chore: remove integration page from the docs

2026-04-20 21:15:23 +05:30
413 changed files with 16573 additions and 30245 deletions
@@ -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"
    }
  ]
 }
@@ -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"
    }
  ]
 }
@@ -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/
@@ -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

@@ -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

@@ -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:
@@ -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": {
@@ -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 {
@@ -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;
@@ -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)}`);
@@ -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
@@ -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) {
@@ -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) {
@@ -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")}`,
 			);
 		}
 	}
@@ -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 {
@@ -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,
 		});
 	});
@@ -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");
  });
 });

@@ -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());
@@ -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);
+  });
 });
@@ -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"
@@ -1,3 +1,3 @@
 """mem0 CLI — the command-line interface for the mem0 memory layer."""

-__version__ = "0.2.4"
+__version__ = "0.2.3"
@@ -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,
    )

@@ -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
@@ -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)
@@ -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:
@@ -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
@@ -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}")
@@ -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))
@@ -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")

@@ -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,
@@ -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"])
@@ -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()
@@ -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):
@@ -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.
@@ -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>
-
@@ -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>
-
@@ -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>
@@ -4,67 +4,6 @@ description: "Release notes for the OpenClaw plugin and agent harness."
 mode: "wide"
 ---

-<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:**
@@ -7,23 +7,6 @@ mode: "wide"
 <Tabs>
 <Tab title="Python">

-<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.
@@ -910,17 +893,6 @@ See the [OSS v1 to v2 migration guide](https://docs.mem0.ai/migration/oss-v1-to-
 </Tab>

 <Tab title="TypeScript">
-<Update label="2026-04-25" 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:**
@@ -1297,16 +1269,6 @@ 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:**
@@ -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
@@ -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>

 ---
@@ -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

@@ -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:
@@ -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.)
@@ -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
@@ -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>

 ---

@@ -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>"

@@ -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)

 ---
@@ -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)

@@ -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/expiration-date">Expiration Policies</Link> to automate retention.

 ## See it live

@@ -236,6 +236,6 @@ memory.delete_all(user_id="alice")
    title="Enable Expiration Policies"
    description="Automate retention with the platform’s expiration feature."
    icon="clock"
-    href="/platform/features/platform-overview"
+    href="/platform/features/expiration-date"
  />
 </CardGroup>
@@ -153,7 +153,6 @@
                    "icon": "rocket",
                    "pages": [
                      "open-source/overview",
-                      "open-source/setup",
                      "vibecoding",
                      "open-source/python-quickstart",
                      "open-source/node-quickstart"
@@ -400,8 +399,6 @@
                      "integrations/langgraph",
                      "integrations/llama-index",
                      "integrations/crewai",
-                      "integrations/autogen",
-                      "integrations/agno",
                      "integrations/camel-ai",
                      "integrations/openai-agents-sdk",
                      "integrations/google-ai-adk",
@@ -430,12 +427,7 @@
                    "group": "Developer Tools",
                    "icon": "wrench",
                    "pages": [
-                      "integrations/dify",
-                      "integrations/flowise",
-                      "integrations/langchain-tools",
-                      "integrations/agentops",
-                      "integrations/keywords",
-                      "integrations/raycast"
+                      "integrations/langchain-tools"
                    ]
                  }
                ]
@@ -579,7 +571,7 @@
    "primary": {
      "type": "button",
      "label": "Your Dashboard",
-      "href": "https://app.mem0.ai?utm_source=oss&utm_medium=docs-nav"
+      "href": "https://app.mem0.ai"
    }
  },
  "footer": {
@@ -609,7 +601,7 @@
        "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"
      }
    ]
  },
@@ -1102,6 +1094,34 @@
      "source": "/v0x/faqs",
      "destination": "/platform/faqs"
    },
+    {
+      "source": "/integrations/raycast",
+      "destination": "/integrations"
+    },
+    {
+      "source": "/integrations/autogen",
+      "destination": "/integrations"
+    },
+    {
+      "source": "/integrations/keywords",
+      "destination": "/integrations"
+    },
+    {
+      "source": "/integrations/agentops",
+      "destination": "/integrations"
+    },
+    {
+      "source": "/integrations/flowise",
+      "destination": "/integrations"
+    },
+    {
+      "source": "/integrations/agno",
+      "destination": "/integrations"
+    },
+    {
+      "source": "/integrations/dify",
+      "destination": "/integrations"
+    },
    {
      "source": "/integrations/multion",
      "destination": "/integrations"
@@ -20,23 +20,6 @@ Here are the available integrations for Mem0:
 ## Integrations

