chore(release): bump SDK, CLI, and plugin versions (#6800)

Co-authored-by: mintlify[bot] <109931778+mintlify[bot]@users.noreply.github.com>

.claude-plugin/marketplace.json

@@ -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": "0.2.12"
+      "version": "0.2.14"
     }
   ]
 }

.cursor-plugin/marketplace.json

@@ -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": "0.2.12"
+      "version": "0.2.14"
     }
   ]
 }

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -9,12 +9,10 @@ body:
       label: Component
       description: Which part of mem0 is affected?
       options:
-        - Core / Python SDK
+        - Python SDK
         - TypeScript SDK
-        - Vector Store (Qdrant, PGVector, Redis, Chroma, etc.)
-        - Graph Memory (Neo4j, Memgraph, etc.)
-        - Ollama / Local Models
-        - OpenClaw
+        - Vector Store
+        - Plugin
         - REST API
         - Other
     validations:

.github/ISSUE_TEMPLATE/feature_request.yml

@@ -9,14 +9,11 @@ body:
       label: Component
       description: Which part of mem0 does this relate to?
       options:
-        - Core / Python SDK
+        - Python SDK
         - TypeScript SDK
-        - Vector Store (Qdrant, PGVector, Redis, Chroma, etc.)
-        - Graph Memory (Neo4j, Memgraph, etc.)
-        - Ollama / Local Models
-        - OpenClaw
+        - Vector Store
+        - Plugin
         - REST API
-        - Benchmarks / Evals
         - Other
     validations:
       required: true

.github/advanced-issue-labeler.yml

@@ -1,18 +1,15 @@
-# 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"
+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']

.github/component-keywords.json

@@ -0,0 +1,40 @@
+{
+  "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"
+    ]
+  }
+}

.github/labeler.yml

@@ -0,0 +1,59 @@
+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'

.github/scripts/infer-component-labels.js

@@ -0,0 +1,44 @@
+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 };

.github/scripts/infer-component-labels.test.js

@@ -0,0 +1,102 @@
+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.`);

.github/workflows/ci-gate.yml

@@ -38,8 +38,11 @@ jobs:
       cli_python: ${{ steps.filter.outputs.cli_python }}
       cli_node: ${{ steps.filter.outputs.cli_node }}
       openclaw: ${{ steps.filter.outputs.openclaw }}
+      mem0_plugin: ${{ steps.filter.outputs.mem0_plugin }}
       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
@@ -71,6 +74,11 @@ jobs:
               - 'integrations/openclaw/**'
               - '.github/workflows/openclaw-checks.yml'
               - '.github/workflows/ci-gate.yml'
+            mem0_plugin:
+              - 'integrations/mem0-plugin/**'
+              - '!integrations/mem0-plugin/.opencode-plugin/**'
+              - '.github/workflows/mem0-plugin-checks.yml'
+              - '.github/workflows/ci-gate.yml'
             opencode_plugin:
               - 'integrations/mem0-plugin/.opencode-plugin/**'
               - '.github/workflows/opencode-plugin-checks.yml'
@@ -79,6 +87,13 @@ 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'
@@ -122,6 +137,13 @@ jobs:
     uses: ./.github/workflows/openclaw-checks.yml
     secrets: inherit
 
+  mem0-plugin:
+    name: Mem0 Plugin
+    needs: changes
+    if: needs.changes.outputs.mem0_plugin == 'true'
+    uses: ./.github/workflows/mem0-plugin-checks.yml
+    secrets: inherit
+
   opencode-plugin:
     name: OpenCode Plugin
     needs: changes
@@ -136,6 +158,18 @@ 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
@@ -152,8 +186,11 @@ jobs:
       - cli-python
       - cli-node
       - openclaw
+      - mem0-plugin
       - opencode-plugin
       - pi-agent-plugin
+      - n8n-nodes-mem0
+      - zapier-mem0
       - docs-llms-txt
     if: always()
     runs-on: ubuntu-latest

.github/workflows/ci.yml

@@ -106,7 +106,7 @@ jobs:
         run: |
           pip install --upgrade pip
           pip install -e ".[test,graph,vector_stores,llms,extras]"
-          pip install ruff
+          pip install ruff==0.16.0
       - name: Run Linting
         if: needs.check_changes.outputs.mem0_changed == 'true'
         run: make lint

.github/workflows/issue-labeler.yml

@@ -12,28 +12,56 @@ 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
 
-      - uses: stefanbuck/github-issue-parser@v3
-        id: feature-parser
-        if: contains(github.event.issue.labels.*.name, 'enhancement')
+      - name: Infer component from text when the form was not used
+        uses: actions/github-script@v7
         with:
-          template-path: .github/ISSUE_TEMPLATE/feature_request.yml
+          script: |
+            const {
+              componentLabels,
+              inferComponentLabels,
+              loadKeywords,
+            } = require(`${process.env.GITHUB_WORKSPACE}/.github/scripts/infer-component-labels.js`);
 
-      - 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: ${{ secrets.GITHUB_TOKEN }}
-          config-path: .github/advanced-issue-labeler.yml
+            const { data: issue } = await github.rest.issues.get({
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+              issue_number: context.issue.number,
+            });
+
+            const keywords = loadKeywords(`${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,
+            });

.github/workflows/mem0-plugin-checks.yml

@@ -0,0 +1,58 @@
+name: Mem0 Plugin Checks
+
+# On PRs this is invoked by ci-gate.yml (the single required check);
+# push-to-main and manual runs remain standalone.
+#
+# Covers the Python plugin (scripts/ + tests/). The nested .opencode-plugin/
+# is a separate package with its own workflow (opencode-plugin-checks.yml).
+on:
+  workflow_dispatch:
+  push:
+    branches: [main]
+    paths:
+      - 'integrations/mem0-plugin/**'
+      - '!integrations/mem0-plugin/.opencode-plugin/**'
+      - '.github/workflows/mem0-plugin-checks.yml'
+  workflow_call:
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.10", "3.11", "3.12"]
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install dependencies
+        working-directory: integrations/mem0-plugin
+        run: |
+          pip install -r requirements.txt
+          pip install pytest
+
+      - name: Verify hook entry points are executable
+        working-directory: integrations/mem0-plugin
+        run: |
+          missing=$(find scripts -name '*.sh' ! -name '_*' ! -perm -u+x -print)
+          if [ -n "$missing" ]; then
+            echo "Hook entry points must be executable:"
+            echo "$missing"
+            exit 1
+          fi
+
+      - name: Check hook manifests are valid JSON
+        working-directory: integrations/mem0-plugin
+        run: |
+          for f in plugin.json mcp_config.json hooks.json hooks/*.json; do
+            jq empty "$f" || (echo "Invalid JSON: $f" && exit 1)
+          done
+
+      - name: Run tests
+        working-directory: integrations/mem0-plugin
+        run: pytest -q

.github/workflows/n8n-nodes-mem0-cd.yml

@@ -0,0 +1,60 @@
+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

.github/workflows/n8n-nodes-mem0-checks.yml

@@ -0,0 +1,88 @@
+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)

.github/workflows/pr-labeler.yml

@@ -0,0 +1,64 @@
+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],
+              });
+            }

.github/workflows/release.yml

@@ -45,6 +45,7 @@ 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."

.github/workflows/zapier-mem0-cd.yml

@@ -0,0 +1,42 @@
+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

.github/workflows/zapier-mem0-checks.yml

@@ -0,0 +1,47 @@
+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

AGENTS.md

@@ -27,8 +27,9 @@ 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) |
@@ -62,7 +63,7 @@ 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/` and `openmemory/` development
+- **Docker**: Required for `server/` development
 
 ### Initial Setup
 
@@ -214,28 +215,6 @@ 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
@@ -331,7 +310,6 @@ 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
 
@@ -382,7 +360,6 @@ 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
@@ -429,8 +406,11 @@ PR testing is orchestrated by a single entry point: **`ci-gate.yml` (CI Gate)**
 | Python CLI | `cli-python-ci.yml` | Push to main (on `cli/python/`), manual | Ruff lint + pytest + hatch build on Python 3.10, 3.11, 3.12 |
 | Node CLI | `cli-node-ci.yml` | Push to main (on `cli/node/`), manual | Biome lint + tsc + vitest + tsup build on Node 20, 22 |
 | OpenClaw | `openclaw-checks.yml` | Push to main (on `integrations/openclaw/`), manual | tsc + vitest (with Codecov) + tsup build on Node 20, 22 |
+| Mem0 Plugin | `mem0-plugin-checks.yml` | Push to main (on `integrations/mem0-plugin/`, excluding `.opencode-plugin/`), manual | pytest + hook entry-point exec bits + JSON manifest validation on Python 3.10, 3.11, 3.12 |
 | 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.
@@ -450,11 +430,13 @@ 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
@@ -462,6 +444,7 @@ 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`. |
 
@@ -584,7 +567,7 @@ 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/` and `openmemory/` work, use Docker Compose for local development.
+- For `server/` 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
@@ -607,5 +590,4 @@ 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/`.

CONTRIBUTING.md

@@ -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/`, `openmemory/`, and the docs site
+(`integrations/`), the self-hosted `server/`, and the docs site
 (`docs/`). See [AGENTS.md](./AGENTS.md) for a full map of the repository.
 
 ## Development Workflow

Makefile

@@ -11,7 +11,7 @@ install:
 	hatch env create
 
 install_all:
-	pip install ruff==0.6.9 groq together boto3 litellm ollama chromadb weaviate weaviate-client sentence_transformers vertexai \
+	pip install ruff==0.16.0 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
 

README.md

@@ -46,12 +46,12 @@
 
 | Benchmark | Old | New  | Tokens  | Latency p50  |
 | --- | --- | --- | --- | --- |
-| **LoCoMo** | 71.4 | **91.6** | 7.0K  | 0.88s  |
-| **LongMemEval** | 67.8 | **94.8** | 6.8K  | 1.09s  |
+| **LoCoMo** | 71.4 | **92.5** | 7.0K  | 0.88s  |
+| **LongMemEval** | 67.8 | **94.4** | 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).
+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.
 
 **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
-- **91.6 on LoCoMo** -- +20 points over the previous algorithm
-- **94.8 on LongMemEval** -- +27 points, with +53.6 on assistant memory recall
+- **92.5 on LoCoMo** -- +21 points over the previous algorithm
+- **94.4 on LongMemEval** -- +27 points, with 98.2 on assistant memory recall
 - **64.1 on BEAM (1M)** -- production-scale memory evaluation at 1M tokens
 - [Read the full paper](https://mem0.ai/research)
 

SECURITY.md

@@ -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, OpenMemory)
+- Affected component or package (e.g. Python SDK, TypeScript SDK, server, CLI)
 - Affected version, tag, or commit
 - Clear, step-by-step reproduction instructions
 - The security impact and a proof of concept, if available

cli/README.md

@@ -60,9 +60,8 @@ mem0 delete <memory-id>
 | `mem0 entity` | List or delete entities (users, agents, apps, runs) |
 | `mem0 event` | Inspect background processing events (bulk deletes, large add jobs) |
 | `mem0 status` | Verify API connection and display current project |
-| `mem0 version` | Print the CLI version |
 
-Run `mem0 <command> --help` for detailed usage on any command.
+Run `mem0 <command> --help` for detailed usage on any command, or `mem0 --version` to print the CLI version.
 
 ## Agent mode
 

cli/cli-spec.json

@@ -207,7 +207,11 @@
         { "name": "immutable", "flags": ["--immutable"], "type": "boolean", "default": false, "help": "Prevent future updates." },
         { "name": "no_infer", "flags": ["--no-infer"], "type": "boolean", "default": false, "help": "Skip inference, store raw." },
         { "name": "expires", "flags": ["--expires"], "type": "string", "help": "Expiration date (YYYY-MM-DD)." },
-        { "name": "categories", "flags": ["--categories"], "type": "string", "help": "Categories (JSON array or comma-separated)." },
+        { "name": "categories", "flags": ["--categories"], "type": "string", "help": "Not supported on add, use --custom-categories instead." },
+        { "name": "custom_instructions", "flags": ["--custom-instructions"], "type": "string", "help": "Custom instructions for fact extraction." },
+        { "name": "custom_categories", "flags": ["--custom-categories"], "type": "string", "help": "Custom categories as a JSON array of {name: description} objects." },
+        { "name": "structured_data_schema", "flags": ["--structured-data-schema"], "type": "string", "help": "Schema for structured data extraction, as JSON." },
+        { "name": "timestamp", "flags": ["--timestamp"], "type": "integer", "help": "Unix timestamp for the memory." },
         { "name": "graph", "flags": ["--graph"], "type": "boolean", "default": false, "help": "Enable graph memory extraction.", "panel": "Scope" },
         { "name": "no_graph", "flags": ["--no-graph"], "type": "boolean", "default": false, "help": "Disable graph memory extraction.", "panel": "Scope" },
         { "name": "output", "flags": ["--output", "-o"], "type": "string", "default": "text", "help": "Output format: text, json, quiet.", "panel": "Output" }
@@ -244,6 +248,9 @@
         { "name": "keyword", "flags": ["--keyword"], "type": "boolean", "default": false, "help": "Use keyword search.", "panel": "Search" },
         { "name": "filter_json", "flags": ["--filter"], "type": "string", "help": "Advanced filter expression (JSON).", "panel": "Search" },
         { "name": "fields", "flags": ["--fields"], "type": "string", "help": "Specific fields to return (comma-separated).", "panel": "Search" },
+        { "name": "show_expired", "flags": ["--show-expired"], "type": "boolean", "default": false, "help": "Include expired memories.", "panel": "Search" },
+        { "name": "reference_date", "flags": ["--reference-date"], "type": "string", "help": "Reference date for relative queries (YYYY-MM-DD or unix timestamp).", "panel": "Search" },
+        { "name": "latest_only", "flags": ["--latest-only"], "type": "boolean", "default": false, "help": "Only return the latest version of each memory.", "panel": "Search" },
         { "name": "graph", "flags": ["--graph"], "type": "boolean", "default": false, "help": "Enable graph in search.", "panel": "Search" },
         { "name": "no_graph", "flags": ["--no-graph"], "type": "boolean", "default": false, "help": "Disable graph in search.", "panel": "Search" },
         { "name": "output", "flags": ["--output", "-o"], "type": "string", "default": "text", "help": "Output: text, json, table.", "panel": "Output" }
@@ -296,6 +303,8 @@
         { "name": "category", "flags": ["--category"], "type": "string", "help": "Filter by category.", "panel": "Filters" },
         { "name": "after", "flags": ["--after"], "type": "string", "help": "Created after (YYYY-MM-DD).", "panel": "Filters" },
         { "name": "before", "flags": ["--before"], "type": "string", "help": "Created before (YYYY-MM-DD).", "panel": "Filters" },
+        { "name": "show_expired", "flags": ["--show-expired"], "type": "boolean", "default": false, "help": "Include expired memories.", "panel": "Filters" },
+        { "name": "latest_only", "flags": ["--latest-only"], "type": "boolean", "default": false, "help": "Only return the latest version of each memory.", "panel": "Filters" },
         { "name": "graph", "flags": ["--graph"], "type": "boolean", "default": false, "help": "Enable graph in listing.", "panel": "Filters" },
         { "name": "no_graph", "flags": ["--no-graph"], "type": "boolean", "default": false, "help": "Disable graph in listing.", "panel": "Filters" },
         { "name": "output", "flags": ["--output", "-o"], "type": "string", "default": "table", "help": "Output: text, json, table.", "panel": "Output" }
@@ -329,6 +338,8 @@
       ],
       "options": [
         { "name": "metadata", "flags": ["--metadata", "-m"], "type": "string", "help": "Update metadata (JSON)." },
+        { "name": "expires", "flags": ["--expires"], "type": "string", "help": "Expiration date (YYYY-MM-DD)." },
+        { "name": "timestamp", "flags": ["--timestamp"], "type": "integer", "help": "Unix timestamp for the memory." },
         { "name": "output", "flags": ["--output", "-o"], "type": "string", "default": "text", "help": "Output: text, json, quiet.", "panel": "Output" }
       ],
       "apiEndpoint": "update"
@@ -356,6 +367,7 @@
         { "name": "all", "flags": ["--all"], "type": "boolean", "default": false, "help": "Delete all memories matching scope filters." },
         { "name": "entity", "flags": ["--entity"], "type": "boolean", "default": false, "help": "Delete the entity itself and all its memories (cascade)." },
         { "name": "project", "flags": ["--project"], "type": "boolean", "default": false, "help": "With --all: delete ALL memories project-wide." },
+        { "name": "delete_linked", "flags": ["--delete-linked"], "type": "boolean", "default": false, "help": "Also delete memories linked to this memory." },
         { "name": "dry_run", "flags": ["--dry-run"], "type": "boolean", "default": false, "help": "Show what would be deleted without deleting." },
         { "name": "force", "flags": ["--force"], "type": "boolean", "default": false, "help": "Skip confirmation." },
         { "name": "user_id", "flags": ["--user-id", "-u"], "type": "string", "help": "Scope to user.", "panel": "Scope" },

cli/node/README.md

@@ -232,14 +232,6 @@ Verify your API connection and display the current project.
 mem0 status
 ```
 
-### `mem0 version`
-
-Print the CLI version.
-
-```bash
-mem0 version
-```
-
 ## Agent mode
 
 Pass `--agent` (or its alias `--json`) as a **global flag** on any command to get output designed for AI agent tool loops:
@@ -298,6 +290,8 @@ These flags are available on all commands:
 | `--base-url` | Override the configured API base URL for this request |
 | `-o, --output` | Set the output format |
 
+`mem0 --version` prints the CLI version. It is only valid before a subcommand, not after one.
+
 ## Environment variables
 
 | Variable | Description |

cli/node/development.md

@@ -51,7 +51,7 @@ pnpm link --global
 
 # Now use it like a normal CLI
 mem0 --help
-mem0 version
+mem0 --version
 ```
 
 > **Warning:** If you also have the Python CLI installed, both register the `mem0` command. The last one linked/installed wins. Unlink with `pnpm unlink --global`.

cli/node/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mem0/cli",
-  "version": "0.2.10",
+  "version": "0.2.12",
   "description": "The official CLI for mem0 — the memory layer for AI agents",
   "type": "module",
   "bin": {
@@ -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",
-      "postcss@<8.5.10": ">=8.5.10",
-      "esbuild": ">=0.28.1"
+      "esbuild": ">=0.28.1",
+      "postcss@<8.5.18": ">=8.5.18 <9.0.0"
     }
   }
 }

cli/node/pnpm-lock.yaml

@@ -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@8.5.15)(tsx@4.21.0)(typescript@5.9.3)
+        version: 8.5.1(postcss@8.5.23)(tsx@4.21.0)(typescript@5.9.3)
       tsx:
         specifier: ^4.7.0
         version: 4.21.0
@@ -78,24 +78,28 @@ 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==}
@@ -316,66 +320,79 @@ 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==}
@@ -653,8 +670,8 @@ packages:
   mz@2.7.0:
     resolution: {integrity: sha512-z81GNO7nnYMEhrGh9LeymoE4+Yr0Wn5McHIZMK5cfQCl+NDX08sCZgUc9/6MHni9IWuFLm1Z3HTCXu2z9fN62Q==}
 
-  nanoid@3.3.12:
-    resolution: {integrity: sha512-ZB9RH/39qpq5Vu6Y+NmUaFhQR6pp+M2Xt76XBnEwDaGcVAqhlvxrl3B2bKS5D3NH3QR76v3aSrKaF/Kiy7lEtQ==}
+  nanoid@3.3.16:
+    resolution: {integrity: sha512-bzlKTyNJ7+LdGIIwy8ijFpIqEQIvafahV7eYykJ8Cvh42EdJeODoJ6gUJXpQJvej1BddH8OqTXZNE/KfbWAu8Q==}
     engines: {node: ^10 || ^12 || ^13.7 || ^14 || >=15.0.1}
     hasBin: true
 
@@ -695,7 +712,7 @@ packages:
     engines: {node: '>= 18'}
     peerDependencies:
       jiti: '>=1.21.0'
-      postcss: '>=8.5.10'
+      postcss: '>=8.5.18 <9.0.0'
       tsx: ^4.8.1
       yaml: ^2.4.2
     peerDependenciesMeta:
@@ -708,8 +725,8 @@ packages:
       yaml:
         optional: true
 
-  postcss@8.5.15:
-    resolution: {integrity: sha512-FfR8sjd4em2T6fb3I2MwAJU7HWVMr9zba+enmQeeWFfCbm+UOC/0X4DS8XtpUTMwWMGbjKYP7xjfNekzyGmB3A==}
+  postcss@8.5.23:
+    resolution: {integrity: sha512-g50586zr4bZmwFiTlflMu8E0bDTb5I5gertgwAKmsdUlTQIhZtunzUlD1WSzwcVWPoAVpsrA6vlfCD7oXvRwgg==}
     engines: {node: ^10 || ^12 || >=14}
 
   readdirp@4.1.2:
@@ -821,7 +838,7 @@ packages:
     peerDependencies:
       '@microsoft/api-extractor': ^7.36.0
       '@swc/core': ^1
-      postcss: '>=8.5.10'
+      postcss: '>=8.5.18 <9.0.0'
       typescript: '>=4.5.0'
     peerDependenciesMeta:
       '@microsoft/api-extractor':
@@ -1388,7 +1405,7 @@ snapshots:
       object-assign: 4.1.1
       thenify-all: 1.6.0
 
-  nanoid@3.3.12: {}
+  nanoid@3.3.16: {}
 
   object-assign@4.1.1: {}
 
@@ -1424,16 +1441,16 @@ snapshots:
       mlly: 1.8.2
       pathe: 2.0.3
 
-  postcss-load-config@6.0.1(postcss@8.5.15)(tsx@4.21.0):
+  postcss-load-config@6.0.1(postcss@8.5.23)(tsx@4.21.0):
     dependencies:
       lilconfig: 3.1.3
     optionalDependencies:
-      postcss: 8.5.15
+      postcss: 8.5.23
       tsx: 4.21.0
 
