docs: update README benchmarks to current temporal-reasoning results

Refresh the README benchmark table and Research Highlights to match the
current numbers on the Memory Evaluation page (docs/core-concepts/
memory-evaluation.mdx), updated in #6056.

- LoCoMo: 91.6 -> 92.5
- LongMemEval: 94.8 -> 94.4 (94.8 was not sourced by any docs page)
- Assistant memory recall highlight: +53.6 delta -> 98.2 absolute
- State the top_200 retrieval budget and the managed-platform caveat

BEAM (1M / 10M), token counts, and latency are unchanged.

.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": "0.2.13"
+      "version": "0.2.12"
     }
   ]
 }

.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": "0.2.13"
+      "version": "0.2.12"
     }
   ]
 }

.github/ISSUE_TEMPLATE/bug_report.yml

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

.github/ISSUE_TEMPLATE/feature_request.yml

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

.github/advanced-issue-labeler.yml

@@ -1,15 +1,18 @@
-policy:
-  - section:
-      - id: ['component']
-        block-list: ['Other']
-        label:
-          - name: 'sdk-python'
-            keys: ['Python SDK']
-          - name: 'sdk-typescript'
-            keys: ['TypeScript SDK']
-          - name: 'vector-store'
-            keys: ['Vector Store']
-          - name: 'plugin'
-            keys: ['Plugin']
-          - name: 'rest-api'
-            keys: ['REST API']
+# Maps dropdown selections to GitHub labels
+# Used by the advanced-issue-labeler GitHub Action
+
+component:
+  - label: "sdk-python"
+    matcher: "Core / Python SDK"
+  - label: "sdk-typescript"
+    matcher: "TypeScript SDK"
+  - label: "vector-store"
+    matcher: "Vector Store"
+  - label: "graph-memory"
+    matcher: "Graph Memory"
+  - label: "ollama"
+    matcher: "Ollama"
+  - label: "openclaw"
+    matcher: "OpenClaw"
+  - label: "rest-api"
+    matcher: "REST API"

.github/component-keywords.json

@@ -1,40 +0,0 @@
-{
-  "language": {
-    "sdk-python": [
-      "python", "pip install", "pypi", "pyproject", "requirements.txt",
-      "from mem0", "import mem0", "traceback", "pydantic", "asyncmemory",
-      "poetry", "virtualenv", "venv", "conda", "pytest", "async def"
-    ],
-    "sdk-typescript": [
-      "typescript", "javascript", "pnpm", "yarn", "node.js", "nodejs",
-      "mem0-ts", "mem0ai/oss", "tsconfig", "await import",
-      "=> {", "undefined is not"
-    ]
-  },
-  "area": {
-    "plugin": [
-      "openclaw", "openclaw-mem0", "openclaw.json", "openclaw plugin",
-      "claude code", "opencode", "pi agent", "mem0-plugin",
-      "cursor plugin", "codex plugin", "editor plugin"
-    ],
-    "cli": ["mem0-cli", "@mem0/cli", "npx mem0", "command line"],
-    "vector-store": [
-      "pgvector", "pinecone", "chroma", "chromadb", "weaviate",
-      "milvus", "faiss", "vector store", "vectorstore",
-      "elasticsearch", "supabase", "azure ai search",
-      "s3 vectors", "mongodb"
-    ],
-    "integrations": [
-      "vercel ai", "vercel-ai-sdk", "@mem0/vercel-ai-provider",
-      "llamaindex", "crewai", "autogen", "langgraph"
-    ],
-    "rest-api": [
-      "rest api", "fastapi", "docker-compose", "/v1/memories",
-      "localhost:8000", "localhost:8888", "curl -x", "http endpoint"
-    ],
-    "documentation": [
-      "docs.mem0.ai", "documentation", "typo", "readme", "docstring", "broken link",
-      "issue on docs", "docs:", "link to the docs page", "issue with current documentation"
-    ]
-  }
-}

.github/labeler.yml

@@ -1,59 +0,0 @@
-sdk-python:
-  - changed-files:
-      - any-glob-to-any-file:
-          - 'mem0/**'
-          - 'tests/**'
-          - 'cli/python/**'
-          - 'pyproject.toml'
-          - 'poetry.lock'
-
-sdk-typescript:
-  - changed-files:
-      - any-glob-to-any-file:
-          - 'mem0-ts/**'
-          - 'cli/node/**'
-
-vector-store:
-  - changed-files:
-      - any-glob-to-any-file:
-          - 'mem0/vector_stores/**'
-          - 'mem0-ts/src/oss/src/vector_stores/**'
-
-rest-api:
-  - changed-files:
-      - any-glob-to-any-file: 'server/**'
-
-integrations:
-  - changed-files:
-      - any-glob-to-any-file: 'integrations/**'
-
-plugin:
-  - changed-files:
-      - all-globs-to-any-file:
-          - 'integrations/**'
-          - '!integrations/vercel-ai-sdk/**'
-      - any-glob-to-any-file:
-          - 'skills/**'
-          - '.agents/**'
-          - '.claude-plugin/**'
-          - '.codex-plugin/**'
-          - '.cursor-plugin/**'
-          - 'marketplace.json'
-
-cli:
-  - changed-files:
-      - any-glob-to-any-file: 'cli/**'
-
-documentation:
-  - changed-files:
-      - any-glob-to-any-file:
-          - 'docs/**'
-          - 'examples/**'
-          - '*.md'
-
-ci:
-  - changed-files:
-      - any-glob-to-any-file:
-          - '.github/**'
-          - 'scripts/**'
-          - '.pre-commit-config.yaml'

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

@@ -1,44 +0,0 @@
-const fs = require('fs');
-
-function componentLabels(keywords) {
-  return Object.values(keywords).flatMap(Object.keys);
-}
-
-function toMatcher(term) {
-  const escaped = term.replace(/[.*+?^${}()|[\]\\]/g, '\\$&');
-  const prefix = /^[a-z0-9]/i.test(term) ? '\\b' : '';
-  return new RegExp(prefix + escaped, 'i');
-}
-
-function scoreGroup(text, group) {
-  let winner = null;
-  let best = 0;
-  for (const [label, terms] of Object.entries(group)) {
-    const score = terms.reduce((n, term) => n + (toMatcher(term).test(text) ? 1 : 0), 0);
-    if (score > best) {
-      winner = label;
-      best = score;
-    }
-  }
-  return winner;
-}
-
-const UMBRELLA = { plugin: 'integrations' };
-
-function inferComponentLabels(text, keywords) {
-  if (!text) return [];
-  const labels = [scoreGroup(text, keywords.language), scoreGroup(text, keywords.area)].filter(
-    Boolean,
-  );
-  for (const label of labels.slice()) {
-    const parent = UMBRELLA[label];
-    if (parent && !labels.includes(parent)) labels.push(parent);
-  }
-  return labels;
-}
-
-function loadKeywords(file) {
-  return JSON.parse(fs.readFileSync(file, 'utf8'));
-}
-
-module.exports = { componentLabels, inferComponentLabels, loadKeywords };