 <CardGroup cols={2}>
-  <Card
-    title="AgentOps"
-    icon={
-      <svg
-        xmlns="http://www.w3.org/2000/svg"
-        width="25"
-        height="26"
-        viewBox="0 0 30 36"
-        fill="none"
-      >
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-      </svg>
-    }
-    href="/integrations/agentops"
-  >
-    Monitor and analyze Mem0 operations with comprehensive AI agent analytics and LLM observability.
-  </Card>
  <Card
    title="Camel AI"
    href="/integrations/camel-ai"
@@ -103,27 +86,6 @@ Here are the available integrations for Mem0:
  >
    Build RAG applications with LlamaIndex and Mem0.
  </Card>
-  <Card
-    title="AutoGen"
-    icon={
-      <svg
-        xmlns="http://www.w3.org/2000/svg"
-        width="24"
-        height="24"
-        viewBox="0 0 96 85"
-        fill="none"
-      >
-        <rect width="96" height="85" rx="6" fill="#2D2D2F" />
-        <path
-          d="M32.6484 28.7109L23.3672 57H15.8906L28.5703 22.875H33.3281L32.6484 28.7109ZM40.3594 57L31.0547 28.7109L30.3047 22.875H35.1094L47.8594 57H40.3594ZM39.9375 44.2969V49.8047H21.9141V44.2969H39.9375ZM77.6484 39.1641V52.6875C77.1172 53.3281 76.2969 54.0234 75.1875 54.7734C74.0781 55.5078 72.6484 56.1406 70.8984 56.6719C69.1484 57.2031 67.0312 57.4688 64.5469 57.4688C62.3438 57.4688 60.3359 57.1094 58.5234 56.3906C56.7109 55.6562 55.1484 54.5859 53.8359 53.1797C52.5391 51.7734 51.5391 50.0547 50.8359 48.0234C50.1328 45.9766 49.7812 43.6406 49.7812 41.0156V38.8828C49.7812 36.2578 50.1172 33.9219 50.7891 31.875C51.4766 29.8281 52.4531 28.1016 53.7188 26.6953C54.9844 25.2891 56.4922 24.2188 58.2422 23.4844C59.9922 22.75 61.9375 22.3828 64.0781 22.3828C67.0469 22.3828 69.4844 22.8672 71.3906 23.8359C73.2969 24.7891 74.75 26.1172 75.75 27.8203C76.7656 29.5078 77.3906 31.4453 77.625 33.6328H70.8047C70.6328 32.4766 70.3047 31.4688 69.8203 30.6094C69.3359 29.75 68.6406 29.0781 67.7344 28.5938C66.8438 28.1094 65.6875 27.8672 64.2656 27.8672C63.0938 27.8672 62.0469 28.1094 61.125 28.5938C60.2188 29.0625 59.4531 29.7578 58.8281 30.6797C58.2031 31.6016 57.7266 32.7422 57.3984 34.1016C57.0703 35.4609 56.9062 37.0391 56.9062 38.8359V41.0156C56.9062 42.7969 57.0781 44.375 57.4219 45.75C57.7656 47.1094 58.2734 48.2578 58.9453 49.1953C59.6328 50.1172 60.4766 50.8125 61.4766 51.2812C62.4766 51.75 63.6406 51.9844 64.9688 51.9844C66.0781 51.9844 67 51.8906 67.7344 51.7031C68.4844 51.5156 69.0859 51.2891 69.5391 51.0234C70.0078 50.7422 70.3672 50.4766 70.6172 50.2266V44.1797H64.1953V39.1641H77.6484Z"
-          fill="white"
-        />
-      </svg>
-    }
-    href="/integrations/autogen"
-  >
-    Build multi-agent systems with persistent memory capabilities.
-  </Card>
  <Card
    title="CrewAI"
    icon={
@@ -205,26 +167,6 @@ Here are the available integrations for Mem0:
  >
    Use Mem0 with LangChain Tools for enhanced agent capabilities.
  </Card>
-  <Card
-    title="Dify"
-    icon={
-      <svg
-        xmlns="http://www.w3.org/2000/svg"
-        width="24"
-        height="24"
-        viewBox="0 0 200 200"
-        fill="none"
-      >
-        <path
-          d="M40 20 H120 C160 20, 160 180, 120 180 H40 V20"
-          fill="currentColor"
-        />
-      </svg>
-    }
-    href="/integrations/dify"
-  >
-    Build AI applications with persistent memory using Dify and Mem0.
-  </Card>
  <Card
    title="Livekit"
    icon={
@@ -290,63 +232,6 @@ Here are the available integrations for Mem0:
  >
    Build conversational AI agents with memory using Pipecat.
  </Card>
-  <Card
-    title="Agno"
-    icon={
-      <svg
-        xmlns="http://www.w3.org/2000/svg"
-        width="24"
-        height="24"
-        viewBox="0 0 24 24"
-        fill="none"
-      >
-        <path d="M8 4h8v12h8" stroke="currentColor" strokeWidth="2" fill="none" transform="rotate(15, 12, 12)"/>
-      </svg>
-    }
-    href="/integrations/agno"
-  >
-    Build autonomous agents with memory using Agno framework.
-  </Card>
-
-  <Card
-    title="Keywords AI"
-    icon={
-      <svg
-        xmlns="http://www.w3.org/2000/svg"
-        width="24"
-        height="24"
-        viewBox="0 0 24 24"
-        fill="none"
-      >
-        <path fill-rule="evenodd" clip-rule="evenodd" d="M9.07513 1.1863C9.21663 1.07722 9.39144 1.01009 9.56624 1.01009C9.83261 1.01009 10.0823 1.12756 10.2405 1.33734L15.0101 7.4964V12.4136L16.4335 13.8401C16.7582 14.1673 16.7582 14.7043 16.4335 15.0316C16.1089 15.3588 15.5762 15.3588 15.2515 15.0316L13.3453 13.1016V8.07538L8.92529 2.36944V2.36105C8.64228 2.00024 8.70887 1.4716 9.07513 1.1863ZM18.976 14.4133C18.8344 14.3778 18.7003 14.3042 18.5894 14.1925L16.9163 12.5059C16.7249 12.3129 16.6416 12.0528 16.6749 11.8094V6.88385H16.6499L11.8553 0.691225C11.7282 0.529117 11.6716 0.333133 11.6803 0.140562C11.134 0.0481292 10.5726 0 10 0C4.47715 0 0 4.47715 0 10C0 15.5228 4.47715 20 10 20C13.9387 20 17.3456 17.7229 18.976 14.4133Z" fill="currentColor"></path>
-      </svg>
-    }
-    href="/integrations/keywords"
-  >
-    Build AI applications with persistent memory and comprehensive LLM observability.
-  </Card>
-  <Card
-    title="Raycast"
-    icon={
-      <svg
-        xmlns="http://www.w3.org/2000/svg"
-        width="24"
-        height="24"
-        viewBox="0 0 24 24"
-        fill="none"
-      >
-        <path
-          d="M3 12L21 12M12 3L12 21M7.5 7.5L16.5 16.5M16.5 7.5L7.5 16.5"
-          stroke="currentColor"
-          strokeWidth="2"
-          strokeLinecap="round"
-        />
-      </svg>
-    }
-    href="/integrations/raycast"
-  >
-    Mem0 Raycast extension for intelligent memory management and retrieval.
-  </Card>
  <Card
    title="Mastra"
    icon={
@@ -395,13 +280,6 @@ Here are the available integrations for Mem0:
  >
    Integrate Mem0 with Google Agent Development Kit for persistent memory across multi-agent workflows.
  </Card>
-  <Card
-    title="Flowise"
-    icon="diagram-project"
-    href="/integrations/flowise"
-  >
-    Add persistent Mem0 memory to Flowise chatflows for context-aware conversations in the low-code builder.
-  </Card>
  <Card
    title="AWS Bedrock"
    icon="cloud"
@@ -1,175 +0,0 @@
---
-title: AgentOps
-description: "Integrate Mem0 with AgentOps for automatic monitoring, analytics, and real-time tracking of memory operations."
---
-
-Integrate [**Mem0**](https://github.com/mem0ai/mem0) with [AgentOps](https://agentops.ai), a comprehensive monitoring and analytics platform for AI agents. This integration enables automatic tracking and analysis of memory operations, providing insights into agent performance and memory usage patterns.
-
-## Overview
-
-1. Automatic monitoring of Mem0 operations and performance metrics
-2. Real-time tracking of memory add, search, and retrieval operations
-3. Analytics dashboard with memory usage patterns and insights
-4. Error tracking and debugging capabilities for memory operations
-
-## Prerequisites
-
-Before setting up Mem0 with AgentOps, ensure you have:
-
-1. Installed the required packages:
-```bash
-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)
-
-## Basic Integration Example
-
-The following example demonstrates how to integrate Mem0 with AgentOps monitoring for comprehensive memory operation tracking:
-
-```python
-#Import the required libraries for local memory management with Mem0
-from mem0 import Memory, AsyncMemory
-import os
-import asyncio
-import logging
-from dotenv import load_dotenv
-import agentops
-import openai
-
-load_dotenv()
-#Set up environment variables for API keys
-os.environ["AGENTOPS_API_KEY"] = os.getenv("AGENTOPS_API_KEY")
-os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
-
-#Set up the configuration for local memory storage and define sample user data. 
-local_config = {
-    "llm": {
-        "provider": "openai",
-        "config": {
-            "model": "gpt-5-mini",
-            "temperature": 0.1,
-            "max_tokens": 2000,
-        },
-    }
-}
-user_id = "alice_demo"
-agent_id = "assistant_demo"
-run_id = "session_001"
-
