chore: drop test scaffold and CI workflow to keep PR minimal

Lays the groundwork for Tasks 2-10 of the REST API authorization fix plan.
Adds an in-memory SQLite test engine, a TestClient fixture that
short-circuits Memory.from_config() (pgvector is unavailable in tests),
and reusable fixtures for admin/member users, JWTs, the legacy
ADMIN_API_KEY env, and AUTH_DISABLED mode.

.claude-plugin/marketplace.json

@@ -12,7 +12,7 @@
       "name": "mem0",
       "source": "./mem0-plugin",
       "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search to Claude workflows.",
-      "version": "0.1.0"
+      "version": "0.1.2"
     }
   ]
 }

.cursor-plugin/marketplace.json

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

.github/workflows/ci.yml

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

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

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

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

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

.gitignore

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

AGENTS.md

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

LLM.md

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

Makefile

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

README.md

@@ -39,7 +39,7 @@
 </p>
 
 <p align="center">
-  <a href="https://mem0.ai/research"><strong>📄 Building Production-Ready AI Agents with Scalable Long-Term Memory →</strong></a>
+  <a href="https://mem0.ai/research"><strong>📄 Benchmarking Mem0's token-efficient memory algorithm →</strong></a>
 </p>
 
 ## New Memory Algorithm (April 2026)
@@ -47,7 +47,7 @@
 | Benchmark | Old | New  | Tokens  | Latency p50  |
 | --- | --- | --- | --- | --- |
 | **LoCoMo** | 71.4 | **91.6** | 7.0K  | 0.88s  |
-| **LongMemEval** | 67.8 | **93.4** | 6.8K  | 1.09s  |
+| **LongMemEval** | 67.8 | **94.8** | 6.8K  | 1.09s  |
 | **BEAM (1M)** | — | **64.1** | 6.7K  | 1.00s  |
 | **BEAM (10M)** | — | **48.6** | 6.9K  | 1.05s  |
 
@@ -58,12 +58,13 @@ All benchmarks run on the same production-representative model stack. Single-pas
 - **Agent-generated facts are first-class** -- when an agent confirms an action, that information is now stored with equal weight.
 - **Entity linking** -- entities are extracted, embedded, and linked across memories for retrieval boosting.
 - **Multi-signal retrieval** -- semantic, BM25 keyword, and entity matching scored in parallel and fused.
+- **Temporal Reasoning** -- time-aware retrieval that ranks the right dated instance for queries about current state, past events, and upcoming plans.
 
 See the [migration guide](https://docs.mem0.ai/migration/oss-v2-to-v3) for upgrade instructions. The [evaluation framework](https://github.com/mem0ai/memory-benchmarks) is open-sourced so anyone can reproduce the numbers.
 
 ## Research Highlights
 - **91.6 on LoCoMo** -- +20 points over the previous algorithm
-- **93.4 on LongMemEval** -- +26 points, with +53.6 on assistant memory recall
+- **94.8 on LongMemEval** -- +27 points, with +53.6 on assistant memory recall
 - **64.1 on BEAM (1M)** -- production-scale memory evaluation at 1M tokens
 - [Read the full paper](https://mem0.ai/research)
 
@@ -85,18 +86,17 @@ See the [migration guide](https://docs.mem0.ai/migration/oss-v2-to-v3) for upgra
 
 ## 🚀 Quickstart Guide <a name="quickstart"></a>
 
-Choose between our hosted platform or self-hosted package:
+| | 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 |
 
-### Hosted Platform
+Just testing? Use the library. Building for a team? Self-hosted. Want zero ops? Cloud.
 
-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:
+### Library (pip / npm)
 
 ```bash
 pip install mem0ai
@@ -110,10 +110,30 @@ 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:
@@ -128,6 +148,27 @@ mem0 search "What does Alice prefer?" --user-id alice
 
 See the [CLI documentation](https://docs.mem0.ai/platform/cli) for the full command reference.
 
+### Agent Skills
+
+Teach your AI coding assistant (Claude Code, Codex, Cursor, Windsurf, OpenCode, OpenClaw, and any tool that supports the skills standard) how to build with Mem0. Two categories:
+
+**Reference skills — always on** (SDK knowledge loaded into the assistant's context):
+
+```bash
+npx skills add https://github.com/mem0ai/mem0 --skill mem0
+npx skills add https://github.com/mem0ai/mem0 --skill mem0-cli
+npx skills add https://github.com/mem0ai/mem0 --skill mem0-vercel-ai-sdk
+```
+
+**Pipeline skills — run on demand** (execute an end-to-end workflow in an existing repo):
+
+```bash
+npx skills add https://github.com/mem0ai/mem0 --skill mem0-integrate
+npx skills add https://github.com/mem0ai/mem0 --skill mem0-test-integration
+```
+
+Use `/mem0-integrate` to wire Mem0 into an existing repo via a test-first pipeline, then `/mem0-test-integration` to verify. See the [skills catalog](./skills/) or [Vibecoding with Mem0](https://docs.mem0.ai/vibecoding) for the full picture.
+
 ### Basic Usage
 
 Mem0 requires an LLM to function, with `gpt-5-mini` from OpenAI as the default. However, it supports a variety of LLMs; for details, refer to our [Supported LLMs documentation](https://docs.mem0.ai/components/llms/overview).

cli/node/package.json

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

cli/node/src/backend/base.ts

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

cli/node/src/backend/platform.ts

@@ -115,10 +115,9 @@ 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", "/v1/memories/", {
+		return (await this._request("POST", "/v3/memories/add/", {
 			json: payload,
 		})) as Record<string, unknown>;
 	}
@@ -176,10 +175,9 @@ 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", "/v2/memories/search/", {
+		const result = (await this._request("POST", "/v3/memories/search/", {
 			json: payload,
 		})) as unknown;
 		if (Array.isArray(result)) return result;
@@ -227,10 +225,9 @@ 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", "/v2/memories/", {
+		const result = (await this._request("POST", "/v3/memories/", {
 			json: payload,
 			params,
 		})) as unknown;

cli/node/src/branding.ts

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

cli/node/src/commands/config.ts

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

cli/node/src/commands/init.ts

@@ -185,7 +185,7 @@ function promptLine(label: string, defaultValue?: string): Promise<string> {
 async function setupPlatform(config: Mem0Config): Promise<void> {
 	console.log();
 	console.log(
-		`  ${dim("Get your API key at https://app.mem0.ai/dashboard/api-keys")}`,
+		`  ${dim("Get your API key at https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cli-node")}`,
 	);
 	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 to get a new key, or run mem0 init again.",
+				"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.",
 			);
 		}
 	} catch (e) {

cli/node/src/commands/memory.ts

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

cli/node/src/commands/utils.ts

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

cli/node/src/config.ts

@@ -28,7 +28,6 @@ export interface DefaultsConfig {
 	agentId: string;
 	appId: string;
 	runId: string;
-	enableGraph: boolean;
 }
 
 export interface TelemetryConfig {
@@ -50,7 +49,6 @@ export function createDefaultConfig(): Mem0Config {
 			agentId: "",
 			appId: "",
 			runId: "",
-			enableGraph: false,
 		},
 		platform: {
 			apiKey: "",
@@ -87,8 +85,6 @@ 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 ?? "";
 	}
@@ -104,12 +100,6 @@ 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;
 }
 
@@ -123,7 +113,6 @@ 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,
@@ -154,7 +143,6 @@ 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"],
@@ -163,7 +151,6 @@ const KEY_MAP: Record<string, [keyof Mem0Config, string]> = {
 	agent_id: ["defaults", "agentId"],
 	app_id: ["defaults", "appId"],
 	run_id: ["defaults", "runId"],
-	enable_graph: ["defaults", "enableGraph"],
 };
 
 export function getNestedValue(config: Mem0Config, dottedKey: string): unknown {

cli/node/src/index.ts

@@ -134,18 +134,6 @@ 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
@@ -236,8 +224,6 @@ 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.")
@@ -253,9 +239,8 @@ 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, enableGraph, output });
+		await cmdAdd(backend, text, { ...ids, ...opts, output });
 	});
 
 // ── Memory: search ────────────────────────────────────────────────────────
@@ -285,8 +270,6 @@ 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.")
@@ -310,7 +293,6 @@ program
 			opts.baseUrl,
 		);
 		const ids = resolveIds(config, opts);
-		const enableGraph = resolveGraph(config, opts);
 		const output = isAgent ? "agent" : opts.output;
 		await cmdSearch(backend, resolvedQuery, {
 			...ids,
@@ -320,7 +302,6 @@ program
 			keyword: opts.keyword,
 			filterJson: opts.filter,
 			fields: opts.fields,
-			enableGraph,
 			output,
 		});
 	});
@@ -364,8 +345,6 @@ 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.")
@@ -381,7 +360,6 @@ program
 			opts.baseUrl,
 		);
 		const ids = resolveIds(config, opts);
-		const enableGraph = resolveGraph(config, opts);
 		const output = isAgent ? "agent" : opts.output;
 		await cmdList(backend, {
 			...ids,
@@ -390,7 +368,6 @@ program
 			category: opts.category,
 			after: opts.after,
 			before: opts.before,
-			enableGraph,
 			output,
 		});
 	});

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

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

cli/node/tests/commands.test.ts

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

cli/node/tests/config.test.ts

@@ -64,7 +64,6 @@ 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);
   });
 });
 
@@ -105,9 +104,4 @@ describe("setNestedValue", () => {
     expect(config.defaults.userId).toBe("bob");
   });
 
-  it("coerces boolean for enable_graph", () => {
-    const config = createDefaultConfig();
-    expect(setNestedValue(config, "defaults.enable_graph", "true")).toBe(true);
-    expect(config.defaults.enableGraph).toBe(true);
-  });
 });

cli/python/pyproject.toml

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

cli/python/src/mem0_cli/__init__.py

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

cli/python/src/mem0_cli/app.py

@@ -267,8 +267,6 @@ 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"
     ),
@@ -295,13 +293,6 @@ 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,
@@ -313,7 +304,6 @@ def add(
         no_infer=no_infer,
         expires=expires,
         categories=categories,
-        enable_graph=graph_enabled,
         output=output,
     )
 
@@ -357,12 +347,6 @@ 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"
     ),
@@ -396,13 +380,6 @@ 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,
@@ -413,7 +390,6 @@ def search(
         keyword=keyword,
         filter_json=filter_json,
         fields=fields,
-        enable_graph=graph_enabled,
         output=output,
     )
 
@@ -480,12 +456,6 @@ 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"
     ),
@@ -511,13 +481,6 @@ 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,
@@ -526,7 +489,6 @@ def list_cmd(
         category=category,
         after=after,
         before=before,
-        enable_graph=graph_enabled,
         output=output,
     )
 

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

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

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

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

cli/python/src/mem0_cli/branding.py

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

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

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

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

@@ -19,7 +19,13 @@ 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)
@@ -352,7 +358,9 @@ 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[/]")
+    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()
 
     console.print(f"  [{BRAND_COLOR}]API Key[/]: ", end="")
@@ -404,7 +412,7 @@ 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 to get a new key, then run mem0 init again.",
+                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.",
             )
     except Exception as e:
         print_error(err_console, f"Connection test failed: {e}")

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

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

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

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

cli/python/src/mem0_cli/config.py

@@ -36,7 +36,6 @@ class DefaultsConfig:
     agent_id: str = ""
     app_id: str = ""
     run_id: str = ""
-    enable_graph: bool = False
 
 
 @dataclass
@@ -60,7 +59,6 @@ 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",
 }
 
 
@@ -91,8 +89,6 @@ 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", "")
 
@@ -121,10 +117,6 @@ 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
 
 
@@ -139,7 +131,6 @@ def save_config(config: Mem0Config) -> None:
             "agent_id": config.defaults.agent_id,
             "app_id": config.defaults.app_id,
             "run_id": config.defaults.run_id,
-            "enable_graph": config.defaults.enable_graph,
         },
         "platform": {
             "api_key": config.platform.api_key,

cli/python/tests/test_cli_integration.py

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

cli/python/tests/test_commands.py

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

cli/python/tests/test_config.py

@@ -121,46 +121,6 @@ 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:
@@ -192,11 +152,6 @@ 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):

cookbooks/helper/mem0_teachability.py

@@ -56,7 +56,7 @@ class Mem0Teachability(AgentCapability):
 
     def process_last_received_message(self, text: Union[Dict, str]):
         expanded_text = text
-        if self.memory.get_all(agent_id=self.agent_id):
+        if self.memory.get_all(filters={"agent_id": self.agent_id}):
             expanded_text = self._consider_memo_retrieval(text)
         self._consider_memo_storage(text)
         return expanded_text
@@ -139,7 +139,7 @@ class Mem0Teachability(AgentCapability):
         return comment + self._concatenate_memo_texts(memo_list)
 
     def _retrieve_relevant_memos(self, input_text: str) -> list:
-        search_results = self.memory.search(input_text, agent_id=self.agent_id, limit=self.max_num_retrievals)
+        search_results = self.memory.search(input_text, filters={"agent_id": self.agent_id}, top_k=self.max_num_retrievals)
         memo_list = [result["memory"] for result in search_results if result["score"] <= self.recall_threshold]
 
         if self.verbosity >= 1 and not memo_list:

docs/_snippets/paper-release.mdx

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

docs/api-reference.mdx

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

docs/api-reference/events/get-event.mdx

@@ -5,3 +5,5 @@ openapi: get /v1/event/{event_id}/
 ---
 
 Retrieve details about a specific event by passing its `event_id`. This endpoint is particularly helpful for tracking the status, payload, and completion details of asynchronous memory operations.
+
+For `POST /v3/memories/add/`, the event confirms that the write pipeline completed. Temporal reasoning enrichment runs asynchronously by default, so the event may be `SUCCEEDED` slightly before temporal ranking signals are available to subsequent `search` calls.

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

@@ -1,18 +1,18 @@
 ---
-title: 'Add Memories'
-description: "Add facts, messages, or metadata to a user memory store with support for async processing and event tracking."
-openapi: post /v1/memories/
+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/
 ---
 
-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.
+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.
 
 ## Endpoint
 
 - **Method**: `POST`
-- **URL**: `/v1/memories/`
+- **URL**: `/v3/memories/add/`
 - **Content-Type**: `application/json`
 
-Memories are processed asynchronously by default. The response contains queued events you can track while the platform finalizes enrichment.
+Processing is asynchronous. The response returns an `event_id` you can poll via `GET /v1/event/{event_id}/`.
 
 ## Required headers
 
@@ -23,7 +23,7 @@ Memories are processed asynchronously by default. The response contains queued e
 
 ## Request body
 
-Provide at least one message or direct memory string. Most callers supply `messages` so Mem0 can infer structured memories as part of ingestion.
+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.
 
 <CodeGroup>
 ```json Basic request
@@ -43,12 +43,15 @@ Provide at least one message or direct memory string. Most callers supply `messa
 
