{DIM_COLOR}]Get your API key at https://app.mem0.ai/dashboard/api-keys[/]")
     console.print()
 
     console.print(f"  [{BRAND_COLOR}]API Key[/]: ", end="")
@@ -412,7 +404,7 @@ def _validate_platform(config: Mem0Config) -> None:
             print_error(
                 err_console,
                 f"Could not connect: {status.get('error', 'Unknown error')}",
-                hint="Visit https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cli-python to get a new key, then run mem0 init again.",
+                hint="Visit https://app.mem0.ai/dashboard/api-keys to get a new key, then run mem0 init again.",
             )
     except Exception as e:
         print_error(err_console, f"Connection test failed: {e}")

cli/python/src/mem0_cli/commands/memory.py

@@ -62,6 +62,7 @@ def cmd_add(
     no_infer: bool,
     expires: str | None,
     categories: str | None,
+    enable_graph: bool = False,
     output: str = "text",
 ) -> None:
     """Add a memory."""
@@ -144,6 +145,7 @@ def cmd_add(
                 infer=not no_infer,
                 expires=expires,
                 categories=cats,
+                enable_graph=enable_graph,
             )
         except Exception as e:
             ts.error_msg = str(e)
@@ -224,6 +226,7 @@ def cmd_search(
     keyword: bool,
     filter_json: str | None,
     fields: str | None,
+    enable_graph: bool = False,
     output: str = "text",
 ) -> None:
     """Search memories."""
@@ -266,6 +269,7 @@ def cmd_search(
                 keyword=keyword,
                 filters=filters,
                 fields=field_list,
+                enable_graph=enable_graph,
             )
         except Exception as e:
             print_error(err_console, str(e))
@@ -352,6 +356,7 @@ def cmd_list(
     category: str | None,
     after: str | None,
     before: str | None,
+    enable_graph: bool = False,
     output: str = "table",
 ) -> None:
     """List memories."""
@@ -380,6 +385,7 @@ def cmd_list(
                 category=category,
                 after=after,
                 before=before,
+                enable_graph=enable_graph,
             )
         except Exception as e:
             print_error(err_console, str(e))

cli/python/src/mem0_cli/commands/utils.py

@@ -77,7 +77,7 @@ def cmd_status(
                 f"  [{DIM_COLOR}]Run [bold]mem0 init[/bold] to reconfigure your API key[/]"
             )
             lines.append(
-                f"  [{DIM_COLOR}]Get a key at [bold]https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cli-python[/bold][/]"
+                f"  [{DIM_COLOR}]Get a key at [bold]https://app.mem0.ai/dashboard/api-keys[/bold][/]"
             )
     lines.append(f"  [{DIM_COLOR}]Latency:[/]  {_elapsed:.2f}s")
 

cli/python/src/mem0_cli/config.py

@@ -36,6 +36,7 @@ class DefaultsConfig:
     agent_id: str = ""
     app_id: str = ""
     run_id: str = ""
+    enable_graph: bool = False
 
 
 @dataclass
@@ -59,6 +60,7 @@ SHORT_KEY_ALIASES: dict[str, str] = {
     "agent_id": "defaults.agent_id",
     "app_id": "defaults.app_id",
     "run_id": "defaults.run_id",
+    "enable_graph": "defaults.enable_graph",
 }
 
 
@@ -89,6 +91,8 @@ def load_config() -> Mem0Config:
         config.defaults.agent_id = defaults.get("agent_id", "")
         config.defaults.app_id = defaults.get("app_id", "")
         config.defaults.run_id = defaults.get("run_id", "")
+        config.defaults.enable_graph = defaults.get("enable_graph", False)
+
         telemetry = data.get("telemetry", {})
         config.telemetry.anonymous_id = telemetry.get("anonymous_id", "")
 
@@ -117,6 +121,10 @@ def load_config() -> Mem0Config:
     if env_run_id:
         config.defaults.run_id = env_run_id
 
+    env_graph = os.environ.get("MEM0_ENABLE_GRAPH")
+    if env_graph:
+        config.defaults.enable_graph = env_graph.lower() in ("true", "1", "yes")
+
     return config
 
 
@@ -131,6 +139,7 @@ def save_config(config: Mem0Config) -> None:
             "agent_id": config.defaults.agent_id,
             "app_id": config.defaults.app_id,
             "run_id": config.defaults.run_id,
+            "enable_graph": config.defaults.enable_graph,
         },
         "platform": {
             "api_key": config.platform.api_key,

cli/python/tests/test_cli_integration.py

@@ -224,13 +224,24 @@ class TestCLIIsolated:
 
 
 class TestCLINewFeatures:
-    """Tests for MCP parity features: --limit, entities delete."""
+    """Tests for MCP parity features: --graph, --limit, entities delete."""
 
-    def test_search_help_has_limit(self):
+    def test_add_help_has_graph(self):
+        result = _run(["add", "--help"])
+        assert result.returncode == 0
+        assert "--graph" in result.stdout
+
+    def test_search_help_has_graph_and_limit(self):
         result = _run(["search", "--help"])
         assert result.returncode == 0
+        assert "--graph" in result.stdout
         assert "--limit" in result.stdout
 
+    def test_list_help_has_graph(self):
+        result = _run(["list", "--help"])
+        assert result.returncode == 0
+        assert "--graph" in result.stdout
+
     def test_delete_entity_via_delete_flag(self):
         """delete --entity should appear in help output."""
         result = _run(["delete", "--help"])

cli/python/tests/test_commands.py

@@ -997,6 +997,85 @@ class TestEntitiesDeleteCommand:
         mock_backend.delete_entities.assert_not_called()
 
 
+class TestEnableGraph:
+    def test_add_with_graph(self, mock_backend):
+        console, _buf = _make_console()
+        err_console, _err_buf = _make_err_console()
+        with (
+            patch("mem0_cli.commands.memory.console", console),
+            patch("mem0_cli.commands.memory.err_console", err_console),
+        ):
+            cmd_add(
+                mock_backend,
+                "test",
+                user_id="alice",
+                agent_id=None,
+                app_id=None,
+                run_id=None,
+                messages=None,
+                file=None,
+                metadata=None,
+                immutable=False,
+                no_infer=False,
+                expires=None,
+                categories=None,
+                enable_graph=True,
+                output="text",
+            )
+        call_kwargs = mock_backend.add.call_args
+        assert call_kwargs.kwargs.get("enable_graph") is True
+
+    def test_search_with_graph(self, mock_backend):
+        console, _buf = _make_console()
+        err_console, _err_buf = _make_err_console()
+        with (
+            patch("mem0_cli.commands.memory.console", console),
+            patch("mem0_cli.commands.memory.err_console", err_console),
+        ):
+            cmd_search(
+                mock_backend,
+                "test",
+                user_id="alice",
+                agent_id=None,
+                app_id=None,
+                run_id=None,
+                top_k=10,
+                threshold=0.3,
+                rerank=False,
+                keyword=False,
+                filter_json=None,
+                fields=None,
+                enable_graph=True,
+                output="text",
+            )
+        call_kwargs = mock_backend.search.call_args
+        assert call_kwargs.kwargs.get("enable_graph") is True
+
+    def test_list_with_graph(self, mock_backend):
+        console, _buf = _make_console()
+        err_console, _err_buf = _make_err_console()
+        with (
+            patch("mem0_cli.commands.memory.console", console),
+            patch("mem0_cli.commands.memory.err_console", err_console),
+        ):
+            cmd_list(
+                mock_backend,
+                user_id="alice",
+                agent_id=None,
+                app_id=None,
+                run_id=None,
+                page=1,
+                page_size=100,
+                category=None,
+                after=None,
+                before=None,
+                enable_graph=True,
+                output="table",
+            )
+        call_kwargs = mock_backend.list_memories.call_args
+        assert call_kwargs.kwargs.get("enable_graph") is True
+
+
 class TestEventCommands:
     def test_event_list_table(self, mock_backend):
         console, buf = _make_console()

cli/python/tests/test_config.py

@@ -121,6 +121,46 @@ class TestConfig:
         assert config.defaults.agent_id == ""
         assert config.defaults.app_id == ""
         assert config.defaults.run_id == ""
+        assert config.defaults.enable_graph is False
+
+    def test_enable_graph_save_and_load(self, isolate_config):
+        config = Mem0Config()
+        config.defaults.enable_graph = True
+        save_config(config)
+        loaded = load_config()
+        assert loaded.defaults.enable_graph is True
+
+    def test_enable_graph_env_var_true(self, isolate_config, monkeypatch):
+        monkeypatch.setenv("MEM0_ENABLE_GRAPH", "true")
+        loaded = load_config()
+        assert loaded.defaults.enable_graph is True
+
+    def test_enable_graph_env_var_false(self, isolate_config, monkeypatch):
+        config = Mem0Config()
+        config.defaults.enable_graph = True
+        save_config(config)
+        monkeypatch.setenv("MEM0_ENABLE_GRAPH", "false")
+        loaded = load_config()
+        assert loaded.defaults.enable_graph is False
+
+    def test_backward_compat_no_enable_graph_key(self, isolate_config):
+        """Old config files without 'enable_graph' key should default to False."""
+        import json
+
+        from mem0_cli.config import CONFIG_FILE, ensure_config_dir
+
+        ensure_config_dir()
+        data = {
+            "version": 1,
+            "defaults": {"user_id": "alice"},
+            "platform": {"api_key": "m0-test", "base_url": "https://api.mem0.ai"},
+        }
+        with open(CONFIG_FILE, "w") as f:
+            json.dump(data, f)
+
+        loaded = load_config()
+        assert loaded.defaults.enable_graph is False
+        assert loaded.defaults.user_id == "alice"
 
 
 class TestNestedAccess:
@@ -152,6 +192,11 @@ class TestNestedAccess:
         assert set_nested_value(config, "defaults.user_id", "bob")
         assert config.defaults.user_id == "bob"
 
+    def test_set_defaults_enable_graph(self):
+        config = Mem0Config()
+        assert set_nested_value(config, "defaults.enable_graph", "true")
+        assert config.defaults.enable_graph is True
+
 
 class TestResolveIds:
     def test_cli_flag_overrides_default(self):

docs/_snippets/paper-release.mdx

@@ -0,0 +1,3 @@
+<Note type="info">
+  <strong>🎉 Mem0 1.0.0 is here!</strong> Enhanced filtering, reranking, and smarter memory management.
+</Note>

docs/api-reference.mdx

@@ -10,7 +10,7 @@ description: "REST APIs for memory management, search, and entity operations"
 Mem0 provides a comprehensive REST API for integrating advanced memory capabilities into your applications. Create, search, update, and manage memories across users, agents, and custom entities with simple HTTP requests.
 
 <Info>
-**Quick start:** Get your API key from the <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=api-reference" rel="nofollow">Mem0 Dashboard</a> and make your first memory operation in minutes.
+**Quick start:** Get your API key from the <a href="https://app.mem0.ai/dashboard/api-keys" rel="nofollow">Mem0 Dashboard</a> and make your first memory operation in minutes.
 </Info>
 
 ---
@@ -87,7 +87,7 @@ All API requests require authentication using Token-based authentication. Includ
 Authorization: Token <your-api-key>
 ```
 
-Get your API key from the <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=api-reference" rel="nofollow">Mem0 Dashboard</a>.
+Get your API key from the <a href="https://app.mem0.ai/dashboard/api-keys" rel="nofollow">Mem0 Dashboard</a>.
 
 <Warning>
 **Keep your API key secure.** Never expose it in client-side code or public repositories. Use environment variables and server-side requests only.

docs/api-reference/memory/add-memories.mdx

@@ -1,18 +1,18 @@
 ---
-title: Add Memories
-description: "Add facts, messages, or metadata to a user memory store with async processing and event tracking via the V3 additive pipeline."
-openapi: post /v3/memories/add/
+title: 'Add Memories'
+description: "Add facts, messages, or metadata to a user memory store with support for async processing and event tracking."
+openapi: post /v1/memories/
 ---
 
-Extract and store memories from a conversation using the V3 additive pipeline. The endpoint uses single-pass ADD-only extraction — one LLM call, no UPDATE/DELETE. Memories accumulate over time; nothing is overwritten.
+Add new facts, messages, or metadata to a user’s memory store. The Add Memories endpoint accepts either raw text or conversational turns and commits them asynchronously so the memory is ready for later search, retrieval, and graph queries.
 
 ## Endpoint
 
 - **Method**: `POST`
-- **URL**: `/v3/memories/add/`
+- **URL**: `/v1/memories/`
 - **Content-Type**: `application/json`
 
-Processing is asynchronous. The response returns an `event_id` you can poll via `GET /v1/event/{event_id}/`.
+Memories are processed asynchronously by default. The response contains queued events you can track while the platform finalizes enrichment.
 
 ## Required headers
 
@@ -23,7 +23,7 @@ Processing is asynchronous. The response returns an `event_id` you can poll via
 
 ## Request body
 
-Provide conversation messages for Mem0 to extract memories from. At least one entity ID (`user_id`, `agent_id`, `app_id`, or `run_id`) is required so the memory is scoped to a session. Entity IDs are accepted at the top level.
+Provide at least one message or direct memory string. Most callers supply `messages` so Mem0 can infer structured memories as part of ingestion.
 
 <CodeGroup>
 ```json Basic request
@@ -43,15 +43,12 @@ Provide conversation messages for Mem0 to extract memories from. At least one en
 
 | Field | Type | Required | Description |
 | --- | --- | --- | --- |
-| `messages` | array | Yes | Conversation turns for Mem0 to extract memories from. Each object should include `role` and `content`. |
-| `user_id` | string | No* | Associates the memory with a user. |
-| `agent_id` | string | No* | Associates the memory with an agent. |
-| `run_id` | string | No* | Associates the memory with a run. |
-| `app_id` | string | No* | Associates the memory with an app. |
+| `user_id` | string | No* | Associates the memory with a user. Provide when you want the memory scoped to a specific identity. |
+| `messages` | array | No* | Conversation turns for Mem0 to infer memories from. Each object should include `role` and `content`. |
 | `metadata` | object | Optional | Custom key/value metadata (e.g., `{"topic": "preferences"}`). |
 | `infer` | boolean (default `true`) | Optional | Set to `false` to skip inference and store the provided text as-is. |
 
-> \* At least one entity ID (`user_id`, `agent_id`, `app_id`, or `run_id`) is required.
+> \* Provide at least one `messages` entry to describe what you are storing. For scoped memories, include `user_id`. You can also attach `agent_id`, `app_id`, `run_id`, `project_id`, or `org_id` to refine ownership.
 
 <Tip>
   Need more details? See [all request parameters](#body-messages) below for complete field descriptions, types, and constraints.
@@ -59,15 +56,19 @@ Provide conversation messages for Mem0 to extract memories from. At least one en
 
 ## Response
 
-The request is queued for background processing. The response contains an `event_id` for tracking status.
+Successful requests return an array of events queued for processing. Each event includes the generated memory text and an identifier you can persist for auditing.
 
 <CodeGroup>
 ```json 200 response
-{
-  "message": "Memory processing has been queued for background execution",
-  "status": "PENDING",
-  "event_id": "evt-uuid"
-}
+[
+  {
+    "id": "mem_01JF8ZS4Y0R0SPM13R5R6H32CJ",
+    "event": "ADD",
+    "data": {
+      "memory": "The user moved to Austin in 2025."
+    }
+  }
+]
 ```
 
 ```json 400 response
@@ -80,7 +81,3 @@ The request is queued for background processing. The response contains an `event
 ```
 </CodeGroup>
 
-<Info>
-Poll the event status via `GET /v1/event/{event_id}/`. Status will be `SUCCEEDED` or `FAILED` once processing completes.
-</Info>
-

docs/api-reference/memory/get-memories.mdx

@@ -1,12 +1,10 @@
 ---
 title: "Get Memories"
-description: "Retrieve memories with paginated results and advanced filtering using logical operators like AND, OR, NOT, and comparison queries."
-openapi: post /v3/memories/
+description: "Retrieve memories with advanced filtering using logical operators like AND, OR, NOT, and comparison queries."
+openapi: post /v2/memories/
 ---
 
-List memories scoped by filters with paginated results. Entity IDs (`user_id`, `agent_id`, `app_id`, `run_id`) **must** be passed inside the `filters` object — top-level entity IDs are rejected with 400.
-
-The `filters` object supports complex logical operations (AND, OR, NOT) and comparison operators:
+The v2 get memories API is powerful and flexible, allowing for more precise memory listing without the need for a search query. It supports complex logical operations (AND, OR, NOT) and comparison operators for advanced filtering capabilities. The comparison operators include:
 
 - `in`: Matches any of the values specified
 - `gte`: Greater than or equal to
@@ -17,8 +15,6 @@ The `filters` object supports complex logical operations (AND, OR, NOT) and comp
 - `icontains`: Case-insensitive containment check
 - `*`: Wildcard character that matches everything
 
-Pass `page` and `page_size` as query parameters to paginate through results.
-
 <CodeGroup>
 ```python Code
 memories = client.get_all(
@@ -31,17 +27,12 @@ memories = client.get_all(
                 "created_at": {"gte": "2024-07-01", "lte": "2024-07-31"}
             }
         ]
-    },
-    page=1,
-    page_size=50
+    }
 )
 ```
 
 ```python Output
 {
-    "count": 2,
-    "next": null,
-    "previous": null,
     "results": [
         {
             "id": "f4cbdb08-7062-4f3e-8eb2-9f5c80dfe64c",
@@ -55,13 +46,10 @@ memories = client.get_all(
             "created_at": "2024-07-05T15:30:00Z",
             "updated_at": "2024-07-05T15:30:00Z"
         }
-    ]
+    ],
+    "total": 2
 }
 ```
 
 </CodeGroup>
 
-<Info>
-The response is a paginated envelope with `count`, `next`, `previous`, and `results`. Use `page` and `page_size` query params to step through results.
-</Info>
-

docs/api-reference/memory/search-memories.mdx

@@ -1,14 +1,10 @@
 ---
 title: 'Search Memories'
-description: "Search memories with hybrid retrieval (semantic + BM25 + entity matching) and advanced filtering using logical and comparison operators."
-openapi: post /v3/memories/search/
+description: "Search memories with semantic queries and advanced filtering using logical and comparison operators."
+openapi: post /v2/memories/search/
 ---
 
-Relevance-ranked hybrid search across stored memories. V3 uses multi-signal retrieval — semantic, BM25 keyword, and entity matching scored in parallel and fused. The returned `score` is a combined `[0, 1]` value.
-
-Entity IDs (`user_id`, `agent_id`, `app_id`, `run_id`) **must** be passed inside the `filters` object — top-level entity IDs are rejected with 400. At least one entity ID is required.
-
-The `filters` object supports complex logical operations (AND, OR, NOT) and comparison operators:
+The v2 search API is powerful and flexible, allowing for more precise memory retrieval. It supports complex logical operations (AND, OR, NOT) and comparison operators for advanced filtering capabilities. The comparison operators include:
 - `in`: Matches any of the values specified
 - `gte`: Greater than or equal to
 - `lte`: Less than or equal to
@@ -18,14 +14,6 @@ The `filters` object supports complex logical operations (AND, OR, NOT) and comp
 - `icontains`: Case-insensitive containment check
 - `*`: Wildcard character that matches everything
 
-### Search parameter defaults
-
-| Parameter | V1/V2 | V3 |
-| --- | --- | --- |
-| `top_k` | Supported (default 10) | Supported (1-1000, default 10) |
-| `threshold` | No default | Default `0.1` (pass `0.0` to disable) |
-| `rerank` | Default `true` | Default `false` (pass `true` to enable) |
-
 <CodeGroup>
 ```python Platform API Example
 related_memories = client.search(
@@ -45,19 +33,20 @@ related_memories = client.search(
 
 ```json Output
 {
-  "results": [
+  "memories": [
     {
       "id": "ea925981-272f-40dd-b576-be64e4871429",
       "memory": "Likes to play cricket and plays cricket on weekends.",
       "metadata": {
         "category": "hobbies"
       },
-      "score": 0.82,
+      "score": 0.32116443111457704,
       "created_at": "2024-07-26T10:29:36.630547-07:00",
       "updated_at": null,
-      "categories": ["hobbies"]
+      "user_id": "alice",
+      "agent_id": "sports-agent"
     }
-  ]
+  ],
 }
 ```
 </CodeGroup>

docs/api-reference/organizations-projects.mdx

@@ -109,19 +109,6 @@ client.project.update(
 )
 ```
 
-#### Toggle Memory Decay
-
-`decay` is a per-project boolean that turns on [Memory Decay](/platform/features/memory-decay) — a search-time ranking bias that reinforces recently-accessed memories and gently dampens stale ones. The flag is `false` by default; set it via the same project-update endpoint:
-
-```bash cURL
-curl -X PATCH https://api.mem0.ai/api/v1/orgs/organizations/$ORG_ID/projects/$PROJECT_ID/ \
-  -H "Authorization: Token $MEM0_API_KEY" \
-  -H "Content-Type: application/json" \
-  -d '{"decay": true}'
-```
-
-The current state is returned on every project read (and supports `?fields=decay` for a minimal response). Toggling has no effect on stored memories, only on how v3 search ranks them.
-
 ### Delete Project
 
 <Warning>

docs/changelog/highlights.mdx

@@ -4,19 +4,6 @@ description: "Major product launches, headline features, and milestones for Mem0
 mode: "wide"
 ---
 
-<Update label="2026-05-08" description="Memory Decay">
-
-**Memory Decay — Recently-Used Memories Surface Higher, Automatically**
-
-Per-project search-time ranking bias that boosts recently-touched memories and gently dampens stale ones. Off by default; opt in per project via the `decay` field on the project endpoint, or via `client.project.update(decay=True)` in the SDKs (Python `v2.0.2` / TypeScript `v3.0.3`).
-
-- **Soft bias, never a filter.** The scaling factor stays in `0.3×–1.5×`. Decay can reorder candidates but never zeros them out — anything that surfaced before decay can still surface after.
-- **Reinforcement loop.** Every memory returned in a search has its access history updated, so frequently-used facts naturally float to the top over time.
-- **Public score still clamped to `[0, 1]`.** Existing API contract preserved; no client-side changes needed.
-- **v3 search only**, fully reversible. See [Memory Decay docs](/platform/features/memory-decay).
-
-</Update>
-
 <Update label="2026-04-14" description="Mem0 SDK v2.0.0 / v3.0.0">
 
 **New Memory Algorithm — State-of-the-Art Accuracy at ~3-4x Lower Cost**

docs/changelog/openclaw.mdx

@@ -4,110 +4,6 @@ description: "Release notes for the OpenClaw plugin and agent harness."
 mode: "wide"
 ---
 
-<Update label="2026-04-29" description="v1.0.11">
-
-**New Features:**
-- **Skills-mode auto-setup:** `enableSkillsConfig()` now runs automatically after onboarding — enables triage, recall (with reranking + keyword search), and dream consolidation with `tools.profile = "full"` and disables the built-in session-memory hook to avoid conflicts
-- **Memory runtime capability:** Plugin now exposes `runtime.getMemorySearchManager()` and `resolveMemoryBackendConfig()` on the registered memory capability, enabling OpenClaw gateway to query memory status and backend config directly
-- **Dimension-aware collections:** OSS wizard detects embedder dimension changes and creates a new collection (`mem0_<dims>d`) automatically, with a warning about old memories being inaccessible under the new embedder
-- **Tool documentation in skills:** Both `memory-triage` and `memory-dream` SKILL.md files now include full tool reference sections listing all available tools with parameters
-
-**Improvements:**
-- **Auto-capture and auto-recall default to enabled:** `autoCapture` and `autoRecall` now default to `true` (was `false`). Manifest descriptions updated accordingly. Ignored in skills mode
-- **`memory_update` over delete+add:** Skills now prefer `memory_update` for in-place edits — atomic and preserves edit history. Consolidation pattern updated: update best memory, delete redundant ones
-- **Search threshold lowered:** Default `searchThreshold` reduced from `0.5` to `0.1` for broader recall. Removed hardcoded `0.6` recall-specific override — all searches now use the configured threshold
-- **Embedder dimension propagation:** Vector store config auto-resolves dimensions from embedder config when not explicitly set. Syncs `dimension` and `embeddingModelDims` fields for Qdrant/PGVector compatibility
-- **Config file write safety:** `writeFullConfig()` now re-reads and deep-merges the `plugins` section before writing, preserving `installs` and `slots` written by the OpenClaw gateway
-- **Additional embedder models:** Added `mxbai-embed-large` (1024), `all-minilm` (384), and `snowflake-arctic-embed` (1024) to known embedder dimensions
-
-**Security:**
-- Bumped `protobufjs` to `>=7.5.5` via pnpm overrides (GHSA-xq3m-2v4x-88gg) ([#5012](https://github.com/mem0ai/mem0/pull/5012))
-
-**Fixes:**
-- Moved `bootstrapTelemetryFlag()` and removed `ensureInstallRecord()` from module-level side effects — both now run inside `register()` to avoid crashes when loaded outside OpenClaw gateway
-- Fixed OSS history DB path resolution: absolute paths no longer passed through `resolvePath()`, preventing double-prefix bugs
-- Manifest `providerAuthEnvVars` replaced with spec-compliant `setup.providers` format using `id` + `envVars`
-
-**Dependencies:**
-- Bumped `mem0ai` from `3.0.1` to `3.0.2`
-- Bumped `pluginApi` and `minGatewayVersion` compat to `>=2026.4.24`
-
-</Update>
-
-<Update label="2026-04-23" description="v1.0.10">
-
-**Security:**
-- Telemetry `distinct_id` now uses SHA-256 instead of MD5 — prevents rainbow-table reversal of API key hashes
-- User email is now SHA-256 hashed before sending as `distinct_id` — no PII in telemetry payloads
-- Declared PostHog telemetry endpoint (`us.i.posthog.com`) in `providerEndpoints`
-
-**Fixes:**
-- Fixed version-pinned install records preventing plugin updates. `ensureInstallRecord()` now detects semver-pinned specs (e.g. `@mem0/openclaw-mem0@1.0.7`) and rewrites them to `@latest` or `clawhub:` prefix so `openclaw plugins update` resolves to the newest release
-- Fixed `searchThreshold` default inconsistency: standardized to `0.3` across docs, README, and manifest
-- `PLUGIN_VERSION` now injected at build time via tsup `define` from `package.json` — no more hardcoded version strings
-
-**Manifest Compliance:**
-- Removed non-spec fields: `requiredEnvVars`, `dataLocations`, `privacy`, `setup` (with `externalEndpoints`, `providers`, `requiresRuntime`, `postInstallHint`)
-- Replaced `setup.externalEndpoints` with spec-compliant `providerEndpoints` using `endpointClass` + `hosts` format
-- Env var declarations now rely solely on `providerAuthEnvVars` (already spec-compliant)
-
-**Docs:**
-- Fixed `openclaw plugins update` command: uses plugin ID (`openclaw-mem0`), not npm package name (`@mem0/openclaw-mem0`)
-- Added update section to README
-- Removed redundant "Key Features" and "Conclusion" sections from integration docs
-
-</Update>
-
-<Update label="2026-04-22" description="v1.0.9">
-
-**Security & Compliance:**
-- Added top-level `requiredEnvVars` to plugin manifest, declaring env vars per mode (platform, OSS OpenAI, OSS Anthropic, OSS Ollama). Fixes ClaHub scanner "required env vars: none" mismatch
-- Added `sensitive: true` and descriptions to `apiKey` and `userEmail` in `configSchema` — previously only declared in `uiHints`
-- Added `default: false` with descriptions to `autoCapture` and `autoRecall` in `configSchema` so scanner can confirm opt-in defaults
-- Added `dataLocations` field to manifest declaring all persistence paths (config, vectorStore, historyDb, dreamState)
-- Added `privacy` field to manifest documenting data flow for platform vs open-source mode and credential storage guidance
-- Added `externalEndpoints` to `setup` section declaring api.mem0.ai and app.mem0.ai with purpose and requirement context
-
-**Tests:**
-- Replaced direct `process.env` access in `tests/cli-commands.test.ts` and `tests/fs-safe.test.ts` with `vi.stubEnv`/`vi.unstubAllEnvs`. Fixes ClaHub static analysis flag for "environment variable access combined with network send"
-- 421 tests across 15 test files
-
-</Update>
-
-<Update label="2026-04-21" description="v1.0.8">
-
-**New Features:**
-- **OSS Onboarding Wizard:** New guided 4-step interactive setup for open-source mode — walks through LLM provider, embedding provider, vector store, and user ID selection with prefilled defaults
-- **Agent-Friendly CLI:** Added `--json` flag to all 16 CLI commands for machine-readable output. Agents can call `openclaw mem0 help --json` to discover every command and flag
-- **Non-Interactive OSS Setup:** Added `--mode open-source` with `--oss-llm`, `--oss-embedder`, `--oss-vector` flags for fully automated OSS configuration without prompts
-- **JSON Helpers Module:** New `cli/json-helpers.ts` with `jsonOut`, `jsonErr`, and `redactSecrets` utilities for consistent structured output
-
-**Improvements:**
-- **Init Flow Redesigned:** Replaced 3-option flat menu with 2-level structure: Platform (email login or API key) and Open Source (guided wizard)
-- **Provider Selection:** LLM providers: OpenAI, Ollama, Anthropic. Embedding providers: OpenAI, Ollama. Vector stores: Qdrant, PGVector
-- **Input Prefill:** All prompts with defaults (base URL, user ID) now prefill the input field instead of showing defaults in brackets
-- **Smart Reuse:** When LLM and embedder use the same provider, API key and base URL are automatically reused from the LLM step
-- **Default Model:** Updated default LLM model to `gpt-5-mini`
-- **Manifest Compliance:** Removed undocumented fields, aligned env var declarations between SKILL.md and manifest, fixed `configSchema.required` for clean installs
-
-**Tests:**
-- 404 tests across 15 test files (+3 new: `json-helpers.test.ts`, `oss-wizard.test.ts`, `cli-commands.test.ts`)
-
-</Update>
-
-<Update label="2026-04-20" description="v1.0.7">
-
-**New Features:**
-- **Chat-Based Setup:** Added chat-based Platform setup flow — users can now configure the plugin conversationally instead of editing config files manually
-- **Installation Docs Rewrite:** Rewrote README and integration docs with chat-first setup, numbered manual steps.
-
-**Improvements:**
-- **SDK Upgrade:** Bumped `mem0ai` dependency to 3.0.1 for V3 API compatibility
-- **Config Cleanup:** Dropped deprecated `orgId`, `projectId`, `enableGraph` config options; updated CLI prompts ([#4734](https://github.com/mem0ai/mem0/pull/4734), [#4764](https://github.com/mem0ai/mem0/pull/4764))
-- **Noise Filtering:** Expanded noise patterns in memory add tool; handle leading text in JSON extraction
-
-</Update>
-
 <Update label="2026-04-11" description="v1.0.6">
 
