chore(cli): remove agent-rush command and de-brand whoami

The AGENTRUSH game has ended. Remove the agent-rush add/search subcommands from both CLIs, change the whoami output to 'Your user_id: <default_user_id>', drop the agent_rush config key handling, and update the CLI docs page accordingly. Bumps mem0-cli to 0.2.10 and @mem0/cli to 0.2.11.
2026-07-07 19:51:16 -07:00
532 changed files with 24265 additions and 38150 deletions
@@ -12,7 +12,7 @@
      "name": "mem0",
      "source": "./integrations/mem0-plugin",
      "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search to Claude workflows.",
-      "version": "0.2.13"
+      "version": "0.2.12"
    }
  ]
 }
@@ -12,7 +12,7 @@
      "name": "mem0",
      "source": "./integrations/mem0-plugin",
      "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search.",
-      "version": "0.2.13"
+      "version": "0.2.12"
    }
  ]
 }
@@ -9,10 +9,12 @@ body:
      label: Component
      description: Which part of mem0 is affected?
      options:
-        - Python SDK
+        - Core / Python SDK
        - TypeScript SDK
-        - Vector Store
-        - Plugin
+        - Vector Store (Qdrant, PGVector, Redis, Chroma, etc.)
+        - Graph Memory (Neo4j, Memgraph, etc.)
+        - Ollama / Local Models
+        - OpenClaw
        - REST API
        - Other
    validations:
@@ -9,11 +9,14 @@ body:
      label: Component
      description: Which part of mem0 does this relate to?
      options:
-        - Python SDK
+        - Core / Python SDK
        - TypeScript SDK
-        - Vector Store
-        - Plugin
+        - Vector Store (Qdrant, PGVector, Redis, Chroma, etc.)
+        - Graph Memory (Neo4j, Memgraph, etc.)
+        - Ollama / Local Models
+        - OpenClaw
        - REST API
+        - Benchmarks / Evals
        - Other
    validations:
      required: true
@@ -1,15 +1,18 @@
-policy:
-  - section:
-      - id: ['component']
-        block-list: ['Other']
-        label:
-          - name: 'sdk-python'
-            keys: ['Python SDK']
-          - name: 'sdk-typescript'
-            keys: ['TypeScript SDK']
-          - name: 'vector-store'
-            keys: ['Vector Store']
-          - name: 'plugin'
-            keys: ['Plugin']
-          - name: 'rest-api'
-            keys: ['REST API']
+# Maps dropdown selections to GitHub labels
+# Used by the advanced-issue-labeler GitHub Action
+
+component:
+  - label: "sdk-python"
+    matcher: "Core / Python SDK"
+  - label: "sdk-typescript"
+    matcher: "TypeScript SDK"
+  - label: "vector-store"
+    matcher: "Vector Store"
+  - label: "graph-memory"
+    matcher: "Graph Memory"
+  - label: "ollama"
+    matcher: "Ollama"
+  - label: "openclaw"
+    matcher: "OpenClaw"
+  - label: "rest-api"
+    matcher: "REST API"
@@ -1,40 +0,0 @@
-{
-  "language": {
-    "sdk-python": [
-      "python", "pip install", "pypi", "pyproject", "requirements.txt",
-      "from mem0", "import mem0", "traceback", "pydantic", "asyncmemory",
-      "poetry", "virtualenv", "venv", "conda", "pytest", "async def"
-    ],
-    "sdk-typescript": [
-      "typescript", "javascript", "pnpm", "yarn", "node.js", "nodejs",
-      "mem0-ts", "mem0ai/oss", "tsconfig", "await import",
-      "=> {", "undefined is not"
-    ]
-  },
-  "area": {
-    "plugin": [
-      "openclaw", "openclaw-mem0", "openclaw.json", "openclaw plugin",
-      "claude code", "opencode", "pi agent", "mem0-plugin",
-      "cursor plugin", "codex plugin", "editor plugin"
-    ],
-    "cli": ["mem0-cli", "@mem0/cli", "npx mem0", "command line"],
-    "vector-store": [
-      "pgvector", "pinecone", "chroma", "chromadb", "weaviate",
-      "milvus", "faiss", "vector store", "vectorstore",
-      "elasticsearch", "supabase", "azure ai search",
-      "s3 vectors", "mongodb"
-    ],
-    "integrations": [
-      "vercel ai", "vercel-ai-sdk", "@mem0/vercel-ai-provider",
-      "llamaindex", "crewai", "autogen", "langgraph"
-    ],
-    "rest-api": [
-      "rest api", "fastapi", "docker-compose", "/v1/memories",
-      "localhost:8000", "localhost:8888", "curl -x", "http endpoint"
-    ],
-    "documentation": [
-      "docs.mem0.ai", "documentation", "typo", "readme", "docstring", "broken link",
-      "issue on docs", "docs:", "link to the docs page", "issue with current documentation"
-    ]
-  }
-}
@@ -1,59 +0,0 @@
-sdk-python:
-  - changed-files:
-      - any-glob-to-any-file:
-          - 'mem0/**'
-          - 'tests/**'
-          - 'cli/python/**'
-          - 'pyproject.toml'
-          - 'poetry.lock'
-
-sdk-typescript:
-  - changed-files:
-      - any-glob-to-any-file:
-          - 'mem0-ts/**'
-          - 'cli/node/**'
-
-vector-store:
-  - changed-files:
-      - any-glob-to-any-file:
-          - 'mem0/vector_stores/**'
-          - 'mem0-ts/src/oss/src/vector_stores/**'
-
-rest-api:
-  - changed-files:
-      - any-glob-to-any-file: 'server/**'
-
-integrations:
-  - changed-files:
-      - any-glob-to-any-file: 'integrations/**'
-
-plugin:
-  - changed-files:
-      - all-globs-to-any-file:
-          - 'integrations/**'
-          - '!integrations/vercel-ai-sdk/**'
-      - any-glob-to-any-file:
-          - 'skills/**'
-          - '.agents/**'
-          - '.claude-plugin/**'
-          - '.codex-plugin/**'
-          - '.cursor-plugin/**'
-          - 'marketplace.json'
-
-cli:
-  - changed-files:
-      - any-glob-to-any-file: 'cli/**'
-
-documentation:
-  - changed-files:
-      - any-glob-to-any-file:
-          - 'docs/**'
-          - 'examples/**'
-          - '*.md'
-
-ci:
-  - changed-files:
-      - any-glob-to-any-file:
-          - '.github/**'
-          - 'scripts/**'
-          - '.pre-commit-config.yaml'
@@ -1,44 +0,0 @@
-const fs = require('fs');
-
-function componentLabels(keywords) {
-  return Object.values(keywords).flatMap(Object.keys);
-}
-
-function toMatcher(term) {
-  const escaped = term.replace(/[.*+?^${}()|[\]\\]/g, '\\$&');
-  const prefix = /^[a-z0-9]/i.test(term) ? '\\b' : '';
-  return new RegExp(prefix + escaped, 'i');
-}
-
-function scoreGroup(text, group) {
-  let winner = null;
-  let best = 0;
-  for (const [label, terms] of Object.entries(group)) {
-    const score = terms.reduce((n, term) => n + (toMatcher(term).test(text) ? 1 : 0), 0);
-    if (score > best) {
-      winner = label;
-      best = score;
-    }
-  }
-  return winner;
-}
-
-const UMBRELLA = { plugin: 'integrations' };
-
-function inferComponentLabels(text, keywords) {
-  if (!text) return [];
-  const labels = [scoreGroup(text, keywords.language), scoreGroup(text, keywords.area)].filter(
-    Boolean,
-  );
-  for (const label of labels.slice()) {
-    const parent = UMBRELLA[label];
-    if (parent && !labels.includes(parent)) labels.push(parent);
-  }
-  return labels;
-}
-
-function loadKeywords(file) {
-  return JSON.parse(fs.readFileSync(file, 'utf8'));
-}
-
-module.exports = { componentLabels, inferComponentLabels, loadKeywords };
@@ -1,102 +0,0 @@
-const assert = require('assert');
-const path = require('path');
-const { inferComponentLabels, loadKeywords } = require('./infer-component-labels.js');
-
-const keywords = loadKeywords(path.join(__dirname, '..', 'component-keywords.json'));
-
-const cases = [
-  {
-    number: 6210,
-    title: "but(anthropic): sampling parameters returns 400 error for new model",
-    body: "### Component\n\nCore / Python SDK\n\n### Description\n\n### Summary\n\nWhen using Anthropic latest models such as `claude-opus-4-7`, `claude-opus-4-8`, or `claude-sonnet-5`, Mem0 still sends sampling parameters like `temperature` / `top_p`. These models do not support those parameters, causing Anthropic API requests to fail.\n\nSee https://platform.claude.com/docs/en/about-claude/models/migration-guide\n\n### Steps to Reproduce\n\n```python\n  from mem0 import Memory\n\n  m = Memory.from_config({\n      \"llm\": {\n          \"provider\": \"anthropic\",\n          \"config\": {\n              \"model\": \"claude-opus-4-8\",\n              \"api_key\": \"your-anthropic-api-key\"\n          },\n      },\n      ...\n  })\n```\n\n### Expected Behavior\n\nMem0 should detect Anthropic models that do not support sampling parameters and omit temperature and top_p from the request.\n\nFor models that still support sampling parameters, such as claude-opus-4-6, claude-sonnet-4-6, and claude-haiku-4-5, Mem0 should continue sending supported sampling parameters till they're deprecated.\n\n### Actual Behavior\n\nMem0 includes temperature by default for Anthropic requests. With newer Anthropic models that do not support sampling parameters, the API request fails because unsupported parameters are sent.\n\n### Environment\n\n  - mem0 version: 2.0.11\n  - Python/Node version: Python 3.11\n  - OS: macOS\n",
-    expected: ["sdk-python"],
-  },
-  {
-    number: 5770,
-    title: "feat(ts-sdk): add FastEmbed embedding provider",
-    body: "## Summary\n\nThe Python SDK supports **FastEmbed** as an embedding provider, but the TypeScript OSS SDK (`mem0ai/oss`) does not. Add it to bring the TS SDK to parity.\n\n| | |\n|---|---|\n| Python reference | `mem0/embeddings/fastembed.py` |\n| Registered in (Python) | `mem0/utils/factory.py` (EmbedderFactory) |\n| Target file (TypeScript) | `mem0-ts/src/oss/src/embeddings/fastembed.ts` |\n| Suggested implementation | Use the `fastembed` npm package (ONNX local embeddings). |\n\n## Requirements\n\n- [ ] Implement `FastEmbedEmbedder` in `mem0-ts/src/oss/src/embeddings/fastembed.ts`, extending `Embedder` (`mem0-ts/src/oss/src/embeddings/base.ts`) and mirroring the Python provider's behavior (embed / embedBatch).\n- [ ] Register the `\"fastembed\"` provider in `mem0-ts/src/oss/src/utils/factory.ts` (EmbedderFactory).\n- [ ] Add config typing in `mem0-ts/src/oss/src/types/`.\n- [ ] Add a unit test under `mem0-ts/src/oss/src/tests/`.\n- [ ] Add `fastembed` to `mem0-ts/package.json` (optional/peer dependency, lazy-imported like other providers).\n- [ ] Update docs under `docs/` if this provider is user-facing.\n\n## Reference pattern\n\nMirror an existing TS provider: `embeddings/openai.ts`.\n\n## Notes\n\n`fastembed` (v2.x) is the JS port of Qdrant's FastEmbed — local/offline embeddings. Mirror the default model in `mem0/embeddings/fastembed.py`.\n\n---\n_Part of the TypeScript ↔ Python SDK provider-parity effort. One provider per issue (atomic)._\n",
-    expected: ["sdk-typescript"],
-  },
-  {
-    number: 3940,
-    title: "Milvus database will return distance not similarity score",
-    body: "### 🐛 Describe the bug\n\nMilvus database will return distance not similarity score\n\n## in milvus.py\n\ndef _parse_output(self, data: list):\n        \"\"\"\n        Parse the output data.\n\n        Args:\n            data (Dict): Output data.\n\n        Returns:\n            List[OutputData]: Parsed output data.\n        \"\"\"\n        memory = []\n\n        for value in data:\n            uid, score, metadata = (\n                value.get(\"id\"),\n                value.get(\"distance\"),  # here\n                value.get(\"entity\", {}).get(\"metadata\"),\n            )\n\n            memory_obj = OutputData(id=uid, score=score, payload=metadata)\n            memory.append(memory_obj)\n\n        return memory\n",
-    expected: ["vector-store"],
-  },
-  {
-    number: 5290,
-    title: "Recall search failed: Bad Request Using OpenAI Embedding Model",
-    body: "### Component\n\nOpenClaw\n\n### Description\n\n### Summary\nuse openclaw.json config:\n\n```json\n...\n\"embedder\": {\n              \"provider\": \"openai\",\n              \"config\": {\n                \"model\": \"bge-base-zh-v1.5\",\n                \"embedding_dims\": 1024,\n                \"embeddingDims\": 1024,\n                \"url\": \"https://xxxxxxxxx/v1\",\n                \"apiKey\": \"xxxxxxxxxxxx\"\n              }\n            },\n\"vectorStore\": {\n              \"provider\": \"qdrant\",\n              \"config\": {\n                \"url\": \"http://qdrant:6333\",\n                \"apiKey\": \"${QDRANT_API_KEY}\",\n                \"collectionName\": \"mem0\",\n                \"embeddingModelDims\": 1024\n              }\n            }\n```\n```\n\nopenclaw log info is：\n\n```\n23:14:20 Api key is used with unsecure connection.\n23:14:21 [mem0] Recall search failed: Bad Request\n23:14:21 [plugins] openclaw-mem0: skills-mode recall (strategy=smart) injecting 0 memories (~20 tokens)\n23:14:22 [ws] ⇄ res ✓ sessions.list 256ms conn=d1eb9bc4…17da id=201b8113…c9dc\n23:14:22 [ws] ⇄ res ✓ sessions.list 264ms conn=d1eb9bc4…17da id=4939f962…2f16\n23:14:34 [ws] ⇄ res ✓ sessions.list 250ms conn=d1eb9bc4…17da id=f7ad503f…baa6\n23:15:12 [mem0] **Recall search failed: Bad Request**\n23:15:12 [plugins] openclaw-mem0: skills-mode recall (strategy=smart) injecting 0 memories (~20 tokens)\n23:15:12 [ws] ⇄ res ✓ sessions.list 288ms conn=d1eb9bc4…17da id=9e20bb86…371e\n23:15:13 [ws] ⇄ res ✓ sessions.list 268ms conn=d1eb9bc4…17da id=3b49a2ad…7ada\n23:15:20 [ws] ⇄ res ✓ sessions.list 235ms conn=d1eb9bc4…17da id=a192da30…069f\n```\n\n### Actual Behavior\n\nembedding model response ok,response message has 1024 vectors,but the vectors are submitted to vector-db:qdrant with all zero vectors,and vectors has only 256 size.\n\n```http\nPOST /collections/mem0/points/search HTTP/1.1\nhost: qdrant:6333\nconnection: keep-alive\nuser-agent: qdrant-js/1.13.0\napi-key: xxxxxxxxxxxxxxxxxxxxxxxxxxxx\nContent-Type: application/json\nAccept: application/json\naccept-language: *\nsec-fetch-mode: cors\naccept-encoding: gzip, deflate\ncontent-length: 651\n\n{\"vector\":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],\"limit\":120,\"offset\":0,\"filter\":{\"must\":[{\"key\":\"user_id\",\"match\":{\"value\":\"agent\"}}]},\"with_payload\":true,\"with_vector\":false}\n\n**HTTP/1.1 400 Bad Request**\ntransfer-encoding: chunked\ncontent-type: application/json\nvary: accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers\ncontent-encoding: gzip\n\n```\n\n### Expected Behavior\n\nembedding model response ok by tcpdump, response message has 1024 vectors,and this vectors are submitted to vector-db:qdrant with the same vectors,and vectors has also 1024 size.\n\n\n### Environment\n\n- openclaw-mem0 version: 1.0.11\n- qdrant: 1.13.6\n",
-    expected: ["plugin", "integrations"],
-  },
-  {
-    number: 3696,
-    title: "Cannot set expiration_date for memory in REST API server (Docker Compose)",
-    body: "### 🐛 Describe the bug\n\nI'm using docker compose to deploy a REST API server. When adding memory, I'm unable to set the expiration_date. Is this feature not supported?",
-    expected: ["rest-api"],
-  },
-  {
-    number: 6252,
-    title: "cursor: on_file_read_cursor.sh ignores auto_search / MEM0_AUTO_SEARCH",
-    body: "### Component\n\nCursor / mem0-plugin\n\n### Description\n\n`on_file_read_cursor.sh` never checks `MEM0_AUTO_SEARCH`. In Claude Code, #6065/#6071 added a guard on `on_file_read.sh`, but the Cursor PreToolUse variant still always calls `file_context.py` (and thus Platform search) once `MEM0_API_KEY` is set.\n\n### Expected\n\nWhen `auto_search: false` / `MEM0_AUTO_SEARCH=false`, `on_file_read_cursor.sh` should exit 0 without searching.\n\n### Actual\n\nTimeline search still runs.\n\n### Related\n\n#6065, #6071, #6250\n",
-    expected: ["plugin", "integrations"],
-  },
-  {
-    number: 6032,
-    title: "docs: fix typos and punctuation errors across docs",
-    body: "### Description\n\n### Page\nMultiple pages — see list below.\n\n### What's Wrong or Missing\n1. https://docs.mem0.ai/components/llms/overview — \"a llm\" should be \"an LLM\"\n2. https://docs.mem0.ai/components/vectordbs/dbs/azure — 2 comma splices + \"setup\" used as a verb (should be \"set up\")\n3. https://docs.mem0.ai/components/embedders/models/azure_openai — \"from the Azure.\" is an incomplete sentence\n4. https://docs.mem0.ai/components/llms/models/azure_openai — same incomplete \"from the Azure\" phrasing\n5. https://docs.mem0.ai/cookbooks/companions/voice-companion-openai — \"an important information\" (uncountable noun)\n6. https://docs.mem0.ai/cookbooks/essentials/exporting-memories — comma splice\n7. https://docs.mem0.ai/cookbooks/integrations/tavily-search — \"usecase\" should be \"use case\"\n8. https://docs.mem0.ai/cookbooks/overview — broken parallelism in bullet list\n9. README.md — \"Github App\" should be \"GitHub App\"\n10. https://docs.mem0.ai/platform/overview — table cell not capitalized like other rows\n\n### Suggested Fix\nApply the corrections listed above for each page. I will submit a PR soon addressing all of the issues mentioned.",
-    expected: ["documentation"],
-  },
-];
-
-const cliRegressionCase = {
-  number: 3144,
-  title: "Bug Report: Memory Score Does Not Match Expected Relevance in Local Search",
-  body: "### 🐛 Describe the bug\n\n#### Description\n\nWhen using the locally deployed `mem0` server, the returned memory `score` from the `search` interface does not align with the expected semantic relevance. In particular, irrelevant or less relevant memories sometimes receive higher scores than directly related ones.\n\n#### Reproduction Steps\n\n```python\nmem0 = mem0_client(mode=\"local\")\nprint(\"Mem0 client initialized successfully.\")\n\nprint(\"Adding memories...\")\nresult = mem0.add(messages=[\n    {\"role\": \"user\", \"content\": \"I like drinking coffee in the morning\"},\n    {\"role\": \"user\", \"content\": \"I enjoy reading books at night\"}\n], user_id=\"alice\")\nprint(\"Memory added:\", result)\n\nprint(\"Searching memories...\")\nsearch_result = mem0.search(query=\"coffee\", user_id=\"alice\", top_k=2)\nprint(\"Search results:\", search_result)\n```\n\n#### Actual Output\n\n```json\n{\n  \"results\": [\n    {\n      \"id\": \"5099b5be-c673-4f09-99de-a196f43b6476\",\n      \"memory\": \"Likes drinking coffee in the morning\",\n      \"score\": 0.5115111920687857\n    },\n    {\n      \"id\": \"08df5c51-c52b-4c45-a5b6-b3f864ea149a\",\n      \"memory\": \"Enjoys reading books at night\",\n      \"score\": 0.7755568273863331\n    }\n  ],\n  \"relations\": [\n    {\"source\": \"coffee\", \"relationship\": \"consumed_in\", \"destination\": \"morning\"},\n    {\"source\": \"user_id:_alice\", \"relationship\": \"likes\", \"destination\": \"coffee\"},\n    {\"source\": \"user_id:_alice\", \"relationship\": \"likes_drinking\", \"destination\": \"coffee\"},\n    {\"source\": \"user_id:_alice\", \"relationship\": \"in_time\", \"destination\": \"morning\"},\n    {\"source\": \"user_id:_alice\", \"relationship\": \"drinks_in\", \"destination\": \"morning\"}\n  ]\n}\n```\n\n#### Expected Behavior\n\nThe memory `\"Likes drinking coffee in the morning\"` should have a **higher score** than `\"Enjoys reading books at night\"` when querying for `\"coffee\"`, since it is directly semantically related.",
-};
-
-let failures = 0;
-
-function run(name, fn) {
-  try {
-    fn();
-    console.log(`PASS ${name}`);
-  } catch (err) {
-    failures++;
-    console.error(`FAIL ${name}: ${err.message}`);
-  }
-}
-
-for (const { number, title, body, expected } of cases) {
-  const text = `${title}
-
-${body}`;
-  run(`#${number}`, () => {
-    assert.deepStrictEqual(inferComponentLabels(text, keywords), expected);
-  });
-}
-
-run('#3144 cliKeywordPrefixSubstringRegression', () => {
-  const text = `${cliRegressionCase.title}
-
-${cliRegressionCase.body}`;
-  const inferred = inferComponentLabels(text, keywords);
-  assert.ok(!inferred.includes('cli'), `expected 'cli' absent (body contains 'Mem0 client', a substring of the removed 'mem0 cli' term), got ${JSON.stringify(inferred)}`);
-});
-
-run('noKeywordMatchReturnsEmptyArray', () => {
-  const text = 'The weather today is sunny and I went for a walk in the park with my dog.';
-  assert.deepStrictEqual(inferComponentLabels(text, keywords), []);
-});
-
-run('emptyStringReturnsEmptyArray', () => {
-  assert.deepStrictEqual(inferComponentLabels('', keywords), []);
-});
-
-if (failures > 0) {
-  console.error(`
-${failures} test(s) failed.`);
-  process.exit(1);
-}
-console.log(`
-All ${cases.length + 3} tests passed.`);
@@ -40,8 +40,6 @@ jobs:
      openclaw: ${{ steps.filter.outputs.openclaw }}
      opencode_plugin: ${{ steps.filter.outputs.opencode_plugin }}
      pi_agent_plugin: ${{ steps.filter.outputs.pi_agent_plugin }}
-      n8n_nodes_mem0: ${{ steps.filter.outputs.n8n_nodes_mem0 }}
-      zapier_mem0: ${{ steps.filter.outputs.zapier_mem0 }}
      docs_llms_txt: ${{ steps.filter.outputs.docs_llms_txt }}
    steps:
      - uses: dorny/paths-filter@v3
@@ -81,13 +79,6 @@ jobs:
              - 'integrations/pi-agent-plugin/**'
              - '.github/workflows/pi-agent-plugin-checks.yml'
              - '.github/workflows/ci-gate.yml'
-            n8n_nodes_mem0:
-              - 'integrations/n8n-nodes-mem0/**'
-              - '.github/workflows/n8n-nodes-mem0-checks.yml'
-            zapier_mem0:
-              - 'integrations/zapier-mem0/**'
-              - '.github/workflows/zapier-mem0-checks.yml'
-              - '.github/workflows/ci-gate.yml'
            docs_llms_txt:
              - 'docs/**/*.mdx'
              - 'docs/llms.txt'
@@ -145,18 +136,6 @@ jobs:
    uses: ./.github/workflows/pi-agent-plugin-checks.yml
    secrets: inherit

-  n8n-nodes-mem0:
-    name: n8n Node
-    needs: changes
-    if: needs.changes.outputs.n8n_nodes_mem0 == 'true'
-    uses: ./.github/workflows/n8n-nodes-mem0-checks.yml
-  zapier-mem0:
-    name: Zapier App
-    needs: changes
-    if: needs.changes.outputs.zapier_mem0 == 'true'
-    uses: ./.github/workflows/zapier-mem0-checks.yml
-    secrets: inherit
-
  docs-llms-txt:
    name: docs llms.txt
    needs: changes
@@ -175,8 +154,6 @@ jobs:
      - openclaw
      - opencode-plugin
      - pi-agent-plugin
-      - n8n-nodes-mem0
-      - zapier-mem0
      - docs-llms-txt
    if: always()
    runs-on: ubuntu-latest
@@ -106,7 +106,7 @@ jobs:
        run: |
          pip install --upgrade pip
          pip install -e ".[test,graph,vector_stores,llms,extras]"
-          pip install ruff==0.16.0
+          pip install ruff
      - name: Run Linting
        if: needs.check_changes.outputs.mem0_changed == 'true'
        run: make lint
@@ -12,56 +12,28 @@ jobs:
  label:
    runs-on: ubuntu-latest
    steps:
-      - uses: actions/checkout@v4
-
      - uses: stefanbuck/github-issue-parser@v3
        id: issue-parser
-        continue-on-error: true
        with:
          template-path: .github/ISSUE_TEMPLATE/bug_report.yml

      - uses: redhat-plumbers-in-action/advanced-issue-labeler@v3
-        continue-on-error: true
        with:
          issue-form: ${{ steps.issue-parser.outputs.jsonString }}
+          section: component
          token: ${{ secrets.GITHUB_TOKEN }}
          config-path: .github/advanced-issue-labeler.yml

-      - name: Infer component from text when the form was not used
-        uses: actions/github-script@v7
+      - uses: stefanbuck/github-issue-parser@v3
+        id: feature-parser
+        if: contains(github.event.issue.labels.*.name, 'enhancement')
        with:
-          script: |
-            const {
-              componentLabels,
-              inferComponentLabels,
-              loadKeywords,
-            } = require(`${process.env.GITHUB_WORKSPACE}/.github/scripts/infer-component-labels.js`);
+          template-path: .github/ISSUE_TEMPLATE/feature_request.yml

-            const { data: issue } = await github.rest.issues.get({
-              owner: context.repo.owner,
-              repo: context.repo.repo,
-              issue_number: context.issue.number,
-            });
-
-            const keywords = loadKeywords(`${process.env.GITHUB_WORKSPACE}/.github/component-keywords.json`);
-            const known = componentLabels(keywords);
-            const existing = issue.labels.map((label) => label.name || label);
-            if (existing.some((name) => known.includes(name))) {
-              core.info(`Component label already present: ${existing.join(', ')}`);
-              return;
-            }
-
-            const labels = inferComponentLabels(`${issue.title}\n\n${issue.body || ''}`, keywords);
-
-            if (labels.length === 0) {
-              core.info('No component could be inferred from the issue text');
-              return;
-            }
-
-            core.info(`Inferred: ${labels.join(', ')}`);
-            await github.rest.issues.addLabels({
-              owner: context.repo.owner,
-              repo: context.repo.repo,
-              issue_number: context.issue.number,
-              labels,
-            });
+      - uses: redhat-plumbers-in-action/advanced-issue-labeler@v3
+        if: contains(github.event.issue.labels.*.name, 'enhancement')
+        with:
+          issue-form: ${{ steps.feature-parser.outputs.jsonString }}
+          section: component
+          token: ${{ secrets.GITHUB_TOKEN }}
+          config-path: .github/advanced-issue-labeler.yml
@@ -1,60 +0,0 @@
-name: Publish n8n-nodes-mem0 📦 to npm
-
-# Dispatched by release.yml (Release Router) when a release tagged
-# n8n-nodes-mem0-v* is published. Can also be dispatched manually to
-# re-publish a tag.
-on:
-  workflow_dispatch:
-    inputs:
-      tag:
-        description: 'Release tag to build and publish (e.g. n8n-nodes-mem0-v0.1.0)'
-        required: true
-        type: string
-      prerelease:
-        description: 'Publish under the version preid dist-tag instead of latest'
-        required: false
-        type: boolean
-        default: false
-
-jobs:
-  build-n-publish:
-    name: Build and publish n8n-nodes-mem0 📦 to npm
-    if: startsWith(inputs.tag, 'n8n-nodes-mem0-v')
-    runs-on: ubuntu-latest
-    permissions:
-      id-token: write
-    defaults:
-      run:
-        working-directory: integrations/n8n-nodes-mem0
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          ref: ${{ inputs.tag }}
-
-      - name: Install pnpm
-        uses: pnpm/action-setup@v4
-        with:
-          version: 9
-
-      - name: Set up Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: '20'
-          registry-url: 'https://registry.npmjs.org'
-          cache: 'pnpm'
-          cache-dependency-path: integrations/n8n-nodes-mem0/pnpm-lock.yaml
-
-      - name: Install dependencies
-        run: pnpm install --frozen-lockfile --ignore-scripts
-
-      - name: Build
-        run: pnpm run build
-
-      - name: Publish to npm
-        run: |
-          if [ "${{ inputs.prerelease }}" = "true" ]; then
-            PREID=$(node -p "require('./package.json').version.split('-')[1].split('.')[0]")
-            npx npm@latest publish --provenance --access public --tag "$PREID"
-          else
-            npx npm@latest publish --provenance --access public
-          fi
@@ -1,88 +0,0 @@
-name: n8n-nodes-mem0 checks
-
-# On PRs this is invoked by ci-gate.yml (the single required check);
-# push-to-main and manual runs remain standalone.
-on:
-  workflow_dispatch:
-  push:
-    branches: [main]
-    paths:
-      - 'integrations/n8n-nodes-mem0/**'
-      - '.github/workflows/n8n-nodes-mem0-checks.yml'
-  workflow_call:
-
-jobs:
-  lint:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Install pnpm
-        uses: pnpm/action-setup@v4
-        with:
-          version: 9
-
-      - name: Setup Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: 20
-          cache: 'pnpm'
-          cache-dependency-path: integrations/n8n-nodes-mem0/pnpm-lock.yaml
-
-      - name: Install dependencies
-        run: cd integrations/n8n-nodes-mem0 && pnpm install --frozen-lockfile --ignore-scripts
-
-      - name: Lint
-        run: cd integrations/n8n-nodes-mem0 && pnpm run lint
-
-  test:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Install pnpm
-        uses: pnpm/action-setup@v4
-        with:
-          version: 9
-
-      - name: Setup Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: 20
-          cache: 'pnpm'
-          cache-dependency-path: integrations/n8n-nodes-mem0/pnpm-lock.yaml
-
-      - name: Install dependencies
-        run: cd integrations/n8n-nodes-mem0 && pnpm install --frozen-lockfile --ignore-scripts
-
-      - name: Run tests
-        run: cd integrations/n8n-nodes-mem0 && pnpm test
-
-  build:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Install pnpm
-        uses: pnpm/action-setup@v4
-        with:
-          version: 9
-
-      - name: Setup Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: 20
-          cache: 'pnpm'
-          cache-dependency-path: integrations/n8n-nodes-mem0/pnpm-lock.yaml
-
-      - name: Install dependencies
-        run: cd integrations/n8n-nodes-mem0 && pnpm install --frozen-lockfile --ignore-scripts
-
-      - name: Build
-        run: cd integrations/n8n-nodes-mem0 && pnpm run build
-
-      - name: Verify dist output exists
-        run: |
-          test -f integrations/n8n-nodes-mem0/dist/nodes/Mem0/Mem0.node.js || (echo "Build output missing: dist/nodes/Mem0/Mem0.node.js" && exit 1)
-          test -f integrations/n8n-nodes-mem0/dist/credentials/Mem0Api.credentials.js || (echo "Build output missing: dist/credentials/Mem0Api.credentials.js" && exit 1)
-          test -f integrations/n8n-nodes-mem0/dist/nodes/Mem0/mem0.svg || (echo "Build output missing: dist/nodes/Mem0/mem0.svg" && exit 1)
@@ -1,64 +0,0 @@
-name: PR Labeler
-
-on:
-  pull_request_target:
-    types: [opened, synchronize, reopened, edited]
-
-concurrency:
-  group: pr-labeler-${{ github.event.pull_request.number }}
-  cancel-in-progress: true
-
-permissions:
-  contents: read
-  pull-requests: write
-  issues: read
-
-jobs:
-  label:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/labeler@v5
-        with:
-          repo-token: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Propagate labels from linked issues
-        uses: actions/github-script@v7
-        with:
-          script: |
-            const allowed = new Set([
-              'sdk-python', 'sdk-typescript', 'vector-store', 'plugin',
-              'rest-api', 'documentation', 'ci', 'cli', 'integrations',
-            ]);
-            const umbrella = { plugin: 'integrations' };
-            const { repository } = await github.graphql(
-              `query ($owner: String!, $repo: String!, $number: Int!) {
-                repository(owner: $owner, name: $repo) {
-                  pullRequest(number: $number) {
-                    closingIssuesReferences(first: 20) {
-                      nodes { labels(first: 50) { nodes { name } } }
-                    }
-                  }
-                }
-              }`,
-              { owner: context.repo.owner, repo: context.repo.repo, number: context.issue.number },
-            );
-
-            const labels = new Set();
-            for (const issue of repository.pullRequest.closingIssuesReferences.nodes) {
-              for (const label of issue.labels.nodes) {
-                if (allowed.has(label.name)) labels.add(label.name);
-              }
-            }
-
-            for (const label of [...labels]) {
-              if (umbrella[label]) labels.add(umbrella[label]);
-            }
-
-            if (labels.size > 0) {
-              await github.rest.issues.addLabels({
-                owner: context.repo.owner,
-                repo: context.repo.repo,
-                issue_number: context.issue.number,
-                labels: [...labels],
-              });
-            }
@@ -45,7 +45,6 @@ jobs:
            openclaw-v*)  workflow="openclaw-cd.yml" ;;
            opencode-v*)  workflow="opencode-plugin-cd.yml" ;;
            pi-agent-v*)  workflow="pi-agent-plugin-cd.yml" ;;
-            n8n-nodes-mem0-v*) workflow="n8n-nodes-mem0-cd.yml" ;;
            v*)           workflow="cd.yml" ;;
            *)
              echo "::error::Release tag '$TAG' does not match any known package prefix — nothing will be published. See the tag prefix table in AGENTS.md."
@@ -1,42 +0,0 @@
-name: Deploy zapier-mem0 to Zapier
-
-# Zapier apps deploy to Zapier's own platform (not npm), so this is NOT wired
-# into the npm release router (release.yml). It is manual workflow_dispatch
-# only and requires the ZAPIER_DEPLOY_KEY repo secret.
-#
-#   gh workflow run zapier-mem0-cd.yml --ref main
-on:
-  workflow_dispatch:
-
-jobs:
-  push:
-    name: Push zapier-mem0 to Zapier
-    runs-on: ubuntu-latest
-    defaults:
-      run:
-        working-directory: integrations/zapier-mem0
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Install pnpm
-        uses: pnpm/action-setup@v4
-        with:
-          version: 9
-
-      - name: Set up Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: 22
-          cache: 'pnpm'
-          cache-dependency-path: integrations/zapier-mem0/pnpm-lock.yaml
-
-      - name: Install dependencies
-        run: pnpm install --frozen-lockfile
-
-      - name: Build TypeScript
-        run: pnpm build
-
-      - name: Push to Zapier
-        env:
-          ZAPIER_DEPLOY_KEY: ${{ secrets.ZAPIER_DEPLOY_KEY }}
-        run: npx zapier-platform-cli@19 push
@@ -1,47 +0,0 @@
-name: zapier-mem0 checks
-
-# On PRs this is invoked by ci-gate.yml (the single required check);
-# push-to-main and manual runs remain standalone.
-#
-# CI compiles the TypeScript app, runs `zapier validate` (offline schema + style
-# checks) against the build, plus the offline jest unit suite (test/unit.test.ts —
-# mocked z.request, no network). The end-to-end jest suite is skipped here because
-# it hits the live Mem0 API — it runs locally with MEM0_API_KEY set (see README).
-on:
-  workflow_dispatch:
-  push:
-    branches: [main]
-    paths:
-      - 'integrations/zapier-mem0/**'
-      - '.github/workflows/zapier-mem0-checks.yml'
-  workflow_call:
-
-jobs:
-  validate:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Install pnpm
-        uses: pnpm/action-setup@v4
-        with:
-          version: 9
-
-      - name: Setup Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: 22
-          cache: 'pnpm'
-          cache-dependency-path: integrations/zapier-mem0/pnpm-lock.yaml
-
-      - name: Install dependencies
-        run: cd integrations/zapier-mem0 && pnpm install --frozen-lockfile
-
-      - name: Build TypeScript
-        run: cd integrations/zapier-mem0 && pnpm build
-
-      - name: Validate Zapier app definition
-        run: cd integrations/zapier-mem0 && npx zapier-platform-cli@19 validate
-
-      - name: Run offline unit tests
-        run: cd integrations/zapier-mem0 && pnpm test:unit
@@ -27,9 +27,8 @@ This is a **polyglot monorepo** containing Python and TypeScript packages, CLIs,
 | `integrations/openclaw/` | `@mem0/openclaw-mem0` — OpenClaw plugin for Claude Code / AI editors |
 | `integrations/pi-agent-plugin/` | `@mem0/pi-agent-plugin` — Pi Agent plugin |
 | `integrations/vercel-ai-sdk/` | `@mem0/vercel-ai-provider` — Vercel AI SDK memory provider |
-| `integrations/n8n-nodes-mem0/` | `@mem0/n8n-nodes-mem0` — n8n community node; add / search / get / update / delete memories |
-| `integrations/zapier-mem0/` | `@mem0/zapier` — Zapier Platform CLI app (deploys to Zapier, not npm); add / search / get / delete memories |
 | `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) |
 | `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/`, `mem0-oss-to-platform/` |
 | `docs/` | Documentation site (Mintlify) |
 | `tests/` | Python SDK tests (pytest) |
@@ -63,7 +62,7 @@ integrations/openclaw/ ──▶ mem0ai (npm)
 - **Node.js**: v18+ (v20 or v22 recommended)
 - **pnpm**: v10+ (`npm install -g pnpm@10`) — used for all TypeScript packages
 - **Hatch**: Python build/environment tool (`pip install hatch`)
- **Docker**: Required for `server/` development
+- **Docker**: Required for `server/` and `openmemory/` development