-sample_messages = [
-    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller? They can be quite engaging."},
-    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
-    {
-        "role": "assistant",
-        "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future.",
-    },
-]
-
-sample_preferences = [
-    "I prefer dark roast coffee over light roast",
-    "I exercise every morning at 6 AM",
-    "I'm vegetarian and avoid all meat products",
-    "I love reading science fiction novels",
-    "I work in software engineering",
-]
-
-#This function demonstrates sequential memory operations using the synchronous Memory class
-def demonstrate_sync_memory(local_config, sample_messages, sample_preferences, user_id):
-    """
-    Demonstrate synchronous Memory class operations.
-    """
-
-    agentops.start_trace("mem0_memory_example", tags=["mem0_memory_example"])
-    try:
-        
-        memory = Memory.from_config(local_config)
-
-        result = memory.add(
-            sample_messages, user_id=user_id, metadata={"category": "movie_preferences", "session": "demo"}
-        )
-
-        for i, preference in enumerate(sample_preferences):
-            result = memory.add(preference, user_id=user_id, metadata={"type": "preference", "index": i})
-       
-        search_queries = [
-            "What movies does the user like?",
-            "What are the user's food preferences?",
-            "When does the user exercise?",
-        ]
-
-        for query in search_queries:
-            results = memory.search(query, filters={"user_id": user_id})
-        
-            if results and "results" in results:
-                for j, result in enumerate(results['results']): 
-                    print(f"Result {j+1}: {result.get('memory', 'N/A')}")
-            else:
-                print("No results found")
-
-        all_memories = memory.get_all(filters={"user_id": user_id})
-        if all_memories and "results" in all_memories:
-            print(f"Total memories: {len(all_memories['results'])}")
-
-        delete_all_result = memory.delete_all(user_id=user_id)
-        print(f"Delete all result: {delete_all_result}")
-
-        agentops.end_trace(end_state="success")
-    except Exception as e:
-        agentops.end_trace(end_state="error")
-
-# Execute sync demonstrations
-demonstrate_sync_memory(local_config, sample_messages, sample_preferences, user_id)
-
-```
-
-For detailed information on this integration, refer to the official [Agentops Mem0 integration documentation](https://docs.agentops.ai/v2/integrations/mem0).
-
-
-## Key Features
-
-### 1. Automatic Operation Tracking
-
-AgentOps automatically monitors all Mem0 operations:
-
- **Memory Operations**: Track add, search, get_all, delete operations and much more
- **Performance Metrics**: Monitor response times and success rates
- **Error Tracking**: Capture and analyze operation failures
-
-### 2. Real-time Analytics Dashboard
-
-Access comprehensive analytics through the AgentOps dashboard:
-
- **Usage Patterns**: Visualize memory usage trends over time
- **User Behavior**: Analyze how different users interact with memory
- **Performance Insights**: Identify bottlenecks and optimization opportunities
-
-### 3. Session Management
-
-Organize your monitoring with structured sessions:
-
- **Session Tracking**: Group related operations into logical sessions
- **Success/Failure Rates**: Track session outcomes for reliability monitoring
- **Custom Metadata**: Add context to sessions for better analysis
-
-## Best Practices
-
-1. **Initialize Early**: Always initialize AgentOps before importing Mem0 classes
-2. **Session Management**: Use meaningful session names and end sessions appropriately
-3. **Error Handling**: Wrap operations in try-catch blocks and report failures
-4. **Tagging**: Use tags to organize different types of memory operations
-5. **Environment Separation**: Use different projects or tags for dev/staging/prod
-
-<CardGroup cols={2}>
-  <Card title="CrewAI Integration" icon="users" href="/integrations/crewai">
-    Monitor multi-agent CrewAI systems
-  </Card>
-  <Card title="LangChain Integration" icon="link" href="/integrations/langchain">
-    Track LangChain agent performance
-  </Card>
-</CardGroup>
-
@@ -1,207 +0,0 @@
---
-title: Agno
-description: "Add persistent multimodal memory to Agno-based agents using Mem0 for text and image interactions."
---
-
-This integration of [**Mem0**](https://github.com/mem0ai/mem0) with [Agno](https://github.com/agno-agi/agno) enables persistent, multimodal memory for Agno-based agents - improving personalization, context awareness, and continuity across conversations.
-
-## Overview
-
-1. Store and retrieve memories from Mem0 within Agno agents
-2. Support for multimodal interactions (text and images)
-3. Semantic search for relevant past conversations
-4. Personalized responses based on user history
-5. One-line memory integration via `Mem0Tools`
-
-## Prerequisites
-
-Before setting up Mem0 with Agno, ensure you have:
-
-1. Installed the required packages:
-```bash
-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>
-   - OpenAI API Key (for the agent model)
-
-## Quick Integration (Using `Mem0Tools`)
-
-The simplest way to integrate Mem0 with Agno Agents is to use Mem0 as a tool using built-in `Mem0Tools`:
-
-```python
-from agno.agent import Agent
-from agno.models.openai import OpenAIChat
-from agno.tools.mem0 import Mem0Tools
-
-agent = Agent(
-    name="Memory Agent",
-    model=OpenAIChat(id="gpt-5-mini"),
-    tools=[Mem0Tools()],
-    description="An assistant that remembers and personalizes using Mem0 memory."
-)
-```
-
-This enables memory functionality out of the box:
-
- **Persistent memory writing**: `Mem0Tools` uses `MemoryClient.add(...)` to store messages from user-agent interactions, including optional metadata such as user ID or session.
- **Contextual memory search**: Compatible queries use `MemoryClient.search(...)` to retrieve relevant past messages, improving contextual understanding.
- **Multimodal support**: Both text and image inputs are supported, allowing richer memory records.
-
-> `Mem0Tools` uses the `MemoryClient` under the hood and requires no additional setup. You can customize its behavior by modifying your tools list or extending it in code.
-
-## Full Manual Example
-
-> Note: Mem0 can also be used with Agno Agents as a separate memory layer.
-
-The following example demonstrates how to create an Agno agent with Mem0 memory integration, including support for image processing:
-
-```python
-import base64
-from pathlib import Path
-from typing import Optional
-
-from agno.agent import Agent
-from agno.media import Image
-from agno.models.openai import OpenAIChat
-from mem0 import MemoryClient
-
-# Initialize the Mem0 client
-client = MemoryClient()
-
-# Define the agent
-agent = Agent(
-    name="Personal Agent",
-    model=OpenAIChat(id="gpt-4"),
-    description="You are a helpful personal agent that helps me with day to day activities."
-                "You can process both text and images.",
-    markdown=True
-)
-
-
-def chat_user(
-    user_input: Optional[str] = None,
-    user_id: str = "alex",
-    image_path: Optional[str] = None
-) -> str:
-    """
-    Handle user input with memory integration, supporting both text and images.
-
-    Args:
-        user_input: The user's text input
-        user_id: Unique identifier for the user
-        image_path: Path to an image file if provided
-
-    Returns:
-        The agent's response as a string
-    """
-    if image_path:
-        # Convert image to base64
-        with open(image_path, "rb") as image_file:
-            base64_image = base64.b64encode(image_file.read()).decode("utf-8")
-
-        # Create message objects for text and image
-        messages = []
-
-        if user_input:
-            messages.append({
-                "role": "user",
-                "content": user_input
-            })
-
-        messages.append({