 **Bug Fixes:**

docs/changelog/platform.mdx

@@ -4,13 +4,6 @@ description: "Release notes for the Mem0 hosted platform — backend, dashboard,
 mode: "wide"
 ---
 
-<Update label="2026-05-04" description="">
-
-**New Features:**
-- **Memory Decay:** Per-project search-time ranking bias that boosts recently-used memories and gently dampens stale ones. Opt-in via `decay` on the project endpoint; off by default. The scaling factor stays in `0.3×–1.5×`, the public `score` remains clamped to `[0, 1]`, and the bias never filters a candidate out. See [Memory Decay docs](/platform/features/memory-decay).
-
-</Update>
-
 <Update label="2026-04-16" description="">
 
 **Improvements:**

docs/changelog/sdk.mdx

@@ -7,37 +7,6 @@ mode: "wide"
 <Tabs>
 <Tab title="Python">
 
-<Update label="2026-05-08" description="v2.0.2">
-
-**Bug Fixes:**
-- **Telemetry:** Stitch OSS and platform PostHog identities on `MemoryClient` init so `$identify` events fire and a single user is no longer tracked as two or three disconnected personas ([#5040](https://github.com/mem0ai/mem0/pull/5040))
-- **Security:** Harden against SQL injection and prompt injection ([#4997](https://github.com/mem0ai/mem0/pull/4997))
-
-**New Features:**
-- **SDK:** Expose `decay` on `project.update` ([#5062](https://github.com/mem0ai/mem0/pull/5062))
-
-**Improvements:**
-- **Plugin:** Hand `mem0` search decisions to the agent ([#4992](https://github.com/mem0ai/mem0/pull/4992))
-
-</Update>
-
-<Update label="2026-04-25" description="v2.0.1">
-
-**Bug Fixes:**
-- **Client:** Map `user_id`, `agent_id`, `run_id` entity params to filters in `GET /memories` ([#4960](https://github.com/mem0ai/mem0/pull/4960))
-- **Memory:** Honor `prompt` param in vector store extraction pipeline ([#4914](https://github.com/mem0ai/mem0/pull/4914))
-- **Memory:** Add missing `text_lemmatized` field in `AsyncMemory._create_memory` ([#4886](https://github.com/mem0ai/mem0/pull/4886))
-- **Memory:** Merge same-key operator dicts in AND metadata filters ([#4853](https://github.com/mem0ai/mem0/pull/4853))
-- **LLMs:** Narrow `_is_reasoning_model` check to not match `gpt-5.x` variants ([#4746](https://github.com/mem0ai/mem0/pull/4746))
-- **Vector Stores:** Add `ca_certs` config option for Elasticsearch vector store ([#3993](https://github.com/mem0ai/mem0/pull/3993))
-- **Vector Stores:** Add `agent_id` and `run_id` to Elasticsearch/OpenSearch default mappings ([#4906](https://github.com/mem0ai/mem0/pull/4906))
-- **Embeddings:** Set FastEmbed `embedding_dims` from model metadata at init ([#4711](https://github.com/mem0ai/mem0/pull/4711))
-
-**Security:**
-- Bump vulnerable dependencies to patched versions ([#4835](https://github.com/mem0ai/mem0/pull/4835))
-
-</Update>
-
 <Update label="2026-04-14" description="v2.0.0">
 
 **Major Release** — Python SDK with V3 memory pipeline, ADD-only extraction, and cleaned-up API surface.
@@ -924,36 +893,6 @@ See the [OSS v1 to v2 migration guide](https://docs.mem0.ai/migration/oss-v1-to-
 </Tab>
 
 <Tab title="TypeScript">
-<Update label="2026-05-08" description="v3.0.3">
-
-**Bug Fixes:**
-- **Telemetry:** Stitch OSS and platform PostHog identities on `MemoryClient` init so `$identify` events fire and a single user is no longer tracked as two or three disconnected personas ([#5040](https://github.com/mem0ai/mem0/pull/5040))
-- **Vector Stores:** Fix inverted vector distance in PGVector implementation ([#4944](https://github.com/mem0ai/mem0/pull/4944))
-- **Security:** Harden against SQL injection and prompt injection ([#4997](https://github.com/mem0ai/mem0/pull/4997))
-
-**New Features:**
-- **SDK:** Expose `decay` on `project.update` ([#5062](https://github.com/mem0ai/mem0/pull/5062))
-
-</Update>
-
-<Update label="2026-04-25" description="v3.0.2">
-
-**Bug Fixes:**
-- **LLMs:** Forward `timeout` config to OpenAI client in JS OSS LLM providers ([#4770](https://github.com/mem0ai/mem0/pull/4770))
-
-**Improvements:**
-- **Telemetry:** Harden TS telemetry version injection and require changelog entry on version bump ([#4900](https://github.com/mem0ai/mem0/pull/4900))
-- **Docs:** Update memory tool list, CLI usage, and config file reading logic ([#4861](https://github.com/mem0ai/mem0/pull/4861))
-
-</Update>
-
-<Update label="2026-04-20" description="v3.0.1">
-
-**Bug Fixes:**
-- **Telemetry:** SDK version is now injected into telemetry at build time via esbuild's `define`, replacing the two hardcoded version strings in `src/client/telemetry.ts` and `src/oss/src/utils/telemetry.ts`. Previously these were stuck at `2.1.36` and `2.1.34` while the published package was on `3.x`, so every telemetry event was reporting the wrong `client_version`. The placeholder is substituted with a string literal at bundle time — no runtime `require("./package.json")` in the shipped bundle ([#4897](https://github.com/mem0ai/mem0/pull/4897)).
-
-</Update>
-
 <Update label="2026-04-14" description="v3.0.0">
 
 **Major Release** — TypeScript SDK with V3 memory pipeline, camelCase parameters, and cleaned-up API surface.
@@ -1323,16 +1262,6 @@ See the [TypeScript SDK migration guide](https://docs.mem0.ai/migration/ts-v2-to
 
 <Tab title="CLI">
 
-<Update label="2026-04-22" description="Python v0.2.4 / Node v0.2.4">
-
-**New Features:**
-- **V3 API Routes:** Migrated `add`, `search`, and `list` commands from v1/v2 to v3 API endpoints — `POST /v3/memories/add/`, `POST /v3/memories/search/`, `POST /v3/memories/`. Aligns both CLIs with the Python and TypeScript SDKs which already use v3 ([#4916](https://github.com/mem0ai/mem0/pull/4916))
-
-**Breaking Changes:**
-- **`--graph` / `--no-graph` removed:** The `enable_graph` config option, `--graph` and `--no-graph` CLI flags, and `MEM0_ENABLE_GRAPH` environment variable have been removed from both CLIs. Graph memory is now a project-level setting on the Platform ([#4916](https://github.com/mem0ai/mem0/pull/4916))
-
-</Update>
-
 <Update label="2026-04-11" description="Python v0.2.3 / Node v0.2.3">
 
 **Bug Fixes:**

docs/cookbooks/companions/quickstart-demo.mdx

@@ -45,7 +45,7 @@ Before you begin, follow these steps to set up the demo application:
    OPENAI_API_KEY=your_openai_api_key
    MEM0_API_KEY=your_mem0_api_key
    ```
-   You can obtain your `MEM0_API_KEY` by signing up at <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cookbook-companions-quickstart" rel="nofollow">Mem0 API Dashboard</a>.
+   You can obtain your `MEM0_API_KEY` by signing up at <a href="https://app.mem0.ai/dashboard/api-keys" rel="nofollow">Mem0 API Dashboard</a>.
 
 5. Start the development server:
    ```bash

docs/cookbooks/companions/travel-assistant.mdx

@@ -54,6 +54,7 @@ config = {
             "embedding_model_dims": 3072,
         }
     },
+    "version": "v1.1",
 }
 
 class PersonalTravelAssistant:
@@ -153,7 +154,7 @@ class PersonalTravelAssistant:
         return answer
 
     def get_memories(self, user_id):
-        memories = self.memory.get_all(filters={"user_id": user_id})
+        memories = self.memory.get_all(user_id=user_id)
         return [m['memory'] for m in memories.get('results', [])]
 
     def search_memories(self, query, user_id):

docs/cookbooks/essentials/controlling-memory-ingestion.mdx

@@ -38,7 +38,7 @@ client = MemoryClient(api_key="your-api-key")
 ```
 
 <Note>
-Replace `your-api-key` with your actual Mem0 API key from the <a href="https://app.mem0.ai?utm_source=oss&utm_medium=cookbook-memory-ingestion" rel="nofollow">dashboard</a>. Without proper API authentication, memory operations will fail.
+Replace `your-api-key` with your actual Mem0 API key from the <a href="https://app.mem0.ai" rel="nofollow">dashboard</a>. Without proper API authentication, memory operations will fail.
 </Note>
 
 ---

docs/cookbooks/essentials/entity-partitioning-playbook.mdx

@@ -17,7 +17,7 @@ from mem0 import MemoryClient
 client = MemoryClient(api_key="m0-...")
 ```
 
-Grab an API key from the <a href="https://app.mem0.ai/?utm_source=oss&utm_medium=cookbook-entity-partitioning" rel="nofollow">Mem0 dashboard</a> to get started.
+Grab an API key from the <a href="https://app.mem0.ai/" rel="nofollow">Mem0 dashboard</a> to get started.
 
 ## Store and Retrieve Scoped Memories
 

docs/cookbooks/essentials/exporting-memories.mdx

@@ -20,7 +20,7 @@ client = MemoryClient(api_key="your-api-key")
 ```
 
 <Note>
-Your API key needs export permissions to download memory data. Check your project settings on the <a href="https://app.mem0.ai?utm_source=oss&utm_medium=cookbook-exporting-memories" rel="nofollow">dashboard</a> if export operations fail with authentication errors.
+Your API key needs export permissions to download memory data. Check your project settings on the <a href="https://app.mem0.ai" rel="nofollow">dashboard</a> if export operations fail with authentication errors.
 </Note>
 
 Let's add some sample memories to work with:

docs/cookbooks/frameworks/eliza-os-character.mdx

@@ -42,7 +42,7 @@ Create a `.env` file in the root of the project and add the following (you can u
 
 ```bash
 # Mem0 Configuration
-MEM0_API_KEY= # Mem0 API Key (get from https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cookbook-eliza-os)
+MEM0_API_KEY= # Mem0 API Key (get from https://app.mem0.ai/dashboard/api-keys)
 MEM0_USER_ID= # Default: eliza-os-user
 MEM0_PROVIDER= # Default: openai
 MEM0_PROVIDER_API_KEY= # API Key for the provider (OpenAI, Anthropic, etc.)

docs/cookbooks/frameworks/gemini-3-with-mem0-mcp.mdx

@@ -55,7 +55,7 @@ GEMINI_API_KEY=your-gemini-api-key-here
 ```
 
 <Note>
-Ensure you have your Mem0 API key from the <a href="https://app.mem0.ai?utm_source=oss&utm_medium=cookbook-gemini-3" rel="nofollow">Mem0 Dashboard</a> and your Gemini API key from the [Google AI Studio](https://ai.studio/app/api-keys).
+Ensure you have your Mem0 API key from the <a href="https://app.mem0.ai" rel="nofollow">Mem0 Dashboard</a> and your Gemini API key from the [Google AI Studio](https://ai.studio/app/api-keys).
 </Note>
 
 ## Gemini Memory Agent

docs/cookbooks/frameworks/llamaindex-multiagent.mdx

@@ -41,7 +41,7 @@ Set up your environment variables:
 - `MEM0_API_KEY`: Your Mem0 Platform API key
 - `OPENAI_API_KEY`: Your OpenAI API key
 
-You can obtain your Mem0 Platform API key from the <a href="https://app.mem0.ai?utm_source=oss&utm_medium=cookbook-llamaindex-multiagent" rel="nofollow">Mem0 Platform</a>.
+You can obtain your Mem0 Platform API key from the <a href="https://app.mem0.ai" rel="nofollow">Mem0 Platform</a>.
 
 ## Complete Implementation
 
@@ -357,7 +357,7 @@ Based on our previous session, I remember we covered Vision Language Models and
 ## Help & Resources
 
 - [LlamaIndex Agent Workflows](https://docs.llamaindex.ai/en/stable/use_cases/agents/)
-- <a href="https://app.mem0.ai/?utm_source=oss&utm_medium=cookbook-llamaindex-multiagent" rel="nofollow">Mem0 Platform</a>
+- <a href="https://app.mem0.ai/" rel="nofollow">Mem0 Platform</a>
 
 ---
 

docs/cookbooks/frameworks/llamaindex-react.mdx

@@ -25,7 +25,7 @@ os.environ["OPENAI_API_KEY"] = "<your-openai-api-key>"
 llm = OpenAI(model="gpt-5-mini")
 ```
 
-Initialize the Mem0 client. You can find your API key <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cookbook-llamaindex-react" rel="nofollow">here</a>. Read about Mem0 [Open Source](https://docs.mem0.ai/open-source/overview).
+Initialize the Mem0 client. You can find your API key <a href="https://app.mem0.ai/dashboard/api-keys" rel="nofollow">here</a>. Read about Mem0 [Open Source](https://docs.mem0.ai/open-source/overview).
 ```python
 os.environ["MEM0_API_KEY"] = "<your-mem0-api-key>"
 

docs/cookbooks/integrations/agents-sdk-tool.mdx

@@ -223,7 +223,7 @@ context = Mem0Context(user_id="user123")
 ## Resources
 
 - [Mem0 Documentation](https://docs.mem0.ai/introduction)
-- <a href="https://app.mem0.ai/dashboard?utm_source=oss&utm_medium=cookbook-agents-sdk-tool" rel="nofollow">Mem0 Dashboard</a>
+- <a href="https://app.mem0.ai/dashboard" rel="nofollow">Mem0 Dashboard</a>
 - [API Reference](https://docs.mem0.ai/api-reference)
 
 ---

docs/cookbooks/integrations/aws-bedrock.mdx

@@ -42,7 +42,7 @@ This sets up Mem0 with:
 ```python
 import boto3
 from opensearchpy import RequestsHttpConnection, AWSV4SignerAuth
-from mem0 import Memory
+from mem0.memory.main import Memory
 
 region = 'us-west-2'
 service = 'aoss'

docs/cookbooks/integrations/openai-tool-calls.mdx

@@ -23,7 +23,7 @@ MEM0_API_KEY=your_mem0_api_key
 OPENAI_API_KEY=your_openai_api_key
 ```
 
-Get your Mem0 API key from the <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cookbook-openai-tool-calls" rel="nofollow">Mem0 Dashboard</a>.
+Get your Mem0 API key from the <a href="https://app.mem0.ai/dashboard/api-keys" rel="nofollow">Mem0 Dashboard</a>.
 
 ### Configuration
 
@@ -303,7 +303,7 @@ run().catch(console.error);
 ## Resources
 
 - [Mem0 Documentation](https://docs.mem0.ai/introduction)
-- <a href="https://app.mem0.ai/dashboard?utm_source=oss&utm_medium=cookbook-openai-tool-calls" rel="nofollow">Mem0 Dashboard</a>
+- <a href="https://app.mem0.ai/dashboard" rel="nofollow">Mem0 Dashboard</a>
 - [API Reference](https://docs.mem0.ai/api-reference)
 - [OpenAI Documentation](https://platform.openai.com/docs)
 

docs/core-concepts/memory-operations/delete.mdx

@@ -216,7 +216,7 @@ memory.delete_all(user_id="alice")
 ## Put it into practice
 
 - Review the <Link href="/api-reference/memory/delete-memory">Delete Memory API reference</Link>, plus <Link href="/api-reference/memory/batch-delete">Batch Delete</Link> and <Link href="/api-reference/memory/delete-memories">Filtered Delete</Link>.
-- Pair deletes with <Link href="/platform/features/platform-overview">Expiration Policies</Link> to automate retention.
+- Pair deletes with <Link href="/platform/features/entity-scoped-memory">Entity-Scoped Memory</Link> to manage retention per user, agent, or session.
 
 ## See it live
 
@@ -233,9 +233,9 @@ memory.delete_all(user_id="alice")
     href="/core-concepts/memory-operations/add"
   />
   <Card
-    title="Enable Expiration Policies"
-    description="Automate retention with the platform’s expiration feature."
+    title="Scope memories by entity"
+    description="Manage retention per user, agent, or session with entity-scoped memory."
     icon="clock"
-    href="/platform/features/platform-overview"
+    href="/platform/features/entity-scoped-memory"
   />
 </CardGroup>

docs/docs.json

@@ -83,8 +83,7 @@
                           "platform/advanced-memory-operations",
                           "platform/features/criteria-retrieval",
                           "platform/features/contextual-add",
-                          "platform/features/custom-instructions",
-                          "platform/features/memory-decay"
+                          "platform/features/custom-instructions"
                         ]
                       },
                       {
@@ -154,7 +153,6 @@
                     "icon": "rocket",
                     "pages": [
                       "open-source/overview",
-                      "open-source/setup",
                       "vibecoding",
                       "open-source/python-quickstart",
                       "open-source/node-quickstart"
@@ -580,7 +578,7 @@
     "primary": {
       "type": "button",
       "label": "Your Dashboard",
-      "href": "https://app.mem0.ai?utm_source=oss&utm_medium=docs-nav"
+      "href": "https://app.mem0.ai"
     }
   },
   "footer": {
@@ -610,7 +608,7 @@
         "title": "Try in Playground",
         "description": "Open this example in the interactive Mem0 playground",
         "icon": "play",
-        "href": "https://app.mem0.ai/playground?utm_source=oss&utm_medium=docs-nav"
+        "href": "https://app.mem0.ai/playground"
       }
     ]
   },

docs/integrations/agentops.mdx

@@ -24,7 +24,7 @@ pip install mem0ai agentops python-dotenv
 2. Valid API keys:
    - [AgentOps API Key](https://app.agentops.ai/dashboard/api-keys)
    - OpenAI API Key (for LLM operations)
-   - <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-agentops" rel="nofollow">Mem0 API Key</a> (optional, for cloud operations)
+   - <a href="https://app.mem0.ai/dashboard/api-keys" rel="nofollow">Mem0 API Key</a> (optional, for cloud operations)
 
 ## Basic Integration Example
 

docs/integrations/agno.mdx

@@ -23,7 +23,7 @@ pip install agno mem0ai python-dotenv
 ```
 
 2. Valid API keys:
-   - <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-agno" rel="nofollow">Mem0 API Key</a>
+   - <a href="https://app.mem0.ai/dashboard/api-keys" rel="nofollow">Mem0 API Key</a>
    - OpenAI API Key (for the agent model)
 
 ## Quick Integration (Using `Mem0Tools`)

docs/integrations/autogen.mdx

@@ -19,7 +19,7 @@ pip install autogen mem0ai openai python-dotenv
 
 First, we'll import the necessary libraries and set up our configurations.
 
-<Note>Remember to get the Mem0 API key from <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-autogen" rel="nofollow">Mem0 Platform</a>.</Note>
+<Note>Remember to get the Mem0 API key from <a href="https://app.mem0.ai" rel="nofollow">Mem0 Platform</a>.</Note>
 
 ```python
 import os
@@ -32,7 +32,7 @@ load_dotenv()
 
 # Configuration
 # OPENAI_API_KEY = 'sk-xxx'  # Replace with your actual OpenAI API key
-# MEM0_API_KEY = 'your-mem0-key'  # Replace with your actual Mem0 API key from https://app.mem0.ai?utm_source=oss&utm_medium=integration-autogen
+# MEM0_API_KEY = 'your-mem0-key'  # Replace with your actual Mem0 API key from https://app.mem0.ai
 USER_ID = "alice"
 
 # Set up OpenAI API key

docs/integrations/aws-bedrock.mdx

@@ -49,7 +49,7 @@ Import necessary modules and configure Mem0:
 ```python
 import boto3
 from opensearchpy import OpenSearch, RequestsHttpConnection, AWSV4SignerAuth
-from mem0 import Memory
+from mem0.memory.main import Memory
 
 region = 'us-west-2'
 service = 'aoss'

docs/integrations/chatdev.mdx

@@ -18,7 +18,7 @@ In this guide, you'll:
 - **Python 3.12+**
 - **[uv](https://docs.astral.sh/uv/)** — Python package manager
 - **Node.js 18+** and **npm** — only needed if using the web console
-- A **Mem0 API key** from <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-chatdev" rel="nofollow">app.mem0.ai</a>
+- A **Mem0 API key** from <a href="https://app.mem0.ai" rel="nofollow">app.mem0.ai</a>
 - An **OpenAI API key** (or another LLM provider supported by ChatDev)
 
 ## Setup and Configuration
@@ -39,7 +39,7 @@ cd frontend && npm install && cd ..
 
 Set up your environment variables in a `.env` file:
 
-<Note>Get your Mem0 API key from <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-chatdev" rel="nofollow">Mem0 Platform</a>.</Note>
+<Note>Get your Mem0 API key from <a href="https://app.mem0.ai" rel="nofollow">Mem0 Platform</a>.</Note>
 
 ```bash
 MEM0_API_KEY=your-mem0-api-key
@@ -194,7 +194,7 @@ This means retrieval returns memories from **both** the user's scope and the age
 
 | Field | Required | Description |
 |-------|----------|-------------|
-| `api_key` | Yes | Mem0 API key from <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-chatdev" rel="nofollow">app.mem0.ai</a> |
+| `api_key` | Yes | Mem0 API key from <a href="https://app.mem0.ai" rel="nofollow">app.mem0.ai</a> |
 | `user_id` | No | Scope memories to a specific user |
 | `agent_id` | No | Scope memories to a specific agent |
 
@@ -216,9 +216,9 @@ This means retrieval returns memories from **both** the user's scope and the age
 
 - **No memories returned on first run** — This is expected. Memories are stored *after* the agent responds, so the first interaction has no prior context. Memories appear starting from the second interaction onward.
 - **`mem0ai` not installed** — If you see `ImportError: mem0ai is required for Mem0Memory`, run `uv add mem0ai` or `pip install mem0ai` to add the dependency.
-- **Invalid API key** — A wrong or expired `MEM0_API_KEY` will log errors like `Mem0 search failed` or `Mem0 add failed` but won't crash the agent. Check your key at <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-chatdev" rel="nofollow">app.mem0.ai</a>.
+- **Invalid API key** — A wrong or expired `MEM0_API_KEY` will log errors like `Mem0 search failed` or `Mem0 add failed` but won't crash the agent. Check your key at <a href="https://app.mem0.ai" rel="nofollow">app.mem0.ai</a>.
 - **Pipeline headers in memories** — ChatDev automatically strips internal pipeline headers (e.g., `=== INPUT FROM TASK (user) ===`) before sending text to Mem0, so your memories stay clean.
-- **Clearing test memories** — To delete memories created during testing, use the Mem0 dashboard at <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-chatdev" rel="nofollow">app.mem0.ai</a> or the Python SDK: `MemoryClient().delete_all(user_id="your-test-user")`.
+- **Clearing test memories** — To delete memories created during testing, use the Mem0 dashboard at <a href="https://app.mem0.ai" rel="nofollow">app.mem0.ai</a> or the Python SDK: `MemoryClient().delete_all(user_id="your-test-user")`.
 
 ## Key Features
 

docs/integrations/claude-code.mdx

@@ -17,8 +17,8 @@ Add persistent memory to [**Claude Code**](https://docs.anthropic.com/en/docs/cl
 Before setting up Mem0 with Claude Code, ensure you have:
 
 1. A Mem0 Platform account and API key:
-   - <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-claude-code" rel="nofollow">Sign up at app.mem0.ai</a>
-   - <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-claude-code" rel="nofollow">Get your API key</a> (starts with `m0-`)
+   - <a href="https://app.mem0.ai" rel="nofollow">Sign up at app.mem0.ai</a>
+   - <a href="https://app.mem0.ai/dashboard/api-keys" rel="nofollow">Get your API key</a> (starts with `m0-`)
 
 2. Claude Code CLI or Claude Cowork desktop app installed
 
@@ -32,19 +32,12 @@ export MEM0_API_KEY="m0-your-api-key"
 
 ### Option A — Plugin Marketplace (Recommended)
 
-Install the full plugin including MCP server, lifecycle hooks, and SDK skill.
+Install the full plugin including MCP server, lifecycle hooks, and SDK skill:
 
-1. Add the Mem0 marketplace:
-
-   ```
-   /plugin marketplace add mem0ai/mem0
-   ```
-
-2. Install the plugin:
-
-   ```
-   /plugin install mem0@mem0-plugins
-   ```
+```
+/plugin marketplace add mem0ai/mem0
+/plugin install mem0@mem0-plugins
+```
 
 **Claude Cowork desktop app:** Open the Cowork tab, click **Customize** in the sidebar, click **Browse plugins**, and install Mem0.
 

docs/integrations/codex.mdx

@@ -17,8 +17,8 @@ Add persistent memory to [**OpenAI Codex**](https://openai.com/index/codex/) wit
 Before setting up Mem0 with Codex, ensure you have:
 
 1. A Mem0 Platform account and API key:
-   - <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-codex" rel="nofollow">Sign up at app.mem0.ai</a>
-   - <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-codex" rel="nofollow">Get your API key</a> (starts with `m0-`)
+   - <a href="https://app.mem0.ai" rel="nofollow">Sign up at app.mem0.ai</a>
+   - <a href="https://app.mem0.ai/dashboard/api-keys" rel="nofollow">Get your API key</a> (starts with `m0-`)
 
 2. OpenAI Codex access
 
@@ -30,100 +30,91 @@ export MEM0_API_KEY="m0-your-api-key"
 
 ## Installation
 
-### Option A — Direct MCP (Recommended)
+### Option A — Repo Marketplace (Recommended for Teams)
 
-The fastest way to connect Codex to Mem0 — no downloads, no marketplace. Codex reads MCP servers from `~/.codex/config.toml` as TOML. Add:
+Add a `.agents/plugins/marketplace.json` to your repository root:
 
-```toml
-[mcp_servers.mem0]
-url = "https://mcp.mem0.ai/mcp"
-bearer_token_env_var = "MEM0_API_KEY"
+```json
+{
+  "name": "mem0-plugins",
+  "interface": {
+    "displayName": "Mem0 Plugins"
+  },
+  "plugins": [
+    {
+      "name": "mem0",
+      "source": {
+        "source": "local",
+        "path": "./plugins/mem0"
+      },
+      "policy": {
+        "installation": "AVAILABLE",
+        "authentication": "ON_INSTALL"
+      },
+      "category": "Productivity"
+    }
+  ]
+}
 ```
 
-Make sure `MEM0_API_KEY` is exported in the shell you launch Codex from, then restart Codex.
+Then in Codex, browse the repo's plugin directory and install Mem0.
 