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

@@ -1,102 +0,0 @@
-const assert = require('assert');
-const path = require('path');
-const { inferComponentLabels, loadKeywords } = require('./infer-component-labels.js');
-
-const keywords = loadKeywords(path.join(__dirname, '..', 'component-keywords.json'));
-
-const cases = [
-  {
-    number: 6210,
-    title: "but(anthropic): sampling parameters returns 400 error for new model",
-    body: "### Component\n\nCore / Python SDK\n\n### Description\n\n### Summary\n\nWhen using Anthropic latest models such as `claude-opus-4-7`, `claude-opus-4-8`, or `claude-sonnet-5`, Mem0 still sends sampling parameters like `temperature` / `top_p`. These models do not support those parameters, causing Anthropic API requests to fail.\n\nSee https://platform.claude.com/docs/en/about-claude/models/migration-guide\n\n### Steps to Reproduce\n\n```python\n  from mem0 import Memory\n\n  m = Memory.from_config({\n      \"llm\": {\n          \"provider\": \"anthropic\",\n          \"config\": {\n              \"model\": \"claude-opus-4-8\",\n              \"api_key\": \"your-anthropic-api-key\"\n          },\n      },\n      ...\n  })\n```\n\n### Expected Behavior\n\nMem0 should detect Anthropic models that do not support sampling parameters and omit temperature and top_p from the request.\n\nFor models that still support sampling parameters, such as claude-opus-4-6, claude-sonnet-4-6, and claude-haiku-4-5, Mem0 should continue sending supported sampling parameters till they're deprecated.\n\n### Actual Behavior\n\nMem0 includes temperature by default for Anthropic requests. With newer Anthropic models that do not support sampling parameters, the API request fails because unsupported parameters are sent.\n\n### Environment\n\n  - mem0 version: 2.0.11\n  - Python/Node version: Python 3.11\n  - OS: macOS\n",
-    expected: ["sdk-python"],
-  },
-  {
-    number: 5770,
-    title: "feat(ts-sdk): add FastEmbed embedding provider",
-    body: "## Summary\n\nThe Python SDK supports **FastEmbed** as an embedding provider, but the TypeScript OSS SDK (`mem0ai/oss`) does not. Add it to bring the TS SDK to parity.\n\n| | |\n|---|---|\n| Python reference | `mem0/embeddings/fastembed.py` |\n| Registered in (Python) | `mem0/utils/factory.py` (EmbedderFactory) |\n| Target file (TypeScript) | `mem0-ts/src/oss/src/embeddings/fastembed.ts` |\n| Suggested implementation | Use the `fastembed` npm package (ONNX local embeddings). |\n\n## Requirements\n\n- [ ] Implement `FastEmbedEmbedder` in `mem0-ts/src/oss/src/embeddings/fastembed.ts`, extending `Embedder` (`mem0-ts/src/oss/src/embeddings/base.ts`) and mirroring the Python provider's behavior (embed / embedBatch).\n- [ ] Register the `\"fastembed\"` provider in `mem0-ts/src/oss/src/utils/factory.ts` (EmbedderFactory).\n- [ ] Add config typing in `mem0-ts/src/oss/src/types/`.\n- [ ] Add a unit test under `mem0-ts/src/oss/src/tests/`.\n- [ ] Add `fastembed` to `mem0-ts/package.json` (optional/peer dependency, lazy-imported like other providers).\n- [ ] Update docs under `docs/` if this provider is user-facing.\n\n## Reference pattern\n\nMirror an existing TS provider: `embeddings/openai.ts`.\n\n## Notes\n\n`fastembed` (v2.x) is the JS port of Qdrant's FastEmbed — local/offline embeddings. Mirror the default model in `mem0/embeddings/fastembed.py`.\n\n---\n_Part of the TypeScript ↔ Python SDK provider-parity effort. One provider per issue (atomic)._\n",
-    expected: ["sdk-typescript"],
-  },
-  {
-    number: 3940,
-    title: "Milvus database will return distance not similarity score",
-    body: "### 🐛 Describe the bug\n\nMilvus database will return distance not similarity score\n\n## in milvus.py\n\ndef _parse_output(self, data: list):\n        \"\"\"\n        Parse the output data.\n\n        Args:\n            data (Dict): Output data.\n\n        Returns:\n            List[OutputData]: Parsed output data.\n        \"\"\"\n        memory = []\n\n        for value in data:\n            uid, score, metadata = (\n                value.get(\"id\"),\n                value.get(\"distance\"),  # here\n                value.get(\"entity\", {}).get(\"metadata\"),\n            )\n\n            memory_obj = OutputData(id=uid, score=score, payload=metadata)\n            memory.append(memory_obj)\n\n        return memory\n",
-    expected: ["vector-store"],
-  },
-  {
-    number: 5290,
-    title: "Recall search failed: Bad Request Using OpenAI Embedding Model",
-    body: "### Component\n\nOpenClaw\n\n### Description\n\n### Summary\nuse openclaw.json config:\n\n```json\n...\n\"embedder\": {\n              \"provider\": \"openai\",\n              \"config\": {\n                \"model\": \"bge-base-zh-v1.5\",\n                \"embedding_dims\": 1024,\n                \"embeddingDims\": 1024,\n                \"url\": \"https://xxxxxxxxx/v1\",\n                \"apiKey\": \"xxxxxxxxxxxx\"\n              }\n            },\n\"vectorStore\": {\n              \"provider\": \"qdrant\",\n              \"config\": {\n                \"url\": \"http://qdrant:6333\",\n                \"apiKey\": \"${QDRANT_API_KEY}\",\n                \"collectionName\": \"mem0\",\n                \"embeddingModelDims\": 1024\n              }\n            }\n```\n```\n\nopenclaw log info is：\n\n```\n23:14:20 Api key is used with unsecure connection.\n23:14:21 [mem0] Recall search failed: Bad Request\n23:14:21 [plugins] openclaw-mem0: skills-mode recall (strategy=smart) injecting 0 memories (~20 tokens)\n23:14:22 [ws] ⇄ res ✓ sessions.list 256ms conn=d1eb9bc4…17da id=201b8113…c9dc\n23:14:22 [ws] ⇄ res ✓ sessions.list 264ms conn=d1eb9bc4…17da id=4939f962…2f16\n23:14:34 [ws] ⇄ res ✓ sessions.list 250ms conn=d1eb9bc4…17da id=f7ad503f…baa6\n23:15:12 [mem0] **Recall search failed: Bad Request**\n23:15:12 [plugins] openclaw-mem0: skills-mode recall (strategy=smart) injecting 0 memories (~20 tokens)\n23:15:12 [ws] ⇄ res ✓ sessions.list 288ms conn=d1eb9bc4…17da id=9e20bb86…371e\n23:15:13 [ws] ⇄ res ✓ sessions.list 268ms conn=d1eb9bc4…17da id=3b49a2ad…7ada\n23:15:20 [ws] ⇄ res ✓ sessions.list 235ms conn=d1eb9bc4…17da id=a192da30…069f\n```\n\n### Actual Behavior\n\nembedding model response ok,response message has 1024 vectors,but the vectors are submitted to vector-db:qdrant with all zero vectors,and vectors has only 256 size.\n\n```http\nPOST /collections/mem0/points/search HTTP/1.1\nhost: qdrant:6333\nconnection: keep-alive\nuser-agent: qdrant-js/1.13.0\napi-key: xxxxxxxxxxxxxxxxxxxxxxxxxxxx\nContent-Type: application/json\nAccept: application/json\naccept-language: *\nsec-fetch-mode: cors\naccept-encoding: gzip, deflate\ncontent-length: 651\n\n{\"vector\":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],\"limit\":120,\"offset\":0,\"filter\":{\"must\":[{\"key\":\"user_id\",\"match\":{\"value\":\"agent\"}}]},\"with_payload\":true,\"with_vector\":false}\n\n**HTTP/1.1 400 Bad Request**\ntransfer-encoding: chunked\ncontent-type: application/json\nvary: accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers\ncontent-encoding: gzip\n\n```\n\n### Expected Behavior\n\nembedding model response ok by tcpdump, response message has 1024 vectors,and this vectors are submitted to vector-db:qdrant with the same vectors,and vectors has also 1024 size.\n\n\n### Environment\n\n- openclaw-mem0 version: 1.0.11\n- qdrant: 1.13.6\n",
-    expected: ["plugin", "integrations"],
-  },
-  {
-    number: 3696,
-    title: "Cannot set expiration_date for memory in REST API server (Docker Compose)",
-    body: "### 🐛 Describe the bug\n\nI'm using docker compose to deploy a REST API server. When adding memory, I'm unable to set the expiration_date. Is this feature not supported?",
-    expected: ["rest-api"],
-  },
-  {
-    number: 6252,
-    title: "cursor: on_file_read_cursor.sh ignores auto_search / MEM0_AUTO_SEARCH",
-    body: "### Component\n\nCursor / mem0-plugin\n\n### Description\n\n`on_file_read_cursor.sh` never checks `MEM0_AUTO_SEARCH`. In Claude Code, #6065/#6071 added a guard on `on_file_read.sh`, but the Cursor PreToolUse variant still always calls `file_context.py` (and thus Platform search) once `MEM0_API_KEY` is set.\n\n### Expected\n\nWhen `auto_search: false` / `MEM0_AUTO_SEARCH=false`, `on_file_read_cursor.sh` should exit 0 without searching.\n\n### Actual\n\nTimeline search still runs.\n\n### Related\n\n#6065, #6071, #6250\n",
-    expected: ["plugin", "integrations"],
-  },
-  {
-    number: 6032,
-    title: "docs: fix typos and punctuation errors across docs",
-    body: "### Description\n\n### Page\nMultiple pages — see list below.\n\n### What's Wrong or Missing\n1. https://docs.mem0.ai/components/llms/overview — \"a llm\" should be \"an LLM\"\n2. https://docs.mem0.ai/components/vectordbs/dbs/azure — 2 comma splices + \"setup\" used as a verb (should be \"set up\")\n3. https://docs.mem0.ai/components/embedders/models/azure_openai — \"from the Azure.\" is an incomplete sentence\n4. https://docs.mem0.ai/components/llms/models/azure_openai — same incomplete \"from the Azure\" phrasing\n5. https://docs.mem0.ai/cookbooks/companions/voice-companion-openai — \"an important information\" (uncountable noun)\n6. https://docs.mem0.ai/cookbooks/essentials/exporting-memories — comma splice\n7. https://docs.mem0.ai/cookbooks/integrations/tavily-search — \"usecase\" should be \"use case\"\n8. https://docs.mem0.ai/cookbooks/overview — broken parallelism in bullet list\n9. README.md — \"Github App\" should be \"GitHub App\"\n10. https://docs.mem0.ai/platform/overview — table cell not capitalized like other rows\n\n### Suggested Fix\nApply the corrections listed above for each page. I will submit a PR soon addressing all of the issues mentioned.",
-    expected: ["documentation"],
-  },
-];
-
-const cliRegressionCase = {
-  number: 3144,
-  title: "Bug Report: Memory Score Does Not Match Expected Relevance in Local Search",
-  body: "### 🐛 Describe the bug\n\n#### Description\n\nWhen using the locally deployed `mem0` server, the returned memory `score` from the `search` interface does not align with the expected semantic relevance. In particular, irrelevant or less relevant memories sometimes receive higher scores than directly related ones.\n\n#### Reproduction Steps\n\n```python\nmem0 = mem0_client(mode=\"local\")\nprint(\"Mem0 client initialized successfully.\")\n\nprint(\"Adding memories...\")\nresult = mem0.add(messages=[\n    {\"role\": \"user\", \"content\": \"I like drinking coffee in the morning\"},\n    {\"role\": \"user\", \"content\": \"I enjoy reading books at night\"}\n], user_id=\"alice\")\nprint(\"Memory added:\", result)\n\nprint(\"Searching memories...\")\nsearch_result = mem0.search(query=\"coffee\", user_id=\"alice\", top_k=2)\nprint(\"Search results:\", search_result)\n```\n\n#### Actual Output\n\n```json\n{\n  \"results\": [\n    {\n      \"id\": \"5099b5be-c673-4f09-99de-a196f43b6476\",\n      \"memory\": \"Likes drinking coffee in the morning\",\n      \"score\": 0.5115111920687857\n    },\n    {\n      \"id\": \"08df5c51-c52b-4c45-a5b6-b3f864ea149a\",\n      \"memory\": \"Enjoys reading books at night\",\n      \"score\": 0.7755568273863331\n    }\n  ],\n  \"relations\": [\n    {\"source\": \"coffee\", \"relationship\": \"consumed_in\", \"destination\": \"morning\"},\n    {\"source\": \"user_id:_alice\", \"relationship\": \"likes\", \"destination\": \"coffee\"},\n    {\"source\": \"user_id:_alice\", \"relationship\": \"likes_drinking\", \"destination\": \"coffee\"},\n    {\"source\": \"user_id:_alice\", \"relationship\": \"in_time\", \"destination\": \"morning\"},\n    {\"source\": \"user_id:_alice\", \"relationship\": \"drinks_in\", \"destination\": \"morning\"}\n  ]\n}\n```\n\n#### Expected Behavior\n\nThe memory `\"Likes drinking coffee in the morning\"` should have a **higher score** than `\"Enjoys reading books at night\"` when querying for `\"coffee\"`, since it is directly semantically related.",
-};
-
-let failures = 0;
-
-function run(name, fn) {
-  try {
-    fn();
-    console.log(`PASS ${name}`);
-  } catch (err) {
-    failures++;
-    console.error(`FAIL ${name}: ${err.message}`);
-  }
-}
-
-for (const { number, title, body, expected } of cases) {
-  const text = `${title}
-
-${body}`;
-  run(`#${number}`, () => {
-    assert.deepStrictEqual(inferComponentLabels(text, keywords), expected);
-  });
-}
-
-run('#3144 cliKeywordPrefixSubstringRegression', () => {
-  const text = `${cliRegressionCase.title}
-
-${cliRegressionCase.body}`;
-  const inferred = inferComponentLabels(text, keywords);
-  assert.ok(!inferred.includes('cli'), `expected 'cli' absent (body contains 'Mem0 client', a substring of the removed 'mem0 cli' term), got ${JSON.stringify(inferred)}`);
-});
-
-run('noKeywordMatchReturnsEmptyArray', () => {
-  const text = 'The weather today is sunny and I went for a walk in the park with my dog.';
-  assert.deepStrictEqual(inferComponentLabels(text, keywords), []);
-});
-
-run('emptyStringReturnsEmptyArray', () => {
-  assert.deepStrictEqual(inferComponentLabels('', keywords), []);
-});
-
-if (failures > 0) {
-  console.error(`
-${failures} test(s) failed.`);
-  process.exit(1);
-}
-console.log(`
-All ${cases.length + 3} tests passed.`);