-            "role": "user",
-            "content": {
-                "type": "image_url",
-                "image_url": {
-                    "url": f"data:image/jpeg;base64,{base64_image}"
-                }
-            }
-        })
-
-        # Store messages in memory
-        client.add(messages, user_id=user_id)
-        print("✅ Image and text stored in memory.")
-
-    if user_input:
-        # Search for relevant memories
-        memories = client.search(user_input, filters={"user_id": user_id})
-        memory_context = "\n".join(f"- {m['memory']}" for m in memories['results'])
-
-        # Construct the prompt
-        prompt = f"""
-You are a helpful personal assistant who helps users with their day-to-day activities and keeps track of everything.
-
-Your task is to:
-1. Analyze the given image (if present) and extract meaningful details to answer the user's question.
-2. Use your past memory of the user to personalize your answer.
-3. Combine the image content and memory to generate a helpful, context-aware response.
-
-Here is what I remember about the user:
-{memory_context}
-
-User question:
-{user_input}
-"""
-        # Get response from agent
-        if image_path:
-            response = agent.run(prompt, images=[Image(filepath=Path(image_path))])
-        else:
-            response = agent.run(prompt)
-
-        # Store the interaction in memory
-        interaction_message = [{"role": "user", "content": f"User: {user_input}\nAssistant: {response.content}"}]
-        client.add(interaction_message, user_id=user_id)
-        return response.content
-
-    return "No user input or image provided."
-
-
-# Example Usage
-if __name__ == "__main__":
-    response = chat_user(
-        "I like to travel and my favorite destination is London",
-        image_path="travel_items.jpeg",
-        user_id="alex"
-    )
-    print(response)
-```
-
-## Key Features
-
-### 1. Multimodal Memory Storage
-
-The integration supports storing both text and image data:
-
- **Text Storage**: Conversation history is saved in a structured format
- **Image Analysis**: Agents can analyze images and store visual information
- **Combined Context**: Memory retrieval combines both text and visual data
-
-### 2. Personalized Agent Responses
-
-Improve your agent's context awareness:
-
- **Memory Retrieval**: Semantic search finds relevant past interactions
- **User Preferences**: Personalize responses based on stored user information
- **Continuity**: Maintain conversation threads across multiple sessions
-
-### 3. Flexible Configuration
-
-Customize the integration to your needs:
-
- **Use `Mem0Tools()`** for drop-in memory support
- **Use `MemoryClient` directly** for advanced control
- **User Identification**: Organize memories by user ID
- **Memory Search**: Configure search relevance and result count
- **Memory Formatting**: Support for various OpenAI message formats
-
-<CardGroup cols={2}>
-  <Card title="OpenAI Agents SDK" icon="cube" href="/integrations/openai-agents-sdk">
-    Build agents with OpenAI SDK and Mem0
-  </Card>
-  <Card title="Mastra Integration" icon="star" href="/integrations/mastra">
-    Create intelligent agents with Mastra framework
-  </Card>
-</CardGroup>
-
@@ -1,142 +0,0 @@
---
-title: AutoGen
-description: "Build conversational AI agents with AutoGen and Mem0 for context-aware, personalized interactions."
---
-
-Build conversational AI agents with memory capabilities. This integration combines AutoGen for creating AI agents with Mem0 for memory management, enabling context-aware and personalized interactions.
-
-## Overview
-
-This guide demonstrates creating a conversational AI system with memory. We'll build a customer service bot that can recall previous interactions and provide personalized responses.
-
-## Setup and Configuration
-
-Install necessary libraries:
-
-```bash
-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>
-
-```python
-import os
-from autogen import ConversableAgent
-from mem0 import MemoryClient
-from openai import OpenAI
-from dotenv import load_dotenv
-
-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
-USER_ID = "alice"
-
-# Set up OpenAI API key
-OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY')
-# os.environ['MEM0_API_KEY'] = MEM0_API_KEY
-
-# Initialize Mem0 and AutoGen agents
-memory_client = MemoryClient()
-agent = ConversableAgent(
-    "chatbot",
-    llm_config={"config_list": [{"model": "gpt-4", "api_key": OPENAI_API_KEY}]},
-    code_execution_config=False,
-    human_input_mode="NEVER",
-)
-```
-
-## Storing Conversations in Memory
-
-Add conversation history to Mem0 for future reference:
-
-```python
-conversation = [
-    {"role": "assistant", "content": "Hi, I'm Best Buy's chatbot! How can I help you?"},
-    {"role": "user", "content": "I'm seeing horizontal lines on my TV."},
-    {"role": "assistant", "content": "I'm sorry to hear that. Can you provide your TV model?"},
-    {"role": "user", "content": "It's a Sony - 77\" Class BRAVIA XR A80K OLED 4K UHD Smart Google TV"},
-    {"role": "assistant", "content": "Thank you for the information. Let's troubleshoot this issue..."}
-]
-
-memory_client.add(messages=conversation, user_id=USER_ID)
-print("Conversation added to memory.")
-```
-
-## Retrieving and Using Memory
-
-Create a function to get context-aware responses based on user's question and previous interactions:
-
-```python
-def get_context_aware_response(question):
-    relevant_memories = memory_client.search(question, filters={"user_id": USER_ID})
-    context = "\n".join([m["memory"] for m in relevant_memories.get('results', [])])
-
-    prompt = f"""Answer the user question considering the previous interactions:
-    Previous interactions:
-    {context}
-
-    Question: {question}
-    """
-
-    reply = agent.generate_reply(messages=[{"content": prompt, "role": "user"}])
-    return reply
-
-# Example usage
-question = "What was the issue with my TV?"
-answer = get_context_aware_response(question)
-print("Context-aware answer:", answer)
-```
-
-## Multi-Agent Conversation
-
-For more complex scenarios, you can create multiple agents:
-
-```python
-manager = ConversableAgent(
-    "manager",
-    system_message="You are a manager who helps in resolving complex customer issues.",
-    llm_config={"config_list": [{"model": "gpt-4", "api_key": OPENAI_API_KEY}]},
-    human_input_mode="NEVER"
-)
-
-def escalate_to_manager(question):
-    relevant_memories = memory_client.search(question, filters={"user_id": USER_ID})
-    context = "\n".join([m["memory"] for m in relevant_memories.get('results', [])])
-
-    prompt = f"""
-    Context from previous interactions:
-    {context}
-
-    Customer question: {question}
-
-    As a manager, how would you address this issue?
-    """
-
-    manager_response = manager.generate_reply(messages=[{"content": prompt, "role": "user"}])
-    return manager_response
-
-# Example usage
-complex_question = "I'm not satisfied with the troubleshooting steps. What else can be done?"
-manager_answer = escalate_to_manager(complex_question)
-print("Manager's response:", manager_answer)
-```
-
-## Conclusion
-
-By integrating AutoGen with Mem0, you've created a conversational AI system with memory capabilities. This example demonstrates a customer service bot that can recall previous interactions and provide context-aware responses, with the ability to escalate complex issues to a manager agent.
-
-This integration enables the creation of more intelligent and personalized AI agents for various applications, such as customer support, virtual assistants, and interactive chatbots.
-
-<CardGroup cols={2}>
-  <Card title="CrewAI Integration" icon="users" href="/integrations/crewai">
-    Build multi-agent systems with CrewAI and Mem0
-  </Card>
-  <Card title="LangGraph Integration" icon="diagram-project" href="/integrations/langgraph">
-    Create stateful workflows with LangGraph
-  </Card>
-</CardGroup>
-
@@ -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