-  postcss@8.5.15:
+  postcss@8.5.23:
     dependencies:
-      nanoid: 3.3.12
+      nanoid: 3.3.16
       picocolors: 1.1.1
       source-map-js: 1.2.1
 
@@ -1554,7 +1571,7 @@ snapshots:
 
   ts-interface-checker@0.1.13: {}
 
-  tsup@8.5.1(postcss@8.5.15)(tsx@4.21.0)(typescript@5.9.3):
+  tsup@8.5.1(postcss@8.5.23)(tsx@4.21.0)(typescript@5.9.3):
     dependencies:
       bundle-require: 5.1.0(esbuild@0.28.1)
       cac: 6.7.14
@@ -1565,7 +1582,7 @@ snapshots:
       fix-dts-default-cjs-exports: 1.0.1
       joycon: 3.1.1
       picocolors: 1.1.1
-      postcss-load-config: 6.0.1(postcss@8.5.15)(tsx@4.21.0)
+      postcss-load-config: 6.0.1(postcss@8.5.23)(tsx@4.21.0)
       resolve-from: 5.0.0
       rollup: 4.60.0
       source-map: 0.7.6
@@ -1574,7 +1591,7 @@ snapshots:
       tinyglobby: 0.2.15
       tree-kill: 1.2.2
     optionalDependencies:
-      postcss: 8.5.15
+      postcss: 8.5.23
       typescript: 5.9.3
     transitivePeerDependencies:
       - jiti
@@ -1602,7 +1619,7 @@ snapshots:
       esbuild: 0.28.1
       fdir: 6.5.0(picomatch@4.0.4)
       picomatch: 4.0.4
-      postcss: 8.5.15
+      postcss: 8.5.23
       rollup: 4.60.0
       tinyglobby: 0.2.15
     optionalDependencies:

cli/node/pnpm-workspace.yaml

@@ -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"

cli/node/src/backend/base.ts

@@ -14,7 +14,10 @@ export interface AddOptions {
 	immutable?: boolean;
 	infer?: boolean;
 	expires?: string;
-	categories?: string[];
+	customInstructions?: string;
+	customCategories?: Record<string, string>[];
+	structuredDataSchema?: Record<string, unknown>;
+	timestamp?: number;
 }
 
 export interface SearchOptions {
@@ -28,6 +31,9 @@ export interface SearchOptions {
 	keyword?: boolean;
 	filters?: Record<string, unknown>;
 	fields?: string[];
+	showExpired?: boolean;
+	referenceDate?: string | number;
+	latestOnly?: boolean;
 }
 
 export interface ListOptions {
@@ -40,6 +46,8 @@ export interface ListOptions {
 	category?: string;
 	after?: string;
 	before?: string;
+	showExpired?: boolean;
+	latestOnly?: boolean;
 }
 
 export interface DeleteOptions {
@@ -48,6 +56,12 @@ export interface DeleteOptions {
 	agentId?: string;
 	appId?: string;
 	runId?: string;
+	deleteLinked?: boolean;
+}
+
+export interface UpdateOptions {
+	expirationDate?: string;
+	timestamp?: number;
 }
 
 export interface EntityIds {
@@ -77,6 +91,7 @@ export interface Backend {
 		memoryId: string,
 		content?: string,
 		metadata?: Record<string, unknown>,
+		opts?: UpdateOptions,
 	): Promise<Record<string, unknown>>;
 
 	delete(

cli/node/src/backend/platform.ts

@@ -15,8 +15,13 @@ import {
 	type ListOptions,
 	NotFoundError,
 	type SearchOptions,
+	type UpdateOptions,
 } from "./base.js";
 
+function encodePathSegment(value: unknown): string {
+	return encodeURIComponent(String(value));
+}
+
 export class PlatformBackend implements Backend {
 	private baseUrl: string;
 	private headers: Record<string, string>;
@@ -146,7 +151,13 @@ export class PlatformBackend implements Backend {
 		if (opts.immutable) payload.immutable = true;
 		if (opts.infer === false) payload.infer = false;
 		if (opts.expires) payload.expiration_date = opts.expires;
-		if (opts.categories) payload.categories = opts.categories;
+		if (opts.customInstructions)
+			payload.custom_instructions = opts.customInstructions;
+		if (opts.customCategories)
+			payload.custom_categories = opts.customCategories;
+		if (opts.structuredDataSchema)
+			payload.structured_data_schema = opts.structuredDataSchema;
+		if (opts.timestamp !== undefined) payload.timestamp = opts.timestamp;
 		payload.source = "CLI";
 
 		return (await this._request("POST", "/v3/memories/add/", {
@@ -207,6 +218,10 @@ export class PlatformBackend implements Backend {
 		if (opts.rerank) payload.rerank = true;
 		if (opts.keyword) payload.keyword_search = true;
 		if (opts.fields) payload.fields = opts.fields;
+		if (opts.showExpired) payload.show_expired = true;
+		if (opts.referenceDate !== undefined)
+			payload.reference_date = opts.referenceDate;
+		if (opts.latestOnly) payload.latest_only = true;
 		payload.source = "CLI";
 
 		const result = (await this._request("POST", "/v3/memories/search/", {
@@ -218,9 +233,13 @@ export class PlatformBackend implements Backend {
 	}
 
 	async get(memoryId: string): Promise<Record<string, unknown>> {
-		return (await this._request("GET", `/v1/memories/${memoryId}/`, {
-			params: { source: "CLI" },
-		})) as Record<string, unknown>;
+		return (await this._request(
+			"GET",
+			`/v1/memories/${encodePathSegment(memoryId)}/`,
+			{
+				params: { source: "CLI" },
+			},
+		)) as Record<string, unknown>;
 	}
 
 	async listMemories(
@@ -257,6 +276,8 @@ export class PlatformBackend implements Backend {
 			extraFilters: Object.keys(extra).length > 0 ? extra : undefined,
 		});
 		if (apiFilters) payload.filters = apiFilters;
+		if (opts.showExpired) payload.show_expired = true;
+		if (opts.latestOnly) payload.latest_only = true;
 		payload.source = "CLI";
 
 		const result = (await this._request("POST", "/v3/memories/", {
@@ -272,14 +293,21 @@ export class PlatformBackend implements Backend {
 		memoryId: string,
 		content?: string,
 		metadata?: Record<string, unknown>,
+		opts: UpdateOptions = {},
 	): Promise<Record<string, unknown>> {
 		const payload: Record<string, unknown> = {};
 		if (content) payload.text = content;
 		if (metadata) payload.metadata = metadata;
+		if (opts.expirationDate) payload.expiration_date = opts.expirationDate;
+		if (opts.timestamp !== undefined) payload.timestamp = opts.timestamp;
 		payload.source = "CLI";
-		return (await this._request("PUT", `/v1/memories/${memoryId}/`, {
-			json: payload,
-		})) as Record<string, unknown>;
+		return (await this._request(
+			"PUT",
+			`/v1/memories/${encodePathSegment(memoryId)}/`,
+			{
+				json: payload,
+			},
+		)) as Record<string, unknown>;
 	}
 
 	async delete(
@@ -297,9 +325,13 @@ export class PlatformBackend implements Backend {
 			})) as Record<string, unknown>;
 		}
 		if (memoryId) {
-			return (await this._request("DELETE", `/v1/memories/${memoryId}/`, {
-				params: { source: "CLI" },
-			})) as Record<string, unknown>;
+			const params: Record<string, string> = { source: "CLI" };
+			if (opts.deleteLinked) params.delete_linked = "true";
+			return (await this._request(
+				"DELETE",
+				`/v1/memories/${encodePathSegment(memoryId)}/`,
+				{ params },
+			)) as Record<string, unknown>;
 		}
 		throw new Error("Either memoryId or --all is required");
 	}
@@ -323,7 +355,7 @@ export class PlatformBackend implements Backend {
 		for (const [entityType, entityId] of entities) {
 			results[entityType] = (await this._request(
 				"DELETE",
-				`/v2/entities/${entityType}/${entityId}/`,
+				`/v2/entities/${encodePathSegment(entityType)}/${encodePathSegment(entityId)}/`,
 				{ params: { source: "CLI" } },
 			)) as Record<string, unknown>;
 		}
@@ -386,9 +418,9 @@ export class PlatformBackend implements Backend {
 	}
 
 	async getEvent(eventId: string): Promise<Record<string, unknown>> {
-		return (await this._request("GET", `/v1/event/${eventId}/`)) as Record<
-			string,
-			unknown
-		>;
+		return (await this._request(
+			"GET",
+			`/v1/event/${encodePathSegment(eventId)}/`,
+		)) as Record<string, unknown>;
 	}
 }

cli/node/src/commands/memory.ts

@@ -21,16 +21,19 @@ import {
 	formatSingleMemory,
 	printResultSummary,
 } from "../output.js";
-import { isAgentMode, setCurrentCommand } from "../state.js";
+import { isAgentMode, setCurrentCommand, stdinIsPiped } from "../state.js";
 
-/** True only when stdin is an actual pipe or file redirect — never in agent mode. */
-function _stdinIsPiped(): boolean {
-	if (isAgentMode()) return false;
-	try {
-		const stat = fs.fstatSync(0);
-		return stat.isFIFO() || stat.isFile();
-	} catch {
-		return false;
+/** Exit 1 if value is not a future YYYY-MM-DD date. */
+function _validateExpires(value: string): void {
+	if (!/^\d{4}-\d{2}-\d{2}$/.test(value)) {
+		printError(
+			"Invalid date format for --expires. Use YYYY-MM-DD (e.g. 2025-12-31).",
+		);
+		process.exit(1);
+	}
+	if (new Date(value) <= new Date()) {
+		printError("--expires date must be in the future.");
+		process.exit(1);
 	}
 }
 
@@ -49,10 +52,22 @@ export async function cmdAdd(
 		infer?: boolean;
 		expires?: string;
 		categories?: string;
+		customInstructions?: string;
+		customCategories?: string;
+		structuredDataSchema?: string;
+		timestamp?: number;
 		output: string;
 	},
 ): Promise<void> {
 	setCurrentCommand("add");
+
+	if (opts.categories) {
+		printError(
+			"--categories is not supported on add. Use --custom-categories instead.",
+		);
+		process.exit(1);
+	}
+
 	let msgs: Record<string, unknown>[] | undefined;
 	let content = text;
 
@@ -78,7 +93,7 @@ export async function cmdAdd(
 		}
 	}
 	// Read from stdin only if stdin is an actual pipe or file redirect
-	else if (!content && _stdinIsPiped()) {
+	else if (!content && stdinIsPiped()) {
 		content = fs.readFileSync(0, "utf-8").trim();
 	}
 
@@ -93,20 +108,6 @@ export async function cmdAdd(
 		process.exit(1);
 	}
 
-	// Validate --expires
-	if (opts.expires) {
-		if (!/^\d{4}-\d{2}-\d{2}$/.test(opts.expires)) {
-			printError(
-				"Invalid date format for --expires. Use YYYY-MM-DD (e.g. 2025-12-31).",
-			);
-			process.exit(1);
-		}
-		if (new Date(opts.expires) <= new Date()) {
-			printError("--expires date must be in the future.");
-			process.exit(1);
-		}
-	}
-
 	let meta: Record<string, unknown> | undefined;
 	if (opts.metadata) {
 		try {
@@ -117,15 +118,28 @@ export async function cmdAdd(
 		}
 	}
 
-	let cats: string[] | undefined;
-	if (opts.categories) {
+	let customCats: Record<string, string>[] | undefined;
+	if (opts.customCategories) {
 		try {
-			cats = JSON.parse(opts.categories);
+			customCats = JSON.parse(opts.customCategories);
 		} catch {
-			cats = opts.categories.split(",").map((c) => c.trim());
+			printError("Invalid JSON in --custom-categories.");
+			process.exit(1);
 		}
 	}
 
+	let schema: Record<string, unknown> | undefined;
+	if (opts.structuredDataSchema) {
+		try {
+			schema = JSON.parse(opts.structuredDataSchema);
+		} catch {
+			printError("Invalid JSON in --structured-data-schema.");
+			process.exit(1);
+		}
+	}
+
+	if (opts.expires) _validateExpires(opts.expires);
+
 	let result: Record<string, unknown>;
 	try {
 		result = await timedStatus("Adding memory...", async () => {
@@ -138,7 +152,10 @@ export async function cmdAdd(
 				immutable: opts.immutable,
 				infer: opts.infer !== false,
 				expires: opts.expires,
-				categories: cats,
+				customInstructions: opts.customInstructions,
+				customCategories: customCats,
+				structuredDataSchema: schema,
+				timestamp: opts.timestamp,
 			});
 		});
 	} catch (e) {
@@ -223,6 +240,9 @@ export async function cmdSearch(
 		keyword: boolean;
 		filterJson?: string;
 		fields?: string;
+		showExpired?: boolean;
+		referenceDate?: string;
+		latestOnly?: boolean;
 		output: string;
 	},
 ): Promise<void> {
@@ -271,6 +291,9 @@ export async function cmdSearch(
 				keyword: opts.keyword,
 				filters,
 				fields: fieldList,
+				showExpired: opts.showExpired,
+				referenceDate: opts.referenceDate,
+				latestOnly: opts.latestOnly,
 			});
 		});
 	} catch (e) {
@@ -364,6 +387,8 @@ export async function cmdList(
 		category?: string;
 		after?: string;
 		before?: string;
+		showExpired?: boolean;
+		latestOnly?: boolean;
 		output: string;
 	},
 ): Promise<void> {
@@ -391,6 +416,8 @@ export async function cmdList(
 				category: opts.category,
 				after: opts.after,
 				before: opts.before,
+				showExpired: opts.showExpired,
+				latestOnly: opts.latestOnly,
 			});
 		});
 	} catch (e) {
@@ -450,7 +477,12 @@ export async function cmdUpdate(
 	backend: Backend,
 	memoryId: string,
 	text: string | undefined,
-	opts: { metadata?: string; output: string },
+	opts: {
+		metadata?: string;
+		expires?: string;
+		timestamp?: number;
+		output: string;
+	},
 ): Promise<void> {
 	setCurrentCommand("update");
 	let meta: Record<string, unknown> | undefined;
@@ -463,11 +495,16 @@ export async function cmdUpdate(
 		}
 	}
 
+	if (opts.expires) _validateExpires(opts.expires);
+
 	const start = performance.now();
 	let result: Record<string, unknown>;
 	try {
 		result = await timedStatus("Updating memory...", async () => {
-			return backend.update(memoryId, text, meta);
+			return backend.update(memoryId, text, meta, {
+				expirationDate: opts.expires,
+				timestamp: opts.timestamp,
+			});
 		});
 	} catch (e) {
 		printError(e instanceof Error ? e.message : String(e));
@@ -493,7 +530,12 @@ export async function cmdUpdate(
 export async function cmdDelete(
 	backend: Backend,
 	memoryId: string,
-	opts: { output: string; dryRun?: boolean; force?: boolean },
+	opts: {
+		output: string;
+		dryRun?: boolean;
+		force?: boolean;
+		deleteLinked?: boolean;
+	},
 ): Promise<void> {
 	setCurrentCommand("delete");
 	if (opts.dryRun) {
@@ -514,7 +556,7 @@ export async function cmdDelete(
 	let result: Record<string, unknown>;
 	try {
 		result = await timedStatus("Deleting...", async () => {
-			return backend.delete(memoryId);
+			return backend.delete(memoryId, { deleteLinked: opts.deleteLinked });
 		});
 	} catch (e) {
 		printError(e instanceof Error ? e.message : String(e));

cli/node/src/commands/utils.ts

@@ -1,5 +1,5 @@
 /**
- * Utility commands: status, version, import.
+ * Utility commands: status, import.
  */
 
 import fs from "node:fs";
@@ -8,7 +8,6 @@ import type { Backend } from "../backend/base.js";
 import { colors, printError, printSuccess, timedStatus } from "../branding.js";
 import { formatAgentEnvelope, formatJsonEnvelope } from "../output.js";
 import { setCurrentCommand } from "../state.js";
-import { CLI_VERSION } from "../version.js";
 
 const { brand, dim, success, error: errorColor } = colors;
 
@@ -82,10 +81,6 @@ export async function cmdStatus(
 	console.log();
 }
 
-export function cmdVersion(): void {
-	console.log(`  ${brand("◆ Mem0")} CLI v${CLI_VERSION}`);
-}
-
 export async function cmdImport(
 	backend: Backend,
 	filePath: string,

cli/node/src/index.ts

@@ -17,6 +17,7 @@ import {
 	isAgentMode,
 	setAgentMode,
 	setCurrentCommand,
+	stdinIsPiped,
 	takeNotice,
 } from "./state.js";
 import { captureEvent } from "./telemetry.js";
@@ -319,7 +320,25 @@ program
 	.option("--immutable", "Prevent future updates.", false)
 	.option("--no-infer", "Skip inference, store raw.")
 	.option("--expires <date>", "Expiration date (YYYY-MM-DD).")
-	.option("--categories <value>", "Categories (JSON array or comma-separated).")
+	.option(
+		"--categories <value>",
+		"Not supported on add, use --custom-categories instead.",
+	)
+	.option(
+		"--custom-instructions <text>",
+		"Custom instructions for fact extraction.",
+	)
+	.option(
+		"--custom-categories <json>",
+		"Custom categories as a JSON array of {name: description} objects.",
+	)
+	.option(
+		"--structured-data-schema <json>",
+		"Schema for structured data extraction, as JSON.",
+	)
+	.option("--timestamp <unix>", "Unix timestamp for the memory.", (v) =>
+		Number.parseInt(v),
+	)
 	.option("-o, --output <format>", "Output format: text, json, quiet.", "text")
 	.option("--api-key <key>", "Override API key.")
 	.option("--base-url <url>", "Override API base URL.")
@@ -366,6 +385,16 @@ program
 	.option("--keyword", "Use keyword search.", false)
 	.option("--filter <json>", "Advanced filter expression (JSON).")
 	.option("--fields <list>", "Specific fields to return (comma-separated).")
+	.option("--show-expired", "Include expired memories.", false)
+	.option(
+		"--reference-date <date>",
+		"Reference date for relative queries (YYYY-MM-DD or unix timestamp).",
+	)
+	.option(
+		"--latest-only",
+		"Only return the latest version of each memory.",
+		false,
+	)
 	.option("-o, --output <format>", "Output: text, json, table.", "text")
 	.option("--api-key <key>", "Override API key.")
 	.option("--base-url <url>", "Override API base URL.")
@@ -375,7 +404,7 @@ program
 	)
 	.action(async (query, opts) => {
 		let resolvedQuery = query;
-		if (!resolvedQuery && !process.stdin.isTTY) {
+		if (!resolvedQuery && stdinIsPiped()) {
 			resolvedQuery = fs.readFileSync(0, "utf-8").trim();
 		}
 		if (!resolvedQuery) {
@@ -398,6 +427,9 @@ program
 			keyword: opts.keyword,
 			filterJson: opts.filter,
 			fields: opts.fields,
+			showExpired: opts.showExpired,
+			referenceDate: opts.referenceDate,
+			latestOnly: opts.latestOnly,
 			output,
 		});
 	});
@@ -441,6 +473,12 @@ program
 	.option("--category <name>", "Filter by category.")
 	.option("--after <date>", "Created after (YYYY-MM-DD).")
 	.option("--before <date>", "Created before (YYYY-MM-DD).")
+	.option("--show-expired", "Include expired memories.", false)
+	.option(
+		"--latest-only",
+		"Only return the latest version of each memory.",
+		false,
+	)
 	.option("-o, --output <format>", "Output: text, json, table.", "table")
 	.option("--api-key <key>", "Override API key.")
 	.option("--base-url <url>", "Override API base URL.")
@@ -464,6 +502,8 @@ program
 			category: opts.category,
 			after: opts.after,
 			before: opts.before,
+			showExpired: opts.showExpired,
+			latestOnly: opts.latestOnly,
 			output,
 		});
 	});
@@ -474,6 +514,10 @@ program
 	.command("update <memoryId> [text]")
 	.description("Update a memory's text or metadata.")
 	.option("-m, --metadata <json>", "Update metadata (JSON).")
+	.option("--expires <date>", "Expiration date (YYYY-MM-DD).")
+	.option("--timestamp <unix>", "Unix timestamp for the memory.", (v) =>
+		Number.parseInt(v),
+	)
 	.option("-o, --output <format>", "Output: text, json, quiet.", "text")
 	.option("--api-key <key>", "Override API key.")
 	.option("--base-url <url>", "Override API base URL.")
@@ -483,7 +527,7 @@ program
 	)
 	.action(async (memoryId, text, opts) => {
 		let resolvedText = text;
-		if (!resolvedText && !opts.metadata && !process.stdin.isTTY) {
+		if (!resolvedText && stdinIsPiped()) {
 			resolvedText = fs.readFileSync(0, "utf-8").trim();
 		}
 		const { cmdUpdate } = await import("./commands/memory.js");
@@ -492,6 +536,8 @@ program
 		const output = isAgent ? "agent" : opts.output;
 		await cmdUpdate(backend, memoryId, resolvedText, {
 			metadata: opts.metadata,
+			expires: opts.expires,
+			timestamp: opts.timestamp,
 			output,
 		});
 	});
@@ -510,6 +556,11 @@ program
 	.option("--project", "With --all: delete ALL memories project-wide.", false)
 	.option("--dry-run", "Show what would be deleted without deleting.", false)
 	.option("--force", "Skip confirmation.", false)
+	.option(
+		"--delete-linked",
+		"Also delete memories linked to this memory.",
+		false,
+	)
 	.option("-u, --user-id <id>", "Scope to user.")
 	.option("--agent-id <id>", "Scope to agent.")
 	.option("--app-id <id>", "Scope to app.")
@@ -563,6 +614,7 @@ program
 				output,
 				dryRun: opts.dryRun,
 				force: opts.force,
+				deleteLinked: opts.deleteLinked,
 			});
 			return;
 		}
@@ -806,8 +858,8 @@ program
 	.addHelpText("after", "\nExamples:\n  $ mem0 help\n  $ mem0 help --json")
 	.action((opts) => {
 		// opts.json is set when `mem0 help --json` is used (subcommand flag).
-		// program.opts().json is set when the root --json global flag was used first.
-		if (opts.json || program.opts().json) {
+		// program.opts().json/.agent is set when a root global flag was used first.
+		if (opts.json || program.opts().json || program.opts().agent) {
 			// Load spec from parent directory
 			const __dirname = path.dirname(fileURLToPath(import.meta.url));
 			const specPath = path.join(__dirname, "..", "..", "cli-spec.json");

cli/node/src/output.ts

@@ -282,11 +282,24 @@ export function sanitizeAgentData(command: string, data: unknown): unknown {
 		}
 		case "search":
 			return (data as Record<string, unknown>[]).map((r) =>
-				pick(r, ["id", "memory", "score", "created_at", "categories"]),
+				pick(r, [
+					"id",
+					"memory",
+					"score",
+					"created_at",
+					"categories",
+					"expiration_date",
+				]),
 			);
 		case "list":
 			return (data as Record<string, unknown>[]).map((r) =>
-				pick(r, ["id", "memory", "created_at", "categories"]),
+				pick(r, [
+					"id",
+					"memory",
+					"created_at",
+					"categories",
+					"expiration_date",
+				]),
 			);
 		case "get": {
 			const r = data as Record<string, unknown>;
@@ -297,11 +310,12 @@ export function sanitizeAgentData(command: string, data: unknown): unknown {
 				"updated_at",
 				"categories",
 				"metadata",
+				"expiration_date",
 			]);
 		}
 		case "update": {
 			const r = data as Record<string, unknown>;
-			return pick(r, ["id", "memory"]);
+			return pick(r, ["id", "memory", "expiration_date"]);
 		}
 		case "delete":
 		case "delete-all":

cli/node/src/state.ts

@@ -3,6 +3,8 @@
  * read by commands and branding functions.
  */
 
+import fs from "node:fs";
+
 let _agentMode = false;
 let _currentCommand = "";
 let _pendingNotice = "";
@@ -38,3 +40,14 @@ export function takeNotice(): string {
 	_pendingNotice = "";
 	return msg;
 }
+
+/** True only when stdin is an actual pipe or file redirect (never in agent mode). */
+export function stdinIsPiped(): boolean {
+	if (isAgentMode()) return false;
+	try {
+		const stat = fs.fstatSync(0);
+		return stat.isFIFO() || stat.isFile();
+	} catch {
+		return false;
+	}
+}