.github/workflows/ci-gate.yml

@@ -40,8 +40,6 @@ jobs:
       openclaw: ${{ steps.filter.outputs.openclaw }}
       opencode_plugin: ${{ steps.filter.outputs.opencode_plugin }}
       pi_agent_plugin: ${{ steps.filter.outputs.pi_agent_plugin }}
-      n8n_nodes_mem0: ${{ steps.filter.outputs.n8n_nodes_mem0 }}
-      zapier_mem0: ${{ steps.filter.outputs.zapier_mem0 }}
       docs_llms_txt: ${{ steps.filter.outputs.docs_llms_txt }}
     steps:
       - uses: dorny/paths-filter@v3
@@ -81,13 +79,6 @@ jobs:
               - 'integrations/pi-agent-plugin/**'
               - '.github/workflows/pi-agent-plugin-checks.yml'
               - '.github/workflows/ci-gate.yml'
-            n8n_nodes_mem0:
-              - 'integrations/n8n-nodes-mem0/**'
-              - '.github/workflows/n8n-nodes-mem0-checks.yml'
-            zapier_mem0:
-              - 'integrations/zapier-mem0/**'
-              - '.github/workflows/zapier-mem0-checks.yml'
-              - '.github/workflows/ci-gate.yml'
             docs_llms_txt:
               - 'docs/**/*.mdx'
               - 'docs/llms.txt'
@@ -145,18 +136,6 @@ jobs:
     uses: ./.github/workflows/pi-agent-plugin-checks.yml
     secrets: inherit
 
-  n8n-nodes-mem0:
-    name: n8n Node
-    needs: changes
-    if: needs.changes.outputs.n8n_nodes_mem0 == 'true'
-    uses: ./.github/workflows/n8n-nodes-mem0-checks.yml
-  zapier-mem0:
-    name: Zapier App
-    needs: changes
-    if: needs.changes.outputs.zapier_mem0 == 'true'
-    uses: ./.github/workflows/zapier-mem0-checks.yml
-    secrets: inherit
-
   docs-llms-txt:
     name: docs llms.txt
     needs: changes
@@ -175,8 +154,6 @@ jobs:
       - openclaw
       - opencode-plugin
       - pi-agent-plugin
-      - n8n-nodes-mem0
-      - zapier-mem0
       - docs-llms-txt
     if: always()
     runs-on: ubuntu-latest

.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==0.16.0
+          pip install ruff
       - name: Run Linting
         if: needs.check_changes.outputs.mem0_changed == 'true'
         run: make lint

.github/workflows/issue-labeler.yml

@@ -12,56 +12,28 @@ jobs:
   label:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
-
       - uses: stefanbuck/github-issue-parser@v3
         id: issue-parser
-        continue-on-error: true
         with:
           template-path: .github/ISSUE_TEMPLATE/bug_report.yml
 
       - uses: redhat-plumbers-in-action/advanced-issue-labeler@v3
-        continue-on-error: true
         with:
           issue-form: ${{ steps.issue-parser.outputs.jsonString }}
+          section: component
           token: ${{ secrets.GITHUB_TOKEN }}
           config-path: .github/advanced-issue-labeler.yml
 
-      - name: Infer component from text when the form was not used
-        uses: actions/github-script@v7
+      - uses: stefanbuck/github-issue-parser@v3
+        id: feature-parser
+        if: contains(github.event.issue.labels.*.name, 'enhancement')
         with:
-          script: |
-            const {
-              componentLabels,
-              inferComponentLabels,
-              loadKeywords,
-            } = require(`${process.env.GITHUB_WORKSPACE}/.github/scripts/infer-component-labels.js`);
+          template-path: .github/ISSUE_TEMPLATE/feature_request.yml
 
-            const { data: issue } = await github.rest.issues.get({
-              owner: context.repo.owner,
-              repo: context.repo.repo,
-              issue_number: context.issue.number,
-            });
-
-            const keywords = loadKeywords(`${process.env.GITHUB_WORKSPACE}/.github/component-keywords.json`);
-            const known = componentLabels(keywords);
-            const existing = issue.labels.map((label) => label.name || label);
-            if (existing.some((name) => known.includes(name))) {
-              core.info(`Component label already present: ${existing.join(', ')}`);
-              return;
-            }
-
-            const labels = inferComponentLabels(`${issue.title}\n\n${issue.body || ''}`, keywords);
-
-            if (labels.length === 0) {
-              core.info('No component could be inferred from the issue text');
-              return;
-            }
-
-            core.info(`Inferred: ${labels.join(', ')}`);
-            await github.rest.issues.addLabels({
-              owner: context.repo.owner,
-              repo: context.repo.repo,
-              issue_number: context.issue.number,
-              labels,
-            });
+      - uses: redhat-plumbers-in-action/advanced-issue-labeler@v3
+        if: contains(github.event.issue.labels.*.name, 'enhancement')
+        with:
+          issue-form: ${{ steps.feature-parser.outputs.jsonString }}
+          section: component
+          token: ${{ secrets.GITHUB_TOKEN }}
+          config-path: .github/advanced-issue-labeler.yml