 | Field | Type | Required | Description |
 | --- | --- | --- | --- |
-| `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`. |
+| `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. |
 | `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. |
 
-> \* 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.
+> \* At least one entity ID (`user_id`, `agent_id`, `app_id`, or `run_id`) is required.
 
 <Tip>
   Need more details? See [all request parameters](#body-messages) below for complete field descriptions, types, and constraints.
@@ -56,19 +59,15 @@ Provide at least one message or direct memory string. Most callers supply `messa
 
 ## Response
 
-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.
+The request is queued for background processing. The response contains an `event_id` for tracking status.
 
 <CodeGroup>
 ```json 200 response
-[
-  {
-    "id": "mem_01JF8ZS4Y0R0SPM13R5R6H32CJ",
-    "event": "ADD",
-    "data": {
-      "memory": "The user moved to Austin in 2025."
-    }
-  }
-]
+{
+  "message": "Memory processing has been queued for background execution",
+  "status": "PENDING",
+  "event_id": "evt-uuid"
+}
 ```
 
 ```json 400 response
@@ -81,3 +80,6 @@ Successful requests return an array of events queued for processing. Each event
 ```
 </CodeGroup>
 
+<Info>
+Poll the event status via `GET /v1/event/{event_id}/`. Status will be `SUCCEEDED` or `FAILED` once processing completes.
+</Info>

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

@@ -1,10 +1,12 @@
 ---
 title: "Get Memories"
-description: "Retrieve memories with advanced filtering using logical operators like AND, OR, NOT, and comparison queries."
-openapi: post /v2/memories/
+description: "Retrieve memories with paginated results and advanced filtering using logical operators like AND, OR, NOT, and comparison queries."
+openapi: post /v3/memories/
 ---
 
-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:
+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:
 
 - `in`: Matches any of the values specified
 - `gte`: Greater than or equal to
@@ -15,6 +17,8 @@ The v2 get memories API is powerful and flexible, allowing for more precise memo
 - `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(
@@ -27,12 +31,17 @@ 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",
@@ -46,54 +55,12 @@ memories = client.get_all(
             "created_at": "2024-07-05T15:30:00Z",
             "updated_at": "2024-07-05T15:30:00Z"
         }
-    ],
-    "total": 2
-}
-```
-
-</CodeGroup>
-
-## Graph Memory
-
-To retrieve graph memory relationships between entities, pass `output_format="v1.1"` in your request. This will return memories with entity and relationship information from the knowledge graph.
-
-<CodeGroup>
-```python Code
-memories = client.get_all(
-    filters={
-        "user_id": "alex"
-    }
-)
-```
-
-```python Output
-{
-    "results": [
-        {
-            "id": "f4cbdb08-7062-4f3e-8eb2-9f5c80dfe64c",
-            "memory": "Alex is planning a trip to San Francisco",
-            "entities": [
-                {
-                    "id": "entity-1",
-                    "name": "Alex",
-                    "type": "person"
-                },
-                {
-                    "id": "entity-2",
-                    "name": "San Francisco",
-                    "type": "location"
-                }
-            ],
-            "relations": [
-                {
-                    "source": "entity-1",
-                    "target": "entity-2",
-                    "relationship": "traveling_to"
-                }
-            ]
-        }
     ]
 }
 ```
 
 </CodeGroup>
+
+<Info>
+The response is a paginated envelope with `count`, `next`, `previous`, and `results`. Use `page` and `page_size` query params to step through results.
+</Info>

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

@@ -1,10 +1,14 @@
 ---
 title: 'Search Memories'
-description: "Search memories with semantic queries and advanced filtering using logical and comparison operators."
-openapi: post /v2/memories/search/
+description: "Search memories with hybrid retrieval (semantic + BM25 + entity matching) and advanced filtering using logical and comparison operators."
+openapi: post /v3/memories/search/
 ---
 
-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:
+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:
 - `in`: Matches any of the values specified
 - `gte`: Greater than or equal to
 - `lte`: Less than or equal to
@@ -14,6 +18,14 @@ The v2 search API is powerful and flexible, allowing for more precise memory ret
 - `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(
@@ -33,20 +45,20 @@ related_memories = client.search(
 