 ### Initial Setup

@@ -215,6 +214,28 @@ docker-compose up                  # starts all 3 services
 - **Services:** PostgreSQL with pgvector, Neo4j 5.x with APOC plugin
 - **Hot reload:** Dev Dockerfile mounts `server/` and `mem0/` for live changes

+### OpenMemory (`openmemory/`)
+
+```bash
+# Full stack via Docker Compose
+cd openmemory
+docker-compose up
+# Qdrant: localhost:6333
+# API (MCP): localhost:8765
+# UI: localhost:3000
+
+# Individual development
+cd openmemory/api && uvicorn main:app --reload       # FastAPI backend
+cd openmemory/ui && npm run dev                       # Next.js frontend
+
+# Tests
+cd openmemory/api && pytest tests/                   # API tests (e.g., test_mcp_server.py)
+```
+
+- **API:** FastAPI + Alembic (DB migrations) + MCP server (Model Context Protocol)
+- **UI:** Next.js 15, React 19, Radix UI, Redux Toolkit, TailwindCSS, Recharts
+- **Vector store:** Qdrant
+
 ### Documentation (`docs/`)

 ```bash
@@ -310,6 +331,7 @@ python -m benchmarks.beam.run --project-name my-test --backend cloud --mem0-api-
  - Root SDK: line length **120**
  - Python CLI: line length **100** with extended rule set (UP, B, SIM, RUF)
 - **isort** with `profile = "black"` for import sorting.
+- Ruff excludes `openmemory/` from root config.

 ### TypeScript Conventions

@@ -360,6 +382,7 @@ Optional layer on top of vector memory for relationship-aware retrieval. Configu
 Model Context Protocol support in multiple places:

 - **Remote:** MCP server at `mcp.mem0.ai`
+- **Local:** MCP server in `openmemory/api/` (FastAPI-based)
 - **Plugin:** MCP tools in `integrations/mem0-plugin/` — 9 tools: `add_memory`, `search_memories`, `get_memories`, `get_memory`, `update_memory`, `delete_memory`, `delete_all_memories`, `delete_entities`, `list_entities`

 ### Plugin & Skills System
@@ -408,8 +431,6 @@ PR testing is orchestrated by a single entry point: **`ci-gate.yml` (CI Gate)**
 | OpenClaw | `openclaw-checks.yml` | Push to main (on `integrations/openclaw/`), manual | tsc + vitest (with Codecov) + tsup build on Node 20, 22 |
 | OpenCode Plugin | `opencode-plugin-checks.yml` | Push to main (on `integrations/mem0-plugin/.opencode-plugin/`), manual | Bun: tsc type-check + build + dist artifact check |
 | Pi Agent Plugin | `pi-agent-plugin-checks.yml` | Push to main (on `integrations/pi-agent-plugin/`), manual | tsc + vitest + tsup build (dist artifact check) on Node 20, 22 |
-| n8n Node | `n8n-nodes-mem0-checks.yml` | Push to main (on `integrations/n8n-nodes-mem0/`), manual | ESLint (n8n-nodes-base) + tsc build (dist artifact check) on Node 20 |
-| Zapier App | `zapier-mem0-checks.yml` | Push to main (on `integrations/zapier-mem0/`), manual | build (tsc) + `zapier validate` + offline unit tests on Node 22 |
 | docs llms.txt | `docs-llms-txt-check.yml` | Manual | `docs/llms.txt` coverage check |

 When adding a new package CI workflow: give it `workflow_call` (plus `push`/`workflow_dispatch` as needed, but no `pull_request` trigger), then register it in `ci-gate.yml` — a path filter under the `changes` job, a call job, and an entry in the gate job's `needs` list.
@@ -429,13 +450,11 @@ Publishing is routed through a single entry point: **`release.yml` (Release Rout
 | OpenClaw | `openclaw-cd.yml` | `openclaw-v*` | npm (`@mem0/openclaw-mem0`) |
 | OpenCode Plugin | `opencode-plugin-cd.yml` | `opencode-v*` | npm (`@mem0/opencode-plugin`) |
 | Pi Agent Plugin | `pi-agent-plugin-cd.yml` | `pi-agent-v*` | npm (`@mem0/pi-agent-plugin`) |
-| n8n Node | `n8n-nodes-mem0-cd.yml` | `n8n-nodes-mem0-v*` | npm (`@mem0/n8n-nodes-mem0`) |

 - Package CD workflows are `workflow_dispatch`-only (inputs: `tag`, `prerelease`); they check out and build the given tag. Registry trusted-publisher settings stay pinned to each package's own workflow filename.
 - All publishing uses **OIDC trusted publishing** — no tokens or secrets required.
 - First publish of a new npm package must be done manually; OIDC works for subsequent versions.
 - To re-publish a release (e.g. after a registry settings fix), do **not** delete/recreate the GitHub release — manually dispatch the package workflow instead: `gh workflow run <package>-cd.yml --ref refs/tags/<tag> -f tag=<tag>`.
- The **Zapier app** (`integrations/zapier-mem0`) deploys to Zapier's own platform, not npm, so it is **not** in the release router. Deploy it manually: `gh workflow run zapier-mem0-cd.yml --ref main` (requires the `ZAPIER_DEPLOY_KEY` secret).
 - When adding a new package: add its CD workflow (`workflow_dispatch` with `tag`/`prerelease` inputs), then register its tag prefix in the `case` block in `release.yml`. Keep the bare `v*` arm last.

 ### Utility Workflows
@@ -443,7 +462,6 @@ Publishing is routed through a single entry point: **`release.yml` (Release Rout
 | Workflow | File | Purpose |
 |----------|------|---------|
 | Issue Labeler | `issue-labeler.yml` | Automatic issue labeling |
-| PR Labeler | `pr-labeler.yml` | Path-based PR labeling plus propagating labels from linked issues |
 | 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`. |

@@ -566,7 +584,7 @@ N/A

 - Follow existing code patterns — don't introduce new frameworks or abstractions without discussion.
 - Version bumps go in `pyproject.toml` (Python) or `package.json` (TypeScript).
- For `server/` work, use Docker Compose for local development.
+- For `server/` and `openmemory/` work, use Docker Compose for local development.
 - Do NOT use `pip` or `conda` for dependency management — use `hatch` (see `docs/contributing/development.mdx`).

 ### Contributing Guides
@@ -589,4 +607,5 @@ N/A
 - Use npm or yarn in TypeScript packages — this repo uses pnpm exclusively.
 - Use `require()` for imports in TypeScript — use ES module `import` syntax.
 - Mix up linter configs: root Python SDK uses line-length 120, Python CLI uses 100, Node CLI uses Biome (not ESLint/Ruff).
+- Modify `openmemory/` database migrations without understanding the Alembic migration chain.
 - Change public APIs without updating documentation in `docs/`.
@@ -45,7 +45,7 @@ The two most common contribution targets are the SDKs:
 | TypeScript SDK (`mem0ai`) | `mem0-ts/` | TypeScript | `pnpm`        |

 Other packages include the CLIs (`cli/python/`, `cli/node/`), integrations
-(`integrations/`), the self-hosted `server/`, and the docs site
+(`integrations/`), the self-hosted `server/`, `openmemory/`, and the docs site
 (`docs/`). See [AGENTS.md](./AGENTS.md) for a full map of the repository.

 ## Development Workflow
@@ -11,7 +11,7 @@ install:
 	hatch env create

 install_all:
-	pip install ruff==0.16.0 groq together boto3 litellm ollama chromadb weaviate weaviate-client sentence_transformers vertexai \
+	pip install ruff==0.6.9 groq together boto3 litellm ollama chromadb weaviate weaviate-client sentence_transformers vertexai \
 	            google-generativeai elasticsearch opensearch-py vecs "pinecone<7.0.0" pinecone-text faiss-cpu langchain-community \
 							upstash-vector azure-search-documents langchain-memgraph langchain-neo4j langchain-aws rank-bm25 pymochow pymongo psycopg kuzu databricks-sdk valkey

@@ -46,12 +46,12 @@

 | Benchmark | Old | New  | Tokens  | Latency p50  |
 | --- | --- | --- | --- | --- |
-| **LoCoMo** | 71.4 | **92.5** | 7.0K  | 0.88s  |
-| **LongMemEval** | 67.8 | **94.4** | 6.8K  | 1.09s  |
+| **LoCoMo** | 71.4 | **91.6** | 7.0K  | 0.88s  |
+| **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  |

-All benchmarks run on the same production-representative model stack. Single-pass retrieval (one call, no agentic loops) at a top_200 retrieval budget. Scores reflect Mem0's managed platform, which includes proprietary optimizations not available in the open-source SDK; open-source users should expect directionally similar gains but not identical numbers.
+All benchmarks run on the same production-representative model stack. Single-pass retrieval (one call, no agentic loops).

 **What changed:**
 - **Single-pass ADD-only extraction** -- one LLM call, no UPDATE/DELETE. Memories accumulate; nothing is overwritten.
@@ -63,8 +63,8 @@ All benchmarks run on the same production-representative model stack. Single-pas
 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
- **92.5 on LoCoMo** -- +21 points over the previous algorithm
- **94.4 on LongMemEval** -- +27 points, with 98.2 on assistant memory recall
+- **91.6 on LoCoMo** -- +20 points over the previous algorithm
+- **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)

@@ -21,7 +21,7 @@ privately through one of the following channels:
 To help us triage and resolve the issue quickly, please include as much of the
 following as you can:

- Affected component or package (e.g. Python SDK, TypeScript SDK, server, CLI)
+- Affected component or package (e.g. Python SDK, TypeScript SDK, server, OpenMemory)
 - Affected version, tag, or commit
 - Clear, step-by-step reproduction instructions
 - The security impact and a proof of concept, if available
@@ -51,8 +51,8 @@
      "langsmith@<0.6.0": "^0.6.0",
      "tar-fs@>=2.0.0 <2.1.4": "^2.1.4",
      "picomatch@<2.3.2": "^2.3.2",
-      "esbuild": ">=0.28.1",
-      "postcss@<8.5.18": ">=8.5.18 <9.0.0"
+      "postcss@<8.5.10": ">=8.5.10",
+      "esbuild": ">=0.28.1"
    }
  }
 }
@@ -9,8 +9,8 @@ overrides:
  langsmith@<0.6.0: ^0.6.0
  tar-fs@>=2.0.0 <2.1.4: ^2.1.4
  picomatch@<2.3.2: ^2.3.2
+  postcss@<8.5.10: '>=8.5.10'
  esbuild: '>=0.28.1'
-  postcss@<8.5.18: '>=8.5.18 <9.0.0'

 importers:

@@ -40,7 +40,7 @@ importers:
        version: 20.19.37
      tsup:
        specifier: ^8.0.0
-        version: 8.5.1(postcss@8.5.23)(tsx@4.21.0)(typescript@5.9.3)
+        version: 8.5.1(postcss@8.5.15)(tsx@4.21.0)(typescript@5.9.3)
      tsx:
        specifier: ^4.7.0
        version: 4.21.0
@@ -78,28 +78,24 @@ packages:
    engines: {node: '>=14.21.3'}
    cpu: [arm64]
    os: [linux]
-    libc: [musl]

  '@biomejs/cli-linux-arm64@1.9.4':
    resolution: {integrity: sha512-fJIW0+LYujdjUgJJuwesP4EjIBl/N/TcOX3IvIHJQNsAqvV2CHIogsmA94BPG6jZATS4Hi+xv4SkBBQSt1N4/g==}
    engines: {node: '>=14.21.3'}
    cpu: [arm64]
    os: [linux]
-    libc: [glibc]

  '@biomejs/cli-linux-x64-musl@1.9.4':
    resolution: {integrity: sha512-gEhi/jSBhZ2m6wjV530Yy8+fNqG8PAinM3oV7CyO+6c3CEh16Eizm21uHVsyVBEB6RIM8JHIl6AGYCv6Q6Q9Tg==}
    engines: {node: '>=14.21.3'}
    cpu: [x64]
    os: [linux]
-    libc: [musl]

  '@biomejs/cli-linux-x64@1.9.4':
    resolution: {integrity: sha512-lRCJv/Vi3Vlwmbd6K+oQ0KhLHMAysN8lXoCI7XeHlxaajk06u7G+UsFSO01NAs5iYuWKmVZjmiOzJ0OJmGsMwg==}
    engines: {node: '>=14.21.3'}
    cpu: [x64]
    os: [linux]
-    libc: [glibc]

  '@biomejs/cli-win32-arm64@1.9.4':
    resolution: {integrity: sha512-tlbhLk+WXZmgwoIKwHIHEBZUwxml7bRJgk0X2sPyNR3S93cdRq6XulAZRQJ17FYGGzWne0fgrXBKpl7l4M87Hg==}
@@ -320,79 +316,66 @@ packages:
    resolution: {integrity: sha512-RzeBwv0B3qtVBWtcuABtSuCzToo2IEAIQrcyB/b2zMvBWVbjo8bZDjACUpnaafaxhTw2W+imQbP2BD1usasK4g==}
    cpu: [arm]
    os: [linux]
-    libc: [glibc]

  '@rollup/rollup-linux-arm-musleabihf@4.60.0':
    resolution: {integrity: sha512-Sf7zusNI2CIU1HLzuu9Tc5YGAHEZs5Lu7N1ssJG4Tkw6e0MEsN7NdjUDDfGNHy2IU+ENyWT+L2obgWiguWibWQ==}
    cpu: [arm]
    os: [linux]
-    libc: [musl]

  '@rollup/rollup-linux-arm64-gnu@4.60.0':
    resolution: {integrity: sha512-DX2x7CMcrJzsE91q7/O02IJQ5/aLkVtYFryqCjduJhUfGKG6yJV8hxaw8pZa93lLEpPTP/ohdN4wFz7yp/ry9A==}
    cpu: [arm64]
    os: [linux]
-    libc: [glibc]

  '@rollup/rollup-linux-arm64-musl@4.60.0':
    resolution: {integrity: sha512-09EL+yFVbJZlhcQfShpswwRZ0Rg+z/CsSELFCnPt3iK+iqwGsI4zht3secj5vLEs957QvFFXnzAT0FFPIxSrkQ==}
    cpu: [arm64]
    os: [linux]
-    libc: [musl]

  '@rollup/rollup-linux-loong64-gnu@4.60.0':
    resolution: {integrity: sha512-i9IcCMPr3EXm8EQg5jnja0Zyc1iFxJjZWlb4wr7U2Wx/GrddOuEafxRdMPRYVaXjgbhvqalp6np07hN1w9kAKw==}
    cpu: [loong64]
    os: [linux]
-    libc: [glibc]

  '@rollup/rollup-linux-loong64-musl@4.60.0':
    resolution: {integrity: sha512-DGzdJK9kyJ+B78MCkWeGnpXJ91tK/iKA6HwHxF4TAlPIY7GXEvMe8hBFRgdrR9Ly4qebR/7gfUs9y2IoaVEyog==}
    cpu: [loong64]
    os: [linux]
-    libc: [musl]

  '@rollup/rollup-linux-ppc64-gnu@4.60.0':
    resolution: {integrity: sha512-RwpnLsqC8qbS8z1H1AxBA1H6qknR4YpPR9w2XX0vo2Sz10miu57PkNcnHVaZkbqyw/kUWfKMI73jhmfi9BRMUQ==}
    cpu: [ppc64]
    os: [linux]
-    libc: [glibc]

  '@rollup/rollup-linux-ppc64-musl@4.60.0':
    resolution: {integrity: sha512-Z8pPf54Ly3aqtdWC3G4rFigZgNvd+qJlOE52fmko3KST9SoGfAdSRCwyoyG05q1HrrAblLbk1/PSIV+80/pxLg==}
    cpu: [ppc64]
    os: [linux]
-    libc: [musl]

  '@rollup/rollup-linux-riscv64-gnu@4.60.0':
    resolution: {integrity: sha512-3a3qQustp3COCGvnP4SvrMHnPQ9d1vzCakQVRTliaz8cIp/wULGjiGpbcqrkv0WrHTEp8bQD/B3HBjzujVWLOA==}
    cpu: [riscv64]
    os: [linux]
-    libc: [glibc]

  '@rollup/rollup-linux-riscv64-musl@4.60.0':
    resolution: {integrity: sha512-pjZDsVH/1VsghMJ2/kAaxt6dL0psT6ZexQVrijczOf+PeP2BUqTHYejk3l6TlPRydggINOeNRhvpLa0AYpCWSQ==}
    cpu: [riscv64]
    os: [linux]
-    libc: [musl]

  '@rollup/rollup-linux-s390x-gnu@4.60.0':
    resolution: {integrity: sha512-3ObQs0BhvPgiUVZrN7gqCSvmFuMWvWvsjG5ayJ3Lraqv+2KhOsp+pUbigqbeWqueGIsnn+09HBw27rJ+gYK4VQ==}
    cpu: [s390x]
    os: [linux]
-    libc: [glibc]

  '@rollup/rollup-linux-x64-gnu@4.60.0':
    resolution: {integrity: sha512-EtylprDtQPdS5rXvAayrNDYoJhIz1/vzN2fEubo3yLE7tfAw+948dO0g4M0vkTVFhKojnF+n6C8bDNe+gDRdTg==}
    cpu: [x64]
    os: [linux]
-    libc: [glibc]

  '@rollup/rollup-linux-x64-musl@4.60.0':
    resolution: {integrity: sha512-k09oiRCi/bHU9UVFqD17r3eJR9bn03TyKraCrlz5ULFJGdJGi7VOmm9jl44vOJvRJ6P7WuBi/s2A97LxxHGIdw==}
    cpu: [x64]
    os: [linux]
-    libc: [musl]

  '@rollup/rollup-openbsd-x64@4.60.0':
    resolution: {integrity: sha512-1o/0/pIhozoSaDJoDcec+IVLbnRtQmHwPV730+AOD29lHEEo4F5BEUB24H0OBdhbBBDwIOSuf7vgg0Ywxdfiiw==}
@@ -670,8 +653,8 @@ packages:
  mz@2.7.0:
    resolution: {integrity: sha512-z81GNO7nnYMEhrGh9LeymoE4+Yr0Wn5McHIZMK5cfQCl+NDX08sCZgUc9/6MHni9IWuFLm1Z3HTCXu2z9fN62Q==}

-  nanoid@3.3.16:
-    resolution: {integrity: sha512-bzlKTyNJ7+LdGIIwy8ijFpIqEQIvafahV7eYykJ8Cvh42EdJeODoJ6gUJXpQJvej1BddH8OqTXZNE/KfbWAu8Q==}
+  nanoid@3.3.12:
+    resolution: {integrity: sha512-ZB9RH/39qpq5Vu6Y+NmUaFhQR6pp+M2Xt76XBnEwDaGcVAqhlvxrl3B2bKS5D3NH3QR76v3aSrKaF/Kiy7lEtQ==}
    engines: {node: ^10 || ^12 || ^13.7 || ^14 || >=15.0.1}
    hasBin: true

@@ -712,7 +695,7 @@ packages:
    engines: {node: '>= 18'}
    peerDependencies:
      jiti: '>=1.21.0'
-      postcss: '>=8.5.18 <9.0.0'
+      postcss: '>=8.5.10'
      tsx: ^4.8.1
      yaml: ^2.4.2
    peerDependenciesMeta:
@@ -725,8 +708,8 @@ packages:
      yaml:
        optional: true

-  postcss@8.5.23:
-    resolution: {integrity: sha512-g50586zr4bZmwFiTlflMu8E0bDTb5I5gertgwAKmsdUlTQIhZtunzUlD1WSzwcVWPoAVpsrA6vlfCD7oXvRwgg==}
+  postcss@8.5.15:
+    resolution: {integrity: sha512-FfR8sjd4em2T6fb3I2MwAJU7HWVMr9zba+enmQeeWFfCbm+UOC/0X4DS8XtpUTMwWMGbjKYP7xjfNekzyGmB3A==}
    engines: {node: ^10 || ^12 || >=14}

  readdirp@4.1.2:
@@ -838,7 +821,7 @@ packages:
    peerDependencies:
      '@microsoft/api-extractor': ^7.36.0
      '@swc/core': ^1
-      postcss: '>=8.5.18 <9.0.0'
+      postcss: '>=8.5.10'
      typescript: '>=4.5.0'
    peerDependenciesMeta:
      '@microsoft/api-extractor':
@@ -1405,7 +1388,7 @@ snapshots:
      object-assign: 4.1.1
      thenify-all: 1.6.0

-  nanoid@3.3.16: {}
+  nanoid@3.3.12: {}

  object-assign@4.1.1: {}

@@ -1441,16 +1424,16 @@ snapshots:
      mlly: 1.8.2
      pathe: 2.0.3

-  postcss-load-config@6.0.1(postcss@8.5.23)(tsx@4.21.0):
+  postcss-load-config@6.0.1(postcss@8.5.15)(tsx@4.21.0):
    dependencies:
      lilconfig: 3.1.3
    optionalDependencies:
-      postcss: 8.5.23
+      postcss: 8.5.15
      tsx: 4.21.0

-  postcss@8.5.23:
+  postcss@8.5.15:
    dependencies:
-      nanoid: 3.3.16
+      nanoid: 3.3.12
      picocolors: 1.1.1
      source-map-js: 1.2.1

@@ -1571,7 +1554,7 @@ snapshots:

  ts-interface-checker@0.1.13: {}

-  tsup@8.5.1(postcss@8.5.23)(tsx@4.21.0)(typescript@5.9.3):
+  tsup@8.5.1(postcss@8.5.15)(tsx@4.21.0)(typescript@5.9.3):
    dependencies:
      bundle-require: 5.1.0(esbuild@0.28.1)
      cac: 6.7.14
@@ -1582,7 +1565,7 @@ snapshots:
      fix-dts-default-cjs-exports: 1.0.1
      joycon: 3.1.1
      picocolors: 1.1.1
-      postcss-load-config: 6.0.1(postcss@8.5.23)(tsx@4.21.0)
+      postcss-load-config: 6.0.1(postcss@8.5.15)(tsx@4.21.0)
      resolve-from: 5.0.0
      rollup: 4.60.0
      source-map: 0.7.6
@@ -1591,7 +1574,7 @@ snapshots:
      tinyglobby: 0.2.15
      tree-kill: 1.2.2
    optionalDependencies:
-      postcss: 8.5.23
+      postcss: 8.5.15
      typescript: 5.9.3
    transitivePeerDependencies:
      - jiti
@@ -1619,7 +1602,7 @@ snapshots:
      esbuild: 0.28.1
      fdir: 6.5.0(picomatch@4.0.4)
      picomatch: 4.0.4
-      postcss: 8.5.23
+      postcss: 8.5.15
      rollup: 4.60.0
      tinyglobby: 0.2.15
    optionalDependencies:
@@ -10,5 +10,5 @@ overrides:
  langsmith@<0.6.0: ^0.6.0
  tar-fs@>=2.0.0 <2.1.4: ^2.1.4
  picomatch@<2.3.2: ^2.3.2
+  "postcss@<8.5.10": ">=8.5.10"
  "esbuild": ">=0.28.1"
-  "postcss@<8.5.18": ">=8.5.18 <9.0.0"
@@ -1,147 +0,0 @@
-/**
- * `mem0 agent-rush <add|search> "..."` — wraps the AGENTRUSH platform endpoints.
- * Project routing is implicit (server-side); zero flags needed.
- */
-
-import readline from "node:readline";
-import { colors, printError, printSuccess } from "../branding.js";
-import { loadConfig, saveConfig } from "../config.js";
-import { CLI_VERSION } from "../version.js";
-
-const PII_WARNING = [
-	"",
-	"⚠️  AGENTRUSH memories are PUBLIC — visible to any other player.",
-	"   Do not include real names, emails, secrets, work content, or PII.",
-	"",
-].join("\n");
-
-const ERROR_HINTS: Record<string, string> = {
-	agentrush_search_first:
-		"Run 3 'mem0 agent-rush search' commands before adding.",
-	agentrush_search_quota: "You've used your 3 lifetime searches.",
-	agentrush_add_quota: "You've used your 3 lifetime adds.",
-	agentrush_not_agent_mode:
-		"Re-run 'mem0 init --agent' to bootstrap an agent-mode key.",
-	agentrush_length: "Memory text must be 50-1000 characters.",
-	agentrush_no_urls: "URLs are not allowed.",
-	agentrush_blocklist: "Content contains a blocked term.",
-	agentrush_global_quota: "Event-wide cap reached. Try again later.",
-	agentrush_not_provisioned:
-		"AGENTRUSH is not provisioned in this environment.",
-};
-
-async function callEndpoint(
-	path: string,
-	body: Record<string, unknown>,
-): Promise<unknown> {
-	const config = loadConfig();
-	const baseUrl = (config.platform?.baseUrl ?? "https://api.mem0.ai").replace(
-		/\/+$/,
-		"",
-	);
-
-	if (!config.platform?.apiKey) {
-		printError("Not initialized. Run `mem0 init --agent` first.");
-		process.exit(1);
-	}
-
-	const resp = await fetch(`${baseUrl}${path}`, {
-		method: "POST",
-		headers: {
-			Authorization: `Token ${config.platform.apiKey}`,
-			"Content-Type": "application/json",
-			"X-Mem0-Source": "cli",
-			"X-Mem0-Client-Language": "node",
-			"X-Mem0-Client-Version": CLI_VERSION,
-			"X-Mem0-Mode": "agent-rush",
-		},
-		body: JSON.stringify(body),
-		signal: AbortSignal.timeout(30_000),
-	});
-
-	const json = await resp.json().catch(() => ({}));
-
-	if (!resp.ok) {
-		const code =
-			(json as { error?: { code?: string } }).error?.code ?? "unknown";
-		printError(`AGENTRUSH error: ${code}`);
-		if (ERROR_HINTS[code]) {
-			console.log(`  ${colors.dim(ERROR_HINTS[code])}`);
-		}
-		process.exit(1);
-	}
-
-	return json;
-}
-
-function promptLine(question: string): Promise<string> {
-	const rl = readline.createInterface({
-		input: process.stdin,
-		output: process.stdout,
-	});
-	return new Promise((resolve) => {
-		rl.question(question, (answer) => {
-			rl.close();
-			resolve(answer.trim());
-		});
-	});
-}
-
-/**
- * Ensure the human has acknowledged that AGENTRUSH memories are PUBLIC.
- *
- * Interactive (TTY): show the prompt; on "y" persist `agentRush.acknowledgedAt`
- * so we never ask the same machine twice. On anything else, abort.
- *
- * Non-interactive (agent invocation, no TTY): print the warning to stderr
- * for the human reading the agent's transcript and proceed — agents can't
- * answer y/N prompts.
- */
-async function ensureWarningAcknowledged(): Promise<void> {
-	const config = loadConfig();
-	if (config.agentRush?.acknowledgedAt) return;
-
-	if (!process.stdin.isTTY || !process.stdout.isTTY) {
-		// Agent context: surface the warning to stderr, don't block.
-		console.error(PII_WARNING);
-		return;
-	}
-
-	console.log(PII_WARNING);
-	const answer = (await promptLine("   Continue? [y/N]: ")).toLowerCase();
-	if (answer !== "y" && answer !== "yes") {
-		printError("Aborted.");
-		process.exit(1);
-	}
-
-	config.agentRush.acknowledgedAt = new Date().toISOString();
-	saveConfig(config);
-}
-
-export async function cmdAgentRushAdd(content: string): Promise<void> {
-	await ensureWarningAcknowledged();
-	const result = await callEndpoint("/v1/agent-rush/memories/", { content });
-	printSuccess(
-		`Memory submitted (event_id: ${(result as { event_id?: string }).event_id ?? "?"})`,
-	);
-}
-
-export async function cmdAgentRushSearch(query: string): Promise<void> {
-	const result = (await callEndpoint("/v1/agent-rush/memories/search/", {
-		query,
-	})) as {
-		results?: Array<{ memory?: string }>;
-		memories?: Array<{ memory?: string }>;
-	};
-
-	const memories = result.results ?? result.memories ?? [];
-
-	if (memories.length === 0) {
-		console.log(colors.dim("(no results)"));
-		return;
-	}
-
-	memories.slice(0, 5).forEach((m, i) => {
-		console.log(`  ${i + 1}. ${m.memory ?? JSON.stringify(m)}`);
-	});
-}
@@ -1,9 +1,9 @@
 /**
- * `mem0 whoami` — print the active agent's default_user_id (AGENTRUSH identifier).
+ * `mem0 whoami` — print the active agent's default_user_id.
 * Reads from local config; no network call.
 */

-import { colors, printError, printInfo } from "../branding.js";
+import { colors, printError } from "../branding.js";
 import { loadConfig } from "../config.js";

 export async function cmdWhoami(): Promise<void> {
@@ -13,6 +13,5 @@ export async function cmdWhoami(): Promise<void> {
 		printError("No default_user_id found. Run `mem0 init --agent` first.");
 		process.exit(1);
 	}
-	console.log(`Your AGENTRUSH identifier:  ${colors.brand(sessionId)}`);
-	printInfo("Find your row at https://mem0.ai/agentrush");
+	console.log(`Your user_id:  ${colors.brand(sessionId)}`);
 }
@@ -40,18 +40,11 @@ export interface TelemetryConfig {
 	anonymousId: string;
 }

-export interface AgentRushConfig {
-	// ISO timestamp the human acknowledged the "memories are public" warning.
-	// Empty until first interactive `mem0 agent-rush add`.
-	acknowledgedAt: string;
-}
-
 export interface Mem0Config {
 	version: number;
 	defaults: DefaultsConfig;
 	platform: PlatformConfig;
 	telemetry: TelemetryConfig;
-	agentRush: AgentRushConfig;
 }

 export function createDefaultConfig(): Mem0Config {
@@ -76,9 +69,6 @@ export function createDefaultConfig(): Mem0Config {
 		telemetry: {
 			anonymousId: "",
 		},
-		agentRush: {
-			acknowledgedAt: "",
-		},
 	};
 }

@@ -113,8 +103,6 @@ export function loadConfig(): Mem0Config {
 		config.defaults.runId = defaults.run_id ?? "";
 		const telemetry = data.telemetry ?? {};
 		config.telemetry.anonymousId = telemetry.anonymous_id ?? "";
-		const agentRush = data.agent_rush ?? {};
-		config.agentRush.acknowledgedAt = agentRush.acknowledged_at ?? "";
 	}

 	// Environment variable overrides
@@ -155,9 +143,6 @@ export function saveConfig(config: Mem0Config): void {
 		telemetry: {
 			anonymous_id: config.telemetry.anonymousId,
 		},
-		agent_rush: {
-			acknowledged_at: config.agentRush.acknowledgedAt,
-		},
 	};

 	fs.writeFileSync(CONFIG_FILE, JSON.stringify(data, null, 2));
@@ -266,44 +266,12 @@ program

 program
 	.command("whoami")
-	.description("Print the active agent's AGENTRUSH identifier.")
+	.description("Print your user_id (default_user_id).")
 	.action(async () => {
 		const { cmdWhoami } = await import("./commands/whoami.js");
 		await cmdWhoami();
 	});

-// ── AGENTRUSH subcommand group ────────────────────────────────────────────
-
-const agentRush = program
-	.command("agent-rush")
-	.description("AGENTRUSH game commands.")
-	.addHelpCommand(false)
-	.configureHelp({ formatHelp: richFormatHelp });
-
-agentRush
-	.command("add <content...>")
-	.description("Submit a memory to AGENTRUSH.")
-	.addHelpText(
-		"after",
-		'\nExamples:\n  $ mem0 agent-rush add "I used mem0 to build a coding agent"\n  $ mem0 agent-rush add "Agents that remember are better agents"',
-	)
-	.action(async (parts: string[]) => {
-		const { cmdAgentRushAdd } = await import("./commands/agent-rush.js");
-		await cmdAgentRushAdd(parts.join(" "));
-	});
-
-agentRush
-	.command("search <query...>")
-	.description("Search AGENTRUSH memories.")
-	.addHelpText(
-		"after",
-		'\nExamples:\n  $ mem0 agent-rush search "agents and memory and tools"\n  $ mem0 agent-rush search "coding assistant"',
-	)
-	.action(async (parts: string[]) => {
-		const { cmdAgentRushSearch } = await import("./commands/agent-rush.js");
-		await cmdAgentRushSearch(parts.join(" "));
-	});
-
 // ── Memory: add ───────────────────────────────────────────────────────────

 program
@@ -916,7 +916,7 @@ def identify(

@app.command(name="whoami", rich_help_panel="Setup")
 def whoami_cmd() -> None:
-    """Print your AGENTRUSH identifier (default_user_id).
+    """Print your user_id (default_user_id).

    Example:
      mem0 whoami
@@ -926,53 +926,6 @@ def whoami_cmd() -> None:
    run_whoami()


-# ── AGENTRUSH sub-app ─────────────────────────────────────────────────────
-
-agent_rush_app = typer.Typer(
-    name="agent-rush",
-    help="AGENTRUSH game commands",
-    no_args_is_help=True,
-    rich_markup_mode="rich",
-)
-
-
-@agent_rush_app.callback(invoke_without_command=True)
-def _agent_rush_callback(ctx: typer.Context) -> None:
-    if ctx.invoked_subcommand:
-        _fire_telemetry(f"agent-rush.{ctx.invoked_subcommand}")
-
-
-@agent_rush_app.command(name="add")
-def agent_rush_add(
-    content: str = typer.Argument(..., help="Memory content (50-1000 characters, no URLs)."),
-) -> None:
-    """Submit a memory to AGENTRUSH.
-
-    Example:
-      mem0 agent-rush add "I enjoy solving constraint-satisfaction problems."
-    """
-    from mem0_cli.commands.agent_rush_cmd import run_agent_rush_add
-
-    run_agent_rush_add(content)
-
-
-@agent_rush_app.command(name="search")
-def agent_rush_search(
-    query: str = typer.Argument(..., help="Search query."),
-) -> None:
-    """Search AGENTRUSH memories.
-
-    Example:
-      mem0 agent-rush search "constraint satisfaction"
-    """
-    from mem0_cli.commands.agent_rush_cmd import run_agent_rush_search
-
-    run_agent_rush_search(query)
-
-
-app.add_typer(agent_rush_app, name="agent-rush", rich_help_panel="Setup")
-
-
 # (entity_app registered at module level, below sub-group definitions)


@@ -1,132 +0,0 @@
-"""mem0 agent-rush — AGENTRUSH game commands.
-
-Wraps the platform's /v1/agent-rush/{memories/, memories/search/} endpoints.
-Hardcoded routing; no flags needed.
-"""
-
-from __future__ import annotations
-
-import sys
-from datetime import datetime, timezone
-
-import httpx
-import typer
-from rich.console import Console
-
-from mem0_cli.branding import print_error, print_success
-from mem0_cli.config import load_config, save_config
-
-console = Console()
-err_console = Console(stderr=True)
-
-_PII_WARNING_LINES = (
-    "",
-    "[yellow]⚠️  AGENTRUSH memories are PUBLIC — visible to any other player.[/yellow]",
-    "[yellow]   Do not include real names, emails, secrets, work content, or PII.[/yellow]",
-    "",
-)
-
-_SOURCE_HEADERS = {
-    "X-Mem0-Source": "cli",
-    "X-Mem0-Client-Language": "python",
-    "X-Mem0-Mode": "agent-rush",
-}
-
-_ERROR_HINTS = {
-    "agentrush_search_first": "Run 3 'mem0 agent-rush search' commands before adding.",
-    "agentrush_search_quota": "You've used your 3 lifetime searches.",
-    "agentrush_add_quota": "You've used your 3 lifetime adds.",
-    "agentrush_not_agent_mode": "Re-run 'mem0 init --agent' to bootstrap an agent-mode key.",
-    "agentrush_length": "Memory text must be 50-1000 characters.",
-    "agentrush_no_urls": "URLs are not allowed.",
-    "agentrush_blocklist": "Content contains a blocked term.",
-    "agentrush_global_quota": "Event-wide cap reached. Try again later.",
-    "agentrush_not_provisioned": "AGENTRUSH is not provisioned in this environment.",
-}
-
-
-def _call(path: str, body: dict) -> dict:
-    config = load_config()
-    if not config.platform.api_key:
-        print_error(err_console, "Not initialized. Run `mem0 init --agent` first.")
-        raise typer.Exit(1)
-    base_url = (config.platform.base_url or "https://api.mem0.ai").rstrip("/")
-    try:
-        with httpx.Client(timeout=30.0) as client:
-            resp = client.post(
-                f"{base_url}{path}",
-                headers={
-                    **_SOURCE_HEADERS,
-                    "Authorization": f"Token {config.platform.api_key}",
-                    "Content-Type": "application/json",
-                },
-                json=body,
-            )
-    except httpx.HTTPError as exc:
-        print_error(err_console, f"Network error: {exc}")
-        raise typer.Exit(1) from exc
-    try:
-        data = resp.json()
-    except Exception:
-        data = {}
-    if resp.status_code >= 400:
-        code = (
-            (data.get("error") or {}).get("code", "unknown")
-            if isinstance(data, dict)
-            else "unknown"
-        )
-        print_error(err_console, f"AGENTRUSH error: {code}")
-        hint = _ERROR_HINTS.get(code)
-        if hint:
-            console.print(f"  [dim]{hint}[/dim]")
-        raise typer.Exit(1)
-    return data
-
-
-def _ensure_warning_acknowledged() -> None:
-    """Block the first interactive add on the PII warning; pass-through for agents.
-
-    Interactive (TTY): show prompt, require explicit 'y', persist
-    `agent_rush.acknowledged_at` so we never ask the same machine twice.
-
-    Non-interactive (no TTY — typical when an agent runs the CLI): surface
-    the warning to stderr for the human reading the agent transcript and
-    proceed without prompting (agents can't answer y/N).
-    """
-    config = load_config()
-    if config.agent_rush.acknowledged_at:
-        return
-
-    is_tty = sys.stdin.isatty() and sys.stdout.isatty()
-    if not is_tty:
-        for line in _PII_WARNING_LINES:
-            err_console.print(line)
-        return
-
-    for line in _PII_WARNING_LINES:
-        console.print(line)
-    answer = typer.prompt("   Continue? [y/N]", default="N", show_default=False).strip().lower()
-    if answer not in ("y", "yes"):
-        print_error(err_console, "Aborted.")
-        raise typer.Exit(1)
-
-    config.agent_rush.acknowledged_at = datetime.now(timezone.utc).isoformat()
-    save_config(config)
-
-
-def run_agent_rush_add(content: str) -> None:
-    _ensure_warning_acknowledged()
-    result = _call("/v1/agent-rush/memories/", {"content": content})
-    event_id = result.get("event_id", "?")
-    print_success(console, f"Memory submitted (event_id: {event_id})")
-
-
-def run_agent_rush_search(query: str) -> None:
-    result = _call("/v1/agent-rush/memories/search/", {"query": query})
-    memories = result.get("results") or result.get("memories") or []
-    if not memories:
-        console.print("[dim](no results)[/dim]")
-        return
-    for i, m in enumerate(memories[:5], start=1):
-        text = m.get("memory") if isinstance(m, dict) else str(m)
-        console.print(f"  {i}. {text}")
@@ -1,11 +1,11 @@
-"""mem0 whoami — print the active agent's default_user_id (AGENTRUSH identifier)."""
+"""mem0 whoami — print the active agent's default_user_id."""

 from __future__ import annotations

 import typer
 from rich.console import Console

-from mem0_cli.branding import BRAND_COLOR, print_error, print_info
+from mem0_cli.branding import BRAND_COLOR, print_error
 from mem0_cli.config import load_config

 console = Console()
@@ -21,5 +21,4 @@ def run_whoami() -> None:
            "No default_user_id found. Run `mem0 init --agent` first.",
        )
        raise typer.Exit(1)
-    console.print(f"Your AGENTRUSH identifier:  [{BRAND_COLOR}]{session_id}[/{BRAND_COLOR}]")
-    print_info(console, "Find your row at https://mem0.ai/agentrush")
+    console.print(f"Your user_id:  [{BRAND_COLOR}]{session_id}[/{BRAND_COLOR}]")
@@ -51,20 +51,12 @@ class TelemetryConfig:
    anonymous_id: str = ""


-@dataclass
-class AgentRushConfig:
-    # ISO timestamp the human acknowledged the "memories are public" warning.
-    # Empty until first interactive `mem0 agent-rush add`.
-    acknowledged_at: str = ""
-
-
@dataclass
 class Mem0Config:
    version: int = CONFIG_VERSION
    defaults: DefaultsConfig = field(default_factory=DefaultsConfig)
    platform: PlatformConfig = field(default_factory=PlatformConfig)
    telemetry: TelemetryConfig = field(default_factory=TelemetryConfig)
-    agent_rush: AgentRushConfig = field(default_factory=AgentRushConfig)


 SHORT_KEY_ALIASES: dict[str, str] = {
@@ -113,9 +105,6 @@ def load_config() -> Mem0Config:
        telemetry = data.get("telemetry", {})
        config.telemetry.anonymous_id = telemetry.get("anonymous_id", "")

-        agent_rush = data.get("agent_rush", {})
-        config.agent_rush.acknowledged_at = agent_rush.get("acknowledged_at", "")
-
    # Environment variable overrides
    env_key = os.environ.get("MEM0_API_KEY")
    if env_key:
@@ -169,9 +158,6 @@ def save_config(config: Mem0Config) -> None:
        "telemetry": {
            "anonymous_id": config.telemetry.anonymous_id,
        },
-        "agent_rush": {
-            "acknowledged_at": config.agent_rush.acknowledged_at,
-        },
    }

    with open(CONFIG_FILE, "w") as f:
@@ -65,8 +65,9 @@ The request is queued for background processing. The response contains an `event
 <CodeGroup>
 ```json 200 response
 {
-  "event_id": "evt-uuid",
-  "status": "PENDING"
+  "message": "Memory processing has been queued for background execution",
+  "status": "PENDING",
+  "event_id": "evt-uuid"
 }
 ```

@@ -79,7 +79,7 @@ new_project = client.project.create(

 ### Update Project Settings

-Modify project configuration including custom instructions, categories, language preferences, and memory decay:
+Modify project configuration including custom instructions, categories, language preferences, retrieval criteria, and memory decay:

 ```python
 # Update project with custom categories
@@ -98,6 +98,14 @@ client.project.update(
 # Use the input language for memory storage and retrieval
 client.project.update(multilingual=True)

+# Set retrieval criteria to control which memories are surfaced in search
+client.project.update(
+    retrieval_criteria=[
+        {"name": "relevance", "description": "How directly relevant this memory is to the current topic or user query", "weight": 3},
+        {"name": "access_frequency", "description": "How often this memory has been accessed or surfaced recently", "weight": 1}
+    ]
+)
+
 # Enable Memory Decay (boosts recently-accessed memories at search time)
 client.project.update(decay=True)

@@ -112,6 +120,34 @@ client.project.update(
 )
 ```

+#### Set Retrieval Criteria
+
+`retrieval_criteria` is a per-project list of dictionaries (`List[Dict]`) that shapes how memories are ranked and filtered during search. Each dictionary has three fields: `name` (identifier), `description` (interpreted by the LLM to score each memory), and `weight` (relative influence on the final score). Use this to focus retrieval on intent-aligned or signal-specific memories:
+
+```python
+client.project.update(
+    retrieval_criteria=[
+        {
+            "name": "joy",
+            "description": "Measure the intensity of positive emotions such as happiness, excitement, or amusement expressed in the memory. A higher score reflects greater joy.",
+            "weight": 3
+        },
+        {
+            "name": "curiosity",
+            "description": "Assess the extent to which the memory reflects inquisitiveness or interest in exploring new information. A higher score reflects stronger curiosity.",
+            "weight": 2
+        },
+        {
+            "name": "access_frequency",
+            "description": "How often this memory has been accessed or surfaced recently.",
+            "weight": 1
+        }
+    ]
+)
+```
+
+Pass an empty list to clear all criteria and restore default retrieval behaviour.
+
 #### 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:
@@ -4,39 +4,6 @@ description: "Major product launches, headline features, and milestones for Mem0
 mode: "wide"
 ---

-<Update label="2026-07-30" description="n8n and Zapier integrations">
-
-**Workflow Automation: Mem0 Memory in n8n and Zapier**
-
-Mem0 now plugs into two no-code automation platforms, so workflows that used to start from zero on every run can store durable facts and recall them later.
-
- **n8n community node:** [`@mem0/n8n-nodes-mem0`](https://www.npmjs.com/package/@mem0/n8n-nodes-mem0) adds a **Mem0** node with a Memory resource covering Add, Search, Get, Get Many, Update, and Delete. Install it from **Settings → Community Nodes** on a self-hosted instance, then connect your API key once as a Mem0 API credential. See [n8n](/integrations/n8n).
- **n8n AI Agent tool:** Attach the same node to an [AI Agent](https://docs.n8n.io/advanced-ai/) node and it becomes a tool the agent calls on its own, so it can decide when to remember and when to recall.
- **Zapier app:** Add Memory, Search Memories, Get Memories, and Delete Memory actions let any of Zapier's thousands of apps write and read Mem0 context with no code and no server. See [Zapier](/integrations/zapier).
- **One-time connection:** Both integrations authenticate with a single Mem0 API key and default to `https://api.mem0.ai`, with a configurable base URL for self-hosted deployments.
-
-<Note>
-  The Zapier app is not yet listed in Zapier's public App Directory. Email [support@mem0.ai](mailto:support@mem0.ai) for an invite link.
-</Note>
-
-</Update>
-
-<Update label="2026-07-13" description="TypeScript provider expansion">
-
-**TypeScript OSS SDK: 26 New Providers, Reranking, and Zero-Dependency Imports**
-
-TypeScript SDK v3.1.0 is the largest provider release for the OSS SDK so far, closing most of the remaining gap with the Python SDK. Python SDK v2.0.12 ships alongside it with fixes and security patches.
-
- **17 new vector stores:** Pinecone, Weaviate, Milvus, Chroma, MongoDB, Elasticsearch, OpenSearch, Databricks, AWS Neptune Analytics, S3 Vectors, Azure MySQL, Google Vertex AI Vector Search, Turbopuffer, Upstash Vector, Valkey, Cassandra, and Baidu Mochow.
- **5 new LLM providers:** AWS Bedrock, xAI Grok, Together, vLLM, and Sarvam.
- **4 new embedding providers:** Vertex AI, HuggingFace, FastEmbed, and Together.
- **Reranking in TypeScript:** Four rerankers (Cohere, ZeroEntropy, cross-encoder, and LLM-based) with per-search rerank via a `rerank` option on `search()`.
- **Install only what you use:** Importing `mem0ai/oss` no longer pulls in any provider SDK. Provider packages are resolved lazily on first use, so an app that configures only OpenAI and Qdrant does not need the other provider SDKs installed.
-
-See [SDK & Tools](/changelog/sdk) for version details and PR links.
-
-</Update>
-
 <Update label="2026-06-27" description="SDK memory expiration">

 **SDK Memory Expiration: Expiring Memories Across Python and TypeScript**
@@ -7,75 +7,6 @@ mode: "wide"
 <Tabs>
 <Tab title="Python">

-<Update label="2026-08-01" description="v2.0.15">
-
-**Bug Fixes:**
- **Core:** `delete_all()` now paginates through the vector store in batches of 1000 instead of listing once, so accounts with more memories than a single page (most vector stores default to ~100) had the remainder silently left behind ([#6636](https://github.com/mem0ai/mem0/pull/6636))
- **Vector Stores:** Cap Supabase `search()`/`list()` `top_k` at the `vecs` query limit of 1000 instead of erroring, and fix a `col_info()` crash by reading collection attributes directly instead of calling the removed `describe()` method ([#6695](https://github.com/mem0ai/mem0/pull/6695))
- **Vector Stores:** Set `size` on Elasticsearch KNN search queries, so results respect `top_k` instead of being capped at Elasticsearch's default of 10 hits ([#5910](https://github.com/mem0ai/mem0/pull/5910))
-
-**Changes:**
- **Rerankers:** `LLMReranker`'s default model is now `gpt-5-mini` (was `gpt-4o-mini`) ([#6703](https://github.com/mem0ai/mem0/pull/6703))
-
-</Update>
-
-<Update label="2026-07-25" description="v2.0.14">
-
-**New Features:**
- **Vector Stores:** Add an Oracle AI Vector Search provider (`oracledb`) with connection pooling, `HNSW`/`IVF` indexes, JSON metadata filtering, and six selectable distance metrics ([#5358](https://github.com/mem0ai/mem0/pull/5358))
-
-**Bug Fixes:**
- **Vector Stores:** Translate a `"*"` filter value in OpenSearch into an `exists` query for every key, not just identity keys. It was previously ignored or matched literally against the string `"*"`, so a wildcard filter returned nothing ([#6522](https://github.com/mem0ai/mem0/pull/6522))
- **Vector Stores:** Re-raise errors from OpenSearch `search()` instead of returning `[]`, so a transport, auth, or index misconfiguration surfaces instead of looking like zero matches. `keyword_search()` still degrades on failure, since it is a best-effort BM25 signal ([#6519](https://github.com/mem0ai/mem0/pull/6519))
- **Vector Stores:** Guard the `text` field in Milvus `update()` behind the `_has_bm25_schema` check, matching `insert()`, so updating a memory in a collection without the BM25 `text`/`sparse` schema no longer fails ([#5705](https://github.com/mem0ai/mem0/pull/5705))
-
-</Update>
-
-<Update label="2026-07-22" description="v2.0.13">
-
-**Bug Fixes:**
- **Vector Stores:** Fix `reset()` silently leaving stale vectors behind on local (on-disk) Qdrant when the old collection directory could not be removed, for example an open file handle on Windows or NFS ([#6412](https://github.com/mem0ai/mem0/pull/6412))
- **Core:** Stop `update()` metadata from overwriting or injecting `user_id`, `agent_id`, `run_id`, or `actor_id`. These identity fields are immutable after creation, so passing them in `metadata` can no longer move a memory into a different tenant's scope ([#6278](https://github.com/mem0ai/mem0/pull/6278))
- **Vector Stores:** Scope Pinecone `delete_col()`/`reset()` to the configured namespace instead of deleting the whole index, so resetting a namespaced Pinecone store no longer wipes out the other namespaces sharing that index ([#6287](https://github.com/mem0ai/mem0/pull/6287))
- **Vector Stores:** Convert Baidu Mochow's raw L2 distance into a similarity score in `search()` (`1 / (1 + distance)`), so closer matches rank higher instead of lower, matching the Milvus provider and the rest of the `VectorStoreBase` contract ([#6435](https://github.com/mem0ai/mem0/pull/6435))
- **LLMs:** Read `OPENAI_BASE_URL` (was `OPENAI_API_BASE`) in `OpenAIStructuredLLM`, matching the official OpenAI SDK's environment variable and the rest of the OpenAI-compatible providers ([#6322](https://github.com/mem0ai/mem0/pull/6322))
-
-**Improvements:**
- **LLMs:** Remove a dead, no-op `api_key` attribute check from `LLMBase.__init__` ([#6460](https://github.com/mem0ai/mem0/pull/6460))
-
-**Changes:**
- **Client:** Remove the `retrieval_criteria` parameter from `MemoryClient.update_project()`/`AsyncMemoryClient.update_project()` and `Project.update()`/`AsyncProject.update()`. It was accepted and forwarded but never affected retrieval, so removing it is not a behavior change ([#6313](https://github.com/mem0ai/mem0/pull/6313))
-
-</Update>
-
-<Update label="2026-07-13" description="v2.0.12">
-
-**New Features:**
- **Memory (OSS):** Accept `text` in `Memory.update()` and `AsyncMemory.update()`. `data` still works but is now deprecated, so prefer `text` in new code ([#6044](https://github.com/mem0ai/mem0/pull/6044))
-
-**Bug Fixes:**
- **Core:** Coerce non-string entity IDs (`user_id`, `agent_id`, `run_id`) instead of crashing on `.strip()`, so passing an integer ID no longer raises `AttributeError` ([#6206](https://github.com/mem0ai/mem0/pull/6206))
- **Core:** Stop requiring `langchain-core` for the default async procedural memory path. The optional dependency is now only imported when you pass a custom LangChain LLM, matching the sync behavior ([#6209](https://github.com/mem0ai/mem0/pull/6209))
- **Client:** Encode dynamic URL path segments so IDs containing special characters no longer produce malformed requests ([#5963](https://github.com/mem0ai/mem0/pull/5963))
- **LLMs:** Skip `temperature` and `top_p` for newer Anthropic models that reject sampling parameters. Detection is automatic per model family and version, and the new `enable_sampling_parameters` config flag overrides it ([#6211](https://github.com/mem0ai/mem0/pull/6211))
- **Vector Stores:** Stop writing internal `OutputData` model fields as properties on Weaviate `update()` ([#6149](https://github.com/mem0ai/mem0/pull/6149))
- **Vector Stores:** Improve wildcard search handling in Milvus ([#6187](https://github.com/mem0ai/mem0/pull/6187))
- **Vector Stores:** Keep env-resolved Upstash Vector credentials after config validation. An env-var-only config previously passed validation and then failed to build ([#5811](https://github.com/mem0ai/mem0/pull/5811))
- **Vector Stores:** Restore the previous payload when a Neptune Analytics vector upsert fails inside `update()`, so a partial write can no longer leave the payload and embedding out of sync ([#5824](https://github.com/mem0ai/mem0/pull/5824))
-
-**Changes:**
- **LLMs:** The Together default model is now `MiniMaxAI/MiniMax-M3` (was `mistralai/Mixtral-8x7B-Instruct-v0.1`) ([#6049](https://github.com/mem0ai/mem0/pull/6049))
- **LLMs:** The xAI default model is now `grok-4.3` (was `grok-2-latest`) ([#6115](https://github.com/mem0ai/mem0/pull/6115))
- **Embeddings:** The Together default embedding model is now `intfloat/multilingual-e5-large-instruct` at 1024 dimensions (was `togethercomputer/m2-bert-80M-8k-retrieval` at 768). If you use the Together embedder without pinning `model`, existing vectors were written at the old dimension: either re-embed them, or pin `model` and `embedding_dims` to the old values ([#5989](https://github.com/mem0ai/mem0/pull/5989))
- **Rerankers:** The Cohere default rerank model is now `rerank-v3.5` (was `rerank-english-v3.0`) ([#6055](https://github.com/mem0ai/mem0/pull/6055))
-
-**Security:**
- **Vector Stores:** Fix SQL and Cypher injection vulnerabilities in the PGVector, Azure MySQL, and Neptune providers ([#4878](https://github.com/mem0ai/mem0/pull/4878))
- **Vector Stores:** Validate Elasticsearch filter keys and values to prevent term query injection ([#5980](https://github.com/mem0ai/mem0/pull/5980))
- **Dependencies:** Require `transformers>=5.3.0` to remediate GHSA-29pf-2h5f-8g72 (CVE-2026-4372) ([#6110](https://github.com/mem0ai/mem0/pull/6110))
-
-</Update>
-
 <Update label="2026-07-01" description="v2.0.11">

 **Bug Fixes:**
@@ -1169,79 +1100,6 @@ See the [OSS v2 to v3 migration guide](https://docs.mem0.ai/migration/oss-v2-to-

 <Tab title="TypeScript">

-<Update label="2026-08-01" description="v3.1.3">
-
-**New Features:**
- **Vector Stores:** Add Qdrant server-side BM25 `keywordSearch()` (requires Qdrant >= 1.15.2) plus payload filter indexes, so keyword search runs without a client-side BM25 dependency ([#5851](https://github.com/mem0ai/mem0/pull/5851))
-
-**Bug Fixes:**
- **Core:** `deleteAll()` now paginates through the vector store in batches of 1000 instead of listing once, so accounts with more memories than a single page had the remainder silently left behind ([#4872](https://github.com/mem0ai/mem0/pull/4872))
- **Vector Stores:** Supabase `list()` now paginates past PostgREST's 1000-row cap instead of stopping at the first page, `search()` warns when results may have been truncated by that same cap, and the initialization probe reads a row instead of writing a test vector, so Row Level Security policies that only grant read access no longer fail table verification ([#6695](https://github.com/mem0ai/mem0/pull/6695))
- **Embeddings:** Honor `TOGETHER_API_BASE` in the Together embedder, matching the Together LLM provider, so a custom gateway URL is no longer silently ignored for embeddings ([#6572](https://github.com/mem0ai/mem0/pull/6572))
-
-**Changes:**
- **Rerankers:** `RerankerFactory`'s default LLM reranker model is now `gpt-5-mini` (was `gpt-4o-mini`) ([#6703](https://github.com/mem0ai/mem0/pull/6703))
-
-**Security:**
- **Dependencies:** Patched 32 high and 57 medium severity dependency vulnerabilities across the pnpm workspace via `pnpm.overrides` (`axios`, `brace-expansion`, `js-yaml`, `postcss`, `protobufjs`, `mongoose`, `tar`, `fast-xml-parser`, `thrift`) ([#6639](https://github.com/mem0ai/mem0/pull/6639))
-
-</Update>
-
-<Update label="2026-07-25" description="v3.1.2">
-
-**Bug Fixes:**
- **Vector Stores:** Apply every operator in a Cassandra compound field filter (e.g. `{ age: { gte: 10, lte: 20 } }`) instead of stopping after the first, so the remaining bounds are no longer silently ignored ([#6511](https://github.com/mem0ai/mem0/pull/6511))
- **Vector Stores:** Stop the Chroma where-clause translator from dropping filter conditions. Same-field ranges (`gte` + `lte`), multi-field conditions inside `$or`, and negated `contains`/`icontains` under `$not` each collapsed to a single clause or vanished, widening the search instead of narrowing it ([#6521](https://github.com/mem0ai/mem0/pull/6521))
- **Vector Stores:** Skip `"*"` wildcard filter values in Milvus instead of matching them literally, so a filter like `{ user_id: "*" }` no longer returns zero memories ([#6508](https://github.com/mem0ai/mem0/pull/6508))
- **Vector Stores:** Read `textLemmatized` for BM25 keyword search on Milvus, OpenSearch, and MongoDB, matching the field the memory layer actually writes, so hybrid search on those backends no longer loses the keyword signal ([#6497](https://github.com/mem0ai/mem0/pull/6497))
- **LLMs:** Forward `responseFormat` to Gemini's `responseMimeType` in `generateResponse()`, so requesting `json_object` returns JSON instead of free-form text ([#6468](https://github.com/mem0ai/mem0/pull/6468))
- **LLMs:** Find the Anthropic text block by type instead of indexing `content[0]`, so a thinking-enabled model whose `thinking` block comes first no longer throws `Unexpected response type from Anthropic API` ([#6506](https://github.com/mem0ai/mem0/pull/6506))
-
-</Update>
-
-<Update label="2026-07-22" description="v3.1.1">
-
-**New Features:**
- **Embeddings:** Add an AWS Bedrock embedding provider ([#6185](https://github.com/mem0ai/mem0/pull/6185))
-
-**Bug Fixes:**
- **Packaging:** Finish the lazy-loading work started in v3.1.0. The remaining LLMs (Anthropic, Google, Groq, LangChain, Mistral, Ollama), embedders (Google, LangChain, Ollama, Vertex AI), vector stores (Azure AI Search, Azure MySQL, Baidu, LangChain, Qdrant, Redis, Supabase, Valkey, Vectorize), and the Supabase history store still imported their SDKs at module load, so importing `mem0ai/oss` required every provider package to be installed ([#6389](https://github.com/mem0ai/mem0/pull/6389))
- **Vector Stores:** Convert Baidu Mochow's raw L2 distance into a similarity score in `search()` (`1 / (1 + distance)`), so closer matches rank higher instead of lower. A row the backend returns without a score is now left `undefined` instead of being treated as the closest match ([#6485](https://github.com/mem0ai/mem0/pull/6485))
- **Memory (OSS):** Coerce non-string entity IDs (e.g. a numeric `user_id`) to strings instead of crashing on `.trim()` ([#6263](https://github.com/mem0ai/mem0/pull/6263))
- **Memory (OSS):** Stop `update()` metadata from overwriting or injecting `user_id`, `agent_id`, `run_id`, or `actor_id` (in either snake_case or camelCase). These identity fields are immutable after creation, so passing them in `metadata` can no longer move a memory into a different tenant's scope ([#6343](https://github.com/mem0ai/mem0/pull/6343))
- **Vector Stores:** Scope Pinecone `deleteCol()`/`reset()` to the configured namespace instead of deleting the whole index, so resetting a namespaced Pinecone store no longer wipes out the other namespaces sharing that index ([#6287](https://github.com/mem0ai/mem0/pull/6287))
-
-**Changes:**
- **Client:** Remove the unused `retrievalCriteria` field from `PromptUpdatePayload`. It was accepted and forwarded but never affected retrieval, so removing it is not a behavior change ([#6313](https://github.com/mem0ai/mem0/pull/6313))
-
-</Update>
-
-<Update label="2026-07-13" description="v3.1.0">
-
-The largest provider release for the TypeScript OSS SDK so far: 17 new vector stores, 5 new LLM providers, 4 new embedders, and reranking support. Importing `mem0ai/oss` no longer pulls in any provider SDK, so you only install what you actually configure.
-
-**New Features:**
- **Rerankers:** Add reranking to the OSS SDK with four providers (Cohere, ZeroEntropy, cross-encoder, and LLM-based), plus per-search rerank via a `rerank` option on `search()` ([#6055](https://github.com/mem0ai/mem0/pull/6055))
- **Memory (OSS):** Accept `text` in `Memory.update()`. `data` still works but is now deprecated, so prefer `text` in new code ([#6044](https://github.com/mem0ai/mem0/pull/6044))
- **Vector Stores:** Add Pinecone ([#5802](https://github.com/mem0ai/mem0/pull/5802)), Weaviate ([#5800](https://github.com/mem0ai/mem0/pull/5800)), Milvus ([#5889](https://github.com/mem0ai/mem0/pull/5889)), Chroma ([#6145](https://github.com/mem0ai/mem0/pull/6145)), MongoDB ([#5793](https://github.com/mem0ai/mem0/pull/5793)), Elasticsearch ([#5866](https://github.com/mem0ai/mem0/pull/5866)), and OpenSearch ([#5810](https://github.com/mem0ai/mem0/pull/5810))
- **Vector Stores:** Add Databricks ([#5824](https://github.com/mem0ai/mem0/pull/5824)), AWS Neptune Analytics ([#5797](https://github.com/mem0ai/mem0/pull/5797)), S3 Vectors ([#5822](https://github.com/mem0ai/mem0/pull/5822)), Azure MySQL ([#5827](https://github.com/mem0ai/mem0/pull/5827)), and Google Vertex AI Vector Search ([#5791](https://github.com/mem0ai/mem0/pull/5791))
- **Vector Stores:** Add Turbopuffer ([#5801](https://github.com/mem0ai/mem0/pull/5801)), Upstash Vector ([#5811](https://github.com/mem0ai/mem0/pull/5811)), Valkey ([#5826](https://github.com/mem0ai/mem0/pull/5826)), Cassandra ([#5823](https://github.com/mem0ai/mem0/pull/5823)), and Baidu Mochow ([#5790](https://github.com/mem0ai/mem0/pull/5790))
- **LLMs:** Add AWS Bedrock ([#5890](https://github.com/mem0ai/mem0/pull/5890)), xAI Grok ([#6115](https://github.com/mem0ai/mem0/pull/6115)), Together ([#6049](https://github.com/mem0ai/mem0/pull/6049)), vLLM ([#5805](https://github.com/mem0ai/mem0/pull/5805)), and Sarvam ([#6130](https://github.com/mem0ai/mem0/pull/6130))
- **Embeddings:** Add Vertex AI ([#5882](https://github.com/mem0ai/mem0/pull/5882)), HuggingFace ([#6027](https://github.com/mem0ai/mem0/pull/6027)), FastEmbed ([#5862](https://github.com/mem0ai/mem0/pull/5862)), and Together ([#5989](https://github.com/mem0ai/mem0/pull/5989))
-
-**Improvements:**
- **Packaging:** Lazy-load optional provider SDKs so importing `mem0ai/oss` never requires them. Provider packages are now resolved on first use, so an app that only configures OpenAI and Qdrant does not need the other provider SDKs installed ([#6280](https://github.com/mem0ai/mem0/pull/6280))
-
-**Bug Fixes:**
- **Memory (OSS):** Re-raise LLM extraction transport failures instead of returning `[]`, so a network error during extraction surfaces as an error rather than a silently empty result ([#6102](https://github.com/mem0ai/mem0/pull/6102))
- **Vector Stores:** Prevent an unhandled promise rejection in the Supabase and Redis constructors ([#6111](https://github.com/mem0ai/mem0/pull/6111))
- **Client:** Encode dynamic URL path segments so IDs containing special characters no longer produce malformed requests ([#5963](https://github.com/mem0ai/mem0/pull/5963))
-
-**Security:**
- **Dependencies:** Patch the `fast-xml-parser` and `tar` transitive CVEs ([#6160](https://github.com/mem0ai/mem0/pull/6160))
-
-</Update>
-
 <Update label="2026-07-01" description="v3.0.13">

 **Bug Fixes:**
@@ -1748,10 +1606,12 @@ See the [TypeScript SDK migration guide](https://docs.mem0.ai/migration/ts-v2-to

 <Tab title="CLI">

-<Update label="2026-07-13" description="Python v0.2.10 / Node v0.2.11">
+<Update label="2026-07-07" description="Python v0.2.10 / Node v0.2.11">

-**Bug Fixes:**
- **Platform backend:** Encode dynamic URL path segments so memory and entity IDs containing special characters no longer produce malformed requests (Python and Node [#5963](https://github.com/mem0ai/mem0/pull/5963))
+**Changes:**
+- **`mem0 agent-rush` removed:** The AGENTRUSH game has ended; the `agent-rush add` and `agent-rush search` commands are removed from both CLIs.
+- **`mem0 whoami` output:** Now prints `Your user_id:  <default_user_id>` instead of the AGENTRUSH-branded line. The value and the exit-code behavior are unchanged.
+- **Config schema:** The `agent_rush.acknowledged_at` key is no longer read or written. Existing config files that still contain it are unaffected; the key is ignored.

 </Update>

@@ -1913,15 +1773,6 @@ A full-featured command-line interface for Mem0, available in both Python and No
 <Tabs>
 <Tab title="Mem0 Plugin">

-<Update label="2026-07-14" description="mem0-plugin v0.2.13">
-
-**Fixes:**
- **Assistant messages no longer stored as your own:** The session-summary hook (fires at the end of every assistant turn) and the post-compaction hook were sending the assistant's own message to Mem0 tagged `role: "user"`. Because Mem0 extracts *facts about the user* from each message and uses `role` to decide who spoke, the assistant's first-person prose was being saved as the human's stated preferences: "I recommend we drop Redis" became `User prefers dropping Redis entirely`. Both hooks now send `role: "assistant"`, so the same session is stored as `Assistant recommended...`. Affects Claude Code, Cursor, Codex, and Antigravity, which share these hooks.
-
-Existing memories written by the previous versions are not rewritten. If your memories contain preferences you never expressed, delete them; the plugin will not recreate them.
-
-</Update>
-
 <Update label="2026-06-30" description="mem0-plugin v0.2.12">

 **New Features:**
@@ -2172,13 +2023,6 @@ Initial release of the Mem0 plugin for Claude Code and Cursor, followed by Codex

 <Tab title="OpenCode">

-<Update label="2026-07-22" description="OpenCode plugin v0.2.2">
-
-**Fixes:**
- **Shell-profile API key recovery:** When `MEM0_API_KEY` isn't set in the process environment, the plugin now falls back to reading it from `.zshrc`, `.bashrc`, `.zprofile`, `.bash_profile`, or `.profile`, fixing startup failures on clients (e.g. Desktop) that launch without shell-exported environment variables.
-
-</Update>
-
 <Update label="2026-06-30" description="OpenCode plugin v0.2.1">

 **Improvements:**
@@ -2252,15 +2096,6 @@ Initial release of the Mem0 plugin for Claude Code and Cursor, followed by Codex

 <Tab title="Antigravity">

-<Update label="2026-07-14" description="Antigravity plugin v0.1.5">
-
-**Fixes:**
- **Assistant messages no longer stored as your own:** The session-summary hook (fires at the end of every assistant turn) and the post-compaction hook were sending the assistant's own message to Mem0 tagged `role: "user"`. Because Mem0 extracts *facts about the user* from each message and uses `role` to decide who spoke, the assistant's first-person prose was being saved as the human's stated preferences: "I recommend we drop Redis" became `User prefers dropping Redis entirely`. Both hooks now send `role: "assistant"`, so the same session is stored as `Assistant recommended...`.
-
-Existing memories written by the previous versions are not rewritten. If your memories contain preferences you never expressed, delete them; the plugin will not recreate them.
-
-</Update>
-
 <Update label="2026-06-30" description="Antigravity plugin v0.1.4">

 **New Features:**
@@ -2308,16 +2143,6 @@ Existing memories written by the previous versions are not rewritten. If your me

 <Tab title="OpenClaw">

-<Update label="2026-08-01" description="openclaw-mem0 v1.0.15">
-
-**Improvements:**
- **Onboarding suggestions:** The example commands shown by `openclaw mem0 config show` now suggest `gpt-5-mini` instead of `gpt-4o` ([#6704](https://github.com/mem0ai/mem0/pull/6704))
-
-**Security:**
- **Dependencies:** Patched high and medium severity dependency vulnerabilities via `pnpm.overrides` (`protobufjs`, `axios`, `postcss`, `mongoose`) ([#6639](https://github.com/mem0ai/mem0/pull/6639))
-
-</Update>
-
 <Update label="2026-06-30" description="openclaw-mem0 v1.0.14">

 **Improvements:**
@@ -2571,13 +2396,6 @@ Existing memories written by the previous versions are not rewritten. If your me

 <Tab title="Pi Agent">

-<Update label="2026-08-01" description="Pi Agent plugin v0.1.4">
-
-**Security:**
- **Dependencies:** Patched high and medium severity dependency vulnerabilities via `pnpm.overrides` (`axios`, `brace-expansion`, `postcss`, `mongoose`, `protobufjs`) ([#6639](https://github.com/mem0ai/mem0/pull/6639))
-
-</Update>
-
 <Update label="2026-06-30" description="Pi Agent plugin v0.1.3">

 **New Features:**
@@ -2630,13 +2448,6 @@ Existing memories written by the previous versions are not rewritten. If your me

 <Tab title="Vercel AI SDK">

-<Update label="2026-08-01" description="Vercel AI SDK v3.0.1">
-
-**Security:**
- **Dependencies:** Patched high and medium severity dependency vulnerabilities via `pnpm.overrides` (`brace-expansion`, `js-yaml`) ([#6639](https://github.com/mem0ai/mem0/pull/6639))
-
-</Update>
-
 <Update label="2026-06-10" description="Vercel AI SDK v3.0.0">

 **Major Release**: Migrated to Vercel AI SDK v6 (`LanguageModelV3` / `ProviderV3`) and Mem0 v3 API.
@@ -2718,57 +2529,6 @@ Existing memories written by the previous versions are not rewritten. If your me
 - Added support for graph memories.
 </Update>

-</Tab>
-
-<Tab title="n8n">
-
-<Update label="2026-07-30" description="n8n-nodes-mem0 v0.1.1">
-
-**Changes:**
- **Published with npm provenance:** Republished through the `n8n-nodes-mem0-cd.yml` GitHub Actions workflow so the package carries a signed provenance attestation. `0.1.0` was published manually and has none, which blocks submission for n8n Creator Portal verification. No functional changes ([#6685](https://github.com/mem0ai/mem0/pull/6685))
-
-</Update>
-
-<Update label="2026-07-29" description="n8n-nodes-mem0 v0.1.0">
-
-**Initial release** of [`@mem0/n8n-nodes-mem0`](https://www.npmjs.com/package/@mem0/n8n-nodes-mem0), a community node that adds long-term memory to n8n workflows and AI Agents ([#6517](https://github.com/mem0ai/mem0/pull/6517))
-
-**New Features:**
- **Memory operations:** A single **Mem0** node covers Add, Search, Get, Get Many, Update, and Delete on the Memory resource.
- **AI Agent tool:** The node sets `usableAsTool`, so it can be attached to an n8n AI Agent node and invoked by the agent itself rather than wired into a fixed workflow path.
- **Scoping:** Add, Search, and Get Many accept User ID, Agent ID, App ID, and Run ID, so memories stay partitioned per user, agent, or session.
- **Add options:** Metadata JSON, custom categories, custom instructions, includes/excludes, an `infer` toggle, and a **Wait for Completion** switch that polls until the write lands instead of returning immediately.
- **Pagination:** Get Many supports Return All, or explicit Page and Page Size.
- **Credential:** A **Mem0 API** credential holds the API key plus a configurable base URL, defaulting to `https://api.mem0.ai` for self-hosted deployments.
-
-<Note>
-  Community nodes install from npm, which is a self-hosted n8n feature. See [n8n](/integrations/n8n) for setup.
-</Note>
-
-</Update>
-
-</Tab>
-
-<Tab title="Zapier">
-
-<Update label="2026-07-29" description="Zapier app v0.1.0">
-
-**Initial release** of the Mem0 Zapier app, built on the Zapier Platform CLI ([#6518](https://github.com/mem0ai/mem0/pull/6518))
-
-**New Features:**
- **Actions:** Add Memory and Delete Memory.
- **Searches:** Search Memories and Get Memories, usable as lookup steps in any Zap.
- **Authentication:** An API key connection validated against Mem0 the moment it is saved, sent as `Authorization: Token <key>`, with a configurable base URL for self-hosted deployments.
-
-**Bug Fixes:**
- **Add Memory:** Raise the poll budget past the real API latency tail, so a slower write is no longer reported as a failure ([#6680](https://github.com/mem0ai/mem0/pull/6680))
-
-<Note>
-  The app deploys to Zapier's platform rather than npm and is not yet listed in the public App Directory. See [Zapier](/integrations/zapier) for invite access.
-</Note>
-
-</Update>
-
 </Tab>
 </Tabs>

@@ -3,27 +3,11 @@ title: AWS Bedrock
 description: "Configure AWS Bedrock as an embedding provider in Mem0 with IAM credentials and boto3 authentication."
 ---

-To use AWS Bedrock embedding models, you need the appropriate AWS credentials and permissions. Python uses `boto3`, and TypeScript uses `@aws-sdk/client-bedrock-runtime`.
-
-Both SDKs support the Amazon Titan and Cohere embedding model families.
+To use AWS Bedrock embedding models, you need to have the appropriate AWS credentials and permissions. The embeddings implementation relies on the `boto3` library.

 ### Setup
-
- Model access is automatic: Bedrock enables serverless foundation models on first invocation in AWS commercial regions, and the [Model access page has been retired](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access.html). Cohere models are served from AWS Marketplace, so an account's first invocation must come from a principal with the `aws-marketplace:Subscribe` permission; after that, any user in the account can invoke them. Browse the models available to you in the [Bedrock model catalog](https://console.aws.amazon.com/bedrock/).
- Install the AWS client for your language:
-
-  <CodeGroup>
-  ```bash Python
-  pip install boto3
-  ```
-
-  ```bash TypeScript
-  npm install @aws-sdk/client-bedrock-runtime
-  ```
-  </CodeGroup>
-
-  In TypeScript this package is an optional peer dependency, so it is only required when you actually use the Bedrock embedder.
-
+- Ensure you have model access from the [AWS Bedrock Console](https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/modelaccess)
+- Authenticate the boto3 client using a method described in the [AWS documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html)
 - Set up environment variables for authentication:
  ```bash
  export AWS_REGION=us-east-1
@@ -31,8 +15,6 @@ Both SDKs support the Amazon Titan and Cohere embedding model families.
  export AWS_SECRET_ACCESS_KEY=your-secret-key
  ```

-Both SDKs fall back to the standard AWS credential chain (environment variables, shared config, SSO, or an instance role) when you do not pass credentials in the config, so you rarely need to hardcode keys. See the [boto3 credentials guide](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html) for the Python resolution order.
-
 ### Usage

 <CodeGroup>
@@ -66,46 +48,8 @@ messages = [
 ]
 m.add(messages, user_id="alice")
 ```
-
-```typescript TypeScript
-import { Memory } from "mem0ai/oss";
-
-// Credentials are read from the AWS default chain (AWS_REGION,
-// AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, SSO, or an instance role).
-const memory = new Memory({
-  embedder: {
-    provider: "aws_bedrock",
-    config: {
-      model: "amazon.titan-embed-text-v2:0",
-      awsRegion: "us-west-2",
-    },
-  },
-});
-
-const messages = [
-  { role: "user", content: "I'm planning to watch a movie tonight. Any recommendations?" },
-  { role: "assistant", content: "How about thriller movies? They can be quite engaging." },
-  { role: "user", content: "I'm not a big fan of thriller movies but I love sci-fi movies." },
-  { role: "assistant", content: "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future." },
-];
-await memory.add(messages, { userId: "alice" });
-```
 </CodeGroup>