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

@@ -1,60 +0,0 @@
-name: Publish n8n-nodes-mem0 📦 to npm
-
-# Dispatched by release.yml (Release Router) when a release tagged
-# n8n-nodes-mem0-v* is published. Can also be dispatched manually to
-# re-publish a tag.
-on:
-  workflow_dispatch:
-    inputs:
-      tag:
-        description: 'Release tag to build and publish (e.g. n8n-nodes-mem0-v0.1.0)'
-        required: true
-        type: string
-      prerelease:
-        description: 'Publish under the version preid dist-tag instead of latest'
-        required: false
-        type: boolean
-        default: false
-
-jobs:
-  build-n-publish:
-    name: Build and publish n8n-nodes-mem0 📦 to npm
-    if: startsWith(inputs.tag, 'n8n-nodes-mem0-v')
-    runs-on: ubuntu-latest
-    permissions:
-      id-token: write
-    defaults:
-      run:
-        working-directory: integrations/n8n-nodes-mem0
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          ref: ${{ inputs.tag }}
-
-      - name: Install pnpm
-        uses: pnpm/action-setup@v4
-        with:
-          version: 9
-
-      - name: Set up Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: '20'
-          registry-url: 'https://registry.npmjs.org'
-          cache: 'pnpm'
-          cache-dependency-path: integrations/n8n-nodes-mem0/pnpm-lock.yaml
-
-      - name: Install dependencies
-        run: pnpm install --frozen-lockfile --ignore-scripts
-
-      - name: Build
-        run: pnpm run build
-
-      - name: Publish to npm
-        run: |
-          if [ "${{ inputs.prerelease }}" = "true" ]; then
-            PREID=$(node -p "require('./package.json').version.split('-')[1].split('.')[0]")
-            npx npm@latest publish --provenance --access public --tag "$PREID"
-          else
-            npx npm@latest publish --provenance --access public
-          fi

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

@@ -1,88 +0,0 @@
-name: n8n-nodes-mem0 checks
-
-# On PRs this is invoked by ci-gate.yml (the single required check);
-# push-to-main and manual runs remain standalone.
-on:
-  workflow_dispatch:
-  push:
-    branches: [main]
-    paths:
-      - 'integrations/n8n-nodes-mem0/**'
-      - '.github/workflows/n8n-nodes-mem0-checks.yml'
-  workflow_call:
-
-jobs:
-  lint:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Install pnpm
-        uses: pnpm/action-setup@v4
-        with:
-          version: 9
-
-      - name: Setup Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: 20
-          cache: 'pnpm'
-          cache-dependency-path: integrations/n8n-nodes-mem0/pnpm-lock.yaml
-
-      - name: Install dependencies
-        run: cd integrations/n8n-nodes-mem0 && pnpm install --frozen-lockfile --ignore-scripts
-
-      - name: Lint
-        run: cd integrations/n8n-nodes-mem0 && pnpm run lint
-
-  test:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Install pnpm
-        uses: pnpm/action-setup@v4
-        with:
-          version: 9
-
-      - name: Setup Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: 20
-          cache: 'pnpm'
-          cache-dependency-path: integrations/n8n-nodes-mem0/pnpm-lock.yaml
-
-      - name: Install dependencies
-        run: cd integrations/n8n-nodes-mem0 && pnpm install --frozen-lockfile --ignore-scripts
-
-      - name: Run tests
-        run: cd integrations/n8n-nodes-mem0 && pnpm test
-
-  build:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Install pnpm
-        uses: pnpm/action-setup@v4
-        with:
-          version: 9
-
-      - name: Setup Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: 20
-          cache: 'pnpm'
-          cache-dependency-path: integrations/n8n-nodes-mem0/pnpm-lock.yaml
-
-      - name: Install dependencies
-        run: cd integrations/n8n-nodes-mem0 && pnpm install --frozen-lockfile --ignore-scripts
-
-      - name: Build
-        run: cd integrations/n8n-nodes-mem0 && pnpm run build
-
-      - name: Verify dist output exists
-        run: |
-          test -f integrations/n8n-nodes-mem0/dist/nodes/Mem0/Mem0.node.js || (echo "Build output missing: dist/nodes/Mem0/Mem0.node.js" && exit 1)
-          test -f integrations/n8n-nodes-mem0/dist/credentials/Mem0Api.credentials.js || (echo "Build output missing: dist/credentials/Mem0Api.credentials.js" && exit 1)
-          test -f integrations/n8n-nodes-mem0/dist/nodes/Mem0/mem0.svg || (echo "Build output missing: dist/nodes/Mem0/mem0.svg" && exit 1)

.github/workflows/pr-labeler.yml

@@ -1,64 +0,0 @@
-name: PR Labeler
-
-on:
-  pull_request_target:
-    types: [opened, synchronize, reopened, edited]
-
-concurrency:
-  group: pr-labeler-${{ github.event.pull_request.number }}
-  cancel-in-progress: true
-
-permissions:
-  contents: read
-  pull-requests: write
-  issues: read
-
-jobs:
-  label:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/labeler@v5
-        with:
-          repo-token: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Propagate labels from linked issues
-        uses: actions/github-script@v7
-        with:
-          script: |
-            const allowed = new Set([
-              'sdk-python', 'sdk-typescript', 'vector-store', 'plugin',
-              'rest-api', 'documentation', 'ci', 'cli', 'integrations',
-            ]);
-            const umbrella = { plugin: 'integrations' };
-            const { repository } = await github.graphql(
-              `query ($owner: String!, $repo: String!, $number: Int!) {
-                repository(owner: $owner, name: $repo) {
-                  pullRequest(number: $number) {
-                    closingIssuesReferences(first: 20) {
-                      nodes { labels(first: 50) { nodes { name } } }
-                    }
-                  }
-                }
-              }`,
-              { owner: context.repo.owner, repo: context.repo.repo, number: context.issue.number },
-            );
-
-            const labels = new Set();
-            for (const issue of repository.pullRequest.closingIssuesReferences.nodes) {
-              for (const label of issue.labels.nodes) {
-                if (allowed.has(label.name)) labels.add(label.name);
-              }
-            }
-
-            for (const label of [...labels]) {
-              if (umbrella[label]) labels.add(umbrella[label]);
-            }
-
-            if (labels.size > 0) {
-              await github.rest.issues.addLabels({
-                owner: context.repo.owner,
-                repo: context.repo.repo,
-                issue_number: context.issue.number,
-                labels: [...labels],
-              });
-            }

.github/workflows/release.yml

@@ -45,7 +45,6 @@ jobs:
             openclaw-v*)  workflow="openclaw-cd.yml" ;;
             opencode-v*)  workflow="opencode-plugin-cd.yml" ;;
             pi-agent-v*)  workflow="pi-agent-plugin-cd.yml" ;;
-            n8n-nodes-mem0-v*) workflow="n8n-nodes-mem0-cd.yml" ;;
             v*)           workflow="cd.yml" ;;
             *)
               echo "::error::Release tag '$TAG' does not match any known package prefix — nothing will be published. See the tag prefix table in AGENTS.md."

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

@@ -1,42 +0,0 @@
-name: Deploy zapier-mem0 to Zapier
-
-# Zapier apps deploy to Zapier's own platform (not npm), so this is NOT wired
-# into the npm release router (release.yml). It is manual workflow_dispatch
-# only and requires the ZAPIER_DEPLOY_KEY repo secret.
-#
-#   gh workflow run zapier-mem0-cd.yml --ref main
-on:
-  workflow_dispatch:
-
-jobs:
-  push:
-    name: Push zapier-mem0 to Zapier
-    runs-on: ubuntu-latest
-    defaults:
-      run:
-        working-directory: integrations/zapier-mem0
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Install pnpm
-        uses: pnpm/action-setup@v4
-        with:
-          version: 9
-
-      - name: Set up Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: 22
-          cache: 'pnpm'
-          cache-dependency-path: integrations/zapier-mem0/pnpm-lock.yaml
-
-      - name: Install dependencies
-        run: pnpm install --frozen-lockfile
-
-      - name: Build TypeScript
-        run: pnpm build
-
-      - name: Push to Zapier
-        env:
-          ZAPIER_DEPLOY_KEY: ${{ secrets.ZAPIER_DEPLOY_KEY }}
-        run: npx zapier-platform-cli@19 push

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

@@ -1,47 +0,0 @@
-name: zapier-mem0 checks
-
-# On PRs this is invoked by ci-gate.yml (the single required check);
-# push-to-main and manual runs remain standalone.
-#
-# CI compiles the TypeScript app, runs `zapier validate` (offline schema + style
-# checks) against the build, plus the offline jest unit suite (test/unit.test.ts —
-# mocked z.request, no network). The end-to-end jest suite is skipped here because
-# it hits the live Mem0 API — it runs locally with MEM0_API_KEY set (see README).
-on:
-  workflow_dispatch:
-  push:
-    branches: [main]
-    paths:
-      - 'integrations/zapier-mem0/**'
-      - '.github/workflows/zapier-mem0-checks.yml'
-  workflow_call:
-
-jobs:
-  validate:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Install pnpm
-        uses: pnpm/action-setup@v4
-        with:
-          version: 9
-
-      - name: Setup Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: 22
-          cache: 'pnpm'
-          cache-dependency-path: integrations/zapier-mem0/pnpm-lock.yaml
-
-      - name: Install dependencies
-        run: cd integrations/zapier-mem0 && pnpm install --frozen-lockfile
-
-      - name: Build TypeScript
-        run: cd integrations/zapier-mem0 && pnpm build
-
-      - name: Validate Zapier app definition
-        run: cd integrations/zapier-mem0 && npx zapier-platform-cli@19 validate
-
-      - name: Run offline unit tests
-        run: cd integrations/zapier-mem0 && pnpm test:unit