 ```json Output
 {
-  "memories": [
+  "results": [
     {
       "id": "ea925981-272f-40dd-b576-be64e4871429",
       "memory": "Likes to play cricket and plays cricket on weekends.",
+      "user_id": "alice",
       "metadata": {
         "category": "hobbies"
       },
-      "score": 0.32116443111457704,
+      "score": 0.82,
       "created_at": "2024-07-26T10:29:36.630547-07:00",
       "updated_at": null,
-      "user_id": "alice",
-      "agent_id": "sports-agent"
+      "categories": ["hobbies"]
     }
-  ],
+  ]
 }
 ```
 </CodeGroup>

docs/api-reference/organizations-projects.mdx

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

docs/changelog/highlights.mdx

@@ -4,9 +4,37 @@ description: "Major product launches, headline features, and milestones for Mem0
 mode: "wide"
 ---
 
+<Update label="2026-05-13" description="Temporal Reasoning for Mem0 Platform v3">
+
+**Temporal Reasoning — Time-Aware Retrieval for Platform v3**
+
+Mem0 Platform v3 can now interpret time-aware memories and queries so assistants retrieve the right information for questions about the past, upcoming plans, and current state.
+
+- **Time-aware search intent** — Queries like `last week`, `upcoming`, `right now`, and `as of March 2025` return contextually appropriate results automatically
+- **Enabled by default** — No per-request toggle required for v3 writes or searches
+- **Anchored relative queries** — `reference_date` anchors relative search phrases for tests, backfills, and reproducible demos
+- **Normal response shape** — Temporal reasoning affects ranking while preserving existing client response patterns
+
+See [Temporal Reasoning](/platform/features/temporal-reasoning) for usage details.
+
+</Update>
+
+<Update label="2026-05-08" description="Memory Decay">
+
+**Memory Decay — Recently-Used Memories Surface Higher, Automatically**
+
+Per-project search-time ranking bias that boosts recently-touched memories and gently dampens stale ones. Off by default; opt in per project via the `decay` field on the project endpoint, or via `client.project.update(decay=True)` in the SDKs (Python `v2.0.2` / TypeScript `v3.0.3`).
+
+- **Soft bias, never a filter.** The scaling factor stays in `0.3×–1.5×`. Decay can reorder candidates but never zeros them out — anything that surfaced before decay can still surface after.
+- **Reinforcement loop.** Every memory returned in a search has its access history updated, so frequently-used facts naturally float to the top over time.
+- **Public score still clamped to `[0, 1]`.** Existing API contract preserved; no client-side changes needed.
+- **v3 search only**, fully reversible. See [Memory Decay docs](/platform/features/memory-decay).
+
+</Update>
+
 <Update label="2026-04-14" description="Mem0 SDK v2.0.0 / v3.0.0">
 
-**New Memory Algorithm — State-of-the-Art Accuracy at 90% Lower Cost**
+**New Memory Algorithm — State-of-the-Art Accuracy at ~3-4x Lower Cost**
 
 Ground-up rewrite of the memory pipeline with 20+ point benchmark improvements:
 
@@ -15,7 +43,7 @@ Ground-up rewrite of the memory pipeline with 20+ point benchmark improvements:
 - **BEAM (1M tokens):** **64.1** — production-scale memory evaluation
 - **Agent memories are first-class** — Previous algorithm: 46% on assistant recall. New: **100%**
 - **Temporal reasoning works** — "Where did I live before SF?" Previous: 51%. New: **93%**
-- **90% fewer tokens** — Under 7K tokens per retrieval vs 25K+ for full-context approaches
+- **~3-4x fewer tokens** — Under 7K tokens per retrieval vs 25K+ for full-context approaches
 - **ADD-only extraction** — Memories accumulate; nothing is overwritten or deleted
 - **Hybrid retrieval** — Semantic + BM25 keyword + entity boost, scored in parallel
 - **Entity linking** — Entities extracted, embedded, and linked across memories
@@ -99,4 +127,4 @@ Major expansion of the provider ecosystem:
 
 First skill launch — a dedicated Mem0 skill providing platform API reference, quickstart patterns, and integration examples directly inside agent sessions. Available on [skills.sh](https://skills.sh) for any compatible AI coding agent.
 
-</Update>
+</Update>

docs/changelog/openclaw.mdx

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

docs/changelog/platform.mdx

@@ -4,6 +4,31 @@ description: "Release notes for the Mem0 hosted platform — backend, dashboard,
 mode: "wide"
 ---
 
+<Update label="2026-05-13" description="">
+
+**New Features:**
+- **Memory:** Added Temporal Reasoning for Platform v3 to improve ranking for time-aware queries such as `last week`, `upcoming`, `right now`, and `as of ...`
+- **Search:** Added `reference_date` support to anchor relative temporal queries for tests, backfills, and reproducible demos
+
+**Improvements:**
+- **API:** Temporal reasoning preserves the normal client response shape for search and get-all results
+
+</Update>
+
+<Update label="2026-05-04" description="">
+
+**New Features:**
+- **Memory Decay:** Per-project search-time ranking bias that boosts recently-used memories and gently dampens stale ones. Opt-in via `decay` on the project endpoint; off by default. The scaling factor stays in `0.3×–1.5×`, the public `score` remains clamped to `[0, 1]`, and the bias never filters a candidate out. See [Memory Decay docs](/platform/features/memory-decay).
+
+</Update>
+
+<Update label="2026-04-16" description="">
+
+**Improvements:**
+- **UI:** Removed Graph Memory tab, page, and all references from dashboard, sidebar, project settings, playground, and billing
+
+</Update>
+
 <Update label="2025-07-23" description="">
 
 **Bug Fixes:**
@@ -287,4 +312,3 @@ mode: "wide"
 - **Core:** Fixed unicode error in user_id, agent_id, run_id and app_id
 
 </Update>
-

docs/changelog/sdk.mdx

@@ -7,6 +7,37 @@ mode: "wide"
 <Tabs>
 <Tab title="Python">
 
+<Update label="2026-05-08" description="v2.0.2">
+
+**Bug Fixes:**
+- **Telemetry:** Stitch OSS and platform PostHog identities on `MemoryClient` init so `$identify` events fire and a single user is no longer tracked as two or three disconnected personas ([#5040](https://github.com/mem0ai/mem0/pull/5040))
+- **Security:** Harden against SQL injection and prompt injection ([#4997](https://github.com/mem0ai/mem0/pull/4997))
+
+**New Features:**
+- **SDK:** Expose `decay` on `project.update` ([#5062](https://github.com/mem0ai/mem0/pull/5062))
+
+**Improvements:**
+- **Plugin:** Hand `mem0` search decisions to the agent ([#4992](https://github.com/mem0ai/mem0/pull/4992))
+
+</Update>
+
+<Update label="2026-04-25" description="v2.0.1">
+
+**Bug Fixes:**
+- **Client:** Map `user_id`, `agent_id`, `run_id` entity params to filters in `GET /memories` ([#4960](https://github.com/mem0ai/mem0/pull/4960))
+- **Memory:** Honor `prompt` param in vector store extraction pipeline ([#4914](https://github.com/mem0ai/mem0/pull/4914))
+- **Memory:** Add missing `text_lemmatized` field in `AsyncMemory._create_memory` ([#4886](https://github.com/mem0ai/mem0/pull/4886))
+- **Memory:** Merge same-key operator dicts in AND metadata filters ([#4853](https://github.com/mem0ai/mem0/pull/4853))
+- **LLMs:** Narrow `_is_reasoning_model` check to not match `gpt-5.x` variants ([#4746](https://github.com/mem0ai/mem0/pull/4746))
+- **Vector Stores:** Add `ca_certs` config option for Elasticsearch vector store ([#3993](https://github.com/mem0ai/mem0/pull/3993))
+- **Vector Stores:** Add `agent_id` and `run_id` to Elasticsearch/OpenSearch default mappings ([#4906](https://github.com/mem0ai/mem0/pull/4906))
+- **Embeddings:** Set FastEmbed `embedding_dims` from model metadata at init ([#4711](https://github.com/mem0ai/mem0/pull/4711))
+
+**Security:**
+- Bump vulnerable dependencies to patched versions ([#4835](https://github.com/mem0ai/mem0/pull/4835))
+
+</Update>
+
 <Update label="2026-04-14" description="v2.0.0">
 
 **Major Release** — Python SDK with V3 memory pipeline, ADD-only extraction, and cleaned-up API surface.
@@ -24,7 +55,7 @@ mode: "wide"
 **Breaking Changes:**
 - **`add()` returns ADD-only events** — No more `"UPDATE"` or `"DELETE"` events. Memories accumulate; nothing is overwritten ([#4805](https://github.com/mem0ai/mem0/pull/4805))
 - **`search()` default `threshold` is now `0.1`** — Pass `threshold=0.0` for previous behavior ([#4805](https://github.com/mem0ai/mem0/pull/4805))
-- **`search()` `score` is now a combined multi-signal score** — The top-level `score` fuses semantic similarity, BM25 keyword match, and entity boost into one value. Absolute numbers shift versus the old raw cosine score; retune any hard thresholds against representative queries. Per-signal scores are not exposed on the response ([#4805](https://github.com/mem0ai/mem0/pull/4805), [#4836](https://github.com/mem0ai/mem0/pull/4836))
+- **`search()` `score` is now a combined multi-signal score** — The top-level `score` fuses semantic similarity, BM25 keyword match, entity signals, and temporal boosts into one value. Absolute numbers shift versus the old raw cosine score; retune any hard thresholds against representative queries ([#4805](https://github.com/mem0ai/mem0/pull/4805), [#4836](https://github.com/mem0ai/mem0/pull/4836))
 - **`search()` default `rerank` is now `False`** — Pass `rerank=True` for previous behavior ([#4805](https://github.com/mem0ai/mem0/pull/4805))
 - **`top_k` default changed 100 → 20** in `Memory.get_all()` and `Memory.search()` (sync + async). Pass `top_k=100` explicitly to restore the old behavior ([#4843](https://github.com/mem0ai/mem0/pull/4843))
 - **Entity ID validation:** `user_id` / `agent_id` / `run_id` are trimmed; empty-string and whitespace-only values now raise `ValueError` ([#4843](https://github.com/mem0ai/mem0/pull/4843))
@@ -893,6 +924,36 @@ See the [OSS v1 to v2 migration guide](https://docs.mem0.ai/migration/oss-v1-to-
 </Tab>
 
 <Tab title="TypeScript">
+<Update label="2026-05-08" description="v3.0.3">
+
+**Bug Fixes:**
+- **Telemetry:** Stitch OSS and platform PostHog identities on `MemoryClient` init so `$identify` events fire and a single user is no longer tracked as two or three disconnected personas ([#5040](https://github.com/mem0ai/mem0/pull/5040))
+- **Vector Stores:** Fix inverted vector distance in PGVector implementation ([#4944](https://github.com/mem0ai/mem0/pull/4944))
+- **Security:** Harden against SQL injection and prompt injection ([#4997](https://github.com/mem0ai/mem0/pull/4997))
+
+**New Features:**
+- **SDK:** Expose `decay` on `project.update` ([#5062](https://github.com/mem0ai/mem0/pull/5062))
+
+</Update>
+
+<Update label="2026-04-25" description="v3.0.2">
+
+**Bug Fixes:**
+- **LLMs:** Forward `timeout` config to OpenAI client in JS OSS LLM providers ([#4770](https://github.com/mem0ai/mem0/pull/4770))
+
+**Improvements:**
+- **Telemetry:** Harden TS telemetry version injection and require changelog entry on version bump ([#4900](https://github.com/mem0ai/mem0/pull/4900))
+- **Docs:** Update memory tool list, CLI usage, and config file reading logic ([#4861](https://github.com/mem0ai/mem0/pull/4861))
+
+</Update>
+
+<Update label="2026-04-20" description="v3.0.1">
+
+**Bug Fixes:**
+- **Telemetry:** SDK version is now injected into telemetry at build time via esbuild's `define`, replacing the two hardcoded version strings in `src/client/telemetry.ts` and `src/oss/src/utils/telemetry.ts`. Previously these were stuck at `2.1.36` and `2.1.34` while the published package was on `3.x`, so every telemetry event was reporting the wrong `client_version`. The placeholder is substituted with a string literal at bundle time — no runtime `require("./package.json")` in the shipped bundle ([#4897](https://github.com/mem0ai/mem0/pull/4897)).
+
+</Update>
+
 <Update label="2026-04-14" description="v3.0.0">
 
 **Major Release** — TypeScript SDK with V3 memory pipeline, camelCase parameters, and cleaned-up API surface.
@@ -1262,6 +1323,16 @@ See the [TypeScript SDK migration guide](https://docs.mem0.ai/migration/ts-v2-to
 
 <Tab title="CLI">
 
+<Update label="2026-04-22" description="Python v0.2.4 / Node v0.2.4">
+
+**New Features:**
+- **V3 API Routes:** Migrated `add`, `search`, and `list` commands from v1/v2 to v3 API endpoints — `POST /v3/memories/add/`, `POST /v3/memories/search/`, `POST /v3/memories/`. Aligns both CLIs with the Python and TypeScript SDKs which already use v3 ([#4916](https://github.com/mem0ai/mem0/pull/4916))
+
+**Breaking Changes:**
+- **`--graph` / `--no-graph` removed:** The `enable_graph` config option, `--graph` and `--no-graph` CLI flags, and `MEM0_ENABLE_GRAPH` environment variable have been removed from both CLIs. Graph memory is now a project-level setting on the Platform ([#4916](https://github.com/mem0ai/mem0/pull/4916))
+
+</Update>
+
 <Update label="2026-04-11" description="Python v0.2.3 / Node v0.2.3">
 
 **Bug Fixes:**

docs/cookbooks/companions/quickstart-demo.mdx

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

docs/cookbooks/companions/travel-assistant.mdx

@@ -54,7 +54,6 @@ config = {
             "embedding_model_dims": 3072,
         }
     },
-    "version": "v1.1",
 }
 
 class PersonalTravelAssistant:
@@ -154,11 +153,11 @@ class PersonalTravelAssistant:
         return answer
 
     def get_memories(self, user_id):
-        memories = self.memory.get_all(user_id=user_id)
+        memories = self.memory.get_all(filters={"user_id": user_id})
         return [m['memory'] for m in memories.get('results', [])]
 
     def search_memories(self, query, user_id):
-        memories = self.memory.search(query, user_id=user_id)
+        memories = self.memory.search(query, filters={"user_id": user_id})
         return [m['memory'] for m in memories.get('results', [])]
 
 # Usage example

docs/cookbooks/essentials/building-ai-companion.mdx

@@ -354,10 +354,10 @@ Exclude:
 ```
   </Tab>
   <Tab title="Open Source">
-Tell Mem0 what matters by including `custom_fact_extraction_prompt` in the config dict:
+Tell Mem0 what matters by including `custom_instructions` in the config dict:
 
 ```python
-MEMORY_CONFIG["custom_fact_extraction_prompt"] = """
+MEMORY_CONFIG["custom_instructions"] = """
 Extract from running coach conversations:
 - Training goals and race targets
 - Physical constraints or injuries
@@ -375,7 +375,7 @@ Return JSON with key "facts" as a list of strings (use [] if nothing to store).
 memory = Memory.from_config(MEMORY_CONFIG)
 ```
 