-### Choosing a model
-
-| Model | Notes |
-| --- | --- |
-| `amazon.titan-embed-text-v1` | Default. Fixed 1536-dimension output. |
-| `amazon.titan-embed-text-v2:0` | Supports a configurable output size of 256, 512, or 1024. |
-| `cohere.embed-english-v3` | English text. Embeds up to 96 texts per request. |
-| `cohere.embed-multilingual-v3` | Multilingual text. Embeds up to 96 texts per request. |
-| `cohere.embed-v4:0` | Text. Embeds up to 96 texts per request. Supports a configurable output size of 256, 512, 1024, or 1536. TypeScript only. |
-
-Custom output sizes are model specific. In Python, only Titan Text Embeddings V2 accepts one. In TypeScript, Titan Text Embeddings V2 and Cohere Embed v4 both do, and `embeddingDims` is ignored on Titan V1 and on Cohere v3, which have no such parameter. When you do set it, make sure your vector store dimension matches, otherwise inserts will fail.
-
-Bedrock caps a Cohere embedding call at 96 texts. The TypeScript SDK splits larger batches into multiple requests for you, so a 200 text batch becomes 3 calls.
-
 ### Config

 Here are the parameters available for configuring AWS Bedrock embedder:
@@ -120,16 +64,4 @@ Here are the parameters available for configuring AWS Bedrock embedder:
 | `aws_secret_access_key` | AWS secret access key for authentication | `None` |
 | `aws_session_token` | AWS session token for temporary credentials | `None` |
 </Tab>
-<Tab title="TypeScript">
-| Parameter | Description | Default Value |
-| --- | --- | --- |
-| `model` | The name of the embedding model to use | `amazon.titan-embed-text-v1` |
-| `awsRegion` | AWS region for the Bedrock client. Falls back to the `AWS_REGION` environment variable | `us-west-2` |
-| `embeddingDims` | Output vector size. Titan Text Embeddings V2 (256, 512, or 1024) and Cohere Embed v4 (256, 512, 1024, or 1536) only | `undefined` |
-| `awsAccessKeyId` | AWS access key ID for authentication | `undefined` |
-| `awsSecretAccessKey` | AWS secret access key for authentication | `undefined` |
-| `awsSessionToken` | AWS session token for temporary credentials | `undefined` |
-
-Omit the three credential fields to use the AWS default credential chain. If you do pass them, `awsAccessKeyId` and `awsSecretAccessKey` are both required.
-</Tab>
 </Tabs>
@@ -5,10 +5,6 @@ description: "Configure Hugging Face as an embedding provider in Mem0 for local

 You can use embedding models from Huggingface to run Mem0 locally.

-<Note>
-The TypeScript SDK supports Hugging Face only through a hosted [Text Embeddings Inference (TEI)](#using-text-embeddings-inference-tei) endpoint, or any OpenAI-compatible Hugging Face endpoint. The local `sentence-transformers` mode shown first is Python-only.
-</Note>
-
 ### Usage

 ```python
@@ -38,10 +34,9 @@ m.add(messages, user_id="john")

 ### Using Text Embeddings Inference (TEI)

-You can also use Hugging Face's Text Embeddings Inference service for faster and more efficient embeddings. This is the mode the TypeScript SDK uses.
+You can also use Hugging Face's Text Embeddings Inference service for faster and more efficient embeddings:

-<CodeGroup>
-```python Python
+```python
 import os
 from mem0 import Memory

@@ -61,24 +56,6 @@ m = Memory.from_config(config)
 m.add("This text will be embedded using the TEI service.", user_id="john")
 ```

-```typescript TypeScript
-import { Memory } from 'mem0ai/oss';
-
-// Point at a running TEI server, or any OpenAI-compatible HF endpoint
-const config = {
-  embedder: {
-    provider: 'huggingface',
-    config: {
-      huggingfaceBaseUrl: 'http://localhost:3000/v1',
-    },
-  },
-};
-
-const memory = new Memory(config);
-await memory.add("This text will be embedded using the TEI service.", { userId: "john" });
-```
-</CodeGroup>
-
 To run the TEI service, you can use Docker:

 ```bash
@@ -89,22 +66,11 @@ docker run -d -p 3000:80 -v huggingfacetei:/data --platform linux/amd64 \

 ### Config

-Here are the parameters available for configuring the Hugging Face embedder:
+Here are the parameters available for configuring Huggingface embedder:

-<Tabs>
-<Tab title="Python">
 | Parameter | Description | Default Value |
 | --- | --- | --- |
 | `model` | The name of the model to use | `multi-qa-MiniLM-L6-cos-v1` |
 | `embedding_dims` | Dimensions of the embedding model | `selected_model_dimensions` |
 | `model_kwargs` | Additional arguments for the model | `None` |
-| `huggingface_base_url` | URL to connect to Text Embeddings Inference (TEI) API | `None` |
-</Tab>
-<Tab title="TypeScript">
-| Parameter | Description | Default Value |
-| --- | --- | --- |
-| `huggingfaceBaseUrl` | TEI or OpenAI-compatible endpoint URL. Required; falls back to `baseURL`, `url`, then the `HUGGINGFACE_BASE_URL` env var | `None` |
-| `model` | Model name sent to the endpoint (TEI ignores it) | `tei` |
-| `apiKey` | API key for the endpoint; falls back to the `HUGGINGFACE_API_KEY` env var | `"hf"` |
-</Tab>
-</Tabs>
+| `huggingface_base_url` | URL to connect to Text Embeddings Inference (TEI) API | `None` |
@@ -4,36 +4,11 @@ description: "Configure Google Cloud Vertex AI as an embedding provider in Mem0
 ---
 ### Vertex AI

-Google Cloud's Vertex AI serves text embedding models such as `gemini-embedding-001`. Mem0 uses them through the provider's own SDK, which you install alongside Mem0.
-
-### Installation
-
-The Vertex AI client is an optional dependency, so install it yourself.
-
-<CodeGroup>
-```bash Python
-pip install vertexai
-```
-
-```bash TypeScript
-npm install @google-cloud/aiplatform
-```
-</CodeGroup>
-
-### Authentication
-
-Both SDKs authenticate with [Application Default Credentials](https://cloud.google.com/docs/authentication/application-default-credentials). Pick whichever fits your environment:
-
- **Local development:** run `gcloud auth application-default login`.
- **Service account:** create a key in the [Google Cloud Console](https://console.cloud.google.com/) and point `GOOGLE_APPLICATION_CREDENTIALS` at the JSON file, or pass its path through the embedder config.
- **Google Cloud runtimes** (Cloud Run, GKE, Compute Engine): the attached service account is picked up automatically.
-
-The TypeScript SDK reads the project ID from `googleProjectId`, then the `GCP_PROJECT_ID`, `GOOGLE_CLOUD_PROJECT`, and `GCLOUD_PROJECT` environment variables, and finally from your credentials. Set it explicitly when your credentials cover more than one project.
+To use Google Cloud's Vertex AI for text embedding models, set the `GOOGLE_APPLICATION_CREDENTIALS` environment variable to point to the path of your service account's credentials JSON file. These credentials can be created in the [Google Cloud Console](https://console.cloud.google.com/).

 ### Usage

-<CodeGroup>
-```python Python
+```python
 import os
 from mem0 import Memory

@@ -57,87 +32,28 @@ m = Memory.from_config(config)
 messages = [
    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
-    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]
 m.add(messages, user_id="john")
 ```
-
-```typescript TypeScript
-import { Memory } from "mem0ai/oss";
-
-const config = {
-  embedder: {
-    provider: "vertexai",
-    config: {
-      model: "gemini-embedding-001",
-      // Optional. Falls back to GCP_PROJECT_ID / GOOGLE_CLOUD_PROJECT /
-      // GCLOUD_PROJECT, then to the project on your credentials.
-      googleProjectId: process.env.GCP_PROJECT_ID,
-      location: "us-central1",
-      // Optional. Path to a service account key file, or pass the JSON inline
-      // via googleServiceAccountJson.
-      vertexCredentialsJson: "/path/to/your/credentials.json",
-      embeddingDims: 256,
-      memoryAddEmbeddingType: "RETRIEVAL_DOCUMENT",
-      memoryUpdateEmbeddingType: "RETRIEVAL_DOCUMENT",
-      memorySearchEmbeddingType: "RETRIEVAL_QUERY",
-    },
-  },
-};
-
-const memory = new Memory(config);
-await memory.add("I love sci-fi movies but not thrillers", { userId: "john" });
-```
-</CodeGroup>
-
-### Embedding types
-
-Vertex AI embeds the same text differently depending on the task you declare. The embedding types can be one of the following:
+The embedding types can be one of the following:
 - SEMANTIC_SIMILARITY
 - CLASSIFICATION
 - CLUSTERING
 - RETRIEVAL_DOCUMENT, RETRIEVAL_QUERY, QUESTION_ANSWERING, FACT_VERIFICATION
- CODE_RETRIEVAL_QUERY
-
-Check out the [Vertex AI documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/task-types#supported_task_types) for more information.
-
-<Note>
-These embedding types map to the add, update, and search memory actions in both the Python and TypeScript SDKs. Stored memories use the add or update type, and searches use the search type.
-</Note>
-
-### Choosing a model
-
-<Warning>
-`gemini-embedding-001` accepts **one input text per request**. When Mem0 embeds several texts at once, such as the memories extracted from a single conversation turn, it issues one request per text. The older `text-embedding-005` and `text-multilingual-embedding-002` models accept up to 250 texts per request, so they are faster and cheaper for large batches. See [Get text embeddings](https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-text-embeddings).
-</Warning>
-
+- CODE_RETRIEVAL_QUERY  
+Check out the [Vertex AI documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/task-types#supported_task_types) for more information.  
+  
 ### Config

 Here are the parameters available for configuring the Vertex AI embedder:

-<Tabs>
-<Tab title="Python">
-| Parameter                        | Description                                                | Default Value          |
-| -------------------------------- | ---------------------------------------------------------- | ---------------------- |
-| `model`                          | The name of the Vertex AI embedding model to use           | `gemini-embedding-001` |
-| `vertex_credentials_json`        | Path to the Google Cloud credentials JSON file             | `None`                 |
-| `embedding_dims`                 | Dimensions of the embedding model                          | `256`                  |
-| `memory_add_embedding_type`      | The embedding type to use for the add memory action        | `RETRIEVAL_DOCUMENT`   |
-| `memory_update_embedding_type`   | The embedding type to use for the update memory action     | `RETRIEVAL_DOCUMENT`   |
-| `memory_search_embedding_type`   | The embedding type to use for the search memory action     | `RETRIEVAL_QUERY`      |
-</Tab>
-<Tab title="TypeScript">
-| Parameter                     | Description                                                                 | Default Value          |
-| ----------------------------- | -------------------------------------------------------------------------- | ---------------------- |
-| `model`                       | The name of the Vertex AI embedding model to use                           | `gemini-embedding-001` |
-| `googleProjectId`             | Google Cloud project ID (falls back to `GCP_PROJECT_ID` env var, then to your credentials) | Resolved from credentials |
-| `location`                    | Google Cloud region (falls back to `GCP_LOCATION` env var)                 | `us-central1`          |
-| `vertexCredentialsJson`       | Path to the Google Cloud credentials JSON file                             | `None`                 |
-| `googleServiceAccountJson`    | Service account credentials as a JSON string or object                     | `None`                 |
-| `embeddingDims`               | Dimensions of the embedding model                                          | `256`                  |
-| `memoryAddEmbeddingType`      | The embedding type to use for the add memory action                        | `RETRIEVAL_DOCUMENT`   |
-| `memoryUpdateEmbeddingType`   | The embedding type to use for the update memory action                     | `RETRIEVAL_DOCUMENT`   |
-| `memorySearchEmbeddingType`   | The embedding type to use for the search memory action                     | `RETRIEVAL_QUERY`      |
-</Tab>
-</Tabs>
+| Parameter                 | Description                                      | Default Value        |
+| ------------------------- | ------------------------------------------------ | -------------------- |
+| `model`                   | The name of the Vertex AI embedding model to use | `gemini-embedding-001` |
+| `vertex_credentials_json` | Path to the Google Cloud credentials JSON file   | `None`               |
+| `embedding_dims`          | Dimensions of the embedding model                | `256`                |
+| `memory_add_embedding_type` | The type of embedding to use for the add memory action | `RETRIEVAL_DOCUMENT` |
+| `memory_update_embedding_type` | The type of embedding to use for the update memory action | `RETRIEVAL_DOCUMENT` |
+| `memory_search_embedding_type` | The type of embedding to use for the search memory action | `RETRIEVAL_QUERY` |
@@ -10,7 +10,7 @@ Mem0 offers support for various embedding models, allowing users to choose the o
 See the list of supported embedders below.

 <Note>
-  All embedders listed below are supported in the Python implementation. The TypeScript implementation supports: **OpenAI**, **Azure OpenAI**, **AWS Bedrock**, **FastEmbed**, **Google AI**, **Hugging Face**, **Langchain**, **LM Studio**, **Ollama**, **Together**, and **Vertex AI**.
+  All embedders listed below are supported in the Python implementation. The TypeScript implementation supports: **OpenAI**, **Azure OpenAI**, **FastEmbed**, **Google AI**, **Langchain**, **LM Studio**, **Ollama**, and **Together**.
 </Note>

 <CardGroup cols={4}>
@@ -5,18 +5,16 @@ description: "Configure AWS Bedrock as an LLM provider in Mem0 with IAM authenti

 ### Setup
 - Before using the AWS Bedrock LLM, make sure you have the appropriate model access from [Bedrock Console](https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/modelaccess).
- Model availability is per-region. `anthropic.claude-sonnet-4-20250514-v1:0` supports on-demand inference in `us-east-1` and `ap-southeast-4`; from any other region, use the cross-region inference profile ID `us.anthropic.claude-sonnet-4-20250514-v1:0` instead.
- Install the AWS SDK for your language: `pip install boto3` (Python) or `npm install @aws-sdk/client-bedrock-runtime` (TypeScript).
- Both SDKs fall back to the standard AWS credential chain (environment variables, `~/.aws/credentials`, or an attached IAM role), so exporting `AWS_REGION`, `AWS_ACCESS_KEY_ID`, and `AWS_SECRET_ACCESS_KEY` is the quickest way to get started. In TypeScript you can also pass credentials inline with `awsRegion`, `awsAccessKeyId`, `awsSecretAccessKey`, and `awsSessionToken`, as shown below.
+- You will also need to authenticate the `boto3` client by using a method in the [AWS documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html#configuring-credentials)
+- You will have to export `AWS_REGION`, `AWS_ACCESS_KEY_ID`, and `AWS_SECRET_ACCESS_KEY` to set environment variables.

 ### Usage

-<CodeGroup>
-```python Python
+```python
 import os
 from mem0 import Memory

-os.environ['AWS_REGION'] = 'us-east-1'
+os.environ['AWS_REGION'] = 'us-west-2'
 os.environ["AWS_ACCESS_KEY_ID"] = "xx"
 os.environ["AWS_SECRET_ACCESS_KEY"] = "xx"

@@ -24,7 +22,7 @@ config = {
    "llm": {
        "provider": "aws_bedrock",
        "config": {
-            "model": "anthropic.claude-sonnet-4-20250514-v1:0",
+            "model": "anthropic.claude-3-5-haiku-20241022-v1:0",
            "temperature": 0.2,
            "max_tokens": 2000,
        }
@@ -41,43 +39,6 @@ messages = [
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```

-```typescript TypeScript
-import { Memory } from 'mem0ai/oss';
-
-const config = {
-  llm: {
-    provider: 'aws_bedrock',
-    config: {
-      model: 'anthropic.claude-sonnet-4-20250514-v1:0',
-      temperature: 0.2,
-      maxTokens: 2000,
-      // Optional. Omit these to use the default AWS credential chain.
-      awsRegion: process.env.AWS_REGION,
-      awsAccessKeyId: process.env.AWS_ACCESS_KEY_ID,
-      awsSecretAccessKey: process.env.AWS_SECRET_ACCESS_KEY,
-    },
-  },
-};
-
-const memory = new Memory(config);
-const messages = [
-    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
-    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
-    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
-];
-await memory.add(messages, { userId: 'alice', metadata: { category: 'movies' } });
-```
-</CodeGroup>
-
-<Note>
-  `@aws-sdk/client-bedrock-runtime` is an optional peer dependency of `mem0ai`, so npm will not install it for you. The TypeScript provider loads it lazily and throws a clear error on the first request if the package is missing.
-</Note>
-
-<Note>
-  The TypeScript provider calls the Bedrock [Converse API](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html), a single uniform interface across the current Bedrock model families. Streaming and `InvokeModel`-only models are not supported yet.
-</Note>
-
 ### Config

-All available parameters for the `aws_bedrock` config are present in [Master List of All Params in Config](../config).
+All available parameters for the `aws_bedrock` config are present in [Master List of All Params in Config](../config).
@@ -9,8 +9,7 @@ To use Sarvam AI's models, please set the `SARVAM_API_KEY` which you can get fro

 ## Usage

-<CodeGroup>
-```python Python
+```python
 import os
 from mem0 import Memory

@@ -35,35 +34,8 @@ messages = [
    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]
 m.add(messages, user_id="alex")
-
 ```

-```typescript TypeScript
-import { Memory } from 'mem0ai/oss';
-
-const config = {
-  llm: {
-    provider: 'sarvam',
-    config: {
-      apiKey: process.env.SARVAM_API_KEY || '',
-      model: 'sarvam-m',
-      temperature: 0.7,
-    },
-  },
-};
-
-const memory = new Memory(config);
-const messages = [
-    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
-    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
-    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
-];
-await memory.add(messages, { userId: 'alex' });
-```
-
-</CodeGroup>
-
 ## Advanced Usage with Sarvam-Specific Features

 ```python
@@ -16,7 +16,7 @@ For a comprehensive list of available parameters for llm configuration, please r
 See the list of supported LLMs below.

 <Note>
-  All LLMs are supported in Python. The following LLMs are also supported in TypeScript: **OpenAI**, **Anthropic**, **AWS Bedrock**, **Groq**, **Azure OpenAI**, **DeepSeek**, **Google AI**, **Langchain**, **LM Studio**, **Mistral AI**, and **Ollama**.
+  All LLMs are supported in Python. The following LLMs are also supported in TypeScript: **OpenAI**, **Anthropic**, **Groq**, **Azure OpenAI**, **DeepSeek**, **Google AI**, **Langchain**, **LM Studio**, **Mistral AI**, and **Ollama**.
 </Note>

 <CardGroup cols={4}>
@@ -26,7 +26,7 @@ All rerankers share these common configuration parameters:

 | Parameter            | Description                                  | Type   | Default                 |
 | -------------------- | -------------------------------------------- | ------ | ----------------------- |
-| `model`              | Cohere rerank model                          | `str`  | `"rerank-v3.5"` |
+| `model`              | Cohere rerank model                          | `str`  | `"rerank-english-v3.0"` |
 | `api_key`            | Cohere API key                               | `str`  | `None`                  |
 | `return_documents`   | Whether to return document texts in response | `bool` | `False`                 |
 | `max_chunks_per_doc` | Maximum chunks per document                  | `int`  | `None`                  |
@@ -52,7 +52,7 @@ All rerankers share these common configuration parameters:

 | Parameter        | Description                                | Type    | Default                |
 | ---------------- | ------------------------------------------ | ------- | ---------------------- |
-| `model`          | LLM model to use for scoring               | `str`   | `"gpt-5-mini"`        |
+| `model`          | LLM model to use for scoring               | `str`   | `"gpt-4o-mini"`        |
 | `provider`       | LLM provider (`openai`, `anthropic`, etc.) | `str`   | `"openai"`             |
 | `api_key`        | API key for LLM provider                   | `str`   | `None`                 |
 | `temperature`    | Temperature for LLM generation             | `float` | `0.0`                  |
@@ -103,30 +103,3 @@ config = {
    }
 }
 ```
-
-## TypeScript SDK
-
-The self-hosted [TypeScript SDK](/open-source/features/reranker-search#typescript-sdk) (`mem0ai/oss`) supports the same five providers. Config keys are camelCase (`apiKey`, `topK`, `maxLength`) and each provider's SDK is a peer dependency you install per reranker.
-
-| Provider | Install | Default model | Key config fields |
-| --- | --- | --- | --- |
-| `cohere` | `pnpm add cohere-ai` | `rerank-v3.5` | `apiKey`, `model`, `topK` |
-| `zero_entropy` | `pnpm add zeroentropy` | `zerank-1` | `apiKey`, `model`, `topK` |
-| `sentence_transformer` | `pnpm add @huggingface/transformers` | `Xenova/ms-marco-MiniLM-L-6-v2` | `model`, `device`, `maxLength`, `normalize`, `topK` |
-| `huggingface` | `pnpm add @huggingface/transformers` | `Xenova/bge-reranker-base` | `model`, `device`, `maxLength`, `normalize`, `topK` |
-| `llm_reranker` | None (uses your LLM provider's own SDK) | `openai` / `gpt-5-mini` | `provider`, `model`, `apiKey`, `llm` (nested override), `topK` |
-
-```typescript
-import { Memory } from "mem0ai/oss";
-
-const memory = new Memory({
-  reranker: {
-    provider: "zero_entropy",
-    config: { apiKey: process.env.ZERO_ENTROPY_API_KEY, topK: 5 },
-  },
-});
-```
-
-<Note>
-  The local cross-encoder providers (`sentence_transformer`, `huggingface`) run on [Transformers.js](https://huggingface.co/docs/transformers.js) and default to ONNX (`Xenova/*`) model mirrors, so Python default model strings must be swapped for their ONNX equivalents. `batchSize` and `showProgressBar` are accepted for parity with Python but are no-ops in the TypeScript runtime. See the [reranker feature guide](/open-source/features/reranker-search#typescript-sdk) for full examples.
-</Note>
@@ -48,7 +48,7 @@ config = {
        "provider": "llm_reranker",
        "config": {
            "provider": "openai",
-            "model": "gpt-5-mini",
+            "model": "gpt-4o-mini",
            "api_key": "your-openai-key",
            "scoring_prompt": custom_prompt,
            "top_k": 5
@@ -9,9 +9,9 @@ Cohere provides enterprise-grade reranking models with excellent multilingual su

 Cohere offers several reranking models:

- **`rerank-v3.5`** (default): Latest reranker, multilingual, best performance
- **`rerank-english-v3.0`**: Previous generation, English only
- **`rerank-multilingual-v3.0`**: Previous generation, multilingual
+- **`rerank-english-v3.0`**: Latest English reranker with best performance
+- **`rerank-multilingual-v3.0`**: Multilingual support for global applications
+- **`rerank-english-v2.0`**: Previous generation English reranker

 ## Installation

@@ -41,7 +41,7 @@ config = {
    "reranker": {
        "provider": "cohere",
        "config": {
-            "model": "rerank-v3.5",
+            "model": "rerank-english-v3.0",
            "api_key": "your-cohere-api-key",  # or set COHERE_API_KEY
            "top_k": 5,
            "return_documents": False,
@@ -53,34 +53,6 @@ config = {
 memory = Memory.from_config(config)
 ```

-## TypeScript (self-hosted)
-
-The [TypeScript OSS SDK](/open-source/features/reranker-search#typescript-sdk) (`mem0ai/oss`) ships the Cohere reranker. Config keys are camelCase, it defaults to the `rerank-v3.5` model, and you opt in per search with `rerank: true`.
-
-```bash
-pnpm add cohere-ai
-```
-
-```typescript
-import { Memory } from "mem0ai/oss";
-
-const memory = new Memory({
-  reranker: {
-    provider: "cohere",
-    config: {
-      apiKey: process.env.COHERE_API_KEY, // or set COHERE_API_KEY
-      // model: "rerank-v3.5",     // default
-      topK: 5,
-    },
-  },
-});
-
-const results = await memory.search("What is the user's profession?", {
-  filters: { userId: "bob" },
-  rerank: true,
-});
-```
-
 ## Environment Variables

 Set your API key as an environment variable:
@@ -101,11 +73,11 @@ os.environ["COHERE_API_KEY"] = "your-api-key"
 # Initialize memory with Cohere reranker
 config = {
    "vector_store": {"provider": "chroma"},
-    "llm": {"provider": "openai", "config": {"model": "gpt-5-mini"}},
+    "llm": {"provider": "openai", "config": {"model": "gpt-4o-mini"}},
    "rerank": {
        "provider": "cohere",
        "config": {
-            "model": "rerank-v3.5",
+            "model": "rerank-english-v3.0",
            "top_k": 3
        }
    }
@@ -152,7 +124,7 @@ config = {

 | Parameter            | Description                      | Type   | Default                 |
 | -------------------- | -------------------------------- | ------ | ----------------------- |
-| `model`              | Cohere rerank model to use       | `str`  | `"rerank-v3.5"` |
+| `model`              | Cohere rerank model to use       | `str`  | `"rerank-english-v3.0"` |
 | `api_key`            | Cohere API key                   | `str`  | `None`                  |
 | `top_k`              | Maximum documents to return      | `int`  | `None`                  |
 | `return_documents`   | Whether to return document texts | `bool` | `False`                 |
@@ -167,7 +139,7 @@ config = {

 ## Best Practices

-1. **Model Selection**: `rerank-v3.5` handles English and multilingual workloads; pin an older `v3.0` model only if you need to reproduce prior results
+1. **Model Selection**: Use `rerank-english-v3.0` for English, `rerank-multilingual-v3.0` for other languages
 2. **Batch Processing**: Process multiple queries efficiently
 3. **Error Handling**: Implement retry logic for production systems
 4. **Monitoring**: Track reranking performance and costs
@@ -57,40 +57,6 @@ config = {
 }
 ```

-## TypeScript (self-hosted)
-
-The [TypeScript OSS SDK](/open-source/features/reranker-search#typescript-sdk) (`mem0ai/oss`) runs this reranker locally with [Transformers.js](https://huggingface.co/docs/transformers.js), the same cross-encoder path as `sentence_transformer`, just a different default model. It executes ONNX weights, so the default is the ONNX mirror `Xenova/bge-reranker-base`. Point `model` at any ONNX-exported reranker on the Hub (a raw `BAAI/bge-reranker-*` PyTorch checkpoint will not load in this runtime).
-
-```bash
-pnpm add @huggingface/transformers
-```
-
-```typescript
-import { Memory } from "mem0ai/oss";
-
-const memory = new Memory({
-  reranker: {
-    provider: "huggingface",
-    config: {
-      // model: "Xenova/bge-reranker-base", // default (ONNX)
-      device: "cpu", // "cpu" | "wasm" | "webgpu"
-      maxLength: 512, // max tokens per query-document pair
-      normalize: true, // sigmoid-normalize logits to [0, 1] (default)
-      topK: 5,
-    },
-  },
-});
-
-const results = await memory.search("What are the user's interests?", {
-  filters: { userId: "alice" },
-  rerank: true,
-});
-```
-
-<Note>
-  `batchSize` and `showProgressBar` are accepted for parity with the Python SDK but are no-ops in the TypeScript runtime. `trust_remote_code` and `model_kwargs` are Python-only.
-</Note>
-
 ## Popular Models

 ### BGE Rerankers (Recommended)
@@ -19,7 +19,7 @@ config = {
        "provider": "llm_reranker",
        "config": {
            "provider": "openai",
-            "model": "gpt-5-mini",
+            "model": "gpt-4o-mini",
            "api_key": "your-openai-api-key"
        }
    }
@@ -33,7 +33,7 @@ m = Memory.from_config(config)
 | Parameter | Type | Default | Description |
 |-----------|------|---------|-------------|
 | `provider` | str | `"openai"` | LLM provider (openai, anthropic, etc.) |
-| `model` | str | `"gpt-5-mini"` | LLM model to use for reranking |
+| `model` | str | `"gpt-4o-mini"` | LLM model to use for reranking |
 | `api_key` | str | None | API key for the LLM provider |
 | `top_k` | int | None | Number of top documents to return after reranking |
 | `temperature` | float | 0.0 | LLM temperature for consistency |
@@ -67,43 +67,6 @@ config = {
 }
 ```