AGENTS.md

@@ -27,9 +27,8 @@ This is a **polyglot monorepo** containing Python and TypeScript packages, CLIs,
 | `integrations/openclaw/` | `@mem0/openclaw-mem0` — OpenClaw plugin for Claude Code / AI editors |
 | `integrations/pi-agent-plugin/` | `@mem0/pi-agent-plugin` — Pi Agent plugin |
 | `integrations/vercel-ai-sdk/` | `@mem0/vercel-ai-provider` — Vercel AI SDK memory provider |
-| `integrations/n8n-nodes-mem0/` | `@mem0/n8n-nodes-mem0` — n8n community node; add / search / get / update / delete memories |
-| `integrations/zapier-mem0/` | `@mem0/zapier` — Zapier Platform CLI app (deploys to Zapier, not npm); add / search / get / delete memories |
 | `server/` | FastAPI REST server for self-hosted Mem0 (Docker: FastAPI + PostgreSQL/pgvector + Neo4j) |
+| `openmemory/` | Self-hosted memory platform — `api/` (FastAPI + Alembic + MCP server) and `ui/` (Next.js 15 + React 19) |
 | `skills/` | Claude Code skill definitions. Reference skills (SDK knowledge, always-on): `mem0/`, `mem0-cli/`, `mem0-vercel-ai-sdk/`. Pipeline skills (run on demand): `mem0-integrate/`, `mem0-test-integration/`, `mem0-oss-to-platform/` |
 | `docs/` | Documentation site (Mintlify) |
 | `tests/` | Python SDK tests (pytest) |
@@ -63,7 +62,7 @@ integrations/openclaw/ ──▶ mem0ai (npm)
 - **Node.js**: v18+ (v20 or v22 recommended)
 - **pnpm**: v10+ (`npm install -g pnpm@10`) — used for all TypeScript packages
 - **Hatch**: Python build/environment tool (`pip install hatch`)
-- **Docker**: Required for `server/` development
+- **Docker**: Required for `server/` and `openmemory/` development
 
 ### Initial Setup
 
@@ -215,6 +214,28 @@ docker-compose up                  # starts all 3 services
 - **Services:** PostgreSQL with pgvector, Neo4j 5.x with APOC plugin
 - **Hot reload:** Dev Dockerfile mounts `server/` and `mem0/` for live changes
 
+### OpenMemory (`openmemory/`)
+
+```bash
+# Full stack via Docker Compose
+cd openmemory
+docker-compose up
+# Qdrant: localhost:6333
+# API (MCP): localhost:8765
+# UI: localhost:3000
+
+# Individual development
+cd openmemory/api && uvicorn main:app --reload       # FastAPI backend
+cd openmemory/ui && npm run dev                       # Next.js frontend
+
+# Tests
+cd openmemory/api && pytest tests/                   # API tests (e.g., test_mcp_server.py)
+```
+
+- **API:** FastAPI + Alembic (DB migrations) + MCP server (Model Context Protocol)
+- **UI:** Next.js 15, React 19, Radix UI, Redux Toolkit, TailwindCSS, Recharts
+- **Vector store:** Qdrant
+
 ### Documentation (`docs/`)
 
 ```bash
@@ -310,6 +331,7 @@ python -m benchmarks.beam.run --project-name my-test --backend cloud --mem0-api-
   - Root SDK: line length **120**
   - Python CLI: line length **100** with extended rule set (UP, B, SIM, RUF)
 - **isort** with `profile = "black"` for import sorting.
+- Ruff excludes `openmemory/` from root config.
 
 ### TypeScript Conventions
 
@@ -360,6 +382,7 @@ Optional layer on top of vector memory for relationship-aware retrieval. Configu
 Model Context Protocol support in multiple places:
 
 - **Remote:** MCP server at `mcp.mem0.ai`
+- **Local:** MCP server in `openmemory/api/` (FastAPI-based)
 - **Plugin:** MCP tools in `integrations/mem0-plugin/` — 9 tools: `add_memory`, `search_memories`, `get_memories`, `get_memory`, `update_memory`, `delete_memory`, `delete_all_memories`, `delete_entities`, `list_entities`
 
 ### Plugin & Skills System
@@ -408,8 +431,6 @@ PR testing is orchestrated by a single entry point: **`ci-gate.yml` (CI Gate)**
 | OpenClaw | `openclaw-checks.yml` | Push to main (on `integrations/openclaw/`), manual | tsc + vitest (with Codecov) + tsup build on Node 20, 22 |
 | OpenCode Plugin | `opencode-plugin-checks.yml` | Push to main (on `integrations/mem0-plugin/.opencode-plugin/`), manual | Bun: tsc type-check + build + dist artifact check |
 | Pi Agent Plugin | `pi-agent-plugin-checks.yml` | Push to main (on `integrations/pi-agent-plugin/`), manual | tsc + vitest + tsup build (dist artifact check) on Node 20, 22 |
-| n8n Node | `n8n-nodes-mem0-checks.yml` | Push to main (on `integrations/n8n-nodes-mem0/`), manual | ESLint (n8n-nodes-base) + tsc build (dist artifact check) on Node 20 |
-| Zapier App | `zapier-mem0-checks.yml` | Push to main (on `integrations/zapier-mem0/`), manual | build (tsc) + `zapier validate` + offline unit tests on Node 22 |
 | docs llms.txt | `docs-llms-txt-check.yml` | Manual | `docs/llms.txt` coverage check |
 
 When adding a new package CI workflow: give it `workflow_call` (plus `push`/`workflow_dispatch` as needed, but no `pull_request` trigger), then register it in `ci-gate.yml` — a path filter under the `changes` job, a call job, and an entry in the gate job's `needs` list.
@@ -429,13 +450,11 @@ Publishing is routed through a single entry point: **`release.yml` (Release Rout
 | OpenClaw | `openclaw-cd.yml` | `openclaw-v*` | npm (`@mem0/openclaw-mem0`) |
 | OpenCode Plugin | `opencode-plugin-cd.yml` | `opencode-v*` | npm (`@mem0/opencode-plugin`) |
 | Pi Agent Plugin | `pi-agent-plugin-cd.yml` | `pi-agent-v*` | npm (`@mem0/pi-agent-plugin`) |
-| n8n Node | `n8n-nodes-mem0-cd.yml` | `n8n-nodes-mem0-v*` | npm (`@mem0/n8n-nodes-mem0`) |
 
 - Package CD workflows are `workflow_dispatch`-only (inputs: `tag`, `prerelease`); they check out and build the given tag. Registry trusted-publisher settings stay pinned to each package's own workflow filename.
 - All publishing uses **OIDC trusted publishing** — no tokens or secrets required.
 - First publish of a new npm package must be done manually; OIDC works for subsequent versions.
 - To re-publish a release (e.g. after a registry settings fix), do **not** delete/recreate the GitHub release — manually dispatch the package workflow instead: `gh workflow run <package>-cd.yml --ref refs/tags/<tag> -f tag=<tag>`.
-- The **Zapier app** (`integrations/zapier-mem0`) deploys to Zapier's own platform, not npm, so it is **not** in the release router. Deploy it manually: `gh workflow run zapier-mem0-cd.yml --ref main` (requires the `ZAPIER_DEPLOY_KEY` secret).
 - When adding a new package: add its CD workflow (`workflow_dispatch` with `tag`/`prerelease` inputs), then register its tag prefix in the `case` block in `release.yml`. Keep the bare `v*` arm last.
 
 ### Utility Workflows
@@ -443,7 +462,6 @@ Publishing is routed through a single entry point: **`release.yml` (Release Rout
 | Workflow | File | Purpose |
 |----------|------|---------|
 | Issue Labeler | `issue-labeler.yml` | Automatic issue labeling |
-| PR Labeler | `pr-labeler.yml` | Path-based PR labeling plus propagating labels from linked issues |
 | Stale Bot | `stale.yml` | Marks stale issues and PRs |
 | llms.txt Check | `docs-llms-txt-check.yml` | Blocks PRs touching `docs/**/*.mdx` when `docs/llms.txt` is out of sync. Fix locally with `python scripts/check-llms-txt-coverage.py --write`. |
 
@@ -566,7 +584,7 @@ N/A
 
 - Follow existing code patterns — don't introduce new frameworks or abstractions without discussion.
 - Version bumps go in `pyproject.toml` (Python) or `package.json` (TypeScript).
-- For `server/` work, use Docker Compose for local development.
+- For `server/` and `openmemory/` work, use Docker Compose for local development.
 - Do NOT use `pip` or `conda` for dependency management — use `hatch` (see `docs/contributing/development.mdx`).
 
 ### Contributing Guides
@@ -589,4 +607,5 @@ N/A
 - Use npm or yarn in TypeScript packages — this repo uses pnpm exclusively.
 - Use `require()` for imports in TypeScript — use ES module `import` syntax.
 - Mix up linter configs: root Python SDK uses line-length 120, Python CLI uses 100, Node CLI uses Biome (not ESLint/Ruff).
+- Modify `openmemory/` database migrations without understanding the Alembic migration chain.
 - Change public APIs without updating documentation in `docs/`.

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

Makefile

@@ -11,7 +11,7 @@ install:
 	hatch env create
 
 install_all:
-	pip install ruff==0.16.0 groq together boto3 litellm ollama chromadb weaviate weaviate-client sentence_transformers vertexai \
+	pip install ruff==0.6.9 groq together boto3 litellm ollama chromadb weaviate weaviate-client sentence_transformers vertexai \
 	            google-generativeai elasticsearch opensearch-py vecs "pinecone<7.0.0" pinecone-text faiss-cpu langchain-community \
 							upstash-vector azure-search-documents langchain-memgraph langchain-neo4j langchain-aws rank-bm25 pymochow pymongo psycopg kuzu databricks-sdk valkey
 

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

cli/node/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mem0/cli",
-  "version": "0.2.11",
+  "version": "0.2.10",
   "description": "The official CLI for mem0 — the memory layer for AI agents",
   "type": "module",
   "bin": {

cli/python/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "mem0-cli"
-version = "0.2.10"
+version = "0.2.9"
 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__ = "0.2.10"
+__version__ = "0.2.9"

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, and memory decay:
+Modify project configuration including custom instructions, categories, language preferences, retrieval criteria, and memory decay:
 