-<Note>`custom_fact_extraction_prompt` is a top-level key in the config dictionary passed to `Memory.from_config()`. Make sure it's set before creating the Memory instance — not after.</Note>
+<Note>`custom_instructions` is a top-level key in the config dictionary passed to `Memory.from_config()`. Make sure it's set before creating the Memory instance — not after.</Note>
   </Tab>
 </Tabs>
 
@@ -520,7 +520,7 @@ memory.add(
 # "hey" → don't store
 # "cool thanks" → don't store
 
-# Or rely on custom_fact_extraction_prompt to filter automatically
+# Or rely on custom_instructions to filter automatically
 ```
   </Tab>
 </Tabs>
@@ -545,11 +545,11 @@ expiration = (datetime.now() + timedelta(days=14)).strftime("%Y-%m-%d")
 mem0_client.add(
     [{"role": "user", "content": "Rolled my left ankle, needs rest"}],
     user_id="max",
-    expiration_date=expiration
+    metadata={"memory_bucket": "constraints", "expires_on": expiration}
 )
 ```
 
-In 14 days, this memory disappears automatically. Ray stops asking about the ankle.
+Store `expires_on` in metadata and periodically clean up expired memories. Ray stops asking about the ankle once it's removed.
   </Tab>
   <Tab title="Open Source">
 ```python
@@ -627,7 +627,7 @@ MEMORY_CONFIG = {
             "ollama_base_url": "http://localhost:11434",
         },
     },
-    "custom_fact_extraction_prompt": """
+    "custom_instructions": """
     Extract: goals, constraints, preferences, progress
     Exclude: greetings, filler, casual chat
     Return JSON with key "facts" as a list of strings.
@@ -684,8 +684,7 @@ expiration = (datetime.now() + timedelta(days=14)).strftime("%Y-%m-%d")
 mem0_client.add(
     [{"role": "user", "content": "Rolled ankle, need light workouts"}],
     user_id="max",
-    categories=["constraints"],
-    expiration_date=expiration
+    metadata={"memory_bucket": "constraints", "expires_on": expiration}
 )
 ```
   </Tab>