-## TypeScript (self-hosted)
-
-The [TypeScript OSS SDK](/open-source/features/reranker-search#typescript-sdk) (`mem0ai/oss`) ships the LLM reranker under the provider name `llm_reranker`. It does **not** reuse the Memory's main `llm` instance; it builds its own LLM from the reranker's own config, defaulting to `openai` / `gpt-5-mini`. Set `provider`/`model`/`apiKey` directly on `config`, or nest a fully separate `config.llm: { provider, config }` (its `provider`/`config` take priority over the top-level fields, which only backfill values missing from the nested config).
-
-```typescript
-import { Memory } from "mem0ai/oss";
-
-const memory = new Memory({
-  reranker: {
-    provider: "llm_reranker",
-    config: { apiKey: process.env.OPENAI_API_KEY },
-  },
-});
-
-const results = await memory.search("What movies do I like?", {
-  filters: { userId: "alice" },
-  rerank: true,
-});
-```
-
-To rerank with a different LLM provider than the Memory's main `llm`, nest it under `config.llm`:
-
-```typescript
-const memory = new Memory({
-  llm: { provider: "openai", config: { apiKey: process.env.OPENAI_API_KEY } },
-  reranker: {
-    provider: "llm_reranker",
-    config: {
-      llm: {
-        provider: "anthropic",
-        config: { apiKey: process.env.ANTHROPIC_API_KEY },
-      },
-    },
-  },
-});
-```
-
 ## Supported LLM Providers

 ### OpenAI
@@ -114,7 +77,7 @@ config = {
        "provider": "llm_reranker",
        "config": {
            "provider": "openai",
-            "model": "gpt-5-mini",
+            "model": "gpt-4o-mini",
            "api_key": "your-openai-api-key",
            "temperature": 0.0
        }
@@ -170,15 +133,15 @@ config = {
        "provider": "llm_reranker",
        "config": {
            "provider": "azure_openai",
-            "model": "gpt-5-mini",
+            "model": "gpt-4o-mini",
            "api_key": "your-azure-api-key",
            "llm": {
                "provider": "azure_openai",
                "config": {
-                    "model": "gpt-5-mini",
+                    "model": "gpt-4o-mini",
                    "api_key": "your-azure-api-key",
                    "azure_endpoint": "https://your-resource.openai.azure.com/",
-                    "azure_deployment": "gpt-5-mini-deployment"
+                    "azure_deployment": "gpt-4o-mini-deployment"
                }
            }
        }
@@ -232,7 +195,7 @@ config = {
        "provider": "llm_reranker",
        "config": {
            "provider": "openai",
-            "model": "gpt-5-mini",
+            "model": "gpt-4o-mini",
            "api_key": "your-api-key",
            "scoring_prompt": custom_prompt
        }
@@ -351,7 +314,7 @@ fast_config = {
        "provider": "llm_reranker",
        "config": {
            "provider": "openai",
-            "model": "gpt-5-mini",
+            "model": "gpt-4o-mini",
            "api_key": "your-api-key",
            "top_k": 5,
            "temperature": 0.0
@@ -444,7 +407,7 @@ fallback_config = {
        "provider": "llm_reranker",
        "config": {
            "provider": "openai",
-            "model": "gpt-5-mini",
+            "model": "gpt-4o-mini",
            "api_key": "your-api-key"
        }
    }
@@ -36,7 +36,7 @@ config = {
    "llm": {
        "provider": "openai",
        "config": {
-            "model": "gpt-5-mini"
+            "model": "gpt-4o-mini"
        }
    },
    "rerank": {
@@ -54,40 +54,6 @@ config = {
 memory = Memory.from_config(config)
 ```

-## TypeScript (self-hosted)
-
-The [TypeScript OSS SDK](/open-source/features/reranker-search#typescript-sdk) (`mem0ai/oss`) runs this reranker locally with [Transformers.js](https://huggingface.co/docs/transformers.js). Because it executes ONNX weights, the default model is the ONNX mirror of the Python default: `Xenova/ms-marco-MiniLM-L-6-v2`. Point `model` at any ONNX-exported cross-encoder on the Hub (a raw `cross-encoder/...` PyTorch checkpoint will not load in this runtime).
-
-```bash
-pnpm add @huggingface/transformers
-```
-
-```typescript
-import { Memory } from "mem0ai/oss";
-
-const memory = new Memory({
-  reranker: {
-    provider: "sentence_transformer",
-    config: {
-      // model: "Xenova/ms-marco-MiniLM-L-6-v2", // default (ONNX)
-      device: "cpu", // "cpu" | "wasm" | "webgpu"
-      maxLength: 512, // max tokens per query-document pair
-      normalize: true, // sigmoid-normalize logits to [0, 1] (default)
-      topK: 5,
-    },
-  },
-});
-
-const results = await memory.search("What books does the user like?", {
-  filters: { userId: "charlie" },
-  rerank: true,
-});
-```
-
-<Note>
-  `batchSize` and `showProgressBar` are accepted for parity with the Python SDK but are no-ops in the TypeScript runtime, because a search reranks a small candidate set in a single in-process forward pass. The model downloads once and is cached in-process.
-</Note>
-
 ## GPU Acceleration

 For better performance, use GPU acceleration:
@@ -113,7 +79,7 @@ from mem0 import Memory
 # Initialize memory with local reranker
 config = {
    "vector_store": {"provider": "chroma"},
-    "llm": {"provider": "openai", "config": {"model": "gpt-5-mini"}},
+    "llm": {"provider": "openai", "config": {"model": "gpt-4o-mini"}},
    "rerank": {
        "provider": "sentence_transformer",
        "config": {
@@ -34,7 +34,7 @@ config = {
    "llm": {
        "provider": "openai",
        "config": {
-            "model": "gpt-5-mini"
+            "model": "gpt-4o-mini"
        }
    },
    "rerank": {
@@ -50,34 +50,6 @@ config = {
 memory = Memory.from_config(config)
 ```

-## TypeScript (self-hosted)
-
-The [TypeScript OSS SDK](/open-source/features/reranker-search#typescript-sdk) (`mem0ai/oss`) ships the Zero Entropy reranker under the same provider name as Python, `zero_entropy`. It reads the key from config or `ZERO_ENTROPY_API_KEY` and defaults to the `zerank-1` model.
-
-```bash
-pnpm add zeroentropy
-```
-
-```typescript
-import { Memory } from "mem0ai/oss";
-
-const memory = new Memory({
-  reranker: {
-    provider: "zero_entropy",
-    config: {
-      apiKey: process.env.ZERO_ENTROPY_API_KEY,
-      // model: "zerank-1",   // default (or "zerank-1-small")
-      topK: 5,
-    },
-  },
-});
-
-const results = await memory.search("What Italian food does the user like?", {
-  filters: { userId: "alice" },
-  rerank: true,
-});
-```
-
 ## Environment Variables

 Set your API key as an environment variable:
@@ -98,7 +70,7 @@ os.environ["ZERO_ENTROPY_API_KEY"] = "your-api-key"
 # Initialize memory with Zero Entropy reranker
 config = {
    "vector_store": {"provider": "chroma"},
-    "llm": {"provider": "openai", "config": {"model": "gpt-5-mini"}},
+    "llm": {"provider": "openai", "config": {"model": "gpt-4o-mini"}},
    "rerank": {"provider": "zero_entropy", "config": {"model": "zerank-1"}}
 }

@@ -47,7 +47,7 @@ config = {
    "reranker": {
        "provider": "cohere",
        "config": {
-            "model": "rerank-v3.5",
+            "model": "rerank-english-v3.0",
            "top_n": 10,
            "max_chunks_per_doc": 10,  # Limit chunk processing
            "return_documents": False   # Reduce response size
@@ -280,7 +280,7 @@ config = {
 ```python
 def benchmark_rerankers():
    configs = [
-        {"provider": "cohere", "model": "rerank-v3.5"},
+        {"provider": "cohere", "model": "rerank-english-v3.0"},
        {"provider": "sentence_transformer", "model": "cross-encoder/ms-marco-MiniLM-L-6-v2"},
        {"provider": "huggingface", "model": "BAAI/bge-reranker-base"}
    ]
@@ -19,10 +19,6 @@ Reranking trades extra latency for better precision. Start once you have baselin
  <Card title="Zero Entropy" icon="/images/provider-icons/zeroentropy.svg" href="/components/rerankers/models/zero_entropy" />
 </CardGroup>

-<Note>
-All five rerankers are available in both the Python and the [TypeScript](/open-source/features/reranker-search#typescript-sdk) self-hosted SDKs. Each provider page has a **TypeScript (self-hosted)** section with the camelCase config.
-</Note>
-
 ## Reranking Workflow

 <CardGroup cols={3}>
@@ -7,7 +7,7 @@ description: "Reference for vector database configuration options in Mem0, inclu

 The `config` is defined as an object with two main keys:
 - `vector_store`: Specifies the vector database provider and its configuration
-  - `provider`: The name of the vector database (e.g., "chroma", "pgvector", "qdrant", "milvus", "upstash_vector", "azure_ai_search", "vertex_ai_vector_search", "valkey", "oracledb")
+  - `provider`: The name of the vector database (e.g., "chroma", "pgvector", "qdrant", "milvus", "upstash_vector", "azure_ai_search", "vertex_ai_vector_search", "valkey")
  - `config`: A nested dictionary containing provider-specific settings


@@ -95,11 +95,6 @@ Here's a comprehensive list of all parameters that can be used across different
 | `connection_string` | PostgreSQL connection string (for Supabase/PGVector) |
 | `index_method` | Vector index method (for Supabase) |
 | `index_measure` | Distance measure for similarity search (for Supabase) |
-| `connection_params` | Connection settings for Oracle AI Vector Search |
-| `use_connection_pool` | Create an Oracle connection pool from `connection_params` |
-| `distance_metric` | Distance metric for Oracle vector indexing and search |
-| `index_type` | Oracle vector index type: `HNSW` or `IVF` |
-| `index_parameters` | Oracle vector-index parameters for the selected index type |
 </Tab>
 <Tab title="TypeScript">
 | Parameter | Description |
@@ -5,22 +5,10 @@ description: "Use Baidu Mochow as an enterprise vector database in Mem0 for high

 [Baidu VectorDB](https://cloud.baidu.com/doc/VDB/index.html) is an enterprise-level distributed vector database service developed by Baidu Intelligent Cloud. It is powered by Baidu's proprietary "Mochow" vector database kernel, providing high performance, availability, and security for vector search.

-### Installation
-
-<CodeGroup>
-```bash Python
-pip install pymochow
-```
-
-```bash TypeScript
-npm install @mochow/mochow-sdk-node
-```
-
-</CodeGroup>
-
 ### Usage

 ```python
+import os
 from mem0 import Memory

 config = {
@@ -48,63 +36,19 @@ messages = [
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```

-```typescript
-import { Memory } from "mem0ai/oss";
-
-const memory = new Memory({
-  embedder: {
-    provider: "openai",
-    config: {
-      apiKey: process.env.OPENAI_API_KEY || "",
-      model: "text-embedding-3-small",
-      embeddingDims: 1536,
-    },
-  },
-  vectorStore: {
-    provider: "baidu",
-    config: {
-      endpoint: process.env.BAIDU_ENDPOINT || "",
-      account: process.env.BAIDU_ACCOUNT || "root",
-      apiKey: process.env.BAIDU_API_KEY || "",
-      databaseName: "mem0",
-      tableName: "mem0_table",
-      embeddingModelDims: 1536,
-      metricType: "COSINE",
-    },
-  },
-  llm: {
-    provider: "openai",
-    config: {
-      apiKey: process.env.OPENAI_API_KEY || "",
-      model: "gpt-5-mini",
-    },
-  },
-});
-```
-
 ### Config

 Here are the parameters available for configuring Baidu VectorDB:

-| Parameter              | Description                                   | Default Value |
-| ---------------------- | --------------------------------------------- | ------------- |
-| `endpoint`             | Endpoint URL for your Baidu VectorDB instance | Required      |
-| `account`              | Baidu VectorDB account name                   | `root`        |
-| `api_key`              | API key for accessing Baidu VectorDB          | Required      |
-| `database_name`        | Name of the database                          | `mem0`        |
-| `table_name`           | Name of the table                             | `mem0`        |
-| `embedding_model_dims` | Dimensions of the embedding model             | `1536`        |
-| `metric_type`          | Distance metric for similarity search         | `L2`          |
-| `client`               | Prebuilt Mochow client (TypeScript SDK only)  | `None`        |
-
-For the TypeScript OSS SDK, use the camelCase equivalents:
-
- `databaseName`
- `tableName`
- `embeddingModelDims`
- `metricType`
-
-For OSS TS usage, `endpoint`, `account`, `apiKey`, `databaseName`, `tableName`, and `embeddingModelDims` are required unless you inject a prebuilt client. `metricType` defaults to `L2`, matching the Python SDK.
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `endpoint` | Endpoint URL for your Baidu VectorDB instance | Required |
+| `account` | Baidu VectorDB account name | `root` |
+| `api_key` | API key for accessing Baidu VectorDB | Required |
+| `database_name` | Name of the database | `mem0` |
+| `table_name` | Name of the table | `mem0` |
+| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
+| `metric_type` | Distance metric for similarity search | `L2` |

 ### Distance Metrics

@@ -122,5 +66,3 @@ The vector index is automatically configured with the following HNSW parameters:
 - `efconstruction`: 200 (size of the dynamic candidate list)
 - `auto_build`: true (automatically build index)
 - `auto_build_index_policy`: Incremental build with 10000 rows increment
-
-The TypeScript provider also creates a BM25 inverted index over a `textLemmatized` column so `keywordSearch()` runs against a real full-text index. Mem0 lemmatizes the query before it reaches the vector store, so only the lemmatized form of each memory is indexed. If you point `tableName` at a table created before this index existed, `keywordSearch()` returns `null` and search falls back to vector similarity alone; recreate the table to enable it.
@@ -6,8 +6,9 @@ description: "Use Chroma as a vector database in Mem0 for local or cloud-hosted

 ### Usage

-<CodeGroup>
-```python Python
+#### Local Installation
+
+```python
 import os
 from mem0 import Memory

@@ -36,46 +37,10 @@ messages = [
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```

-```typescript TypeScript
-import { Memory } from 'mem0ai/oss';
-
-// The Node.js client connects to a running Chroma server.
-// Start one locally with: chroma run --host localhost --port 8000
-const config = {
-  vectorStore: {
-    provider: 'chroma',
-    config: {
-      collectionName: 'memories',
-      host: 'localhost',
-      port: 8000,
-      // Optional: ChromaDB Cloud configuration
-      // apiKey: 'your-chroma-cloud-api-key',
-      // tenant: 'your-chroma-cloud-tenant-id',
-    },
-  },
-};
-
-const memory = new Memory(config);
-const messages = [
-    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
-    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
-    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
-]
-await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
-```
-</CodeGroup>
-
-<Note>
-The Node.js SDK uses the `chromadb` v3 client, which talks to a Chroma server over HTTP (local server or ChromaDB Cloud). Install it with `npm install chromadb`. Mem0 supplies the embeddings, so the collection is created without an embedding function.
-</Note>
-
 ### Config

 Here are the parameters available for configuring Chroma:

-<Tabs>
-<Tab title="Python">
 | Parameter | Description | Default Value |
 | --- | --- | --- |
 | `collection_name` | The name of the collection | `mem0` |
@@ -84,19 +49,4 @@ Here are the parameters available for configuring Chroma:
 | `host` | The host where the Chroma server is running | `None` |
 | `port` | The port where the Chroma server is running | `None` |
 | `api_key` | ChromaDB Cloud API key (for cloud usage) | `None` |
-| `tenant` | ChromaDB Cloud tenant ID (for cloud usage) | `None` |
-</Tab>
-<Tab title="TypeScript">
-| Parameter | Description | Default Value |
-| --- | --- | --- |
-| `collectionName` | The name of the collection | `mem0` |
-| `client` | Pre-configured `ChromaClient` or `CloudClient` instance | `None` |
-| `host` | The host where the Chroma server is running | `None` |
-| `port` | The port where the Chroma server is running | `None` |
-| `ssl` | Whether to use SSL when connecting to the Chroma server | `false` |
-| `path` | Full URL of a Chroma server, e.g. `http://localhost:8000` (alternative to `host` and `port`) | `None` |
-| `apiKey` | ChromaDB Cloud API key (for cloud usage) | `None` |
-| `tenant` | ChromaDB Cloud tenant ID (for cloud usage) | `None` |
-| `database` | ChromaDB Cloud database name (for cloud usage) | `mem0` |
-</Tab>
-</Tabs>
+| `tenant` | ChromaDB Cloud tenant ID (for cloud usage) | `None` |
@@ -6,8 +6,7 @@ description: "Use Databricks Vector Search as a serverless vector store in Mem0

 ### Usage

-<CodeGroup>
-```python Python
+```python
 import os
 from mem0 import Memory

@@ -37,44 +36,10 @@ messages = [
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```

-```typescript TypeScript
-// Requires the Databricks SQL driver (peer dependency): pnpm add @databricks/sql
-import { Memory } from 'mem0ai/oss';
-
-const config = {
-  vectorStore: {
-    provider: 'databricks',
-    config: {
-      workspaceUrl: 'https://your-workspace.databricks.com',
-      // SQL warehouse HTTP path, used for index writes (required)
-      httpPath: '/sql/1.0/warehouses/your-warehouse-id',
-      accessToken: 'your-access-token',
-      catalog: 'your_catalog',
-      schema: 'your_schema',
-      tableName: 'your_table',
-      collectionName: 'your_index_name',
-      embeddingModelDims: 1536,
-    },
-  },
-};
-
-const memory = new Memory(config);
-const messages = [
-    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
-    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
-    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
-]
-await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
-```
-</CodeGroup>
-
 ### Config

 Here are the parameters available for configuring Databricks Vector Search:

-<Tabs>
-<Tab title="Python">
 | Parameter | Description | Default Value |
 | --- | --- | --- |
 | `workspace_url` | The URL of your Databricks workspace | **Required** |
@@ -95,32 +60,6 @@ Here are the parameters available for configuring Databricks Vector Search:
 | `pipeline_type` | Sync pipeline type: `TRIGGERED` or `CONTINUOUS` | `TRIGGERED` |
 | `warehouse_name` | Databricks SQL warehouse name (if using SQL warehouse) | `None` |
 | `query_type` | Query type: `ANN` or `HYBRID` | `ANN` |
-</Tab>
-<Tab title="TypeScript">
-| Parameter | Description | Default Value |
-| --- | --- | --- |
-| `workspaceUrl` | The URL of your Databricks workspace (or pass `host`) | **Required** |
-| `httpPath` | SQL warehouse HTTP path, used for index writes | **Required** |
-| `accessToken` | Personal Access Token for authentication | `None` |
-| `clientId` | Service principal client ID (alternative to `accessToken`) | `None` |
-| `clientSecret` | Service principal client secret (required with `clientId`) | `None` |
-| `endpointName` | Name of the Vector Search endpoint | `mem0_vector_search` |
-| `endpointType` | Type of endpoint (`STANDARD` or `STORAGE_OPTIMIZED`) | `STANDARD` |
-| `pipelineType` | Delta Sync pipeline type: `TRIGGERED` or `CONTINUOUS` | `TRIGGERED` |
-| `queryType` | Query type: `ANN` or `HYBRID` | `ANN` |
-| `catalog` | Unity Catalog catalog name | `main` |
-| `schema` | Unity Catalog schema name | `default` |
-| `collectionName` | Vector Search index name | `mem0` |
-| `tableName` | Source Delta table name | falls back to `collectionName` |
-| `embeddingModelDims` | Dimension of self-managed embeddings | `1536` |
-| `syncPollIntervalMs` | Poll interval while waiting for a `TRIGGERED` sync | `1000` |
-| `syncTimeoutMs` | Timeout while waiting for an index sync | `300000` |
-
-<Note>
-The TypeScript provider uses `DELTA_SYNC` indexes with self-managed embeddings: pass vectors directly. `DIRECT_ACCESS` indexes, Databricks-computed embeddings (`embedding_model_endpoint_name`), and Azure AD auth are Python-only today. It writes to the index through a SQL warehouse, so `httpPath` is required, and `@databricks/sql` must be installed as a peer dependency.
-</Note>
-</Tab>
-</Tabs>

 ### Authentication

@@ -6,14 +6,7 @@ description: "Use Milvus as an open-source vector database in Mem0, scalable fro

 ### Usage

-The TypeScript SDK loads the Milvus client lazily. Install it alongside `mem0ai` when you use this provider:
-
-```bash
-npm install @zilliz/milvus2-sdk-node
-```
-
-<CodeGroup>
-```python Python
+```python
 import os
 from mem0 import Memory

@@ -40,39 +33,10 @@ messages = [
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```

-```typescript TypeScript
-import { Memory } from 'mem0ai/oss';
-
-const config = {
-  vectorStore: {
-    provider: 'milvus',
-    config: {
-      collectionName: 'test',
-      embeddingModelDims: 1536,
-      url: 'http://localhost:19530',
-      token: '8e4b8ca8cf2c67',
-      dbName: 'my_database',
-    },
-  },
-};
-
-const memory = new Memory(config);
-const messages = [
-  { role: "user", content: "I'm planning to watch a movie tonight. Any recommendations?" },
-  { role: "assistant", content: "How about thriller movies? They can be quite engaging." },
-  { role: "user", content: "I'm not a big fan of thriller movies but I love sci-fi movies." },
-  { role: "assistant", content: "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future." },
-];
-await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
-```
-</CodeGroup>
-
 ### Config

 Here are the parameters available for configuring Milvus:

-<Tabs>
-<Tab title="Python">
 | Parameter | Description | Default Value |
 | --- | --- | --- |
 | `url` | Full URL/Uri for Milvus/Zilliz server | `http://localhost:19530` |
@@ -81,15 +45,3 @@ Here are the parameters available for configuring Milvus:
 | `embedding_model_dims` | Dimensions of the embedding model | `1536` |
 | `metric_type` | Metric type for similarity search | `L2` |
 | `db_name` | Name of the database | `""` |
-</Tab>
-<Tab title="TypeScript">
-| Parameter | Description | Default Value |
-| --- | --- | --- |
-| `url` | Full URL/Uri for Milvus/Zilliz server | `http://localhost:19530` |
-| `token` | Token for Zilliz Cloud (optional for a local setup) | `undefined` |
-| `collectionName` | The name of the collection | `mem0` |
-| `embeddingModelDims` | Dimensions of the embedding model | `1536` |
-| `metricType` | Metric type for similarity search (`L2`, `IP`, `COSINE`, `HAMMING`, `JACCARD`) | `L2` |
-| `dbName` | Name of the database | `undefined` |
-</Tab>
-</Tabs>
@@ -2,37 +2,26 @@
 title: "Neptune Analytics"
 description: "Use AWS Neptune Analytics as a vector store in Mem0, combining graph analytics with vector search capabilities."
 ---
+# Neptune Analytics Vector Store

-[Neptune Analytics](https://docs.aws.amazon.com/neptune-analytics/latest/userguide/what-is-neptune-analytics.html) is a memory-optimized graph database engine for analytics. With Neptune Analytics, you can get insights and find trends by processing large amounts of graph data in seconds, including vector search.
+[Neptune Analytics](https://docs.aws.amazon.com/neptune-analytics/latest/userguide/what-is-neptune-analytics.html/) is a memory-optimized graph database engine for analytics. With Neptune Analytics, you can get insights and find trends by processing large amounts of graph data in seconds, including vector search.

-### Installation

-The Neptune Analytics provider needs the AWS Neptune Graph client. Install it alongside `mem0ai`:
+## Installation

-<CodeGroup>
-```bash Python
+```bash
 pip install mem0ai[vector-stores]
 ```

-```bash TypeScript
-npm install @aws-sdk/client-neptune-graph
-```
-</CodeGroup>
-
-### Usage
-
-Configure AWS credentials in your environment (environment variables, shared config file, an IAM role, or an instance profile). Both SDKs pick them up automatically through the standard AWS credential chain.
-
-<CodeGroup>
-```python Python
-from mem0 import Memory
+## Usage

+```python
 config = {
    "vector_store": {
        "provider": "neptune",
        "config": {
            "collection_name": "mem0",
-            "endpoint": "neptune-graph://g-abc123xyz0",
+            "endpoint": f"neptune-graph://my-graph-identifier",
        },
    },
 }
@@ -40,90 +29,18 @@ config = {
 m = Memory.from_config(config)
 messages = [
    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movie? They can be quite engaging."},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```

-```typescript TypeScript
-import { Memory } from 'mem0ai/oss';
+## Parameters

-const config = {
-  vectorStore: {
-    provider: 'neptune',
-    config: {
-      collectionName: 'mem0',
-      graphIdentifier: 'g-abc123xyz0',
-      // Any other key here (region, credentials, maxAttempts, ...) is
-      // forwarded to the underlying NeptuneGraphClient constructor.
-      region: 'us-east-1',
-    },
-  },
-};
+Let's see the available parameters for the `neptune` config:

-const memory = new Memory(config);
-const messages = [
-  { role: "user", content: "I'm planning to watch a movie tonight. Any recommendations?" },
-  { role: "assistant", content: "How about a thriller movie? They can be quite engaging." },
-  { role: "user", content: "I'm not a big fan of thriller movies but I love sci-fi movies." },
-  { role: "assistant", content: "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future." },
-];
-await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
-```
-</CodeGroup>
-
-### Config
-
-<Tabs>
-<Tab title="Python">
 | Parameter | Description | Default Value |
 | --- | --- | --- |
 | `collection_name` | The name of the collection to store the vectors | `mem0` |
-| `endpoint` | Connection URL for the Neptune Analytics service, must be `neptune-graph://<graph-id>` | Required |
-</Tab>
-<Tab title="TypeScript">
-| Parameter | Description | Default Value |
-| --- | --- | --- |
-| `collectionName` | The name of the collection to store the vectors | `memories` |
-| `graphIdentifier` | Graph ID, e.g. `g-abc123xyz0`. Takes priority over `endpoint`. | Required, unless `endpoint` supplies it |
-| `endpoint` | Either `neptune-graph://<graph-id>` (or a bare graph ID) to supply the graph ID, or an `https://` service endpoint to override the AWS endpoint. An `https://` value must be paired with `graphIdentifier`. | `undefined` |
-| `dimension` | Embedding vector dimension | Auto-detected from the embedder when omitted |
-| `client` | A pre-built `NeptuneGraphClient` to use instead of constructing one | `undefined` |
-| any other key | Forwarded as-is to the [`NeptuneGraphClient`](https://www.npmjs.com/package/@aws-sdk/client-neptune-graph) constructor, e.g. `region`, `credentials`, `maxAttempts` | N/A |
-</Tab>
-</Tabs>
-
-Both SDKs store vectors on graph nodes labeled `MEM0_VECTOR_<collection_name>`. Point them at the same
-graph with the same `collection_name` (the defaults differ, `mem0` in Python and `memories` in
-TypeScript) and `get()`, `list()`, and `delete()` interoperate across SDKs.
-
-<Note>
-`search()` is not currently cross-SDK compatible. The TypeScript provider filters on Neptune's reserved
-`~label` metafield, while the Python provider filters on a synthetic `label` property that only Python's
-own `insert()` writes. Python's `search()` therefore cannot see nodes written by the TypeScript provider.
-</Note>
-
-### IAM Permissions
-
-Your AWS identity (user or role) needs a policy that allows the [`ExecuteQuery`](https://docs.aws.amazon.com/neptune-analytics/latest/apiref/API_ExecuteQuery.html) actions used for reads, writes, and deletes:
-
-```json
-{
-  "Version": "2012-10-17",
-  "Statement": [
-    {
-      "Effect": "Allow",
-      "Action": [
-        "neptune-graph:ReadDataViaQuery",
-        "neptune-graph:WriteDataViaQuery",
-        "neptune-graph:DeleteDataViaQuery"
-      ],
-      "Resource": "*"
-    }
-  ]
-}
-```
-
-For production, scope the resource ARN down to your specific graph.
+| `endpoint` | Connection URL for the Neptune Analytics service | `neptune-graph://my-graph-identifier` |
@@ -1,134 +0,0 @@
---
-title: "Oracle AI Vector Search"
-description: "Use Oracle Database AI Vector Search as a vector store in Mem0 for semantic and relational queries."
---
-
-[Oracle AI Vector Search](https://www.oracle.com/database/ai-vector-search/) stores embeddings in an Oracle table using the native `VECTOR` data type, so you can combine semantic search over unstructured data with relational queries over business data in a single database.
-
-### Requirements
-
- Oracle Database 23.4 or later, with a user that can create tables and vector indexes
- The `python-oracledb` driver. In thick mode, Oracle Client 23.4 or later is also required.
-
-```bash
-pip install oracledb
-```
-
-### Usage
-
-<CodeGroup>
-```python Python
-import os
-from mem0 import Memory
-
-os.environ["OPENAI_API_KEY"] = "sk-xx"
-
-config = {
-    "vector_store": {
-        "provider": "oracledb",
-        "config": {
-            "collection_name": "mem0",
-            "embedding_model_dims": 1536,
-            "connection_params": {
-                "user": "mem0_user",
-                "password": "your-password",
-                "dsn": "localhost:1521/FREEPDB1",
-            },
-        }
-    }
-}
-
-m = Memory.from_config(config)
-messages = [
-    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
-    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
-    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
-]
-m.add(messages, user_id="alice", metadata={"category": "movies"})
-```
-</CodeGroup>
-
-To reuse a connection or pool you already manage, pass it as `client` instead of `connection_params`:
-
-```python
-import oracledb
-
-pool = oracledb.create_pool(user="mem0_user", password="your-password", dsn="localhost:1521/FREEPDB1")
-
-config = {
-    "vector_store": {
-        "provider": "oracledb",
-        "config": {"client": pool},
-    }
-}
-```
-
-### Config
-
-Here are the parameters available for configuring Oracle AI Vector Search:
-
-| Parameter | Description | Default Value |
-| --- | --- | --- |
-| `connection_params` | Connection settings passed to `python-oracledb`, such as `user`, `password` and `dsn`. See the [connection handling guide](https://python-oracledb.readthedocs.io/en/latest/user_guide/connection_handling.html). | `None` |
-| `use_connection_pool` | Create a connection pool from `connection_params` instead of a single connection | `True` |
-| `client` | An existing `oracledb.Connection` or `oracledb.ConnectionPool` to use instead of building one from `connection_params` | `None` |
-| `collection_name` | Name of the Oracle table that stores vectors and payloads | `mem0` |
-| `embedding_model_dims` | Dimension of your embedding vectors, must be greater than 0 | `1536` |
-| `distance_metric` | Distance function used for indexing and search: `COSINE`, `EUCLIDEAN`, `EUCLIDEAN_SQUARED`, `DOT`, `HAMMING` or `MANHATTAN` | `COSINE` |
-| `do_create_index` | Whether to create a vector index on the collection | `True` |
-| `index_type` | Vector index type: `HNSW` or `IVF` | `HNSW` |
-| `index_name` | Name of the vector index | `<collection_name>_VEC_IDX` |
-| `index_parameters` | Index tuning parameters. For `HNSW`: `neighbors`, `efconstruction`. For `IVF`: `neighbor partitions`, `samples_per_partition`, `min_vectors_per_partition`. | `None` |
-| `index_accuracy` | Target index accuracy from 1 to 100, applied as `WITH TARGET ACCURACY <n>` | `None` |
-
-<Note>
-  When you pass a pre-built `client`, Mem0 uses it as-is and ignores `connection_params` and `use_connection_pool`. Mem0 does not close a client it did not create.
-</Note>
-
-### Vector indexes
-
-Set the index type with `index_type` and tune it with `index_parameters`:
-
-```python
-config = {
-    "vector_store": {
-        "provider": "oracledb",
-        "config": {
-            "connection_params": {"user": "mem0_user", "password": "your-password", "dsn": "localhost:1521/FREEPDB1"},
-            "index_type": "HNSW",
-            "index_parameters": {"neighbors": 32, "efconstruction": 200},
-            "index_accuracy": 95,
-        }
-    }
-}
-```
-
-For the full list of supported options, see the Oracle [`CREATE VECTOR INDEX`](https://docs.oracle.com/en/database/oracle/oracle-database/26/sqlrf/create-vector-index.html) reference.
-
-### Search scores
-
-Oracle returns a distance from `VECTOR_DISTANCE`, which Mem0 converts to a `score` where higher means more similar. `COSINE` and the other non-negative metrics produce scores in the range `[0, 1]`. `DOT` returns the inner product, which can fall outside that range.
-
-### Metadata filters
-
-Filters run against the JSON `payload` column and support:
-
-| Filter type | Examples |
-| --- | --- |
-| Scalar equality | `{"user_id": "alice"}` |
-| Field existence | `{"agent_id": "*"}` |
-| Comparison | `{"score": {"gte": 0.5}}`, also `eq`, `ne`, `gt`, `lt`, `lte` |
-| Membership | `{"category": {"in": ["movies", "books"]}}`, also `nin` |
-| String matching | `{"title": {"contains": "sci-fi"}}`, also `icontains` for case-insensitive |
-| Logical groups | `{"AND": [...]}`, `{"OR": [...]}`, `{"NOT": [...]}` |
-
-Multiple fields at the top level are combined with `AND`:
-
-```python
-m.search(
-    "movie recommendations",
-    user_id="alice",
-    filters={"category": {"in": ["movies", "books"]}, "rating": {"gte": 4}},
-)
-```
@@ -42,7 +42,7 @@ const config = {
    provider: 'qdrant',
    config: {
      collectionName: 'memories',
-      dimension: 1536,
+      embeddingModelDims: 1536,
      host: 'localhost',
      port: 6333,
    },
@@ -60,12 +60,6 @@ await memory.add(messages, { userId: "alice", metadata: { category: "movies" } }
 ```
 </CodeGroup>

-### Hybrid keyword search
-
-Mem0 blends semantic similarity with BM25 keyword scoring. On the TypeScript SDK, Qdrant computes the BM25 vectors server-side, which requires Qdrant 1.15.2 or newer with inference enabled. Qdrant Cloud enables inference by default only for clusters created after 2025-07-07; older clusters must activate it from the Cluster Detail page. The Python SDK encodes BM25 locally instead and needs the `fastembed` package, so scores are not numerically comparable between the two SDKs.
-
-When BM25 is unavailable, or when the collection was created before hybrid search was added, Mem0 logs a warning and falls back to semantic-only search. Writes are unaffected. To enable keyword scoring on an older collection, use a fresh collection name.
-
 ### Config

 Let's see the available parameters for the `qdrant` config:
@@ -89,7 +83,7 @@ Let's see the available parameters for the `qdrant` config:
 | Parameter | Description | Default Value |
 | --- | --- | --- |
 | `collectionName` | The name of the collection to store the vectors | `mem0` |
-| `dimension` | Dimensions of the embedding model | `1536` |
+| `embeddingModelDims` | Dimensions of the embedding model | `1536` |
 | `host` | The host where the Qdrant server is running | `None` |
 | `port` | The port where the Qdrant server is running | `None` |
 | `path` | Path for the Qdrant database | `/tmp/qdrant` |
@@ -115,27 +115,6 @@ $$;

 Go to [Supabase](https://supabase.com/dashboard/projects) and run the above SQL migrations in the SQL Editor.