 ```python
 # Update project with custom categories
@@ -98,6 +98,14 @@ client.project.update(
 # Use the input language for memory storage and retrieval
 client.project.update(multilingual=True)
 
+# Set retrieval criteria to control which memories are surfaced in search
+client.project.update(
+    retrieval_criteria=[
+        {"name": "relevance", "description": "How directly relevant this memory is to the current topic or user query", "weight": 3},
+        {"name": "access_frequency", "description": "How often this memory has been accessed or surfaced recently", "weight": 1}
+    ]
+)
+
 # Enable Memory Decay (boosts recently-accessed memories at search time)
 client.project.update(decay=True)
 
@@ -112,6 +120,34 @@ client.project.update(
 )
 ```
 
+#### Set Retrieval Criteria
+
+`retrieval_criteria` is a per-project list of dictionaries (`List[Dict]`) that shapes how memories are ranked and filtered during search. Each dictionary has three fields: `name` (identifier), `description` (interpreted by the LLM to score each memory), and `weight` (relative influence on the final score). Use this to focus retrieval on intent-aligned or signal-specific memories:
+
+```python
+client.project.update(
+    retrieval_criteria=[
+        {
+            "name": "joy",
+            "description": "Measure the intensity of positive emotions such as happiness, excitement, or amusement expressed in the memory. A higher score reflects greater joy.",
+            "weight": 3
+        },
+        {
+            "name": "curiosity",
+            "description": "Assess the extent to which the memory reflects inquisitiveness or interest in exploring new information. A higher score reflects stronger curiosity.",
+            "weight": 2
+        },
+        {
+            "name": "access_frequency",
+            "description": "How often this memory has been accessed or surfaced recently.",
+            "weight": 1
+        }
+    ]
+)
+```
+
+Pass an empty list to clear all criteria and restore default retrieval behaviour.
+
 #### Toggle Memory Decay
 
 `decay` is a per-project boolean that turns on [Memory Decay](/platform/features/memory-decay): a search-time ranking bias that reinforces recently-accessed memories and gently dampens stale ones. The flag is `false` by default; set it via the same project-update endpoint:

docs/changelog/highlights.mdx

@@ -4,39 +4,6 @@ description: "Major product launches, headline features, and milestones for Mem0
 mode: "wide"
 ---
 
-<Update label="2026-07-30" description="n8n and Zapier integrations">
-
-**Workflow Automation: Mem0 Memory in n8n and Zapier**
-
-Mem0 now plugs into two no-code automation platforms, so workflows that used to start from zero on every run can store durable facts and recall them later.
-
-- **n8n community node:** [`@mem0/n8n-nodes-mem0`](https://www.npmjs.com/package/@mem0/n8n-nodes-mem0) adds a **Mem0** node with a Memory resource covering Add, Search, Get, Get Many, Update, and Delete. Install it from **Settings → Community Nodes** on a self-hosted instance, then connect your API key once as a Mem0 API credential. See [n8n](/integrations/n8n).
-- **n8n AI Agent tool:** Attach the same node to an [AI Agent](https://docs.n8n.io/advanced-ai/) node and it becomes a tool the agent calls on its own, so it can decide when to remember and when to recall.
-- **Zapier app:** Add Memory, Search Memories, Get Memories, and Delete Memory actions let any of Zapier's thousands of apps write and read Mem0 context with no code and no server. See [Zapier](/integrations/zapier).
-- **One-time connection:** Both integrations authenticate with a single Mem0 API key and default to `https://api.mem0.ai`, with a configurable base URL for self-hosted deployments.
-
-<Note>
-  The Zapier app is not yet listed in Zapier's public App Directory. Email [support@mem0.ai](mailto:support@mem0.ai) for an invite link.
-</Note>
-
-</Update>
-
-<Update label="2026-07-13" description="TypeScript provider expansion">
-
-**TypeScript OSS SDK: 26 New Providers, Reranking, and Zero-Dependency Imports**
-
-TypeScript SDK v3.1.0 is the largest provider release for the OSS SDK so far, closing most of the remaining gap with the Python SDK. Python SDK v2.0.12 ships alongside it with fixes and security patches.
-
-- **17 new vector stores:** Pinecone, Weaviate, Milvus, Chroma, MongoDB, Elasticsearch, OpenSearch, Databricks, AWS Neptune Analytics, S3 Vectors, Azure MySQL, Google Vertex AI Vector Search, Turbopuffer, Upstash Vector, Valkey, Cassandra, and Baidu Mochow.
-- **5 new LLM providers:** AWS Bedrock, xAI Grok, Together, vLLM, and Sarvam.
-- **4 new embedding providers:** Vertex AI, HuggingFace, FastEmbed, and Together.
-- **Reranking in TypeScript:** Four rerankers (Cohere, ZeroEntropy, cross-encoder, and LLM-based) with per-search rerank via a `rerank` option on `search()`.
-- **Install only what you use:** Importing `mem0ai/oss` no longer pulls in any provider SDK. Provider packages are resolved lazily on first use, so an app that configures only OpenAI and Qdrant does not need the other provider SDKs installed.
-
-See [SDK & Tools](/changelog/sdk) for version details and PR links.
-
-</Update>
-
 <Update label="2026-06-27" description="SDK memory expiration">
 
 **SDK Memory Expiration: Expiring Memories Across Python and TypeScript**

docs/changelog/sdk.mdx

@@ -7,63 +7,6 @@ mode: "wide"
 <Tabs>
 <Tab title="Python">
 
-<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:**
@@ -1157,61 +1100,6 @@ See the [OSS v2 to v3 migration guide](https://docs.mem0.ai/migration/oss-v2-to-
 
 <Tab title="TypeScript">
 
-<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:**
@@ -1718,13 +1606,6 @@ See the [TypeScript SDK migration guide](https://docs.mem0.ai/migration/ts-v2-to
 
 <Tab title="CLI">
 
-<Update label="2026-07-13" description="Python v0.2.10 / Node v0.2.11">
-
-**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:**
@@ -1883,15 +1764,6 @@ A full-featured command-line interface for Mem0, available in both Python and No
 <Tabs>
 <Tab title="Mem0 Plugin">
 
-<Update label="2026-07-14" description="mem0-plugin v0.2.13">
-
-**Fixes:**
-- **Assistant messages no longer stored as your own:** The session-summary hook (fires at the end of every assistant turn) and the post-compaction hook were sending the assistant's own message to Mem0 tagged `role: "user"`. Because Mem0 extracts *facts about the user* from each message and uses `role` to decide who spoke, the assistant's first-person prose was being saved as the human's stated preferences — "I recommend we drop Redis" became `User prefers dropping Redis entirely`. Both hooks now send `role: "assistant"`, so the same session is stored as `Assistant recommended...`. Affects Claude Code, Cursor, Codex, and Antigravity, which share these hooks.
-
-Existing memories written by the previous versions are not rewritten. If your memories contain preferences you never expressed, delete them — the plugin will not recreate them.
-
-</Update>
-
 <Update label="2026-06-30" description="mem0-plugin v0.2.12">
 
 **New Features:**
@@ -2142,13 +2014,6 @@ Initial release of the Mem0 plugin for Claude Code and Cursor, followed by Codex
 
 <Tab title="OpenCode">
 
-<Update label="2026-07-22" description="OpenCode plugin v0.2.2">
-
-**Fixes:**
-- **Shell-profile API key recovery:** When `MEM0_API_KEY` isn't set in the process environment, the plugin now falls back to reading it from `.zshrc`, `.bashrc`, `.zprofile`, `.bash_profile`, or `.profile`, fixing startup failures on clients (e.g. Desktop) that launch without shell-exported environment variables.
-
-</Update>
-
 <Update label="2026-06-30" description="OpenCode plugin v0.2.1">
 
 **Improvements:**
@@ -2222,15 +2087,6 @@ Initial release of the Mem0 plugin for Claude Code and Cursor, followed by Codex
 
 <Tab title="Antigravity">
 
-<Update label="2026-07-14" description="Antigravity plugin v0.1.5">
-
-**Fixes:**
-- **Assistant messages no longer stored as your own:** The session-summary hook (fires at the end of every assistant turn) and the post-compaction hook were sending the assistant's own message to Mem0 tagged `role: "user"`. Because Mem0 extracts *facts about the user* from each message and uses `role` to decide who spoke, the assistant's first-person prose was being saved as the human's stated preferences — "I recommend we drop Redis" became `User prefers dropping Redis entirely`. Both hooks now send `role: "assistant"`, so the same session is stored as `Assistant recommended...`.
-
-Existing memories written by the previous versions are not rewritten. If your memories contain preferences you never expressed, delete them — the plugin will not recreate them.
-
-</Update>
-
 <Update label="2026-06-30" description="Antigravity plugin v0.1.4">
 
 **New Features:**
@@ -2664,57 +2520,6 @@ Existing memories written by the previous versions are not rewritten. If your me
 - Added support for graph memories.
 </Update>
 
-</Tab>
-
-<Tab title="n8n">
-
-<Update label="2026-07-30" description="n8n-nodes-mem0 v0.1.1">
-
-**Changes:**
-- **Published with npm provenance:** Republished through the `n8n-nodes-mem0-cd.yml` GitHub Actions workflow so the package carries a signed provenance attestation. `0.1.0` was published manually and has none, which blocks submission for n8n Creator Portal verification. No functional changes ([#6685](https://github.com/mem0ai/mem0/pull/6685))
-
-</Update>
-
-<Update label="2026-07-29" description="n8n-nodes-mem0 v0.1.0">
-
-**Initial release** of [`@mem0/n8n-nodes-mem0`](https://www.npmjs.com/package/@mem0/n8n-nodes-mem0), a community node that adds long-term memory to n8n workflows and AI Agents ([#6517](https://github.com/mem0ai/mem0/pull/6517))
-
-**New Features:**
-- **Memory operations:** A single **Mem0** node covers Add, Search, Get, Get Many, Update, and Delete on the Memory resource.
-- **AI Agent tool:** The node sets `usableAsTool`, so it can be attached to an n8n AI Agent node and invoked by the agent itself rather than wired into a fixed workflow path.
-- **Scoping:** Add, Search, and Get Many accept User ID, Agent ID, App ID, and Run ID, so memories stay partitioned per user, agent, or session.
-- **Add options:** Metadata JSON, custom categories, custom instructions, includes/excludes, an `infer` toggle, and a **Wait for Completion** switch that polls until the write lands instead of returning immediately.
-- **Pagination:** Get Many supports Return All, or explicit Page and Page Size.
-- **Credential:** A **Mem0 API** credential holds the API key plus a configurable base URL, defaulting to `https://api.mem0.ai` for self-hosted deployments.
-
-<Note>
-  Community nodes install from npm, which is a self-hosted n8n feature. See [n8n](/integrations/n8n) for setup.
-</Note>
-
-</Update>
-
-</Tab>
-
-<Tab title="Zapier">
-
-<Update label="2026-07-29" description="Zapier app v0.1.0">
-
-**Initial release** of the Mem0 Zapier app, built on the Zapier Platform CLI ([#6518](https://github.com/mem0ai/mem0/pull/6518))
-
-**New Features:**
-- **Actions:** Add Memory and Delete Memory.
-- **Searches:** Search Memories and Get Memories, usable as lookup steps in any Zap.
-- **Authentication:** An API key connection validated against Mem0 the moment it is saved, sent as `Authorization: Token <key>`, with a configurable base URL for self-hosted deployments.
-
-**Bug Fixes:**
-- **Add Memory:** Raise the poll budget past the real API latency tail, so a slower write is no longer reported as a failure ([#6680](https://github.com/mem0ai/mem0/pull/6680))
-
-<Note>
-  The app deploys to Zapier's platform rather than npm and is not yet listed in the public App Directory. See [Zapier](/integrations/zapier) for invite access.
-</Note>
-
-</Update>
-
 </Tab>
 </Tabs>
 

docs/components/embedders/models/aws_bedrock.mdx

@@ -3,27 +3,11 @@ title: AWS Bedrock
 description: "Configure AWS Bedrock as an embedding provider in Mem0 with IAM credentials and boto3 authentication."
 ---
 
-To use AWS Bedrock embedding models, you need the appropriate AWS credentials and permissions. Python uses `boto3`, and TypeScript uses `@aws-sdk/client-bedrock-runtime`.
-
-Both SDKs support the Amazon Titan and Cohere embedding model families.
+To use AWS Bedrock embedding models, you need to have the appropriate AWS credentials and permissions. The embeddings implementation relies on the `boto3` library.
 
 ### Setup
-
-- Model access is automatic: Bedrock enables serverless foundation models on first invocation in AWS commercial regions, and the [Model access page has been retired](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access.html). Cohere models are served from AWS Marketplace, so an account's first invocation must come from a principal with the `aws-marketplace:Subscribe` permission; after that, any user in the account can invoke them. Browse the models available to you in the [Bedrock model catalog](https://console.aws.amazon.com/bedrock/).
-- Install the AWS client for your language:
-
-  <CodeGroup>
-  ```bash Python
-  pip install boto3
-  ```
-
-  ```bash TypeScript
-  npm install @aws-sdk/client-bedrock-runtime
-  ```
-  </CodeGroup>
-
-  In TypeScript this package is an optional peer dependency, so it is only required when you actually use the Bedrock embedder.
-
+- Ensure you have model access from the [AWS Bedrock Console](https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/modelaccess)
+- Authenticate the boto3 client using a method described in the [AWS documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html)
 - Set up environment variables for authentication:
   ```bash
   export AWS_REGION=us-east-1
@@ -31,8 +15,6 @@ Both SDKs support the Amazon Titan and Cohere embedding model families.
   export AWS_SECRET_ACCESS_KEY=your-secret-key
   ```
 
-Both SDKs fall back to the standard AWS credential chain (environment variables, shared config, SSO, or an instance role) when you do not pass credentials in the config, so you rarely need to hardcode keys. See the [boto3 credentials guide](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html) for the Python resolution order.
-
 ### Usage
 
 <CodeGroup>
@@ -66,46 +48,8 @@ messages = [
 ]
 m.add(messages, user_id="alice")
 ```
-
-```typescript TypeScript
-import { Memory } from "mem0ai/oss";
-
-// Credentials are read from the AWS default chain (AWS_REGION,
-// AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, SSO, or an instance role).
-const memory = new Memory({
-  embedder: {
-    provider: "aws_bedrock",
-    config: {
-      model: "amazon.titan-embed-text-v2:0",
-      awsRegion: "us-west-2",
-    },
-  },
-});
-
-const messages = [
-  { role: "user", content: "I'm planning to watch a movie tonight. Any recommendations?" },
-  { role: "assistant", content: "How about thriller movies? They can be quite engaging." },
-  { role: "user", content: "I'm not a big fan of thriller movies but I love sci-fi movies." },
-  { role: "assistant", content: "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future." },
-];
-await memory.add(messages, { userId: "alice" });
-```
 </CodeGroup>
 