-<Info>
-  Codex's `codex mcp add` CLI only supports stdio MCP servers. Because Mem0's MCP is HTTP/streamable, you configure it by editing `config.toml` directly (or via the **Plugins → Connect to a custom MCP → Streamable HTTP** UI in the Codex app).
-</Info>
+### Option B — Personal Marketplace
 
-### Option B — Sideload the Plugin (Advanced)
+Add to `~/.agents/plugins/marketplace.json`:
 
-For the full plugin experience — MCP server **plus** the Mem0 SDK skill, memory protocol skill, and opt-in lifecycle hooks — sideload the plugin from a local clone. The Mem0 repo already ships a marketplace manifest at [`.agents/plugins/marketplace.json`](https://github.com/mem0ai/mem0/blob/main/.agents/plugins/marketplace.json), so there's no JSON to author by hand. This follows the Codex [build-plugins](https://developers.openai.com/codex/plugins/build) local-testing workflow.
-
-<Info>
-  Don't combine Option B with Option A. The plugin manifest declares its MCP server via [`.codex-mcp.json`](https://github.com/mem0ai/mem0/blob/main/mem0-plugin/.codex-mcp.json), so Codex auto-registers the `mem0` MCP server when the plugin loads. Adding the same `[mcp_servers.mem0]` block to `~/.codex/config.toml` will create a duplicate registration.
-</Info>
-
-**Step 1.** Clone the Mem0 repository anywhere on disk:
-
-```bash
-git clone https://github.com/mem0ai/mem0.git ~/codex-plugins/mem0-source
+```json
+{
+  "name": "mem0-plugins",
+  "interface": {
+    "displayName": "Mem0 Plugins"
+  },
+  "plugins": [
+    {
+      "name": "mem0",
+      "source": {
+        "source": "local",
+        "path": "/path/to/mem0-plugin"
+      },
+      "policy": {
+        "installation": "AVAILABLE",
+        "authentication": "ON_INSTALL"
+      },
+      "category": "Productivity"
+    }
+  ]
+}
 ```
 
-**Step 2.** Register the bundled marketplace with Codex's CLI:
+### Option C — Manual MCP Configuration
 
-```bash
-codex plugin marketplace add ~/codex-plugins/mem0-source
+Add to your Codex MCP config:
+
+```json
+{
+  "mcpServers": {
+    "mem0": {
+      "type": "http",
+      "url": "https://mcp.mem0.ai/mcp/",
+      "headers": {
+        "Authorization": "Token ${MEM0_API_KEY}"
+      }
+    }
+  }
+}
 ```
 
-This points Codex at the repo's `.agents/plugins/marketplace.json`. The bundled file uses `path: "./mem0-plugin"`, which Codex resolves relative to the clone root.
-
-<Info>
-  **Why we recommend this over hand-authoring `~/.agents/plugins/marketplace.json`:** Codex requires `source.path` in any marketplace manifest to be **relative** (starting with `./`) and **inside the marketplace root**. The repo's bundled manifest already satisfies this — the marketplace root is the clone directory, and `mem0-plugin/` lives inside it. With a personal `~/.agents/plugins/marketplace.json`, the root is `~/` and the clone has to live under `~/` too. The CLI form sidesteps that constraint.
-</Info>
-
-**Step 3.** Restart Codex, run `/plugins`, browse the `Mem0 Plugins` marketplace, and install **Mem0**.
-
-**Step 4 (optional) — enable lifecycle hooks.** Codex doesn't auto-wire hooks from plugin manifests; it only reads them from `~/.codex/hooks.json` (or `<repo>/.codex/hooks.json`). Run the bundled installer once to merge the Mem0 entries into your global hooks file:
-
-```bash
-python3 ~/codex-plugins/mem0-source/mem0-plugin/scripts/install_codex_hooks.py
-```
-
-Then enable the hooks feature flag in `~/.codex/config.toml`:
-
-```toml
-[features]
-codex_hooks = true
-```
-
-Restart Codex. The installer registers three hooks pointing at scripts inside your clone:
-
-| Event | Behavior |
-|-------|----------|
-| `SessionStart` | Loads prior memories as bootstrap context |
-| `UserPromptSubmit` | Injects relevant memories before each prompt |
-| `Stop` | Reminds the agent to persist learnings at turn end |
-
-Re-running the installer is idempotent. To remove the hooks: `python3 ~/codex-plugins/mem0-source/mem0-plugin/scripts/install_codex_hooks.py --uninstall`.
-
-<Warning>
-  The hooks file stores absolute paths into your clone (e.g. `~/codex-plugins/mem0-source/mem0-plugin/scripts/...`). If you move or delete the clone, the hooks will break silently — re-run the installer from the new location, or run `--uninstall` first.
-</Warning>
-
-### Managing the Plugin
-
-Codex provides CLI commands for managing marketplaces after install:
-
-```bash
-codex plugin marketplace upgrade               # pull latest plugin versions
-codex plugin marketplace remove mem0-plugins   # unregister the marketplace
-```
-
-To pull updates to the plugin source itself, `git pull` inside your clone (`~/codex-plugins/mem0-source`) and then run `codex plugin marketplace upgrade` to refresh Codex's plugin cache. Plugins are cached at `~/.codex/plugins/cache/<marketplace>/<plugin>/<version>/`.
-
 <Info icon="check">
-  After either option, start a new Codex task and ask: *"List my mem0 entities"* or *"Search my memories for hello"*. If the `mem0` tools appear and respond, you're all set.
+  Start a new Codex task and ask: *"List my mem0 entities"* or *"Search my memories for hello"*. If the `mem0` tools appear and respond, you're all set.
 </Info>
 
 ## What's Included
 
-| Component | Sideloaded Plugin | Direct MCP |
-|-----------|:-----------------:|:----------:|
+| Component | Plugin Install | MCP Only |
+|-----------|:--------------:|:--------:|
 | MCP Server (9 memory tools) | Yes | Yes |
 | Memory Protocol Skill | Yes | No |
 | Mem0 SDK Skill | Yes | No |
-| Lifecycle Hooks (opt-in) | Yes | No |
 
 ## Available MCP Tools
 
@@ -143,7 +134,7 @@ Once installed, the following tools are available in every Codex session:
 
 ## Memory Protocol Skill
 
-When the plugin is sideloaded, the memory protocol skill instructs the agent to:
+Codex uses a skill-based approach instead of lifecycle hooks. When installed via the plugin marketplace, the memory protocol skill instructs the agent to:
 
 ### On Every New Task
 1. Call `search_memories` with a query related to the current task to load relevant context
@@ -208,12 +199,8 @@ You: Add WebSocket support for real-time notification delivery.
 
 - **"Connection failed"** — Verify `MEM0_API_KEY` is set in your shell: `echo $MEM0_API_KEY`
 - **No tools appearing** — Restart your Codex session after plugin installation
-- **Duplicate `mem0` MCP server / "tool collision" errors** — You combined Option A (Direct MCP) with Option B (sideload). The sideloaded plugin auto-registers `mem0` from `.codex-mcp.json`, so remove the `[mcp_servers.mem0]` block from `~/.codex/config.toml`.
-- **`plugin/read failed in TUI`** — Codex can't find the plugin directory the marketplace points at. If you used `codex plugin marketplace add <path>`, confirm the path is your clone root and that `<clone>/.agents/plugins/marketplace.json` exists. If you hand-authored `~/.agents/plugins/marketplace.json`, `source.path` must be relative (start with `./`), inside the marketplace root (`~/` for personal installs), and end in `mem0-plugin` — e.g. `"./codex-plugins/mem0-source/mem0-plugin"`.
-- **Plugin not found in `/plugins`** — Run `codex plugin marketplace add ~/path/to/clone` again, or confirm the marketplace was registered with `codex plugin marketplace remove mem0-plugins` then re-add.
-- **Skills not loading** — Verify the `skills` field in `plugin.json` points to a valid directory containing `SKILL.md` files.
-- **Hooks not firing** — Confirm `codex_hooks = true` is in `~/.codex/config.toml` under `[features]`, and that `~/.codex/hooks.json` contains the Mem0 entries (re-run the installer if not). Restart Codex after enabling the flag.
-- **Hooks broke after moving the clone** — The installer bakes absolute paths into `~/.codex/hooks.json` pointing at scripts inside your clone. If you moved or renamed the clone directory, run `python3 <new-clone>/mem0-plugin/scripts/install_codex_hooks.py` from the new location — the installer is idempotent and replaces the old entries.
+- **Plugin not found** — Ensure `.agents/plugins/marketplace.json` is at the repository root and `source.path` points to the correct plugin directory
+- **Skills not loading** — Verify the `skills` field in `plugin.json` points to a valid directory containing `SKILL.md` files
 
 <CardGroup cols={2}>
   <Card title="Mem0 MCP Setup" icon="puzzle-piece" href="/platform/mem0-mcp">

docs/integrations/crewai.mdx

@@ -22,7 +22,7 @@ pip install crewai crewai-tools mem0ai
 
 Import required modules and set up configurations:
 
-<Note>Remember to get your API keys from <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-crewai" rel="nofollow">Mem0 Platform</a>, [OpenAI](https://platform.openai.com) and [Serper Dev](https://serper.dev) for search capabilities.</Note>
+<Note>Remember to get your API keys from <a href="https://app.mem0.ai" rel="nofollow">Mem0 Platform</a>, [OpenAI](https://platform.openai.com) and [Serper Dev](https://serper.dev) for search capabilities.</Note>
 
 ```python
 import os

docs/integrations/cursor.mdx

@@ -17,8 +17,8 @@ Add persistent memory to [**Cursor**](https://cursor.com) with the Mem0 plugin.
 Before setting up Mem0 with Cursor, ensure you have:
 
 1. A Mem0 Platform account and API key:
-   - <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-cursor" rel="nofollow">Sign up at app.mem0.ai</a>
-   - <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-cursor" rel="nofollow">Get your API key</a> (starts with `m0-`)
+   - <a href="https://app.mem0.ai" rel="nofollow">Sign up at app.mem0.ai</a>
+   - <a href="https://app.mem0.ai/dashboard/api-keys" rel="nofollow">Get your API key</a> (starts with `m0-`)
 
 2. Cursor installed ([cursor.com](https://cursor.com))
 

docs/integrations/flowise.mdx

@@ -38,7 +38,7 @@ npx flowise start
 
 ### 2. Obtain Your Mem0 API Key
 
-1. Navigate to the <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-flowise" rel="nofollow">Mem0 API Key dashboard</a>.
+1. Navigate to the <a href="https://app.mem0.ai/dashboard/api-keys" rel="nofollow">Mem0 API Key dashboard</a>.
 2. Generate or copy your existing Mem0 API Key.
 
 ![Mem0 API Key](https://raw.githubusercontent.com/FlowiseAI/FlowiseDocs/main/en/.gitbook/assets/mem0/api-key.png)
@@ -70,7 +70,7 @@ Test your memory configuration:
 
 1. Save your Flowise configuration
 2. Run a test chat and store some information
-3. Verify the stored memories in the <a href="https://app.mem0.ai/dashboard/requests?utm_source=oss&utm_medium=integration-flowise" rel="nofollow">Mem0 Dashboard</a>
+3. Verify the stored memories in the <a href="https://app.mem0.ai/dashboard/requests" rel="nofollow">Mem0 Dashboard</a>
 
 ![Flowise Test Chat](https://raw.githubusercontent.com/FlowiseAI/FlowiseDocs/main/en/.gitbook/assets/mem0/flowise-chat-1.png)
 
@@ -103,7 +103,7 @@ Available settings include:
 
 ### Platform Configuration
 
-Additional settings available in <a href="https://app.mem0.ai/dashboard/project-settings?utm_source=oss&utm_medium=integration-flowise" rel="nofollow">Mem0 Project Settings</a>:
+Additional settings available in <a href="https://app.mem0.ai/dashboard/project-settings" rel="nofollow">Mem0 Project Settings</a>:
 
 1. **Custom Instructions**: Define memory extraction rules
 2. **Expiration Date**: Set automatic memory cleanup periods

docs/integrations/google-ai-adk.mdx

@@ -22,7 +22,7 @@ pip install google-adk mem0ai python-dotenv
 ```
 
 2. Valid API keys:
-   - <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-google-ai-adk" rel="nofollow">Mem0 API Key</a>
+   - <a href="https://app.mem0.ai/dashboard/api-keys" rel="nofollow">Mem0 API Key</a>
    - Google AI Studio API Key
 
 ## Basic Integration Example

docs/integrations/hermes.mdx

@@ -52,7 +52,7 @@ hermes memory setup
 
 Select **mem0** as the provider and enter your Mem0 API key when prompted. The wizard writes your config to `~/.hermes/mem0.json`.
 
-<Note>Get your API key from <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-hermes" rel="nofollow">app.mem0.ai</a>.</Note>
+<Note>Get your API key from <a href="https://app.mem0.ai" rel="nofollow">app.mem0.ai</a>.</Note>
 
 ### Option 2: Manual Configuration
 

docs/integrations/keywords.mdx

@@ -16,7 +16,7 @@ Combining Mem0 with Keywords AI allows you to:
 4. Optimize token usage and reduce costs
 
 <Note>
-You can get your Mem0 API key from the <a href="https://app.mem0.ai/?utm_source=oss&utm_medium=integration-keywords" rel="nofollow">Mem0 dashboard</a>.
+You can get your Mem0 API key from the <a href="https://app.mem0.ai/" rel="nofollow">Mem0 dashboard</a>.
 </Note>
 
 ## Setup and Configuration
@@ -24,7 +24,7 @@ You can get your Mem0 API key from the <a href="https://app.mem0.ai/?utm_source=
 Install the necessary libraries:
 
 ```bash
-pip install mem0ai keywordsai-sdk
+pip install mem0 keywordsai-sdk
 ```
 
 Set up your environment variables:
@@ -65,7 +65,7 @@ config = {
 }
 
 # Initialize Memory
-memory = Memory.from_config(config)
+memory = Memory.from_config(config_dict=config)
 
 # Add a memory
 result = memory.add(

docs/integrations/langchain.mdx

@@ -22,7 +22,7 @@ pip install langchain langchain_openai mem0ai python-dotenv
 
 Import required modules and set up configurations:
 
-<Note>Remember to get the Mem0 API key from <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-langchain" rel="nofollow">Mem0 Platform</a>.</Note>
+<Note>Remember to get the Mem0 API key from <a href="https://app.mem0.ai" rel="nofollow">Mem0 Platform</a>.</Note>
 
 ```python
 import os

docs/integrations/langgraph.mdx

@@ -23,7 +23,7 @@ pip install langgraph langchain-openai mem0ai python-dotenv
 
 Import required modules and set up configurations:
 
-<Note>Remember to get the Mem0 API key from <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-langgraph" rel="nofollow">Mem0 Platform</a>.</Note>
+<Note>Remember to get the Mem0 API key from <a href="https://app.mem0.ai" rel="nofollow">Mem0 Platform</a>.</Note>
 
 ```python
 from typing import Annotated, TypedDict, List

docs/integrations/llama-index.mdx

@@ -22,7 +22,7 @@ pip install llama-index-core llama-index-memory-mem0 python-dotenv
 Set your Mem0 Platform API key as an environment variable. You can replace `<your-mem0-api-key>` with your actual API key:
 
 <Note type="info">
-  You can obtain your Mem0 Platform API key from the <a href="https://app.mem0.ai/login?utm_source=oss&utm_medium=integration-llama-index" rel="nofollow">Mem0 Platform</a>.
+  You can obtain your Mem0 Platform API key from the <a href="https://app.mem0.ai/login" rel="nofollow">Mem0 Platform</a>.
 </Note>
 
 ```python
@@ -92,6 +92,7 @@ config = {
         "provider": "openai",
         "config": {"model": "text-embedding-3-small"},
     },
+    "version": "v1.1",
 }
 ```
 

docs/integrations/mastra.mdx

@@ -23,7 +23,7 @@ npm install @mastra/core @mastra/mem0 @ai-sdk/openai zod
 
 Set up your environment variables:
 
-<Note>Remember to get the Mem0 API key from <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-mastra" rel="nofollow">Mem0 Platform</a>.</Note>
+<Note>Remember to get the Mem0 API key from <a href="https://app.mem0.ai" rel="nofollow">Mem0 Platform</a>.</Note>
 
 ```bash
 MEM0_API_KEY=your-mem0-api-key

docs/integrations/openai-agents-sdk.mdx

@@ -22,7 +22,7 @@ pip install openai-agents mem0ai
 ```
 
 2. Valid API keys:
-   - <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-openai-agents-sdk" rel="nofollow">Mem0 API Key</a>
+   - <a href="https://app.mem0.ai/dashboard/api-keys" rel="nofollow">Mem0 API Key</a>
    - [OpenAI API Key](https://platform.openai.com/api-keys)
 
 ## Basic Integration Example
@@ -214,7 +214,7 @@ Customize memory behavior:
 # Configure memory search
 memories = mem0.search(
     query="travel preferences",
-    filters={"user_id": "alex"},
+    user_id="alex",
     top_k=5  # Number of memories to retrieve
 )
 

docs/integrations/openclaw.mdx

@@ -1,6 +1,6 @@
 ---
 title: OpenClaw
-description: "Add long-term memory to OpenClaw agents using the Mem0 plugin with skills-based memory extraction and recall."
+description: "Add long-term memory to OpenClaw agents using the Mem0 plugin with auto-recall and auto-capture support."
 ---
 
 Add long-term memory to [OpenClaw](https://github.com/openclaw/openclaw) agents with the `@mem0/openclaw-mem0` plugin. Your agent forgets everything between sessions — this plugin fixes that by automatically watching conversations, extracting what matters, and bringing it back when relevant.
@@ -12,39 +12,17 @@ Add long-term memory to [OpenClaw](https://github.com/openclaw/openclaw) agents
 </Frame>
 
 The plugin provides:
-1. **Triage** — The agent extracts durable facts from conversations using a structured protocol with importance gates and domain overlays
-2. **Recall** — Before each turn, relevant memories are retrieved with reranking and injected into context
-3. **Dream** — Periodic memory consolidation: merges duplicates, resolves conflicts, prunes stale entries
-4. **Agent Tools** — Eight tools for explicit memory operations during conversations
+1. **Auto-Recall** — Before the agent responds, memories matching the current message are injected into context
+2. **Auto-Capture** — After the agent responds, the exchange is sent to Mem0 which decides what's worth keeping
+3. **Agent Tools** — Five tools for explicit memory operations during conversations
 
-Skills mode, `autoRecall`, and `autoCapture` are all enabled by default during `openclaw mem0 init`.
-
-## Requirements
-
-Check your OpenClaw version:
-
-```bash
-openclaw --version
-# OpenClaw 2026.4.25 (aa36ee6)
-```
-
-| OpenClaw Version | Plugin Support |
-|------------------|----------------|
-| `>= 2026.4.25`   | Fully supported |
+Both auto-recall and auto-capture run silently with no manual configuration required.
 
 ## Installation
 
-The fastest way is to install directly from your OpenClaw chat, no CLI or config editing needed.
-
-**Copy and paste this into your OpenClaw chat**; Telegram, WhatsApp, default chat, or any channel where your agent lives:
-
+```bash
+openclaw plugins install @mem0/openclaw-mem0
 ```
-Setup Mem0 from mem0.ai/claw-setup
-```
-
-OpenClaw installs the plugin, prompts you for your email, and connects your Mem0 account with OTP verification. See [Chat Setup](#option-1-chat-setup-recommended) below for the full walkthrough.
-
-If you prefer the OpenClaw CLI, or are setting up self-hosted / open-source mode, see [Manual Config](#option-2-manual-config) and [Open-Source Mode](#open-source-mode-self-hosted) below.
 
 ## Setup and Configuration
 
@@ -58,225 +36,51 @@ Pick any stable, unique identifier for the user. Common choices:
 - A UUID (e.g. `"550e8400-e29b-41d4-a716-446655440000"`)
 - A simple username (e.g. `"alice"`)
 
-All memories are scoped to this `userId` — different values create separate memory namespaces. If you don't set it, it defaults to your OS username.
+All memories are scoped to this `userId` — different values create separate memory namespaces. If you don't set it, it defaults to `"default"`, which means all users share the same memory space.
 
 <Tip>In a multi-user application, set `userId` dynamically per user (e.g. from your auth system) rather than hardcoding a single value.</Tip>
 
 ### Platform Mode (Mem0 Cloud)
 
-There are two ways to set up `@mem0/openclaw-mem0` on the Mem0 platform:
+<Note>Get your API key from <a href="https://app.mem0.ai" rel="nofollow">app.mem0.ai</a>.</Note>
 
-- **Chat setup (recommended)** — run the setup inside any OpenClaw chat. No config editing, no API key handling.
-- **Manual config** — edit `openclaw.json` directly.
-
-#### Option 1: Chat Setup (Recommended)
-
-You no longer need manual config editing to get started. Everything happens inside the OpenClaw chat itself.
-
-<Steps>
-  <Step title="Send the setup command to your OpenClaw agent">
-    Open any OpenClaw channel — Telegram, WhatsApp, your default chat, wherever your agent lives. Paste and send this command:
-
-    ```
-    Setup Mem0 from mem0.ai/claw-setup
-    ```
-
-    OpenClaw responds with a Mem0 setup card and immediately asks:
-
-    > "What's your email address? I'll send you a verification code to connect your Mem0 account."
-  </Step>
-
-  <Step title="Enter your email">
-    Type your email address and send it. Mem0 sends back:
-
-    > "Check your email for a 6-digit code and paste it here."
-  </Step>
-
-  <Step title="Paste the OTP">
-    Copy the 6-digit code from your email inbox and paste it into the chat.
-
-    You'll see the confirmation:
-
-    > "Connected to Mem0."
-  </Step>
-</Steps>
-
-That's it. No API key, no config file editing, no environment variables. The plugin is now active with skills-based memory (triage, recall, and dream) running automatically.
-
-<Note>The chat flow uses the same underlying config as manual setup — it writes `apiKey`, `userId`, and `skills` config into `openclaw.json` for you. You can still open the file to inspect or override values afterward.</Note>
-
-#### Option 2: Manual Config
-
-<Steps>
-  <Step title="Install the plugin via the OpenClaw CLI">
-    ```bash
-    openclaw plugins install @mem0/openclaw-mem0
-    ```
-  </Step>
-
-  <Step title="Get your API key">
-    Get your API key from <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-openclaw" rel="nofollow">app.mem0.ai</a>.
-  </Step>
-
-  <Step title="Select the plugin as your memory backend in `openclaw.json`">
-    Add the full config to your `openclaw.json`:
-
-    ```json5
-    {
-      "plugins": {
-        "slots": {
-          "memory": "openclaw-mem0"
-        },
-        "entries": {
-          "openclaw-mem0": {
-            "enabled": true,
-            "config": {
-              "apiKey": "${MEM0_API_KEY}",
-              "userId": "alice",  // any unique identifier you choose for this user
-              "skills": {
-                "triage": { "enabled": true },
-                "recall": {
-                  "enabled": true,
-                  "tokenBudget": 1500,
-                  "rerank": true,
-                  "keywordSearch": true,
-                  "identityAlwaysInclude": true
-                },
-                "dream": { "enabled": true },
-                "domain": "companion"
-              }
-            }
-          }
-        }
-      }
-    }
-    ```
-  </Step>
-</Steps>
-
-<Warning>
-OpenClaw treats memory plugins as an exclusive slot. Installing the plugin alone does **not** activate it — you must also set `plugins.slots.memory` as shown above.
-</Warning>
-
-### Open-Source Mode (Self-hosted)
-
-No Mem0 key needed. Defaults use OpenAI (`gpt-5-mini` for LLM, `text-embedding-3-small` for embeddings) — requires `OPENAI_API_KEY`. For a fully local setup, use Ollama for both.
-
-#### Option 1: Interactive Wizard (Recommended)
-
-Run the guided 4-step wizard:
-
-```bash
-openclaw mem0 init --mode open-source
-```
-
-The wizard walks you through:
-
-<Steps>
-  <Step title="LLM provider">
-    Choose OpenAI (`gpt-5-mini`), Ollama (`llama3.1:8b`, fully local), or Anthropic (`claude-sonnet-4-5-20250514`). Provide an API key or base URL as needed.
-  </Step>
-  <Step title="Embedding provider">
-    Choose OpenAI (`text-embedding-3-small`) or Ollama (`nomic-embed-text`, local). If the same provider was chosen for LLM, the API key and URL are reused automatically.
-  </Step>
-  <Step title="Vector store">
-    Choose Qdrant (`http://localhost:6333`) or PGVector (PostgreSQL). Connectivity is verified before proceeding.
-  </Step>
-  <Step title="User ID">
-    Set your memory namespace identifier.
-  </Step>
-</Steps>
-
-#### Option 2: Non-Interactive Setup
-
-For CI/CD, scripts, or agent-driven setup — pass all options as flags:
-
-```bash
-# Fully local with Ollama + Qdrant
-openclaw mem0 init --mode open-source \
-  --oss-llm ollama --oss-embedder ollama --oss-vector qdrant
-
-# OpenAI + Qdrant
-openclaw mem0 init --mode open-source \
-  --oss-llm openai --oss-llm-key <key> \
-  --oss-embedder openai --oss-embedder-key <key> \
-  --oss-vector qdrant
-
-# Anthropic LLM + OpenAI embeddings + PGVector
-openclaw mem0 init --mode open-source \
-  --oss-llm anthropic --oss-llm-key <key> \
-  --oss-embedder openai --oss-embedder-key <key> \
-  --oss-vector pgvector --oss-vector-user postgres --oss-vector-password secret
-```
-
-Add `--json` for machine-readable output (useful when an LLM agent is driving the setup).
-
-<Accordion title="All --oss-* flags">
-| Flag | Description |
-|------|-------------|
-| `--oss-llm <provider>` | `openai`, `ollama`, or `anthropic` |
-| `--oss-llm-key <key>` | API key for LLM provider |
-| `--oss-llm-model <model>` | Override default LLM model |
-| `--oss-llm-url <url>` | Base URL (Ollama only) |
-| `--oss-embedder <provider>` | `openai` or `ollama` |
-| `--oss-embedder-key <key>` | API key for embedder |
-| `--oss-embedder-model <model>` | Override default embedder model |
-| `--oss-embedder-url <url>` | Base URL (Ollama only) |
-| `--oss-vector <provider>` | `qdrant` or `pgvector` |
-| `--oss-vector-url <url>` | Qdrant server URL (default: `http://localhost:6333`) |
-| `--oss-vector-host <host>` | PGVector host |
-| `--oss-vector-port <port>` | PGVector port |
-| `--oss-vector-user <user>` | PGVector user |
-| `--oss-vector-password <pw>` | PGVector password |
-| `--oss-vector-dbname <db>` | PGVector database name |
-| `--oss-vector-dims <n>` | Override embedding dimensions |
-</Accordion>
-
-#### Option 3: Manual Config
-
-Minimal config — uses OpenAI defaults:
+Add to your `openclaw.json`:
 
 ```json5
-{
-  "plugins": {
-    "slots": {
-      "memory": "openclaw-mem0"
-    },
-    "entries": {
-      "openclaw-mem0": {
-        "enabled": true,
-        "config": {
-          "mode": "open-source",
-          "userId": "alice"  // any unique identifier you choose for this user
-        }
-      }
-    }
+// plugins.entries
+"openclaw-mem0": {
+  "enabled": true,
+  "config": {
+    "apiKey": "${MEM0_API_KEY}",
+    "userId": "alice"  // any unique identifier you choose for this user
   }
 }
 ```
 
-To customize providers:
+### Open-Source Mode (Self-hosted)
+
+No Mem0 key needed. Requires `OPENAI_API_KEY` for default embeddings/LLM.
 