docs/cookbooks/essentials/choosing-memory-architecture-vector-vs-graph.mdx

@@ -1,361 +0,0 @@
----
-title: Choose Vector vs Graph Memory
-description: "Blend vector search with graph relationships to answer multi-hop questions."
----
-
-
-Most AI agents use vector stores for RAG operations - they work great for semantic search and retrieving relevant context. But there's a gap when queries require understanding connections between entities.
-
-Mem0 brings graph memory into the picture to fill this gap. In this cookbook, we'll create a company knowledge base with Mem0, using both vector and graph stores. You'll learn when each one helps along the way.
-
----
-
-## Vector and Graph Stores
-
-When you add a memory to Mem0, it goes into a **vector store** by default. Vector stores are excellent at semantic search - finding memories that match the meaning of your query.
-
-**Graph stores** work differently. They extract **entities** (people, projects, teams) and **relationships between them** (works_with, reports_to, member_of). This lets you answer questions that need connecting information across multiple memories.
-
-We will go through examples in this cookbook while building a company's knowledge base along the way.
-
----
-
-## Starting Simple
-
-Since we're building a company knowledge base, let's add some employee information:
-
-```python
-from mem0 import MemoryClient
-
-client = MemoryClient(api_key="your-api-key")
-# Add employee info
-client.add("Emma is a software engineer in Seattle", user_id="company_kb")
-client.add("David is a product manager in Austin", user_id="company_kb")
-
-```
-
-Now let's search for Emma's role:
-
-```python
-results = client.search("What does Emma do?", filters={"user_id": "company_kb"})
-print(results['results'][0]['memory'])
-
-```
-
-**Output:**
-
-```
-Emma is a software engineer in Seattle
-
-```
-
-<Info>
-**Expected output:** Vector search returned Emma's role instantly. When queries ask for facts directly stored in one memory, vector semantic search is perfect—fast and accurate.
-</Info>
-
-This works perfectly. Vector search found the memory that semantically matches "What does Emma do?" and returned Emma's role.
-
----
-
-## Adding Team Structure
-
-Let's add some information about how the team works together:
-
-```python
-client.add("Emma works with David on the mobile app redesign", user_id="company_kb")
-client.add("David reports to Rachel, who manages the design team", user_id="company_kb")
-
-```
-
-Now we have two pieces of information stored:
-
-1. Emma works with David
-2. David reports to Rachel
-
-Let's try asking something that needs both pieces:
-
-```python
-results = client.search(
-    "Who is Emma's teammate's manager?",
-    filters={"user_id": "company_kb"}
-)
-
-for r in results['results']:
-    print(r['memory'])
-
-```
-
-**Output:**
-
-```
-Emma works with David on the mobile app redesign
-David reports to Rachel, who manages the design team
-
-```
-
-Vector search returned both memories, but it didn't connect them. You'd need to manually figure out:
-
-- Emma's teammate is David (from memory 1)
-- David's manager is Rachel (from memory 2)
-- So the answer is Rachel
-
-<Warning>
-Vector search can't traverse relationships. It returns relevant memories, but you must connect the dots manually. For "Who is Emma's teammate's manager?", vector search gives you the pieces—not the answer. This breaks down as queries get more complex (3+ hops).
-</Warning>
-
----
-
-## Enter Graph Memory
-
-Let's add the same information with graph memory enabled:
-
-```python
-client.add(
-    "Emma works with David on the mobile app redesign",
-    user_id="company_kb",
-    enable_graph=True
-)
-
-client.add(
-    "David reports to Rachel, who manages the design team",
-    user_id="company_kb",
-    enable_graph=True
-)
-
-```
-
-When you set `enable_graph=True`, Mem0 extracts entities and relationships:
-
-- `emma --[works_with]--> david`
-- `david --[reports_to]--> rachel`
-- `rachel --[manages]--> design_team`
-
-Now the same query works differently:
-
-```python
-results = client.search(
-    "Who is Emma's teammate's manager?",
-    filters={"user_id": "company_kb"},
-    enable_graph=True
-)
-
-print(results['results'][0]['memory'])
-print("\\nRelationships found:")
-for rel in results.get('relations', []):
-    print(f"  {rel['source']}, {rel['target']} ({rel['relationship']})")
-
-```
-
-**Output:**
-
-```
-David reports to Rachel, who manages the design team
-
-Relationships found:
-  emma, david (works_with)
-  david, rachel (reports_to)
-
-```
-
-<Info>
-**Expected behavior:** Graph memory returns the direct answer—"David reports to Rachel"—plus the relationship chain that got there. No manual connecting needed. The graph traversed: Emma → works_with → David → reports_to → Rachel.
-</Info>
-
-Graph memory traversed the relationships automatically: Emma works with David, David reports to Rachel, so Rachel is the answer.
-
----
-
-## How It Connects
-
-Here's what the graph looks like behind the scenes:
-
-```mermaid
-graph LR
-    Emma[Emma] -->|works_with| David[David]
-    David -->|reports_to| Rachel[Rachel]
-    Rachel -->|manages| DesignTeam[Design Team]
-    David -->|works_on| MobileApp[Mobile App]
-    Emma -->|works_on| MobileApp
-
-```
-
-Graph memory lets you discover relations and memories which are tricky to do with direct vector stores.
-
-Vector search would need the exact words in your query to match. Graph memory follows the connections.
-
----
-
-## When to Use Each
-
-Use **vector store** (default) when:
-
-- Searching documents by semantic similarity
-- Looking up facts that don't need relationships
-- Building FAQs or knowledge bases where each item stands alone
-
-Use **graph memory** when:
-
-- Tracking organizational hierarchies (who reports to whom)
-- Understanding project teams (who collaborates with whom)
-- Building CRMs (which contacts connect to which companies)
-- Product recommendations (what items are bought together)
-
-For our company knowledge base, we'll use both:
-
-- Vector for individual facts: "Emma specializes in React"
-- Graph for relationships: "Emma works with David"
-
----
-
-## Putting It Together
-
-Let's build a small company knowledge base with both approaches:
-
-```python
-# Facts about individuals - vector store is fine
-client.add("Emma specializes in React and TypeScript", user_id="company_kb")
-client.add("David has 5 years of product management experience", user_id="company_kb")
-
-# Relationships - use graph memory
-client.add(
-    "Emma and David work together on the mobile app",
-    user_id="company_kb",
-    enable_graph=True
-)
-
-client.add(
-    "David reports to Rachel",
-    user_id="company_kb",
-    enable_graph=True
-)
-
-client.add(
-    "Rachel runs weekly team syncs every Tuesday",
-    user_id="company_kb",
-    enable_graph=True
-)
-
-```
-
-Now we can ask different types of questions:
-
-```python
-# Direct fact - vector search
-results = client.search("What are Emma's skills?", filters={"user_id": "company_kb"})
-print(results['results'][0]['memory'])
-
-```
-
-**Output:**
-
-```
-Emma specializes in React and TypeScript
-
-```
-
-```python
-# Multi-hop relationship - graph search
-results = client.search(
-    "What meetings does Emma's project manager's boss run?",
-    filters={"user_id": "company_kb"},
-    enable_graph=True
-)
-print(results['results'][0]['memory'])
-
-```
-
-**Output:**
-
-```
-Rachel runs weekly team syncs every Tuesday
-
-```
-
-Graph memory connected: Emma works with David, David reports to Rachel, Rachel runs team syncs.
-
-<Tip>
-Enable graph memory when your queries need multi-hop traversal: org charts (who reports to whom), project teams (who collaborates), CRMs (which contacts connect to companies). For single-fact lookups, stick with vector search—it's faster and cheaper.
-</Tip>
-
----
-
-## The Tradeoff
-
-Graph memory adds processing time and cost. When you call `client.add()` with `enable_graph=True`, Mem0 makes extra LLM calls to extract entities and relationships.
-
-<Note>
-**Cost consideration:** Graph memory extraction adds ~2-3 extra LLM calls per `add()` operation to identify entities and relationships. Use it selectively—enable graph for organizational structure and long-term relationships, skip it for temporary notes and simple facts.
-</Note>
-
-Use graph memory when the relationship traversal adds real value. For most use cases, vector search is sufficient and faster.
-
-```python
-# Long-term organizational structure - worth using graph
-client.add(
-    "Emma mentors two junior engineers on the frontend team",
-    user_id="company_kb",
-    enable_graph=True
-)
-
-# Temporary notes - skip graph, not worth the cost
-client.add(
-    "Emma is out sick today",
-    user_id="company_kb",
-    run_id="daily_notes"
-)
-
-```
-
----
-
-## Enabling Graph Memory
-
-You can enable graph memory in two ways:
-
-**Per-call** (recommended to start):
-
-```python
-client.add("Emma works with David", user_id="company_kb", enable_graph=True)
-client.search("team structure", filters={"user_id": "company_kb"}, enable_graph=True)
-
-```
-
-**Project-wide** (if most of your data has relationships):
-
-```python
-client.project.update(enable_graph=True)
-
-# Now every add uses graph automatically
-client.add("Emma mentors Jordan", user_id="company_kb")
-
-```
-
----
-
-## What You Built
-
-A hybrid company knowledge base that combines both architectures:
-
-- **Vector search** - Fast semantic lookups for individual facts (Emma's skills, David's experience)
-- **Graph memory** - Multi-hop relationship traversal (Emma's teammate's manager, project hierarchies)
-- **Selective enablement** - Graph only for long-term organizational structure, vector for everything else
-- **Cost optimization** - Skip graph extraction for temporary notes and simple facts
-
-This pattern scales from 10-person startups to enterprise org charts with thousands of employees.
-
----
-
-## Summary
-
-Vector stores handle most memory operations efficiently—semantic search works great for finding relevant information. Add graph memory when your queries need to understand how entities connect across multiple hops.
-
-The key is knowing which tool fits your query pattern: direct questions work with vectors, multi-hop relationship queries need graphs.
-
-<CardGroup cols={2}>
-  <Card title="Partition Memories by Entity" icon="layers" href="/cookbooks/essentials/entity-partitioning-playbook">
-    Scope memories across users, agents, apps, and sessions to balance personalization and reuse.
-  </Card>
-  <Card title="Export Everything Safely" icon="download" href="/cookbooks/essentials/exporting-memories">
-    Learn how to migrate or audit stored memories with structured exports.
-  </Card>
-</CardGroup>