-### Choosing a model
-
-| Model | Notes |
-| --- | --- |
-| `amazon.titan-embed-text-v1` | Default. Fixed 1536-dimension output. |
-| `amazon.titan-embed-text-v2:0` | Supports a configurable output size of 256, 512, or 1024. |
-| `cohere.embed-english-v3` | English text. Embeds up to 96 texts per request. |
-| `cohere.embed-multilingual-v3` | Multilingual text. Embeds up to 96 texts per request. |
-| `cohere.embed-v4:0` | Text. Embeds up to 96 texts per request. Supports a configurable output size of 256, 512, 1024, or 1536. TypeScript only. |
-
-Custom output sizes are model specific. In Python, only Titan Text Embeddings V2 accepts one. In TypeScript, Titan Text Embeddings V2 and Cohere Embed v4 both do, and `embeddingDims` is ignored on Titan V1 and on Cohere v3, which have no such parameter. When you do set it, make sure your vector store dimension matches, otherwise inserts will fail.
-
-Bedrock caps a Cohere embedding call at 96 texts. The TypeScript SDK splits larger batches into multiple requests for you, so a 200 text batch becomes 3 calls.
-
 ### Config
 
 Here are the parameters available for configuring AWS Bedrock embedder:
@@ -120,16 +64,4 @@ Here are the parameters available for configuring AWS Bedrock embedder:
 | `aws_secret_access_key` | AWS secret access key for authentication | `None` |
 | `aws_session_token` | AWS session token for temporary credentials | `None` |
 </Tab>
-<Tab title="TypeScript">
-| Parameter | Description | Default Value |
-| --- | --- | --- |
-| `model` | The name of the embedding model to use | `amazon.titan-embed-text-v1` |
-| `awsRegion` | AWS region for the Bedrock client. Falls back to the `AWS_REGION` environment variable | `us-west-2` |
-| `embeddingDims` | Output vector size. Titan Text Embeddings V2 (256, 512, or 1024) and Cohere Embed v4 (256, 512, 1024, or 1536) only | `undefined` |
-| `awsAccessKeyId` | AWS access key ID for authentication | `undefined` |
-| `awsSecretAccessKey` | AWS secret access key for authentication | `undefined` |
-| `awsSessionToken` | AWS session token for temporary credentials | `undefined` |
-
-Omit the three credential fields to use the AWS default credential chain. If you do pass them, `awsAccessKeyId` and `awsSecretAccessKey` are both required.
-</Tab>
 </Tabs>

docs/components/embedders/models/vertexai.mdx

@@ -4,36 +4,11 @@ description: "Configure Google Cloud Vertex AI as an embedding provider in Mem0
 ---
 ### Vertex AI
 