 ```json5
-{
-  "plugins": {
-    "slots": {
-      "memory": "openclaw-mem0"
-    },
-    "entries": {
-      "openclaw-mem0": {
-        "enabled": true,
-        "config": {
-          "mode": "open-source",
-          "userId": "your-user-id",
-          "oss": {
-            "embedder": { "provider": "openai", "config": { "model": "text-embedding-3-small" } },
-            "vectorStore": { "provider": "qdrant", "config": { "url": "http://localhost:6333" } },
-            "llm": { "provider": "openai", "config": { "model": "gpt-5-mini" } }
-          }
-        }
-      }
-    }
+"openclaw-mem0": {
+  "enabled": true,
+  "config": {
+    "mode": "open-source",
+    "userId": "alice"  // any unique identifier you choose for this user
+  }
+}
+```
+
+Sensible defaults work out of the box. To customize the embedder, vector store, or LLM:
+
+```json5
+"config": {
+  "mode": "open-source",
+  "userId": "your-user-id",
+  "oss": {
+    "embedder": { "provider": "openai", "config": { "model": "text-embedding-3-small" } },
+    "vectorStore": { "provider": "qdrant", "config": { "host": "localhost", "port": 6333 } },
+    "llm": { "provider": "openai", "config": { "model": "gpt-4o" } }
   }
 }
 ```
@@ -289,31 +93,26 @@ Memories are organized into two scopes:
 
 - **Session (short-term)** — Auto-capture stores memories scoped to the current session via Mem0's `run_id` / `runId` parameter. These are contextual to the ongoing conversation.
 
-- **User (long-term)** — The agent can explicitly store long-term memories using the `memory_add` tool (with `longTerm: true`, the default). These persist across all sessions for the user.
+- **User (long-term)** — The agent can explicitly store long-term memories using the `memory_store` tool (with `longTerm: true`, the default). These persist across all sessions for the user.
 
 During **auto-recall**, the plugin searches both scopes and presents them separately — long-term memories first, then session memories — so the agent has full context.
 
 ## Agent Tools
 
-The agent gets eight tools it can call during conversations:
+The agent gets five tools it can call during conversations:
 
 | Tool | Description |
 |------|-------------|
-| `memory_search` | Search memories by natural language query. Supports `scope`, `categories`, `filters`. |
-| `memory_add` | Store facts. Accepts `text` or `facts` array, `category`, `importance`, `metadata`. |
-| `memory_get` | Retrieve a single memory by ID |
-| `memory_list` | List all memories. Filter by `userId`, `agentId`, `scope`. |
-| `memory_update` | Update a memory's text in place. Preserves history. |
-| `memory_delete` | Delete by `memoryId`, `query` (search-and-delete), or `all: true`. |
-| `memory_event_list` | List recent background processing events (platform mode only). |
-| `memory_event_status` | Get status of a specific event by ID (platform mode only). |
+| `memory_search` | Search memories by natural language |
+| `memory_list` | List all stored memories for a user |
+| `memory_store` | Explicitly save a fact |
+| `memory_get` | Retrieve a memory by ID |
+| `memory_forget` | Delete by ID or by query |
 
-The `memory_search` and `memory_list` tools accept a `scope` parameter (`"session"`, `"long-term"`, or `"all"`) to control which memories are queried.
+The `memory_search` and `memory_list` tools accept a `scope` parameter (`"session"`, `"long-term"`, or `"all"`) to control which memories are queried. The `memory_store` tool accepts a `longTerm` boolean (default: `true`) to choose where to store.
 
 ## CLI Commands
 
-All commands support `--json` for machine-readable output — useful when an LLM agent drives the CLI programmatically. Run `openclaw mem0 help --json` to discover every command and flag.
-
 ```bash
 # Search all memories (long-term + session)
 openclaw mem0 search "what languages does the user know"
@@ -324,13 +123,8 @@ openclaw mem0 search "what languages does the user know" --scope long-term
 # Search only session/short-term memories
 openclaw mem0 search "what languages does the user know" --scope session
 