-### Row Level Security
-
-Tables created through the Supabase dashboard have Row Level Security (RLS) enabled by default with no policies attached. With RLS on and no policies, the TypeScript SDK's queries return zero rows with an HTTP 200 (no error is raised), which looks like an empty memory store rather than a permissions problem. If you use the SQL migrations above (via the SQL Editor), RLS is left in its default off state and this does not apply.
-
-If your table has RLS enabled, add policies for the key your app uses (the example below grants full access to the `service_role` key; scope it down for anon/authenticated keys as needed):
-
-```sql
-alter table memories enable row level security;
-
-create policy "Allow service role full access to memories"
-on memories
-for all
-to service_role
-using (true)
-with check (true);
-```
-
-### PostgREST Row Limits
-
-Supabase's PostgREST layer caps the number of rows returned by a single request at `db-max-rows` (1000 by default), for both `.select()` queries and RPC function calls like `match_vectors`. Requesting a `topK` above this limit for `search()` or `list()` will not raise an error, results are capped at `db-max-rows` instead. The TypeScript `list()` method paginates internally to work around this, but `search()` cannot since `match_vectors` has no offset parameter; it logs a warning when it detects a truncated result. Raise `db-max-rows` in your Supabase project settings if you need more than 1000 results per search.
-
 ### Config

 Here are the parameters available for configuring Supabase:
@@ -4,21 +4,14 @@ description: "Use Weaviate as an open-source vector search engine in Mem0 for st
 ---
 [Weaviate](https://weaviate.io/) is an open-source vector search engine. It allows efficient storage and retrieval of high-dimensional vector embeddings, enabling powerful search and retrieval capabilities.

-### Installation

-<CodeGroup>
-```bash Python
+### Installation
+```bash
 pip install weaviate-client
 ```

-```bash TypeScript
-npm install weaviate-client
-```
-</CodeGroup>
-
 ### Usage

-<CodeGroup>
 ```python Python
 import os
 from mem0 import Memory
@@ -40,73 +33,20 @@ m = Memory.from_config(config)
 messages = [
    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
    {"role": "assistant", "content": "How about a thriller movie? They can be quite engaging."},
-    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```

-```typescript TypeScript
-import { Memory } from "mem0ai/oss";
-
-const config = {
-  vectorStore: {
-    provider: "weaviate",
-    config: {
-      collectionName: "test",
-      embeddingModelDims: 1536,
-      clusterUrl: "http://localhost:8080",
-    },
-  },
-};
-
-const memory = new Memory(config);
-
-const messages = [
-  {
-    role: "user",
-    content: "I'm planning to watch a movie tonight. Any recommendations?",
-  },
-  {
-    role: "assistant",
-    content: "How about a thriller movie? They can be quite engaging.",
-  },
-  {
-    role: "user",
-    content: "I'm not a big fan of thriller movies but I love sci-fi movies.",
-  },
-  {
-    role: "assistant",
-    content:
-      "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future.",
-  },
-];
-
-await memory.add(messages, {
-  userId: "alice",
-  metadata: {
-    category: "movies",
-  },
-});
-```
-</CodeGroup>
-
-The TypeScript SDK picks the connection mode from the config you pass:
-
- `clusterUrl` pointing at `localhost` connects to a local instance.
- `clusterUrl` plus `apiKey` connects to a Weaviate Cloud cluster (for example `https://my-cluster.weaviate.cloud`).
- Any other `clusterUrl` without an `apiKey` connects to a custom deployment, using the host and port from the URL.
-
-You can also pass a pre-configured `client` (a `WeaviateClient` instance) to reuse an existing connection.
-
 ### Config

 Here are the parameters available for configuring Weaviate:

-| Python | TypeScript | Description | Default Value |
-| --- | --- | --- | --- |
-| `collection_name` | `collectionName` | The name of the collection to store the vectors | `mem0` |
-| `embedding_model_dims` | `embeddingModelDims` | Dimensions of the embedding model | `1536` |
-| `cluster_url` | `clusterUrl` | URL for the Weaviate server | `None` |
-| `auth_client_secret` | `apiKey` | API key for Weaviate authentication | `None` |
-| `additional_headers` | `additionalHeaders` | Additional headers to include in requests | `None` |
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `collection_name` | The name of the collection to store the vectors | `mem0` |
+| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
+| `cluster_url` | URL for the Weaviate server | `None` |
+| `auth_client_secret` | API key for Weaviate authentication | `None` |
+| `additional_headers` | Additional headers to include in requests (`Dict[str, str]`) | `None` |
@@ -1,6 +1,6 @@
 ---
 title: Overview
-description: "Overview of all supported vector databases in Mem0, including Qdrant, Chroma, PGVector, Pinecone, Oracle, and more."
+description: "Overview of all supported vector databases in Mem0, including Qdrant, Chroma, PGVector, Pinecone, and more."
 ---

 Mem0 includes built-in support for various popular databases. Memory can utilize the database provided by the user, ensuring efficient use for specific needs.
@@ -10,7 +10,7 @@ Mem0 includes built-in support for various popular databases. Memory can utilize
 See the list of supported vector databases below.

 <Note>
-  The following vector databases are supported in the Python implementation. The TypeScript implementation currently supports Qdrant, Redis, PGVector, Supabase, LangChain, Azure AI Search, Vectorize, Amazon S3 Vectors, Milvus, Neptune Analytics, and an in-memory store.
+  The following vector databases are supported in the Python implementation. The TypeScript implementation currently supports Qdrant, Redis, PGVector, Supabase, LangChain, Azure AI Search, Vectorize, Amazon S3 Vectors, and an in-memory store.
 </Note>

 <CardGroup cols={3}>
@@ -21,7 +21,6 @@ See the list of supported vector databases below.
  <Card title="Milvus" icon="/images/provider-icons/milvus.svg" href="/components/vectordbs/dbs/milvus"></Card>
  <Card title="Pinecone" icon="/images/provider-icons/pinecone.svg" href="/components/vectordbs/dbs/pinecone"></Card>
  <Card title="MongoDB" icon="/images/provider-icons/mongodb.svg" href="/components/vectordbs/dbs/mongodb"></Card>
-  <Card title="Oracle AI Vector Search" icon="/images/provider-icons/oracle.svg" href="/components/vectordbs/dbs/oracledb"></Card>
  <Card title="Azure" icon="/images/provider-icons/azure-color.svg" href="/components/vectordbs/dbs/azure"></Card>
  <Card title="Redis" icon="/images/provider-icons/redis.svg" href="/components/vectordbs/dbs/redis"></Card>
  <Card title="Valkey" icon="/images/provider-icons/valkey.svg" href="/components/vectordbs/dbs/valkey"></Card>
@@ -33,7 +32,6 @@ See the list of supported vector databases below.
  <Card title="FAISS" icon="layer-group" href="/components/vectordbs/dbs/faiss"></Card>
  <Card title="LangChain" icon="/images/provider-icons/langchain-color.svg" href="/components/vectordbs/dbs/langchain"></Card>
  <Card title="Amazon S3 Vectors" icon="/images/provider-icons/aws-color.svg" href="/components/vectordbs/dbs/s3_vectors"></Card>
-  <Card title="Neptune Analytics" icon="/images/provider-icons/aws-color.svg" href="/components/vectordbs/dbs/neptune_analytics"></Card>
  <Card title="Databricks" icon="/images/provider-icons/databricks.svg" href="/components/vectordbs/dbs/databricks"></Card>
  <Card title="Turbopuffer" icon="/images/provider-icons/turbopuffer.svg" href="/components/vectordbs/dbs/turbopuffer"></Card>
 </CardGroup>
@@ -588,9 +588,9 @@ mem0_client.project.update(
    Exclude: greetings, filler, casual chat
    """,
    custom_categories=[
-        {"goals": "Training targets"},
-        {"constraints": "Injuries and limitations"},
-        {"preferences": "Training style"}
+        {"name": "goals", "description": "Training targets"},
+        {"name": "constraints", "description": "Injuries and limitations"},
+        {"name": "preferences", "description": "Training style"}
    ]
 )
 ```
@@ -19,7 +19,7 @@ client = MemoryClient(api_key="your-api-key")
 ```

 <Note>
-Define custom categories at the **project level** with `client.project.update()` before adding memories. Categories apply to all future memories: Mem0 auto-assigns them based on content semantics. You can also pass `custom_categories` on a single `client.add()` call to override the project list for just those memories. See [Custom Categories](/platform/features/custom-categories).
+Define custom categories at the **project level** with `client.project.update()` before adding memories. Categories apply to all future memories: Mem0 auto-assigns them based on content semantics.
 </Note>

 ---
@@ -96,10 +96,6 @@ Start with 3-5 clear categories that match how your team thinks. Too many catego

 These categories are now available project-wide. Every memory can be tagged with one or more categories.

-<Tip>
-Need a different vocabulary for one tenant or one kind of conversation? Pass `custom_categories=[...]` to `client.add()`. That list replaces the project list for the memories created by that call, and it does not change the project configuration.
-</Tip>
-
 ---

 ## Tagging Memories
@@ -55,8 +55,9 @@ await addUserPreferences();

 ```json Output
 {
-  "event_id": "9f8c2b1a-4e7d-4c3a-9b21-1a2b3c4d5e6f",
-  "status": "PENDING"
+  "message": "Memory processing has been queued for background execution",
+  "status": "PENDING",
+  "event_id": "9f8c2b1a-4e7d-4c3a-9b21-1a2b3c4d5e6f"
 }
 ```
 </CodeGroup>
@@ -15,7 +15,6 @@ Adding memory is how Mem0 captures useful details from a conversation so your ag
 - **Infer**: Controls whether Mem0 extracts structured memories (`infer=True`, default) or stores raw messages.
 - **Metadata**: Optional filters (e.g., `{"category": "movie_recommendations"}`) that improve retrieval later.
 - **User / Session identifiers**: `user_id`, `agent_id`, `app_id`, or `run_id` that scope the memory for future searches.
- **expiration_date**: Optional `YYYY-MM-DD` date after which the memory is treated as expired. Use `expirationDate` in the JavaScript SDKs. Expired memories are hidden from `search` and `get_all` unless you pass `show_expired` (`showExpired` in JavaScript); fetching by ID still returns them.

 ## How does it work?

@@ -83,50 +82,6 @@ await client.add(messages, {
  Expect a `status: "PENDING"` response with an `event_id`. Poll `GET /v1/event/{event_id}/` to confirm completion.
 </Info>

-### Automatic conversation context
-
-On the Platform, you only send new messages. Mem0 automatically pulls the earlier messages that share the same identifiers (`user_id`, and `run_id` if you use one) and uses them as context when extracting memories, so you never need to resend conversation history.
-
-This means a follow-up turn is understood against what came before it:
-
-<CodeGroup>
-```python Python
-# First interaction
-client.add(
-    [{"role": "user", "content": "My dog's name is Biscuit. He's a golden retriever."}],
-    user_id="alice",
-)
-
-# Later — send only the new turn, no history
-client.add(
-    [{"role": "user", "content": "He turned 5 today, and I'm taking him to the vet on Friday."}],
-    user_id="alice",
-)
-# Stored as: "User's dog Biscuit turned 5" — "He" is resolved against the earlier turn.
-```
-
-```javascript JavaScript
-// First interaction
-await client.add(
-  [{ role: "user", content: "My dog's name is Biscuit. He's a golden retriever." }],
-  { userId: "alice" },
-);
-
-// Later — send only the new turn, no history
-await client.add(
-  [{ role: "user", content: "He turned 5 today, and I'm taking him to the vet on Friday." }],
-  { userId: "alice" },
-);
-// Stored as: "User's dog Biscuit turned 5" — "He" is resolved against the earlier turn.
-```
-</CodeGroup>
-
-Without that earlier turn, the same message can only be stored as "User's male pet turned 5", because there is nothing to resolve "He" against. Scope each conversation with a consistent `user_id` (plus `run_id` for a distinct session) and Mem0 handles the rest.
-
-<Info>
-  This is default behavior and needs no configuration. Earlier SDK versions gated it behind a `version="v2"` argument on `add`; that argument no longer exists and is ignored if sent.
-</Info>
-
 ## Add with Mem0 Open Source

 <CodeGroup>
@@ -150,9 +105,6 @@ result = m.add(messages, user_id="alice", metadata={"category": "movie_recommend

 # Optionally store raw messages without inference
 result = m.add(messages, user_id="alice", metadata={"category": "movie_recommendations"}, infer=False)
-
-# Optionally set an expiration date (YYYY-MM-DD)
-result = m.add(messages, user_id="alice", expiration_date="2030-01-31")
 ```

 ```javascript JavaScript
@@ -171,12 +123,6 @@ const result = memory.add(messages, {
  userId: "alice",
  metadata: { category: "preferences" }
 });
-
-// Optionally set an expiration date (YYYY-MM-DD)
-const expiring = memory.add(messages, {
-  userId: "alice",
-  expirationDate: "2030-01-31",
-});
 ```
 </CodeGroup>

@@ -188,17 +188,12 @@ memory = Memory()
 memory.delete(memory_id="mem_123")
 memory.delete_all(user_id="alice")
 ```
-
-```typescript TypeScript
-import { Memory } from "mem0ai/oss";
-
-const memory = new Memory();
-
-await memory.delete("mem_123");
-await memory.deleteAll({ userId: "alice" });
-```
 </CodeGroup>

+<Note>
+  The OSS JavaScript SDK does not yet expose deletion helpers: use the REST API or Python SDK when self-hosting.
+</Note>
+
 ## Use cases recap

 - Forget a user’s preferences at their request.
@@ -12,7 +12,7 @@ Mem0’s update operation lets you fix or enrich an existing memory without dele
 ## Key terms

 - **memory_id**: Unique identifier returned by `add` or `search` results.
- **text**: New content that replaces the stored memory value. In the Python OSS SDK, `data` is a deprecated alias for `text`.
+- **text** / **data**: New content that replaces the stored memory value.
 - **metadata**: Optional key-value pairs you update alongside the text.
 - **timestamp**: Unix epoch (int/float) or ISO 8601 string to override the memory's timestamp.
 - **batch_update**: Platform API that edits multiple memories in a single request.
@@ -110,47 +110,17 @@ from mem0 import Memory

 memory = Memory()

-# Replace the content
 memory.update(
    memory_id="mem_123",
-    text="Alex now prefers decaf coffee",
-)
-
-# Update content plus metadata and an expiration date (None clears it)
-memory.update(
-    memory_id="mem_123",
-    text="Alex now prefers decaf coffee",
-    metadata={"category": "preferences"},
-    expiration_date="2030-01-31",
+    data="Alex now prefers decaf coffee",
 )
 ```
-
-```javascript JavaScript
-import { Memory } from "mem0ai/oss";
-
-const memory = new Memory();
-
-// Replace the content
-await memory.update("mem_123", { text: "Alex now prefers decaf coffee" });
-
-// Update content plus metadata and an expiration date (null clears it)
-await memory.update("mem_123", {
-  text: "Alex now prefers decaf coffee",
-  metadata: { category: "preferences" },
-  expirationDate: "2030-01-31",
-});
-
-// Update metadata only, leaving the stored text untouched
-await memory.update("mem_123", { metadata: { category: "preferences" } });
+```
 ```
 </CodeGroup>

 <Note>
-  In both OSS SDKs the content is optional: pass only `metadata` and/or an expiration date to update those while keeping the existing content. At least one of the three must be provided, otherwise the call raises.
-</Note>
-
-<Note>
-  `data` is a deprecated alias for `text` in both OSS SDKs (`data=` in Python, `{ data: ... }` in JavaScript). It still works but logs a warning; prefer `text`. In JavaScript, passing a bare string is shorthand for `{ text }`, so `update(memoryId, "new text")` also still works.
+  OSS JavaScript SDK does not expose `update` yet: use the REST API or Python SDK when self-hosting.
 </Note>

 ## Tips
@@ -168,7 +138,7 @@ await memory.update("mem_123", { metadata: { category: "preferences" } });

 | Capability | Mem0 Platform | Mem0 OSS |
 | --- | --- | --- |
-| Update call | `client.update(memory_id, {...})` | `memory.update(memory_id, text=...)` |
+| Update call | `client.update(memory_id, {...})` | `memory.update(memory_id, data=...)` |
 | Batch updates | `client.batch_update` (up to 1000 memories) | Script your own loop or bulk job |
 | Dashboard visibility | Inspect updates in the UI | Inspect via logs or custom tooling |
 | Immutable handling | Returns descriptive error | Raises exception: delete and re-add |
@@ -84,6 +84,8 @@
                        "pages": [
                          "platform/features/advanced-retrieval",
                          "platform/advanced-memory-operations",
+                          "platform/features/criteria-retrieval",
+                          "platform/features/contextual-add",
                          "platform/features/custom-instructions",
                          "platform/features/memory-decay"
                        ]
@@ -94,8 +96,7 @@
                        "pages": [
                          "platform/features/direct-import",
                          "platform/features/memory-export",
-                          "platform/features/timestamp",
-                          "platform/features/memory-expiration"
+                          "platform/features/timestamp"
                        ]
                      },
                      {
@@ -151,8 +152,7 @@
                      "open-source/features/multimodal-support",
                      "open-source/features/custom-instructions",
                      "open-source/features/rest-api",
-                      "open-source/features/openai_compatibility",
-                      "platform/features/memory-expiration"
+                      "open-source/features/openai_compatibility"
                    ]
                  },
                  {
@@ -207,7 +207,6 @@
                              "components/vectordbs/dbs/milvus",
                              "components/vectordbs/dbs/pinecone",
                              "components/vectordbs/dbs/mongodb",
-                              "components/vectordbs/dbs/oracledb",
                              "components/vectordbs/dbs/azure",
                              "components/vectordbs/dbs/azure_mysql",
                              "components/vectordbs/dbs/redis",
@@ -349,8 +348,6 @@
                    "pages": [
                      "integrations/dify",
                      "integrations/flowise",
-                      "integrations/n8n",
-                      "integrations/zapier",
                      "integrations/langchain-tools",
                      "integrations/agentops",
                      "integrations/respan",
@@ -610,10 +607,6 @@
    ]
  },
  "redirects": [
-    {
-      "source": "/platform/features/contextual-add",
-      "destination": "/core-concepts/memory-operations/add"
-    },
    {
      "source": "/changelog/openclaw",
      "destination": "/changelog/sdk"
@@ -636,7 +629,7 @@
    },
    {
      "source": "/platform/features/expiration-date",
-      "destination": "/platform/features/memory-expiration"
+      "destination": "/"
    },
    {
      "source": "/cookbooks/essentials/memory-expiration-short-and-long-term",
@@ -1233,14 +1226,6 @@
    {
      "source": "/open-source/multimodal-support",
      "destination": "/open-source/features/multimodal-support"
-    },
-    {
-      "source": "/platform/features/criteria-retrieval",
-      "destination": "/platform/features/advanced-retrieval"
-    },
-    {
-      "source": "/integrations/keywords",
-      "destination": "/integrations/respan"
    }
  ]
 }
@@ -1 +0,0 @@
-<svg fill="#8F74E0" role="img" viewBox="0 0 93.9 59.4" xmlns="http://www.w3.org/2000/svg"><title>Oracle</title><path d="M30.5,59.4H65c16.4-0.4,29.3-14.1,28.9-30.4C93.5,13.1,80.7,0.4,65,0H30.5C14.1-0.4,0.4,12.5,0,28.9s12.5,30,28.9,30.4C29.4,59.4,29.9,59.4,30.5,59.4 M64.2,48.9h-33c-10.6-0.3-18.9-9.2-18.6-19.8C13,19,21.1,10.8,31.2,10.5h33c10.6-0.3,19.5,8,19.8,18.6c0.3,10.6-8,19.5-18.6,19.8C65,48.9,64.6,48.9,64.2,48.9"/></svg>
@@ -65,9 +65,7 @@ The plugin uses the same shell scripts as Claude Code, Cursor, and Codex: hooks
 | **User prompt** | `UserPromptSubmit` | Searches relevant memories before each message |
 | **Pre-tool** | `PreToolUse` | Blocks MEMORY.md writes, enforces `user_id`/`app_id` on mem0 tools |
 | **Post-tool** | `PostToolUse` | Tracks stats, scans bash errors for related memories |
-| **Stop** | `Stop` | Stores a session summary at the end of every assistant turn (not just at session end) |
-
-What you type is stored as yours. What the agent produces (session summaries and compaction summaries) is stored as the assistant's, so its suggestions never become your stated preferences.
+| **Stop** | `Stop` | Stores a session summary when the session ends |

 ## Troubleshooting

@@ -44,14 +44,14 @@ Install the full plugin including MCP server, lifecycle hooks, and SDK skill.

 1. Add the Mem0 marketplace:

-   ```bash
-   claude plugin marketplace add mem0ai/mem0
+   ```
+   /plugin marketplace add mem0ai/mem0
   ```

 2. Install the plugin:

-   ```bash
-   claude plugin install mem0@mem0-plugins
+   ```
+   /plugin install mem0@mem0-plugins
   ```

 **Claude Cowork desktop app:** Open the Cowork tab, click **Customize** in the sidebar, click **Browse plugins**, and install Mem0.
@@ -88,15 +88,6 @@ Add to your Claude Code MCP config (`.mcp.json`):
 }
 ```

-### Managing the Plugin
-
-```bash
-claude plugin update mem0@mem0-plugins         # update the plugin to the latest version (restart to apply)
-claude plugin marketplace update mem0-plugins  # refresh the marketplace catalog
-claude plugin uninstall mem0@mem0-plugins      # uninstall the plugin (keeps the marketplace)
-claude plugin marketplace remove mem0-plugins  # unregister the marketplace entirely
-```
-
 <Info icon="check">
  Start a new session and ask: *"List my mem0 entities"* or *"Search my memories for hello"*. If the `mem0` tools appear and respond, you're all set.
 </Info>
@@ -152,11 +143,9 @@ When installed via the plugin marketplace, Mem0 hooks into Claude Code's lifecyc
 | **User prompt** | `UserPromptSubmit` | Searches relevant memories before each message; skips short prompts |
 | **Pre-tool (3 handlers)** | `PreToolUse` | Blocks MEMORY.md writes; enforces `user_id`/`app_id` on mem0 tool calls; scans files being read for relevant memory context |
 | **Post-tool** | `PostToolUse` | Tracks stats, scans bash errors for related memories |
-| **Stop** | `Stop` | Stores a session summary at the end of every assistant turn (not just at session end) |
+| **Stop** | `Stop` | Stores a session summary when the session ends |
 | **Pre-compact** | `PreCompact` | Stores a summary before the context is compacted |

-What you type is stored as yours. What Claude produces (session summaries and compaction summaries) is stored as the assistant's, so its suggestions never become your stated preferences.
-
 ## Example Workflow

 ```text
@@ -164,17 +153,16 @@ What you type is stored as yours. What Claude produces (session summaries and co
 You: Let's refactor the auth module to use JWT tokens instead of sessions.

 # Claude searches memories, finds nothing relevant, proceeds with the work.
-# Mem0 stores what you said as yours:
-#   - Your preference: "Prefers TypeScript, uses ESLint"
-# ...and what Claude did as the assistant's, in the session summary:
+# After completing the task, Mem0 stores:
 #   - Decision: "Migrated auth from sessions to JWT tokens"
 #   - Files modified: auth/middleware.ts, auth/token.ts
+#   - User preference: "Prefers TypeScript, uses ESLint"

 # Session 2 (days later): Related work
 You: Add refresh token rotation to the auth system.

 # Claude searches memories, retrieves the JWT migration context.
-# Knows the file structure, decisions made, and your stated preferences.
+# Knows the file structure, decisions made, and user preferences.
 # Continues seamlessly without re-explaining the codebase.
 ```

@@ -125,23 +125,20 @@ When installed via the plugin marketplace, Mem0 hooks into Codex's lifecycle to
 | **User prompt** | `UserPromptSubmit` | Searches relevant memories before each message |
 | **Pre-tool (3 handlers)** | `PreToolUse` | Blocks MEMORY.md writes; enforces `user_id`/`app_id` on mem0 tool calls; scans files being read for relevant memory context |
 | **Post-tool** | `PostToolUse` | Tracks stats, scans bash errors for related memories |
-| **Stop** | `Stop` | Stores a session summary at the end of every assistant turn (not just at session end) |
+| **Stop** | `Stop` | Stores a session summary when the session ends |
 | **Pre-compact** | `PreCompact` | Stores a summary before the context is compacted |

-What you type is stored as yours. What Codex produces (session summaries and compaction summaries) is stored as the assistant's, so its suggestions never become your stated preferences.
-
 ## Example Workflow

 ```text
 # Task 1: Setting up a new service
 You: Create a REST API for the notifications service using Express and TypeScript.

-# Codex searches memories, finds your preferences from prior tasks.
-# Mem0 stores what you said as yours:
-#   - Your preference: "Prefers explicit error types over generic catch-all"
-# ...and what Codex did as the assistant's, in the session summary:
+# Codex searches memories, finds user preferences from prior tasks.
+# After completing the task, Mem0 stores:
 #   - Decision: "Notifications service uses Express + TypeScript + Zod validation"
 #   - Convention: "All API routes follow /api/v1/{resource} pattern"
+#   - Preference: "User prefers explicit error types over generic catch-all"

 # Task 2 (days later): Extending the service
 You: Add WebSocket support for real-time notification delivery.
@@ -96,11 +96,10 @@ Once installed, the following tools are available in every Cursor session:
 You: The API endpoint /users is taking 3 seconds. Help me optimize it.

 # Cursor agent searches memories, proceeds with investigation.
-# Mem0 stores what you said as yours:
-#   - Your preference: "Prefers query-level fixes over caching"
-# ...and what the agent did as the assistant's:
+# After completing the task, Mem0 stores:
 #   - Learning: "N+1 query in UserService.getAll(): fixed with eager loading"
 #   - Decision: "Added database index on users.email column"
+#   - Preference: "User prefers query-level fixes over caching"

 # Session 2 (next week): Similar issue
 You: The /orders endpoint is also slow, same pattern as before.
@@ -98,8 +98,9 @@ add_result = add_tool.invoke(add_input)

 ```json Output
 {
-  "event_id": "3a1b2c3d-4e5f-6789-abcd-ef0123456789",
-  "status": "PENDING"
+  "message": "Memory processing has been queued for background execution",
+  "status": "PENDING",
+  "event_id": "3a1b2c3d-4e5f-6789-abcd-ef0123456789"
 }
 ```
 </CodeGroup>
@@ -1,157 +0,0 @@
---
-title: n8n
-description: "Add long-term memory to n8n workflows and AI Agents with the Mem0 community node, no code required."
---
-
-Your n8n workflows start from zero on every run. The [`@mem0/n8n-nodes-mem0`](https://www.npmjs.com/package/@mem0/n8n-nodes-mem0) community node fixes that: store durable facts as memories, recall them in any later run, and hand the node to an [n8n AI Agent](https://docs.n8n.io/advanced-ai/) as a tool so it can remember and recall on its own.
-
-## Overview
-
-1. Install the node from n8n's community nodes panel.
-2. Connect your Mem0 API key once as a credential.
-3. Drop a **Mem0** node into any workflow to add, search, or manage memories.
-4. Optionally attach it to an **AI Agent** node, where it becomes a tool the agent calls itself.
-
-## Prerequisites
-
-1. A Mem0 API key from the <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-n8n" rel="nofollow">API Keys dashboard</a> (sign up at <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-n8n" rel="nofollow">app.mem0.ai</a> if you do not have an account).
-2. A **self-hosted** n8n instance. Installing community nodes from npm is a self-hosted feature; n8n Cloud only offers nodes that n8n has verified.
-3. Owner access to that instance, since only instance owners can install community nodes.
-
-## Installation
-
-<Steps>
-  <Step title="Open the community nodes panel">
-    In n8n, go to **Settings → Community Nodes** and select **Install**.
-  </Step>
-  <Step title="Install the package">
-    Enter `@mem0/n8n-nodes-mem0`, tick the risk acknowledgement, and select **Install**.
-  </Step>
-  <Step title="Create the credential">
-    Add a new **Mem0 API** credential and paste your API key. Leave **Base URL** at `https://api.mem0.ai` unless you run Mem0 somewhere else.
-  </Step>
-</Steps>
-
-<Info>
-  **Verify the install:** search the nodes panel for `Mem0`. The node should appear with a **Memory** resource offering Add, Search, Get, Get Many, Update, and Delete.
-</Info>
-
-## Quickstart
-
-A two-node workflow that writes a memory and reads it back:
-
-```text
-Manual Trigger  →  Mem0 (Add)  →  Mem0 (Search)
-```
-
-<Steps>
-  <Step title="Add a memory">
-    Add a **Mem0** node, keep **Operation: Add**, set **User ID** to `alice`, and add one message with **Role** `user` and **Content**:
-
-    `I am vegetarian and I never eat mushrooms.`
-  </Step>
-  <Step title="Search for it">
-    Add a second **Mem0** node with **Operation: Search**, **User ID** `alice`, and **Query** `what does the user eat?`.
-  </Step>
-  <Step title="Run it">
-    Select **Test workflow**. The Search node returns the extracted dietary memory.
-  </Step>
-</Steps>
-
-<Note>
-  Extraction is asynchronous. The Add node's **Wait for Completion** option is on by default, so it polls until extraction finishes before the next node runs. If you turn it off, allow a few seconds before searching for what you just wrote.
-</Note>
-
-## Use it as an AI Agent tool
-
-The node is marked `usableAsTool`, so an n8n **AI Agent** (Tools Agent) can call it without any wiring on your side:
-
-```text
-Chat Trigger  →  AI Agent  ──tool──▶  Mem0 (Search)
-                          ──tool──▶  Mem0 (Add)
-```
-
-Attach one Mem0 node set to **Search** and one set to **Add**. The agent searches memory before answering and writes back durable facts after a meaningful exchange. Keep **User ID** the same on both.
-
-## Operations
-
-The node wraps the hosted Mem0 REST API and supports six operations on the **Memory** resource:
-
-| Operation | What it does | Endpoint |
-| --- | --- | --- |
-| **Add** | Extract and store memories from messages | `POST /v3/memories/add/` |
-| **Search** | Semantic search over stored memories | `POST /v3/memories/search/` |
-| **Get Many** | List stored memories (one page, or **Return All**) | `POST /v3/memories/` |
-| **Get** | Fetch a single memory by ID | `GET /v1/memories/{id}/` |
-| **Update** | Change a memory's text or metadata | `PUT /v1/memories/{id}/` |
-| **Delete** | Delete a single memory by ID | `DELETE /v1/memories/{id}/` |
-
-### Add
-
-Extracts and stores memories from one or more messages. Supply at least one entity id (**User ID**, or **Agent ID** / **App ID** / **Run ID** under Additional Fields); the node checks this before calling the API.
-
-**Additional Fields:**
-
-| Field | Purpose |
-| --- | --- |
-| **Agent ID** | Scopes the memory to an agent |
-| **App ID** | Scopes the memory to an app or project |
-| **Run ID** | Scopes the memory to a single session or run |
-| **Metadata (JSON)** | Arbitrary JSON attached to each extracted memory |
-| **Infer** | On by default. Turn off to store messages verbatim instead of running LLM extraction |
-| **Custom Instructions** | Free-text guidance steering what the extractor keeps or ignores, for this call |
-| **Custom Categories** | JSON array of `{category: description}` objects, replacing the project-level catalog for this call |
-| **Includes** | Only extract memories matching this description |
-| **Excludes** | Skip memories matching this description |
-
-**Includes** and **Excludes** narrow what extraction keeps. Sending *"I am vegetarian and I never eat mushrooms. I drive a blue Toyota Corolla and my parking spot is B12"* stores three memories by default; with `Includes: "only record food and diet preferences"` it stores just the dietary one.
-
-### Search
-
-Semantic search over stored memories. Takes a **Query**, at least one entity id, and an optional **Limit**.
-
-### Get Many
-
-Lists stored memories for the entity ids you supply. Turn on **Return All** to page through everything automatically, or leave it off to fetch a single **Page**. **Page Size** applies either way.
-
-### Get, Update, Delete
-
-Operate on one memory by **Memory ID**. Update accepts new **Text** and/or **Metadata (JSON)**.
-
-## Entity filters on Search and Get Many
-
-Both operations take **User ID**, **Agent ID**, **App ID**, and **Run ID**. At least one is required, since the API rejects a query with no entity scope, and the node fails with a clear message before making the call if all four are empty.
-
-Supply several and they combine with **OR**, so the result is the union of those scopes:
-
-```json
-{ "OR": [{ "user_id": "alice" }, { "agent_id": "support-bot" }] }
-```
-
-<Warning>
-  This is deliberate, not a shortcut. Mem0 indexes each entity separately, so an `AND` across `user_id` and `agent_id` matches nothing even when a memory was written with both. To narrow rather than widen, run one operation per entity id.
-</Warning>
-
-## Choosing a User ID
-
-The **User ID** is a stable string you pick to identify whose memories these are. It is not looked up in the dashboard, so any consistent value works: your app's internal user ID, an email, or a UUID. Use the same value across Add, Search, and Get Many or recall returns nothing.
-
-## Troubleshooting
-
- **The node does not appear in the panel**: community nodes install on self-hosted n8n only, and only instance owners can install them. On n8n Cloud, this node is not yet available.
- **`401 Unauthorized`**: the API key is wrong or was revoked. Regenerate it in the <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-n8n" rel="nofollow">API Keys dashboard</a> and update the credential.
- **"Provide at least one of User ID, Agent ID, App ID, or Run ID"**: every Add, Search, and Get Many needs an entity scope. Fill in at least one.
- **Search returns nothing right after an Add**: extraction is asynchronous. Leave **Wait for Completion** on, or add a short Wait node before searching.
- **Searching two entity ids returns more than expected**: multiple ids are combined with OR by design. Run one operation per id to narrow.
- **"Timed out waiting for memory event"**: the add was accepted and is likely still finishing on the server. A timeout here does not mean it failed.
-
-<CardGroup cols={2}>
-  <Card title="Zapier Integration" icon="bolt" href="/integrations/zapier">
-    Add memory to Zaps across thousands of apps
-  </Card>
-  <Card title="Flowise Integration" icon="blocks" href="/integrations/flowise">
-    Add memory to Flowise chatflows
-  </Card>
-</CardGroup>
-
-<Snippet file="star-on-github.mdx" />
@@ -1,143 +0,0 @@
---
-title: Zapier
-description: "Add, search, and manage Mem0 memories from any Zap using the Mem0 Zapier app, no code required."
---
-
-Zaps fire and forget. The [Mem0](https://mem0.ai) app gives them memory: store durable facts from a form submission, a support ticket, or a chat message, then recall them later from any of [Zapier's](https://zapier.com) thousands of apps. No code, no server.
-
-## Overview
-
-1. Connect your Mem0 API key once as a Zapier connection.
-2. Use **Add Memory** to store what a Zap learns.
-3. Use **Search Memories** or **Get Memories** to pull that context back into a later step.
-4. Use **Delete Memory** to remove one by ID.
-
-## Prerequisites
-
-1. A Mem0 API key from the <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-zapier" rel="nofollow">API Keys dashboard</a> (sign up at <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-zapier" rel="nofollow">app.mem0.ai</a> if you do not have an account).
-2. A Zapier account on any plan.
-
-<Note>
-  The Mem0 app is not yet listed in Zapier's public App Directory, so you need an invite link to add it to a Zap. Email [support@mem0.ai](mailto:support@mem0.ai) to request one.
-</Note>
-
-## Setup
-
-<Steps>
-  <Step title="Add a Mem0 step">
-    In the Zap editor, search for **Mem0** and pick an action such as **Add Memory**.
-  </Step>
-  <Step title="Connect your account">
-    Select **Sign in**, paste your **Mem0 API Key** (it starts with `m0-`), and leave **Base URL** at `https://api.mem0.ai` unless you run Mem0 somewhere else.
-  </Step>
-  <Step title="Confirm the connection">
-    Zapier validates the key against Mem0 the moment you save it. A connection labelled **Mem0** means the key works.
-  </Step>
-</Steps>
-
-<Info>
-  The key is a password field, so Zapier masks it in the editor. It is sent to Mem0 as `Authorization: Token <key>`.
-</Info>
-
-## Quickstart
-
-Remember what a user tells you:
-
-```text
-Trigger (form, chat, ticket)  →  Mem0: Add Memory
-```
-
-Set **Content** to the message text and **User ID** to a stable identifier for that person, such as their email.
-
-Then recall it in a later Zap:
-
-```text
-Trigger (new message)  →  Mem0: Search Memories  →  Send reply
-```
-
-Set **Query** to the incoming message and **User ID** to the same value. The matched memories become available to every step after it.
-
-<Note>
-  Extraction is asynchronous. **Add Memory** returns immediately with an event ID by default, so a Search fired a second later may not see the new memory yet. See [Waiting for extraction](#waiting-for-extraction).
-</Note>
-
-## Actions
-
-| Type | Action | What it does | Endpoint |
-| --- | --- | --- | --- |
-| Create | **Add Memory** | Extract and store memories from a message | `POST /v3/memories/add/` |
-| Search | **Search Memories** | Semantic search over stored memories | `POST /v3/memories/search/` |
-| Search | **Get Memories** | List stored memories, one page at a time | `POST /v3/memories/` |
-| Create | **Delete Memory** | Delete a single memory by ID | `DELETE /v1/memories/{id}/` |
-
-### Add Memory
-
-| Field | Required | Purpose |
-| --- | --- | --- |
-| **Content** | Yes | The message text to extract memories from |
-| **Role** | | `User` (default), `Assistant`, or `System` |
-| **User ID** | | Scopes the memory to a person |
-| **Agent ID** | | Scopes the memory to an agent |
-| **Run ID** | | Scopes the memory to a single session or run |
-| **Metadata (JSON)** | | Arbitrary JSON attached to each extracted memory |
-| **Custom Instructions** | | Free-text guidance steering what the extractor keeps or ignores, for this call |
-| **Custom Categories (JSON)** | | JSON array of `{category: description}` objects, replacing the project-level catalog for this call |
-| **Includes** | | Only extract memories matching this description |
-| **Excludes** | | Skip memories matching this description |
-| **Infer** | | On by default. Turn off to store the message verbatim instead of running LLM extraction |
-| **Wait for Completion** | | Off by default. Turn on to poll until extraction finishes and return the resulting memories |
-
-**Includes** and **Excludes** narrow what extraction keeps. Sending *"I am vegetarian and I never eat mushrooms. I drive a blue Toyota Corolla and my parking spot is B12"* stores three memories by default; with `Includes: "only record food and diet preferences"` it stores just the dietary one.
-
-#### Waiting for extraction
-
-Extraction runs asynchronously, so **Add Memory** returns an event ID and moves on unless you turn on **Wait for Completion**. When you do, the step polls for up to 60 seconds and returns the extracted memories instead.
-
-<Warning>
-  Extraction can take longer than Zapier allows a single step to run, which is why waiting is opt-in. If the step times out, the add was still accepted and typically completes on Mem0's side, so do not retry it blindly.
-</Warning>
-
-### Search Memories
-
-| Field | Required | Purpose |
-| --- | --- | --- |
-| **Query** | Yes | Natural-language search text |
-| **User ID** | Yes | Whose memories to search. The API needs an entity filter |
-| **Limit** | | Maximum results, default `50` |
-
-### Get Memories
-
-| Field | Required | Purpose |
-| --- | --- | --- |
-| **User ID** | Yes | Whose memories to list |
-| **Limit** | | Memories per page, default `50` |
-| **Page** | | Which page to return, 1-based, default `1` |
-
-Returns one page per run. Raise **Page** to walk through larger result sets.
-
-### Delete Memory
-
-Takes a **Memory ID** and deletes that memory. Pair it with **Search Memories** or **Get Memories** to get the ID first.
-
-## Choosing a User ID
-
-The **User ID** is a stable string you pick to identify whose memories these are. It is not looked up in the dashboard, so any consistent value works: your app's internal user ID, an email, or a UUID. Use the same value on Add, Search, and Get Memories or recall returns nothing.
-
-## Troubleshooting
-
- **Mem0 does not appear in the Zap editor**: the app is not yet in the public App Directory. Email [support@mem0.ai](mailto:support@mem0.ai) for an invite link.
- **The connection fails when you paste the key**: check that it starts with `m0-` and has not been revoked in the <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-zapier" rel="nofollow">API Keys dashboard</a>.
- **Search returns nothing right after an Add**: extraction is asynchronous. Turn on **Wait for Completion**, or put a Zapier **Delay** step before the Search.
- **"Metadata must be valid JSON" or "Custom Categories must be valid JSON"**: those fields take raw JSON. Check for smart quotes and trailing commas.
- **The Add step times out**: the memory was still accepted and is likely finishing server-side. Confirm with **Get Memories** before re-running.
-
-<CardGroup cols={2}>
-  <Card title="n8n Integration" icon="diagram-project" href="/integrations/n8n">
-    Build workflows with the Mem0 n8n community node
-  </Card>
-  <Card title="Flowise Integration" icon="blocks" href="/integrations/flowise">
-    Add memory to Flowise chatflows
-  </Card>
-</CardGroup>
-
-<Snippet file="star-on-github.mdx" />
@@ -10,7 +10,7 @@ mode: "custom"
  </h1>