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

@@ -44,15 +44,25 @@ describe("CLI Integration — help and version", () => {
     expect(result.stdout).toContain("search");
   });
 
-  it("help --json produces valid JSON", () => {
-    const result = run(["help", "--json"]);
-    expect(result.exitCode).toBe(0);
-    const parsed = JSON.parse(result.stdout);
-    // spec may have cli.name or top-level name
-    const name = parsed.name ?? parsed.cli?.name;
-    expect(name).toBe("mem0");
+  it("prints the version with --version, and has no version subcommand", () => {
+    const flag = run(["--version"]);
+    expect(flag.exitCode).toBe(0);
+    expect(flag.stdout).toContain("Mem0");
+    expect(run(["version"]).exitCode).not.toBe(0);
   });
 
+  it.each([["help", "--json"], ["--json", "help"], ["--agent", "help"]])(
+    "%s %s produces valid JSON",
+    (...args) => {
+      const result = run(args);
+      expect(result.exitCode).toBe(0);
+      const parsed = JSON.parse(result.stdout);
+      // spec may have cli.name or top-level name
+      const name = parsed.name ?? parsed.cli?.name;
+      expect(name).toBe("mem0");
+    },
+  );
+
   it("shows add help", () => {
     const result = run(["add", "--help"]);
     expect(result.exitCode).toBe(0);

cli/node/tests/option-parity.test.ts

@@ -0,0 +1,163 @@
+/**
+ * Drift test: every documented v3 add/search/list param must be reachable from the Node CLI.
+ */
+
+import { execSync } from "node:child_process";
+import fs from "node:fs";
+import path from "node:path";
+import { describe, expect, it } from "vitest";
+
+const OPENAPI_PATH = path.join(
+	__dirname,
+	"..",
+	"..",
+	"..",
+	"docs",
+	"openapi.json",
+);
+
+const KNOWN_UNSURFACED: Record<string, Record<string, string>> = {
+	"/v3/memories/add/": {
+		includes: "extraction hint, no CLI flag yet",
+		excludes: "extraction hint, no CLI flag yet",
+		enable_graph: "graph memory toggle, no CLI flag yet",
+		output_format: "response envelope is pinned by the CLI",
+		prompt_profile_id: "no CLI flag yet",
+		temporal_reasoning: "no CLI flag yet",
+		timezone: "no CLI flag yet",
+		observation_datetime: "no CLI flag yet, --timestamp backdates instead",
+		observation_date: "no CLI flag yet, --timestamp backdates instead",
+	},
+	"/v3/memories/search/": {
+		categories: "expressible through --filter",
+		metadata: "expressible through --filter",
+	},
+	"/v3/memories/": {
+		start_date: "covered by --after via filters.created_at.gte",
+		end_date: "covered by --before via filters.created_at.lte",
+		categories: "covered by --category via filters.categories",
+		fields: "no CLI flag yet",
+		keywords: "no CLI flag yet",
+	},
+};
+
+const ADD_MAPPING: Record<string, string[]> = {
+	messages: ["--messages", "--file", "text"],
+	user_id: ["--user-id"],
+	agent_id: ["--agent-id"],
+	app_id: ["--app-id"],
+	run_id: ["--run-id"],
+	metadata: ["--metadata"],
+	expiration_date: ["--expires"],
+	custom_instructions: ["--custom-instructions"],
+	custom_categories: ["--custom-categories"],
+	infer: ["--no-infer"],
+	immutable: ["--immutable"],
+	structured_data_schema: ["--structured-data-schema"],
+	timestamp: ["--timestamp"],
+};
+
+const SEARCH_MAPPING: Record<string, string[]> = {
+	query: ["query"],
+	filters: ["--filter", "--user-id", "--agent-id", "--run-id"],
+	show_expired: ["--show-expired"],
+	top_k: ["--top-k"],
+	threshold: ["--threshold"],
+	rerank: ["--rerank"],
+	reference_date: ["--reference-date"],
+	fields: ["--fields"],
+};
+
+const LIST_MAPPING: Record<string, string[]> = {
+	filters: [
+		"--user-id",
+		"--agent-id",
+		"--run-id",
+		"--category",
+		"--after",
+		"--before",
+	],
+	show_expired: ["--show-expired"],
+	page: ["--page"],
+	page_size: ["--page-size"],
+};
+
+function documentedFields(endpoint: string): string[] {
+	const spec = JSON.parse(fs.readFileSync(OPENAPI_PATH, "utf-8"));
+	const schema =
+		spec.paths[endpoint].post.requestBody.content["application/json"].schema;
+	return Object.keys(schema.properties);
+}
+
+function helpText(command: string): string {
+	return execSync(`npx tsx src/index.ts ${command} --help`, {
+		cwd: path.join(__dirname, ".."),
+		encoding: "utf-8",
+		timeout: 15000,
+	});
+}
+
+function assertAllReachable(
+	endpoint: string,
+	mapping: Record<string, string[]>,
+	command: string,
+) {
+	const documented = documentedFields(endpoint);
+	const help = helpText(command);
+	for (const field of documented) {
+		if (KNOWN_UNSURFACED[endpoint]?.[field]) continue;
+		const candidates = mapping[field];
+		expect(
+			candidates,
+			`${endpoint}: documented field "${field}" has no mapping entry for command "${command}"`,
+		).toBeDefined();
+		const reachable = candidates.some((flag) =>
+			flag.startsWith("--") ? help.includes(flag) : true,
+		);
+		expect(
+			reachable,
+			`${endpoint}: documented field "${field}" not reachable via any of ${JSON.stringify(candidates)} on command "${command}"`,
+		).toBe(true);
+	}
+}
+
+describe("Option parity: Node CLI reachability of documented v3 params", () => {
+	it("add covers documented fields", () => {
+		assertAllReachable("/v3/memories/add/", ADD_MAPPING, "add");
+	});
+
+	it("search covers documented fields", () => {
+		assertAllReachable("/v3/memories/search/", SEARCH_MAPPING, "search");
+	});
+
+	it("list covers documented fields", () => {
+		assertAllReachable("/v3/memories/", LIST_MAPPING, "list");
+	});
+});
+
+describe("stdin fallback uses the shared piped-stdin guard", () => {
+	const SOURCES = ["src/index.ts", "src/commands/memory.ts"];
+
+	for (const rel of SOURCES) {
+		it(`${rel} never checks process.stdin.isTTY directly`, () => {
+			const src = fs.readFileSync(path.join(__dirname, "..", rel), "utf-8");
+			expect(
+				src.includes("process.stdin.isTTY"),
+				`${rel}: use stdinIsPiped() from state.ts. A bare !isTTY check is also true for /dev/null and sockets, so readFileSync(0) crashes with EAGAIN in scripts, CI, and agent mode.`,
+			).toBe(false);
+		});
+
+		it(`${rel} guards every readFileSync(0) with stdinIsPiped()`, () => {
+			const src = fs.readFileSync(path.join(__dirname, "..", rel), "utf-8");
+			const lines = src.split("\n");
+			for (const [i, line] of lines.entries()) {
+				if (!line.includes("readFileSync(0")) continue;
+				const guard = lines.slice(Math.max(0, i - 3), i).join("\n");
+				expect(
+					guard.includes("stdinIsPiped()"),
+					`${rel}:${i + 1}: readFileSync(0) must be guarded by stdinIsPiped()`,
+				).toBe(true);
+			}
+		});
+	}
+});

cli/node/tests/platform-backend.test.ts

@@ -2,51 +2,236 @@
  * Tests for the Platform backend (mem0 Platform API client).
  */
 
-import { describe, it, expect, vi } from "vitest";
+import { beforeEach, describe, expect, it, vi } from "vitest";
 import { PlatformBackend } from "../src/backend/platform.js";
 import { createDefaultConfig } from "../src/config.js";
 
 function makeBackend(): PlatformBackend {
-  // apiKey/baseUrl only build request headers; every test spies on _request,
-  // so no real network calls are made.
-  return new PlatformBackend(createDefaultConfig().platform);
+	return new PlatformBackend(createDefaultConfig().platform);
 }
 
-describe("deleteEntities", () => {
-  it("returns all results keyed by entity type for a multi-entity delete", async () => {
-    const backend = makeBackend();
-    const responses: Record<string, unknown> = {
-      "/v2/entities/user/alice/": { message: "user deleted" },
-      "/v2/entities/agent/bob/": { message: "agent deleted" },
-    };
-    const spy = vi
-      // biome-ignore lint/suspicious/noExplicitAny: spying on a private method
-      .spyOn(backend as any, "_request")
-      .mockImplementation(async (_method: string, path: string) => responses[path]);
+function mockFetch() {
+	const fetchMock = vi.fn().mockResolvedValue({
+		ok: true,
+		status: 200,
+		headers: { get: vi.fn().mockReturnValue(null) },
+		json: vi.fn().mockResolvedValue({ message: "ok" }),
+	});
+	vi.stubGlobal("fetch", fetchMock);
+	return fetchMock;
+}
 
-    const result = await backend.deleteEntities({ userId: "alice", agentId: "bob" });
-
-    // Regression: previously only the last entity's response survived.
-    expect(result).toEqual({
-      user: { message: "user deleted" },
-      agent: { message: "agent deleted" },
-    });
-    expect(spy).toHaveBeenCalledTimes(2);
-  });
-
-  it("keys a single-entity delete by its type", async () => {
-    const backend = makeBackend();
-    // biome-ignore lint/suspicious/noExplicitAny: spying on a private method
-    vi.spyOn(backend as any, "_request").mockResolvedValue({ message: "user deleted" });
-
-    const result = await backend.deleteEntities({ userId: "alice" });
-    expect(result).toEqual({ user: { message: "user deleted" } });
-  });
-
-  it("throws when no entity id is provided", async () => {
-    const backend = makeBackend();
-    await expect(backend.deleteEntities({})).rejects.toThrow(
-      "At least one entity ID is required",
-    );
-  });
+beforeEach(() => {
+	vi.restoreAllMocks();
+	vi.unstubAllGlobals();
+});
+
+describe("deleteEntities", () => {
+	it("returns all results keyed by entity type for a multi-entity delete", async () => {
+		const backend = makeBackend();
+		const responses: Record<string, unknown> = {
+			"/v2/entities/user/alice/": { message: "user deleted" },
+			"/v2/entities/agent/bob/": { message: "agent deleted" },
+		};
+		const spy = vi
+			// biome-ignore lint/suspicious/noExplicitAny: spying on a private method
+			.spyOn(backend as any, "_request")
+			.mockImplementation(
+				async (_method: string, path: string) => responses[path],
+			);
+
+		const result = await backend.deleteEntities({
+			userId: "alice",
+			agentId: "bob",
+		});
+
+		expect(result).toEqual({
+			user: { message: "user deleted" },
+			agent: { message: "agent deleted" },
+		});
+		expect(spy).toHaveBeenCalledTimes(2);
+	});
+
+	it("keys a single-entity delete by its type", async () => {
+		const backend = makeBackend();
+		// biome-ignore lint/suspicious/noExplicitAny: spying on a private method
+		vi.spyOn(backend as any, "_request").mockResolvedValue({
+			message: "user deleted",
+		});
+
+		const result = await backend.deleteEntities({ userId: "alice" });
+		expect(result).toEqual({ user: { message: "user deleted" } });
+	});
+
+	it("throws when no entity id is provided", async () => {
+		const backend = makeBackend();
+		await expect(backend.deleteEntities({})).rejects.toThrow(
+			"At least one entity ID is required",
+		);
+	});
+});
+
+describe("PlatformBackend option-parity payloads (MEM-5893)", () => {
+	it("add: custom_instructions, custom_categories, structured_data_schema, timestamp reach the payload alongside existing fields", async () => {
+		const backend = makeBackend();
+		const spy = vi
+			// biome-ignore lint/suspicious/noExplicitAny: spying on a private method
+			.spyOn(backend as any, "_request")
+			.mockResolvedValue({ results: [] });
+
+		await backend.add("hello", undefined, {
+			userId: "alice",
+			metadata: { source: "test" },
+			expires: "2099-01-01",
+			customInstructions: "Extract only preferences.",
+			customCategories: [{ prefs: "user preferences" }],
+			structuredDataSchema: { type: "object" },
+			timestamp: 1700000000,
+		});
+
+		const payload = spy.mock.calls[0][2].json;
+		expect(payload.custom_instructions).toBe("Extract only preferences.");
+		expect(payload.custom_categories).toEqual([{ prefs: "user preferences" }]);
+		expect(payload.structured_data_schema).toEqual({ type: "object" });
+		expect(payload.timestamp).toBe(1700000000);
+		expect(payload.metadata).toEqual({ source: "test" });
+		expect(payload.expiration_date).toBe("2099-01-01");
+	});
+
+	it("add: omitted optional fields are absent from the payload", async () => {
+		const backend = makeBackend();
+		const spy = vi
+			// biome-ignore lint/suspicious/noExplicitAny: spying on a private method
+			.spyOn(backend as any, "_request")
+			.mockResolvedValue({ results: [] });
+
+		await backend.add("hello", undefined, { userId: "alice" });
+
+		const payload = spy.mock.calls[0][2].json;
+		expect(payload).not.toHaveProperty("custom_instructions");
+		expect(payload).not.toHaveProperty("custom_categories");
+		expect(payload).not.toHaveProperty("structured_data_schema");
+		expect(payload).not.toHaveProperty("timestamp");
+	});
+
+	it("search: show_expired, reference_date, latest_only reach the payload", async () => {
+		const backend = makeBackend();
+		// biome-ignore lint/suspicious/noExplicitAny: spying on a private method
+		const spy = vi.spyOn(backend as any, "_request").mockResolvedValue([]);
+
+		await backend.search("query", {
+			showExpired: true,
+			referenceDate: "2024-01-01",
+			latestOnly: true,
+		});
+
+		const payload = spy.mock.calls[0][2].json;
+		expect(payload.show_expired).toBe(true);
+		expect(payload.reference_date).toBe("2024-01-01");
+		expect(payload.latest_only).toBe(true);
+	});
+
+	it("search: keyword_search and fields reach the payload", async () => {
+		const backend = makeBackend();
+		// biome-ignore lint/suspicious/noExplicitAny: spying on a private method
+		const spy = vi.spyOn(backend as any, "_request").mockResolvedValue([]);
+
+		await backend.search("query", {
+			keyword: true,
+			fields: ["memory", "score"],
+		});
+
+		const payload = spy.mock.calls[0][2].json;
+		expect(payload.keyword_search).toBe(true);
+		expect(payload.fields).toEqual(["memory", "score"]);
+	});
+
+	it("search: omitted keyword and fields are absent from the payload", async () => {
+		const backend = makeBackend();
+		// biome-ignore lint/suspicious/noExplicitAny: spying on a private method
+		const spy = vi.spyOn(backend as any, "_request").mockResolvedValue([]);
+
+		await backend.search("query", {});
+
+		const payload = spy.mock.calls[0][2].json;
+		expect(payload).not.toHaveProperty("keyword_search");
+		expect(payload).not.toHaveProperty("fields");
+	});
+
+	it("listMemories: show_expired and latest_only are top-level, not nested inside filters", async () => {
+		const backend = makeBackend();
+		// biome-ignore lint/suspicious/noExplicitAny: spying on a private method
+		const spy = vi.spyOn(backend as any, "_request").mockResolvedValue([]);
+
+		await backend.listMemories({
+			userId: "alice",
+			showExpired: true,
+			latestOnly: true,
+		});
+
+		const payload = spy.mock.calls[0][2].json;
+		expect(payload.show_expired).toBe(true);
+		expect(payload.latest_only).toBe(true);
+		expect(payload.filters ?? {}).not.toHaveProperty("show_expired");
+		expect(payload.filters ?? {}).not.toHaveProperty("latest_only");
+	});
+
+	it("update: expiration_date and timestamp reach the payload", async () => {
+		const backend = makeBackend();
+		// biome-ignore lint/suspicious/noExplicitAny: spying on a private method
+		const spy = vi.spyOn(backend as any, "_request").mockResolvedValue({});
+
+		await backend.update("mem-123", undefined, undefined, {
+			expirationDate: "2099-01-01",
+			timestamp: 1700000000,
+		});
+
+		const payload = spy.mock.calls[0][2].json;
+		expect(payload.expiration_date).toBe("2099-01-01");
+		expect(payload.timestamp).toBe(1700000000);
+	});
+
+	it("delete: delete_linked is a query param, not part of the JSON body", async () => {
+		const backend = makeBackend();
+		// biome-ignore lint/suspicious/noExplicitAny: spying on a private method
+		const spy = vi.spyOn(backend as any, "_request").mockResolvedValue({});
+
+		await backend.delete("mem-123", { deleteLinked: true });
+
+		const opts = spy.mock.calls[0][2];
+		expect(opts.params.delete_linked).toBe("true");
+		expect(opts.json).toBeUndefined();
+	});
+});
+
+describe("PlatformBackend path encoding", () => {
+	it("encodes memory IDs before interpolating them into paths", async () => {
+		const fetchMock = mockFetch();
+		const backend = makeBackend();
+
+		await backend.get("mem/a?b#c");
+		await backend.update("mem/a?b#c", "updated");
+		await backend.delete("mem/a?b#c");
+
+		const urls = fetchMock.mock.calls.map((call) => call[0]);
+		expect(urls).toEqual([
+			"https://api.mem0.ai/v1/memories/mem%2Fa%3Fb%23c/?source=CLI",
+			"https://api.mem0.ai/v1/memories/mem%2Fa%3Fb%23c/",
+			"https://api.mem0.ai/v1/memories/mem%2Fa%3Fb%23c/?source=CLI",
+		]);
+	});
+
+	it("encodes entity and event IDs before interpolating them into paths", async () => {
+		const fetchMock = mockFetch();
+		const backend = makeBackend();
+
+		await backend.deleteEntities({ userId: "org/team?active#frag" });
+		await backend.getEvent("evt/a?b#c");
+
+		const urls = fetchMock.mock.calls.map((call) => call[0]);
+		expect(urls).toEqual([
+			"https://api.mem0.ai/v2/entities/user/org%2Fteam%3Factive%23frag/?source=CLI",
+			"https://api.mem0.ai/v1/event/evt%2Fa%3Fb%23c/",
+		]);
+	});
 });