-# List all memories
-openclaw mem0 list
-openclaw mem0 list --user-id alice --top-k 20
-
-# JSON output (any command)
-openclaw mem0 search "preferences" --json
-openclaw mem0 status --json
+# View stats
+openclaw mem0 stats
 ```
 
 ## Configuration Options
@@ -340,9 +134,9 @@ openclaw mem0 status --json
 | Key | Type | Default | Description |
 |-----|------|---------|-------------|
 | `mode` | `"platform"` \| `"open-source"` | `"platform"` | Which backend to use |
-| `userId` | `string` | OS username | Scope memories per user |
-| `autoRecall` | `boolean` | `true` | Inject memories before each turn. Ignored when `skills` is configured. |
-| `autoCapture` | `boolean` | `true` | Store facts after each turn. Ignored when `skills` is configured. |
+| `userId` | `string` | `"default"` | Scope memories per user |
+| `autoRecall` | `boolean` | `true` | Inject memories before each turn |
+| `autoCapture` | `boolean` | `true` | Store facts after each turn |
 | `topK` | `number` | `5` | Max memories per recall |
 | `searchThreshold` | `number` | `0.3` | Min similarity (0–1) |
 
@@ -351,6 +145,8 @@ openclaw mem0 status --json
 | Key | Type | Default | Description |
 |-----|------|---------|-------------|
 | `apiKey` | `string` | — | **Required.** Mem0 API key (supports `${MEM0_API_KEY}`) |
+| `orgId` | `string` | — | Organization ID |
+| `projectId` | `string` | — | Project ID |
 | `customInstructions` | `string` | *(built-in)* | Extraction rules — what to store, how to format |
 | `customCategories` | `object` | *(12 defaults)* | Category name → description map for tagging |
 
@@ -366,129 +162,19 @@ openclaw mem0 status --json
 | `oss.llm.provider` | `string` | `"openai"` | LLM provider (`"openai"`, `"anthropic"`, `"ollama"`, etc.) |
 | `oss.llm.config` | `object` | — | Provider config: `apiKey`, `model`, `baseURL`, `temperature` |
 | `oss.historyDbPath` | `string` | — | SQLite path for memory edit history |
-| `oss.disableHistory` | `boolean` | `false` | Disable memory edit history tracking |
 
-Everything inside `oss` is optional — defaults use OpenAI embeddings (`text-embedding-3-small`), in-memory vector store, and OpenAI LLM (`gpt-5-mini`).
+Everything inside `oss` is optional — defaults use OpenAI embeddings (`text-embedding-3-small`), in-memory vector store, and OpenAI LLM.
 
-## Plugin Management
+## Key Features
 
-### Updating the Plugin
+1. **Zero Configuration** — Auto-recall and auto-capture work out of the box with no prompting required
+2. **Dual Memory Scopes** — Session-scoped short-term and user-scoped long-term memories
+3. **Flexible Backend** — Use Mem0 Cloud for managed service or self-host with open-source mode
+4. **Rich Tool Suite** — Five agent tools for explicit memory operations when needed
 
-```bash
-openclaw plugins update openclaw-mem0
-```
+## Conclusion
 
-### Checking Plugin Status
-
-```bash
-openclaw plugins list
-openclaw plugins inspect openclaw-mem0
-```
-
-## Troubleshooting
-
-### "plugins.allow excludes mem0" Error
-
-If you see an error like:
-
-```
-[openclaw] Failed to start CLI: Error: The `openclaw mem0` command is unavailable
-because `plugins.allow` excludes "mem0". Add "mem0" to `plugins.allow` if you want
-that bundled plugin CLI surface.
-```
-
-Add `mem0` to your `plugins.allow` list in `openclaw.json`:
-
-```json5
-{
-  "plugins": {
-    "allow": ["mem0"],
-    "slots": {
-      "memory": "openclaw-mem0"
-    }
-  }
-}
-```
-
-### Plugin Not Activating
-
-If the plugin installs but doesn't work:
-
-1. Verify `plugins.slots.memory` is set to `"openclaw-mem0"` (not the npm package name)
-2. Check `openclaw plugins list --enabled` to confirm the plugin is loaded
-3. Run `openclaw mem0 status` to verify configuration
-
-### Plugin Update Not Working
-
-If `openclaw plugins update` fails:
-
-1. Use the plugin ID: `openclaw plugins update openclaw-mem0`
-2. Update all plugins at once: `openclaw plugins update --all`
-3. If that fails, uninstall and reinstall:
-   ```bash
-   openclaw plugins uninstall openclaw-mem0
-   openclaw plugins install @mem0/openclaw-mem0
-   ```
-
-## Privacy & Security
-
-### Data Flow
-
-| Mode | Where data goes | Storage |
-|------|----------------|---------|
-| **Platform** | Conversations sent to `api.mem0.ai` for extraction and storage | Mem0 cloud |
-| **Open-source** | Embeddings generated via configured provider (default: OpenAI API). Vectors stored locally. | `~/.mem0/vector_store.db` (SQLite) |
-
-### Auto-Capture and Auto-Recall
-
-Auto-capture and auto-recall are **enabled by default**. When skills mode is configured (the default after `openclaw mem0 init`), these are ignored in favor of the skills-based triage/recall/dream protocol.
-
-To disable either:
-
-```json5
-{
-  "plugins": {
-    "entries": {
-      "openclaw-mem0": {
-        "config": {
-          "autoCapture": false,   // disable automatic fact extraction
-          "autoRecall": false     // disable automatic memory injection
-        }
-      }
-    }
-  }
-}
-```
-
-The agent can always use memory tools (`memory_add`, `memory_search`, etc.) explicitly regardless of these settings.
-
-### Credential Protection
-
-The plugin never stores API keys, tokens, or secrets as memories. Five independent layers enforce this:
-
-1. **Triage gate** — The extraction prompt rejects values matching known credential patterns (`sk-`, `m0-`, `ghp_`, `AKIA`, `Bearer`, `password=`, `token=`, `secret=`)
-2. **Dream cleanup** — Periodic memory consolidation deletes any memories that slipped through containing credential patterns
-3. **Extraction instructions** — Default extraction rules explicitly instruct the model to store only that a credential was configured, never the value
-4. **Configurable patterns** — Add custom credential patterns via `skills.triage.credentialPatterns`
-5. **CLI redaction** — `openclaw mem0 config show` redacts sensitive fields (`apiKey`, `oss.*.config.apiKey`)
-
-### API Key Storage
-
-Plugin config is stored in `~/.openclaw/openclaw.json` with file permissions `0o600` (owner-read-only). For production deployments, use environment variable references (`${MEM0_API_KEY}`) or SecretRef objects instead of plaintext keys.
-
-### Telemetry
-
-Anonymous usage telemetry (PostHog) is enabled by default to help improve the plugin. No conversation content or memory values are included — only event counts (recall, capture, tool usage, CLI commands).
-
-To opt out, set the environment variable:
-
-```bash
-export MEM0_TELEMETRY=false
-```
-
-### System Prompt Context
-
-The plugin injects memory-related instructions into the agent's system context via OpenClaw's `prependSystemContext` mechanism. This includes the memory triage protocol and recalled memories. This is the standard OpenClaw plugin SDK pattern for memory backends — no user-facing prompts are modified.
+The `@mem0/openclaw-mem0` plugin gives OpenClaw agents persistent memory with minimal setup. Whether using Mem0 Cloud or self-hosting, your agents can now remember user preferences, facts, and context across sessions automatically.
 
 <CardGroup cols={2}>
   <Card title="OpenAI Agents SDK" icon="robot" href="/integrations/openai-agents-sdk">

docs/integrations/raycast.mdx

@@ -9,7 +9,7 @@ Mem0 is a self-improving memory layer for LLM applications, enabling personalize
 
 **Get your API Key**: You'll need a Mem0 API key to use this extension:
 
-a. Sign up at <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-raycast" rel="nofollow">app.mem0.ai</a>
+a. Sign up at <a href="https://app.mem0.ai" rel="nofollow">app.mem0.ai</a>
 
 b. Navigate to your API Keys page
 

docs/integrations/vercel-ai-sdk.mdx

@@ -29,7 +29,7 @@ npm install @mem0/vercel-ai-provider
 
 ### Setting Up Mem0
 
-1. Get your **Mem0 API Key** from the <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-vercel-ai-sdk" rel="nofollow">Mem0 Dashboard</a>.
+1. Get your **Mem0 API Key** from the <a href="https://app.mem0.ai/dashboard/api-keys" rel="nofollow">Mem0 Dashboard</a>.
 
 2. Initialize the Mem0 Client in your application:
 

docs/llms.txt

@@ -1,476 +1,302 @@
 # Mem0
 
-> Mem0 is a memory layer for LLM agents - persistent, self-improving context that survives across sessions. Two products share one mental model: Mem0 Platform (managed) and Mem0 Open Source (self-hosted). Every link below is tagged `[Platform]`, `[OSS]`, or `[Both]` so you can load only what the current user needs.
+> Mem0 is a self-improving memory layer for LLM applications, enabling personalized AI experiences that retain context across sessions, adapt over time, and reduce costs by intelligently storing and retrieving relevant information.
 
-## For agents reading this file
+Mem0 provides both a managed platform and open-source solutions for adding persistent memory to AI agents and applications. Unlike traditional RAG systems that are stateless, Mem0 creates stateful agents that remember user preferences, learn from interactions, and evolve behavior over time.
 
-- Use `MemoryClient` (Python) / `mem0ai` (npm) when the user has a Mem0 Platform API key. Docs under `/platform/` and `/api-reference/` apply; the managed product handles providers server-side, so you can ignore `## Optional` below.
-- Use `Memory` (Python) / `mem0ai/oss` (npm) when the user self-hosts. Docs under `/open-source/` and `/components/` apply; Platform-only features (entity filters v2, custom categories, webhooks, advanced retrieval) may not be available.
-- Scope tag reference: `[Platform]` = managed only, `[OSS]` = self-hosted only, `[Both]` = same API surface on both.
-- OpenAPI spec: https://docs.mem0.ai/openapi.json
-- Live MCP server: https://mcp.mem0.ai (see `platform/mem0-mcp`).
-- Source repo: https://github.com/mem0ai/mem0
-
-## Install
-
-- Python SDK: `pip install mem0ai`
-- Node SDK: `npm install mem0ai`
-- Python CLI: `pip install mem0-cli`
-- Node CLI: `npm install -g @mem0/cli`
-
-## Identify the User's Setup
-
-Look at the user's imports first - they determine which product (Platform vs OSS) and which language you should quote docs from. **Mem0 Platform (managed) is the recommended path** - 4-line integration, sub-50ms retrieval, no infra. Route to OSS only when the user has an explicit self-hosting requirement.
-
-### Platform - Python [Platform]
-
-Import signature: `from mem0 import MemoryClient`
-
-```python
-from mem0 import MemoryClient
-
-client = MemoryClient(api_key="your-api-key")
-
-# Create
-client.add(
-    [{"role": "user", "content": "I love hiking on weekends"}],
-    user_id="alice",
-)
-
-# Read
-client.search("What does Alice like to do?", user_id="alice")
-client.get_all(user_id="alice")
-client.get(memory_id="<id>")
-
-# Update
-client.update(memory_id="<id>", data="Alice loves mountain hiking")
-
-# Delete
-client.delete(memory_id="<id>")
-client.delete_all(user_id="alice")
-```
-
-Relevant docs: `platform/quickstart`, `platform/features/*`, `api-reference/*`.
-
-### Platform - TypeScript / JavaScript [Platform]
-
-Import signature: `import MemoryClient from "mem0ai"`
-
-```ts
-import MemoryClient from "mem0ai";
-
-const client = new MemoryClient({ apiKey: "your-api-key" });
-
-// Create
-await client.add(
-  [{ role: "user", content: "I love hiking on weekends" }],
-  { user_id: "alice" },
-);
-
-// Read
-await client.search("What does Alice like to do?", { user_id: "alice" });
-await client.getAll({ user_id: "alice" });
-await client.get("<memory_id>");
-
-// Update
-await client.update("<memory_id>", { text: "Alice loves mountain hiking" });
-
-// Delete
-await client.delete("<memory_id>");
-await client.deleteAll({ user_id: "alice" });
-```
-
-Relevant docs: same as Platform Python.
-
-### OSS - Python [OSS]
-
-Import signature: `from mem0 import Memory`
-
-```python
-from mem0 import Memory
-
-m = Memory()   # needs OPENAI_API_KEY; see components/ for custom providers
-
-# Create
-m.add("I love hiking on weekends", user_id="alice")
-
-# Read
-m.search("What does Alice like to do?", user_id="alice")
-m.get_all(user_id="alice")
-m.get(memory_id="<id>")
-
-# Update
-m.update(memory_id="<id>", data="Alice loves mountain hiking")
-
-# Delete
-m.delete(memory_id="<id>")
-m.delete_all(user_id="alice")
-```
-
-Relevant docs: `open-source/*` plus provider pages under `## Optional`.
-
-### OSS - Node [OSS]
-
-Import signature: `import { Memory } from "mem0ai/oss"`
-
-```ts
-import { Memory } from "mem0ai/oss";
-
-const memory = new Memory();
-
-// Create
-await memory.add("I love hiking on weekends", { userId: "alice" });
-
-// Read
-await memory.search("What does Alice like to do?", { userId: "alice" });
-await memory.getAll({ userId: "alice" });
-await memory.get("<memory_id>");
-
-// Update
-await memory.update("<memory_id>", "Alice loves mountain hiking");
-
-// Delete
-await memory.delete("<memory_id>");
-await memory.deleteAll({ userId: "alice" });
-```
-
-Relevant docs: same as OSS Python.
-
-### Version Probes
-
-Once you know which product, check the installed version - v2 vs v3 APIs differ in both OSS and Platform. Current published versions: Python `mem0ai` 2.x, TypeScript `mem0ai` 3.x, Node CLI `@mem0/cli` 0.2.x.
-
-```bash
-pip show mem0ai | grep -i ^version
-npm list mem0ai --depth 0 2>/dev/null | grep mem0ai
-mem0 --version          # Python or Node CLI, whichever is on PATH
-```
-
-If the user is on a pre-current major (Python < 2, TS < 3, or Platform `output_format: "v1.1"`), route them through the matching migration guide in the Platform section before quoting current docs. If no Mem0 package is installed, recommend `pip install mem0ai` or `npm install mem0ai` and the corresponding quickstart above.
+Key differentiators:
+- **Stateful vs Stateless**: Retains context across sessions rather than forgetting after each interaction
+- **Intelligent Memory Management**: Uses LLMs to extract, filter, and organize relevant information
+- **Dual Storage Architecture**: Combines vector embeddings with graph databases for comprehensive memory
+- **Sub-50ms Retrieval**: Lightning-fast memory lookups for real-time applications
+- **Multimodal Support**: Handles text, images, and documents seamlessly
 
 ## Getting Started
 
-- [Introduction](https://docs.mem0.ai/introduction) [Both]: Use when the user wants a one-page overview of how memory fits between the LLM and the app.
-- [Vibe Code with Mem0](https://docs.mem0.ai/vibecoding) [Both]: Use when the user is in Claude Code, Cursor, or Windsurf and wants memory wired into their editor.
-- [Platform Overview](https://docs.mem0.ai/platform/overview) [Platform]: Use when the user picks the managed product - 4-line integration, sub-50ms retrieval, dashboard.
-- [Platform vs Open Source](https://docs.mem0.ai/platform/platform-vs-oss) [Both]: Use when the user is deciding between managed and self-hosted.
-- [Platform Quickstart](https://docs.mem0.ai/platform/quickstart) [Platform]: Use for the first Platform integration - API key plus `MemoryClient.add/search`.
-- [Platform CLI](https://docs.mem0.ai/platform/cli) [Platform]: Use when the user wants to manage Platform memories from the terminal.
-- [Mem0 MCP Server](https://docs.mem0.ai/platform/mem0-mcp) [Platform]: Use when connecting memory to AI coding tools over MCP.
-- [Open Source Overview](https://docs.mem0.ai/open-source/overview) [OSS]: Use when the user needs full infra control and custom provider wiring.
-- [Open Source Configuration](https://docs.mem0.ai/open-source/configuration) [OSS]: Use when configuring `Memory` - LLM, embedder, vector store, graph store.
-- [Open Source Python Quickstart](https://docs.mem0.ai/open-source/python-quickstart) [OSS]: Use for the first self-hosted Python integration.
-- [Open Source Node.js Quickstart](https://docs.mem0.ai/open-source/node-quickstart) [OSS]: Use for the first self-hosted Node integration.
-- [Self-Hosted Setup](https://docs.mem0.ai/open-source/setup) [OSS]: Use when standing up the bundled REST server and dashboard via Docker Compose, including auth, API keys, and the setup wizard.
+- [Introduction](https://docs.mem0.ai/introduction): Overview of Mem0's memory layer for AI agents, including stateless vs stateful agents and how memory fits in the agent stack
+- [Platform Overview](https://docs.mem0.ai/platform/overview): Managed solution with 4-line integration, sub-50ms latency, and intuitive dashboard
+- [Vibe Code with Mem0](https://docs.mem0.ai/vibecoding): Single entry point for developers using AI coding tools (Claude Code, Cursor, Windsurf) with Mem0
+- [Mem0 MCP Server](https://docs.mem0.ai/platform/mem0-mcp): Model Context Protocol server for integrating Mem0 with AI coding assistants
+- [Platform vs Open Source](https://docs.mem0.ai/platform/platform-vs-oss): Compare managed platform vs self-hosted options
+- [Platform Quickstart](https://docs.mem0.ai/platform/quickstart): Get started with Mem0 Platform (managed) in minutes
+- [Open Source Overview](https://docs.mem0.ai/open-source/overview): Self-hosted solution with full infrastructure control and customization
+- [Open Source Python Quickstart](https://docs.mem0.ai/open-source/python-quickstart): Get started with Mem0 Open Source using Python
+- [Open Source Node.js Quickstart](https://docs.mem0.ai/open-source/node-quickstart): Get started with Mem0 Open Source using Node.js
 
 ## Core Concepts
 
-- [Memory Types](https://docs.mem0.ai/core-concepts/memory-types) [Both]: Use when explaining working, factual, episodic, and semantic memory distinctions.
-- [Memory Operations - Add](https://docs.mem0.ai/core-concepts/memory-operations/add) [Both]: Use when explaining how `add()` extracts facts, resolves conflicts, and writes to both stores.
-- [Memory Operations - Search](https://docs.mem0.ai/core-concepts/memory-operations/search) [Both]: Use when explaining how queries are processed and ranked.
-- [Memory Operations - Update](https://docs.mem0.ai/core-concepts/memory-operations/update) [Both]: Use when memories need to be edited in place or reconciled against new info.
-- [Memory Operations - Delete](https://docs.mem0.ai/core-concepts/memory-operations/delete) [Both]: Use when outdated memories must be removed.
-- [Memory Evaluation](https://docs.mem0.ai/core-concepts/memory-evaluation) [Both]: Use when benchmarking memory quality or comparing against baselines.
+- [Memory Types](https://docs.mem0.ai/core-concepts/memory-types): Working memory (short-term session awareness), factual memory (structured knowledge), episodic memory (past conversations), and semantic memory (general knowledge)
+- [Memory Operations - Add](https://docs.mem0.ai/core-concepts/memory-operations/add): How Mem0 processes conversations through information extraction, conflict resolution, and dual storage
+- [Memory Operations - Search](https://docs.mem0.ai/core-concepts/memory-operations/search): Retrieval of relevant memories using semantic search with query processing and result ranking
+- [Memory Operations - Update](https://docs.mem0.ai/core-concepts/memory-operations/update): Modifying existing memories when new information conflicts or supplements stored data
+- [Memory Operations - Delete](https://docs.mem0.ai/core-concepts/memory-operations/delete): Removing outdated or irrelevant memories to maintain memory quality
 
-## Platform
+## Platform Features
 
-### Features - Essential
-- [Platform Features Overview](https://docs.mem0.ai/platform/features/platform-overview) [Platform]: Use when surveying what managed offers beyond CRUD.
-- [V2 Memory Filters](https://docs.mem0.ai/platform/features/v2-memory-filters) [Platform]: Use when compound filters (AND/OR on metadata, entity, time) are needed at search.
-- [Entity-Scoped Memory](https://docs.mem0.ai/platform/features/entity-scoped-memory) [Platform]: Use when partitioning memories by user, agent, app, or run.
-- [Async Client](https://docs.mem0.ai/platform/features/async-client) [Platform]: Use when the app issues many concurrent Mem0 calls and needs non-blocking I/O.
-- [Multimodal Support](https://docs.mem0.ai/platform/features/multimodal-support) [Platform]: Use when storing images or PDFs as memory input.
-- [Custom Categories](https://docs.mem0.ai/platform/features/custom-categories) [Platform]: Use when the default categories do not match the domain.
+- [Platform Features Overview](https://docs.mem0.ai/platform/features/platform-overview): High-level overview of all Mem0 Platform capabilities
+- [Advanced Memory Operations](https://docs.mem0.ai/platform/advanced-memory-operations): Sophisticated memory management techniques for complex applications
 
-### Features - Advanced Retrieval
-- [Advanced Retrieval](https://docs.mem0.ai/platform/features/advanced-retrieval) [Platform]: Use when the user needs keyword search, reranking, or hybrid retrieval.
-- [Criteria-Based Retrieval](https://docs.mem0.ai/platform/features/criteria-retrieval) [Platform]: Use when targeting memories by custom criteria, not just semantic similarity.
-- [Contextual Add](https://docs.mem0.ai/platform/features/contextual-add) [Platform]: Use when `add()` should consider the surrounding conversation, not just the latest turn.
-- [Custom Instructions](https://docs.mem0.ai/platform/features/custom-instructions) [Platform]: Use when tailoring what Mem0 extracts and stores on Platform.
-- [Memory Decay](https://docs.mem0.ai/platform/features/memory-decay) [Platform]: Use when search results should boost recently-reinforced memories and dampen stale ones — opt-in per project, search-time only, never filters candidates out.
-- [Advanced Memory Operations](https://docs.mem0.ai/platform/advanced-memory-operations) [Platform]: Use when basic CRUD is not enough - batch ops, complex filters, workflows.
+### Essential Features
+- [V2 Memory Filters](https://docs.mem0.ai/platform/features/v2-memory-filters): Advanced filtering and querying capabilities for memories
+- [Entity-Scoped Memory](https://docs.mem0.ai/platform/features/entity-scoped-memory): Organize memories by user, agent, app, and session identifiers
+- [Async Client](https://docs.mem0.ai/platform/features/async-client): Non-blocking operations for high-concurrency applications
+- [Async Mode Default Changes](https://docs.mem0.ai/platform/features/async-mode-default-change): Understanding new async behavior defaults
+- [Multimodal Support](https://docs.mem0.ai/platform/features/multimodal-support): Integration of images and documents (JPG, PNG, MDX, TXT, PDF) via URLs or Base64
+- [Custom Categories](https://docs.mem0.ai/platform/features/custom-categories): Define domain-specific categories to improve memory organization
 
-### Features - Data Management
-- [Direct Import](https://docs.mem0.ai/platform/features/direct-import) [Platform]: Use when seeding a Mem0 project from existing data.
-- [Memory Export](https://docs.mem0.ai/platform/features/memory-export) [Platform]: Use when exporting memories via a Pydantic schema.
-- [Timestamp Support](https://docs.mem0.ai/platform/features/timestamp) [Platform]: Use when temporal queries or time-based filtering matter.
+### Advanced Features
+- [Graph Threshold](https://docs.mem0.ai/platform/features/graph-threshold): Configure graph relationship sensitivity and strength
+- [Advanced Retrieval](https://docs.mem0.ai/platform/features/advanced-retrieval): Enhanced search with keyword search, reranking, and filtering capabilities
+- [Criteria-Based Retrieval](https://docs.mem0.ai/platform/features/criteria-retrieval): Targeted memory retrieval using custom criteria
+- [Contextual Add](https://docs.mem0.ai/platform/features/contextual-add): Add memories with enhanced context awareness
+- [Custom Instructions](https://docs.mem0.ai/platform/features/custom-instructions): Customize how Mem0 processes and stores information
 
-### Features - Integration & Ops
-- [Webhooks](https://docs.mem0.ai/platform/features/webhooks) [Platform]: Use when another system needs to react to memory changes in real time.
-- [Feedback Mechanism](https://docs.mem0.ai/platform/features/feedback-mechanism) [Platform]: Use when capturing user feedback to improve memory quality.
-- [Group Chat Support](https://docs.mem0.ai/platform/features/group-chat) [Platform]: Use when the conversation has multiple participants.
-- [MCP Integration](https://docs.mem0.ai/platform/features/mcp-integration) [Platform]: Use when wiring Mem0 into Claude/Cursor/other MCP clients.
+### Data Management
+- [Direct Import](https://docs.mem0.ai/platform/features/direct-import): Bulk import existing data into Mem0 memory
+- [Memory Export](https://docs.mem0.ai/platform/features/memory-export): Export memories in structured formats using customizable Pydantic schemas
+- [Timestamp Support](https://docs.mem0.ai/platform/features/timestamp): Temporal memory management with time-based queries
+- [Expiration Dates](https://docs.mem0.ai/platform/features/expiration-date): Automatic memory cleanup with configurable expiration
+
+### Integration Features
+- [Webhooks](https://docs.mem0.ai/platform/features/webhooks): Real-time notifications for memory events
+- [Feedback Mechanism](https://docs.mem0.ai/platform/features/feedback-mechanism): Improve memory quality through user feedback
+- [Group Chat Support](https://docs.mem0.ai/platform/features/group-chat): Multi-conversation memory management
+- [MCP Integration](https://docs.mem0.ai/platform/features/mcp-integration): Model Context Protocol integration for AI coding tools
 
 ### Support & Migration
-- [FAQs](https://docs.mem0.ai/platform/faqs) [Platform]: Use when answering common Platform questions.
-- [Contribute to Platform](https://docs.mem0.ai/platform/contribute) [Platform]: Use when a user wants to contribute to Platform docs or code.
-- [OSS to Platform Migration](https://docs.mem0.ai/migration/oss-to-platform) [Both]: Use when moving from self-hosted to managed.
-- [OSS v2 to v3 Migration](https://docs.mem0.ai/migration/oss-v2-to-v3) [OSS]: Use when upgrading a self-hosted deployment across major versions.
-- [Platform v2 to v3 Migration](https://docs.mem0.ai/migration/platform-v2-to-v3) [Platform]: Use when upgrading a Platform integration across major versions.
-- [API Changes](https://docs.mem0.ai/migration/api-changes) [Both]: Use when the upgrade involves API surface changes.
-- [Changelog](https://docs.mem0.ai/changelog/highlights) [Both]: Use when the user asks what shipped recently.
+- [FAQs](https://docs.mem0.ai/platform/faqs): Frequently asked questions about Mem0 Platform
+- [Contribute Guide](https://docs.mem0.ai/platform/contribute): Contributing to Mem0 Platform development
+- [OSS to Platform Migration](https://docs.mem0.ai/migration/oss-to-platform): Guide for migrating from open-source to managed platform
+- [V0 to V1 Migration](https://docs.mem0.ai/migration/v0-to-v1): Upgrading from Mem0 v0 to v1
+- [Breaking Changes](https://docs.mem0.ai/migration/breaking-changes): List of breaking changes across versions
+- [API Changes](https://docs.mem0.ai/migration/api-changes): Detailed API changes and migration paths
 
 ## Open Source
 
-- [Open Source Features Overview](https://docs.mem0.ai/open-source/features/overview) [OSS]: Use when surveying OSS-only capabilities.
-- [Metadata Filtering](https://docs.mem0.ai/open-source/features/metadata-filtering) [OSS]: Use when filtering by custom metadata fields in self-hosted.
-- [Reranker Search](https://docs.mem0.ai/open-source/features/reranker-search) [OSS]: Use when improving OSS search quality with a reranker.
-- [Reranking](https://docs.mem0.ai/open-source/features/reranking) [OSS]: Use when configuring reranking end-to-end in OSS.
-- [Async Memory](https://docs.mem0.ai/open-source/features/async-memory) [OSS]: Use when the self-hosted app needs `AsyncMemory`.
-- [OSS Multimodal Support (features)](https://docs.mem0.ai/open-source/features/multimodal-support) [OSS]: Use when handling images and PDFs self-hosted (feature guide).
-- [OSS Multimodal Support](https://docs.mem0.ai/open-source/multimodal-support) [OSS]: Use when handling images and PDFs self-hosted (concept overview).
-- [Custom Instructions (OSS)](https://docs.mem0.ai/open-source/features/custom-instructions) [OSS]: Use when tailoring extraction prompts in OSS.
-- [REST API Server](https://docs.mem0.ai/open-source/features/rest-api) [OSS]: Use when exposing a self-hosted Mem0 as a FastAPI service.
-- [OpenAI Compatibility](https://docs.mem0.ai/open-source/features/openai_compatibility) [OSS]: Use when hitting an OpenAI-compatible endpoint with self-hosted.
+### Getting Started
+- [Python Quickstart](https://docs.mem0.ai/open-source/python-quickstart): Installation, configuration, and usage examples for Python SDK
+- [Node.js Quickstart](https://docs.mem0.ai/open-source/node-quickstart): Installation, configuration, and usage examples for Node.js SDK
+- [Configuration Guide](https://docs.mem0.ai/open-source/configuration): Complete configuration options for self-hosted deployment
+
+### Open Source Features
+- [Features Overview](https://docs.mem0.ai/open-source/features/overview): Overview of all open-source features
+- [Metadata Filtering](https://docs.mem0.ai/open-source/features/metadata-filtering): Advanced filtering using custom metadata fields
+- [Reranker Search](https://docs.mem0.ai/open-source/features/reranker-search): Enhanced search results with reranking models
+- [Async Memory](https://docs.mem0.ai/open-source/features/async-memory): Asynchronous memory operations for better performance
+- [Multimodal Support](https://docs.mem0.ai/open-source/features/multimodal-support): Handle text, images, and documents in self-hosted setup
+- [Custom Instructions](https://docs.mem0.ai/open-source/features/custom-instructions): Tailor information extraction for specific use cases
+- [Custom Memory Update Prompt](https://docs.mem0.ai/open-source/features/custom-update-memory-prompt): Customize how memories are updated and merged
+- [REST API Server](https://docs.mem0.ai/open-source/features/rest-api): FastAPI-based server with core operations and OpenAPI documentation
+- [OpenAI Compatibility](https://docs.mem0.ai/open-source/features/openai_compatibility): Seamless integration with OpenAI-compatible APIs
+
+## Components
+
+### LLMs
+- [LLM Overview](https://docs.mem0.ai/components/llms/overview): Comprehensive guide to Large Language Model integration and configuration options
+- [LLM Configuration](https://docs.mem0.ai/components/llms/config): Configuration reference for LLM providers
+- [OpenAI](https://docs.mem0.ai/components/llms/models/openai): Integration with OpenAI models including GPT-4
+- [Anthropic](https://docs.mem0.ai/components/llms/models/anthropic): Claude model integration with advanced reasoning capabilities
+- [Azure OpenAI](https://docs.mem0.ai/components/llms/models/azure_openai): Microsoft Azure hosted OpenAI models for enterprise environments
+- [Ollama](https://docs.mem0.ai/components/llms/models/ollama): Local model deployment for privacy-focused applications
+- [Together](https://docs.mem0.ai/components/llms/models/together): Open-source model inference platform
+- [Groq](https://docs.mem0.ai/components/llms/models/groq): High-performance LPU optimized models for fast inference
+- [LiteLLM](https://docs.mem0.ai/components/llms/models/litellm): Unified LLM interface and proxy
+- [Mistral AI](https://docs.mem0.ai/components/llms/models/mistral_AI): Mistral model integration
+- [Google AI](https://docs.mem0.ai/components/llms/models/google_AI): Gemini model integration for multimodal applications
+- [AWS Bedrock](https://docs.mem0.ai/components/llms/models/aws_bedrock): Enterprise-grade AWS managed model integration
+- [DeepSeek](https://docs.mem0.ai/components/llms/models/deepseek): Advanced reasoning models
+- [MiniMax](https://docs.mem0.ai/components/llms/models/minimax): MiniMax model integration
+- [xAI](https://docs.mem0.ai/components/llms/models/xAI): xAI Grok models integration
+- [Sarvam](https://docs.mem0.ai/components/llms/models/sarvam): Indian language models
+- [LM Studio](https://docs.mem0.ai/components/llms/models/lmstudio): Local model management and deployment
+- [LangChain LLM](https://docs.mem0.ai/components/llms/models/langchain): LangChain LLM integration
+- [vLLM](https://docs.mem0.ai/components/llms/models/vllm): High-performance inference framework
+
+### Vector Databases
+- [Vector Database Overview](https://docs.mem0.ai/components/vectordbs/overview): Guide to supported vector databases for semantic memory storage
+- [Vector Database Configuration](https://docs.mem0.ai/components/vectordbs/config): Configuration reference for vector database providers
+- [Qdrant](https://docs.mem0.ai/components/vectordbs/dbs/qdrant): High-performance vector similarity search engine
+- [Chroma](https://docs.mem0.ai/components/vectordbs/dbs/chroma): AI-native open-source vector database optimized for speed
+- [PGVector](https://docs.mem0.ai/components/vectordbs/dbs/pgvector): PostgreSQL extension for vector similarity search
+- [Milvus](https://docs.mem0.ai/components/vectordbs/dbs/milvus): Open-source vector database for AI applications at scale
+- [Pinecone](https://docs.mem0.ai/components/vectordbs/dbs/pinecone): Managed vector database with serverless and pod deployment options
+- [MongoDB](https://docs.mem0.ai/components/vectordbs/dbs/mongodb): Document database with vector search capabilities
+- [Azure AI Search](https://docs.mem0.ai/components/vectordbs/dbs/azure): Microsoft's enterprise search service
+- [Azure MySQL](https://docs.mem0.ai/components/vectordbs/dbs/azure_mysql): Azure Database for MySQL with vector search
+- [Redis](https://docs.mem0.ai/components/vectordbs/dbs/redis): Real-time vector storage and search with Redis Stack
+- [Valkey](https://docs.mem0.ai/components/vectordbs/dbs/valkey): Open-source Redis alternative with vector search
+- [Elasticsearch](https://docs.mem0.ai/components/vectordbs/dbs/elasticsearch): Distributed search and analytics engine
+- [OpenSearch](https://docs.mem0.ai/components/vectordbs/dbs/opensearch): Open-source search and analytics platform
+- [Supabase](https://docs.mem0.ai/components/vectordbs/dbs/supabase): Open-source Firebase alternative with vector support
+- [Upstash Vector](https://docs.mem0.ai/components/vectordbs/dbs/upstash-vector): Serverless vector database
+- [Vectorize](https://docs.mem0.ai/components/vectordbs/dbs/vectorize): Vectorize vector database integration
+- [Vertex AI Vector Search](https://docs.mem0.ai/components/vectordbs/dbs/vertex_ai): Google Cloud's vector search service
+- [Weaviate](https://docs.mem0.ai/components/vectordbs/dbs/weaviate): Open-source vector search engine with built-in ML capabilities
+- [FAISS](https://docs.mem0.ai/components/vectordbs/dbs/faiss): Facebook AI Similarity Search library
+- [LangChain Vector Store](https://docs.mem0.ai/components/vectordbs/dbs/langchain): LangChain vector store integration
+- [Baidu](https://docs.mem0.ai/components/vectordbs/dbs/baidu): Baidu vector database integration
+- [Cassandra](https://docs.mem0.ai/components/vectordbs/dbs/cassandra): Apache Cassandra with vector search capabilities
+- [S3 Vectors](https://docs.mem0.ai/components/vectordbs/dbs/s3_vectors): Amazon S3 Vectors integration
+- [Databricks](https://docs.mem0.ai/components/vectordbs/dbs/databricks): Delta Lake integration for vector search
+- [Neptune Analytics](https://docs.mem0.ai/components/vectordbs/dbs/neptune_analytics): AWS Neptune Analytics for graph and vector search
+- [Turbopuffer](https://docs.mem0.ai/components/vectordbs/dbs/turbopuffer): High-performance serverless vector database
+
+### Embedding Models
+- [Embeddings Overview](https://docs.mem0.ai/components/embedders/overview): Embedding model configuration for semantic understanding
+- [Embeddings Configuration](https://docs.mem0.ai/components/embedders/config): Configuration reference for embedding providers
+- [OpenAI Embeddings](https://docs.mem0.ai/components/embedders/models/openai): High-quality text embeddings with customizable dimensions
+- [Azure OpenAI Embeddings](https://docs.mem0.ai/components/embedders/models/azure_openai): Enterprise Azure-hosted embedding models
+- [Ollama Embeddings](https://docs.mem0.ai/components/embedders/models/ollama): Local embedding models for privacy-focused applications
+- [Hugging Face Embeddings](https://docs.mem0.ai/components/embedders/models/huggingface): Open-source embedding models for local deployment
+- [Vertex AI Embeddings](https://docs.mem0.ai/components/embedders/models/vertexai): Google Cloud's enterprise embedding models
+- [Google AI Embeddings](https://docs.mem0.ai/components/embedders/models/google_AI): Gemini embedding models
+- [LM Studio Embeddings](https://docs.mem0.ai/components/embedders/models/lmstudio): Local model embeddings
+- [Together Embeddings](https://docs.mem0.ai/components/embedders/models/together): Open-source model embeddings
+- [LangChain Embeddings](https://docs.mem0.ai/components/embedders/models/langchain): LangChain embedder integration
+- [AWS Bedrock Embeddings](https://docs.mem0.ai/components/embedders/models/aws_bedrock): Amazon embedding models through Bedrock
+
+### Rerankers
+- [Reranker Overview](https://docs.mem0.ai/components/rerankers/overview): Guide to reranking models for improving search result quality
+- [Reranker Configuration](https://docs.mem0.ai/components/rerankers/config): Configuration reference for reranker providers
+- [Reranker Optimization](https://docs.mem0.ai/components/rerankers/optimization): Performance tuning and optimization strategies for rerankers
+- [Custom Reranker Prompts](https://docs.mem0.ai/components/rerankers/custom-prompts): Customize reranker behavior with custom prompts
+- [Cohere Reranker](https://docs.mem0.ai/components/rerankers/models/cohere): Cohere reranking model integration
+- [Sentence Transformer Reranker](https://docs.mem0.ai/components/rerankers/models/sentence_transformer): Cross-encoder reranking with sentence transformers
+- [Hugging Face Reranker](https://docs.mem0.ai/components/rerankers/models/huggingface): Hugging Face reranking models
+- [LLM Reranker](https://docs.mem0.ai/components/rerankers/models/llm_reranker): Use LLMs as rerankers for flexible relevance scoring
+- [Zero Entropy Reranker](https://docs.mem0.ai/components/rerankers/models/zero_entropy): Zero Entropy reranking model
 
 ## Integrations
 
-- [Integrations Overview](https://docs.mem0.ai/integrations) [Both]: Use when surveying every available integration.
+- [Integrations Overview](https://docs.mem0.ai/integrations): Overview of all available Mem0 integrations
 
 ### Agent Frameworks
-- [LangChain](https://docs.mem0.ai/integrations/langchain) [Both]: Use when the user is on LangChain.
-- [LangGraph](https://docs.mem0.ai/integrations/langgraph) [Both]: Use when building stateful multi-actor LangGraph apps.
-- [LangChain Tools](https://docs.mem0.ai/integrations/langchain-tools) [Both]: Use when Mem0 should be exposed as a LangChain tool.
-- [LlamaIndex](https://docs.mem0.ai/integrations/llama-index) [Both]: Use when layering memory on a LlamaIndex RAG app.
-- [CrewAI](https://docs.mem0.ai/integrations/crewai) [Both]: Use when building CrewAI multi-agent systems.
-- [AutoGen](https://docs.mem0.ai/integrations/autogen) [Both]: Use when the user is on Microsoft AutoGen.
-- [Agno](https://docs.mem0.ai/integrations/agno) [Both]: Use when the user is on Agno.
-- [Camel AI](https://docs.mem0.ai/integrations/camel-ai) [Both]: Use when the user is on Camel AI.
-- [ChatDev](https://docs.mem0.ai/integrations/chatdev) [Both]: Use when the user is on ChatDev.
-- [Hermes](https://docs.mem0.ai/integrations/hermes) [Both]: Use when the user is on Hermes.
-- [OpenAI Agents SDK](https://docs.mem0.ai/integrations/openai-agents-sdk) [Both]: Use when the user is on the OpenAI Agents SDK.
-- [Google AI ADK](https://docs.mem0.ai/integrations/google-ai-adk) [Both]: Use when the user is on Google's Agent Development Kit.
-- [Mastra](https://docs.mem0.ai/integrations/mastra) [Both]: Use when the user is on Mastra (TypeScript).
-- [OpenClaw](https://docs.mem0.ai/integrations/openclaw) [Both]: Use when wiring Mem0 into Claude Code or editors via OpenClaw.
-- [Vercel AI SDK](https://docs.mem0.ai/integrations/vercel-ai-sdk) [Both]: Use when the user is on the Vercel AI SDK.
-
-### AI Coding Tools
-- [Claude Code](https://docs.mem0.ai/integrations/claude-code) [Both]: Use when wiring memory into Claude Code.
-- [Cursor](https://docs.mem0.ai/integrations/cursor) [Both]: Use when wiring memory into Cursor.
-- [Codex](https://docs.mem0.ai/integrations/codex) [Both]: Use when wiring memory into Codex / other editor assistants.
+- [LangChain](https://docs.mem0.ai/integrations/langchain): Seamless integration with LangChain framework for enhanced agent capabilities
+- [LangGraph](https://docs.mem0.ai/integrations/langgraph): Build stateful, multi-actor applications with persistent memory
+- [LlamaIndex](https://docs.mem0.ai/integrations/llama-index): Enhanced RAG applications with intelligent memory layer
+- [CrewAI](https://docs.mem0.ai/integrations/crewai): Multi-agent systems with shared and individual memory capabilities
+- [AutoGen](https://docs.mem0.ai/integrations/autogen): Microsoft's multi-agent conversation framework with memory
+- [Agno](https://docs.mem0.ai/integrations/agno): Agno framework integration with persistent memory
+- [Camel AI](https://docs.mem0.ai/integrations/camel-ai): Camel AI multi-agent framework with memory support
+- [OpenClaw](https://docs.mem0.ai/integrations/openclaw): OpenClaw framework integration
+- [OpenAI Agents SDK](https://docs.mem0.ai/integrations/openai-agents-sdk): OpenAI's agent framework with Mem0 memory
+- [Google AI ADK](https://docs.mem0.ai/integrations/google-ai-adk): Google AI Agent Development Kit with persistent memory
+- [Mastra](https://docs.mem0.ai/integrations/mastra): Mastra TypeScript agent framework integration
+- [Vercel AI SDK](https://docs.mem0.ai/integrations/vercel-ai-sdk): Build AI-powered web applications with persistent memory
 
 ### Voice & Real-time
-- [LiveKit](https://docs.mem0.ai/integrations/livekit) [Both]: Use when building real-time voice/video with memory.
-- [Pipecat](https://docs.mem0.ai/integrations/pipecat) [Both]: Use when the voice pipeline is Pipecat.
-- [ElevenLabs](https://docs.mem0.ai/integrations/elevenlabs) [Both]: Use when voice synthesis uses ElevenLabs.
+- [LiveKit](https://docs.mem0.ai/integrations/livekit): Real-time voice and video AI with persistent memory
+- [Pipecat](https://docs.mem0.ai/integrations/pipecat): Voice AI pipeline framework with memory capabilities
+- [ElevenLabs](https://docs.mem0.ai/integrations/elevenlabs): Voice synthesis integration with conversational memory
 
 ### Cloud & Infrastructure
-- [AWS Bedrock](https://docs.mem0.ai/integrations/aws-bedrock) [Both]: Use when the user is on AWS Bedrock managed AI services.
+- [AWS Bedrock](https://docs.mem0.ai/integrations/aws-bedrock): Enterprise AWS integration for managed AI services
 
 ### Developer Tools
-- [Dify](https://docs.mem0.ai/integrations/dify) [Both]: Use when the user is on Dify LLMOps.
-- [Flowise](https://docs.mem0.ai/integrations/flowise) [Both]: Use when the user is on Flowise no-code.
-- [AgentOps](https://docs.mem0.ai/integrations/agentops) [Both]: Use when tracking agent observability with memory metadata.
-- [Keywords AI](https://docs.mem0.ai/integrations/keywords) [Both]: Use when monitoring with Keywords AI.
-- [Raycast](https://docs.mem0.ai/integrations/raycast) [Both]: Use when the user wants quick memory access via Raycast.
+- [Dify](https://docs.mem0.ai/integrations/dify): LLMOps platform integration for production AI applications
+- [Flowise](https://docs.mem0.ai/integrations/flowise): No-code LLM workflow builder with memory capabilities
+- [LangChain Tools](https://docs.mem0.ai/integrations/langchain-tools): Use Mem0 as a LangChain tool for agents
+- [AgentOps](https://docs.mem0.ai/integrations/agentops): Agent observability and monitoring with memory tracking
+- [Keywords AI](https://docs.mem0.ai/integrations/keywords): Keywords AI integration for LLM monitoring
+- [Raycast](https://docs.mem0.ai/integrations/raycast): Raycast extension for quick memory access
 
-## Cookbooks
+## Cookbooks and Examples
 
-- [Cookbooks Overview](https://docs.mem0.ai/cookbooks/overview) [Both]: Use when surveying all reference examples.
+- [Cookbooks Overview](https://docs.mem0.ai/cookbooks/overview): Complete guide to Mem0 examples and implementation patterns
 
-### Essentials
-- [Building an AI Companion](https://docs.mem0.ai/cookbooks/essentials/building-ai-companion) [Both]: Use when starting a companion app from scratch.
-- [Partition Memories by Entity](https://docs.mem0.ai/cookbooks/essentials/entity-partitioning-playbook) [Both]: Use when isolating multi-tenant memories.
-- [Controlling Memory Ingestion](https://docs.mem0.ai/cookbooks/essentials/controlling-memory-ingestion) [Both]: Use when deciding what to store and what to skip.
-- [Tagging and Organizing Memories](https://docs.mem0.ai/cookbooks/essentials/tagging-and-organizing-memories) [Both]: Use when memory taxonomy matters.
-- [Exporting Memories](https://docs.mem0.ai/cookbooks/essentials/exporting-memories) [Both]: Use when backing up or migrating memory data.
+### Essential Guides
+- [Building AI Companion](https://docs.mem0.ai/cookbooks/essentials/building-ai-companion): Core patterns for building AI agents with memory
+- [Partition Memories by Entity](https://docs.mem0.ai/cookbooks/essentials/entity-partitioning-playbook): Keep multi-tenant assistants isolated by tagging user, agent, app, and session identifiers
+- [Controlling Memory Ingestion](https://docs.mem0.ai/cookbooks/essentials/controlling-memory-ingestion): Fine-tune what gets stored in memory and when
+- [Tagging and Organizing Memories](https://docs.mem0.ai/cookbooks/essentials/tagging-and-organizing-memories): Advanced memory organization and categorization
+- [Exporting Memories](https://docs.mem0.ai/cookbooks/essentials/exporting-memories): Backup and transfer memory data between systems
 
-### AI Companions
-- [Quickstart Demo](https://docs.mem0.ai/cookbooks/companions/quickstart-demo) [Both]: Use when showing the smallest end-to-end companion.
-- [Node.js Companion](https://docs.mem0.ai/cookbooks/companions/nodejs-companion) [Both]: Use when the companion is in JavaScript/TypeScript.
-- [AI Tutor](https://docs.mem0.ai/cookbooks/companions/ai-tutor) [Both]: Use when the agent adapts to a learner over time.
-- [Travel Assistant](https://docs.mem0.ai/cookbooks/companions/travel-assistant) [Both]: Use when the agent learns travel preferences.
-- [YouTube Research Assistant](https://docs.mem0.ai/cookbooks/companions/youtube-research) [Both]: Use when building an agent that ingests video content over sessions.
-- [Voice Companion (OpenAI)](https://docs.mem0.ai/cookbooks/companions/voice-companion-openai) [Both]: Use when the companion is voice-first with OpenAI Realtime.
-- [Local Companion (Ollama)](https://docs.mem0.ai/cookbooks/companions/local-companion-ollama) [OSS]: Use when the companion must run entirely on local models.
+### AI Companion Examples
+- [Quickstart Demo](https://docs.mem0.ai/cookbooks/companions/quickstart-demo): Quick demo of building an AI companion with memory
+- [Node.js Companion](https://docs.mem0.ai/cookbooks/companions/nodejs-companion): JavaScript-based AI companion applications
+- [AI Tutor](https://docs.mem0.ai/cookbooks/companions/ai-tutor): Educational AI that adapts to learning progress
+- [Travel Assistant](https://docs.mem0.ai/cookbooks/companions/travel-assistant): Travel planning agent that learns preferences
+- [YouTube Research Assistant](https://docs.mem0.ai/cookbooks/companions/youtube-research): AI that researches and learns from video content
+- [Voice Companion](https://docs.mem0.ai/cookbooks/companions/voice-companion-openai): Voice-enabled AI with conversational memory
+- [Local Companion](https://docs.mem0.ai/cookbooks/companions/local-companion-ollama): Privacy-focused companion using local models
 
 ### Operations & Automation
-- [Support Inbox](https://docs.mem0.ai/cookbooks/operations/support-inbox) [Both]: Use when a support agent needs conversation history across tickets.
-- [Email Automation](https://docs.mem0.ai/cookbooks/operations/email-automation) [Both]: Use when processing email with contextual memory.
-- [Content Writing](https://docs.mem0.ai/cookbooks/operations/content-writing) [Both]: Use when an AI writer must maintain brand voice across sessions.
-- [Deep Research](https://docs.mem0.ai/cookbooks/operations/deep-research) [Both]: Use when research agents build on previous findings.
-- [Team Task Agent](https://docs.mem0.ai/cookbooks/operations/team-task-agent) [Both]: Use when collaborative agents share project memory.
+- [Support Inbox](https://docs.mem0.ai/cookbooks/operations/support-inbox): Customer service agents with conversation history
+- [Email Automation](https://docs.mem0.ai/cookbooks/operations/email-automation): Smart email processing with contextual memory
+- [Content Writing](https://docs.mem0.ai/cookbooks/operations/content-writing): AI writers that maintain brand voice and style
+- [Deep Research](https://docs.mem0.ai/cookbooks/operations/deep-research): Research assistants that build on previous findings
+- [Team Task Agent](https://docs.mem0.ai/cookbooks/operations/team-task-agent): Collaborative AI agents with shared project memory
 
 ### Integration Examples
-- [Agents SDK Tool](https://docs.mem0.ai/cookbooks/integrations/agents-sdk-tool) [Platform]: Use when exposing Mem0 as a tool in OpenAI Agents SDK.
-- [OpenAI Tool Calls](https://docs.mem0.ai/cookbooks/integrations/openai-tool-calls) [Platform]: Use when hooking Mem0 into OpenAI function calling.
-- [Mastra Agent](https://docs.mem0.ai/cookbooks/integrations/mastra-agent) [Both]: Use when the agent is built in Mastra.
-- [Healthcare Google ADK](https://docs.mem0.ai/cookbooks/integrations/healthcare-google-adk) [Both]: Use when the domain is medical and the framework is Google ADK.
-- [AWS Bedrock](https://docs.mem0.ai/cookbooks/integrations/aws-bedrock) [Both]: Use when deploying with AWS managed model services.
-- [Tavily Search](https://docs.mem0.ai/cookbooks/integrations/tavily-search) [Both]: Use when the agent layers web search on memory.
+- [Agents SDK Tool](https://docs.mem0.ai/cookbooks/integrations/agents-sdk-tool): Using Mem0 as a tool with OpenAI Agents SDK
+- [OpenAI Tool Calls](https://docs.mem0.ai/cookbooks/integrations/openai-tool-calls): Mem0 integrated with OpenAI function calling
+- [Mastra Agent](https://docs.mem0.ai/cookbooks/integrations/mastra-agent): Mastra framework integration with memory
+- [Healthcare Google ADK](https://docs.mem0.ai/cookbooks/integrations/healthcare-google-adk): Medical AI applications with memory
+- [AWS Bedrock](https://docs.mem0.ai/cookbooks/integrations/aws-bedrock): Enterprise memory with AWS managed services
+- [Neptune Analytics](https://docs.mem0.ai/cookbooks/integrations/neptune-analytics): Graph and vector search with AWS Neptune
+- [Tavily Search](https://docs.mem0.ai/cookbooks/integrations/tavily-search): Web search with persistent memory of results
 
 ### Framework Examples
-- [LlamaIndex React](https://docs.mem0.ai/cookbooks/frameworks/llamaindex-react) [Both]: Use when building a React UI with LlamaIndex and memory.
-- [LlamaIndex Multiagent](https://docs.mem0.ai/cookbooks/frameworks/llamaindex-multiagent) [Both]: Use when running LlamaIndex multi-agent systems with shared memory.
-- [Multimodal Retrieval](https://docs.mem0.ai/cookbooks/frameworks/multimodal-retrieval) [Both]: Use when memory must handle text, images, and docs together.
-- [Eliza OS Character](https://docs.mem0.ai/cookbooks/frameworks/eliza-os-character) [Both]: Use when building a character-based agent with persistent personality.
-- [Gemini with Mem0 MCP](https://docs.mem0.ai/cookbooks/frameworks/gemini-3-with-mem0-mcp) [Platform]: Use when Gemini connects to Mem0 over MCP.
+- [LlamaIndex React](https://docs.mem0.ai/cookbooks/frameworks/llamaindex-react): React applications with LlamaIndex and memory
+- [LlamaIndex Multiagent](https://docs.mem0.ai/cookbooks/frameworks/llamaindex-multiagent): Multi-agent systems with shared memory
+- [Multimodal Retrieval](https://docs.mem0.ai/cookbooks/frameworks/multimodal-retrieval): Memory systems handling text, images, and documents
+- [Eliza OS Character](https://docs.mem0.ai/cookbooks/frameworks/eliza-os-character): Character-based AI with persistent personality
+- [Gemini with Mem0 MCP](https://docs.mem0.ai/cookbooks/frameworks/gemini-3-with-mem0-mcp): Google Gemini integration using MCP server
 
 ## API Reference
 
-All API Reference docs describe Mem0 Platform REST endpoints (requires API key).
+- [API Reference Overview](https://docs.mem0.ai/api-reference): REST API overview with authentication and quick start guide
+- [Organizations & Projects](https://docs.mem0.ai/api-reference/organizations-projects): Managing organizations and projects for multi-tenant setups
 
-- [API Reference Overview](https://docs.mem0.ai/api-reference) [Platform]: Use when explaining authentication and the general request/response shape.
-- [Organizations & Projects](https://docs.mem0.ai/api-reference/organizations-projects) [Platform]: Use when the user needs multi-tenant isolation.
+### Core Memory APIs
+- [Add Memories](https://docs.mem0.ai/api-reference/memory/add-memories): REST API for storing new memories with detailed request/response formats
+- [Get All Memories](https://docs.mem0.ai/api-reference/memory/get-memories): Retrieve all memories with pagination and filtering options
+- [Search Memories](https://docs.mem0.ai/api-reference/memory/search-memories): Advanced search API with filtering and ranking capabilities
+- [Update Memory](https://docs.mem0.ai/api-reference/memory/update-memory): Modify existing memories with conflict resolution
+- [Delete Memory](https://docs.mem0.ai/api-reference/memory/delete-memory): Remove a specific memory by ID
 
-### Core Memory
-- [Add Memories](https://docs.mem0.ai/api-reference/memory/add-memories) [Platform]: Use when writing one or more memories.
-- [Get All Memories](https://docs.mem0.ai/api-reference/memory/get-memories) [Platform]: Use when paginating memories for a user/agent.
-- [Get Memory](https://docs.mem0.ai/api-reference/memory/get-memory) [Platform]: Use when fetching one memory by ID.
-- [Search Memories](https://docs.mem0.ai/api-reference/memory/search-memories) [Platform]: Use when running a semantic query with filters.
-- [Update Memory](https://docs.mem0.ai/api-reference/memory/update-memory) [Platform]: Use when editing a memory in place.
-- [Delete Memory](https://docs.mem0.ai/api-reference/memory/delete-memory) [Platform]: Use when removing one memory.
-- [Delete All Memories](https://docs.mem0.ai/api-reference/memory/delete-memories) [Platform]: Use when purging memories matching a scope.
-- [Batch Update](https://docs.mem0.ai/api-reference/memory/batch-update) [Platform]: Use when updating many memories in one call.
-- [Batch Delete](https://docs.mem0.ai/api-reference/memory/batch-delete) [Platform]: Use when deleting many memories in one call.
-- [Memory History](https://docs.mem0.ai/api-reference/memory/history-memory) [Platform]: Use when the user needs the change log for a memory.
-- [Feedback](https://docs.mem0.ai/api-reference/memory/feedback) [Platform]: Use when capturing user signals on memory quality.
-- [Create Memory Export](https://docs.mem0.ai/api-reference/memory/create-memory-export) [Platform]: Use when kicking off an async export job.
-- [Get Memory Export](https://docs.mem0.ai/api-reference/memory/get-memory-export) [Platform]: Use when fetching the result of an export job.
+### Additional Memory APIs
+- [Create Memory Export](https://docs.mem0.ai/api-reference/memory/create-memory-export): Export memories in bulk
+- [Feedback](https://docs.mem0.ai/api-reference/memory/feedback): Submit feedback on memory quality
+- [Get Memory](https://docs.mem0.ai/api-reference/memory/get-memory): Retrieve a single memory by ID
+- [Memory History](https://docs.mem0.ai/api-reference/memory/history-memory): View the history of changes to a memory
+- [Get Memory Export](https://docs.mem0.ai/api-reference/memory/get-memory-export): Retrieve a previously created memory export
+- [Batch Update](https://docs.mem0.ai/api-reference/memory/batch-update): Update multiple memories in a single request
+- [Batch Delete](https://docs.mem0.ai/api-reference/memory/batch-delete): Delete multiple memories in a single request
+- [Delete All Memories](https://docs.mem0.ai/api-reference/memory/delete-memories): Remove all memories matching criteria
 
-### Events
-- [Get Events](https://docs.mem0.ai/api-reference/events/get-events) [Platform]: Use when listing async memory operation events.
-- [Get Event](https://docs.mem0.ai/api-reference/events/get-event) [Platform]: Use when fetching one event by ID.
+### Events APIs
+- [Get Events](https://docs.mem0.ai/api-reference/events/get-events): List asynchronous memory operation events
+- [Get Event](https://docs.mem0.ai/api-reference/events/get-event): Retrieve details of a specific event
 
-### Entities
-- [Get Users](https://docs.mem0.ai/api-reference/entities/get-users) [Platform]: Use when listing users, agents, or apps known to a project.
-- [Delete User](https://docs.mem0.ai/api-reference/entities/delete-user) [Platform]: Use when removing an entity and all its memories.
+### Entities APIs
+- [Get Users](https://docs.mem0.ai/api-reference/entities/get-users): List all entities (users, agents, apps)
+- [Delete User](https://docs.mem0.ai/api-reference/entities/delete-user): Remove an entity and all associated memories
 
-### Organizations
-- [Create Organization](https://docs.mem0.ai/api-reference/organization/create-org) [Platform]: Use when setting up a new org.
-- [Get Organizations](https://docs.mem0.ai/api-reference/organization/get-orgs) [Platform]: Use when listing orgs.
-- [Get Organization](https://docs.mem0.ai/api-reference/organization/get-org) [Platform]: Use when fetching one org.
-- [Get Organization Members](https://docs.mem0.ai/api-reference/organization/get-org-members) [Platform]: Use when listing org members.
-- [Add Organization Member](https://docs.mem0.ai/api-reference/organization/add-org-member) [Platform]: Use when inviting a member to an org.
-- [Delete Organization](https://docs.mem0.ai/api-reference/organization/delete-org) [Platform]: Use when removing an org.
+### Organizations APIs
+- [Create Organization](https://docs.mem0.ai/api-reference/organization/create-org): Create a new organization
+- [Get Organizations](https://docs.mem0.ai/api-reference/organization/get-orgs): List all organizations
+- [Get Organization](https://docs.mem0.ai/api-reference/organization/get-org): Retrieve organization details
+- [Get Organization Members](https://docs.mem0.ai/api-reference/organization/get-org-members): List organization members
+- [Add Organization Member](https://docs.mem0.ai/api-reference/organization/add-org-member): Add a member to an organization
+- [Delete Organization](https://docs.mem0.ai/api-reference/organization/delete-org): Remove an organization
 
-### Projects
-- [Create Project](https://docs.mem0.ai/api-reference/project/create-project) [Platform]: Use when creating a project inside an org.
-- [Get Projects](https://docs.mem0.ai/api-reference/project/get-projects) [Platform]: Use when listing projects.
-- [Get Project](https://docs.mem0.ai/api-reference/project/get-project) [Platform]: Use when fetching one project.
-- [Get Project Members](https://docs.mem0.ai/api-reference/project/get-project-members) [Platform]: Use when listing project members.
-- [Add Project Member](https://docs.mem0.ai/api-reference/project/add-project-member) [Platform]: Use when inviting a member to a project.
-- [Delete Project](https://docs.mem0.ai/api-reference/project/delete-project) [Platform]: Use when removing a project.
+### Project APIs
+- [Create Project](https://docs.mem0.ai/api-reference/project/create-project): Create a new project within an organization
+- [Get Projects](https://docs.mem0.ai/api-reference/project/get-projects): List all projects
+- [Get Project](https://docs.mem0.ai/api-reference/project/get-project): Retrieve project details
+- [Get Project Members](https://docs.mem0.ai/api-reference/project/get-project-members): List project members
+- [Add Project Member](https://docs.mem0.ai/api-reference/project/add-project-member): Add a member to a project
+- [Delete Project](https://docs.mem0.ai/api-reference/project/delete-project): Remove a project
 
-### Webhooks
-- [Create Webhook](https://docs.mem0.ai/api-reference/webhook/create-webhook) [Platform]: Use when registering a webhook endpoint.
-- [Get Webhook](https://docs.mem0.ai/api-reference/webhook/get-webhook) [Platform]: Use when fetching webhook config.
-- [Update Webhook](https://docs.mem0.ai/api-reference/webhook/update-webhook) [Platform]: Use when modifying webhook settings.
-- [Delete Webhook](https://docs.mem0.ai/api-reference/webhook/delete-webhook) [Platform]: Use when removing a webhook.
-
-## Skills & Plugins
-
-Mem0 ships first-class integrations for AI coding editors and MCP-aware tools. When the user is in Claude Code, Cursor, Codex, or any MCP client, load this section first.
-
-### Claude Code Skills (in-repo, not on docs.mem0.ai)
-
-Source: https://github.com/mem0ai/mem0/tree/main/skills
-
-- **skills/mem0** - Default Mem0 skill. Trigger on mentions of `MemoryClient`, "memory layer", personalization, or adding long-term memory to chatbots/agents. Covers Python SDK, TS SDK, and every framework integration.
-- **skills/mem0-cli** - Trigger on CLI / terminal / shell usage of Mem0.
-- **skills/mem0-vercel-ai-sdk** - Trigger when the stack includes `@mem0/vercel-ai-provider` or `createMem0`.
-
-Each subdirectory is a Claude Code Skill (`SKILL.md` + supporting assets). Load only the one that matches the user's stack.
-
-### Editor Plugin (shared glue)
-
-Source: https://github.com/mem0ai/mem0/tree/main/mem0-plugin
-
-The `mem0-plugin/` directory provides MCP server connection, lifecycle hooks, and skill bundling for Claude Code, Cursor, and Codex. It exposes 9 MCP tools: `add_memory`, `search_memories`, `get_memories`, `get_memory`, `update_memory`, `delete_memory`, `delete_all_memories`, `delete_entities`, `list_entities`.
-
-Editor-specific setup docs (already listed above under `## Integrations > AI Coding Tools`):
-
-- `integrations/claude-code` [Both]
-- `integrations/cursor` [Both]
-- `integrations/codex` [Both]
-- `integrations/openclaw` [Both]
-
-### MCP Endpoints
-
-- Hosted MCP server: `https://mcp.mem0.ai` - requires Platform API key. See `platform/mem0-mcp`.
-- Self-hosted MCP server: ships with `openmemory/api/` (FastAPI) - runs against your own Qdrant + LLM stack.
+### Webhook APIs
+- [Create Webhook](https://docs.mem0.ai/api-reference/webhook/create-webhook): Register a new webhook endpoint
+- [Get Webhook](https://docs.mem0.ai/api-reference/webhook/get-webhook): Retrieve webhook configuration
+- [Update Webhook](https://docs.mem0.ai/api-reference/webhook/update-webhook): Modify webhook settings
+- [Delete Webhook](https://docs.mem0.ai/api-reference/webhook/delete-webhook): Remove a webhook
 
 ## Community & Support
 
-- [Contributing - Development](https://docs.mem0.ai/contributing/development) [Both]: Use when the user wants to contribute code.
-- [Contributing - Documentation](https://docs.mem0.ai/contributing/documentation) [Both]: Use when the user wants to contribute docs.
-
-## Optional
-
-Everything below is OSS-only provider configuration. Skip this entire section when the user is on Mem0 Platform (providers are managed server-side). When the user is self-hosting, load only the subsection that matches the provider they are configuring.
-
-### LLM Providers [OSS]
-- [LLM Overview](https://docs.mem0.ai/components/llms/overview) [OSS]: Use when the user is choosing an LLM for memory extraction.
-- [LLM Configuration](https://docs.mem0.ai/components/llms/config) [OSS]: Use for the `llm` config schema.
-- [OpenAI](https://docs.mem0.ai/components/llms/models/openai) [OSS]: Use when the extraction LLM is OpenAI.
-- [Anthropic](https://docs.mem0.ai/components/llms/models/anthropic) [OSS]: Use when the extraction LLM is Claude.
-- [Azure OpenAI](https://docs.mem0.ai/components/llms/models/azure_openai) [OSS]: Use when the user is on Azure-hosted OpenAI.
-- [AWS Bedrock](https://docs.mem0.ai/components/llms/models/aws_bedrock) [OSS]: Use when the LLM runs through Bedrock.
-- [Google AI](https://docs.mem0.ai/components/llms/models/google_AI) [OSS]: Use when the LLM is Gemini.
-- [Groq](https://docs.mem0.ai/components/llms/models/groq) [OSS]: Use when the user wants Groq's low-latency inference.
-- [DeepSeek](https://docs.mem0.ai/components/llms/models/deepseek) [OSS]: Use when the LLM is DeepSeek.
-- [Mistral AI](https://docs.mem0.ai/components/llms/models/mistral_AI) [OSS]: Use when the LLM is Mistral.
-- [MiniMax](https://docs.mem0.ai/components/llms/models/minimax) [OSS]: Use when the LLM is MiniMax.
-- [xAI](https://docs.mem0.ai/components/llms/models/xAI) [OSS]: Use when the LLM is xAI Grok.
-- [Sarvam](https://docs.mem0.ai/components/llms/models/sarvam) [OSS]: Use for Indian-language Sarvam models.
-- [Together](https://docs.mem0.ai/components/llms/models/together) [OSS]: Use when the LLM runs on Together.
-- [Ollama](https://docs.mem0.ai/components/llms/models/ollama) [OSS]: Use when the LLM is a local Ollama model.
-- [LM Studio](https://docs.mem0.ai/components/llms/models/lmstudio) [OSS]: Use when the LLM is served from LM Studio.
-- [LiteLLM](https://docs.mem0.ai/components/llms/models/litellm) [OSS]: Use when multiplexing many providers behind LiteLLM.
-- [vLLM](https://docs.mem0.ai/components/llms/models/vllm) [OSS]: Use when self-hosting inference with vLLM.
-- [LangChain LLM](https://docs.mem0.ai/components/llms/models/langchain) [OSS]: Use when the LLM is wrapped behind a LangChain adapter.
-
-### Embedding Providers [OSS]
-- [Embeddings Overview](https://docs.mem0.ai/components/embedders/overview) [OSS]: Use when choosing an embedding model.
-- [Embeddings Configuration](https://docs.mem0.ai/components/embedders/config) [OSS]: Use for the `embedder` config schema.
-- [OpenAI Embeddings](https://docs.mem0.ai/components/embedders/models/openai) [OSS]: Use when embeddings come from OpenAI.
-- [Azure OpenAI Embeddings](https://docs.mem0.ai/components/embedders/models/azure_openai) [OSS]: Use for Azure-hosted OpenAI embeddings.
-- [AWS Bedrock Embeddings](https://docs.mem0.ai/components/embedders/models/aws_bedrock) [OSS]: Use for Bedrock-hosted embeddings.
-- [Google AI Embeddings](https://docs.mem0.ai/components/embedders/models/google_AI) [OSS]: Use for Gemini embeddings.
-- [Vertex AI Embeddings](https://docs.mem0.ai/components/embedders/models/vertexai) [OSS]: Use for Google Cloud Vertex AI embeddings.
-- [Hugging Face Embeddings](https://docs.mem0.ai/components/embedders/models/huggingface) [OSS]: Use for open-source HF embedding models.
-- [Ollama Embeddings](https://docs.mem0.ai/components/embedders/models/ollama) [OSS]: Use when embeddings run through local Ollama.
-- [LM Studio Embeddings](https://docs.mem0.ai/components/embedders/models/lmstudio) [OSS]: Use when embeddings run through LM Studio.
-- [Together Embeddings](https://docs.mem0.ai/components/embedders/models/together) [OSS]: Use when embeddings run on Together.
-- [LangChain Embeddings](https://docs.mem0.ai/components/embedders/models/langchain) [OSS]: Use when embeddings are wrapped behind a LangChain adapter.
-
-### Vector Databases [OSS]
-- [Vector Database Overview](https://docs.mem0.ai/components/vectordbs/overview) [OSS]: Use when choosing a vector store.
-- [Vector Database Configuration](https://docs.mem0.ai/components/vectordbs/config) [OSS]: Use for the `vector_store` config schema.
-- [Qdrant](https://docs.mem0.ai/components/vectordbs/dbs/qdrant) [OSS]: Use as the default self-hosted vector store (best-tested).
-- [Chroma](https://docs.mem0.ai/components/vectordbs/dbs/chroma) [OSS]: Use when the user wants a lightweight embedded store.
-- [PGVector](https://docs.mem0.ai/components/vectordbs/dbs/pgvector) [OSS]: Use when Postgres is already in the stack.
-- [Milvus](https://docs.mem0.ai/components/vectordbs/dbs/milvus) [OSS]: Use for large-scale Milvus deployments.
-- [Pinecone](https://docs.mem0.ai/components/vectordbs/dbs/pinecone) [OSS]: Use when the user is on Pinecone managed.
-- [MongoDB](https://docs.mem0.ai/components/vectordbs/dbs/mongodb) [OSS]: Use when Mongo Atlas Vector Search is the backing store.
-- [Azure AI Search](https://docs.mem0.ai/components/vectordbs/dbs/azure) [OSS]: Use when the user is on Azure AI Search.
-- [Azure MySQL](https://docs.mem0.ai/components/vectordbs/dbs/azure_mysql) [OSS]: Use when vector search runs on Azure Database for MySQL.
-- [Redis](https://docs.mem0.ai/components/vectordbs/dbs/redis) [OSS]: Use when Redis Stack is the backing store.
-- [Valkey](https://docs.mem0.ai/components/vectordbs/dbs/valkey) [OSS]: Use when the user is on Valkey (Redis fork).
-- [Elasticsearch](https://docs.mem0.ai/components/vectordbs/dbs/elasticsearch) [OSS]: Use when Elasticsearch is the backing store.
-- [OpenSearch](https://docs.mem0.ai/components/vectordbs/dbs/opensearch) [OSS]: Use when OpenSearch is the backing store.
-- [Supabase](https://docs.mem0.ai/components/vectordbs/dbs/supabase) [OSS]: Use when Supabase with pgvector is the backing store.
-- [Upstash Vector](https://docs.mem0.ai/components/vectordbs/dbs/upstash-vector) [OSS]: Use for serverless Upstash Vector.
-- [Vectorize](https://docs.mem0.ai/components/vectordbs/dbs/vectorize) [OSS]: Use when the store is Cloudflare Vectorize.
-- [Vertex AI Vector Search](https://docs.mem0.ai/components/vectordbs/dbs/vertex_ai) [OSS]: Use when the store is Google Cloud Vertex Vector Search.
-- [Weaviate](https://docs.mem0.ai/components/vectordbs/dbs/weaviate) [OSS]: Use when Weaviate is the backing store.
-- [FAISS](https://docs.mem0.ai/components/vectordbs/dbs/faiss) [OSS]: Use for local FAISS-based similarity search.
-- [LangChain Vector Store](https://docs.mem0.ai/components/vectordbs/dbs/langchain) [OSS]: Use when the vector store is wrapped behind LangChain.
-- [Baidu](https://docs.mem0.ai/components/vectordbs/dbs/baidu) [OSS]: Use when the user is on Baidu Cloud vector service.
-- [Cassandra](https://docs.mem0.ai/components/vectordbs/dbs/cassandra) [OSS]: Use when Cassandra is the backing store.
-- [S3 Vectors](https://docs.mem0.ai/components/vectordbs/dbs/s3_vectors) [OSS]: Use for AWS S3 Vectors.
-- [Databricks](https://docs.mem0.ai/components/vectordbs/dbs/databricks) [OSS]: Use when the user is on Databricks with Delta Lake.
-- [Neptune Analytics](https://docs.mem0.ai/components/vectordbs/dbs/neptune_analytics) [OSS]: Use when the user is on AWS Neptune Analytics (graph + vector).
-- [Turbopuffer](https://docs.mem0.ai/components/vectordbs/dbs/turbopuffer) [OSS]: Use when the user is on Turbopuffer serverless.
-
-### Rerankers [OSS]
-- [Reranker Overview](https://docs.mem0.ai/components/rerankers/overview) [OSS]: Use when the user wants to improve OSS search result quality.
-- [Reranker Configuration](https://docs.mem0.ai/components/rerankers/config) [OSS]: Use for the `reranker` config schema.
-- [Reranker Optimization](https://docs.mem0.ai/components/rerankers/optimization) [OSS]: Use when tuning reranker performance.
-- [Custom Reranker Prompts](https://docs.mem0.ai/components/rerankers/custom-prompts) [OSS]: Use when rewriting reranker prompts.
-- [Cohere Reranker](https://docs.mem0.ai/components/rerankers/models/cohere) [OSS]: Use for Cohere Rerank.
-- [Sentence Transformer Reranker](https://docs.mem0.ai/components/rerankers/models/sentence_transformer) [OSS]: Use for local cross-encoder rerankers.
-- [Hugging Face Reranker](https://docs.mem0.ai/components/rerankers/models/huggingface) [OSS]: Use for HF-hosted reranker models.
-- [LLM Reranker (prompt)](https://docs.mem0.ai/components/rerankers/models/llm) [OSS]: Use when the reranker is a prompted LLM (config guide).
-- [LLM Reranker](https://docs.mem0.ai/components/rerankers/models/llm_reranker) [OSS]: Use when the reranker is a prompted LLM (implementation reference).
-- [Zero Entropy Reranker](https://docs.mem0.ai/components/rerankers/models/zero_entropy) [OSS]: Use for the Zero Entropy reranker.
+- [Contributing - Development](https://docs.mem0.ai/contributing/development): Guidelines for contributing to Mem0's open-source development
+- [Contributing - Documentation](https://docs.mem0.ai/contributing/documentation): Guidelines for contributing to Mem0's documentation
+- [Changelog](https://docs.mem0.ai/changelog): Detailed product updates and version history

docs/migration/oss-to-platform.mdx

@@ -28,7 +28,7 @@ Move your Mem0 implementation to managed infrastructure with enterprise features
 
 ## Plan
 
-1. **Sign up**: Create an account on <a href="https://app.mem0.ai?utm_source=oss&utm_medium=migration-oss-to-platform" rel="nofollow">Mem0 Platform</a>.
+1. **Sign up**: Create an account on <a href="https://app.mem0.ai" rel="nofollow">Mem0 Platform</a>.
 2. **Get API Key**: Navigate to **Settings > API Keys** and generate a new key.
 3. **Review Usage**: Identify where you instantiate `Memory` and where you call `search` or `get_all`.
 
@@ -372,7 +372,7 @@ If you encounter issues, you can revert immediately by switching your import bac
 
 ## Next Steps
 
-- <a href="https://app.mem0.ai?utm_source=oss&utm_medium=migration-oss-to-platform" rel="nofollow">Platform Dashboard</a> - Monitor usage and manage settings.
+- <a href="https://app.mem0.ai" rel="nofollow">Platform Dashboard</a> - Monitor usage and manage settings.
 - [Webhooks Setup](/platform/features/webhooks) - Configure real-time event notifications.
 - [Organizations & Projects](/api-reference/organizations-projects) - Set up multi-tenancy for your team.
 

docs/open-source/features/custom-instructions.mdx

@@ -115,15 +115,17 @@ config = {
         }
     },
     "custom_instructions": custom_instructions,
+    "version": "v1.1"
 }
 
-m = Memory.from_config(config)
+m = Memory.from_config(config_dict=config)
 ```
 
 ```ts TypeScript
 import { Memory } from "mem0ai/oss";
 
 const config = {
+  version: "v1.1",
   llm: {
     provider: "openai",
     config: {

docs/open-source/features/metadata-filtering.mdx

@@ -51,7 +51,8 @@ m = Memory()
 # Search with simple metadata filters
 results = m.search(
     "What are my preferences?",
-    filters={"user_id": "alice", "category": "preferences"}
+    user_id="alice",
+    filters={"category": "preferences"}
 )
 ```
 