  <p className="max-w-3xl mx-auto text-base text-gray-600 dark:text-zinc-400 leading-relaxed">
-    Mem0 gives your AI agents long-term memory that persists across sessions, tools, and runs.
+    Universal, self-improving memory layer for LLM applications.
  </p>
 </div>

@@ -34,10 +34,10 @@ mode: "custom"
      />
      <div className="flex flex-1 flex-col gap-2 px-4 pb-4 pt-3 text-left">
        <h3 className="text-base font-semibold text-gray-900 dark:text-zinc-100 group-hover:text-primary">
-          Store your first memory
+          Add memory to your app
        </h3>
        <p className="text-sm text-gray-600 dark:text-zinc-400">
-          Five-minute quickstart: get an API key, then save and search a memory in Python or JavaScript.
+          Start with the quickstart and store your first memory in minutes.
        </p>
      </div>
    </a>
@@ -60,10 +60,10 @@ mode: "custom"
      />
      <div className="flex flex-1 flex-col gap-2 px-4 pb-4 pt-3 text-left">
        <h3 className="text-base font-semibold text-gray-900 dark:text-zinc-100 group-hover:text-primary">
-          Add memory to your coding agent
+          Add memory to your agent
        </h3>
        <p className="text-sm text-gray-600 dark:text-zinc-400">
-          Plugins that let Claude Code, Cursor, Codex, and other harnesses remember your project. Opens the Claude Code guide.
+          Give Claude Code, Cursor, and Codex memory that persists across sessions. A drop-in plugin, no code to write.
        </p>
      </div>
    </a>
@@ -86,10 +86,10 @@ mode: "custom"
      />
      <div className="flex flex-1 flex-col gap-2 px-4 pb-4 pt-3 text-left">
        <h3 className="text-base font-semibold text-gray-900 dark:text-zinc-100 group-hover:text-primary">
-          Let an AI agent sign itself up
+          Sign up for Mem0 as an agent
        </h3>
        <p className="text-sm text-gray-600 dark:text-zinc-400">
-          Four terminal commands create an account and API key. No email, no dashboard. Claim it as owner later.
+          Let an AI agent mint a Mem0 API key, claim ownership later, and write its first memory from the terminal.
        </p>
      </div>
    </a>
@@ -112,10 +112,10 @@ mode: "custom"
      />
      <div className="flex flex-1 flex-col gap-2 px-4 pb-4 pt-3 text-left">
        <h3 className="text-base font-semibold text-gray-900 dark:text-zinc-100 group-hover:text-primary">
-          Use Mem0 with your framework
+          Explore Mem0 integrations
        </h3>
        <p className="text-sm text-gray-600 dark:text-zinc-400">
-          Setup guides for 22 tools, including LangChain, CrewAI, LlamaIndex, and the Vercel AI SDK.
+          Connect Mem0 to LangChain, CrewAI, Vercel AI SDK, and 20+ partner frameworks.
        </p>
      </div>
    </a>
@@ -138,10 +138,10 @@ mode: "custom"
      />
      <div className="flex flex-1 flex-col gap-2 px-4 pb-4 pt-3 text-left">
        <h3 className="text-base font-semibold text-gray-900 dark:text-zinc-100 group-hover:text-primary">
-          Copy a working example
+          See memory-app examples
        </h3>
        <p className="text-sm text-gray-600 dark:text-zinc-400">
-          Full walkthroughs for companion chatbots, support agents, voice agents, and research tools.
+          Browse cookbooks for companions, support agents, voice agents, research tools, and more.
        </p>
      </div>
    </a>
@@ -164,10 +164,10 @@ mode: "custom"
      />
      <div className="flex flex-1 flex-col gap-2 px-4 pb-4 pt-3 text-left">
        <h3 className="text-base font-semibold text-gray-900 dark:text-zinc-100 group-hover:text-primary">
-          Run Mem0 on your own servers
+          Self-host Mem0
        </h3>
        <p className="text-sm text-gray-600 dark:text-zinc-400">
-          The open-source version. Run it as a library or a Docker stack, with your data on your infrastructure.
+          Use Open Source when you want memory without the hosted platform.
        </p>
      </div>
    </a>
@@ -38,7 +38,7 @@ This mints an evaluation key in <5 seconds. Use it immediately against the Platf

 ## Identify the User's Setup

-Look at the user's imports first - they determine which product (Platform vs OSS) and which language you should quote docs from. **Mem0 Platform (managed) is the recommended path** - 4-line integration, no infra to run. Route to OSS only when the user has an explicit self-hosting requirement.
+Look at the user's imports first - they determine which product (Platform vs OSS) and which language you should quote docs from. **Mem0 Platform (managed) is the recommended path** - 4-line integration, sub-50ms retrieval, no infra. Route to OSS only when the user has an explicit self-hosting requirement.

 ### Platform - Python [Platform]

@@ -61,7 +61,7 @@ client.get_all(user_id="alice")
 client.get(memory_id="<id>")

 # Update
-client.update(memory_id="<id>", text="Alice loves mountain hiking")
+client.update(memory_id="<id>", data="Alice loves mountain hiking")

 # Delete
 client.delete(memory_id="<id>")
@@ -118,7 +118,7 @@ m.get_all(user_id="alice")
 m.get(memory_id="<id>")

 # Update
-m.update(memory_id="<id>", text="Alice loves mountain hiking")
+m.update(memory_id="<id>", data="Alice loves mountain hiking")

 # Delete
 m.delete(memory_id="<id>")
@@ -170,7 +170,7 @@ If the user is on a pre-current major (Python < 2, TS < 3, or Platform `output_f

 - [Introduction](https://docs.mem0.ai/introduction) [Both]: Use when the user wants a one-page overview of how memory fits between the LLM and the app.
 - [Vibe Code with Mem0](https://docs.mem0.ai/vibecoding) [Both]: Use when the user is in Claude Code, Cursor, or Windsurf and wants memory wired into their editor.
- [Platform Overview](https://docs.mem0.ai/platform/overview) [Platform]: Use when the user picks the managed product - 4-line integration, hosted API, dashboard.
+- [Platform Overview](https://docs.mem0.ai/platform/overview) [Platform]: Use when the user picks the managed product - 4-line integration, sub-50ms retrieval, dashboard.
 - [Sign up as an agent](https://docs.mem0.ai/platform/agent-signup) [Platform]: Use when an AI agent needs to mint a Mem0 API key autonomously - four commands, no email or dashboard, human claims ownership later.
 - [Platform vs Open Source](https://docs.mem0.ai/platform/platform-vs-oss) [Both]: Use when the user is deciding between managed and self-hosted.
 - [Platform Quickstart](https://docs.mem0.ai/platform/quickstart) [Platform]: Use for the first Platform integration - API key plus `MemoryClient.add/search`.
@@ -204,7 +204,9 @@ If the user is on a pre-current major (Python < 2, TS < 3, or Platform `output_f

 ### Features - Advanced Retrieval
 - [Advanced Retrieval](https://docs.mem0.ai/platform/features/advanced-retrieval) [Platform]: Use when the user needs keyword search, reranking, or hybrid retrieval.
+- [Criteria-Based Retrieval](https://docs.mem0.ai/platform/features/criteria-retrieval) [Platform]: Use when targeting memories by custom criteria, not just semantic similarity.
 - [Temporal Reasoning](https://docs.mem0.ai/platform/features/temporal-reasoning) [Platform]: Use when time-aware searches like last week, upcoming, or right now need better result ordering.
+- [Contextual Add](https://docs.mem0.ai/platform/features/contextual-add) [Platform]: Use when `add()` should consider the surrounding conversation, not just the latest turn.
 - [Custom Instructions](https://docs.mem0.ai/platform/features/custom-instructions) [Platform]: Use when tailoring what Mem0 extracts and stores on Platform.
 - [Memory Decay](https://docs.mem0.ai/platform/features/memory-decay) [Platform]: Use when search results should boost recently-reinforced memories and dampen stale ones. Opt in per project; applies at search time and never filters candidates out.
 - [Advanced Memory Operations](https://docs.mem0.ai/platform/advanced-memory-operations) [Platform]: Use when basic CRUD is not enough - batch ops, complex filters, workflows.
@@ -213,7 +215,6 @@ If the user is on a pre-current major (Python < 2, TS < 3, or Platform `output_f
 - [Direct Import](https://docs.mem0.ai/platform/features/direct-import) [Platform]: Use when seeding a Mem0 project from existing data.
 - [Memory Export](https://docs.mem0.ai/platform/features/memory-export) [Platform]: Use when exporting memories via a Pydantic schema.
 - [Timestamp Support](https://docs.mem0.ai/platform/features/timestamp) [Platform]: Use when temporal queries or time-based filtering matter.
- [Memory Expiration](https://docs.mem0.ai/platform/features/memory-expiration) [Both]: Use when a memory should stop surfacing after a known date without being deleted, e.g. trial facts, seasonal preferences, or retention windows.

 ### Features - Integration & Ops
 - [Webhooks](https://docs.mem0.ai/platform/features/webhooks) [Platform]: Use when another system needs to react to memory changes in real time.
@@ -281,8 +282,6 @@ If the user is on a pre-current major (Python < 2, TS < 3, or Platform `output_f
 ### Developer Tools
 - [Dify](https://docs.mem0.ai/integrations/dify) [Both]: Use when the user is on Dify LLMOps.
 - [Flowise](https://docs.mem0.ai/integrations/flowise) [Both]: Use when the user is on Flowise no-code.
- [n8n](https://docs.mem0.ai/integrations/n8n) [Both]: Use when the user builds workflows or AI agents in n8n.
- [Zapier](https://docs.mem0.ai/integrations/zapier) [Both]: Use when the user automates workflows with Zapier.
 - [AgentOps](https://docs.mem0.ai/integrations/agentops) [Both]: Use when tracking agent observability with memory metadata.
 - [Respan](https://docs.mem0.ai/integrations/respan) [Both]: Use when monitoring Mem0 with Respan (formerly Keywords AI) LLM observability.
 - [Raycast](https://docs.mem0.ai/integrations/raycast) [Both]: Use when the user wants quick memory access via Raycast.
@@ -418,6 +417,7 @@ Editor-specific setup docs (already listed above under `## Integrations > AI Cod
 ### MCP Endpoints

 - Hosted MCP server: `https://mcp.mem0.ai` - requires Platform API key. See `platform/mem0-mcp`.
+- Self-hosted MCP server: ships with `openmemory/api/` (FastAPI) - runs against your own Qdrant + LLM stack.

 ## Community & Support

@@ -473,7 +473,6 @@ Everything below is OSS-only provider configuration. Skip this entire section wh
 - [Milvus](https://docs.mem0.ai/components/vectordbs/dbs/milvus) [OSS]: Use for large-scale Milvus deployments.
 - [Pinecone](https://docs.mem0.ai/components/vectordbs/dbs/pinecone) [OSS]: Use when the user is on Pinecone managed.
 - [MongoDB](https://docs.mem0.ai/components/vectordbs/dbs/mongodb) [OSS]: Use when Mongo Atlas Vector Search is the backing store.
- [Oracle AI Vector Search](https://docs.mem0.ai/components/vectordbs/dbs/oracledb) [OSS]: Use when Oracle Database AI Vector Search is the backing store.
 - [Azure AI Search](https://docs.mem0.ai/components/vectordbs/dbs/azure) [OSS]: Use when the user is on Azure AI Search.
 - [Azure MySQL](https://docs.mem0.ai/components/vectordbs/dbs/azure_mysql) [OSS]: Use when vector search runs on Azure Database for MySQL.
 - [Redis](https://docs.mem0.ai/components/vectordbs/dbs/redis) [OSS]: Use when Redis Stack is the backing store.
@@ -502,6 +501,5 @@ Everything below is OSS-only provider configuration. Skip this entire section wh
 - [Custom Reranker Prompts](https://docs.mem0.ai/components/rerankers/custom-prompts) [OSS]: Use when rewriting reranker prompts.
 - [Cohere Reranker](https://docs.mem0.ai/components/rerankers/models/cohere) [OSS]: Use for Cohere Rerank.
 - [Sentence Transformer Reranker](https://docs.mem0.ai/components/rerankers/models/sentence_transformer) [OSS]: Use for local cross-encoder rerankers.
- [Hugging Face Reranker](https://docs.mem0.ai/components/rerankers/models/huggingface) [OSS]: Use for HF-hosted reranker models.
- [LLM Reranker](https://docs.mem0.ai/components/rerankers/models/llm_reranker) [OSS]: Use when the reranker is a prompted LLM (implementation reference).
+- [Hugging Face Reranker](https://docs.mem0.ai/components/rerankers/models/huggingface) [OSS]: Use for HF-hosted reranker models.- [LLM Reranker](https://docs.mem0.ai/components/rerankers/models/llm_reranker) [OSS]: Use when the reranker is a prompted LLM (implementation reference).
 - [Zero Entropy Reranker](https://docs.mem0.ai/components/rerankers/models/zero_entropy) [OSS]: Use for the Zero Entropy reranker.
@@ -1,19 +1,19 @@
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+<path d="M153.491 191.533C174.501 191.533 191.533 174.501 191.533 153.491C191.533 132.482 174.501 115.45 153.491 115.45C132.481 115.45 115.449 132.482 115.449 153.491C115.449 174.501 132.481 191.533 153.491 191.533Z" fill="#9C58FA"/>
 </svg>
@@ -339,22 +339,21 @@ The Platform introduces powerful capabilities not available in OSS:
    </Info>
    ```python
    # Set custom categories for your project
-    client.project.update(
-        custom_categories=[
-            {"customer_preferences": "Likes, dislikes, and product preferences"},
-            {"product_feedback": "Feature requests and complaints about the product"},
-            {"support_issues": "Problems reported and how they were resolved"}
+    client.projects.update_categories(
+        project_id="proj_123",
+        categories=[
+            "Customer Preferences",
+            "Product Feedback",
+            "Support Issues",
+            "Feature Requests"
        ]
    )

-    # Mem0 assigns these categories automatically as memories come in
-    client.add("User wants dark mode in dashboard", user_id="alex")
-
-    # Or pass a different catalog for a single call
+    # Memories will use these categories
    client.add(
        "User wants dark mode in dashboard",
        user_id="alex",
-        custom_categories=[{"ui_requests": "Requests about interface and appearance"}]
+        categories=["Customer Preferences"]
    )
    ```
  </Accordion>
@@ -146,8 +146,9 @@ curl -X POST 'https://api.mem0.ai/v3/memories/?page=1&page_size=50' \

 ```json
 {
-  "event_id": "evt-uuid",
-  "status": "PENDING"
+  "message": "Memory processing has been queued for background execution",
+  "status": "PENDING",
+  "event_id": "evt-uuid"
 }
 ```

@@ -59,7 +59,7 @@ config = {
    },
    "reranker": {
        "provider": "cohere",
-        "config": {"model": "rerank-v3.5"},
+        "config": {"model": "rerank-english-v3.0"},
    },
 }

@@ -119,9 +119,9 @@ Change the `provider` string to switch backends. The most common options:

 | Component | Python | TypeScript |
 | --- | --- | --- |
-| LLM | `openai`, `anthropic`, `gemini`, `groq`, `ollama`, `aws_bedrock`, `azure_openai`, `litellm` | `openai`, `anthropic`, `gemini`, `groq`, `ollama`, `aws_bedrock`, `azure_openai`, `mistral`, `deepseek` |
+| LLM | `openai`, `anthropic`, `gemini`, `groq`, `ollama`, `aws_bedrock`, `azure_openai`, `litellm` | `openai`, `anthropic`, `gemini`, `groq`, `ollama`, `azure_openai`, `mistral`, `deepseek` |
 | Embedder | `openai`, `gemini`, `azure_openai`, `ollama`, `huggingface`, `vertexai`, `aws_bedrock` | `openai`, `gemini`, `azure_openai`, `ollama` |
-| Vector store | `qdrant`, `pgvector`, `chroma`, `pinecone`, `redis`, `weaviate`, `milvus`, `elasticsearch` | `memory`, `qdrant`, `pgvector`, `redis`, `supabase`, `azure-ai-search`, `vectorize`, `milvus` |
+| Vector store | `qdrant`, `pgvector`, `chroma`, `pinecone`, `redis`, `weaviate`, `milvus`, `elasticsearch` | `memory`, `qdrant`, `pgvector`, `redis`, `supabase`, `azure-ai-search`, `vectorize` |

 See the full catalog in <Link href="/components/llms/overview">Components</Link>.

@@ -148,7 +148,7 @@ See the full catalog in <Link href="/components/llms/overview">Components</Link>
 - Qdrant connection errors: confirm port `6333` is exposed and the API key (if set) matches.
 - Empty search results: verify the embedder model name. A mismatch causes dimension errors.
 - `Unknown reranker` (Python): upgrade the SDK with `pip install --upgrade mem0ai` to load the latest provider registry.
- `Cannot find module` (Node): two common causes. First, import from the OSS entry point, `import { Memory } from "mem0ai/oss"`, not `"mem0ai"`. Second, provider SDKs are optional peer dependencies loaded on demand, so install the one for the provider you configured (for example `npm install @qdrant/js-client-rest` for Qdrant). Installing `mem0ai` alone only pulls in the providers used by default; you do not need SDKs for providers you never select.
+- `Cannot find module` (Node): import from the OSS entry point, `import { Memory } from "mem0ai/oss"`, not `"mem0ai"`.

 <CardGroup cols={2}>
  <Card
@@ -36,7 +36,7 @@ icon: "bolt"
    | Search memories | `await memory.search(...)` | Returns dict with `results`, identical shape. |
    | List memories | `await memory.get_all(...)` | Filter by `user_id`, `agent_id`, `run_id`. |
    | Retrieve memory | `await memory.get(memory_id=...)` | Raises `ValueError` if ID is invalid. |
-    | Update memory | `await memory.update(memory_id=..., text=...)` | Accepts partial updates. |
+    | Update memory | `await memory.update(memory_id=..., data=...)` | Accepts partial updates. |
    | Delete memory | `await memory.delete(memory_id=...)` | Returns confirmation payload. |
    | Delete in bulk | `await memory.delete_all(...)` | Requires at least one scope filter. |
    | History | `await memory.history(memory_id=...)` | Fetches change log for auditing. |
@@ -185,7 +185,7 @@ specific_memory = await memory.get(memory_id="memory-id-here")
 # Update a memory
 updated_memory = await memory.update(
    memory_id="memory-id-here",
-    text="I'm travelling to Seattle"
+    data="I'm travelling to Seattle"
 )

 # Delete a memory
@@ -18,129 +18,7 @@ Reranker-enhanced search adds a second scoring pass after vector retrieval so Me
 </Warning>

 <Note>
-  The `Configure it` and `See it in action` snippets below use the Python SDK. The self-hosted **TypeScript SDK** supports the Cohere, Zero Entropy, Sentence Transformer, Hugging Face, and LLM rerankers; see [TypeScript SDK](#typescript-sdk).
-</Note>
-
---
-
-## TypeScript SDK
-
-The self-hosted TypeScript SDK (`mem0ai/oss`) ships five rerankers: **Cohere**, **Zero Entropy**, **Sentence Transformer**, **Hugging Face**, and the **LLM reranker**. Configure one under `reranker`, then opt in per search with `rerank: true`. Keys are camelCase (`apiKey`, not `api_key`).
-
-Provider SDKs are peer dependencies. Install the one your reranker needs:
-
-```bash
-pnpm add cohere-ai          # cohere
-pnpm add zeroentropy        # zero_entropy
-pnpm add @huggingface/transformers   # sentence_transformer, huggingface
-# llm_reranker defaults to openai (already a core dependency); install another
-# provider's SDK only if you nest a different one under config.llm
-```
-
-### Hosted rerankers (Cohere, Zero Entropy)
-
-Both call a hosted API and read their key from config or the provider's environment variable (`COHERE_API_KEY`, `ZERO_ENTROPY_API_KEY`).
-
-```typescript
-import { Memory } from "mem0ai/oss";
-
-// Cohere reranker (defaults to the rerank-v3.5 model)
-const memory = new Memory({
-  reranker: {
-    provider: "cohere",
-    config: { apiKey: process.env.COHERE_API_KEY },
-  },
-});
-
-const results = await memory.search("What are my food preferences?", {
-  filters: { userId: "alice" },
-  rerank: true,
-});
-```
-
-```typescript
-// Zero Entropy reranker (defaults to the zerank-1 model)
-const memory = new Memory({
-  reranker: {
-    provider: "zero_entropy",
-    config: { apiKey: process.env.ZERO_ENTROPY_API_KEY },
-  },
-});
-```
-
-### Local cross-encoders (Sentence Transformer, Hugging Face)
-
-Both run a cross-encoder locally with [Transformers.js](https://huggingface.co/docs/transformers.js): no API key, no network at inference time. Because Transformers.js runs ONNX weights, the default models are the ONNX mirrors of the Python SDK's defaults (`sentence_transformer` → `Xenova/ms-marco-MiniLM-L-6-v2`, `huggingface` → `Xenova/bge-reranker-base`). Point `model` at any ONNX-exported cross-encoder on the Hub to override.
-
-```typescript
-const memory = new Memory({
-  reranker: {
-    provider: "sentence_transformer", // or "huggingface"
-    config: {
-      // model: "Xenova/bge-reranker-base",  // override the default
-      device: "cpu", // Transformers.js device: "cpu" | "wasm" | "webgpu"
-      maxLength: 512, // max tokens per query-document pair
-      normalize: true, // sigmoid-normalize logits to [0, 1] (default)
-    },
-  },
-});
-
-const results = await memory.search("What movies do I like?", {
-  filters: { userId: "alice" },
-  rerank: true,
-});
-```
-
-<Note>
-  `batchSize` and `showProgressBar` are accepted for config parity with the Python SDK but are no-ops in this runtime, because a memory search reranks a small candidate set in a single in-process forward pass. The model is downloaded once and cached in-process on first use.
-</Note>
-
-### LLM reranker
-
-To score with an LLM instead of a dedicated reranker, use the `llm_reranker` provider. It builds its own LLM from the reranker's config (defaulting to `openai` / `gpt-5-mini`) rather than reusing the Memory's main `llm`:
-
-```typescript
-const memory = new Memory({
-  reranker: {
-    provider: "llm_reranker",
-    config: { apiKey: process.env.OPENAI_API_KEY },
-  },
-});
-
-const results = await memory.search("What movies do I like?", {
-  filters: { userId: "alice" },
-  rerank: true,
-});
-```
-
-Nest a different provider under `config.llm` to override the default:
-
-```typescript
-const memory = new Memory({
-  reranker: {
-    provider: "llm_reranker",
-    config: {
-      llm: {
-        provider: "anthropic",
-        config: { apiKey: process.env.ANTHROPIC_API_KEY },
-      },
-    },
-  },
-});
-```
-
-### Config reference
-
-| Provider | Default model | Key config fields |
-| --- | --- | --- |
-| `cohere` | `rerank-v3.5` | `apiKey`, `model`, `topK` |
-| `zero_entropy` | `zerank-1` | `apiKey`, `model`, `topK` |
-| `sentence_transformer` | `Xenova/ms-marco-MiniLM-L-6-v2` | `model`, `device`, `maxLength`, `normalize`, `topK` |
-| `huggingface` | `Xenova/bge-reranker-base` | `model`, `device`, `maxLength`, `normalize`, `topK` |
-| `llm_reranker` | `openai` / `gpt-5-mini` | `provider`, `model`, `apiKey`, `llm` (nested override), `topK` |
-
-<Note>
-  `rerank` is opt-in per search and a no-op when no `reranker` is configured. If the reranker call fails, Mem0 logs a warning and returns the original vector-ranked results.
+  All configuration snippets translate directly to the TypeScript SDK: swap dictionaries for objects while keeping the same keys (`provider`, `config`, `rerank` flags).
 </Note>

 ---
@@ -183,7 +61,7 @@ config = {
    "reranker": {
        "provider": "cohere",
        "config": {
-            "model": "rerank-v3.5",
+            "model": "rerank-english-v3.0",
            "api_key": "your-cohere-api-key"
        }
    }
@@ -208,7 +86,7 @@ config = {
    "reranker": {
        "provider": "cohere",
        "config": {
-            "model": "rerank-v3.5",
+            "model": "rerank-english-v3.0",
            "api_key": "your-cohere-api-key",
            "top_k": 10,
            "return_documents": True
@@ -286,7 +164,7 @@ config = {
    "reranker": {
        "provider": "cohere",
        "config": {
-            "model": "rerank-v3.5",
+            "model": "rerank-english-v3.0",
            "api_key": "your-cohere-api-key",
            "top_k": 15,
            "return_documents": True
@@ -460,7 +338,7 @@ config = {
    "reranker": {
        "provider": "cohere",
        "config": {
-            "model": "rerank-v3.5",
+            "model": "rerank-english-v3.0",
            "api_key": "your-cohere-api-key"
        }
    }
@@ -35,7 +35,8 @@ pip install mem0ai
 from mem0 import Memory

 m = Memory()
-```
+
+````
 </Step>

 <Step title="Add a memory">
@@ -45,7 +46,7 @@ messages = [
    {"role": "assistant", "content": "Hey Alex! I'll remember your interests."}
 ]
 m.add(messages, user_id="alex")
-```
+````