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

@@ -38,7 +38,7 @@ client = MemoryClient(api_key="your-api-key")
 ```
 
 <Note>
-Replace `your-api-key` with your actual Mem0 API key from the <a href="https://app.mem0.ai" 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?utm_source=oss&utm_medium=cookbook-memory-ingestion" rel="nofollow">dashboard</a>. Without proper API authentication, memory operations will fail.
 </Note>
 
 ---
@@ -513,11 +513,6 @@ These controls prevent retrieval failures and ensure your AI assistant works wit
 
 Start with conservative filters (only store confirmed facts) and iterate based on your application's needs. Combine custom instructions with confidence thresholds for the most reliable memory ingestion pipeline.
 
-<CardGroup cols={2}>
-  <Card title="Expire Short-Term Data" icon="timer" href="/cookbooks/essentials/memory-expiration-short-and-long-term">
-    Automatically clean up session context before it clutters retrieval.
-  </Card>
-  <Card title="Choose Your Memory Architecture" icon="sitemap" href="/cookbooks/essentials/choosing-memory-architecture-vector-vs-graph">
-    Learn when to layer graph memory alongside vectors for multi-hop queries.
-  </Card>
-</CardGroup>
+<Card title="Build a Mem0 Companion" icon="users" href="/cookbooks/essentials/building-ai-companion">
+  Learn core memory patterns including temporary vs permanent data handling.
+</Card>

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

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

docs/cookbooks/essentials/exporting-memories.mdx

@@ -20,7 +20,7 @@ client = MemoryClient(api_key="your-api-key")
 ```
 
 <Note>
-Your API key needs export permissions to download memory data. Check your project settings on the <a href="https://app.mem0.ai" 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?utm_source=oss&utm_medium=cookbook-exporting-memories" rel="nofollow">dashboard</a> if export operations fail with authentication errors.
 </Note>
 
 Let's add some sample memories to work with:
@@ -280,8 +280,8 @@ This covers data portability, GDPR compliance, system migrations, and manual rev
 Use **`get_all()`** for bulk retrieval, **`search()`** for specific questions, and **`create_memory_export()`** for structured data exports with custom schemas. Remember exports expire after 7 days—download them locally for long-term archives.
 
 <CardGroup cols={2}>
-  <Card title="Expire Short-Term Data" icon="timer" href="/cookbooks/essentials/memory-expiration-short-and-long-term">
-    Keep exports lean by clearing session context before you archive it.
+  <Card title="Build a Mem0 Companion" icon="users" href="/cookbooks/essentials/building-ai-companion">
+    Learn core memory patterns including temporary vs permanent data handling.
   </Card>
   <Card title="Control Memory Ingestion" icon="filter" href="/cookbooks/essentials/controlling-memory-ingestion">
     Ensure only verified insights make it into your export pipeline.

docs/cookbooks/essentials/memory-expiration-short-and-long-term.mdx

@@ -1,277 +0,0 @@
----
-title: Set Memory Expiration
-description: "Define short-term versus long-term retention so the store stays fresh."
----
-
-
-While building memory systems, we realized their size grows fast. Session notes, temporary context, chat history - everything starts accumulating and bogging down the system. This pollutes search results and increase storage costs. Not every memory needs to persist forever.
-
-In this cookbook, we'll go through how to use short-term vs long-term memories and see where it's best to use them.
-
----
-
-## Overview
-
-By default, Mem0 memories persist forever. This works for user preferences and core facts, but temporary data should expire automatically.
-
-In this tutorial, we will:
-
-- Understand default (permanent) memory behavior
-- Add expiration dates for temporary memories
-- Decide what should be temporary vs permanent
-
----
-
-## Setup
-
-```python
-from mem0 import MemoryClient
-from datetime import datetime, timedelta
-
-client = MemoryClient(api_key="your-api-key")
-```
-
-<Note>
-Import `datetime` and `timedelta` to calculate expiration dates. Without these imports, you'll need to manually format ISO timestamps—error-prone and harder to read.
-</Note>
-
----
-
-## Default Behavior: Everything Persists
-
-By default, all memories persist forever:
-
-```python
-# Store user preference
-client.add("User prefers dark mode", user_id="sarah")
-
-# Store session context
-client.add("Currently browsing electronics category", user_id="sarah")
-
-# 6 months later - both still exist
-results = client.get_all(filters={"user_id": "sarah"})
-print(f"Total memories: {len(results['results'])}")
-
-```
-
-**Output:**
-
-```
-Total memories: 2
-
-```
-
-Both the preference and session context persist. The preference is useful, but the 6-month-old session context is not.
-
----
-
-## The Problem: Memory Bloat
-
-Without expiration, memories accumulate forever. Session notes from weeks ago mix with current preferences. Storage grows, search results get polluted with irrelevant old context, and retrieval quality degrades.
-
-<Warning>
-Memory bloat degrades search quality. When "User prefers dark mode" competes with "Currently browsing electronics" from 6 months ago, semantic search returns stale session data instead of actual preferences. Old memories pollute retrieval.
-</Warning>
-
----
-
-## Short-Term Memories: Adding Expiration
-
-Set `expiration_date` to make memories temporary:
-
-```python
-from datetime import datetime, timedelta
-
-# Session context - expires in 7 days
-expires_at = (datetime.now() + timedelta(days=7)).isoformat()
-
-client.add(
-    "Currently browsing electronics category",
-    user_id="sarah",
-    expiration_date=expires_at
-)
-
-# User preference - no expiration, persists forever
-client.add(
-    "User prefers dark mode",
-    user_id="sarah"
-)
-
-```
-
-<Info icon="check">
-**Expected behavior:** After 7 days, the session context automatically disappears—no cron jobs, no manual cleanup. The preference persists forever. Mem0 handles expiration transparently.
-</Info>
-
-Memories with `expiration_date` are automatically removed after expiring. No cleanup job needed - Mem0 handles it.
-
-<Tip>
-Start conservative with short expiration windows (7 days), then extend them based on usage patterns. It's easier to increase retention than to clean up over-retained stale data. Monitor search quality to find the right balance.
-</Tip>
-
----
-
-## When to Use Each
-
-### Permanent Memories (no expiration_date):
-
-**Use for:**
-
-- User preferences and settings
-- Account information
-- Important facts and milestones
-- Historical data that matters long-term
-
-```python
-client.add("User prefers email notifications", user_id="sarah")
-client.add("User's birthday is March 15th", user_id="sarah")
-client.add("User completed onboarding on Jan 5th", user_id="sarah")
-
-```
-
-### Temporary Memories (with expiration_date):
-
-**Use for:**
-
-- Session context (current page, browsing history)
-- Temporary reminders
-- Recent chat history
-- Cached data
-
-```python
-expires_7d = (datetime.now() + timedelta(days=7)).isoformat()
-
-client.add(
-    "Currently viewing product ABC123",
-    user_id="sarah",
-    expiration_date=expires_7d
-)
-
-client.add(
-    "Asked about return policy",
-    user_id="sarah",
-    expiration_date=expires_7d
-)
-
-```
-
----
-
-## Setting Different Expiration Periods
-
-Different data needs different lifetimes:
-
-```python
-# Session context - 7 days
-expires_7d = (datetime.now() + timedelta(days=7)).isoformat()
-client.add("Browsing electronics", user_id="sarah", expiration_date=expires_7d)
-
-# Recent chat - 30 days
-expires_30d = (datetime.now() + timedelta(days=30)).isoformat()
-client.add("User asked about warranty", user_id="sarah", expiration_date=expires_30d)
-
-# Important preference - no expiration
-client.add("User prefers dark mode", user_id="sarah")
-
-```
-
----
-
-## Using Metadata to Track Memory Types
-
-Tag memories to make filtering easier:
-
-```python
-expires_7d = (datetime.now() + timedelta(days=7)).isoformat()
-
-# Tag session context
-client.add(
-    "Browsing electronics",
-    user_id="sarah",
-    expiration_date=expires_7d,
-    metadata={"type": "session"}
-)
-
-# Tag preference
-client.add(
-    "User prefers dark mode",
-    user_id="sarah",
-    metadata={"type": "preference"}
-)
-
-# Query only preferences
-preferences = client.get_all(
-    filters={
-        "AND": [
-            {"user_id": "sarah"},
-            {"metadata": {"type": "preference"}}
-        ]
-    }
-)
-
-```
-
----
-
-## Checking Expiration Status
-
-See which memories will expire and when:
-
-```python
-results = client.get_all(filters={"user_id": "sarah"})
-
-for memory in results['results']:
-    exp_date = memory.get('expiration_date')
-
-    if exp_date:
-        print(f"Temporary: {memory['memory']}")
-        print(f"  Expires: {exp_date}\\n")
-    else:
-        print(f"Permanent: {memory['memory']}\\n")
-
-```
-
-**Output:**
-
-```
-Temporary: Browsing electronics
-  Expires: 2025-11-01T10:30:00Z
-
-Temporary: Viewed MacBook Pro and Dell XPS
-  Expires: 2025-11-01T10:30:00Z
-
-Permanent: User prefers dark mode
-
-Permanent: User prefers email notifications
-
-```
-
----
-
-## What You Built
-
-A self-cleaning memory system with automatic retention policies:
-
-- **Automatic expiration** - Memories self-destruct after defined periods, no cron jobs needed
-- **Tiered retention** - 7-day session context, 30-day chat history, permanent preferences
-- **Metadata tagging** - Classify memories by type (session, preference, chat) for filtered retrieval
-- **Expiration tracking** - Check which memories will expire and when using `get_all()`
-
-This pattern keeps storage costs low and search quality high as your memory store scales.
-
----
-
-## Summary
-
-Memory expiration keeps storage clean and search results relevant. Use **`expiration_date`** for temporary data (session context, recent chats), skip it for permanent facts (preferences, account info). Mem0 handles cleanup automatically—no background jobs required.
-
-Start by identifying what's temporary versus permanent, then set conservative expiration windows and adjust based on retrieval quality.
-
-<CardGroup cols={2}>
-  <Card title="Control Memory Ingestion" icon="filter" href="/cookbooks/essentials/controlling-memory-ingestion">
-    Pair expirations with ingestion rules so only trusted context persists.
-  </Card>
-  <Card title="Export Memories Safely" icon="download" href="/cookbooks/essentials/exporting-memories">
-    Build compliant archives once your retention windows are dialed in.
-  </Card>
-</CardGroup>