cli/python/README.md

@@ -241,14 +241,6 @@ Verify your API connection and display the current project.
 mem0 status
 ```
 
-### `mem0 version`
-
-Print the CLI version.
-
-```bash
-mem0 version
-```
-
 ## Agent mode
 
 Pass `--agent` (or its alias `--json`) as a **global flag** on any command to get output designed for AI agent tool loops:
@@ -307,6 +299,8 @@ These flags are available on all commands:
 | `--base-url` | Override the configured API base URL for this request |
 | `-o, --output` | Set the output format |
 
+`mem0 --version` prints the CLI version. It is only valid before a subcommand, not after one.
+
 ## Environment variables
 
 | Variable | Description |

cli/python/development.md

@@ -80,7 +80,7 @@ mem0 --help
 # Using Python directly (with venv activated)
 source .venv/bin/activate
 mem0 --help
-mem0 version
+mem0 --version
 
 # Or run without activating
 .venv/bin/mem0 --help

cli/python/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "mem0-cli"
-version = "0.2.9"
+version = "0.2.11"
 description = "The official CLI for mem0 — the memory layer for AI agents"
 readme = "README.md"
 license = "Apache-2.0"

cli/python/src/mem0_cli/__init__.py

@@ -1,3 +1,3 @@
 """mem0 CLI — the command-line interface for the mem0 memory layer."""
 
-__version__ = "0.2.9"
+__version__ = "0.2.11"

cli/python/src/mem0_cli/app.py

@@ -273,7 +273,21 @@ def add(
     no_infer: bool = typer.Option(False, "--no-infer", help="Skip inference, store raw."),
     expires: str | None = typer.Option(None, "--expires", help="Expiration date (YYYY-MM-DD)."),
     categories: str | None = typer.Option(
-        None, "--categories", help="Categories (JSON array or comma-separated)."
+        None, "--categories", help="Not supported on add, use --custom-categories instead."
+    ),
+    custom_instructions: str | None = typer.Option(
+        None, "--custom-instructions", help="Custom instructions for fact extraction."
+    ),
+    custom_categories: str | None = typer.Option(
+        None,
+        "--custom-categories",
+        help="Custom categories as a JSON array of {name: description} objects.",
+    ),
+    structured_data_schema: str | None = typer.Option(
+        None, "--structured-data-schema", help="Schema for structured data extraction, as JSON."
+    ),
+    timestamp: int | None = typer.Option(
+        None, "--timestamp", help="Unix timestamp for the memory."
     ),
     output: str = typer.Option(
         "text", "--output", "-o", help="Output format: text, json, quiet.", rich_help_panel="Output"
@@ -312,6 +326,10 @@ def add(
         no_infer=no_infer,
         expires=expires,
         categories=categories,
+        custom_instructions=custom_instructions,
+        custom_categories=custom_categories,
+        structured_data_schema=structured_data_schema,
+        timestamp=timestamp,
         output=output,
     )
 
@@ -355,6 +373,21 @@ def search(
         help="Specific fields to return (comma-separated).",
         rich_help_panel="Search",
     ),
+    show_expired: bool = typer.Option(
+        False, "--show-expired", help="Include expired memories.", rich_help_panel="Search"
+    ),
+    reference_date: str | None = typer.Option(
+        None,
+        "--reference-date",
+        help="Reference date for relative queries (YYYY-MM-DD or unix timestamp).",
+        rich_help_panel="Search",
+    ),
+    latest_only: bool = typer.Option(
+        False,
+        "--latest-only",
+        help="Only return the latest version of each memory.",
+        rich_help_panel="Search",
+    ),
     output: str = typer.Option(
         "text", "--output", "-o", help="Output: text, json, table.", rich_help_panel="Output"
     ),
@@ -398,6 +431,9 @@ def search(
         keyword=keyword,
         filter_json=filter_json,
         fields=fields,
+        show_expired=show_expired,
+        reference_date=reference_date,
+        latest_only=latest_only,
         output=output,
     )
 
@@ -464,6 +500,15 @@ def list_cmd(
     before: str | None = typer.Option(
         None, "--before", help="Created before (YYYY-MM-DD).", rich_help_panel="Filters"
     ),
+    show_expired: bool = typer.Option(
+        False, "--show-expired", help="Include expired memories.", rich_help_panel="Filters"
+    ),
+    latest_only: bool = typer.Option(
+        False,
+        "--latest-only",
+        help="Only return the latest version of each memory.",
+        rich_help_panel="Filters",
+    ),
     output: str = typer.Option(
         "table", "--output", "-o", help="Output: text, json, table.", rich_help_panel="Output"
     ),
@@ -497,6 +542,8 @@ def list_cmd(
         category=category,
         after=after,
         before=before,
+        show_expired=show_expired,
+        latest_only=latest_only,
         output=output,
     )
 
@@ -509,6 +556,10 @@ def update(
     memory_id: str = typer.Argument(..., help="Memory ID to update."),
     text: str | None = typer.Argument(None, help="New memory text."),
     metadata: str | None = typer.Option(None, "--metadata", "-m", help="Update metadata (JSON)."),
+    expires: str | None = typer.Option(None, "--expires", help="Expiration date (YYYY-MM-DD)."),
+    timestamp: int | None = typer.Option(
+        None, "--timestamp", help="Unix timestamp for the memory."
+    ),
     output: str = typer.Option(
         "text", "--output", "-o", help="Output: text, json, quiet.", rich_help_panel="Output"
     ),
@@ -537,7 +588,15 @@ def update(
         text = _read_stdin()
 
     backend = _get_backend(api_key, base_url)
-    cmd_update(backend, memory_id, text, metadata=metadata, output=output)
+    cmd_update(
+        backend,
+        memory_id,
+        text,
+        metadata=metadata,
+        expires=expires,
+        timestamp=timestamp,
+        output=output,
+    )
 
 
 # ── Memory: delete ────────────────────────────────────────────────────────
@@ -559,6 +618,9 @@ def delete(
         False, "--dry-run", help="Show what would be deleted without deleting."
     ),
     force: bool = typer.Option(False, "--force", help="Skip confirmation."),
+    delete_linked: bool = typer.Option(
+        False, "--delete-linked", help="Also delete memories linked to this memory."
+    ),
     user_id: str | None = typer.Option(
         None, "--user-id", "-u", help="Scope to user.", rich_help_panel="Scope"
     ),
@@ -616,7 +678,14 @@ def delete(
         from mem0_cli.commands.memory import cmd_delete
 
         backend = _get_backend(api_key, base_url)
-        cmd_delete(backend, memory_id, dry_run=dry_run, force=force, output=output)
+        cmd_delete(
+            backend,
+            memory_id,
+            dry_run=dry_run,
+            force=force,
+            delete_linked=delete_linked,
+            output=output,
+        )
 
     elif all_:
         _fire_telemetry("delete", {"delete_mode": "all"})
@@ -1068,7 +1137,11 @@ def _build_help_json() -> dict:
                 "--immutable": "Prevent future updates.",
                 "--no-infer": "Skip inference, store raw.",
                 "--expires": "Expiration date (YYYY-MM-DD).",
-                "--categories": "Categories (JSON array or comma-separated).",
+                "--categories": "Not supported on add, use --custom-categories instead.",
+                "--custom-instructions": "Custom instructions for fact extraction.",
+                "--custom-categories": "Custom categories as a JSON array of {name: description} objects.",
+                "--structured-data-schema": "Schema for structured data extraction, as JSON.",
+                "--timestamp": "Unix timestamp for the memory.",
                 "--graph": "Enable graph memory extraction.",
                 "--no-graph": "Disable graph memory extraction.",
                 "--output, -o": "Output format: text, json, quiet.",
@@ -1087,6 +1160,9 @@ def _build_help_json() -> dict:
                 "--keyword": "Use keyword search instead of semantic.",
                 "--filter": "Advanced filter expression (JSON).",
                 "--fields": "Specific fields to return (comma-separated).",
+                "--show-expired": "Include expired memories.",
+                "--reference-date": "Reference date for relative queries (YYYY-MM-DD or unix timestamp).",
+                "--latest-only": "Only return the latest version of each memory.",
                 "--graph": "Enable graph in search.",
                 "--no-graph": "Disable graph in search.",
                 "--output, -o": "Output format: text, json, table.",
@@ -1110,6 +1186,8 @@ def _build_help_json() -> dict:
                 "--category": "Filter by category.",
                 "--after": "Created after (YYYY-MM-DD).",
                 "--before": "Created before (YYYY-MM-DD).",
+                "--show-expired": "Include expired memories.",
+                "--latest-only": "Only return the latest version of each memory.",
                 "--graph": "Enable graph in listing.",
                 "--no-graph": "Disable graph in listing.",
                 "--output, -o": "Output format: text, json, table.",
@@ -1124,6 +1202,8 @@ def _build_help_json() -> dict:
             },
             "options": {
                 "--metadata, -m": "Update metadata (JSON).",
+                "--expires": "Expiration date (YYYY-MM-DD).",
+                "--timestamp": "Unix timestamp for the memory.",
                 "--output, -o": "Output format: text, json, quiet.",
             },
         },
@@ -1140,6 +1220,7 @@ def _build_help_json() -> dict:
                 "--all": "Delete all memories matching scope filters.",
                 "--entity": "Delete the entity itself and all its memories (cascade).",
                 "--project": "With --all: delete ALL memories project-wide.",
+                "--delete-linked": "Also delete memories linked to this memory.",
                 "--dry-run": "Show what would be deleted without deleting.",
                 "--force": "Skip confirmation.",
                 "--user-id, -u": "Scope to user.",
@@ -1269,8 +1350,10 @@ def help(
       mem0 help
       mem0 help --json
     """
-    if json:
-        console.print(_json.dumps(_build_help_json(), indent=2))
+    from mem0_cli.state import is_agent_mode
+
+    if json or is_agent_mode():
+        console.print_json(_json.dumps(_build_help_json()))
     else:
         console.print(
             f"[{BRAND_COLOR}]◆ mem0 CLI[/] v{__version__} — The Memory Layer for AI Agents\n"

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

@@ -25,7 +25,10 @@ class Backend(ABC):
         immutable: bool = False,
         infer: bool = True,
         expires: str | None = None,
-        categories: list[str] | None = None,
+        custom_instructions: str | None = None,
+        custom_categories: list[dict] | None = None,
+        structured_data_schema: dict | None = None,
+        timestamp: int | None = None,
     ) -> dict: ...
 
     @abstractmethod
@@ -43,6 +46,9 @@ class Backend(ABC):
         keyword: bool = False,
         filters: dict | None = None,
         fields: list[str] | None = None,
+        show_expired: bool = False,
+        reference_date: str | None = None,
+        latest_only: bool = False,
     ) -> list[dict]: ...
 
     @abstractmethod
@@ -61,11 +67,19 @@ class Backend(ABC):
         category: str | None = None,
         after: str | None = None,
         before: str | None = None,
+        show_expired: bool = False,
+        latest_only: bool = False,
     ) -> list[dict]: ...
 
     @abstractmethod
     def update(
-        self, memory_id: str, content: str | None = None, metadata: dict | None = None
+        self,
+        memory_id: str,
+        content: str | None = None,
+        metadata: dict | None = None,
+        *,
+        expiration_date: str | None = None,
+        timestamp: int | None = None,
     ) -> dict: ...
 
     @abstractmethod
@@ -78,6 +92,7 @@ class Backend(ABC):
         agent_id: str | None = None,
         app_id: str | None = None,
         run_id: str | None = None,
+        delete_linked: bool = False,
     ) -> dict: ...
 
     @abstractmethod

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

@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 from typing import Any
+from urllib.parse import quote
 
 import httpx
 
@@ -11,6 +12,10 @@ from mem0_cli.backend.base import Backend
 from mem0_cli.config import PlatformConfig
 
 
+def _encode_path_segment(value: Any) -> str:
+    return quote(str(value), safe="")
+
+
 class PlatformBackend(Backend):
     """Backend that talks to the mem0 Platform API."""
 
@@ -82,7 +87,10 @@ class PlatformBackend(Backend):
         immutable: bool = False,
         infer: bool = True,
         expires: str | None = None,
-        categories: list[str] | None = None,
+        custom_instructions: str | None = None,
+        custom_categories: list[dict] | None = None,
+        structured_data_schema: dict | None = None,
+        timestamp: int | None = None,
     ) -> dict:
         payload: dict[str, Any] = {}
 
@@ -107,8 +115,14 @@ class PlatformBackend(Backend):
             payload["infer"] = False
         if expires:
             payload["expiration_date"] = expires
-        if categories:
-            payload["categories"] = categories
+        if custom_instructions:
+            payload["custom_instructions"] = custom_instructions
+        if custom_categories:
+            payload["custom_categories"] = custom_categories
+        if structured_data_schema:
+            payload["structured_data_schema"] = structured_data_schema
+        if timestamp is not None:
+            payload["timestamp"] = timestamp
         payload["source"] = "CLI"
 
         return self._request("POST", "/v3/memories/add/", json=payload)
@@ -168,6 +182,9 @@ class PlatformBackend(Backend):
         keyword: bool = False,
         filters: dict | None = None,
         fields: list[str] | None = None,
+        show_expired: bool = False,
+        reference_date: str | None = None,
+        latest_only: bool = False,
     ) -> list[dict]:
         payload: dict[str, Any] = {"query": query, "top_k": top_k, "threshold": threshold}
 
@@ -186,6 +203,12 @@ class PlatformBackend(Backend):
             payload["keyword_search"] = True
         if fields:
             payload["fields"] = fields
+        if show_expired:
+            payload["show_expired"] = True
+        if reference_date is not None:
+            payload["reference_date"] = reference_date
+        if latest_only:
+            payload["latest_only"] = True
         payload["source"] = "CLI"
 
         result = self._request("POST", "/v3/memories/search/", json=payload)
@@ -196,7 +219,11 @@ class PlatformBackend(Backend):
         )
 
     def get(self, memory_id: str) -> dict:
-        return self._request("GET", f"/v1/memories/{memory_id}/", params={"source": "CLI"})
+        return self._request(
+            "GET",
+            f"/v1/memories/{_encode_path_segment(memory_id)}/",
+            params={"source": "CLI"},
+        )
 
     def list_memories(
         self,
@@ -210,6 +237,8 @@ class PlatformBackend(Backend):
         category: str | None = None,
         after: str | None = None,
         before: str | None = None,
+        show_expired: bool = False,
+        latest_only: bool = False,
     ) -> list[dict]:
         payload: dict[str, Any] = {}
         params = {"page": str(page), "page_size": str(page_size)}
@@ -232,6 +261,10 @@ class PlatformBackend(Backend):
         )
         if api_filters:
             payload["filters"] = api_filters
+        if show_expired:
+            payload["show_expired"] = True
+        if latest_only:
+            payload["latest_only"] = True
         payload["source"] = "CLI"
 
         result = self._request("POST", "/v3/memories/", json=payload, params=params)
@@ -242,15 +275,29 @@ class PlatformBackend(Backend):
         )
 
     def update(
-        self, memory_id: str, content: str | None = None, metadata: dict | None = None
+        self,
+        memory_id: str,
+        content: str | None = None,
+        metadata: dict | None = None,
+        *,
+        expiration_date: str | None = None,
+        timestamp: int | None = None,
     ) -> dict:
         payload: dict[str, Any] = {}
         if content:
             payload["text"] = content
         if metadata:
             payload["metadata"] = metadata
+        if expiration_date:
+            payload["expiration_date"] = expiration_date
+        if timestamp is not None:
+            payload["timestamp"] = timestamp
         payload["source"] = "CLI"
-        return self._request("PUT", f"/v1/memories/{memory_id}/", json=payload)
+        return self._request(
+            "PUT",
+            f"/v1/memories/{_encode_path_segment(memory_id)}/",
+            json=payload,
+        )
 
     def delete(
         self,
@@ -261,6 +308,7 @@ class PlatformBackend(Backend):
         agent_id: str | None = None,
         app_id: str | None = None,
         run_id: str | None = None,
+        delete_linked: bool = False,
     ) -> dict:
         if all:
             params: dict[str, str] = {"source": "CLI"}
@@ -274,7 +322,14 @@ class PlatformBackend(Backend):
                 params["run_id"] = run_id
             return self._request("DELETE", "/v1/memories/", params=params)
         elif memory_id:
-            return self._request("DELETE", f"/v1/memories/{memory_id}/", params={"source": "CLI"})
+            params = {"source": "CLI"}
+            if delete_linked:
+                params["delete_linked"] = "true"
+            return self._request(
+                "DELETE",
+                f"/v1/memories/{_encode_path_segment(memory_id)}/",
+                params=params,
+            )
         else:
             raise ValueError("Either memory_id or --all is required")
 
@@ -302,7 +357,9 @@ class PlatformBackend(Backend):
         results: dict = {}
         for entity_type, entity_id in entities.items():
             results[entity_type] = self._request(
-                "DELETE", f"/v2/entities/{entity_type}/{entity_id}/", params={"source": "CLI"}
+                "DELETE",
+                f"/v2/entities/{_encode_path_segment(entity_type)}/{_encode_path_segment(entity_id)}/",
+                params={"source": "CLI"},
             )
         return results
 
@@ -348,7 +405,7 @@ class PlatformBackend(Backend):
         return result if isinstance(result, list) else result.get("results", [])
 
     def get_event(self, event_id: str) -> dict:
-        return self._request("GET", f"/v1/event/{event_id}/")
+        return self._request("GET", f"/v1/event/{_encode_path_segment(event_id)}/")
 
 
 class AuthError(Exception):

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

@@ -4,9 +4,11 @@ from __future__ import annotations
 
 import json
 import os
+import re
 import stat as _stat_mod
 import sys
 import time as _time
+from datetime import date
 from pathlib import Path
 
 import typer
@@ -47,6 +49,18 @@ def _stdin_is_piped() -> bool:
         return False
 
 
+def _validate_expires(value: str) -> None:
+    """Exit 1 if value is not a future YYYY-MM-DD date."""
+    if not re.match(r"^\d{4}-\d{2}-\d{2}$", value):
+        print_error(
+            err_console, "Invalid date format for --expires. Use YYYY-MM-DD (e.g. 2025-12-31)."
+        )
+        raise typer.Exit(1)
+    if date.fromisoformat(value) <= date.today():
+        print_error(err_console, "--expires date must be in the future.")
+        raise typer.Exit(1)
+
+
 def cmd_add(
     backend: Backend,
     text: str | None,
@@ -62,6 +76,10 @@ def cmd_add(
     no_infer: bool,
     expires: str | None,
     categories: str | None,
+    custom_instructions: str | None = None,
+    custom_categories: str | None = None,
+    structured_data_schema: str | None = None,
+    timestamp: int | None = None,
     output: str = "text",
 ) -> None:
     """Add a memory."""
@@ -70,6 +88,13 @@ def cmd_add(
     set_current_command("add")
     if is_agent_mode():
         output = "agent"
+
+    if categories:
+        print_error(
+            err_console, "--categories is not supported on add. Use --custom-categories instead."
+        )
+        raise typer.Exit(1)
+
     msgs = None
     content = text
 
@@ -108,27 +133,24 @@ def cmd_add(
             print_error(err_console, "Invalid JSON in --metadata.")
             raise typer.Exit(1) from None
 
-    cats = None
-    if categories:
+    custom_cats = None
+    if custom_categories:
         try:
-            cats = json.loads(categories)
+            custom_cats = json.loads(custom_categories)
         except json.JSONDecodeError:
-            cats = [c.strip() for c in categories.split(",")]
+            print_error(err_console, "Invalid JSON in --custom-categories.")
+            raise typer.Exit(1) from None
+
+    schema = None
+    if structured_data_schema:
+        try:
+            schema = json.loads(structured_data_schema)
+        except json.JSONDecodeError:
+            print_error(err_console, "Invalid JSON in --structured-data-schema.")
+            raise typer.Exit(1) from None
 
-    # Validate --expires
     if expires:
-        import re
-
-        if not re.match(r"^\d{4}-\d{2}-\d{2}$", expires):
-            print_error(
-                err_console, "Invalid date format for --expires. Use YYYY-MM-DD (e.g. 2025-12-31)."
-            )
-            raise typer.Exit(1)
-        from datetime import date
-
-        if date.fromisoformat(expires) <= date.today():
-            print_error(err_console, "--expires date must be in the future.")
-            raise typer.Exit(1)
+        _validate_expires(expires)
 
     with timed_status(err_console, "Adding memory...") as ts:
         try:
@@ -143,7 +165,10 @@ def cmd_add(
                 immutable=immutable,
                 infer=not no_infer,
                 expires=expires,
-                categories=cats,
+                custom_instructions=custom_instructions,
+                custom_categories=custom_cats,
+                structured_data_schema=schema,
+                timestamp=timestamp,
             )
         except Exception as e:
             ts.error_msg = str(e)
@@ -224,6 +249,9 @@ def cmd_search(
     keyword: bool,
     filter_json: str | None,
     fields: str | None,
+    show_expired: bool = False,
+    reference_date: str | None = None,
+    latest_only: bool = False,
     output: str = "text",
 ) -> None:
     """Search memories."""
@@ -266,6 +294,9 @@ def cmd_search(
                 keyword=keyword,
                 filters=filters,
                 fields=field_list,
+                show_expired=show_expired,
+                reference_date=reference_date,
+                latest_only=latest_only,
             )
         except Exception as e:
             print_error(err_console, str(e))
@@ -352,6 +383,8 @@ def cmd_list(
     category: str | None,
     after: str | None,
     before: str | None,
+    show_expired: bool = False,
+    latest_only: bool = False,
     output: str = "table",
 ) -> None:
     """List memories."""
@@ -380,6 +413,8 @@ def cmd_list(
                 category=category,
                 after=after,
                 before=before,
+                show_expired=show_expired,
+                latest_only=latest_only,
             )
         except Exception as e:
             print_error(err_console, str(e))
@@ -446,6 +481,8 @@ def cmd_update(
     text: str | None,
     *,
     metadata: str | None,
+    expires: str | None = None,
+    timestamp: int | None = None,
     output: str,
 ) -> None:
     """Update a memory."""
@@ -462,10 +499,19 @@ def cmd_update(
             print_error(err_console, "Invalid JSON in --metadata.")
             raise typer.Exit(1) from None
 
+    if expires:
+        _validate_expires(expires)
+
     _start = _time.perf_counter()
     with timed_status(err_console, "Updating memory...") as _ts:
         try:
-            result = backend.update(memory_id, content=text, metadata=meta)
+            result = backend.update(
+                memory_id,
+                content=text,
+                metadata=meta,
+                expiration_date=expires,
+                timestamp=timestamp,
+            )
         except Exception as e:
             print_error(err_console, str(e))
             raise typer.Exit(1) from None
@@ -490,6 +536,7 @@ def cmd_delete(
     *,
     dry_run: bool = False,
     force: bool = False,
+    delete_linked: bool = False,
     output: str,
 ) -> None:
     """Delete a single memory by ID."""