@@ -237,7 +237,7 @@ By adding Mem0 as a memory store in ChatDev, your multi-agent workflows gain per
  <Card title="CrewAI Integration" icon="users" href="/integrations/crewai">
    Build multi-agent systems with CrewAI and Mem0
  </Card>
-  <Card title="AutoGen Integration" icon="robot" href="/integrations/autogen">
-    Build conversational agents with AutoGen and Mem0
+  <Card title="OpenAI Agents SDK" icon="robot" href="/integrations/openai-agents-sdk">
+    Build conversational agents with OpenAI Agents SDK and Mem0
  </Card>
 </CardGroup>
@@ -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.

@@ -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">
@@ -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
@@ -162,8 +162,8 @@ if __name__ == "__main__":
 By combining CrewAI with Mem0, you can create sophisticated AI systems that maintain context and provide personalized experiences while leveraging the power of autonomous agents.

 <CardGroup cols={2}>
-  <Card title="AutoGen Integration" icon="users" href="/integrations/autogen">
-    Build multi-agent systems with AutoGen and Mem0
+  <Card title="OpenAI Agents SDK" icon="users" href="/integrations/openai-agents-sdk">
+    Build multi-agent systems with OpenAI Agents SDK and Mem0
  </Card>
  <Card title="LangGraph Integration" icon="diagram-project" href="/integrations/langgraph">
    Create stateful agent workflows with memory
@@ -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))

@@ -1,42 +0,0 @@
---
-title: Dify
-description: "Integrate Mem0 as a plugin in Dify AI workflows for persistent conversation storage and retrieval."
---
-
-# Integrating Mem0 with Dify AI
-
-Mem0 brings a robust memory layer to Dify AI, empowering your AI agents with persistent conversation storage and retrieval capabilities. With Mem0, your Dify applications gain the ability to recall past interactions and maintain context, ensuring more natural and insightful conversations.
-
---
-
-## How to Integrate Mem0 in Your Dify Workflow
-
-1. **Install the Mem0 Plugin:**  
-   Head to the [Dify Marketplace](https://marketplace.dify.ai/plugins/yevanchen/mem0) and install the Mem0 plugin. This is your first step toward adding intelligent memory to your AI applications.
-
-2. **Create or Open Your Dify Project:**  
-   Whether you're starting fresh or updating an existing project, simply create or open your Dify workspace.
-
-3. **Add the Mem0 Plugin to Your Project:**  
-   Within your project, add the Mem0 plugin. This integration connects Mem0’s memory management capabilities directly to your Dify application.
-
-4. **Configure Your Mem0 Settings:**  
-   Customize Mem0 to suit your needs—set preferences for how conversation history is stored, the search parameters, and any other context-aware features.
-
-5. **Leverage Mem0 in Your Workflow:**  
-   Use Mem0 to store every conversation turn and retrieve past interactions seamlessly. This integration ensures that your AI agents can refer back to important context, making multi-turn dialogues more effective and user-centric.
-
---
-
-![Mem0 Dify Integration](/images/dify-mem0-integration.png)
-
-Enhance your Dify-powered AI with Mem0 and transform your conversational experiences. Start integrating intelligent memory management today and give your agents the context they need to excel!
-
-<CardGroup cols={2}>
-  <Card title="Flowise Integration" icon="share-nodes" href="/integrations/flowise">
-    Build visual AI workflows with Flowise
-  </Card>
-  <Card title="LangChain Integration" icon="link" href="/integrations/langchain">
-    Create LangChain-powered applications
-  </Card>
-</CardGroup>
@@ -1,127 +0,0 @@
---
-title: Flowise
-description: "Add persistent Mem0 memory to Flowise chatflows for context-aware conversations in the low-code builder."
---
-
-The [**Mem0 Memory**](https://github.com/mem0ai/mem0) integration with [Flowise](https://github.com/FlowiseAI/Flowise) enables persistent memory capabilities for your AI chatflows. [Flowise](https://flowiseai.com/) is an open-source low-code tool for developers to build customized LLM orchestration flows & AI agents using a drag & drop interface.
-
-## Overview
-
-1. Provides persistent memory storage for Flowise chatflows
-2. Seamless integration with existing Flowise templates
-3. Compatible with various LLM nodes in Flowise
-4. Supports custom memory configurations
-5. Easy to set up and manage
-
-## Prerequisites
-
-Before setting up Mem0 with Flowise, ensure you have:
-
-1. [Flowise installed](https://github.com/FlowiseAI/Flowise#⚡quick-start) (NodeJS >= 18.15.0 required):
-```bash
-npm install -g flowise
-npx flowise start
-```
-
-2. Access to the Flowise UI at http://localhost:3000
-3. Basic familiarity with [Flowise's LLM orchestration](https://flowiseai.com/#features) concepts
-
-## Setup and Configuration
-
-### 1. Set Up Flowise
-
-1. Open the Flowise application and create a new canvas, or select a template from the Flowise marketplace.
-2. In this example, we use the **Conversation Chain** template.
-3. Replace the default **Buffer Memory** with **Mem0 Memory**.
-
-![Flowise Memory Integration](https://raw.githubusercontent.com/FlowiseAI/FlowiseDocs/main/en/.gitbook/assets/mem0/flowise-flow.png)
-
-### 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>.
-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)
-
-### 3. Configure Mem0 Credentials
-
-1. Enter the **Mem0 API Key** in the Mem0 Credentials section.
-2. Configure additional settings as needed:
-
-```typescript
-{
-  "apiKey": "m0-xxx",
-  "userId": "user-123",  // Optional: Specify user ID
-  "projectId": "proj-xxx",  // Optional: Specify project ID
-  "orgId": "org-xxx"  // Optional: Specify organization ID
-}
-```
-
-<figure>
-  <img src="https://raw.githubusercontent.com/FlowiseAI/FlowiseDocs/main/en/.gitbook/assets/mem0/creds.png" alt="Mem0 Credentials" />
-  <figcaption>Configure API Credentials</figcaption>
-</figure>
-
-## Memory Features
-
-### 1. Basic Memory Storage
-
-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>
-
-![Flowise Test Chat](https://raw.githubusercontent.com/FlowiseAI/FlowiseDocs/main/en/.gitbook/assets/mem0/flowise-chat-1.png)
-
-### 2. Memory Retention
-
-Validate memory persistence:
-
-1. Clear the chat history in Flowise
-2. Ask a question about previously stored information
-3. Confirm that the AI remembers the context
-
-![Testing Memory Retention](https://raw.githubusercontent.com/FlowiseAI/FlowiseDocs/main/en/.gitbook/assets/mem0/flowise-chat-2.png)
-
-## Advanced Configuration
-
-### Memory Settings
-
-![Mem0 Settings](https://raw.githubusercontent.com/FlowiseAI/FlowiseDocs/main/en/.gitbook/assets/mem0/settings.png)
-
-Available settings include:
-
-1. **Search Only Mode**: Enable memory retrieval without creating new memories
-2. **Mem0 Entities**: Configure identifiers:
-   - `user_id`: Unique identifier for each user
-   - `run_id`: Specific conversation session ID
-   - `app_id`: Application identifier
-   - `agent_id`: AI agent identifier
-3. **Project ID**: Assign memories to specific projects
-4. **Organization ID**: Organize memories by organization
-
-### 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>:
-
-1. **Custom Instructions**: Define memory extraction rules
-2. **Expiration Date**: Set automatic memory cleanup periods
-
-![Mem0 Project Settings](https://raw.githubusercontent.com/FlowiseAI/FlowiseDocs/main/en/.gitbook/assets/mem0/mem0-settings.png)
-
-## Best Practices
-
-1. **User Identification**: Use consistent `user_id` values for reliable memory retrieval
-2. **Memory Organization**: Utilize projects and organizations for better memory management
-3. **Regular Maintenance**: Monitor and clean up unused memories periodically
-
-<CardGroup cols={2}>
-  <Card title="LangChain Integration" icon="link" href="/integrations/langchain">
-    Build LangChain-powered flows with memory
-  </Card>
-  <Card title="Dify Integration" icon="blocks" href="/integrations/dify">
-    Create AI workflows with Dify platform
-  </Card>
-</CardGroup>
-
@@ -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
@@ -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