@@ -67,8 +68,8 @@ Layer greater-than/less-than comparisons to rank results by score, confidence, o
 # Greater than / Less than
 results = m.search(
     "recent activities",
+    user_id="alice",
     filters={
-        "user_id": "alice",
         "score": {"gt": 0.8},
         "priority": {"gte": 5},
         "confidence": {"lt": 0.9},
@@ -79,8 +80,8 @@ results = m.search(
 # Equality operators
 results = m.search(
     "specific content",
+    user_id="alice",
     filters={
-        "user_id": "alice",
         "status": {"eq": "active"},
         "archived": {"ne": True}
     }
@@ -95,8 +96,8 @@ Use `in` and `nin` when you want to pre-approve or exclude specific values witho
 # In / Not in operators
 results = m.search(
     "multi-category search",
+    user_id="alice",
     filters={
-        "user_id": "alice",
         "category": {"in": ["food", "travel", "entertainment"]},
         "status": {"nin": ["deleted", "archived"]}
     }
@@ -115,8 +116,8 @@ results = m.search(
 # Text matching operators
 results = m.search(
     "content search",
+    user_id="alice",
     filters={
-        "user_id": "alice",
         "title": {"contains": "meeting"},
         "description": {"icontains": "important"},
         "tags": {"contains": "urgent"}
@@ -132,8 +133,8 @@ Allow any value for a field while still requiring the field to exist—handy whe
 # Match any value for a field
 results = m.search(
     "all with category",
+    user_id="alice",
     filters={
-        "user_id": "alice",
         "category": "*"
     }
 )
@@ -147,9 +148,9 @@ Combine filters with `AND`, `OR`, and `NOT` to express complex decision trees. N
 # Logical AND
 results = m.search(
     "complex query",
+    user_id="alice",
     filters={
         "AND": [
-            {"user_id": "alice"},
             {"category": "work"},
             {"priority": {"gte": 7}},
             {"status": {"ne": "completed"}}
@@ -160,16 +161,12 @@ results = m.search(
 # Logical OR
 results = m.search(
     "flexible query",
+    user_id="alice",
     filters={
-        "AND": [
-            {"user_id": "alice"},
-            {
-                "OR": [
-                    {"category": "urgent"},
-                    {"priority": {"gte": 9}},
-                    {"deadline": {"contains": "today"}}
-                ]
-            }
+        "OR": [
+            {"category": "urgent"},
+            {"priority": {"gte": 9}},
+            {"deadline": {"contains": "today"}}
         ]
     }
 )
@@ -177,15 +174,11 @@ results = m.search(
 # Logical NOT
 results = m.search(
     "exclusion query",
+    user_id="alice",
     filters={
-        "AND": [
-            {"user_id": "alice"},
-            {
-                "NOT": [
-                    {"category": "archived"},
-                    {"status": "deleted"}
-                ]
-            }
+        "NOT": [
+            {"category": "archived"},
+            {"status": "deleted"}
         ]
     }
 )
@@ -193,9 +186,9 @@ results = m.search(
 # Complex nested logic
 results = m.search(
     "advanced query",
+    user_id="alice",
     filters={
         "AND": [
-            {"user_id": "alice"},
             {
                 "OR": [
                     {"category": "work"},
@@ -295,15 +288,16 @@ Vector store support varies. Confirm operator coverage before shipping:
 # Before (v0.x) - simple key-value filtering only
 results = m.search(
     "query",
-    filters={"user_id": "alice", "category": "work", "status": "active"}
+    user_id="alice",
+    filters={"category": "work", "status": "active"}
 )
 