@@ -512,7 +559,7 @@ def cmd_delete(
     _start = _time.perf_counter()
     with timed_status(err_console, "Deleting...") as _ts:
         try:
-            result = backend.delete(memory_id=memory_id)
+            result = backend.delete(memory_id=memory_id, delete_linked=delete_linked)
         except Exception as e:
             print_error(err_console, str(e))
             raise typer.Exit(1) from None

cli/python/src/mem0_cli/output.py

@@ -262,16 +262,32 @@ def sanitize_agent_data(command: str, data: Any) -> Any:
         return result
 
     if command == "search":
-        return [pick(r, ["id", "memory", "score", "created_at", "categories"]) for r in data]
+        return [
+            pick(r, ["id", "memory", "score", "created_at", "categories", "expiration_date"])
+            for r in data
+        ]
 
     if command == "list":
-        return [pick(r, ["id", "memory", "created_at", "categories"]) for r in data]
+        return [
+            pick(r, ["id", "memory", "created_at", "categories", "expiration_date"]) for r in data
+        ]
 
     if command == "get":
-        return pick(data, ["id", "memory", "created_at", "updated_at", "categories", "metadata"])
+        return pick(
+            data,
+            [
+                "id",
+                "memory",
+                "created_at",
+                "updated_at",
+                "categories",
+                "metadata",
+                "expiration_date",
+            ],
+        )
 
     if command == "update":
-        return pick(data, ["id", "memory"])
+        return pick(data, ["id", "memory", "expiration_date"])
 
     if command in ("delete", "delete-all", "entity delete"):
         return data

cli/python/tests/test_cli_integration.py

@@ -7,6 +7,7 @@ boundaries).
 
 from __future__ import annotations
 
+import json
 import os
 import re
 import subprocess
@@ -84,6 +85,31 @@ class TestCLIIntegration:
         assert "add" in result.stdout
         assert "search" in result.stdout
 
+    def test_version_flag_only(self):
+        from mem0_cli import __version__
+
+        flag = _run(["--version"])
+        assert flag.returncode == 0
+        assert __version__ in flag.stdout
+        assert _run(["version"]).returncode != 0
+
+    @pytest.mark.parametrize(
+        "args",
+        [["help", "--json"], ["--json", "help"], ["help", "--agent"], ["--agent", "help"]],
+    )
+    def test_help_json_produces_valid_json(self, args):
+        result = _run(args)
+        assert result.returncode == 0
+        spec = json.loads(result.stdout)
+        assert spec["name"] == "mem0"
+        assert "add" in spec["commands"]
+
+    def test_help_without_json_is_text(self):
+        result = _run(["help"])
+        assert result.returncode == 0
+        with pytest.raises(json.JSONDecodeError):
+            json.loads(result.stdout)
+
     def test_add_help(self):
         result = _run(["add", "--help"])
         assert result.returncode == 0

cli/python/tests/test_commands.py

@@ -252,12 +252,13 @@ class TestAddCommand:
             )
         mock_backend.add.assert_called_once()
 
-    def test_add_categories_csv(self, mock_backend):
+    def test_add_categories_rejected(self, mock_backend):
         console, _buf = _make_console()
-        err_console, _err_buf = _make_err_console()
+        err_console, err_buf = _make_err_console()
         with (
             patch("mem0_cli.commands.memory.console", console),
             patch("mem0_cli.commands.memory.err_console", err_console),
+            pytest.raises((SystemExit, TyperExit)),
         ):
             cmd_add(
                 mock_backend,
@@ -275,7 +276,93 @@ class TestAddCommand:
                 categories="health,prefs",
                 output="text",
             )
-        mock_backend.add.assert_called_once()
+        assert "--custom-categories" in err_buf.getvalue()
+        mock_backend.add.assert_not_called()
+
+    def test_add_invalid_custom_categories_json(self, mock_backend):
+        console, _buf = _make_console()
+        err_console, err_buf = _make_err_console()
+        with (
+            patch("mem0_cli.commands.memory.console", console),
+            patch("mem0_cli.commands.memory.err_console", err_console),
+            pytest.raises((SystemExit, TyperExit)),
+        ):
+            cmd_add(
+                mock_backend,
+                "test",
+                user_id="alice",
+                agent_id=None,
+                app_id=None,
+                run_id=None,
+                messages=None,
+                file=None,
+                metadata=None,
+                immutable=False,
+                no_infer=False,
+                expires=None,
+                categories=None,
+                custom_categories="not-json",
+                output="text",
+            )
+        assert "--custom-categories" in err_buf.getvalue()
+        mock_backend.add.assert_not_called()
+
+    def test_add_invalid_structured_data_schema_json(self, mock_backend):
+        console, _buf = _make_console()
+        err_console, err_buf = _make_err_console()
+        with (
+            patch("mem0_cli.commands.memory.console", console),
+            patch("mem0_cli.commands.memory.err_console", err_console),
+            pytest.raises((SystemExit, TyperExit)),
+        ):
+            cmd_add(
+                mock_backend,
+                "test",
+                user_id="alice",
+                agent_id=None,
+                app_id=None,
+                run_id=None,
+                messages=None,
+                file=None,
+                metadata=None,
+                immutable=False,
+                no_infer=False,
+                expires=None,
+                categories=None,
+                structured_data_schema="not-json",
+                output="text",
+            )
+        assert "--structured-data-schema" in err_buf.getvalue()
+        mock_backend.add.assert_not_called()
+
+    def test_add_regression_metadata_expiration_custom_categories_together(self, mock_backend):
+        console, _buf = _make_console()
+        err_console, _err_buf = _make_err_console()
+        with (
+            patch("mem0_cli.commands.memory.console", console),
+            patch("mem0_cli.commands.memory.err_console", err_console),
+        ):
+            cmd_add(
+                mock_backend,
+                "test",
+                user_id="alice",
+                agent_id=None,
+                app_id=None,
+                run_id=None,
+                messages=None,
+                file=None,
+                metadata='{"source": "test"}',
+                immutable=False,
+                no_infer=False,
+                expires="2099-01-01",
+                categories=None,
+                custom_categories='[{"prefs": "user preferences"}]',
+                output="text",
+            )
+        call_kwargs = mock_backend.add.call_args.kwargs
+        assert call_kwargs["metadata"] == {"source": "test"}
+        assert call_kwargs["expires"] == "2099-01-01"
+        assert call_kwargs["custom_categories"] == [{"prefs": "user preferences"}]
 
 
 class TestAddDeduplicatesPending:
@@ -464,6 +551,36 @@ class TestSearchCommand:
             )
         mock_backend.search.assert_called_once()
 
+    def test_search_new_flags_reach_backend(self, mock_backend):
+        console, _buf = _make_console()
+        err_console, _err_buf = _make_err_console()
+        with (
+            patch("mem0_cli.commands.memory.console", console),
+            patch("mem0_cli.commands.memory.err_console", err_console),
+        ):
+            cmd_search(
+                mock_backend,
+                "preferences",
+                user_id="alice",
+                agent_id=None,
+                app_id=None,
+                run_id=None,
+                top_k=10,
+                threshold=0.3,
+                rerank=False,
+                keyword=False,
+                filter_json=None,
+                fields=None,
+                show_expired=True,
+                reference_date="2024-01-01",
+                latest_only=True,
+                output="text",
+            )
+        call_kwargs = mock_backend.search.call_args.kwargs
+        assert call_kwargs["show_expired"] is True
+        assert call_kwargs["reference_date"] == "2024-01-01"
+        assert call_kwargs["latest_only"] is True
+
 
 class TestGetCommand:
     def test_get_text(self, mock_backend):
@@ -561,6 +678,32 @@ class TestListCommand:
         output = buf.getvalue()
         assert "No memories found" in output
 
+    def test_list_new_flags_reach_backend(self, mock_backend):
+        console, _buf = _make_console()
+        err_console, _err_buf = _make_err_console()
+        with (
+            patch("mem0_cli.commands.memory.console", console),
+            patch("mem0_cli.commands.memory.err_console", err_console),
+        ):
+            cmd_list(
+                mock_backend,
+                user_id="alice",
+                agent_id=None,
+                app_id=None,
+                run_id=None,
+                page=1,
+                page_size=100,
+                category=None,
+                after=None,
+                before=None,
+                show_expired=True,
+                latest_only=True,
+                output="table",
+            )
+        call_kwargs = mock_backend.list_memories.call_args.kwargs
+        assert call_kwargs["show_expired"] is True
+        assert call_kwargs["latest_only"] is True
+
 
 class TestUpdateCommand:
     def test_update(self, mock_backend):
@@ -585,6 +728,26 @@ class TestUpdateCommand:
         output = buf.getvalue()
         assert '"memory"' in output
 
+    def test_update_new_fields_reach_backend(self, mock_backend):
+        console, _buf = _make_console()
+        err_console, _err_buf = _make_err_console()
+        with (
+            patch("mem0_cli.commands.memory.console", console),
+            patch("mem0_cli.commands.memory.err_console", err_console),
+        ):
+            cmd_update(
+                mock_backend,
+                "abc-123",
+                "New text",
+                metadata=None,
+                expires="2099-01-01",
+                timestamp=1700000000,
+                output="text",
+            )
+        call_kwargs = mock_backend.update.call_args.kwargs
+        assert call_kwargs["expiration_date"] == "2099-01-01"
+        assert call_kwargs["timestamp"] == 1700000000
+
 
 class TestDeleteCommand:
     def test_delete_single(self, mock_backend):
@@ -610,6 +773,17 @@ class TestDeleteCommand:
         assert "dry run" in output.lower()
         mock_backend.delete.assert_not_called()
 
+    def test_delete_linked_reaches_backend(self, mock_backend):
+        console, _buf = _make_console()
+        err_console, _err_buf = _make_err_console()
+        with (
+            patch("mem0_cli.commands.memory.console", console),
+            patch("mem0_cli.commands.memory.err_console", err_console),
+        ):
+            cmd_delete(mock_backend, "abc-123", delete_linked=True, output="text")
+        call_kwargs = mock_backend.delete.call_args.kwargs
+        assert call_kwargs["delete_linked"] is True
+
 
 class TestDeleteAllCommand:
     def test_delete_all_force(self, mock_backend):

cli/python/tests/test_option_parity.py

@@ -0,0 +1,102 @@
+"""Drift test: every documented v3 add/search/list param must be reachable from the Python CLI."""
+
+import json
+from pathlib import Path
+
+import typer.main
+
+from mem0_cli.app import app
+
+REPO_ROOT = Path(__file__).resolve().parents[3]
+OPENAPI_PATH = REPO_ROOT / "docs" / "openapi.json"
+
+KNOWN_UNSURFACED: dict[tuple[str, str], str] = {
+    ("/v3/memories/add/", "includes"): "extraction hint, no CLI flag yet",
+    ("/v3/memories/add/", "excludes"): "extraction hint, no CLI flag yet",
+    ("/v3/memories/add/", "enable_graph"): "graph memory toggle, no CLI flag yet",
+    ("/v3/memories/add/", "output_format"): "response envelope is pinned by the CLI",
+    ("/v3/memories/add/", "prompt_profile_id"): "no CLI flag yet",
+    ("/v3/memories/add/", "temporal_reasoning"): "no CLI flag yet",
+    ("/v3/memories/add/", "timezone"): "no CLI flag yet",
+    ("/v3/memories/add/", "observation_datetime"): "no CLI flag yet, --timestamp backdates instead",
+    ("/v3/memories/add/", "observation_date"): "no CLI flag yet, --timestamp backdates instead",
+    ("/v3/memories/search/", "categories"): "expressible through --filter",
+    ("/v3/memories/search/", "metadata"): "expressible through --filter",
+    ("/v3/memories/", "start_date"): "covered by --after via filters.created_at.gte",
+    ("/v3/memories/", "end_date"): "covered by --before via filters.created_at.lte",
+    ("/v3/memories/", "categories"): "covered by --category via filters.categories",
+    ("/v3/memories/", "fields"): "no CLI flag yet",
+    ("/v3/memories/", "keywords"): "no CLI flag yet",
+}
+
+ADD_MAPPING: dict[str, list[str]] = {
+    "messages": ["messages", "file", "text"],
+    "user_id": ["user_id"],
+    "agent_id": ["agent_id"],
+    "app_id": ["app_id"],
+    "run_id": ["run_id"],
+    "metadata": ["metadata"],
+    "expiration_date": ["expires"],
+    "custom_instructions": ["custom_instructions"],
+    "custom_categories": ["custom_categories"],
+    "infer": ["no_infer"],
+    "immutable": ["immutable"],
+    "structured_data_schema": ["structured_data_schema"],
+    "timestamp": ["timestamp"],
+}
+
+SEARCH_MAPPING: dict[str, list[str]] = {
+    "query": ["query"],
+    "filters": ["filter_json", "user_id", "agent_id", "run_id"],
+    "show_expired": ["show_expired"],
+    "top_k": ["top_k"],
+    "threshold": ["threshold"],
+    "rerank": ["rerank"],
+    "reference_date": ["reference_date"],
+    "fields": ["fields"],
+}
+
+LIST_MAPPING: dict[str, list[str]] = {
+    "filters": ["user_id", "agent_id", "run_id", "category", "after", "before"],
+    "show_expired": ["show_expired"],
+    "page": ["page"],
+    "page_size": ["page_size"],
+}
+
+
+def _documented_fields(endpoint: str) -> set[str]:
+    spec = json.loads(OPENAPI_PATH.read_text())
+    schema = spec["paths"][endpoint]["post"]["requestBody"]["content"]["application/json"]["schema"]
+    return set(schema["properties"])
+
+
+def _cli_param_names(command_name: str) -> set[str]:
+    click_app = typer.main.get_command(app)
+    command = click_app.commands[command_name]
+    return {param.name for param in command.params}
+
+
+def _assert_all_reachable(endpoint: str, mapping: dict[str, list[str]], command_name: str) -> None:
+    documented = _documented_fields(endpoint)
+    reachable = _cli_param_names(command_name)
+    for field in documented:
+        if (endpoint, field) in KNOWN_UNSURFACED:
+            continue
+        candidates = mapping.get(field)
+        assert candidates, (
+            f"{endpoint}: documented field {field!r} has no mapping entry for command {command_name!r}"
+        )
+        assert any(candidate in reachable for candidate in candidates), (
+            f"{endpoint}: documented field {field!r} not reachable via any of {candidates} on command {command_name!r}"
+        )
+
+
+class TestOptionParity:
+    def test_add_covers_documented_fields(self):
+        _assert_all_reachable("/v3/memories/add/", ADD_MAPPING, "add")
+
+    def test_search_covers_documented_fields(self):
+        _assert_all_reachable("/v3/memories/search/", SEARCH_MAPPING, "search")
+
+    def test_list_covers_documented_fields(self):
+        _assert_all_reachable("/v3/memories/", LIST_MAPPING, "list")

cli/python/tests/test_platform_backend_options.py

@@ -0,0 +1,109 @@
+"""Tests that the MEM-5893 option-parity flags reach the correct request payload/params."""
+
+from __future__ import annotations
+
+from unittest.mock import patch
+
+from mem0_cli.backend.platform import PlatformBackend
+from mem0_cli.config import PlatformConfig
+
+
+def _make_backend() -> PlatformBackend:
+    return PlatformBackend(PlatformConfig(api_key="test-key", base_url="https://api.mem0.ai"))
+
+
+class TestAddOptions:
+    def test_new_fields_and_existing_fields_land_in_payload_together(self):
+        backend = _make_backend()
+        with patch.object(backend, "_request", return_value={"results": []}) as mock_request:
+            backend.add(
+                content="hello",
+                user_id="alice",
+                metadata={"source": "test"},
+                expires="2099-01-01",
+                custom_instructions="Extract only preferences.",
+                custom_categories=[{"prefs": "user preferences"}],
+                structured_data_schema={"type": "object"},
+                timestamp=1700000000,
+            )
+        payload = mock_request.call_args.kwargs["json"]
+        assert payload["custom_instructions"] == "Extract only preferences."
+        assert payload["custom_categories"] == [{"prefs": "user preferences"}]
+        assert payload["structured_data_schema"] == {"type": "object"}
+        assert payload["timestamp"] == 1700000000
+        assert payload["metadata"] == {"source": "test"}
+        assert payload["expiration_date"] == "2099-01-01"
+
+    def test_omitted_fields_are_absent_from_payload(self):
+        backend = _make_backend()
+        with patch.object(backend, "_request", return_value={"results": []}) as mock_request:
+            backend.add(content="hello", user_id="alice")
+        payload = mock_request.call_args.kwargs["json"]
+        assert "custom_instructions" not in payload
+        assert "custom_categories" not in payload
+        assert "structured_data_schema" not in payload
+        assert "timestamp" not in payload
+
+
+class TestSearchOptions:
+    def test_show_expired_reference_date_latest_only_reach_payload(self):
+        backend = _make_backend()
+        with patch.object(backend, "_request", return_value=[]) as mock_request:
+            backend.search(
+                "query",
+                show_expired=True,
+                reference_date="2024-01-01",
+                latest_only=True,
+            )
+        payload = mock_request.call_args.kwargs["json"]
+        assert payload["show_expired"] is True
+        assert payload["reference_date"] == "2024-01-01"
+        assert payload["latest_only"] is True
+
+    def test_keyword_and_fields_reach_payload(self):
+        backend = _make_backend()
+        with patch.object(backend, "_request", return_value=[]) as mock_request:
+            backend.search("query", keyword=True, fields=["memory", "score"])
+        payload = mock_request.call_args.kwargs["json"]
+        assert payload["keyword_search"] is True
+        assert payload["fields"] == ["memory", "score"]
+
+    def test_keyword_and_fields_omitted_are_absent_from_payload(self):
+        backend = _make_backend()
+        with patch.object(backend, "_request", return_value=[]) as mock_request:
+            backend.search("query")
+        payload = mock_request.call_args.kwargs["json"]
+        assert "keyword_search" not in payload
+        assert "fields" not in payload
+
+
+class TestListOptions:
+    def test_show_expired_and_latest_only_are_top_level_not_in_filters(self):
+        backend = _make_backend()
+        with patch.object(backend, "_request", return_value=[]) as mock_request:
+            backend.list_memories(user_id="alice", show_expired=True, latest_only=True)
+        payload = mock_request.call_args.kwargs["json"]
+        assert payload["show_expired"] is True
+        assert payload["latest_only"] is True
+        assert "show_expired" not in payload.get("filters", {})
+        assert "latest_only" not in payload.get("filters", {})
+
+
+class TestUpdateOptions:
+    def test_expires_and_timestamp_reach_payload(self):
+        backend = _make_backend()
+        with patch.object(backend, "_request", return_value={}) as mock_request:
+            backend.update("mem-123", expiration_date="2099-01-01", timestamp=1700000000)
+        payload = mock_request.call_args.kwargs["json"]
+        assert payload["expiration_date"] == "2099-01-01"
+        assert payload["timestamp"] == 1700000000
+
+
+class TestDeleteOptions:
+    def test_delete_linked_is_a_query_param_not_json_body(self):
+        backend = _make_backend()
+        with patch.object(backend, "_request", return_value={}) as mock_request:
+            backend.delete(memory_id="mem-123", delete_linked=True)
+        call = mock_request.call_args
+        assert call.kwargs["params"]["delete_linked"] == "true"
+        assert "json" not in call.kwargs

cli/python/tests/test_platform_backend_paths.py

@@ -0,0 +1,43 @@
+from unittest.mock import MagicMock
+
+from mem0_cli.backend.platform import PlatformBackend
+
+
+def _backend(sample_config):
+    backend = PlatformBackend(sample_config.platform)
+    backend._client = MagicMock()
+    backend._client.request.return_value = MagicMock(
+        status_code=200,
+        json=lambda: {"message": "ok"},
+        headers={},
+        raise_for_status=lambda: None,
+    )
+    return backend
+
+
+def test_memory_id_path_segments_are_encoded(sample_config):
+    backend = _backend(sample_config)
+
+    backend.get("mem/a?b#c")
+    backend.update("mem/a?b#c", content="updated")
+    backend.delete("mem/a?b#c")
+
+    paths = [call.args[1] for call in backend._client.request.call_args_list]
+    assert paths == [
+        "/v1/memories/mem%2Fa%3Fb%23c/",
+        "/v1/memories/mem%2Fa%3Fb%23c/",
+        "/v1/memories/mem%2Fa%3Fb%23c/",
+    ]
+
+
+def test_entity_and_event_path_segments_are_encoded(sample_config):
+    backend = _backend(sample_config)
+
+    backend.delete_entities(user_id="org/team?active#frag")
+    backend.get_event("evt/a?b#c")
+
+    paths = [call.args[1] for call in backend._client.request.call_args_list]
+    assert paths == [
+        "/v2/entities/user/org%2Fteam%3Factive%23frag/",
+        "/v1/event/evt%2Fa%3Fb%23c/",
+    ]

docs/README.md

@@ -24,7 +24,7 @@ mintlify dev
 
 ### Publishing Changes
 
-Install our Github App to auto propagate changes from your repo to your deployment. Changes will be deployed to production automatically after pushing to the default branch. Find the link to install on your dashboard. 
+Install our GitHub App to auto-propagate changes from your repo to your deployment. Changes will be deployed to production automatically after pushing to the default branch. Find the link to install on your dashboard. 
 
 #### Troubleshooting
 

docs/_snippets/star-on-github.mdx

@@ -0,0 +1,5 @@
+{/* Subtle, value-anchored nudge to star the repo. Drop in at peak-end "win" moments in the OSS docs (after a successful add/search, a server bootstrap, etc.). Keep it off the Platform/API pages. */}
+{/* Clicks are tracked via PostHog autocapture: the data-ph-capture-attribute-cta below tags each click with cta="star-on-github" so it's filterable as an event property. Metric = count of $autocapture where cta = star-on-github; break down by Current URL to see which win-moment converts. */}
+<Callout icon="star" iconType="solid" color="#FACC15">
+  **Using Mem0?** <a href="https://github.com/mem0ai/mem0" data-ph-capture-attribute-cta="star-on-github">Star us on GitHub</a> to help more developers discover memory for AI apps.
+</Callout>

docs/api-reference.mdx

@@ -97,7 +97,7 @@ Get your API key from the <a href="https://app.mem0.ai/dashboard/api-keys?utm_so
 
 ## Next Steps
 
-<CardGroup cols={2}>
+<CardGroup cols={3}>
   <Card title="Add Your First Memory" icon="rocket" href="/api-reference/memory/add-memories">
     Start storing memories via the REST API
   </Card>
@@ -105,4 +105,8 @@ Get your API key from the <a href="https://app.mem0.ai/dashboard/api-keys?utm_so
   <Card title="Search with Filters" icon="filter" href="/api-reference/memory/search-memories">
     Learn advanced search and filtering techniques
   </Card>
+
+  <Card title="Build with cookbooks" icon="book-open" href="/cookbooks/overview">
+    See the API used end to end in real projects.
+  </Card>
 </CardGroup>

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

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

docs/api-reference/organizations-projects.mdx

@@ -79,7 +79,7 @@ new_project = client.project.create(
 
 ### Update Project Settings
 
-Modify project configuration including custom instructions, categories, language preferences, retrieval criteria, and memory decay:
+Modify project configuration including custom instructions, categories, language preferences, and memory decay:
 