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

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

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

@@ -55,7 +55,7 @@ GEMINI_API_KEY=your-gemini-api-key-here
 ```
 
 <Note>
-Ensure you have your Mem0 API key from the <a href="https://app.mem0.ai" 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?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).
 </Note>
 
 ## Gemini Memory Agent

docs/cookbooks/frameworks/llamaindex-multiagent.mdx

@@ -41,7 +41,7 @@ Set up your environment variables:
 - `MEM0_API_KEY`: Your Mem0 Platform API key
 - `OPENAI_API_KEY`: Your OpenAI API key
 
-You can obtain your Mem0 Platform API key from the <a href="https://app.mem0.ai" rel="nofollow">Mem0 Platform</a>.
+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>.
 
 ## 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/" rel="nofollow">Mem0 Platform</a>
+- <a href="https://app.mem0.ai/?utm_source=oss&utm_medium=cookbook-llamaindex-multiagent" rel="nofollow">Mem0 Platform</a>
 
 ---
 

docs/cookbooks/frameworks/llamaindex-react.mdx

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

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

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

docs/cookbooks/integrations/aws-bedrock.mdx

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

docs/cookbooks/integrations/healthcare-google-adk.mdx

@@ -77,7 +77,7 @@ def retrieve_patient_info(query: str) -> dict:
     results = mem0_client.search(
         query,
         user_id=USER_ID,
-        limit=5,
+        top_k=5,
         threshold=0.7  # Higher threshold for more relevant results
     )
 

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

@@ -23,18 +23,15 @@ 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" rel="nofollow">Mem0 Dashboard</a>.
+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>.
 
 ### Configuration
 
 ```javascript
-const mem0Config = {
-    apiKey: process.env.MEM0_API_KEY,
-    user_id: "sample-user",
-};
+const USER_ID = "sample-user";
 
 const openAIClient = new OpenAI();
-const mem0Client = new MemoryClient(mem0Config);
+const mem0Client = new MemoryClient({ apiKey: process.env.MEM0_API_KEY });
 ```
 
 ## Adding Memories
@@ -43,14 +40,14 @@ Store user preferences, past interactions, or any relevant information:
 <CodeGroup>
 ```javascript JavaScript
 async function addUserPreferences() {
-    const mem0Client = new MemoryClient(mem0Config);
+    const mem0Client = new MemoryClient({ apiKey: process.env.MEM0_API_KEY });
     
     const userPreferences = "I Love BMW, Audi and Porsche. I Hate Mercedes. I love Red cars and Maroon cars. I have a budget of 120K to 150K USD. I like Audi the most.";
     
     await mem0Client.add([{
         role: "user",
         content: userPreferences,
-    }], mem0Config);
+    }], { userId: "sample-user" });
 }
 
 await addUserPreferences();
@@ -91,7 +88,7 @@ await addUserPreferences();
 Search for relevant memories based on the current user input:
 
 ```javascript
-const relevantMemories = await mem0Client.search(userInput, mem0Config);
+const relevantMemories = await mem0Client.search(userInput, { userId: USER_ID });
 ```
 
 ## Structured Responses with Zod
@@ -152,10 +149,7 @@ import dotenv from 'dotenv';
 
 dotenv.config();
 
-const mem0Config = {
-    apiKey: process.env.MEM0_API_KEY,
-    user_id: "sample-user",
-};
+const USER_ID = "sample-user";
 
 async function run() {
     // Responses without memories
@@ -185,7 +179,7 @@ const Cars = z.object({
 
 async function main(memory = false) {
   const openAIClient = new OpenAI();
-  const mem0Client = new MemoryClient(mem0Config);
+  const mem0Client = new MemoryClient({ apiKey: process.env.MEM0_API_KEY });
 
   const input = "Suggest me some cars that I can buy today.";
 
@@ -195,12 +189,12 @@ async function main(memory = false) {
   await mem0Client.add([{
     role: "user",
     content: input,
-  }], mem0Config);
+  }], { userId: USER_ID });
 
   // Search for relevant memories
   let relevantMemories = []
   if (memory) {
-    relevantMemories = await mem0Client.search(input, mem0Config);
+    relevantMemories = await mem0Client.search(input, { userId: USER_ID });
   }
 
   const response = await openAIClient.responses.create({
@@ -213,14 +207,14 @@ async function main(memory = false) {
 }
 
 async function addSampleMemories() {
-  const mem0Client = new MemoryClient(mem0Config);
+  const mem0Client = new MemoryClient({ apiKey: process.env.MEM0_API_KEY });
 
   const myInterests = "I Love BMW, Audi and Porsche. I Hate Mercedes. I love Red cars and Maroon cars. I have a budget of 120K to 150K USD. I like Audi the most.";
   
   await mem0Client.add([{
     role: "user",
     content: myInterests,
-  }], mem0Config);
+  }], { userId: USER_ID });
 }
 
 const getMemoryString = (memories) => {
@@ -309,7 +303,7 @@ run().catch(console.error);
 ## Resources
 
 - [Mem0 Documentation](https://docs.mem0.ai/introduction)
-- <a href="https://app.mem0.ai/dashboard" rel="nofollow">Mem0 Dashboard</a>
+- <a href="https://app.mem0.ai/dashboard?utm_source=oss&utm_medium=cookbook-openai-tool-calls" rel="nofollow">Mem0 Dashboard</a>
 - [API Reference](https://docs.mem0.ai/api-reference)
 - [OpenAI Documentation](https://platform.openai.com/docs)
 

docs/cookbooks/overview.mdx

@@ -37,13 +37,6 @@ Here are some examples of how Mem0 can be integrated into various applications:
   >
     Filter speculation and low-confidence data.
   </Card>
-  <Card
-    title="Set Memory Expiration"
-    icon="timer"
-    href="/cookbooks/essentials/memory-expiration-short-and-long-term"
-  >
-    Short-term vs long-term retention strategies.
-  </Card>
 </CardGroup>
 
 ## Companion Playbooks

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

@@ -216,7 +216,7 @@ memory.delete_all(user_id="alice")
 ## Put it into practice
 
 - Review the <Link href="/api-reference/memory/delete-memory">Delete Memory API reference</Link>, plus <Link href="/api-reference/memory/batch-delete">Batch Delete</Link> and <Link href="/api-reference/memory/delete-memories">Filtered Delete</Link>.
-- Pair deletes with <Link href="/platform/features/expiration-date">Expiration Policies</Link> to automate retention.
+- Pair deletes with <Link href="/platform/features/platform-overview">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/expiration-date"
+    href="/platform/features/platform-overview"
   />
 </CardGroup>

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

@@ -156,6 +156,10 @@ const memories = memory.search("food preferences", {
   Expect an array of memory documents. Platform responses include vectors, metadata, and timestamps; OSS returns your stored schema.
 </Info>
 
+<Note>
+  On Mem0 Platform v3, time-aware queries use Temporal Reasoning internally while preserving the normal search response shape. See <Link href="/platform/features/temporal-reasoning">Temporal Reasoning</Link>.
+</Note>
+
 ## Filter patterns
 