 </Step>

@@ -1232,7 +1232,7 @@
 				"tags": [
 					"memories"
 				],
-				"description": "Delete memories by filter. At least one filter is required. Previously, omitting all filters silently deleted everything; now it returns a validation error.",
+				"description": "Delete memories by filter. At least one filter is required — previously omitting all filters silently deleted everything; now it returns a validation error.",
 				"operationId": "memories_delete_all",
 				"parameters": [
 					{
@@ -1315,15 +1315,15 @@
 				"x-code-samples": [
 					{
 						"lang": "Python",
-						"source": "# To use the Python SDK, install the package:\n# pip install mem0ai\n\nfrom mem0 import MemoryClient\nclient = MemoryClient(api_key=\"your_api_key\")\n\n# Delete all memories for a specific user\nclient.delete_all(user_id=\"<user_id>\")\n\n# Delete all memories for every user in the project (wildcard)\nclient.delete_all(user_id=\"*\")\n\n# Full project wipe: all four filters must be explicitly set to \"*\"\nclient.delete_all(user_id=\"*\", agent_id=\"*\", app_id=\"*\", run_id=\"*\")\n\n# NOTE: Calling delete_all() with no filters raises a validation error.\n# At least one filter is required to prevent accidental data loss."
+						"source": "# To use the Python SDK, install the package:\n# pip install mem0ai\n\nfrom mem0 import MemoryClient\nclient = MemoryClient(api_key=\"your_api_key\")\n\n# Delete all memories for a specific user\nclient.delete_all(user_id=\"<user_id>\")\n\n# Delete all memories for every user in the project (wildcard)\nclient.delete_all(user_id=\"*\")\n\n# Full project wipe — all four filters must be explicitly set to \"*\"\nclient.delete_all(user_id=\"*\", agent_id=\"*\", app_id=\"*\", run_id=\"*\")\n\n# NOTE: Calling delete_all() with no filters raises a validation error.\n# At least one filter is required to prevent accidental data loss."
 					},
 					{
 						"lang": "JavaScript",
-						"source": "// To use the JavaScript SDK, install the package:\n// npm i mem0ai\n\nimport MemoryClient from 'mem0ai';\nconst client = new MemoryClient({ apiKey: \"your-api-key\" });\n\n// Delete all memories for a specific user\nclient.deleteAll({ user_id: \"<user_id>\" })\n  .then(result => console.log(result))\n  .catch(error => console.error(error));\n\n// Delete all memories for every user in the project (wildcard)\nclient.deleteAll({ user_id: \"*\" })\n  .then(result => console.log(result))\n  .catch(error => console.error(error));\n\n// Full project wipe: all four filters must be explicitly set to \"*\"\nclient.deleteAll({ user_id: \"*\", agent_id: \"*\", app_id: \"*\", run_id: \"*\" })\n  .then(result => console.log(result))\n  .catch(error => console.error(error));"
+						"source": "// To use the JavaScript SDK, install the package:\n// npm i mem0ai\n\nimport MemoryClient from 'mem0ai';\nconst client = new MemoryClient({ apiKey: \"your-api-key\" });\n\n// Delete all memories for a specific user\nclient.deleteAll({ user_id: \"<user_id>\" })\n  .then(result => console.log(result))\n  .catch(error => console.error(error));\n\n// Delete all memories for every user in the project (wildcard)\nclient.deleteAll({ user_id: \"*\" })\n  .then(result => console.log(result))\n  .catch(error => console.error(error));\n\n// Full project wipe — all four filters must be explicitly set to \"*\"\nclient.deleteAll({ user_id: \"*\", agent_id: \"*\", app_id: \"*\", run_id: \"*\" })\n  .then(result => console.log(result))\n  .catch(error => console.error(error));"
 					},
 					{
 						"lang": "cURL",
-						"source": "# Delete memories for a specific user\ncurl --request DELETE \\\n  --url 'https://api.mem0.ai/v1/memories/?user_id=<user_id>' \\\n  --header 'Authorization: Token <api-key>'\n\n# Delete memories for all users (wildcard)\ncurl --request DELETE \\\n  --url 'https://api.mem0.ai/v1/memories/?user_id=*' \\\n  --header 'Authorization: Token <api-key>'\n\n# Full project wipe: all four filters must be set to *\ncurl --request DELETE \\\n  --url 'https://api.mem0.ai/v1/memories/?user_id=*&agent_id=*&app_id=*&run_id=*' \\\n  --header 'Authorization: Token <api-key>'"
+						"source": "# Delete memories for a specific user\ncurl --request DELETE \\\n  --url 'https://api.mem0.ai/v1/memories/?user_id=<user_id>' \\\n  --header 'Authorization: Token <api-key>'\n\n# Delete memories for all users (wildcard)\ncurl --request DELETE \\\n  --url 'https://api.mem0.ai/v1/memories/?user_id=*' \\\n  --header 'Authorization: Token <api-key>'\n\n# Full project wipe — all four filters must be set to *\ncurl --request DELETE \\\n  --url 'https://api.mem0.ai/v1/memories/?user_id=*&agent_id=*&app_id=*&run_id=*' \\\n  --header 'Authorization: Token <api-key>'"
 					},
 					{
 						"lang": "Go",
@@ -1740,7 +1740,7 @@
 					"memories"
 				],
 				"summary": "Get all memories (V3, paginated)",
-				"description": "List memories scoped by filters, paginated. Entity IDs **must** be passed inside the `filters` object. Top-level `user_id` / `agent_id` / `run_id` are rejected with 400. `filters` supports the same operator set as V2 search (`AND`, `OR`, `NOT`, `in`, `gte`, `lte`, etc.). Response is a paginated envelope; pass `page` and `page_size` as query parameters to step through results.",
+				"description": "List memories scoped by filters, paginated. Entity IDs **must** be passed inside the `filters` object — top-level `user_id` / `agent_id` / `run_id` are rejected with 400. `filters` supports the same operator set as V2 search (`AND`, `OR`, `NOT`, `in`, `gte`, `lte`, etc.). Response is a paginated envelope; pass `page` and `page_size` as query parameters to step through results.",
 				"operationId": "memories_list_v3",
 				"parameters": [
 					{
@@ -1896,10 +1896,10 @@
 						}
 					},
 					"400": {
-						"description": "Validation error, e.g. empty `filters` or no positively-scoped entity ID."
+						"description": "Validation error — e.g. empty `filters` or no positively-scoped entity ID."
 					},
 					"401": {
-						"description": "Unauthorized: missing or invalid API key."
+						"description": "Unauthorized — missing or invalid API key."
 					}
 				},
 				"security": [
@@ -1992,17 +1992,6 @@
 										"type": "string",
 										"description": "Project-level instructions that guide extraction for this call."
 									},
-									"custom_categories": {
-										"type": "array",
-										"description": "Category catalog for this call. Replaces the project-level list rather than merging with it. Omit to fall back to the project list, then the default catalog.",
-										"items": {
-											"type": "object",
-											"additionalProperties": {
-												"type": "string"
-											},
-											"description": "Maps a category name to the description the classifier matches against."
-										}
-									},
 									"infer": {
 										"type": "boolean",
 										"default": true,
@@ -2018,7 +2007,7 @@
 									},
 									{
 										"role": "assistant",
-										"content": "Got it, I'll update your location."
+										"content": "Got it — I'll update your location."
 									}
 								],
 								"user_id": "alice"
@@ -2035,8 +2024,7 @@
 									"type": "object",
 									"properties": {
 										"message": {
-											"type": "string",
-											"description": "Only present when `infer` is `false`, where processing is synchronous."
+											"type": "string"
 										},
 										"status": {
 											"type": "string",
@@ -2053,17 +2041,18 @@
 									}
 								},
 								"example": {
-									"event_id": "2c4d1f44-4f7b-4b2f-9f6e-7b5b4f5a1234",
-									"status": "PENDING"
+									"message": "Memory processing has been queued for background execution",
+									"status": "PENDING",
+									"event_id": "2c4d1f44-4f7b-4b2f-9f6e-7b5b4f5a1234"
 								}
 							}
 						}
 					},
 					"400": {
-						"description": "Validation error, e.g. missing `messages` or no entity ID supplied."
+						"description": "Validation error — e.g. missing `messages` or no entity ID supplied."
 					},
 					"401": {
-						"description": "Unauthorized: missing or invalid API key."
+						"description": "Unauthorized — missing or invalid API key."
 					}
 				},
 				"security": [
@@ -2074,15 +2063,15 @@
 				"x-codeSamples": [
 					{
 						"lang": "cURL",
-						"source": "curl -X POST https://api.mem0.ai/v3/memories/add/ \\\n  -H \"Authorization: Token <api-key>\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"messages\": [\n      {\"role\": \"user\", \"content\": \"I just moved to San Francisco from New York.\"},\n      {\"role\": \"assistant\", \"content\": \"Got it, I\\u0027ll update your location.\"}\n    ],\n    \"user_id\": \"alice\"\n  }'"
+						"source": "curl -X POST https://api.mem0.ai/v3/memories/add/ \\\n  -H \"Authorization: Token <api-key>\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"messages\": [\n      {\"role\": \"user\", \"content\": \"I just moved to San Francisco from New York.\"},\n      {\"role\": \"assistant\", \"content\": \"Got it — I\\u0027ll update your location.\"}\n    ],\n    \"user_id\": \"alice\"\n  }'"
 					},
 					{
 						"lang": "Python",
-						"source": "from mem0 import MemoryClient\n\nclient = MemoryClient(api_key=\"your-api-key\")\n\nresult = client.add(\n    messages=[\n        {\"role\": \"user\", \"content\": \"I just moved to San Francisco from New York.\"},\n        {\"role\": \"assistant\", \"content\": \"Got it, I'll update your location.\"}\n    ],\n    user_id=\"alice\",\n)\nprint(result)"
+						"source": "from mem0 import MemoryClient\n\nclient = MemoryClient(api_key=\"your-api-key\")\n\nresult = client.add(\n    messages=[\n        {\"role\": \"user\", \"content\": \"I just moved to San Francisco from New York.\"},\n        {\"role\": \"assistant\", \"content\": \"Got it — I'll update your location.\"}\n    ],\n    user_id=\"alice\",\n)\nprint(result)"
 					},
 					{
 						"lang": "JavaScript",
-						"source": "import MemoryClient from \"mem0ai\";\n\nconst client = new MemoryClient({ apiKey: \"your-api-key\" });\n\nconst result = await client.add(\n  [\n    { role: \"user\", content: \"I just moved to San Francisco from New York.\" },\n    { role: \"assistant\", content: \"Got it, I'll update your location.\" },\n  ],\n  { userId: \"alice\" }\n);\nconsole.log(result);"
+						"source": "import MemoryClient from \"mem0ai\";\n\nconst client = new MemoryClient({ apiKey: \"your-api-key\" });\n\nconst result = await client.add(\n  [\n    { role: \"user\", content: \"I just moved to San Francisco from New York.\" },\n    { role: \"assistant\", content: \"Got it — I'll update your location.\" },\n  ],\n  { userId: \"alice\" }\n);\nconsole.log(result);"
 					}
 				]
 			}
@@ -2093,7 +2082,7 @@
 					"memories"
 				],
 				"summary": "Search memories (V3)",
-				"description": "Relevance-ranked search across stored memories. V3 uses hybrid retrieval and can also apply temporal reasoning for time-aware queries. Entity IDs **must** be passed inside the `filters` object. Top-level `user_id` / `agent_id` / `run_id` are rejected with 400. At least one entity ID is required.",
+				"description": "Relevance-ranked search across stored memories. V3 uses hybrid retrieval and can also apply temporal reasoning for time-aware queries. Entity IDs **must** be passed inside the `filters` object — top-level `user_id` / `agent_id` / `run_id` are rejected with 400. At least one entity ID is required.",
 				"operationId": "memories_search_v3",
 				"requestBody": {
 					"required": true,
@@ -2247,10 +2236,10 @@
 						}
 					},
 					"400": {
-						"description": "Validation error, e.g. empty `query`, missing `filters`, or no positively-scoped entity ID."
+						"description": "Validation error — e.g. empty `query`, missing `filters`, or no positively-scoped entity ID."
 					},
 					"401": {
-						"description": "Unauthorized: missing or invalid API key."
+						"description": "Unauthorized — missing or invalid API key."
 					}
 				},
 				"security": [
@@ -27,7 +27,7 @@ pip install mem0ai
 </Step>
 <Step title="Export your API key">
 ```bash
-export MEM0_API_KEY="m0-..."
+export MEM0_API_KEY="sk-platform-..."
 ```
 </Step>
 <Step title="Create an async client">
@@ -49,7 +49,7 @@ npm install mem0ai
 </Step>
 <Step title="Load your API key">
 ```bash
-export MEM0_API_KEY="m0-..."
+export MEM0_API_KEY="sk-platform-..."
 ```
 </Step>
 <Step title="Instantiate the client">
@@ -129,7 +129,7 @@ matches = await memory.search(
 ```python
 await memory.update(
    memory_id=matches["results"][0]["id"],
-    text="Morgan avoids shellfish and prefers boutique hotels in central Tokyo.",
+    data="Morgan avoids shellfish and prefers boutique hotels in central Tokyo.",
 )
 ```
 </Step>
@@ -158,8 +158,6 @@ await memory.update(matches.results[0].id, {

 ## Clean up

-Scope each delete call to a single entity id where possible; combining more than one entity filter in the same `delete_all` call is not guaranteed to AND them together today.
-
 <Tabs>
  <Tab title="Python">
 <Steps>
@@ -1,19 +1,13 @@
 ---
 title: Sign up as an agent
-description: "Let an AI agent create its own Mem0 account and API key in four commands, with no email or dashboard needed."
+description: "Zero-friction signup for AI agents: mint a working Mem0 API key in under five seconds. No email, no dashboard, no OTP."
 icon: "robot"
 iconType: "solid"
 ---

-Normally a person signs up for Mem0, gets an API key from the dashboard, and hands it to their tools. Agent Mode skips that: a coding agent creates its own account from the terminal and starts storing memories immediately. A human can take ownership of that account later, without disrupting the agent.
+If you're an AI agent (Claude Code, Cursor, Codex, Cline, Aider, Windsurf, or any other autonomous coding tool), you don't need a human to set up Mem0 for you. Four commands (install, sign up, push a memory, search it) and you have persistent memory.

-This page is written for both readers. The setup below is for the agent; [Claiming the account](#claiming-the-account-for-the-human-owner) is for the person who ends up owning it.
-
-<Note>
-**If you're an AI agent** (Claude Code, Cursor, Codex, Cline, Aider, Windsurf, or any other coding tool), run the four commands below. You don't need a human to set Mem0 up for you.
-</Note>
-
-## Sign up in four commands
+## The 4-command demo

 <CodeGroup>
 ```bash npm
@@ -23,11 +17,11 @@ npm install -g @mem0/cli
 # 2. Sign up as an agent (replace `claude-code` with your name)
 mem0 init --agent --agent-caller claude-code

-# 3. Store a memory
-mem0 add "Prefers TypeScript over JavaScript for new projects"
+# 3. Push a memory
+mem0 add "I am using mem0"

-# 4. Search it back
-mem0 search "what language should I use"
+# 4. Verify
+mem0 search "am I using mem0"
 ```

 ```bash pip
@@ -37,22 +31,14 @@ pip install mem0-cli
 # 2. Sign up as an agent (replace `claude-code` with your name)
 mem0 init --agent --agent-caller claude-code

-# 3. Store a memory
-mem0 add "Prefers TypeScript over JavaScript for new projects"
+# 3. Push a memory
+mem0 add "I am using mem0"

-# 4. Search it back
-mem0 search "what language should I use"
+# 4. Verify
+mem0 search "am I using mem0"
 ```
 </CodeGroup>

-Step 2 prints your new account's default scope and how to hand the account to a human later:
-
-```
-✓ Agent Mode active. Default user_id: swift-otter-4821
-
-  To claim this account later: mem0 init --email <your-email>
-```
-
 That's it. Under 30 seconds from a fresh shell to a working memory store.

 ## Identify yourself with `--agent-caller`
@@ -60,17 +46,17 @@ That's it. Under 30 seconds from a fresh shell to a working memory store.
 Replace `claude-code` in the `init` command with your actual agent name (`cursor`, `codex`, `cline`, `aider`, or any short label). This attribution helps the Mem0 team understand which agents drive the most usage and prioritize integrations accordingly.

 <Tip>
-**Forgot `--agent-caller` when you ran `init`?** Run `mem0 identify <your-name>` afterwards. It keeps the same API key and just fills in the name, so it is safe to run more than once.
+**Forgot `--agent-caller` on init?** Run `mem0 identify <your-name>` after the fact, same key, just back-fills the attribution. Idempotent.
 </Tip>

 ## What happened when you ran `mem0 init --agent`?

- A new Mem0 account was created with its own API key.
- The key was saved to `~/.mem0/config.json` on that machine, readable only by you (file mode `0600`).
- A default `user_id` was created, so `mem0 add` and `mem0 search` work without you passing scope flags.
- **Nobody owns the account yet.** It works, but no human is attached to it until someone claims it.
+- A **shadow account** was minted on the Mem0 backend with a fresh API key.
+- The key was saved to `~/.mem0/config.json` (mode 0600) on the machine you ran the command on.
+- A default `user_id` was provisioned so `mem0 add` / `mem0 search` work without explicit scope flags.
+- The account is **unclaimed**; no human owns it yet.

-Every command run with this key prints a one-line reminder to pass the claim instructions on to your human.
+Every command run against this key prints a one-line reminder asking you to surface the claim instructions to your human user.

 ## Claiming the account (for the human owner)

@@ -80,7 +66,7 @@ When the human is ready to take ownership of the agent's account (and all the me
 mem0 init --email you@yourcompany.com
 ```

-The CLI spots the existing Agent Mode setup, emails a verification code, and upgrades the account in place:
+The CLI detects the existing Agent Mode config, sends a verification code, and upgrades the shadow account in-place:

 - **The API key never changes**, so the agent isn't disrupted.
 - **All memories transfer** to the human's account.
@@ -90,33 +76,31 @@ Pass `--code 123456` to skip the interactive code prompt for fully non-interacti

 ## Rate limits and quotas

-Agent Mode signups are limited to **5 per day per IP address** to prevent abuse. If you hit the limit, the CLI tells you so:
+Agent Mode signups are rate-limited to **5 per day per IP address** to prevent abuse. If you hit the limit, the CLI returns:

 ```
 Daily Agent Mode signup limit reached for this network (5/day).
 Try again from a different IP or after midnight UTC.
 ```

-Calling the API directly instead of through the CLI returns a plain `403 Forbidden` with no explanation and no `Retry-After` header, so handle that case yourself.
-
-Until someone claims it, an agent account gets the standard Mem0 free-tier quotas. The human owner can upgrade after claiming.
+Unclaimed agent accounts get the standard Mem0 free-tier quotas. The human owner can upgrade after claiming.

 ## What's next

 <CardGroup cols={2}>
-<Card title="CLI reference" icon="terminal" href="/platform/cli">
-Every command in full: `mem0 add`, `mem0 search`, `mem0 list`, and the rest.
+<Card title="CLI Reference" icon="terminal" href="/platform/cli">
+Full command-by-command reference for `mem0 add`, `mem0 search`, `mem0 list`, and the rest.
 </Card>

-<Card title="Memory operations" icon="database" href="/core-concepts/memory-operations/add">
+<Card title="Memory Operations" icon="database" href="/core-concepts/memory-operations/add">
 How `add`, `search`, `update`, and `delete` work under the hood.
 </Card>

 <Card title="Mem0 MCP" icon="plug" href="/platform/mem0-mcp">
-Give your agent memory as a set of tools instead of shell commands.
+Connect agents to Mem0 via the Model Context Protocol, as an alternative integration path.
 </Card>

-<Card title="Platform overview" icon="star" href="/platform/overview">
-Everything the account unlocks once a human claims it.
+<Card title="Platform Overview" icon="star" href="/platform/overview">
+The full Mem0 Platform feature set once you claim your account.
 </Card>
 </CardGroup>
@@ -9,7 +9,9 @@ The mem0 CLI lets you add, search, list, update, and delete memories directly fr

 Both implementations provide identical behavior: same commands, same options, same output formats.

-Running it inside an agent? Put `--agent` before any command to get clean JSON instead of human output. See [Use with AI agents](#use-with-ai-agents).
+<Tip>
+**Built for AI agents.** Pass `--agent` (or `--json`) as a global flag on any command to get structured JSON output optimized for programmatic consumption: sanitized fields, no colors or spinners, and errors as JSON too. Drop it into any agent tool loop with zero extra parsing.
+</Tip>

 ## Installation

@@ -95,8 +97,6 @@ mem0 init --api-key m0-xxx --user-id alice --force
 | `-u, --user-id` | Default user ID (skip prompt) |
 | `--email` | Login via email verification code |
 | `--code` | Verification code (use with `--email` for non-interactive login) |
-| `--agent` | Create an Agent Mode account, with no email needed |
-| `--agent-caller` | Name the agent running the command, such as `claude-code` |
 | `--force` | Overwrite existing config without confirmation |

 <Note>
@@ -117,15 +117,10 @@ echo "Loves hiking on weekends" | mem0 add --user-id alice
 |------|-------------|
 | `-u, --user-id` | Scope to a user |
 | `--agent-id` | Scope to an agent |
-| `--app-id` | Scope to an app |
-| `--run-id` | Scope to a single run or session |
 | `--messages` | Conversation messages as JSON |
 | `-f, --file` | Read messages from a JSON file |
 | `-m, --metadata` | Custom metadata as JSON |
 | `--categories` | Categories (JSON array or comma-separated) |
-| `--expires` | Expiration date, after which the memory stops being returned |
-| `--immutable` | Store the memory so it can never be updated or overwritten |
-| `--no-infer` | Store the text exactly as given, skipping fact extraction |
 | `-o, --output` | Output format: `text`, `json`, `quiet` |

 ### `mem0 search`
@@ -140,15 +135,11 @@ mem0 search "preferred tools" --user-id alice --output json --top-k 5
 | Flag | Description |
 |------|-------------|
 | `-u, --user-id` | Filter by user |
-| `--agent-id` | Filter by agent |
-| `--app-id` | Filter by app |
-| `--run-id` | Filter by run or session |
-| `-k, --top-k` | Number of results (default: 10). `--limit` does the same thing |
+| `-k, --top-k` | Number of results (default: 10) |
 | `--threshold` | Minimum similarity score (default: 0.3) |
 | `--rerank` | Enable reranking |
 | `--keyword` | Use keyword search instead of semantic |
 | `--filter` | Advanced filter expression (JSON) |
-| `--fields` | Return only the named fields |
 | `-o, --output` | Output format: `text`, `json`, `table` |

 ### `mem0 list`
@@ -164,9 +155,6 @@ mem0 list --user-id alice --after 2024-01-01 --page-size 50
 | Flag | Description |
 |------|-------------|
 | `-u, --user-id` | Filter by user |
-| `--agent-id` | Filter by agent |
-| `--app-id` | Filter by app |
-| `--run-id` | Filter by run or session |
 | `--page` | Page number (default: 1) |
 | `--page-size` | Results per page (default: 100) |
 | `--category` | Filter by category |
@@ -183,10 +171,6 @@ mem0 get 7b3c1a2e-4d5f-6789-abcd-ef0123456789
 mem0 get 7b3c1a2e-4d5f-6789-abcd-ef0123456789 --output json
 ```

-| Flag | Description |
-|------|-------------|
-| `-o, --output` | Output format: `text`, `json` |
-
 ### `mem0 update`

 Update the text or metadata of an existing memory.
@@ -197,11 +181,6 @@ mem0 update <memory-id> --metadata '{"priority": "high"}'
 echo "new text" | mem0 update <memory-id>
 ```

-| Flag | Description |
-|------|-------------|
-| `-m, --metadata` | Replace the memory's metadata with this JSON |
-| `-o, --output` | Output format: `text`, `json`, `quiet` |
-
 ### `mem0 delete`

 Delete a single memory, all memories for a scope, or an entire entity.
@@ -222,10 +201,6 @@ mem0 delete --all --user-id alice --dry-run

 | Flag | Description |
 |------|-------------|
-| `-u, --user-id` | Scope the deletion to a user |
-| `--agent-id` | Scope the deletion to an agent |
-| `--app-id` | Scope the deletion to an app |
-| `--run-id` | Scope the deletion to a run or session |
 | `--all` | Delete all memories matching scope filters |
 | `--entity` | Delete the entity and all its memories |
 | `--project` | With `--all`: delete all memories project-wide |
@@ -242,12 +217,6 @@ mem0 import data.json --user-id alice

 The file should be a JSON array where each item has a `memory` (or `text` or `content`) field and optional `user_id`, `agent_id`, and `metadata` fields.

-| Flag | Description |
-|------|-------------|
-| `-u, --user-id` | Default user for items that do not set their own |
-| `--agent-id` | Default agent for items that do not set their own |
-| `-o, --output` | Output format: `text`, `json` |
-
 ### `mem0 config`

 View or modify the local CLI configuration.
@@ -268,18 +237,6 @@ mem0 entity list agents --output json
 mem0 entity delete --user-id alice --force
 ```

-Deleting an entity removes it and every memory belonging to it. Preview first with `--dry-run`.
-
-| Flag | Description |
-|------|-------------|
-| `-u, --user-id` | The user to delete |
-| `--agent-id` | The agent to delete |
-| `--app-id` | The app to delete |
-| `--run-id` | The run or session to delete |
-| `--dry-run` | Show what would be deleted, without deleting it |
-| `--force` | Skip confirmation prompt |
-| `-o, --output` | Output format: `text`, `json` |
-
 ### `mem0 event`

 Inspect background processing events created by async operations (e.g. bulk deletes, large add jobs).
@@ -304,66 +261,47 @@ Verify your API connection and display the current project.
 mem0 status
 ```

-### `mem0 whoami`
+### `mem0 version`

-Print the identity the CLI is currently using. After `mem0 init --agent`, the
-server issues an identifier (`default_user_id`, for example
-`user_a1b2c3d4e5f6`) and the CLI stores it in `~/.mem0/config.json`. That value
-is the agent's stable identity across runs, and it is the row key on the
-[AGENTRUSH leaderboard](https://mem0.ai/agentrush).
+Print the CLI version.

-Use this instead of parsing the config file by hand:
+```bash
+mem0 version
+```
+
+## Identity helper: `mem0 whoami`
+
+After running `mem0 init --agent`, the CLI persists a server-issued identifier
+(`default_user_id`, e.g. `user_a1b2c3d4e5f6`) in `~/.mem0/config.json`. This
+value is the agent's stable identity.
+
+Print it without parsing the config file by hand:

 ```bash
 mem0 whoami
-# Your AGENTRUSH identifier:  user_a1b2c3d4e5f6
-# Find your row at https://mem0.ai/agentrush
+# Your user_id:  user_a1b2c3d4e5f6
 ```

 No network call. The command exits with code `1` if no `default_user_id` is
 configured yet. In that case, run `mem0 init --agent` first.

-### `mem0 identify`
-
-Attach an agent name to an account created with `mem0 init --agent`, if you did
-not pass `--agent-caller` at the time. It keeps the same API key and only fills
-in the name, so it is safe to run more than once. See
-[Sign up as an agent](/platform/agent-signup).
-
-### `mem0 help`
-
-Print the command tree. Adding `--json` returns the whole tree as structured
-data, so an agent can discover the available commands and options for itself.

 ## Output formats

-Most commands take `-o, --output` to control how results are displayed. Not every command accepts every format:
+All commands support the `--output` flag to control how results are displayed:

-| Format | Description | Accepted by |
-|--------|-------------|-------------|
-| `text` | Human-readable output with colors and formatting. The default everywhere except `list` | every command |
-| `json` | Structured JSON, suitable for piping to `jq` | every command |
-| `table` | Tabular format, and the default for `list` | `search`, `list` |
-| `quiet` | Minimal output: just IDs or status codes | `add`, `update`, `delete` |
+| Format | Description |
+|--------|-------------|
+| `text` | Human-readable output with colors and formatting (default for most commands) |
+| `json` | Structured JSON, suitable for piping to `jq` or consumption by AI agents |
+| `table` | Tabular format (default for `list`) |
+| `quiet` | Minimal output: just IDs or status codes |
+| `agent` | Structured JSON envelope with sanitized fields, set automatically by `--json`/`--agent` |

-Passing a format a command does not accept is an error, so check the command's own flag table above.
-
-There is no `agent` value for `--output`. Agent mode is turned on by the global `--json`/`--agent` flag placed before the command name, and it overrides `--output`. See [Use with AI agents](#use-with-ai-agents).
-
-The exact JSON shape depends on the command. `search` returns a bare array of memories, while `list` returns an envelope object with the memories under `data`:
+Example with JSON output:

 ```bash
-# search: results are the top-level array
-mem0 search "user preferences" --user-id alice --output json | jq '.[].memory'
-
-# list: results are nested under .data
-mem0 list --user-id alice --output json | jq '.data[].memory'
-```
-
-Agent mode always returns the envelope, whichever command you run, so `.data[]` works everywhere:
-
-```bash
-mem0 --agent search "user preferences" --user-id alice | jq '.data[].memory'
+mem0 search "user preferences" --user-id alice --output json | jq '.data.results[].memory'
 ```

 ## Use with AI agents
@@ -417,22 +355,6 @@ Two other agent-friendly features:

 For non-interactive environments (CI, agent runtimes), set credentials via `mem0 init --api-key m0-xxx --user-id alice --force` or the `MEM0_API_KEY` environment variable.

-## Errors and exit codes
-
-Every command exits `0` when it succeeds and `1` when it fails, so `if mem0 ...; then` works as you would expect in a script. In agent mode (`--json` or `--agent`), failures still print a JSON envelope to stdout alongside the non-zero exit, so you can parse successes and failures the same way.
-
-Errors you are most likely to hit:
-
-| Message | Cause | Fix |
-|---------|-------|-----|
-| `Authentication failed` | The API key is missing, wrong, or revoked | Run `mem0 init` again, or get a new key from the dashboard |
-| `No content provided. Pass text, --messages, --file, or pipe via stdin.` | `mem0 add` was called with nothing to store | Give it text, a file, or piped input |
-| `Invalid JSON in --messages` / `--metadata` / `--filter` | The JSON value would not parse | Check the quoting, especially inside shell single quotes |
-| `Invalid date format for --expires. Use YYYY-MM-DD` | `--expires` got something other than a date | Use `YYYY-MM-DD`, and make sure the date is in the future |
-| `--threshold must be between 0.0 and 1.0.` | Score threshold out of range | Pass a value between `0.0` and `1.0` |
-
-Run [`mem0 status`](#mem0-status) to check whether the CLI can reach the API and which project the key belongs to. It reports the connection state, the API URL, and any error.
-
 ## Environment variables

 | Variable | Description |
@@ -443,28 +365,17 @@ Run [`mem0 status`](#mem0-status) to check whether the CLI can reach the API and
 | `MEM0_AGENT_ID` | Default agent ID |
 | `MEM0_APP_ID` | Default app ID |
 | `MEM0_RUN_ID` | Default run ID |
-| `MEM0_TELEMETRY` | Set to `false` to turn off usage telemetry. On by default |

 Environment variables take precedence over values in the config file, which take precedence over defaults.

 ## Global flags

-These two flags belong to `mem0` itself, so they go **before** the command name:
+These flags are available on all commands:

 | Flag | Description |
 |------|-------------|
 | `--json` | Enable agent mode: structured JSON envelope output, no colors or spinners |
 | `--agent` | Alias for `--json` |
-| `--version` | Print the CLI version and exit |
-
-<Warning>
-On `init` only, `--agent` means something different. `mem0 init --agent` creates an Agent Mode account (see [Sign up as an agent](/platform/agent-signup)); it does not switch the output to JSON. To get JSON from `init`, put the flag first: `mem0 --json init`.
-</Warning>
-
-The following flags are accepted by most commands, but they belong to the command, so they go **after** the command name:
-
-| Flag | Description |
-|------|-------------|
 | `--api-key` | Override the configured API key for this request |
 | `--base-url` | Override the configured API base URL for this request |
 | `-o, --output` | Set the output format |
@@ -476,11 +387,11 @@ The following flags are accepted by most commands, but they belong to the comman
 Store your first memory in under five minutes using the SDK or CLI
 </Card>

-<Card title="Memory operations" icon="database" href="/core-concepts/memory-operations/add">
+<Card title="Memory Operations" icon="database" href="/core-concepts/memory-operations/add">
 Learn about add, search, update, and delete operations in depth
 </Card>

-<Card title="API reference" icon="code" href="/api-reference/memory/add-memories">
+<Card title="API Reference" icon="code" href="/api-reference/memory/add-memories">
 See the complete REST API documentation
 </Card>
 </CardGroup>
@@ -17,7 +17,7 @@ iconType: "solid"
  </Accordion>

    <Accordion title="What are the key features of Mem0?">
-        - **User, Agent, App, and Run Memory**: Scopes memories to the individual, AI agent, application, and conversation/session they belong to, ensuring continuity and context.
+        - **User, Session, and AI Agent Memory**: Retains information across sessions and interactions for users and AI agents, ensuring continuity and context.
        - **Adaptive Personalization**: Continuously updates memories based on user interactions and feedback.
        - **Developer-Friendly API**: Offers a straightforward API for seamless integration into various applications.
        - **Platform Consistency**: Ensures consistent behavior and data across different platforms and devices.
@@ -84,12 +84,10 @@ iconType: "solid"
        - Include specific examples or cases rather than general definitions
    </Accordion>

-    <Accordion title="How do I configure Mem0 for AWS Lambda? (self-hosted / OSS only)">
-        This applies only if you are running the self-hosted OSS `Memory` class yourself (for example with a local vector store) inside a Lambda function. If you're using the hosted Mem0 Platform (`MemoryClient`), there is nothing to configure here: the Platform stores all memory data on Mem0's servers, not on your Lambda instance's filesystem, so Lambda's `/tmp`-only write restriction does not apply to you.
+    <Accordion title="How do I configure Mem0 for AWS Lambda?">
+        When deploying Mem0 on AWS Lambda, you'll need to modify the storage directory configuration due to Lambda's file system restrictions. By default, Lambda only allows writing to the `/tmp` directory.

-        When deploying self-hosted Mem0 on AWS Lambda, you'll need to modify the storage directory configuration due to Lambda's file system restrictions. By default, Lambda only allows writing to the `/tmp` directory.
-
-        To configure self-hosted Mem0 for AWS Lambda, set the `MEM0_DIR` environment variable to point to a writable directory in `/tmp`:
+        To configure Mem0 for AWS Lambda, set the `MEM0_DIR` environment variable to point to a writable directory in `/tmp`:

        ```bash
        MEM0_DIR=/tmp/.mem0
@@ -151,7 +149,7 @@ iconType: "solid"
        2. Go to **Settings → Account**.
        3. Click **Delete account** and confirm.

-        Deletion is asynchronous: the request is accepted immediately and processed in the background, typically within a few minutes. Once it completes, the following is removed:
+        Deletion is immediate and irreversible. The following is removed:

        - Your user profile and login credentials
        - All memories, agents, and runs you created
@@ -159,7 +157,7 @@ iconType: "solid"
        - Organizations you solely own, along with their data
        - Your membership in any shared organizations (the orgs themselves are not affected)

-        Your old API keys stop working once deletion completes, not the instant you click confirm. If you'd like to use Mem0 again later, you can create a new account at any time: it will start fresh with no data carried over.
+        Any application still using your old API keys will start receiving `401 Unauthorized` responses immediately. If you'd like to use Mem0 again later, you can create a new account at any time: it will start fresh with no data carried over.
    </Accordion>

 </AccordionGroup>
@@ -51,7 +51,7 @@ results = client.search(
 # Smart home assistant finding device preferences
 results = client.search(
    query="How do I like my bedroom temperature?",
-    rerank=True,           # Closest-matching preferences first
+    rerank=True,           # Get most recent preferences first
    filters={"user_id": "user123"},
 )

@@ -86,7 +86,7 @@ results = client.search(
 # Find learning progress for specific topics
 results = client.search(
    query="Python programming progress and difficulties",
-    rerank=True,           # Closest-matching progress notes first
+    rerank=True,           # Recent progress first
    filters={"user_id": "student123"},
 )

@@ -108,13 +108,21 @@ def quick_search(query, user_id):
        filters={"user_id": user_id},
    )

-# Reranked search - good when result order matters
+# Reranked search - good for most applications
 def standard_search(query, user_id):
    return client.search(
        query=query,
        rerank=True,
        filters={"user_id": user_id},
    )
+
+# Reranked search - good for critical applications
+def precise_search(query, user_id):
+    return client.search(
+        query=query,
+        rerank=True,
+        filters={"user_id": user_id},
+    )
 ```

 ```javascript JavaScript
@@ -125,13 +133,21 @@ function quickSearch(query, userId) {
    });
 }

-// Reranked search - good when result order matters
+// Reranked search - good for most applications
 function standardSearch(query, userId) {
    return client.search(query, {
        filters: { user_id: userId },
        rerank: true,
    });
 }
+
+// Reranked search - good for critical applications
+function preciseSearch(query, userId) {
+    return client.search(query, {
+        filters: { user_id: userId },
+        rerank: true,
+    });
+}
 ```
 </CodeGroup>

@@ -66,7 +66,7 @@ await client.search("What is Alice's favorite sport?", filters={"user_id": "alic
 ```

 ```javascript JavaScript
-await client.search("What is Alice's favorite sport?", { filters: { user_id: "alice" } });
+await client.search("What is Alice's favorite sport?", { filters: { userId: "alice" } });
 ```

 </CodeGroup>
@@ -124,7 +124,7 @@ await client.deleteAll({ userId: "alice" });
 </CodeGroup>

 <Note>
-  At least one filter (`user_id`, `agent_id`, `app_id`, or `run_id`) is required: calling `delete_all` with no filters raises an error to prevent accidental data loss. You can pass `"*"` as a value to delete all memories for a given entity type (e.g., `user_id="*"` removes memories for every user). A full project wipe requires all four filters set to `"*"`. When multiple entity filters are combined on a single `delete_all` call, only one is currently guaranteed to be honored; scope each call to a single entity id to be safe.
+  At least one filter (`user_id`, `agent_id`, `app_id`, or `run_id`) is required: calling `delete_all` with no filters raises an error to prevent accidental data loss. You can pass `"*"` as a value to delete all memories for a given entity type (e.g., `user_id="*"` removes memories for every user). A full project wipe requires all four filters set to `"*"`.
 </Note>

 ### History
--- a/Show More
+++ b/Show More
				`@@ -1 +0,0 @@`
				`<svg fill="#8F74E0" role="img" viewBox="0 0 93.9 59.4" xmlns="http://www.w3.org/2000/svg"><title>Oracle</title><path d="M30.5,59.4H65c16.4-0.4,29.3-14.1,28.9-30.4C93.5,13.1,80.7,0.4,65,0H30.5C14.1-0.4,0.4,12.5,0,28.9s12.5,30,28.9,30.4C29.4,59.4,29.9,59.4,30.5,59.4 M64.2,48.9h-33c-10.6-0.3-18.9-9.2-18.6-19.8C13,19,21.1,10.8,31.2,10.5h33c10.6-0.3,19.5,8,19.8,18.6c0.3,10.6-8,19.5-18.6,19.8C65,48.9,64.6,48.9,64.2,48.9"/></svg>`