 ```python
 # Update project with custom categories
@@ -98,14 +98,6 @@ 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)
 
@@ -120,34 +112,6 @@ 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:

docs/changelog/highlights.mdx

@@ -4,6 +4,39 @@ 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**

docs/changelog/sdk.mdx

@@ -7,6 +7,95 @@ mode: "wide"
 <Tabs>
 <Tab title="Python">
 
+<Update label="2026-08-04" description="v2.0.16">
+
+**New Features:**
+- **Client:** Add `reference_date`, `latest_only`, and `keyword_search` to `SearchMemoryOptions`, and `latest_only` to `GetAllMemoryOptions`, keeping the Python client's typed options in sync with the Platform API and the CLIs ([#6696](https://github.com/mem0ai/mem0/pull/6696))
+
+**Bug Fixes:**
+- **Core:** Stop `add()` metadata from setting a memory's identity scope. `_build_filters_and_metadata()` now strips `user_id`, `agent_id`, `run_id`, and `actor_id` from caller-supplied `metadata` before building the creation template, so metadata can no longer place a memory into a scope that was never passed through the entity params ([#6656](https://github.com/mem0ai/mem0/pull/6656))
+- **Vector Stores:** Validate Upstash filter keys and values in `search()`, `keyword_search()`, and `list()`. Filter keys must match a safe identifier pattern, values must be `str`/`int`/`float`/`bool`, and string values containing a double quote or backslash are now rejected instead of being interpolated unescaped into the generated query string ([#5981](https://github.com/mem0ai/mem0/pull/5981))
+- **Embeddings:** `FastEmbedEmbedding.embed()` now converts its result with `.tolist()` before returning, so callers get a plain `List[float]` instead of a numpy array ([#6770](https://github.com/mem0ai/mem0/pull/6770))
+- **Embeddings:** `HuggingFaceEmbedding` now passes `api_key` to the OpenAI-compatible client when `huggingface_base_url` is set. The configured key was previously dropped, so the client fell back to `OPENAI_API_KEY` from the environment or raised `OpenAIError` at construction when that was unset ([#6770](https://github.com/mem0ai/mem0/pull/6770))
+- **Embeddings:** Replace Ollama's interactive `pip install` prompt on import with a plain `ImportError`. Importing `mem0.embeddings.ollama` without the `ollama` package previously blocked on stdin and then called `sys.exit(1)`, killing the host process instead of raising ([#6770](https://github.com/mem0ai/mem0/pull/6770))
+- **Core:** `remove_code_blocks()` now returns an empty string for `None` input instead of raising `AttributeError` ([#6770](https://github.com/mem0ai/mem0/pull/6770))
+- **Core:** `parse_vision_messages()` now chains the original exception (`raise ... from e`) when an image download fails, so the root cause is preserved in the traceback ([#6770](https://github.com/mem0ai/mem0/pull/6770))
+- **Core:** `process_telemetry_filters(None)` now returns `([], {})`, matching the two-value tuple every caller unpacks, instead of `{}` ([#6770](https://github.com/mem0ai/mem0/pull/6770))
+- **Core:** `LlmFactory.create()` no longer mutates the caller's config dict in place via `.update(kwargs)`; the merge now builds a new dict ([#6770](https://github.com/mem0ai/mem0/pull/6770))
+- **Rerankers:** The Cohere, HuggingFace, SentenceTransformer, and Zero Entropy rerankers' failure-fallback path no longer mutates the caller's document dicts in place when stamping `rerank_score`; it now falls back on copies ([#6770](https://github.com/mem0ai/mem0/pull/6770))
+- **Vector Stores:** Remove `logging.basicConfig()` calls from the MongoDB and Vertex AI Vector Search providers, so selecting either provider no longer reconfigures the host application's root logger as a side effect ([#6770](https://github.com/mem0ai/mem0/pull/6770))
+
+</Update>
+
+<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:**
@@ -1100,6 +1189,92 @@ See the [OSS v2 to v3 migration guide](https://docs.mem0.ai/migration/oss-v2-to-
 
 <Tab title="TypeScript">
 
+<Update label="2026-08-04" description="v3.1.4">
+
+**New Features:**
+- **Client:** Add `referenceDate` and `keywordSearch` to `SearchMemoryOptions`, keeping the TypeScript client's typed search options in sync with the Platform API ([#6696](https://github.com/mem0ai/mem0/pull/6696))
+
+**Bug Fixes:**
+- **Client:** Take the `/v1/ping/` identity/telemetry call off the request critical path. Every method previously did `if (this.telemetryId === "") await this.ping();`, blocking the first call on each client instance on a network round trip; the ping now resolves in the background through a shared, credentials-keyed promise cache (FIFO-capped at 50 entries), so concurrent clients on the same host/API key share one ping instead of issuing one each, and a failed ping is not cached so the process can retry ([#6788](https://github.com/mem0ai/mem0/pull/6788))
+- **Client:** `deleteUsers()` now issues its per-entity DELETE requests through the shared `fetch()`-based helper instead of the axios instance, which defaulted to `keepAlive: false`. Deleting every user, agent, app, and run now reuses pooled connections instead of paying a fresh TCP/TLS handshake per entity ([#6788](https://github.com/mem0ai/mem0/pull/6788))
+- **Memory (OSS):** Stop `add()` metadata from setting or overwriting a memory's identity scope. Metadata now runs through the same `stripIdentityKeys()` helper as `update()`, so `user_id`/`agent_id`/`run_id` (snake_case or camelCase) and `actor_id` passed in metadata can no longer place a memory into a scope the caller didn't request through `userId`/`agentId`/`runId`/`filters` ([#6377](https://github.com/mem0ai/mem0/pull/6377))
+- **Vector Stores:** Fix Redis `search()` and `list()` building an invalid, empty RediSearch filter expression when `filters` was an empty object or contained only null/undefined values. The shared `buildRedisFilterExpr()` helper now falls back to `"*"` (match all) instead of joining zero conditions into `""` ([#6014](https://github.com/mem0ai/mem0/pull/6014))
+
+</Update>
+
+<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:**
@@ -1606,6 +1781,33 @@ See the [TypeScript SDK migration guide](https://docs.mem0.ai/migration/ts-v2-to
 
 <Tab title="CLI">
 
+<Update label="2026-08-04" description="Python v0.2.11 / Node v0.2.12">
+
+**New Features:**
+- **`add`:** New `--custom-instructions`, `--custom-categories`, `--structured-data-schema`, and `--timestamp` flags, matching the Platform `/v3/memories/add/` payload fields (Python and Node [#6696](https://github.com/mem0ai/mem0/pull/6696))
+- **`search`:** New `--show-expired`, `--reference-date`, and `--latest-only` flags, forwarded to `/v3/memories/search/` as `show_expired`, `reference_date`, and `latest_only` (Python and Node [#6696](https://github.com/mem0ai/mem0/pull/6696))
+- **`list`:** New `--show-expired` and `--latest-only` flags (Python and Node [#6696](https://github.com/mem0ai/mem0/pull/6696))
+- **`update`:** New `--expires` and `--timestamp` flags (Python and Node [#6696](https://github.com/mem0ai/mem0/pull/6696))
+- **`delete`:** New `--delete-linked` flag to also delete memories linked to the target memory (Python and Node [#6696](https://github.com/mem0ai/mem0/pull/6696))
+
+**Bug Fixes:**
+- **`help --json`:** Emit JSON automatically under agent mode (`--agent`), not only when `--json` is passed explicitly. Python checks `is_agent_mode()` and now renders through `console.print_json()` instead of a plain `console.print(json.dumps(...))` call; Node also checks the root `--agent` flag in addition to the subcommand's `--json` flag (Python and Node [#6773](https://github.com/mem0ai/mem0/pull/6773))
+
+**Changes:**
+- **`add`:** `--categories` is no longer forwarded on `add`. The `/v3/memories/add/` payload has no `categories` field (only `custom_categories`), so the CLI was sending a key the endpoint does not accept. Passing the flag now exits 1 with a message pointing to `--custom-categories` (Python and Node [#6696](https://github.com/mem0ai/mem0/pull/6696))
+
+**Security:**
+- **Dependencies (Node):** Tighten the `postcss` pnpm override from `<8.5.10 → >=8.5.10` to `<8.5.18 → >=8.5.18 <9.0.0`, closing a newer CVE range the previous floor didn't cover, as part of a wider dependency patch sweep across the pnpm workspaces ([#6639](https://github.com/mem0ai/mem0/pull/6639))
+
+</Update>
+
+<Update label="2026-07-13" 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))
+
+</Update>
+
 <Update label="2026-07-01" description="Python v0.2.9 / Node v0.2.10">
 
 **Bug Fixes:**
@@ -1764,6 +1966,28 @@ A full-featured command-line interface for Mem0, available in both Python and No
 <Tabs>
 <Tab title="Mem0 Plugin">
 
+<Update label="2026-08-04" description="mem0-plugin v0.2.14">
+
+**Fixes:**
+- **Settings loading no longer crashes on a malformed `settings.json`:** If `~/.mem0/settings.json` parsed as valid JSON but wasn't an object (a list or a bare string, for example), `load_settings()` called `.items()` on it and raised. `resolve_config()` guards only against `ImportError`, so the failure escaped into every hook that resolves identity, not just setup. Settings loading now keeps the defaults instead. Setup also stopped claiming it "Created" the settings file when one already existed; it only prints that when it actually wrote one. Shared across Claude Code, Cursor, Codex, and Antigravity.
+
+**New Features:**
+- **Unknown settings key warning:** Setup now flags keys in `settings.json` that the plugin doesn't recognize, so a typo'd setting no longer fails silently.
+
+**Removed:**
+- Dropped the `mem0_doc_search` "openmemory" section now that OpenMemory has been removed from the docs site, so the skill no longer links to pages that don't exist.
+
+</Update>
+
+<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:**
@@ -2014,6 +2238,13 @@ 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:**
@@ -2087,6 +2318,28 @@ Initial release of the Mem0 plugin for Claude Code and Cursor, followed by Codex
 
 <Tab title="Antigravity">
 
+<Update label="2026-08-04" description="Antigravity plugin v0.1.6">
+
+**Fixes:**
+- **Settings loading no longer crashes on a malformed `settings.json`:** If `~/.mem0/settings.json` parsed as valid JSON but wasn't an object, every hook that resolves identity previously raised, not just setup. It now keeps the defaults instead, and setup only reports it "Created" the settings file when it actually wrote one.
+
+**Improvements:**
+- **Unknown settings key warning:** Setup now flags keys in `settings.json` it doesn't recognize instead of ignoring them silently.
+
+**Removed:**
+- Dropped the doc-search skill's "openmemory" section now that OpenMemory has been removed from the docs site.
+
+</Update>
+
+<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:**
@@ -2134,6 +2387,16 @@ Initial release of the Mem0 plugin for Claude Code and Cursor, followed by Codex
 
 <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:**
@@ -2387,6 +2650,13 @@ Initial release of the Mem0 plugin for Claude Code and Cursor, followed by Codex
 
 <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:**
@@ -2439,6 +2709,13 @@ Initial release of the Mem0 plugin for Claude Code and Cursor, followed by Codex
 
 <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.
@@ -2520,6 +2797,72 @@ Initial release of the Mem0 plugin for Claude Code and Cursor, followed by Codex
 - Added support for graph memories.
 </Update>
 
+</Tab>
+
+<Tab title="n8n">
+
+<Update label="2026-08-04" description="n8n-nodes-mem0 v0.1.2">
+
+**Changes:**
+- **Package contact:** `author.email` in the published package is now `integrations@mem0.ai` (was `founders@mem0.ai`), so npm and n8n Creator Portal correspondence reaches the integrations team directly. No functional changes ([#6791](https://github.com/mem0ai/mem0/pull/6791))
+
+</Update>
+
+<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="2026-08-04" description="Zapier app v0.1.1">
+
+**Bug Fixes:**
+- **Add Memory:** **User ID** is now a required field. Mem0 rejects a write that carries no entity ID, so a Zap left blank failed at the API instead of in the editor. If you scoped memories only by Agent ID or Run ID, set a User ID as well ([#6790](https://github.com/mem0ai/mem0/pull/6790))
+- **Deployment:** Add a root `index.js` that re-exports the compiled app from `dist/`, and point `main` at it. Zapier's Lambda wrapper loads `<root>/index.js` and ignores the `package.json` `main` field, so the deployed app could not resolve its entry point ([#6789](https://github.com/mem0ai/mem0/pull/6789))
+
+</Update>
+
+<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>
 

docs/components/embedders/models/aws_bedrock.mdx

@@ -3,11 +3,27 @@ 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 to have the appropriate AWS credentials and permissions. The embeddings implementation relies on the `boto3` library.
+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.
 
 ### Setup
-- 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)
+
+- 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.
+
 - Set up environment variables for authentication:
   ```bash
   export AWS_REGION=us-east-1
@@ -15,6 +31,8 @@ To use AWS Bedrock embedding models, you need to have the appropriate AWS creden
   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>
@@ -48,8 +66,46 @@ 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:
@@ -64,4 +120,16 @@ 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>

docs/components/embedders/models/azure_openai.mdx

@@ -3,7 +3,7 @@ title: Azure OpenAI
 description: "Configure Azure OpenAI as an embedding provider in Mem0 with API key, deployment, and endpoint settings."
 ---
 
-To use Azure OpenAI embedding models, set the `EMBEDDING_AZURE_OPENAI_API_KEY`, `EMBEDDING_AZURE_DEPLOYMENT`, `EMBEDDING_AZURE_ENDPOINT` and `EMBEDDING_AZURE_API_VERSION` environment variables. You can obtain the Azure OpenAI API key from the Azure.
+To use Azure OpenAI embedding models, set the `EMBEDDING_AZURE_OPENAI_API_KEY`, `EMBEDDING_AZURE_DEPLOYMENT`, `EMBEDDING_AZURE_ENDPOINT` and `EMBEDDING_AZURE_API_VERSION` environment variables. You can obtain the Azure OpenAI API key from the Azure Portal.
 
 ### Usage
 

docs/components/embedders/models/fastembed.mdx

@@ -7,10 +7,18 @@ You can use FastEmbed to run embedding models locally in Mem0. FastEmbed is an O
 
 ### Installation
 
-```bash
+FastEmbed is an optional dependency, so install it alongside Mem0.
+
+<CodeGroup>
+```bash Python
 pip install fastembed
 ```
 
+```bash TypeScript
+npm install fastembed
+```
+</CodeGroup>
+
 ### Usage
 
 <CodeGroup>
@@ -38,13 +46,66 @@ messages = [
 ]
 m.add(messages, user_id="john")
 ```
+
+```typescript TypeScript
+import { Memory } from "mem0ai/oss";
+
+// FastEmbed needs no API key. Leave the embedder config empty to use the
+// default model (fast-bge-small-en-v1.5), or set `model` to one of the
+// supported models listed below.
+const memory = new Memory({
+  embedder: {
+    provider: "fastembed",
+    config: {
+      model: "fast-bge-small-en-v1.5",
+    },
+  },
+  llm: {
+    provider: "openai",
+    config: { apiKey: process.env.OPENAI_API_KEY }, // For fact extraction
+  },
+});
+
+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: "john" });
+```
 </CodeGroup>
 
+<Note>
+  **The Python and TypeScript SDKs default to different models.** Python defaults to `thenlper/gte-large` (1024 dimensions), while TypeScript defaults to `fast-bge-small-en-v1.5` (384 dimensions). The TypeScript package (`fastembed` on npm) ships a fixed set of ONNX models and does not include `thenlper/gte-large`. Because the two defaults produce vectors of different dimensions, do not point both SDKs at the same vector store collection unless you configure them to use the same model.
+</Note>
+
+The TypeScript SDK supports these FastEmbed models. Pass the exact string as `model`:
+
+- `fast-bge-small-en-v1.5` (default)
+- `fast-bge-small-en`
+- `fast-bge-base-en`
+- `fast-bge-base-en-v1.5`
+- `fast-bge-small-zh-v1.5`
+- `fast-all-MiniLM-L6-v2`
+- `fast-multilingual-e5-large`
+
 ### Config
 
-Here are the parameters available for configuring FastEmbed embedder:
+Here are the parameters available for configuring the FastEmbed embedder:
 
+<Tabs>
+<Tab title="Python">
 | Parameter | Description | Default Value |
 | --- | --- | --- |
 | `model` | The name of the FastEmbed model to use | `thenlper/gte-large` |
 | `embedding_dims` | Dimensions of the embedding model (auto-derived from the model if not set) | `None` |
+</Tab>
+<Tab title="TypeScript">
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `model` | The FastEmbed model to use (see the supported list above) | `fast-bge-small-en-v1.5` |
+
+The embedding dimension is detected automatically at startup, so you do not need to set it manually.
+</Tab>
+</Tabs>

docs/components/embedders/models/huggingface.mdx

@@ -5,6 +5,10 @@ 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
@@ -34,9 +38,10 @@ 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:
+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.
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -56,6 +61,24 @@ 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
@@ -66,11 +89,22 @@ docker run -d -p 3000:80 -v huggingfacetei:/data --platform linux/amd64 \
 
 ### Config
 
-Here are the parameters available for configuring Huggingface embedder:
+Here are the parameters available for configuring the Hugging Face 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` |
+| `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>

docs/components/embedders/models/together.mdx

@@ -1,15 +1,20 @@
 ---
 title: Together
-description: "Configure Together AI as an embedding provider in Mem0 with support for 768-dimensional embedding models."
+description: "Configure Together AI as an embedding provider in Mem0 with support for 1024-dimensional embedding models."
 ---
 
-To use Together embedding models, set the `TOGETHER_API_KEY` environment variable. You can obtain the Together API key from the [Together Platform](https://api.together.xyz/settings/api-keys).
+To use Together embedding models, set the `TOGETHER_API_KEY` environment variable. You can obtain the Together API key from the [Together Platform](https://api.together.ai/settings/projects/~current/api-keys).
 
 ### Usage
 
-<Note> The `embedding_model_dims` parameter for `vector_store` should be set to `768` for Together embedder. </Note>
+<Note> The `embedding_model_dims` parameter for `vector_store` should be set to `1024` for Together embedder. </Note>
 
-```python
+<Warning>
+**Breaking default change.** The default Together embedding model is now `intfloat/multilingual-e5-large-instruct` (**1024-dim**), replacing the previous default `togethercomputer/m2-bert-80M-8k-retrieval` (**768-dim**). If you created a self-hosted vector store with the old default, its collection is 768-dim and will reject the new 1024-dim vectors **recreate/reindex the collection at 1024 dimensions** after upgrading. To defer the change, pin the previous values explicitly (`model="togethercomputer/m2-bert-80M-8k-retrieval"`, `embedding_dims=768`) note Together no longer lists this model among its recommended embeddings, so reindexing at 1024 is the durable path.
+</Warning>
+
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -20,7 +25,7 @@ config = {
     "embedder": {
         "provider": "together",
         "config": {
-            "model": "togethercomputer/m2-bert-80M-8k-retrieval"
+            "model": "intfloat/multilingual-e5-large-instruct"
         }
     }
 }
@@ -29,18 +34,50 @@ 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: 'together',
+    config: {
+      apiKey: process.env.TOGETHER_API_KEY || '',
+      model: 'intfloat/multilingual-e5-large-instruct',
+      embeddingDims: 1024,
+    },
+  },
+};
+
+const memory = new Memory(config);
+await memory.add("I'm visiting Paris", { userId: "john" });
+```
+
+</CodeGroup>
+
 ### Config
 
 Here are the parameters available for configuring Together embedder:
 
+<Tabs>
+<Tab title="Python">
 | Parameter | Description | Default Value |
 | --- | --- | --- |
-| `model` | The name of the embedding model to use | `togethercomputer/m2-bert-80M-8k-retrieval` |
-| `embedding_dims` | Dimensions of the embedding model | `768` |
+| `model` | The name of the embedding model to use | `intfloat/multilingual-e5-large-instruct` |
+| `embedding_dims` | Dimensions of the embedding model | `1024` |
 | `api_key` | The Together API key | `None` |
+</Tab>
+<Tab title="TypeScript">
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `model` | The name of the embedding model to use | `intfloat/multilingual-e5-large-instruct` |
+| `embeddingDims` | Dimensions of the embedding model for vector store configuration | `1024` |
+| `apiKey` | The Together API key | `TOGETHER_API_KEY` |
+| `baseURL` | Base URL for an OpenAI-compatible Together endpoint | `https://api.together.ai/v1` |
+</Tab>
+</Tabs>

docs/components/embedders/models/vertexai.mdx

@@ -4,11 +4,36 @@ description: "Configure Google Cloud Vertex AI as an embedding provider in Mem0
 ---
 ### Vertex AI
 
-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/).
+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.
 
 ### Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -32,28 +57,87 @@ 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")
 ```
-The embedding types can be one of the following:
+
+```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:
 - 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.  
-  
+- 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>
+
 ### Config
 