 # After (v1.0.0) - enhanced filtering with operators
 results = m.search(
     "query",
+    user_id="alice",
     filters={
         "AND": [
-            {"user_id": "alice"},
             {"category": "work"},
             {"status": {"ne": "archived"}},
             {"priority": {"gte": 5}}
@@ -326,9 +320,9 @@ results = m.search(
 # Find high-priority active tasks
 results = m.search(
     "What tasks need attention?",
+    user_id="project_manager",
     filters={
         "AND": [
-            {"user_id": "project_manager"},
             {"project": {"in": ["alpha", ""]}},
             {"priority": {"gte": 8}},
             {"status": {"ne": "completed"}},
@@ -353,9 +347,9 @@ results = m.search(
 # Find recent unresolved tickets
 results = m.search(
     "pending support issues",
+    agent_id="support_bot",
     filters={
         "AND": [
-            {"agent_id": "support_bot"},
             {"ticket_status": {"ne": "resolved"}},
             {"priority": {"in": ["high", "critical"]}},
             {"created_date": {"gte": "2024-01-01"}},
@@ -370,7 +364,7 @@ results = m.search(
 ```
 
 <Tip>
-  Pair agent ID filters with ticket-specific metadata so shared support bots return only the tickets they can act on in the current session.
+  Pair `agent_id` filters with ticket-specific metadata so shared support bots return only the tickets they can act on in the current session.
 </Tip>
 
 ### Content recommendation filtering
@@ -379,9 +373,9 @@ results = m.search(
 # Personalized content filtering
 results = m.search(
     "recommend content",
+    user_id="reader123",
     filters={
         "AND": [
-            {"user_id": "reader123"},
             {
                 "OR": [
                     {"genre": {"in": ["sci-fi", "fantasy"]}},
@@ -406,8 +400,8 @@ results = m.search(
 try:
     results = m.search(
         "test query",
+        user_id="alice",
         filters={
-            "user_id": "alice",
             "invalid_operator": {"unknown": "value"}
         }
     )
@@ -415,7 +409,8 @@ except ValueError as e:
     print(f"Filter error: {e}")
     results = m.search(
         "test query",
-        filters={"user_id": "alice", "category": "general"}
+        user_id="alice",
+        filters={"category": "general"}
     )
 ```
 

docs/open-source/features/reranker-search.mdx

@@ -189,7 +189,7 @@ async_memory = AsyncMemory.from_config(config)
 async def search_with_rerank():
     return await async_memory.search(
         "What are my preferences?",
-        filters={"user_id": "alice"},
+        user_id="alice",
         rerank=True
     )
 
@@ -272,7 +272,7 @@ results = m.search("query", filters={"user_id": "alice"})
 ```python
 results = m.search(
     "What are my food preferences?",
-    filters={"user_id": "alice"}
+    user_id="alice"
 )
 
 for result in results["results"]:
@@ -289,13 +289,13 @@ for result in results["results"]:
 ```python
 results_with_rerank = m.search(
     "What movies do I like?",
-    filters={"user_id": "alice"},
+    user_id="alice",
     rerank=True
 )
 
 results_without_rerank = m.search(
     "What movies do I like?",
-    filters={"user_id": "alice"},
+    user_id="alice",
     rerank=False
 )
 ```
@@ -313,9 +313,9 @@ results_without_rerank = m.search(
 ```python
 results = m.search(
     "important work tasks",
+    user_id="alice",
     filters={
         "AND": [
-            {"user_id": "alice"},
             {"category": "work"},
             {"priority": {"gte": 7}}
         ]
@@ -348,7 +348,8 @@ m = Memory.from_config(config)
 
 results = m.search(
     "customer having login issues with mobile app",
-    filters={"agent_id": "support_bot", "category": "technical_support"},
+    agent_id="support_bot",
+    filters={"category": "technical_support"},
     rerank=True
 )
 ```
@@ -362,7 +363,8 @@ results = m.search(
 ```python
 results = m.search(
     "science fiction books with space exploration themes",
-    filters={"user_id": "reader123", "content_type": "book_recommendation"},
+    user_id="reader123",
+    filters={"content_type": "book_recommendation"},
     rerank=True,
     top_k=10
 )
@@ -381,9 +383,9 @@ for result in results["results"]:
 ```python
 results = m.search(
     "What restaurants did I enjoy last month that had good vegetarian options?",
+    user_id="foodie_user",
     filters={
         "AND": [
-            {"user_id": "foodie_user"},
             {"category": "dining"},
             {"rating": {"gte": 4}},
             {"date": {"gte": "2024-01-01"}}

docs/open-source/features/rest-api.mdx

@@ -13,10 +13,6 @@ The Mem0 REST API server exposes every OSS memory operation over HTTP. Run it al
   - You plan to explore or debug endpoints through the built-in OpenAPI page at `/docs`.
 </Info>
 
-<Warning>
-  **First time self-hosting, or upgrading from a pre-1.x build?** Start at [Self-Hosted Setup](/open-source/setup). It walks through the stack, the setup wizard, and the upgrade path for deployments that relied on open endpoints or `ADMIN_API_KEY`. This page covers the API surface and auth modes only.
-</Warning>
-
 <Warning>
   **OSS vs Platform API paths:** The self-hosted OSS server does **not** use the `/v1/` prefix. For example, the endpoint is `POST /memories`, not `POST /v1/memories/`. The [API Reference](/api-reference) documents the hosted platform at `api.mem0.ai` which uses `/v1/` paths — those do not apply to the OSS server.
 </Warning>
@@ -30,7 +26,7 @@ The Mem0 REST API server exposes every OSS memory operation over HTTP. Run it al
 ## Feature
 
 - **CRUD endpoints:** Create, retrieve, search, update, delete, and reset memories by `user_id`, `agent_id`, or `run_id`.
-- **Authentication:** On by default. Dashboard sessions use JWTs; programmatic clients use per-user `X-API-Key` headers. Legacy `ADMIN_API_KEY` is still supported.
+- **API key authentication:** Optionally secure all endpoints with a shared API key via the `X-API-Key` header.
 - **Status health check:** Access base routes to confirm the server is online.
 - **OpenAPI explorer:** Visit `/docs` for interactive testing and schema reference.
 
@@ -44,75 +40,51 @@ The Mem0 REST API server exposes every OSS memory operation over HTTP. Run it al
   <Tab title="Steps">
 1. Create `server/.env` with your keys:
 
-    ```bash
-    OPENAI_API_KEY=your-openai-api-key
-    JWT_SECRET=$(openssl rand -base64 48)
-    ```
+```bash
+OPENAI_API_KEY=your-openai-api-key
+```
 
-2. Bootstrap the stack in one command:
+2. Start the stack:
 
-    ```bash
-    cd server
-    make bootstrap    # starts Compose, creates an admin, issues the first API key
-    ```
+```bash
+cd server
+docker compose up
+```
 
-    Or to start the stack only and finish setup via the browser wizard at http://localhost:3000:
-
-    ```bash
-    cd server
-    docker compose up -d
-    ```
-
-3. API is at `http://localhost:8888`. Code edits auto-reload.
+3. Reach the API at `http://localhost:8888`. Edits to the server or library auto-reload.
   </Tab>
 </Tabs>
 
-<AccordionGroup>
-  <Accordion title="Other install paths">
-    **Run with Docker**
+### Run with Docker
 
-    <Tabs>
-      <Tab title="Pull image">
+<Tabs>
+  <Tab title="Pull image">
 ```bash
 docker pull mem0/mem0-api-server
 ```
-      </Tab>
-      <Tab title="Build locally">
+  </Tab>
+  <Tab title="Build locally">
 ```bash
 docker build -t mem0-api-server .
 ```
-      </Tab>
-    </Tabs>
+  </Tab>
+</Tabs>
 
-    1. Create a `.env` file with `OPENAI_API_KEY` and `JWT_SECRET`.
-    2. Run the container:
+1. Create a `.env` file with `OPENAI_API_KEY`.  
+2. Run the container:
 
-    ```bash
-    docker run -p 8000:8000 --env-file .env mem0-api-server
-    ```
+```bash
+docker run -p 8000:8000 --env-file .env mem0-api-server
+```
 
-    3. Visit `http://localhost:8000`.
+3. Visit `http://localhost:8000`.
 
-    **Run directly (no Docker)**
+### Run directly (no Docker)
 
-    <Warning>
-      This path skips Docker and assumes Postgres is already running and reachable at `POSTGRES_HOST:POSTGRES_PORT`. For a single-command local setup with Postgres included, use Docker Compose above.
-    </Warning>
-
-    ```bash
-    pip install -r requirements.txt
-    uvicorn main:app --reload
-    ```
-  </Accordion>
-</AccordionGroup>
-
-<Note>
-  Compose publishes internal port 8000 as 8888 on the host. Raw Docker and raw uvicorn listen on 8000 unless remapped.
-</Note>
-
-<Note>
-  `JWT_SECRET` is required once auth is enabled — the server returns `500` on auth endpoints if it's unset. Generate one with `openssl rand -base64 48`. See [Self-Hosted Setup](/open-source/setup#configure-the-environment) for the full env var table.
-</Note>
+```bash
+pip install -r requirements.txt
+uvicorn main:app --reload
+```
 
 <Tip>
   Use a process manager such as `systemd`, Supervisor, or PM2 when deploying the FastAPI server for production resilience.
@@ -126,74 +98,35 @@ docker build -t mem0-api-server .
 
 ## Authentication
 
-Auth is on by default. Protected endpoints require either a JWT (from the dashboard login flow) or an `X-API-Key` header. The `/` redirect, `/docs`, and `/openapi.json` routes stay open so you can reach the OpenAPI explorer.
+The server supports optional API key authentication. When the `ADMIN_API_KEY` environment variable is set, every endpoint requires a valid `X-API-Key` header. The `/` redirect, `/docs`, and `/openapi.json` routes remain open so you can always reach the interactive API explorer.
 
-| Mode | How to send it | When to use it |
-|---|---|---|
-| Bearer JWT | `Authorization: Bearer <access_token>` | Dashboard sessions; tokens come from `POST /auth/login` and refresh via `POST /auth/refresh` |
-| Per-user API key | `X-API-Key: m0sk_...` | Programmatic access scoped to a single dashboard user |
-| Legacy `ADMIN_API_KEY` | `X-API-Key: <env value>` | Back-compat for deployments that set the `ADMIN_API_KEY` env var |
-| `AUTH_DISABLED=true` | — | Local development only; bypasses auth entirely |
+| `ADMIN_API_KEY` value | Behavior |
+|---|---|
+| Not set / empty | All endpoints are open (no auth) |
+| Any non-empty string | Requests must include `X-API-Key: <your-key>` |
 
-The `/docs` OpenAPI explorer supports both auth modes. Click **Authorize** at the top of the page and paste either `Bearer <access_token>` (JWT) or your `X-API-Key` value. Protected endpoints return `401` until you authorize.
+### Enable authentication
 
-### Log in and use a JWT
-
-Register the first admin (only works when no user exists yet), then log in:
+Add the key to your `.env` file:
 
 ```bash
-# First admin only — returns 403 after the first admin is registered
-curl -X POST http://localhost:8888/auth/register \
-  -H "Content-Type: application/json" \
-  -d '{"name": "Admin", "email": "admin@example.com", "password": "strong-password"}'
+ADMIN_API_KEY=your-secret-api-key
 ```
 
-```bash
-curl -X POST http://localhost:8888/auth/login \
-  -H "Content-Type: application/json" \
-  -d '{"email": "admin@example.com", "password": "your-password"}'
-```
-
-Use the returned `access_token` as a bearer token:
+Then include the header in every request:
 
 ```bash
-curl -X POST http://localhost:8888/memories \
+curl -X POST http://localhost:8000/memories \
   -H "Content-Type: application/json" \
-  -H "Authorization: Bearer <access_token>" \
+  -H "X-API-Key: your-secret-api-key" \
   -d '{
     "messages": [{"role": "user", "content": "I love pizza."}],
     "user_id": "alice"
   }'
 ```
 
-When the access token expires, exchange the refresh token at `POST /auth/refresh`.
-
-### Create and use a per-user API key
-
-Create a key from the dashboard **API Keys** page, or call `POST /api-keys` with a JWT. The full `m0sk_...` value is returned **once** at creation time — store it securely.
-
-```bash
-curl -X POST http://localhost:8888/memories \
-  -H "Content-Type: application/json" \
-  -H "X-API-Key: m0sk_your_key_here" \
-  -d '{
-    "messages": [{"role": "user", "content": "I love pizza."}],
-    "user_id": "alice"
-  }'
-```
-
-Per-user keys inherit the creating user's scope. List or revoke them via `GET /api-keys` and `DELETE /api-keys/{id}`.
-
-### Legacy `ADMIN_API_KEY`
-
-Set the `ADMIN_API_KEY` environment variable and send it as `X-API-Key`. The request is treated as admin-level and is not tied to a dashboard user. This mode is kept for back-compat with older self-hosted deployments — prefer JWT or per-user keys for new setups.
-
-```bash
-ADMIN_API_KEY=your-long-admin-key
-```
-
 <Warning>
-  Setting `AUTH_DISABLED=true` makes every protected endpoint open — the server logs a warning at startup when it's enabled. The server also warns when `ADMIN_API_KEY` is shorter than 16 characters. Never enable `AUTH_DISABLED` in production, and always use a long `ADMIN_API_KEY` if you rely on the legacy fallback.
+  The server logs a warning at startup when `ADMIN_API_KEY` is not set. Always set it in production.
 </Warning>
 
 ---
@@ -203,7 +136,7 @@ ADMIN_API_KEY=your-long-admin-key
 ### Create and search memories via HTTP
 
 ```bash
-curl -X POST http://localhost:8888/memories \
+curl -X POST http://localhost:8000/memories \
   -H "Content-Type: application/json" \
   -d '{
     "messages": [
@@ -218,7 +151,7 @@ curl -X POST http://localhost:8888/memories \
 </Info>
 
 ```bash
-curl -X POST http://localhost:8888/search \
+curl -X POST http://localhost:8000/search \
   -H "Content-Type: application/json" \
   -d '{
     "query": "vegetable",
@@ -228,7 +161,7 @@ curl -X POST http://localhost:8888/search \
 
 ### Explore with OpenAPI docs
 
-1. Navigate to `http://localhost:8888/docs` (Compose) or `http://localhost:8000/docs` (raw Docker / uvicorn).
+1. Navigate to `http://localhost:8000/docs`.
 2. Pick an endpoint (e.g., `POST /search`).  
 3. Fill in parameters and click **Execute** to try requests in-browser.
 
@@ -242,13 +175,9 @@ curl -X POST http://localhost:8888/search \
 
 The OSS REST server exposes the following endpoints. None use the `/v1/` prefix.
 
-### Memory operations
-
 | Method | Path | Description |
 |--------|------|-------------|
-| `POST` | `/configure` | Set memory configuration. Rejects unbundled providers with a 400 |
-| `GET` | `/configure` | Get the current memory configuration |
-| `GET` | `/configure/providers` | List the LLM and embedder providers bundled in the container |
+| `POST` | `/configure` | Set memory configuration |
 | `POST` | `/memories` | Create memories |
 | `GET` | `/memories` | Get all memories (filter by `user_id`, `agent_id`, or `run_id`) |
 | `GET` | `/memories/{memory_id}` | Get a specific memory |
@@ -259,43 +188,6 @@ The OSS REST server exposes the following endpoints. None use the `/v1/` prefix.
 | `POST` | `/search` | Search memories |
 | `POST` | `/reset` | Reset all memories |
 
-### Authentication
-
-| Method | Path | Description |
-|--------|------|-------------|
-| `GET` | `/auth/setup-status` | Returns `{needsSetup: bool}`. Open, no auth required |
-| `POST` | `/auth/register` | Register the first admin. Registration closes after the first admin is created; additional accounts are provisioned by the existing admin. |
-| `POST` | `/auth/login` | Exchange email and password for access and refresh JWTs |
-| `POST` | `/auth/refresh` | Exchange a refresh token for a new access token |
-| `GET` | `/auth/me` | Get the current authenticated user (JWT required) |
-| `PATCH` | `/auth/me` | Update the caller's name or email. 409 if the new email is already in use |
-| `POST` | `/auth/change-password` | Change the caller's password. 401 if the current password is wrong; new password must be at least 8 characters |
-
-### API keys
-
-All `/api-keys` endpoints require a JWT.
-
-| Method | Path | Description |
-|--------|------|-------------|
-| `GET` | `/api-keys` | List the caller's API keys |
-| `POST` | `/api-keys` | Create a new key; the full `m0sk_...` value is returned once |
-| `DELETE` | `/api-keys/{id}` | Revoke an API key |
-
-### Request logs
-
-| Method | Path | Description |
-|--------|------|-------------|
-| `GET` | `/requests?limit=N` | Recent API call log (JWT or admin key) |
-
-### Entities
-
-| Method | Path | Description |
-|--------|------|-------------|
-| `GET` | `/entities` | Distinct `user_id` / `agent_id` / `run_id` values with memory counts |
-| `DELETE` | `/entities/{entity_type}/{entity_id}` | Cascade-delete all memories for an entity; `entity_type` is `user`, `agent`, or `run` |
-
-The `/auth/*`, `/api-keys`, `/requests`, and `/entities` routes are new to the self-hosted server and primarily back the dashboard, but you can call them directly from your own tooling.
-
 ---
 
 ## Verify the feature is working
@@ -309,7 +201,7 @@ The `/auth/*`, `/api-keys`, `/requests`, and `/entities` routes are new to the s
 
 ## Best practices
 
-1. **Keep auth on:** Auth is enabled by default. Never set `AUTH_DISABLED=true` in production. If you rely on `ADMIN_API_KEY`, use a long value (16+ chars) or prefer per-user API keys.
+1. **Enable authentication:** Set `ADMIN_API_KEY` to secure all endpoints, or use an API gateway for more advanced schemes.
 2. **Use HTTPS:** Terminate TLS at your load balancer or reverse proxy.
 3. **Monitor uptime:** Track request rates, latency, and error codes per endpoint.
 4. **Version configs:** Keep environment files and Docker Compose definitions in source control.

docs/open-source/node-quickstart.mdx

@@ -76,6 +76,7 @@ By default the Node SDK uses local-friendly settings (OpenAI `gpt-5-mini`, `text
 import { Memory } from "mem0ai/oss";
 
 const memory = new Memory({
+  version: "v1.1",
   embedder: {
     provider: "openai",
     config: {
@@ -220,6 +221,7 @@ Mem0 offers granular configuration across vector stores, LLMs, embedders, and hi
 | Parameter | Description | Default |
 | --- | --- | --- |
 | `historyDbPath` | Path to history database | `"{mem0_dir}/history.db"` |
+| `version` | API version | `"v1.0"` |
 | `customInstructions` | Custom processing prompt | `undefined` |
   </Accordion>
   <Accordion title="History store">
@@ -232,6 +234,7 @@ Mem0 offers granular configuration across vector stores, LLMs, embedders, and hi
   <Accordion title="Complete config example">
 ```ts
 const config = {
+  version: "v1.1",
   embedder: {
     provider: "openai",
     config: {

docs/open-source/overview.mdx

@@ -8,6 +8,10 @@ icon: "house"
 
 Mem0 Open Source delivers the same adaptive memory engine as the platform, but packaged for teams that need to run everything on their own infrastructure. You own the stack, the data, and the customizations.
 
+<Tip>
+  Mem0 v1.0.0 brought rerankers, async-by-default clients, and Azure OpenAI support. See the <Link href="/changelog">release notes</Link> for the full rundown before upgrading.
+</Tip>
+
 ## What Mem0 OSS provides
 
 - **Full control**: Tune every component, from LLMs to vector stores, inside your environment.
@@ -15,15 +19,12 @@ Mem0 Open Source delivers the same adaptive memory engine as the platform, but p
 - **Extendable codebase**: Fork the repo, add providers, and ship custom automations.
 
 <Info>
-  Two ways to run Mem0 OSS: as a **library** inside your app (Python or Node), or as a **self-hosted server** with a dashboard, per-user API keys, and a request audit log.
+  Begin with the <Link href="/open-source/python-quickstart">Python quickstart</Link> (or the Node.js variant) to clone the repo, configure dependencies, and validate memory reads/writes locally.
 </Info>
 
 ## Choose your path
 
-<CardGroup cols={3}>
-  <Card title="Self-hosted setup" icon="rocket-launch" href="/open-source/setup">
-    Run `make bootstrap` to launch the server + dashboard, create an admin, and issue your first API key.
-  </Card>
+<CardGroup cols={2}>
   <Card title="Python Quickstart" icon="python" href="/open-source/python-quickstart">
     Bootstrap CLI and verify add/search loop.
   </Card>
@@ -44,6 +45,15 @@ Mem0 Open Source delivers the same adaptive memory engine as the platform, but p
   </Card>
 </CardGroup>
 
+<CardGroup cols={2}>
+  <Card title="Deploy with Docker Compose" icon="server" href="/open-source/features/rest-api">
+    Reference deployment with REST endpoints.
+  </Card>
+  <Card title="Use the REST API" icon="code" href="/open-source/features/rest-api">
+    Async add/search flows and automation.
+  </Card>
+</CardGroup>
+
 <Tip>
   Need a managed alternative? Compare hosting models in the <Link href="/platform/platform-vs-oss">Platform vs OSS guide</Link> or switch tabs to the Platform documentation.
 </Tip>
@@ -64,28 +74,20 @@ Mem0 Open Source delivers the same adaptive memory engine as the platform, but p
 ## Default components
 
 <Note>
-  **Library defaults** (when you `import` Mem0 and call `Memory()` directly):
+  Mem0 OSS works out of the box with sensible defaults:
   - LLM: OpenAI `gpt-5-mini` (via `OPENAI_API_KEY`)
   - Embeddings: OpenAI `text-embedding-3-small`
-  - Vector store: Local Qdrant at `/tmp/qdrant`
-  - History store: SQLite at `~/.mem0/history.db`
-  - Reranker: Disabled until configured
+  - Vector store: Local Qdrant instance storing data at `/tmp/qdrant`
+  - History store: SQLite database at `~/.mem0/history.db`
+  - Reranker: Disabled until you configure a provider
 
   Override any component with <Link href="/open-source/configuration">`Memory.from_config`</Link>.
 </Note>
 
-<Note>
-  **Self-hosted server defaults** (the `server/` Docker Compose stack):
-  - LLM: OpenAI `gpt-4.1-nano-2025-04-14` (override with `MEM0_DEFAULT_LLM_MODEL`)
-  - Embeddings: OpenAI `text-embedding-3-small` (override with `MEM0_DEFAULT_EMBEDDER_MODEL`)
-  - Vector store: Postgres + pgvector
-  - Bundled providers: `openai`, `anthropic`, `gemini` — switch from the Configuration page
-
-  See <Link href="/open-source/setup#supported-providers">Self-Hosted Setup</Link> for the full provider list and how to extend it.
-</Note>
-
 ## Keep going
 