@@ -1,142 +0,0 @@
---
-title: Keywords AI
-description: "Combine Mem0 persistent memory with Keywords AI observability for tracked, cost-optimized AI applications."
---
-
-Build AI applications with persistent memory and comprehensive LLM observability by integrating Mem0 with Keywords AI.
-
-## Overview
-
-Mem0 is a self-improving memory layer for LLM applications, enabling personalized AI experiences that save costs and delight users. Keywords AI provides complete LLM observability.
-
-Combining Mem0 with Keywords AI allows you to:
-1. Add persistent memory to your AI applications
-2. Track interactions across sessions
-3. Monitor memory usage and retrieval with Keywords AI observability
-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>.
-</Note>
-
-## Setup and Configuration
-
-Install the necessary libraries:
-
-```bash
-pip install mem0ai keywordsai-sdk
-```
-
-Set up your environment variables:
-
-```python
-import os
-
-# Set your API keys
-os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
-os.environ["KEYWORDSAI_API_KEY"] = "your-keywords-api-key"
-os.environ["KEYWORDSAI_BASE_URL"] = "https://api.keywordsai.co/api/"
-```
-
-## Basic Integration Example
-
-Here's a simple example of using Mem0 with Keywords AI:
-
-```python
-from mem0 import Memory
-import os
-
-# Configuration
-api_key = os.getenv("MEM0_API_KEY")
-keywordsai_api_key = os.getenv("KEYWORDSAI_API_KEY")
-base_url = os.getenv("KEYWORDSAI_BASE_URL") # "https://api.keywordsai.co/api/"
-
-# Set up Mem0 with Keywords AI as the LLM provider
-config = {
-    "llm": {
-        "provider": "openai",
-        "config": {
-            "model": "gpt-5-mini",
-            "temperature": 0.0,
-            "api_key": keywordsai_api_key,
-            "openai_base_url": base_url,
-        },
-    }
-}
-
-# Initialize Memory
-memory = Memory.from_config(config)
-
-# Add a memory
-result = memory.add(
-    "I like to take long walks on weekends.",
-    user_id="alice",
-    metadata={"category": "hobbies"},
-)
-
-print(result)
-```
-
-## Advanced Integration with OpenAI SDK
-
-For more advanced use cases, you can integrate Keywords AI with Mem0 through the OpenAI SDK:
-
-```python
-from openai import OpenAI
-import os
-import json
-
-# Initialize client
-client = OpenAI(
-    api_key=os.environ.get("KEYWORDSAI_API_KEY"),
-    base_url=os.environ.get("KEYWORDSAI_BASE_URL"),
-)
-
-# Sample conversation messages
-messages = [
-    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
-    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
-    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
-]
-
-# Add memory and generate a response
-response = client.chat.completions.create(
-    model="openai/gpt-4.1-nano",
-    messages=messages,
-    extra_body={
-        "mem0_params": {
-            "user_id": "test_user",
-            "api_key": os.environ.get("MEM0_API_KEY"),
-            "add_memories": {
-                "messages": messages,
-            },
-        }
-    },
-)
-
-print(json.dumps(response.model_dump(), indent=4))
-```
-
-For detailed information on this integration, refer to the official [Keywords AI Mem0 integration documentation](https://docs.keywordsai.co/integration/development-frameworks/mem0).
-
-## Key Features
-
-1. **Memory Integration**: Store and retrieve relevant information from past interactions
-2. **LLM Observability**: Track memory usage and retrieval patterns with Keywords AI
-3. **Session Persistence**: Maintain context across multiple user sessions
-4. **Cost Optimization**: Reduce token usage through efficient memory retrieval
-
-## Conclusion
-
-Integrating Mem0 with Keywords AI provides a powerful combination for building AI applications with persistent memory and comprehensive observability. This integration enables more personalized user experiences while providing insights into your application's memory usage.
-
-<CardGroup cols={2}>
-  <Card title="OpenAI Agents SDK" icon="cube" href="/integrations/openai-agents-sdk">
-    Build monitored agents with OpenAI SDK
-  </Card>
-  <Card title="AgentOps Integration" icon="chart-line" href="/integrations/agentops">
-    Monitor agent performance with AgentOps
-  </Card>
-</CardGroup>
-
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -16,7 +16,7 @@ The plugin provides:
 2. **Auto-Capture** — After the agent responds, the exchange is sent to Mem0 which decides what's worth keeping
 3. **Agent Tools** — Eight tools for explicit memory operations during conversations

-Both auto-recall and auto-capture are opt-in (`autoRecall: true`, `autoCapture: true` in config). Once enabled, they run silently with no manual intervention required.
+Both auto-recall and auto-capture run silently with no manual configuration required.

 ## Requirements

@@ -114,7 +114,7 @@ That's it. No API key, no config file editing, no environment variables. The plu
  </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>.
+    Get your API key from <a href="https://app.mem0.ai?utm_source=mem0-docs" rel="nofollow">app.mem0.ai</a>.
  </Step>

  <Step title="Select the plugin as your memory backend in `openclaw.json`">
@@ -147,81 +147,7 @@ OpenClaw treats memory plugins as an exclusive slot. Installing the plugin alone

 ### 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:
+No Mem0 key needed. Requires `OPENAI_API_KEY` for default embeddings/LLM.

 ```json5
 {
@@ -242,7 +168,7 @@ Minimal config — uses OpenAI defaults:
 }
 ```

-To customize providers:
+Sensible defaults work out of the box. To customize the embedder, vector store, or LLM:

 ```json5
 {
@@ -258,8 +184,8 @@ To customize providers:
          "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" } }
+            "vectorStore": { "provider": "qdrant", "config": { "host": "localhost", "port": 6333 } },
+            "llm": { "provider": "openai", "config": { "model": "gpt-4o" } }
          }
        }
      }
@@ -299,8 +225,6 @@ The `memory_search` and `memory_list` tools accept a `scope` parameter (`"sessio

 ## 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"
@@ -314,10 +238,6 @@ 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
 ```