 Here are the parameters available for configuring the Vertex AI embedder:
 
-| 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` |
+<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>

docs/components/embedders/overview.mdx

@@ -10,21 +10,21 @@ 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**, **Google AI**, **Langchain**, **LM Studio**, and **Ollama**.
+  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**.
 </Note>
 
 <CardGroup cols={4}>
-  <Card title="OpenAI" href="/components/embedders/models/openai"></Card>
-  <Card title="Azure OpenAI" href="/components/embedders/models/azure_openai"></Card>
-  <Card title="Ollama" href="/components/embedders/models/ollama"></Card>
-  <Card title="Hugging Face" href="/components/embedders/models/huggingface"></Card>
-  <Card title="Google AI" href="/components/embedders/models/google_AI"></Card>
-  <Card title="Vertex AI" href="/components/embedders/models/vertexai"></Card>
-  <Card title="Together" href="/components/embedders/models/together"></Card>
-  <Card title="LM Studio" href="/components/embedders/models/lmstudio"></Card>
-  <Card title="Langchain" href="/components/embedders/models/langchain"></Card>
-  <Card title="AWS Bedrock" href="/components/embedders/models/aws_bedrock"></Card>
-  <Card title="FastEmbed" href="/components/embedders/models/fastembed"></Card>
+  <Card title="OpenAI" icon="/images/provider-icons/openai.svg" href="/components/embedders/models/openai"></Card>
+  <Card title="Azure OpenAI" icon="/images/provider-icons/azure-color.svg" href="/components/embedders/models/azure_openai"></Card>
+  <Card title="Ollama" icon="/images/provider-icons/ollama.svg" href="/components/embedders/models/ollama"></Card>
+  <Card title="Hugging Face" icon="/images/provider-icons/huggingface.svg" href="/components/embedders/models/huggingface"></Card>
+  <Card title="Google AI" icon="/images/provider-icons/google-color.svg" href="/components/embedders/models/google_AI"></Card>
+  <Card title="Vertex AI" icon="/images/provider-icons/vertexai.svg" href="/components/embedders/models/vertexai"></Card>
+  <Card title="Together" icon="/images/provider-icons/together-color.svg" href="/components/embedders/models/together"></Card>
+  <Card title="LM Studio" icon="/images/provider-icons/lmstudio.svg" href="/components/embedders/models/lmstudio"></Card>
+  <Card title="Langchain" icon="/images/provider-icons/langchain-color.svg" href="/components/embedders/models/langchain"></Card>
+  <Card title="AWS Bedrock" icon="/images/provider-icons/bedrock-color.svg" href="/components/embedders/models/aws_bedrock"></Card>
+  <Card title="FastEmbed" icon="/images/provider-icons/qdrant.svg" href="/components/embedders/models/fastembed"></Card>
 </CardGroup>
 
 ## Usage

docs/components/llms/models/aws_bedrock.mdx

@@ -5,16 +5,18 @@ 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).
-- 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.
+- 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.
 
 ### Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
-os.environ['AWS_REGION'] = 'us-west-2'
+os.environ['AWS_REGION'] = 'us-east-1'
 os.environ["AWS_ACCESS_KEY_ID"] = "xx"
 os.environ["AWS_SECRET_ACCESS_KEY"] = "xx"
 
@@ -22,7 +24,7 @@ config = {
     "llm": {
         "provider": "aws_bedrock",
         "config": {
-            "model": "anthropic.claude-3-5-haiku-20241022-v1:0",
+            "model": "anthropic.claude-sonnet-4-20250514-v1:0",
             "temperature": 0.2,
             "max_tokens": 2000,
         }
@@ -39,6 +41,43 @@ 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).

docs/components/llms/models/azure_openai.mdx

@@ -5,7 +5,7 @@ description: "Configure Azure OpenAI as an LLM provider in Mem0 with Azure Ident
 
 <Note> Mem0 Now Supports Azure OpenAI Models in TypeScript SDK </Note>
 
-To use Azure OpenAI models, you have to set the `LLM_AZURE_OPENAI_API_KEY`, `LLM_AZURE_ENDPOINT`, `LLM_AZURE_DEPLOYMENT` and `LLM_AZURE_API_VERSION` environment variables. You can obtain the Azure API key from the [Azure](https://azure.microsoft.com/).
+To use Azure OpenAI models, you have to set the `LLM_AZURE_OPENAI_API_KEY`, `LLM_AZURE_ENDPOINT`, `LLM_AZURE_DEPLOYMENT` and `LLM_AZURE_API_VERSION` environment variables. You can obtain the Azure API key from the [Azure Portal](https://azure.microsoft.com/).
 
 Optionally, you can use Azure Identity to authenticate with Azure OpenAI, which allows you to use managed identities or service principals for production and Azure CLI login for development instead of an API key. If an Azure Identity is to be used, ***do not*** set the `LLM_AZURE_OPENAI_API_KEY` environment variable or the api_key in the config dictionary.
 

docs/components/llms/models/sarvam.mdx

@@ -9,7 +9,8 @@ To use Sarvam AI's models, please set the `SARVAM_API_KEY` which you can get fro
 
 ## Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -34,8 +35,35 @@ 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

docs/components/llms/models/together.mdx

@@ -1,13 +1,15 @@
 ---
 title: Together
-description: "Configure Together AI as an LLM provider in Mem0 with API key setup and Mixtral model configuration."
+description: "Configure Together AI as an LLM provider in Mem0 with API key setup and optional custom endpoint configuration."
 ---
 
-To use Together LLM models, you have to set the `TOGETHER_API_KEY` environment variable. You can obtain the Together API key from their [Account settings page](https://api.together.xyz/settings/api-keys).
+To use Together LLM models, you have to set the `TOGETHER_API_KEY` environment variable. You can obtain the Together API key from their [Account settings page](https://api.together.ai/settings/projects/~current/api-keys).
+In the TypeScript SDK, you can optionally set `TOGETHER_API_BASE` or pass `baseURL` in the config (defaults to `https://api.together.ai/v1`).
 
 ## Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -18,7 +20,7 @@ config = {
     "llm": {
         "provider": "together",
         "config": {
-            "model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
+            "model": "MiniMaxAI/MiniMax-M3",
             "temperature": 0.2,
             "max_tokens": 2000,
         }
@@ -29,12 +31,68 @@ 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="alice", metadata={"category": "movies"})
 ```
 
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+  llm: {
+    provider: 'together',
+    config: {
+      apiKey: process.env.TOGETHER_API_KEY || '',
+      model: 'MiniMaxAI/MiniMax-M3',
+      temperature: 0.2,
+      maxTokens: 2000,
+    },
+  },
+};
+
+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>
+
+You can also configure the API base URL in the config:
+
+<CodeGroup>
+```python Python
+config = {
+    "llm": {
+        "provider": "together",
+        "config": {
+            "model": "MiniMaxAI/MiniMax-M3",
+            "api_key": "your-api-key"
+        }
+    }
+}
+```
+
+```typescript TypeScript
+const config = {
+  llm: {
+    provider: "together",
+    config: {
+      model: "MiniMaxAI/MiniMax-M3",
+      baseURL: "https://api.together.ai/v1",
+      apiKey: "your-api-key",
+    },
+  },
+};
+```
+
+</CodeGroup>
+
 ## Config
 
-All available parameters for the `together` config are present in [Master List of All Params in Config](../config).
+All available parameters for the `together` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/vllm.mdx

@@ -25,7 +25,8 @@ description: "Configure vLLM as an LLM provider in Mem0 for high-performance loc
 
 ## Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -53,6 +54,46 @@ messages = [
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
+```typescript TypeScript
+import { Memory } from "mem0ai/oss";
+
+const config = {
+  llm: {
+    provider: "vllm",
+    config: {
+      model: "Qwen/Qwen2.5-32B-Instruct",
+      baseURL: "http://localhost:8000/v1",
+      apiKey: process.env.VLLM_API_KEY || "vllm-api-key",
+      temperature: 0.1,
+      maxTokens: 2000,
+    },
+  },
+};
+
+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 thrillers, but I love sci-fi movies.",
+  },
+  {
+    role: "assistant",
+    content: "Got it! I'll avoid thrillers and suggest sci-fi movies instead.",
+  },
+];
+await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
+```
+
+</CodeGroup>
+
 ## Configuration Parameters
 
 | Parameter       | Description                       | Default                       | Environment Variable |

docs/components/llms/models/xAI.mdx

@@ -5,11 +5,12 @@ description: "Configure xAI Grok models as an LLM provider in Mem0 with API key
 
 [xAI](https://x.ai/) is a new AI company founded by Elon Musk that develops large language models, including Grok. Grok is trained on real-time data from X (formerly Twitter) and aims to provide accurate, up-to-date responses with a touch of wit and humor.
 
-In order to use LLMs from xAI, go to their [platform](https://console.x.ai) and get the API key. Set the API key as `XAI_API_KEY` environment variable to use the model as given below in the example.
+In order to use LLMs from xAI, go to their [platform](https://console.x.ai) and get the API key. Set the API key as `XAI_API_KEY` environment variable to use the model as given below in the example. You can also optionally set `XAI_API_BASE` to use a different API endpoint (defaults to `https://api.x.ai/v1`).
 
 ## Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -37,6 +38,31 @@ messages = [
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+  llm: {
+    provider: 'xai',
+    config: {
+      apiKey: process.env.XAI_API_KEY || '',
+      model: 'grok-4.3',
+      temperature: 0.1,
+      maxTokens: 2000,
+    },
+  },
+};
+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
 
 All available parameters for the `xai` config are present in [Master List of All Params in Config](../config).

docs/components/llms/overview.mdx

@@ -7,7 +7,7 @@ Mem0 includes built-in support for various popular large language models. Memory
 
 ## Usage
 
-To use a llm, you must provide a configuration to customize its usage. If no configuration is supplied, a default configuration will be applied, and `OpenAI` will be used as the llm.
+To use an LLM, you must provide a configuration to customize its usage. If no configuration is supplied, a default configuration will be applied, and `OpenAI` will be used as the LLM.
 
 For a comprehensive list of available parameters for llm configuration, please refer to [Config](./config).
 
@@ -16,26 +16,26 @@ 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**, **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**, **AWS Bedrock**, **Groq**, **Azure OpenAI**, **DeepSeek**, **Google AI**, **Langchain**, **LM Studio**, **Mistral AI**, and **Ollama**.
 </Note>
 
 <CardGroup cols={4}>
-  <Card title="OpenAI" href="/components/llms/models/openai" />
-  <Card title="Ollama" href="/components/llms/models/ollama" />
-  <Card title="Azure OpenAI" href="/components/llms/models/azure_openai" />
-  <Card title="Anthropic" href="/components/llms/models/anthropic" />
-  <Card title="Together" href="/components/llms/models/together" />
-  <Card title="Groq" href="/components/llms/models/groq" />
-  <Card title="Litellm" href="/components/llms/models/litellm" />
-  <Card title="Mistral AI" href="/components/llms/models/mistral_AI" />
-  <Card title="Google AI" href="/components/llms/models/google_AI" />
-  <Card title="AWS bedrock" href="/components/llms/models/aws_bedrock" />
-  <Card title="DeepSeek" href="/components/llms/models/deepseek" />
-  <Card title="MiniMax" href="/components/llms/models/minimax" />
-  <Card title="xAI" href="/components/llms/models/xAI" />
-  <Card title="Sarvam AI" href="/components/llms/models/sarvam" />
-  <Card title="LM Studio" href="/components/llms/models/lmstudio" />
-  <Card title="Langchain" href="/components/llms/models/langchain" />
+  <Card title="OpenAI" icon="/images/provider-icons/openai.svg" href="/components/llms/models/openai" />
+  <Card title="Ollama" icon="/images/provider-icons/ollama.svg" href="/components/llms/models/ollama" />
+  <Card title="Azure OpenAI" icon="/images/provider-icons/azure-color.svg" href="/components/llms/models/azure_openai" />
+  <Card title="Anthropic" icon="/images/provider-icons/anthropic.svg" href="/components/llms/models/anthropic" />
+  <Card title="Together" icon="/images/provider-icons/together-color.svg" href="/components/llms/models/together" />
+  <Card title="Groq" icon="/images/provider-icons/groq.svg" href="/components/llms/models/groq" />
+  <Card title="Litellm" icon="shuffle" href="/components/llms/models/litellm" />
+  <Card title="Mistral AI" icon="/images/provider-icons/mistral-color.svg" href="/components/llms/models/mistral_AI" />
+  <Card title="Google AI" icon="/images/provider-icons/google-color.svg" href="/components/llms/models/google_AI" />
+  <Card title="AWS bedrock" icon="/images/provider-icons/bedrock-color.svg" href="/components/llms/models/aws_bedrock" />
+  <Card title="DeepSeek" icon="/images/provider-icons/deepseek-color.svg" href="/components/llms/models/deepseek" />
+  <Card title="MiniMax" icon="/images/provider-icons/minimax-color.svg" href="/components/llms/models/minimax" />
+  <Card title="xAI" icon="/images/provider-icons/xai.svg" href="/components/llms/models/xAI" />
+  <Card title="Sarvam AI" icon="/images/provider-icons/sarvam.svg" href="/components/llms/models/sarvam" />
+  <Card title="LM Studio" icon="/images/provider-icons/lmstudio.svg" href="/components/llms/models/lmstudio" />
+  <Card title="Langchain" icon="/images/provider-icons/langchain-color.svg" href="/components/llms/models/langchain" />
 </CardGroup>
 
 ## Structured vs Unstructured Outputs

docs/components/rerankers/config.mdx

@@ -26,7 +26,7 @@ All rerankers share these common configuration parameters:
 
 | Parameter            | Description                                  | Type   | Default                 |
 | -------------------- | -------------------------------------------- | ------ | ----------------------- |
-| `model`              | Cohere rerank model                          | `str`  | `"rerank-english-v3.0"` |
+| `model`              | Cohere rerank model                          | `str`  | `"rerank-v3.5"` |
 | `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-4o-mini"`        |
+| `model`          | LLM model to use for scoring               | `str`   | `"gpt-5-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`                  |
@@ -103,3 +103,30 @@ 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>

docs/components/rerankers/custom-prompts.mdx

@@ -48,7 +48,7 @@ config = {
         "provider": "llm_reranker",
         "config": {
             "provider": "openai",
-            "model": "gpt-4o-mini",
+            "model": "gpt-5-mini",
             "api_key": "your-openai-key",
             "scoring_prompt": custom_prompt,
             "top_k": 5

docs/components/rerankers/models/cohere.mdx

@@ -9,9 +9,9 @@ Cohere provides enterprise-grade reranking models with excellent multilingual su
 
 Cohere offers several reranking models:
 
-- **`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
+- **`rerank-v3.5`** (default): Latest reranker, multilingual, best performance
+- **`rerank-english-v3.0`**: Previous generation, English only
+- **`rerank-multilingual-v3.0`**: Previous generation, multilingual
 
 ## Installation
 
@@ -41,7 +41,7 @@ config = {
     "reranker": {
         "provider": "cohere",
         "config": {
-            "model": "rerank-english-v3.0",
+            "model": "rerank-v3.5",
             "api_key": "your-cohere-api-key",  # or set COHERE_API_KEY
             "top_k": 5,
             "return_documents": False,
@@ -53,6 +53,34 @@ 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:
@@ -73,11 +101,11 @@ os.environ["COHERE_API_KEY"] = "your-api-key"
 # Initialize memory with Cohere reranker
 config = {
     "vector_store": {"provider": "chroma"},
-    "llm": {"provider": "openai", "config": {"model": "gpt-4o-mini"}},
+    "llm": {"provider": "openai", "config": {"model": "gpt-5-mini"}},
     "rerank": {
         "provider": "cohere",
         "config": {
-            "model": "rerank-english-v3.0",
+            "model": "rerank-v3.5",
             "top_k": 3
         }
     }
@@ -124,7 +152,7 @@ config = {
 
 | Parameter            | Description                      | Type   | Default                 |
 | -------------------- | -------------------------------- | ------ | ----------------------- |
-| `model`              | Cohere rerank model to use       | `str`  | `"rerank-english-v3.0"` |
+| `model`              | Cohere rerank model to use       | `str`  | `"rerank-v3.5"` |
 | `api_key`            | Cohere API key                   | `str`  | `None`                  |
 | `top_k`              | Maximum documents to return      | `int`  | `None`                  |
 | `return_documents`   | Whether to return document texts | `bool` | `False`                 |
@@ -139,7 +167,7 @@ config = {
 
 ## Best Practices
 
-1. **Model Selection**: Use `rerank-english-v3.0` for English, `rerank-multilingual-v3.0` for other languages
+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
 2. **Batch Processing**: Process multiple queries efficiently
 3. **Error Handling**: Implement retry logic for production systems
 4. **Monitoring**: Track reranking performance and costs

docs/components/rerankers/models/huggingface.mdx

@@ -57,6 +57,40 @@ 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)

docs/components/rerankers/models/llm_reranker.mdx

@@ -19,7 +19,7 @@ config = {
         "provider": "llm_reranker",
         "config": {
             "provider": "openai",
-            "model": "gpt-4o-mini",
+            "model": "gpt-5-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-4o-mini"` | LLM model to use for reranking |
+| `model` | str | `"gpt-5-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 |
@@ -67,6 +67,43 @@ 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
@@ -77,7 +114,7 @@ config = {
         "provider": "llm_reranker",
         "config": {
             "provider": "openai",
-            "model": "gpt-4o-mini",
+            "model": "gpt-5-mini",
             "api_key": "your-openai-api-key",
             "temperature": 0.0
         }
@@ -133,15 +170,15 @@ config = {
         "provider": "llm_reranker",
         "config": {
             "provider": "azure_openai",
-            "model": "gpt-4o-mini",
+            "model": "gpt-5-mini",
             "api_key": "your-azure-api-key",
             "llm": {
                 "provider": "azure_openai",
                 "config": {
-                    "model": "gpt-4o-mini",
+                    "model": "gpt-5-mini",
                     "api_key": "your-azure-api-key",
                     "azure_endpoint": "https://your-resource.openai.azure.com/",
-                    "azure_deployment": "gpt-4o-mini-deployment"
+                    "azure_deployment": "gpt-5-mini-deployment"
                 }
             }
         }
@@ -195,7 +232,7 @@ config = {
         "provider": "llm_reranker",
         "config": {
             "provider": "openai",
-            "model": "gpt-4o-mini",
+            "model": "gpt-5-mini",
             "api_key": "your-api-key",
             "scoring_prompt": custom_prompt
         }
@@ -314,7 +351,7 @@ fast_config = {
         "provider": "llm_reranker",
         "config": {
             "provider": "openai",
-            "model": "gpt-4o-mini",
+            "model": "gpt-5-mini",
             "api_key": "your-api-key",
             "top_k": 5,
             "temperature": 0.0
@@ -407,7 +444,7 @@ fallback_config = {
         "provider": "llm_reranker",
         "config": {
             "provider": "openai",
-            "model": "gpt-4o-mini",
+            "model": "gpt-5-mini",
             "api_key": "your-api-key"
         }
     }

docs/components/rerankers/models/sentence_transformer.mdx

@@ -36,7 +36,7 @@ config = {
     "llm": {
         "provider": "openai",
         "config": {
-            "model": "gpt-4o-mini"
+            "model": "gpt-5-mini"
         }
     },
     "rerank": {
@@ -54,6 +54,40 @@ 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:
@@ -79,7 +113,7 @@ from mem0 import Memory
 # Initialize memory with local reranker
 config = {
     "vector_store": {"provider": "chroma"},
-    "llm": {"provider": "openai", "config": {"model": "gpt-4o-mini"}},
+    "llm": {"provider": "openai", "config": {"model": "gpt-5-mini"}},
     "rerank": {
         "provider": "sentence_transformer",
         "config": {

docs/components/rerankers/models/zero_entropy.mdx

@@ -34,7 +34,7 @@ config = {
     "llm": {
         "provider": "openai",
         "config": {
-            "model": "gpt-4o-mini"
+            "model": "gpt-5-mini"
         }
     },
     "rerank": {
@@ -50,6 +50,34 @@ 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:
@@ -70,7 +98,7 @@ 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-4o-mini"}},
+    "llm": {"provider": "openai", "config": {"model": "gpt-5-mini"}},
     "rerank": {"provider": "zero_entropy", "config": {"model": "zerank-1"}}
 }
 

docs/components/rerankers/optimization.mdx

@@ -47,7 +47,7 @@ config = {
     "reranker": {
         "provider": "cohere",
         "config": {
-            "model": "rerank-english-v3.0",
+            "model": "rerank-v3.5",
             "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-english-v3.0"},
+        {"provider": "cohere", "model": "rerank-v3.5"},
         {"provider": "sentence_transformer", "model": "cross-encoder/ms-marco-MiniLM-L-6-v2"},
         {"provider": "huggingface", "model": "BAAI/bge-reranker-base"}
     ]

docs/components/rerankers/overview.mdx

@@ -9,6 +9,22 @@ Mem0 rerankers rescore vector search hits so your agents surface the most releva
 Reranking trades extra latency for better precision. Start once you have baseline search working and measure before/after relevance.
 </Info>
 
+## Supported Rerankers
+
+<CardGroup cols={3}>
+  <Card title="Cohere" icon="/images/provider-icons/cohere.svg" href="/components/rerankers/models/cohere" />
+  <Card title="Sentence Transformers" icon="vector-square" href="/components/rerankers/models/sentence_transformer" />
+  <Card title="Hugging Face" icon="/images/provider-icons/huggingface.svg" href="/components/rerankers/models/huggingface" />
+  <Card title="LLM Reranker" icon="wand-magic-sparkles" href="/components/rerankers/models/llm_reranker" />
+  <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}>
   <Card
     title="Understand Reranking"
@@ -19,13 +35,13 @@ Reranking trades extra latency for better precision. Start once you have baselin
   <Card
     title="Configure Providers"
     description="Add reranker blocks to your memory configuration."
-    icon="settings"
+    icon="gear"
     href="/components/rerankers/config"
   />
   <Card
     title="Optimize Performance"
     description="Balance relevance, latency, and cost with tuning tactics."
-    icon="speedometer"
+    icon="gauge"
     href="/components/rerankers/optimization"
   />
   <Card
@@ -43,7 +59,7 @@ Reranking trades extra latency for better precision. Start once you have baselin
   <Card
     title="Sentence Transformers"
     description="Keep reranking on-device with cross-encoder models."
-    icon="cpu"
+    icon="microchip"
     href="/components/rerankers/models/sentence_transformer"
   />
 </CardGroup>
@@ -66,7 +82,7 @@ Reranking trades extra latency for better precision. Start once you have baselin
   <Card
     title="Set Up Reranking"
     description="Walk through the configuration fields and defaults."
-    icon="settings"
+    icon="gear"
     href="/components/rerankers/config"
   />
   <Card

docs/components/vectordbs/config.mdx

@@ -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")
+  - `provider`: The name of the vector database (e.g., "chroma", "pgvector", "qdrant", "milvus", "upstash_vector", "azure_ai_search", "vertex_ai_vector_search", "valkey", "oracledb")
   - `config`: A nested dictionary containing provider-specific settings
 
 
@@ -95,6 +95,11 @@ 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 |

docs/components/vectordbs/dbs/azure.mdx

@@ -81,13 +81,13 @@ Azure client ID, secret, tenant ID, or certificate in environment variables for
 Utilizes Azure Workload Identity (relevant for Kubernetes and Azure workloads).
 
 3. **Managed Identity Credential:**
-Authenticates as a Managed Identity (for apps/services hosted in Azure with Managed Identity enabled), this is the most secure production credential.
+Authenticates as a Managed Identity (for apps/services hosted in Azure with Managed Identity enabled); this is the most secure production credential.
 
 4. **Shared Token Cache Credential / Visual Studio Credential (Windows only):**
 Uses cached credentials from Visual Studio sign-ins (and sometimes VS Code if SSO is enabled).
 
 5. **Azure CLI Credential:**
-Uses the currently logged-in user from the Azure CLI (`az login`), this is the most common development credential.
+Uses the currently logged-in user from the Azure CLI (`az login`); this is the most common development credential.
 
 6. **Azure PowerShell Credential:**
 Uses the identity from Azure PowerShell (`Connect-AzAccount`).
@@ -135,7 +135,7 @@ config = {
 ```
 