+{/* DEBUG: verify CTA targets */}
+
 <CardGroup cols={2}>
   <Card
     title="Review Platform vs OSS"
@@ -100,3 +102,7 @@ Mem0 Open Source delivers the same adaptive memory engine as the platform, but p
     href="/open-source/python-quickstart"
   />
 </CardGroup>
+
+<Tip>
+  Need a managed alternative? Compare hosting models in the <Link href="/platform/platform-vs-oss">Platform vs OSS guide</Link> or switch tabs to the Platform documentation.
+</Tip>

docs/open-source/setup.mdx

@@ -1,218 +0,0 @@
----
-title: "Self-Hosted Setup"
-description: "Stand up the Mem0 REST server and dashboard in a few minutes — admin account, API keys, and a live audit log included."
-icon: "rocket-launch"
----
-
-The self-hosted bundle ships the REST API and a web dashboard together. Configure your LLM provider and secrets in a `.env` file, start the containers, then choose how to create your admin account: through the browser-based setup wizard, or from the command line.
-
-<Info>
-  **Use this page when…**
-  - You want a self-hosted Mem0 with a dashboard, not just the Python or Node library.
-  - You need per-user API keys and a request audit log for your team.
-  - You're upgrading from a pre-1.x server that relied on `ADMIN_API_KEY` or open endpoints.
-</Info>
-
-<Warning>
-  **Upgrading from 1.x?** Auth is now on by default. Deployments that ran with an empty `ADMIN_API_KEY` will return `401` on every protected endpoint until you either set `ADMIN_API_KEY`, register an admin through the wizard, or set `AUTH_DISABLED=true` for local development. See [Upgrade notes](#upgrade-notes) below.
-</Warning>
-
----
-
-## Prerequisites
-
-- Docker and Docker Compose (the reference path).
-- An `OPENAI_API_KEY` (or equivalent — the server reads the same component config as the library).
-- A free port `8888` for the API and `3000` for the dashboard.
-
----
-
-## Configure the environment
-
-Copy `server/.env.example` to `server/.env` and fill in the required values. The server refuses to start if `JWT_SECRET` is unset once auth is enabled.
-
-| Variable | Required | Purpose |
-|---|---|---|
-| `OPENAI_API_KEY` | Yes | Default LLM and embedder provider. |
-| `JWT_SECRET` | Yes | Signs access and refresh tokens. Use a long random value. A missing secret causes auth endpoints to return `500`. |
-| `ADMIN_API_KEY` | Optional | Legacy shared admin key. Kept for back-compat; prefer per-user keys for new setups. |
-| `AUTH_DISABLED` | Optional | `true` turns off auth for local development only. Never enable in production. |
-| `DASHBOARD_URL` | Optional | Origin the API accepts for CORS. Defaults to `http://localhost:3000`. Set this when you front the dashboard on a custom domain. |
-| `POSTGRES_*` | Optional | Override the bundled Postgres / pgvector connection. |
-
-<Tip>
-  Generate a `JWT_SECRET` with `openssl rand -base64 48` or `python -c "import secrets; print(secrets.token_urlsafe(48))"`.
-</Tip>
-
----
-
-## Start the stack
-
-Pick the path that fits your workflow.
-
-### Browser-first (setup wizard)
-
-```bash
-cd server
-make up
-```
-
-This starts the containers and runs database migrations. The REST API listens on `http://localhost:8888` and the dashboard on `http://localhost:3000`.
-
-Open `http://localhost:3000` — since no admin account exists yet, the dashboard redirects to the one-time setup wizard at `/setup`. See [Run the setup wizard](#run-the-setup-wizard) below.
-
-### Agent-first (command line)
-
-First, set `OPENAI_API_KEY` (or `ANTHROPIC_API_KEY` / `GOOGLE_API_KEY`) in `server/.env`. `make bootstrap` does not prompt for it, and the runtime test will fail without a valid provider key.
-
-```bash
-cd server
-make bootstrap
-```
-
-`make bootstrap` starts the same containers, then automatically creates the admin account and generates the first API key via the CLI. The admin credentials and API key are printed to your terminal — no browser required.
-
-You can override the generated credentials:
-
-```bash
-make bootstrap EMAIL=admin@company.com PASSWORD='strong-password' NAME='Admin'
-```
-
-Because `make bootstrap` already creates the admin, the setup wizard is skipped. Opening `http://localhost:3000` takes you straight to the login page.
-
-<Tip>
-  For machine-readable output (useful in CI), run `OUTPUT=json make seed` after `make up`.
-</Tip>
-
----
-
-## Run the setup wizard
-
-<Info>
-  This section applies to the **browser-first** path (`make up`). If you used `make bootstrap`, the admin and API key were already created — skip ahead to [What the dashboard gives you](#what-the-dashboard-gives-you).
-</Info>
-
-On a fresh install the dashboard redirects to `/setup`. Each step submits on Enter.
-
-**1. Create the admin account.** Name, email, password. This account becomes the first admin. Registration closes after the first admin is created; additional accounts are provisioned by the existing admin.
-
-**2. Review the effective config.** Read-only display of the LLM and embedder the server is running with, sourced from your environment. If anything is wrong here, stop the stack, fix the `.env`, and restart — the dashboard intentionally does not let you change provider secrets at runtime.
-
-**3. Generate your first API key.** The full `m0sk_...` value is shown **once**. Copy it immediately — the server only stores the prefix and a bcrypt hash.
-
-**4. Tell us your use case.** Pick a preset or describe your use case in a few words. Mem0 generates custom instructions that tell the memory system what to prioritize. You can edit the instructions before saving, or skip this step entirely.
-
-**5. Test the key.** A ready-to-paste `curl` exercises `POST /memories` against your new key. Click "Run Test" to fire it from the browser. Success lands you in the dashboard at `/dashboard/requests`, where you'll see the test call in the live audit log.
-
----
-
-## What the dashboard gives you
-
-| Page | What it does |
-|---|---|
-| **Requests** | Default landing page. Live audit log of every API call, with status, latency, and auth mode. |
-| **Memories** | Browse and search the memories your server has stored. |
-| **Entities** | Distinct `user_id` / `agent_id` / `run_id` values with memory counts and cascade-delete. |
-| **API Keys** | Issue per-user keys, label them, and revoke. |
-| **Configuration** | Runtime override for LLM and embedder. Changes persist to the app database and reapply on restart, layered over the values from your `.env`. |
-| **Settings** | Account and session controls. |
-
-For the underlying endpoints (including `/auth/*`, `/api-keys`, `/requests`, `/entities`), see the [REST API reference](/open-source/features/rest-api).
-
----
-
-## Supported providers
-
-The shipped container bundles the Python packages for:
-
-- **LLMs** — `openai`, `anthropic`, `gemini`
-- **Embedders** — `openai`, `gemini`
-
-The Configuration page and `POST /configure` only accept providers from these lists. Anything else returns a 400 up front instead of failing at the first memory write.
-
-**To add another provider**, for example to run embeddings locally with `sentence-transformers`:
-
-1. Add the package to `server/requirements.txt` (e.g. `sentence-transformers>=2.0`).
-2. Extend `BUNDLED_LLM_PROVIDERS` or `BUNDLED_EMBEDDER_PROVIDERS` in `server/main.py`.
-3. Rebuild the image (`make up` or `docker compose build`).
-
-Heavy providers (`sentence-transformers` pulls in PyTorch, ~2 GB) are intentionally kept out of the default image.
-
----
-
-## Upgrade notes
-
-### Upgrading from a pre-auth build
-
-Previous self-hosted builds allowed open access when `ADMIN_API_KEY` was unset. This build enables auth by default. After pulling the new image, pick **one**:
-
-1. **Fastest, zero client changes** — set `ADMIN_API_KEY` to a long random value (16+ characters). Existing clients that send `X-API-Key: <your-key>` keep working unchanged.
-2. **Recommended for teams** — visit `http://<host>:3000`, run the setup wizard, and switch clients to per-user API keys. You get the audit log and revocation for free.
-3. **Local development only** — set `AUTH_DISABLED=true`. The server logs a warning on every boot. Never use this in production.
-
-The server prints an unmissable startup banner when it detects the "upgraded but not configured" state so you know exactly which option to pick.
-
-### Other changes in this release
-
-- Dashboard ships as a second container in the reference Compose stack, wired to the API over the internal Docker network.
-- New tables: `users`, `api_keys`, `request_logs`. Alembic handles the migration automatically on first boot.
-
-If `alembic upgrade head` fails on first boot, see the [Troubleshooting](#troubleshooting) section below.
-
----
-
-## Troubleshooting
-
-<AccordionGroup>
-  <Accordion title="Port 3000 or 8888 is already in use">
-    Find the owning process on either port:
-    ```bash
-    lsof -iTCP:3000 -sTCP:LISTEN
-    lsof -iTCP:8888 -sTCP:LISTEN
-    ```
-    Kill it (`kill <PID>`) or change the host port in `server/docker-compose.yaml`.
-  </Accordion>
-
-  <Accordion title="JWT_SECRET is required">
-    The server refuses to start without one. Generate a secret and add it to `server/.env`:
-    ```bash
-    echo "JWT_SECRET=$(openssl rand -base64 48)" >> server/.env
-    ```
-    `AUTH_DISABLED=true` is valid for local dev only, never production.
-  </Accordion>
-
-  <Accordion title=".env changes aren't applied after editing">
-    `docker compose restart` does not re-read `env_file`. To pick up changes:
-    ```bash
-    cd server && docker compose up -d --force-recreate mem0
-    # or
-    cd server && make up
-    ```
-  </Accordion>
-
-  <Accordion title="Provider returns 401 (bad API key)">
-    Provider credential errors surface as `502 Upstream provider error.`. Check `docker compose logs mem0` for the full trace, then fix the key on the Configuration page and hit **Save**.
-  </Accordion>
-
-  <Accordion title="Alembic migrations fail on startup">
-    Inspect the logs:
-    ```bash
-    docker compose logs mem0 | grep -i alembic
-    ```
-    If the database is unrecoverable, reset the volume (**this destroys all memories and users**):
-    ```bash
-    docker compose down -v
-    ```
-  </Accordion>
-</AccordionGroup>
-
----
-
-<CardGroup cols={2}>
-  <Card title="REST API reference" icon="code" href="/open-source/features/rest-api">
-    Endpoint tables, auth modes, and example requests.
-  </Card>
-  <Card title="Configure components" icon="sliders" href="/open-source/configuration">
-    Swap LLMs, embedders, vector stores, and rerankers.
-  </Card>
-</CardGroup>

docs/platform/features/group-chat.mdx

@@ -3,6 +3,8 @@ title: Group Chat
 description: 'Enable multi-participant conversations with automatic memory attribution to individual speakers'
 ---
 
+<Snippet file="paper-release.mdx" />
+
 ## Overview
 
 The Group Chat feature enables Mem0 to process conversations involving multiple participants and automatically attribute memories to individual speakers. This allows for precise tracking of each participant's preferences, characteristics, and contributions in collaborative discussions, team meetings, or multi-agent conversations.

docs/platform/features/mcp-integration.mdx

@@ -53,7 +53,7 @@ For detailed per-client instructions, see the [Mem0 MCP Quickstart](/platform/me
 
 ## Available tools
 
-The MCP server exposes 11 memory tools to your AI client:
+The MCP server exposes 9 memory tools to your AI client:
 
 | Tool | Purpose |
 |------|---------|
@@ -66,8 +66,6 @@ The MCP server exposes 11 memory tools to your AI client:
 | `delete_entities` | Remove user/agent/app entities |
 | `get_memory` | Retrieve single memory by ID |
 | `list_entities` | View stored entities |
-| `list_events` | List memory operation events with filters and pagination |
-| `get_event_status` | Check the status of an async memory operation by `event_id` |
 
 ## How it works
 

docs/platform/features/memory-decay.mdx

@@ -1,193 +0,0 @@
----
-title: Memory Decay
-description: "Boost recently-used memories and gently dampen stale ones at search time, without filtering anything out."
----
-
-# Memory Decay
-
-Older memories drift in relevance at different speeds. A user's coffee order matters every morning; a one-off project name from last quarter rarely matters again. Memory Decay makes that intuition explicit at search time: every time a memory is returned in a search it gets a small reinforcement, and memories that haven't been touched in a while have their ranking score gently dampened.
-
-It is **a soft ranking bias, never a filter.** Decay never zeroes a candidate out — at worst it scales its score by `0.3×`. Anything that would have surfaced without decay can still surface with decay on, just with a different ranking among similarly-scored results.
-
-<Info>
-  **Use Memory Decay when…**
-  - Search results are crowded with old facts the user no longer cares about.
-  - You want recently-used memories to drift to the top automatically — without writing custom scoring logic.
-  - You want this preference applied per project so cohorts can be compared side-by-side.
-</Info>
-
-<Warning>
-  Memory Decay is **opt-in per project** and **off by default**. Search behavior is bit-identical to today until you turn it on. The toggle applies to v3 search only.
-</Warning>
-
-## How it works
-
-Every memory carries a small piece of bookkeeping: when was it last retrieved, and how often. Memory Decay turns that history into a *scaling factor* in the range `0.3×` to `1.5×` and multiplies it into the ranking score at search time.
-
-| Memory state | Scaling factor | Ranking effect |
-|---|---|---|
-| Just accessed | ≈ **1.5×** | Strong boost |
-| Touched today | 1.2 – 1.4× | Mild boost |
-| Idle for a few days | 0.6 – 1.0× | Mild dampening |
-| Idle for weeks | 0.4 – 0.6× | Stronger dampening |
-| Idle for many months / years | ≈ **0.3×** | Floor — never lower |
-
-The bounds matter: `0.3` is the floor and `1.5` is the ceiling, so decay can meaningfully reorder candidates without ever dominating the underlying relevance score.
-
-At search time the pipeline:
-
-1. Widens the candidate pool (`top_k × 3`, with a floor of 50) so reordering has room.
-2. Multiplies each candidate's score by its scaling factor.
-3. Sorts on the unclamped product so the full `0.3×–1.5×` range can rearrange candidates.
-4. Returns the public `score` clamped to `[0, 1]` so the API contract is preserved.
-5. Truncates to the `top_k` you requested.
-6. Records a fire-and-forget reinforcement against each returned memory — its access history grows by one, capped at the most recent 20 touches.
-
-Memories created before decay was enabled don't yet have an access history. They use a sensible fallback: their `updated_at` is treated as a single past touch, so the same scale above applies based on how stale that update is — a recently-updated legacy memory enters near the neutral band, a long-stale one sits closer to the floor. Once surfaced in a search after decay is on, they accumulate access history naturally and behave like any other memory.
-
-## Configure access
-
-- Set `MEM0_API_KEY` in your environment, or pass it to the SDK constructor.
-- Initialize the client with the organization and project you want to scope to.
-
-The toggle lives on the project. You enable decay by patching the project's `decay` field; everything else — your `add` calls, your `search` calls, your application code — stays exactly the same.
-
-## Enable decay for a project
-
-### 1. Turn the flag on
-
-The toggle is exposed on the standard project-update endpoint, the same place where `multilingual` and `custom_categories` live.
-
-<CodeGroup>
-```bash cURL
-curl -X PATCH https://api.mem0.ai/api/v1/orgs/organizations/$ORG_ID/projects/$PROJECT_ID/ \
-  -H "Authorization: Token $MEM0_API_KEY" \
-  -H "Content-Type: application/json" \
-  -d '{"decay": true}'
-```
-
-```python Python
-import os
-import requests
-
-org_id = os.environ["MEM0_ORG_ID"]
-project_id = os.environ["MEM0_PROJECT_ID"]
-
-requests.patch(
-    f"https://api.mem0.ai/api/v1/orgs/organizations/{org_id}/projects/{project_id}/",
-    headers={"Authorization": f"Token {os.environ['MEM0_API_KEY']}"},
-    json={"decay": True},
-)
-```
-
-```javascript Node.js
-const res = await fetch(
-  `https://api.mem0.ai/api/v1/orgs/organizations/${process.env.MEM0_ORG_ID}/projects/${process.env.MEM0_PROJECT_ID}/`,
-  {
-    method: "PATCH",
-    headers: {
-      Authorization: `Token ${process.env.MEM0_API_KEY}`,
-      "Content-Type": "application/json",
-    },
-    body: JSON.stringify({ decay: true }),
-  },
-);
-```
-
-```json Response
-{ "message": "Updated decay" }
-```
-</CodeGroup>
-
-### 2. Confirm the state
-
-`decay` is returned on every project read. To fetch only this field, use `?fields=decay`.
-
-<CodeGroup>
-```bash cURL
-curl "https://api.mem0.ai/api/v1/orgs/organizations/$ORG_ID/projects/$PROJECT_ID/?fields=decay" \
-  -H "Authorization: Token $MEM0_API_KEY"
-```
-
-```json Response
-{ "decay": true }
-```
-</CodeGroup>
-
-### 3. Turn it back off
-
-The toggle is fully reversible. Setting it to `false` immediately restores the pre-decay ranking; nothing about your stored memories is modified or lost.
-
-<CodeGroup>
-```bash cURL
-curl -X PATCH https://api.mem0.ai/api/v1/orgs/organizations/$ORG_ID/projects/$PROJECT_ID/ \
-  -H "Authorization: Token $MEM0_API_KEY" \
-  -H "Content-Type: application/json" \
-  -d '{"decay": false}'
-```
-</CodeGroup>
-
-<Note>
-  The toggle is idempotent. Re-applying the same value is a no-op, and access history accumulated while decay was on is preserved if you flip it back on later.
-</Note>
-
-## What changes when decay is on
-
-- **Search ranking reorders.** A relevant memory you reinforced an hour ago will tend to outrank an equally-relevant memory that was last touched a month ago.
-- **The candidate pool over-fetches** to give the scaling factor room to reorder. You still get exactly the `top_k` you requested, but the items returned can come from a deeper slice of the pre-decay ranking than before.
-- **The public `score` field stays in `[0, 1]`.** Even when the internal product exceeds 1, the field returned to the client is clamped, so existing assertions and downstream UI logic continue to work.
-
-## What stays the same
-
-- **Public API shape** — every endpoint accepts the same parameters and returns the same fields. You don't touch your client code.
-- **Threshold semantics on the request side** — your `threshold` is still applied during candidate selection.
-- **Memory creation and storage** — every new memory still lands the same way. Decay is a search-time concern.
-- **Per-memory data** — categories, metadata, timestamps, embeddings: untouched.
-
-<Warning>
-  Because the scaling factor is applied *after* the threshold filter has already run, an item that passed the request `threshold` can come back with a public `score` slightly below it (a stale candidate dampened by `0.3×`). This is intentional — decay is a soft bias, not a filter. If you require a hard `score >= threshold` invariant on the response, filter client-side after the call.
-</Warning>
-
-## Lifecycle of a memory under decay
-
-| Stage | Scaling factor | Effect |
-|---|---|---|
-| Just added | ≈ 1.5× | Strong boost — fresh facts surface easily. |
-| Reinforced on a recent search | 1.2 – 1.5× | Sustains its boost for the next several searches. |
-| Idle for a few days | 0.6 – 1.0× | Falls back into the neutral band. |
-| Idle for weeks | 0.4 – 0.6× | Mild dampening — can still surface for strong matches. |
-| Pre-decay legacy memory (no access history) | 0.3 – 1.0× | Falls back to `updated_at`: recently-updated entries land near 1.0×, long-stale entries approach the 0.3× floor. |
-
-The reinforcement is bounded: each memory tracks at most the last 20 access timestamps, so the boost stays well-behaved no matter how many times a memory is retrieved.
-
-## FAQ
-
-**Will decay ever drop a result that would otherwise surface?**
-No. The floor is `0.3×` — the scaling factor can dampen a score, never zero it. Threshold filtering happens *before* decay, so any candidate that cleared the threshold is in the pool decay reorders.
-
-**Why is the public score sometimes below my requested threshold?**
-The threshold is applied to the candidate pool pre-decay; the scaling factor then reshapes scores in the `0.3×–1.5×` band. A stale-but-relevant candidate can come back with a final score slightly under your threshold by design — the candidate stays visible but visibly dampened. Filter client-side if you need a hard floor on the response.
-
-**Does decay change how I add memories?**
-No. The `client.add(...)` path is unchanged. Decay is a search-time ranking adjustment.
-
-**What if I had memories before turning decay on?**
-They use a fallback: the memory's `updated_at` is treated as a single historical touch, so the same scaling applies based on how stale that update is — a recently-updated legacy memory enters near the neutral band (~1.0×), a long-stale one closer to the floor (~0.3×). Once retrieved they accumulate access history and behave like any other memory.
-
-**Can I tune how aggressively decay scales scores?**
-Not in this version. The current scaling is calibrated to be conservative — wide enough to meaningfully reorder candidates, narrow enough to never dominate the underlying relevance score. Per-project tuning is on the roadmap.
-
-**Can I see the scaling factor per result?**
-Internal scoring details are persisted on the search Event for support and debugging. They aren't exposed in the public response by design — the response surface stays a single `score` field.
-
-**Does decay interact with reranking?**
-Yes — they layer cleanly. The reranker produces a richer relevance score; decay then biases that score by reinforcement history before final truncation to `top_k`.
-
-## What's next
-
-This release is deliberately the simplest version of decay we could ship — every memory contributes to ranking through its access history alone, so the signal can be evaluated in isolation. On the roadmap:
-
-- **Category-aware weighting.** A fact tagged `health` will be able to carry more weight than a passing observation tagged `misc`, so important categories don't get dampened the same way as noise.
-- **Auto-tuning per project.** Project-scoped automatic adjustment of how aggressively decay scales scores, based on observed access patterns — replacing the fixed scaling band with one that fits your workload.
-
-Both extensions are forward-compatible — no migration on your side will be needed when they ship.

docs/platform/features/v2-memory-filters.mdx

@@ -15,6 +15,10 @@ When working with large-scale memory stores, you need precise control over which
 * **Time-based queries**: Retrieve memories within specific date ranges
 * **Performance optimization**: Reduce query complexity by pre-filtering
 
+<Callout type="info" icon="info-circle" color="#7A5DFF">
+Filters were introduced in v1.0.0 to provide precise control over memory retrieval.
+</Callout>
+
 ## Filter structure
 
 Filters use a nested JSON structure with logical operators at the root:

docs/platform/mem0-mcp.mdx

@@ -7,10 +7,10 @@ estimatedTime: "~2 minutes"
 
 <Info>
   **Prerequisites**
-  - Mem0 Platform account (<a href="https://app.mem0.ai?utm_source=oss&utm_medium=platform-mem0-mcp" rel="nofollow">Sign up here</a>)
-  - API key (<a href="https://app.mem0.ai/settings/api-keys?utm_source=oss&utm_medium=platform-mem0-mcp" rel="nofollow">Get one from dashboard</a>)
+  - Mem0 Platform account (<a href="https://app.mem0.ai" rel="nofollow">Sign up here</a>)
+  - API key (<a href="https://app.mem0.ai/settings/api-keys" rel="nofollow">Get one from dashboard</a>)
   - Node.js 14+ (for npx)
-  - An MCP-compatible client (Claude, Claude Code, Codex, Cursor, Windsurf, VS Code, OpenCode)
+  - An MCP-compatible client (Claude, Claude Code, Cursor, Windsurf, VS Code, OpenCode)
 </Info>
 
 ## What is Mem0 MCP?
@@ -46,8 +46,6 @@ The MCP server exposes these memory tools to your AI client:
 | `delete_all_memories` | Bulk delete all memories in scope |
 | `delete_entities` | Delete a user/agent/app/run entity and its memories |
 | `list_entities` | Enumerate users/agents/apps/runs stored in Mem0 |
-| `list_events` | List memory operation events with filters and pagination |
-| `get_event_status` | Check the status of an async memory operation by `event_id` |
 
 ---
 
@@ -88,33 +86,6 @@ You can also configure individual clients:
     ```
   </Accordion>
 
-  <Accordion title="Codex">
-    **Direct MCP (fastest, MCP only).** Codex reads MCP servers from `~/.codex/config.toml` as TOML (not JSON). Add:
-
-    ```toml
-    [mcp_servers.mem0]
-    url = "https://mcp.mem0.ai/mcp"
-    bearer_token_env_var = "MEM0_API_KEY"
-    ```
-
-    Export `MEM0_API_KEY` in the shell you launch Codex from, then restart Codex. `codex mcp add` only supports stdio servers, so HTTP servers must be added via `config.toml` directly — or via the **Plugins → Connect to a custom MCP → Streamable HTTP** UI in the Codex app.
-
-    <Note>
-      Codex uses the server name `mem0` (not `mem0-mcp` like the other clients on this page) so it matches the name the bundled plugin registers if you ever sideload it later.
-    </Note>
-
-    **Sideloaded plugin (full experience).** If you want the memory protocol skill, Mem0 SDK skill, and opt-in lifecycle hooks alongside the MCP server, sideload the plugin from a clone of `mem0ai/mem0`. The repo ships a marketplace manifest at `.agents/plugins/marketplace.json`, so you can register it with one CLI call:
-
-    ```bash
-    git clone https://github.com/mem0ai/mem0.git ~/codex-plugins/mem0-source
-    codex plugin marketplace add ~/codex-plugins/mem0-source
-    ```
-
-    Then run `codex` and `/plugins`, browse the **Mem0 Plugins** marketplace, and install **Mem0**. Don't combine this with the Direct MCP setup above — the sideloaded plugin auto-registers `mem0` via `.codex-mcp.json`, so a manual `[mcp_servers.mem0]` block would create a duplicate.
-
-    See the [Codex integration guide](/integrations/codex) for full details, lifecycle-hook setup, and management commands (`codex plugin marketplace upgrade` / `remove`).
-  </Accordion>
-
   <Accordion title="Cursor">
     ```bash
     npx mcp-add \
@@ -192,7 +163,7 @@ Agent: Updated your project status successfully.
 ```
 
 <Info icon="check">
-  If you get "Connection failed", ensure you have a valid API key from <a href="https://app.mem0.ai/settings/api-keys?utm_source=oss&utm_medium=platform-mem0-mcp" rel="nofollow">Mem0 Dashboard</a>.
+  If you get "Connection failed", ensure you have a valid API key from <a href="https://app.mem0.ai/settings/api-keys" rel="nofollow">Mem0 Dashboard</a>.
 </Info>
 
 ---
@@ -200,7 +171,7 @@ Agent: Updated your project status successfully.
 ## Quick Recovery
 
 - **"Connection refused"** → Check your internet connection and ensure the MCP client is correctly configured
-- **"Invalid API key"** → Get a new key from <a href="https://app.mem0.ai/settings/api-keys?utm_source=oss&utm_medium=platform-mem0-mcp" rel="nofollow">Mem0 Dashboard</a>
+- **"Invalid API key"** → Get a new key from <a href="https://app.mem0.ai/settings/api-keys" rel="nofollow">Mem0 Dashboard</a>
 - **"npx command not found"** → Install Node.js from [nodejs.org](https://nodejs.org)
 
 ---

docs/platform/overview.mdx

@@ -8,6 +8,10 @@ icon: "cloud"
 
 Mem0 is the memory engine that keeps conversations contextual so users never repeat themselves and your agents respond with continuity. Mem0 Platform delivers that experience as a fully managed service—scaling, securing, and enriching memories without any infrastructure work on your side.
 
+<Tip>
+  Mem0 v1.0.0 shipped rerankers, async-by-default behavior, and Azure OpenAI support. Catch the full list of changes in the <Link href="/changelog">release notes</Link>.
+</Tip>
+
 ## Why it matters
 
 - **Personalized replies**: Memories persist across users and agents, cutting prompt bloat and repeat questions.
@@ -60,7 +64,7 @@ Mem0 is the memory engine that keeps conversations contextual so users never rep
   <Card title="Connect Integrations" icon="plug" href="/integrations">
     LangChain, CrewAI, Vercel AI SDK.
   </Card>
-  <Card title="Monitor in the Dashboard" icon="presentation" href="https://app.mem0.ai/login?utm_source=oss&utm_medium=platform-overview">
+  <Card title="Monitor in the Dashboard" icon="presentation" href="https://app.mem0.ai/login">
     Track activity and manage workspaces.
   </Card>
 </CardGroup>

docs/platform/platform-vs-oss.mdx

@@ -150,7 +150,7 @@ Mem0 offers two powerful ways to add memory to your AI applications. Choose base
   <Card
     title="Try Platform Free"
     icon="rocket"
-    href="https://app.mem0.ai/login?utm_source=oss&utm_medium=platform-vs-oss"
+    href="https://app.mem0.ai/login"
   >
     Sign up and test the Platform with our free tier. No credit card required.
   </Card>

docs/platform/quickstart.mdx

@@ -9,7 +9,7 @@ Get started with Mem0 Platform's hosted API in under 5 minutes. This guide shows
 
 ## Prerequisites
 
-- Mem0 Platform account (<a href="https://app.mem0.ai?utm_source=oss&utm_medium=platform-quickstart" rel="nofollow">Sign up here</a>)
+- Mem0 Platform account (<a href="https://app.mem0.ai" rel="nofollow">Sign up here</a>)
 - API key (<a href="https://app.mem0.ai/dashboard/settings?tab=api-keys&subtab=configuration" rel="nofollow">Get one from dashboard</a>)
 - Python 3.10+, Node.js 14+, or cURL