 ## Configuration Options
@@ -328,8 +248,8 @@ openclaw mem0 status --json
 |-----|------|---------|-------------|
 | `mode` | `"platform"` \| `"open-source"` | `"platform"` | Which backend to use |
 | `userId` | `string` | OS username | Scope memories per user |
-| `autoRecall` | `boolean` | `false` | Inject memories before each turn (opt-in) |
-| `autoCapture` | `boolean` | `false` | Store facts after each turn (opt-in) |
+| `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) |

@@ -355,16 +275,18 @@ openclaw mem0 status --json
 | `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

 ### Updating the Plugin

 ```bash
-openclaw plugins update openclaw-mem0
+openclaw plugins update @mem0/openclaw-mem0
 ```

+<Note>Use the npm package name (`@mem0/openclaw-mem0`) for plugin management commands, not the plugin ID (`openclaw-mem0`).</Note>
+
 ### Checking Plugin Status

 ```bash
@@ -409,71 +331,23 @@ If the plugin installs but doesn't work:

 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:
+1. Use the full npm package name: `openclaw plugins update @mem0/openclaw-mem0`
+2. If that fails, uninstall and reinstall:
   ```bash
   openclaw plugins uninstall openclaw-mem0
   openclaw plugins install @mem0/openclaw-mem0
   ```

-## Privacy & Security
+## Key Features

-### Data Flow
+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** — Eight agent tools for explicit memory operations when needed

-| 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) |
+## Conclusion

-### Enabling Auto-Capture and Auto-Recall
-
-Auto-capture and auto-recall are disabled by default (opt-in). To enable either or both:
-
-```json5
-{
-  "plugins": {
-    "entries": {
-      "openclaw-mem0": {
-        "config": {
-          "autoCapture": true,   // send conversations to Mem0 for extraction
-          "autoRecall": true     // inject relevant memories into context
-        }
-      }
-    }
-  }
-}
-```
-
-Without these enabled, the agent can still use memory tools (`memory_add`, `memory_search`, etc.) explicitly — only the automatic background behavior is off.
-
-### 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">
@@ -1,50 +0,0 @@
---
-title: "Raycast Extension"
-description: "Mem0 Raycast extension for intelligent memory management"
---
-
-Mem0 is a self-improving memory layer for LLM applications, enabling personalized AI experiences that save costs and delight users. This extension lets you store and retrieve text snippets using Mem0's intelligent memory system. Find Mem0 in [Raycast Store](https://www.raycast.com/dev_khant/mem0) for using it.
-
-## Getting Started
-
-**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>
-
-b. Navigate to your API Keys page
-
-c. Copy your API key
-
-d. Enter this key in the extension preferences
-
-**Basic Usage**:
-
- Store memories and text snippets
- Retrieve context-aware information
- Manage persistent user preferences
- Search through stored memories
-
-## Features
-
-**Remember Everything**: Never lose important information. Store notes, preferences, and conversations that your AI can recall later.
-
-**Smart Connections**: Automatically links related topics, helping you discover useful connections.
-
-**Cost Saver**: Spend less on AI usage by efficiently retrieving relevant information instead of regenerating responses.
-
-## How This Helps You
-
-**More Personal Experience**: Your AI remembers your preferences and past conversations, making interactions feel more natural.
-
-**Learn Your Style**: Adapts to how you work and what you like, becoming more helpful over time.
-
-**No More Repetition**: Stop explaining the same things repeatedly. Your AI remembers your context and preferences.
-
-<CardGroup cols={2}>
-  <Card title="OpenAI Agents SDK" icon="cube" href="/integrations/openai-agents-sdk">
-    Build desktop AI agents with OpenAI SDK
-  </Card>
-  <Card title="Mastra Integration" icon="star" href="/integrations/mastra">
-    Create intelligent desktop workflows
-  </Card>
-</CardGroup>
@@ -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:

@@ -161,7 +161,6 @@ If the user is on a pre-current major (Python < 2, TS < 3, or Platform `output_f
 - [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.

 ## Core Concepts

@@ -232,8 +231,6 @@ If the user is on a pre-current major (Python < 2, TS < 3, or Platform `output_f
 - [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.
@@ -256,12 +253,6 @@ If the user is on a pre-current major (Python < 2, TS < 3, or Platform `output_f
 ### Cloud & Infrastructure
 - [AWS Bedrock](https://docs.mem0.ai/integrations/aws-bedrock) [Both]: Use when the user is on AWS Bedrock 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.

 ## Cookbooks

@@ -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.

@@ -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.
@@ -15,15 +15,12 @@ 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 +41,15 @@ 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 +70,20 @@ 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 +98,7 @@ 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>
@@ -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>
@@ -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?
@@ -86,33 +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 \
@@ -190,7 +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>

 ---
@@ -198,7 +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)

 ---
@@ -60,7 +60,7 @@ 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>
@@ -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>
@@ -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

@@ -12,7 +12,7 @@ We follow the llms.txt standard:
 - [llms.txt](https://docs.mem0.ai/llms.txt)

 <CardGroup cols={2}>
-  <Card title="Get an API Key" icon="key" href="https://app.mem0.ai/login?utm_source=oss&utm_medium=vibecoding">
+  <Card title="Get an API Key" icon="key" href="https://app.mem0.ai/login">
    Sign up for Mem0 Platform and start building
  </Card>
  <Card title="Quickstart" icon="rocket" href="/platform/quickstart">
@@ -34,7 +34,7 @@ Works with Claude Code, Cursor, Windsurf, and any assistant that supports skills

 Connect Claude, Claude Code, Cursor, Windsurf, VS Code, OpenCode, or any MCP-compatible client to Mem0.

-Get your API key from <a href="https://app.mem0.ai?utm_source=oss&utm_medium=vibecoding" rel="nofollow">app.mem0.ai</a>, then add Mem0 MCP with a single command:
+Get your API key from <a href="https://app.mem0.ai" rel="nofollow">app.mem0.ai</a>, then add Mem0 MCP with a single command:

 ```bash
 npx mcp-add \
@@ -0,0 +1,57 @@
+---
+title: 'Full Stack'
+---
+
+The Full Stack app example can be found [here](https://github.com/mem0ai/mem0/tree/main/embedchain/examples/full_stack).
+
+This guide will help you setup the full stack app on your local machine.
+
+### 🐳 Docker Setup
+
+- Create a `docker-compose.yml` file and paste the following code in it.
+
+```yaml
+version: "3.9"
+
+services:
+  backend:
+    container_name: embedchain-backend
+    restart: unless-stopped
+    build:
+      context: backend
+      dockerfile: Dockerfile
+    image: embedchain/backend
+    ports:
+      - "8000:8000"
+
+  frontend:
+    container_name: embedchain-frontend
+    restart: unless-stopped
+    build:
+      context: frontend
+      dockerfile: Dockerfile
+    image: embedchain/frontend
+    ports:
+      - "3000:3000"
+    depends_on:
+      - "backend"
+```
+
+- Run the following command,
+
+```bash
+docker-compose up
+```
+
+📝 Note: The build command might take a while to install all the packages depending on your system resources.
+
+![Fullstack App](https://github.com/embedchain/embedchain/assets/73601258/c7c04bbb-9be7-4669-a6af-039e7e972a13)
+
+### 🚀 Usage Instructions
+
+- Go to [http://localhost:3000/](http://localhost:3000/) in your browser to view the dashboard.
+- Add your `OpenAI API key` 🔑 in the Settings.
+- Create a new bot and you'll be navigated to its page.
+- Here you can add your data sources and then chat with the bot.
+
+🎉 Happy Chatting! 🎉
@@ -182,6 +182,7 @@
            "examples/rest-api/check-status"
          ]
        },
+        "examples/full_stack",
        "examples/openai-assistant",
        "examples/opensource-assistant",
        "examples/nextjs-assistant",
@@ -17,6 +17,9 @@ Chatbots, especially those powered by Large Language Models (LLMs), have a wide
 Embedchain provides the right set of tools to create chatbots for the above use cases. Refer to the following examples of chatbots on and you can built on top of these examples:

 <CardGroup cols={2}>
+  <Card title="Full Stack Chatbot" href="/examples/full_stack" icon="link">
+    Learn to integrate a chatbot within a full-stack application.
+  </Card>
  <Card title="Custom GPT Creation" href="https://app.embedchain.ai/create-your-gpt/" target="_blank" icon="link">
    Build a tailored GPT chatbot suited for your specific needs.
  </Card>
@@ -1,2 +1,2 @@
-gradio>=4.14.0
+gradio==4.11.0
 embedchain
@@ -1,2 +1,2 @@
 chainlit==0.7.700
-embedchain==0.1.57
+embedchain==0.1.31
@@ -1,3 +1,3 @@
 discord==2.3.1
-embedchain==0.1.57
+embedchain==0.0.58
 python-dotenv==1.0.0
@@ -0,0 +1 @@
+.git
@@ -0,0 +1,18 @@
+## 🐳 Docker Setup
+
+- To setup full stack app using docker, run the following command inside this folder using your terminal.
+
+```bash
+docker-compose up --build
+```
+
+📝 Note: The build command might take a while to install all the packages depending on your system resources.
+
+## 🚀 Usage Instructions
+
+- Go to [http://localhost:3000/](http://localhost:3000/) in your browser to view the dashboard.
+- Add your `OpenAI API key` 🔑 in the Settings.
+- Create a new bot and you'll be navigated to its page.
+- Here you can add your data sources and then chat with the bot.
+
+🎉 Happy Chatting! 🎉
@@ -0,0 +1,7 @@
+__pycache__/
+database
+pyenv
+venv
+.env
+.git
+trash_files/
@@ -0,0 +1,6 @@
+__pycache__
+database
+pyenv
+venv
+.env
+trash_files/
@@ -0,0 +1,11 @@
+FROM python:3.11-slim AS backend
+
+WORKDIR /usr/src/app/backend
+COPY requirements.txt .
+RUN pip install -r requirements.txt
+
+COPY . .
+
+EXPOSE 8000
+
+CMD ["python", "server.py"]
@@ -0,0 +1,14 @@
+from flask_sqlalchemy import SQLAlchemy
+
+db = SQLAlchemy()
+
+
+class APIKey(db.Model):
+    id = db.Column(db.Integer, primary_key=True)
+    key = db.Column(db.String(255), nullable=False)
+
+
+class BotList(db.Model):
+    id = db.Column(db.Integer, primary_key=True)
+    name = db.Column(db.String(255), nullable=False)
+    slug = db.Column(db.String(255), nullable=False, unique=True)
@@ -0,0 +1,5 @@
+import os
+
+ROOT_DIRECTORY = os.getcwd()
+DB_DIRECTORY_OPEN_AI = os.path.join(os.getcwd(), "database", "open_ai")
+DB_DIRECTORY_OPEN_SOURCE = os.path.join(os.getcwd(), "database", "open_source")
@@ -0,0 +1,32 @@
+import os
+
+from flask import Blueprint, jsonify, make_response, request
+from models import APIKey
+from paths import DB_DIRECTORY_OPEN_AI
+
+from embedchain import App
+
+chat_response_bp = Blueprint("chat_response", __name__)
+
+
+# Chat Response for user query
+@chat_response_bp.route("/api/get_answer", methods=["POST"])
+def get_answer():
+    try:
+        data = request.get_json()
+        query = data.get("query")
+        embedding_model = data.get("embedding_model")
+        app_type = data.get("app_type")
+
+        if embedding_model == "open_ai":
+            os.chdir(DB_DIRECTORY_OPEN_AI)
+            api_key = APIKey.query.first().key
+            os.environ["OPENAI_API_KEY"] = api_key
+            if app_type == "app":
+                chat_bot = App()
+
+        response = chat_bot.chat(query)
+        return make_response(jsonify({"response": response}), 200)
+
+    except Exception as e:
+        return make_response(jsonify({"error": str(e)}), 400)
@@ -0,0 +1,72 @@
+from flask import Blueprint, jsonify, make_response, request
+from models import APIKey, BotList, db
+
+dashboard_bp = Blueprint("dashboard", __name__)
+
+
+# Set Open AI Key
+@dashboard_bp.route("/api/set_key", methods=["POST"])
+def set_key():
+    data = request.get_json()
+    api_key = data["openAIKey"]
+    existing_key = APIKey.query.first()
+    if existing_key:
+        existing_key.key = api_key
+    else:
+        new_key = APIKey(key=api_key)
+        db.session.add(new_key)
+    db.session.commit()
+    return make_response(jsonify(message="API key saved successfully"), 200)
+
+
+# Check OpenAI Key
+@dashboard_bp.route("/api/check_key", methods=["GET"])
+def check_key():
+    existing_key = APIKey.query.first()
+    if existing_key:
+        return make_response(jsonify(status="ok", message="OpenAI Key exists"), 200)
+    else:
+        return make_response(jsonify(status="fail", message="No OpenAI Key present"), 200)
+
+
+# Create a bot
+@dashboard_bp.route("/api/create_bot", methods=["POST"])
+def create_bot():
+    data = request.get_json()
+    name = data["name"]
+    slug = name.lower().replace(" ", "_")
+    existing_bot = BotList.query.filter_by(slug=slug).first()
+    if existing_bot:
+        return (make_response(jsonify(message="Bot already exists"), 400),)
+    new_bot = BotList(name=name, slug=slug)
+    db.session.add(new_bot)
+    db.session.commit()
+    return make_response(jsonify(message="Bot created successfully"), 200)
+
+
+# Delete a bot
+@dashboard_bp.route("/api/delete_bot", methods=["POST"])
+def delete_bot():
+    data = request.get_json()
+    slug = data.get("slug")
+    bot = BotList.query.filter_by(slug=slug).first()
+    if bot:
+        db.session.delete(bot)
+        db.session.commit()
+        return make_response(jsonify(message="Bot deleted successfully"), 200)
+    return make_response(jsonify(message="Bot not found"), 400)
+
+
+# Get the list of bots
+@dashboard_bp.route("/api/get_bots", methods=["GET"])
+def get_bots():
+    bots = BotList.query.all()
+    bot_list = []
+    for bot in bots:
+        bot_list.append(
+            {
+                "name": bot.name,
+                "slug": bot.slug,
+            }
+        )
+    return jsonify(bot_list)
--- a/Show More
+++ b/Show More