 ### Environment Variables to Use Azure Identity Credential
-* For an Environment Credential, you will need to setup a Service Principal and set the following environment variables:
+* For an Environment Credential, you will need to set up a Service Principal and set the following environment variables:
   - `AZURE_TENANT_ID`: Your Azure Active Directory tenant ID.
   - `AZURE_CLIENT_ID`: The client ID of your service principal or managed identity.
   - `AZURE_CLIENT_SECRET`: The client secret of your service principal.

docs/components/vectordbs/dbs/baidu.mdx

@@ -5,10 +5,22 @@ 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 = {
@@ -36,19 +48,63 @@ 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` |
+| 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.
 
 ### Distance Metrics
 
@@ -66,3 +122,5 @@ 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.

docs/components/vectordbs/dbs/cassandra.mdx

@@ -7,7 +7,8 @@ description: "Use Apache Cassandra as a distributed vector store in Mem0 with se
 
 ### Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -37,11 +38,43 @@ messages = [
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+// Set OPENAI_API_KEY in your environment for the default embedder
+
+const config = {
+  vectorStore: {
+    provider: 'cassandra',
+    config: {
+      contactPoints: ['127.0.0.1'],
+      localDataCenter: 'datacenter1', // required with contactPoints; "datacenter1" is the default for a single-node cluster
+      port: 9042,
+      username: 'cassandra',
+      password: 'cassandra',
+      keyspace: 'mem0',
+      collectionName: 'memories',
+    },
+  },
+};
+
+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>
+
 #### Using DataStax Astra DB
 
 For managed Cassandra with DataStax Astra DB:
 
-```python
+<CodeGroup>
+```python Python
 config = {
     "vector_store": {
         "provider": "cassandra",
@@ -57,8 +90,24 @@ config = {
 }
 ```
 
+```typescript TypeScript
+const config = {
+  vectorStore: {
+    provider: 'cassandra',
+    config: {
+      username: 'token',
+      password: 'AstraCS:...', // Your Astra DB application token
+      keyspace: 'mem0',
+      collectionName: 'memories',
+      secureConnectBundle: '/path/to/secure-connect-bundle.zip',
+    },
+  },
+};
+```
+</CodeGroup>
+
 <Note>
-When using DataStax Astra DB, provide the secure connect bundle path. The contact_points parameter is ignored when a secure connect bundle is provided.
+When using DataStax Astra DB, provide the secure connect bundle path. Contact points and `localDataCenter` are not needed when a secure connect bundle is provided.
 </Note>
 
 ### Config
@@ -78,6 +127,10 @@ Here are the parameters available for configuring Apache Cassandra:
 | `protocol_version` | CQL protocol version | `4` |
 | `load_balancing_policy` | Custom load balancing policy | `None` |
 
+<Note>
+The TypeScript SDK uses camelCase keys: `contactPoints`, `collectionName`, `embeddingModelDims`, `secureConnectBundle`, `protocolVersion`, and `loadBalancingPolicy`. It also requires `localDataCenter` (for example, `datacenter1`) when you connect with `contactPoints` instead of a secure connect bundle. The Node.js driver needs this to route queries; it has no default.
+</Note>
+
 ### Setup
 
 #### Option 1: Local Cassandra Setup using Docker:
@@ -139,14 +192,20 @@ brew services start cassandra
 cqlsh
 ```
 
-### Python Client Installation
+### Client Installation
 
-Install the required Python package:
+Install the driver for your SDK:
 
-```bash
+<CodeGroup>
+```bash Python
 pip install cassandra-driver
 ```
 
+```bash TypeScript
+npm install cassandra-driver
+```
+</CodeGroup>
+
 ### Performance Considerations
 
 - **Replication Factor**: For production, use replication factor of at least 3
@@ -156,7 +215,8 @@ pip install cassandra-driver
 
 ### Advanced Configuration
 
-```python
+<CodeGroup>
+```python Python
 from cassandra.policies import DCAwareRoundRobinPolicy
 
 config = {
@@ -176,6 +236,28 @@ config = {
 }
 ```
 
+```typescript TypeScript
+// The Node.js driver routes to localDataCenter by default, so set it to your
+// primary DC for datacenter-aware routing. Pass loadBalancingPolicy only when
+// you need a custom policy from the cassandra-driver package.
+const config = {
+  vectorStore: {
+    provider: 'cassandra',
+    config: {
+      contactPoints: ['node1.example.com', 'node2.example.com', 'node3.example.com'],
+      localDataCenter: 'DC1',
+      port: 9042,
+      username: 'mem0_user',
+      password: 'secure_password',
+      keyspace: 'mem0_prod',
+      collectionName: 'memories',
+      protocolVersion: 4,
+    },
+  },
+};
+```
+</CodeGroup>
+
 <Warning>
 For production use, configure appropriate replication strategies and consistency levels based on your availability and consistency requirements.
 </Warning>

docs/components/vectordbs/dbs/chroma.mdx

@@ -6,9 +6,8 @@ description: "Use Chroma as a vector database in Mem0 for local or cloud-hosted
 
 ### Usage
 
-#### Local Installation
-
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -37,10 +36,46 @@ 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` |
@@ -49,4 +84,19 @@ 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` |
+| `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>

docs/components/vectordbs/dbs/databricks.mdx

@@ -6,7 +6,8 @@ description: "Use Databricks Vector Search as a serverless vector store in Mem0
 
 ### Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -36,10 +37,44 @@ 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** |
@@ -60,6 +95,32 @@ 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
 

docs/components/vectordbs/dbs/elasticsearch.mdx

@@ -6,15 +6,22 @@ description: "Use Elasticsearch as a vector database in Mem0 for distributed vec
 
 ### Installation
 
-Elasticsearch support requires additional dependencies. Install them with:
+Elasticsearch support requires the Elasticsearch client as an extra dependency.
 
-```bash
+<CodeGroup>
+```bash Python
 pip install elasticsearch>=8.0.0
 ```
 
+```bash TypeScript
+npm install mem0ai @elastic/elasticsearch
+```
+</CodeGroup>
+
 ### Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -36,12 +43,52 @@ 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="alice", metadata={"category": "movies"})
 ```
 
+```typescript TypeScript
+import { Memory } from "mem0ai/oss";
+
+// Set OPENAI_API_KEY in your environment.
+const config = {
+  embedder: {
+    provider: "openai",
+    config: {
+      apiKey: process.env.OPENAI_API_KEY,
+      model: "text-embedding-3-small",
+    },
+  },
+  vectorStore: {
+    provider: "elasticsearch",
+    config: {
+      collectionName: "mem0",
+      embeddingModelDims: 1536,
+      host: "localhost",
+      port: 9200,
+      // For Elastic Cloud, pass cloudId and apiKey instead of host/port.
+      // For basic auth, pass username and password.
+    },
+  },
+};
+
+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 TypeScript SDK uses camelCase config keys: `collectionName`, `embeddingModelDims`, `cloudId`, `apiKey`, `useSsl`, `verifyCerts`, `caCerts`, `autoCreateIndex`, and `username` (in place of the Python `user`). `collectionName` and `embeddingModelDims` are required. Because the vector store embeds text with your configured embedder before writing, set an `embedder` in the config as shown above.
+</Note>
+
 ### Config
 
 Here are the parameters available for configuring Elasticsearch:
@@ -74,6 +121,10 @@ Here are the parameters available for configuring Elasticsearch:
 
 ### Custom Search Query
 
+<Note>
+  `custom_search_query` is available in the Python SDK only. The TypeScript SDK runs a fixed k-NN query with optional metadata filters.
+</Note>
+
 The `custom_search_query` parameter allows you to customize the search query when `Memory.search` is called.  
   
 __Example__  

docs/components/vectordbs/dbs/milvus.mdx

@@ -6,7 +6,14 @@ description: "Use Milvus as an open-source vector database in Mem0, scalable fro
 
 ### Usage
 
-```python
+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
 import os
 from mem0 import Memory
 
@@ -33,10 +40,39 @@ 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` |
@@ -45,3 +81,15 @@ 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>

docs/components/vectordbs/dbs/mongodb.mdx

@@ -2,13 +2,15 @@
 title: "MongoDB"
 description: "Use MongoDB as a vector database in Mem0 with built-in vector search for high-dimensional similarity queries."
 ---
+
 # MongoDB
 
 [MongoDB](https://www.mongodb.com/) is a versatile document database that supports vector search capabilities, allowing for efficient high-dimensional similarity searches over large datasets with robust scalability and performance.
 
 ## Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -20,30 +22,90 @@ config = {
         "config": {
             "db_name": "mem0-db",
             "collection_name": "mem0-collection",
-            "mongo_uri":"mongodb://username:password@localhost:27017"
+            "mongo_uri": "mongodb://username:password@localhost:27017"
         }
     }
 }
 
 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."}
+    {
+        "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"})
 ```
 
+```typescript TypeScript
+import { Memory } from "mem0ai/oss";
+
+const config = {
+  vectorStore: {
+    provider: "mongodb",
+    config: {
+      dbName: "mem0-db",
+      collectionName: "mem0-collection",
+      url: "mongodb://username:password@localhost:27017",
+    },
+  },
+};
+
+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 MongoDB:
 
-| Parameter | Description | Default Value |
-| --- | --- | --- |
-| db_name | Name of the MongoDB database | `"mem0_db"` |
-| collection_name | Name of the MongoDB collection | `"mem0"` |
-| embedding_model_dims | Dimensions of the embedding vectors | `1536` |
-| mongo_uri | The MongoDB URI connection string | `mongodb://localhost:27017` |
+| Python | TypeScript | Description | Default Value |
+| --- | --- | --- | --- |
+| db_name | dbName | Name of the MongoDB database | "mem0_db" |
+| collection_name | collectionName | Name of the MongoDB collection | "mem0" |
+| embedding_model_dims | embeddingModelDims | Dimensions of the embedding vectors | 1536 |
+| mongo_uri | url | The MongoDB URI connection string | mongodb://localhost:27017 |
 
-> **Note**: If `mongo_uri` is not provided, it will default to `mongodb://localhost:27017`.
+> **Note**: If `mongo_uri` (Python) or `url` (TypeScript) is not provided, it defaults to `mongodb://localhost:27017`. A local instance must be running MongoDB v8.2+ for vector search to work.
+
+> **Note**: The vector search index builds asynchronously after the first write. A search issued right after the first `add()` may return no results (and log an "index not initialized" message) until the index finishes building. This takes a few seconds on a local deployment and up to about a minute on Atlas. This is expected; the search returns results once the index is ready.

docs/components/vectordbs/dbs/neptune_analytics.mdx

@@ -2,26 +2,37 @@
 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
 
-## Installation
+The Neptune Analytics provider needs the AWS Neptune Graph client. Install it alongside `mem0ai`:
 
-```bash
+<CodeGroup>
+```bash Python
 pip install mem0ai[vector-stores]
 ```
 
-## Usage
+```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
 
-```python
 config = {
     "vector_store": {
         "provider": "neptune",
         "config": {
             "collection_name": "mem0",
-            "endpoint": f"neptune-graph://my-graph-identifier",
+            "endpoint": "neptune-graph://g-abc123xyz0",
         },
     },
 }
@@ -29,18 +40,90 @@ 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 movies? They can be quite engaging."},
+    {"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."}
 ]
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
-## Parameters
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
 
-Let's see the available parameters for the `neptune` config:
+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',
+    },
+  },
+};
 
+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 | `neptune-graph://my-graph-identifier` |
+| `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.

docs/components/vectordbs/dbs/opensearch.mdx

@@ -6,12 +6,18 @@ description: "Use OpenSearch as a vector database in Mem0 with k-NN search suppo
 
 ### Installation
 
-OpenSearch support requires additional dependencies. Install them with:
+OpenSearch support requires an additional client library. Install the one for your SDK:
 
-```bash
+<CodeGroup>
+```bash Python
 pip install opensearch-py
 ```
 
+```bash TypeScript
+npm install @opensearch-project/opensearch
+```
+</CodeGroup>
+
 ### Prerequisites
 
 Before using OpenSearch with Mem0, you need to set up a collection in AWS OpenSearch Service.
@@ -26,7 +32,8 @@ You can create a collection through the AWS Console:
 
 ### Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 import boto3
@@ -56,8 +63,43 @@ config = {
 }
 ```
 
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+// Basic self-hosted OpenSearch. For AWS OpenSearch Serverless, build an
+// @opensearch-project/opensearch Client with AwsSigv4Signer and pass it as
+// `client` instead of host/port/user/password.
+const config = {
+  vectorStore: {
+    provider: 'opensearch',
+    config: {
+      collectionName: 'mem0',
+      embeddingModelDims: 1024,
+      host: 'localhost',
+      port: 9200,
+      user: 'admin',
+      password: 'admin',
+      useSSL: false,
+      verifyCerts: false,
+    },
+  },
+};
+
+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>
+
 ### Configuration Options
 
+<Tabs>
+<Tab title="Python">
 | Parameter | Type | Default | Description |
 |-----------|------|---------|-------------|
 | `collection_name` | string | required | Name of the OpenSearch index |
@@ -68,6 +110,23 @@ config = {
 | `use_ssl` | bool | False | Enable SSL/TLS connection |
 | `verify_certs` | bool | False | Verify SSL certificates |
 | `auto_refresh` | bool | False | Automatically refresh index after insert. OpenSearch refreshes every ~1 second by default, so this is rarely needed. |
+</Tab>
+<Tab title="TypeScript">
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `collectionName` | string | required | Name of the OpenSearch index |
+| `embeddingModelDims` | number | 1536 | Dimension of embedding vectors |
+| `host` | string | `localhost` | OpenSearch endpoint host |
+| `port` | number | 9200 | Port number |
+| `httpAuth` | object | None | Authentication credentials, an object or `[user, password]` tuple |
+| `user` | string | None | Username for basic auth (used together with `password`) |
+| `password` | string | None | Password for basic auth (used together with `user`) |
+| `useSSL` | boolean | false | Enable SSL/TLS connection |
+| `verifyCerts` | boolean | false | Verify SSL certificates |
+| `autoRefresh` | boolean | false | Refresh the index after each write so new memories are searchable immediately. Not supported on AWS Serverless. |
+| `client` | object | None | Preconfigured OpenSearch client, e.g. one built with AwsSigv4Signer for AWS auth |
+</Tab>
+</Tabs>
 
 <Note>
   The defaults above match a local OpenSearch instance. The AWS OpenSearch Serverless

docs/components/vectordbs/dbs/oracledb.mdx

@@ -0,0 +1,134 @@
+---
+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}},
+)
+```

docs/components/vectordbs/dbs/pinecone.mdx

@@ -10,7 +10,8 @@ description: "Use Pinecone as a fully managed vector database in Mem0 with serve
 
 ### Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -44,10 +45,43 @@ messages = [
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+// Set OPENAI_API_KEY and PINECONE_API_KEY in your environment
+const config = {
+  vectorStore: {
+    provider: 'pinecone',
+    config: {
+      collectionName: 'testing',
+      embeddingModelDims: 1536, // Matches OpenAI's text-embedding-3-small
+      namespace: 'my-namespace', // Optional: specify a namespace for multi-tenancy
+      serverlessConfig: {
+        cloud: 'aws', // 'aws' | 'gcp' | 'azure'
+        region: 'us-east-1',
+      },
+      metric: 'cosine',
+    },
+  },
+};
+
+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 Pinecone:
 
+<Tabs>
+<Tab title="Python">
 | Parameter | Description | Default Value |
 | --- | --- | --- |
 | `collection_name` | Name of the index/collection | Required |
@@ -61,11 +95,28 @@ Here are the parameters available for configuring Pinecone:
 | `metric` | Distance metric for vector similarity | `"cosine"` |
 | `batch_size` | Batch size for operations | `100` |
 | `namespace` | Namespace for the collection, useful for multi-tenancy. | `None` |
+</Tab>
+<Tab title="TypeScript">
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `collectionName` | Name of the index/collection | Required |
+| `embeddingModelDims` | Dimensions of the embedding model (must match your chosen embedding model) | `1536` |
+| `client` | Existing Pinecone client instance | `undefined` |
+| `apiKey` | API key for Pinecone | Environment variable: `PINECONE_API_KEY` |
+| `serverlessConfig` | Configuration for serverless deployment (`cloud`, `region`) | `undefined` |
+| `podConfig` | Configuration for pod-based deployment (`environment`, `podType`, `pods`, `replicas`, `shards`) | `undefined` |
+| `metric` | Distance metric for vector similarity (`cosine`, `dotproduct`, `euclidean`) | `"cosine"` |
+| `batchSize` | Batch size for insert operations | `100` |
+| `namespace` | Namespace for the collection, useful for multi-tenancy. | `undefined` |
+| `extraParams` | Extra parameters spread into the Pinecone `createIndex` call | `{}` |
+</Tab>
+</Tabs>
 
 > **Important**: You must choose either `serverless_config` or `pod_config` for your deployment, but not both.
 
 #### Serverless Config Example
-```python
+<CodeGroup>
+```python Python
 config = {
     "vector_store": {
         "provider": "pinecone",
@@ -82,8 +133,27 @@ config = {
 }
 ```
 
+```typescript TypeScript
+const config = {
+  vectorStore: {
+    provider: 'pinecone',
+    config: {
+      collectionName: 'memory_index',
+      embeddingModelDims: 1536, // For OpenAI's text-embedding-3-small
+      namespace: 'my-namespace', // Optional: custom namespace
+      serverlessConfig: {
+        cloud: 'aws', // 'gcp' | 'azure'
+        region: 'us-east-1', // Choose appropriate region
+      },
+    },
+  },
+};
+```
+</CodeGroup>
+
 #### Pod Config Example
-```python
+<CodeGroup>
+```python Python
 config = {
     "vector_store": {
         "provider": "pinecone",
@@ -99,4 +169,23 @@ config = {
         }
     }
 }
-```
+```
+
+```typescript TypeScript
+const config = {
+  vectorStore: {
+    provider: 'pinecone',
+    config: {
+      collectionName: 'memory_index',
+      embeddingModelDims: 1536, // For OpenAI's text-embedding-ada-002
+      namespace: 'my-namespace', // Optional: custom namespace
+      podConfig: {
+        environment: 'gcp-starter',
+        replicas: 1,
+        podType: 'starter',
+      },
+    },
+  },
+};
+```
+</CodeGroup>

docs/components/vectordbs/dbs/qdrant.mdx

@@ -42,7 +42,7 @@ const config = {
     provider: 'qdrant',
     config: {
       collectionName: 'memories',
-      embeddingModelDims: 1536,
+      dimension: 1536,
       host: 'localhost',
       port: 6333,
     },
@@ -60,6 +60,12 @@ 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:
@@ -83,7 +89,7 @@ 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` |
-| `embeddingModelDims` | Dimensions of the embedding model | `1536` |
+| `dimension` | 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` |