 Filters help narrow down search results. Common use cases:
@@ -251,4 +255,4 @@ For the full list of filter logic, comparison operators, and optional search par
     icon="rocket"
     href="/cookbooks/operations/support-inbox"
   />
-</CardGroup>
+</CardGroup>

docs/docs.json

@@ -72,7 +72,8 @@
                           "platform/features/entity-scoped-memory",
                           "platform/features/async-client",
                           "platform/features/multimodal-support",
-                          "platform/features/custom-categories"
+                          "platform/features/custom-categories",
+                          "platform/features/temporal-reasoning"
                         ]
                       },
                       {
@@ -83,7 +84,8 @@
                           "platform/advanced-memory-operations",
                           "platform/features/criteria-retrieval",
                           "platform/features/contextual-add",
-                          "platform/features/custom-instructions"
+                          "platform/features/custom-instructions",
+                          "platform/features/memory-decay"
                         ]
                       },
                       {
@@ -153,6 +155,7 @@
                     "icon": "rocket",
                     "pages": [
                       "open-source/overview",
+                      "open-source/setup",
                       "vibecoding",
                       "open-source/python-quickstart",
                       "open-source/node-quickstart"
@@ -328,10 +331,8 @@
                       "cookbooks/essentials/building-ai-companion",
                       "cookbooks/essentials/entity-partitioning-playbook",
                       "cookbooks/essentials/controlling-memory-ingestion",
-                      "cookbooks/essentials/memory-expiration-short-and-long-term",
                       "cookbooks/essentials/tagging-and-organizing-memories",
-                      "cookbooks/essentials/exporting-memories",
-                      "cookbooks/essentials/choosing-memory-architecture-vector-vs-graph"
+                      "cookbooks/essentials/exporting-memories"
                     ]
                   },
                   {
@@ -580,7 +581,7 @@
     "primary": {
       "type": "button",
       "label": "Your Dashboard",
-      "href": "https://app.mem0.ai"
+      "href": "https://app.mem0.ai?utm_source=oss&utm_medium=docs-nav"
     }
   },
   "footer": {
@@ -610,7 +611,7 @@
         "title": "Try in Playground",
         "description": "Open this example in the interactive Mem0 playground",
         "icon": "play",
-        "href": "https://app.mem0.ai/playground"
+        "href": "https://app.mem0.ai/playground?utm_source=oss&utm_medium=docs-nav"
       }
     ]
   },
@@ -627,6 +628,10 @@
       "source": "/platform/features/expiration-date",
       "destination": "/"
     },
+    {
+      "source": "/cookbooks/essentials/memory-expiration-short-and-long-term",
+      "destination": "/cookbooks/essentials/building-ai-companion"
+    },
     {
       "source": "/platform/features/async-mode-default-change",
       "destination": "/"
@@ -639,6 +644,10 @@
       "source": "/platform/features/graph-memory",
       "destination": "/migration/oss-v2-to-v3"
     },
+    {
+      "source": "/cookbooks/essentials/choosing-memory-architecture-vector-vs-graph",
+      "destination": "/migration/oss-v2-to-v3"
+    },
     {
       "source": "/changelog",
       "destination": "/changelog/highlights"
@@ -1136,4 +1145,4 @@
       "destination": "/introduction"
     }
   ]
-}
+}

docs/integrations/agentops.mdx

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

docs/integrations/agno.mdx

@@ -23,7 +23,7 @@ pip install agno mem0ai python-dotenv
 ```
 
 2. Valid API keys:
-   - <a href="https://app.mem0.ai/dashboard/api-keys" rel="nofollow">Mem0 API Key</a>
+   - <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`)

docs/integrations/autogen.mdx

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

docs/integrations/aws-bedrock.mdx

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

docs/integrations/chatdev.mdx

@@ -18,7 +18,7 @@ In this guide, you'll:
 - **Python 3.12+**
 - **[uv](https://docs.astral.sh/uv/)** — Python package manager
 - **Node.js 18+** and **npm** — only needed if using the web console
-- A **Mem0 API key** from <a href="https://app.mem0.ai" rel="nofollow">app.mem0.ai</a>
+- A **Mem0 API key** from <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-chatdev" 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" rel="nofollow">Mem0 Platform</a>.</Note>
+<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>
 
 ```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" rel="nofollow">app.mem0.ai</a> |
+| `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> |
 | `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" 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?utm_source=oss&utm_medium=integration-chatdev" 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" 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?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")`.
 
 ## Key Features
 

docs/integrations/claude-code.mdx

@@ -17,8 +17,8 @@ Add persistent memory to [**Claude Code**](https://docs.anthropic.com/en/docs/cl
 Before setting up Mem0 with Claude Code, ensure you have:
 
 1. A Mem0 Platform account and API key:
-   - <a href="https://app.mem0.ai" 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-`)
+   - <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-`)
 
 2. Claude Code CLI or Claude Cowork desktop app installed
 
@@ -32,12 +32,19 @@ 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.
 
-```
-/plugin marketplace add mem0ai/mem0
-/plugin install mem0@mem0-plugins
-```
+1. Add the Mem0 marketplace:
+
+   ```
+   /plugin marketplace add mem0ai/mem0
+   ```
+
+2. Install the plugin:
+
+   ```
+   /plugin install mem0@mem0-plugins
+   ```
 
 **Claude Cowork desktop app:** Open the Cowork tab, click **Customize** in the sidebar, click **Browse plugins**, and install Mem0.
 

docs/integrations/codex.mdx

@@ -17,8 +17,8 @@ Add persistent memory to [**OpenAI Codex**](https://openai.com/index/codex/) wit
 Before setting up Mem0 with Codex, ensure you have:
 
 1. A Mem0 Platform account and API key:
-   - <a href="https://app.mem0.ai" 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-`)
+   - <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-`)
 
 2. OpenAI Codex access
 
@@ -30,91 +30,100 @@ export MEM0_API_KEY="m0-your-api-key"
 
 ## Installation
 
-### Option A — Repo Marketplace (Recommended for Teams)
+### Option A — Direct MCP (Recommended)
 
-Add a `.agents/plugins/marketplace.json` to your repository root:
+The fastest way to connect Codex to Mem0 — no downloads, no marketplace. Codex reads MCP servers from `~/.codex/config.toml` as TOML. Add:
 
-```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"
-    }
-  ]
-}
+```toml
+[mcp_servers.mem0]
+url = "https://mcp.mem0.ai/mcp"
+bearer_token_env_var = "MEM0_API_KEY"
 ```
 
-Then in Codex, browse the repo's plugin directory and install Mem0.
+Make sure `MEM0_API_KEY` is exported in the shell you launch Codex from, then restart Codex.
 
-### Option B — Personal Marketplace
+<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>
 
-Add to `~/.agents/plugins/marketplace.json`:
+### Option B — Sideload the Plugin (Advanced)
 
-```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"
-    }
-  ]
-}
+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
 ```
 
-### Option C — Manual MCP Configuration
+**Step 2.** Register the bundled marketplace with Codex's CLI:
 
-Add to your Codex MCP config:
-
-```json
-{
-  "mcpServers": {
-    "mem0": {
-      "type": "http",
-      "url": "https://mcp.mem0.ai/mcp/",
-      "headers": {
-        "Authorization": "Token ${MEM0_API_KEY}"
-      }
-    }
-  }
-}
+```bash
+codex plugin marketplace add ~/codex-plugins/mem0-source
 ```
 
+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">
-  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.
+  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.
 </Info>
 
 ## What's Included
 
-| Component | Plugin Install | MCP Only |
-|-----------|:--------------:|:--------:|
+| Component | Sideloaded Plugin | Direct MCP |
+|-----------|:-----------------:|:----------:|
 | 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
 
@@ -134,7 +143,7 @@ Once installed, the following tools are available in every Codex session:
 
 ## Memory Protocol Skill
 
-Codex uses a skill-based approach instead of lifecycle hooks. When installed via the plugin marketplace, the memory protocol skill instructs the agent to:
+When the plugin is sideloaded, 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
@@ -199,8 +208,12 @@ 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
-- **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
+- **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.
 
 <CardGroup cols={2}>
   <Card title="Mem0 MCP Setup" icon="puzzle-piece" href="/platform/mem0-mcp">

docs/integrations/crewai.mdx

@@ -22,7 +22,7 @@ pip install crewai crewai-tools mem0ai
 
 Import required modules and set up configurations:
 
-<Note>Remember to get your API keys from <a href="https://app.mem0.ai" 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?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>
 
 ```python
 import os

docs/integrations/cursor.mdx

@@ -17,8 +17,8 @@ Add persistent memory to [**Cursor**](https://cursor.com) with the Mem0 plugin.
 Before setting up Mem0 with Cursor, ensure you have:
 
 1. A Mem0 Platform account and API key:
-   - <a href="https://app.mem0.ai" 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-`)
+   - <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-`)
 
 2. Cursor installed ([cursor.com](https://cursor.com))
 

docs/integrations/flowise.mdx

@@ -38,7 +38,7 @@ npx flowise start
 
 ### 2. Obtain Your Mem0 API Key
 
-1. Navigate to the <a href="https://app.mem0.ai/dashboard/api-keys" rel="nofollow">Mem0 API Key dashboard</a>.
+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)
@@ -70,7 +70,7 @@ Test your memory configuration:
 
 1. Save your Flowise configuration
 2. Run a test chat and store some information
-3. Verify the stored memories in the <a href="https://app.mem0.ai/dashboard/requests" rel="nofollow">Mem0 Dashboard</a>
+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)
 
@@ -103,7 +103,7 @@ Available settings include:
 
 ### Platform Configuration
 
-Additional settings available in <a href="https://app.mem0.ai/dashboard/project-settings" rel="nofollow">Mem0 Project Settings</a>:
+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

docs/integrations/google-ai-adk.mdx

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

docs/integrations/hermes.mdx

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

docs/integrations/keywords.mdx

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

docs/integrations/langchain.mdx

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

docs/integrations/langgraph.mdx

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

docs/integrations/llama-index.mdx

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

docs/integrations/mastra.mdx

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

docs/integrations/openai-agents-sdk.mdx

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

docs/integrations/openclaw.mdx

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

docs/integrations/raycast.mdx

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

docs/integrations/vercel-ai-sdk.mdx

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