-Google Cloud's Vertex AI serves text embedding models such as `gemini-embedding-001`. Mem0 uses them through the provider's own SDK, which you install alongside Mem0.
-
-### Installation
-
-The Vertex AI client is an optional dependency, so install it yourself.
-
-<CodeGroup>
-```bash Python
-pip install vertexai
-```
-
-```bash TypeScript
-npm install @google-cloud/aiplatform
-```
-</CodeGroup>
-
-### Authentication
-
-Both SDKs authenticate with [Application Default Credentials](https://cloud.google.com/docs/authentication/application-default-credentials). Pick whichever fits your environment:
-
-- **Local development:** run `gcloud auth application-default login`.
-- **Service account:** create a key in the [Google Cloud Console](https://console.cloud.google.com/) and point `GOOGLE_APPLICATION_CREDENTIALS` at the JSON file, or pass its path through the embedder config.
-- **Google Cloud runtimes** (Cloud Run, GKE, Compute Engine): the attached service account is picked up automatically.
-
-The TypeScript SDK reads the project ID from `googleProjectId`, then the `GCP_PROJECT_ID`, `GOOGLE_CLOUD_PROJECT`, and `GCLOUD_PROJECT` environment variables, and finally from your credentials. Set it explicitly when your credentials cover more than one project.
+To use Google Cloud's Vertex AI for text embedding models, set the `GOOGLE_APPLICATION_CREDENTIALS` environment variable to point to the path of your service account's credentials JSON file. These credentials can be created in the [Google Cloud Console](https://console.cloud.google.com/).
 
 ### Usage
 
-<CodeGroup>
-```python Python
+```python
 import os
 from mem0 import Memory
 
@@ -57,87 +32,28 @@ m = Memory.from_config(config)
 messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
     {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
-    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]
 m.add(messages, user_id="john")
 ```
-
-```typescript TypeScript
-import { Memory } from "mem0ai/oss";
-
-const config = {
-  embedder: {
-    provider: "vertexai",
-    config: {
-      model: "gemini-embedding-001",
-      // Optional. Falls back to GCP_PROJECT_ID / GOOGLE_CLOUD_PROJECT /
-      // GCLOUD_PROJECT, then to the project on your credentials.
-      googleProjectId: process.env.GCP_PROJECT_ID,
-      location: "us-central1",
-      // Optional. Path to a service account key file, or pass the JSON inline
-      // via googleServiceAccountJson.
-      vertexCredentialsJson: "/path/to/your/credentials.json",
-      embeddingDims: 256,
-      memoryAddEmbeddingType: "RETRIEVAL_DOCUMENT",
-      memoryUpdateEmbeddingType: "RETRIEVAL_DOCUMENT",
-      memorySearchEmbeddingType: "RETRIEVAL_QUERY",
-    },
-  },
-};
-
-const memory = new Memory(config);
-await memory.add("I love sci-fi movies but not thrillers", { userId: "john" });
-```
-</CodeGroup>
-
-### Embedding types
-
-Vertex AI embeds the same text differently depending on the task you declare. The embedding types can be one of the following:
+The embedding types can be one of the following:
 - SEMANTIC_SIMILARITY
 - CLASSIFICATION
 - CLUSTERING
 - RETRIEVAL_DOCUMENT, RETRIEVAL_QUERY, QUESTION_ANSWERING, FACT_VERIFICATION
-- CODE_RETRIEVAL_QUERY
-
-Check out the [Vertex AI documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/task-types#supported_task_types) for more information.
-
-<Note>
-These embedding types map to the add, update, and search memory actions in both the Python and TypeScript SDKs. Stored memories use the add or update type, and searches use the search type.
-</Note>
-
-### Choosing a model
-
-<Warning>
-`gemini-embedding-001` accepts **one input text per request**. When Mem0 embeds several texts at once, such as the memories extracted from a single conversation turn, it issues one request per text. The older `text-embedding-005` and `text-multilingual-embedding-002` models accept up to 250 texts per request, so they are faster and cheaper for large batches. See [Get text embeddings](https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-text-embeddings).
-</Warning>
-
+- CODE_RETRIEVAL_QUERY  
+Check out the [Vertex AI documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/task-types#supported_task_types) for more information.  
+  
 ### Config
 
 Here are the parameters available for configuring the Vertex AI embedder:
 
-<Tabs>
-<Tab title="Python">
-| Parameter                        | Description                                                | Default Value          |
-| -------------------------------- | ---------------------------------------------------------- | ---------------------- |
-| `model`                          | The name of the Vertex AI embedding model to use           | `gemini-embedding-001` |
-| `vertex_credentials_json`        | Path to the Google Cloud credentials JSON file             | `None`                 |
-| `embedding_dims`                 | Dimensions of the embedding model                          | `256`                  |
-| `memory_add_embedding_type`      | The embedding type to use for the add memory action        | `RETRIEVAL_DOCUMENT`   |
-| `memory_update_embedding_type`   | The embedding type to use for the update memory action     | `RETRIEVAL_DOCUMENT`   |
-| `memory_search_embedding_type`   | The embedding type to use for the search memory action     | `RETRIEVAL_QUERY`      |
-</Tab>
-<Tab title="TypeScript">
-| Parameter                     | Description                                                                 | Default Value          |
-| ----------------------------- | -------------------------------------------------------------------------- | ---------------------- |
-| `model`                       | The name of the Vertex AI embedding model to use                           | `gemini-embedding-001` |
-| `googleProjectId`             | Google Cloud project ID (falls back to `GCP_PROJECT_ID` env var, then to your credentials) | Resolved from credentials |
-| `location`                    | Google Cloud region (falls back to `GCP_LOCATION` env var)                 | `us-central1`          |
-| `vertexCredentialsJson`       | Path to the Google Cloud credentials JSON file                             | `None`                 |
-| `googleServiceAccountJson`    | Service account credentials as a JSON string or object                     | `None`                 |
-| `embeddingDims`               | Dimensions of the embedding model                                          | `256`                  |
-| `memoryAddEmbeddingType`      | The embedding type to use for the add memory action                        | `RETRIEVAL_DOCUMENT`   |
-| `memoryUpdateEmbeddingType`   | The embedding type to use for the update memory action                     | `RETRIEVAL_DOCUMENT`   |
-| `memorySearchEmbeddingType`   | The embedding type to use for the search memory action                     | `RETRIEVAL_QUERY`      |
-</Tab>
-</Tabs>
+| Parameter                 | Description                                      | Default Value        |
+| ------------------------- | ------------------------------------------------ | -------------------- |
+| `model`                   | The name of the Vertex AI embedding model to use | `gemini-embedding-001` |
+| `vertex_credentials_json` | Path to the Google Cloud credentials JSON file   | `None`               |
+| `embedding_dims`          | Dimensions of the embedding model                | `256`                |
+| `memory_add_embedding_type` | The type of embedding to use for the add memory action | `RETRIEVAL_DOCUMENT` |
+| `memory_update_embedding_type` | The type of embedding to use for the update memory action | `RETRIEVAL_DOCUMENT` |
+| `memory_search_embedding_type` | The type of embedding to use for the search memory action | `RETRIEVAL_QUERY` |

docs/components/embedders/overview.mdx

@@ -10,7 +10,7 @@ Mem0 offers support for various embedding models, allowing users to choose the o
 See the list of supported embedders below.
 
 <Note>
-  All embedders listed below are supported in the Python implementation. The TypeScript implementation supports: **OpenAI**, **Azure OpenAI**, **AWS Bedrock**, **FastEmbed**, **Google AI**, **Hugging Face**, **Langchain**, **LM Studio**, **Ollama**, **Together**, and **Vertex AI**.
+  All embedders listed below are supported in the Python implementation. The TypeScript implementation supports: **OpenAI**, **Azure OpenAI**, **FastEmbed**, **Google AI**, **Langchain**, **LM Studio**, **Ollama**, and **Together**.
 </Note>
 
 <CardGroup cols={4}>

docs/components/llms/models/aws_bedrock.mdx

@@ -5,18 +5,16 @@ description: "Configure AWS Bedrock as an LLM provider in Mem0 with IAM authenti
 
 ### Setup
 - Before using the AWS Bedrock LLM, make sure you have the appropriate model access from [Bedrock Console](https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/modelaccess).
-- Model availability is per-region. `anthropic.claude-sonnet-4-20250514-v1:0` supports on-demand inference in `us-east-1` and `ap-southeast-4`; from any other region, use the cross-region inference profile ID `us.anthropic.claude-sonnet-4-20250514-v1:0` instead.
-- Install the AWS SDK for your language: `pip install boto3` (Python) or `npm install @aws-sdk/client-bedrock-runtime` (TypeScript).
-- Both SDKs fall back to the standard AWS credential chain (environment variables, `~/.aws/credentials`, or an attached IAM role), so exporting `AWS_REGION`, `AWS_ACCESS_KEY_ID`, and `AWS_SECRET_ACCESS_KEY` is the quickest way to get started. In TypeScript you can also pass credentials inline with `awsRegion`, `awsAccessKeyId`, `awsSecretAccessKey`, and `awsSessionToken`, as shown below.
+- You will also need to authenticate the `boto3` client by using a method in the [AWS documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html#configuring-credentials)
+- You will have to export `AWS_REGION`, `AWS_ACCESS_KEY_ID`, and `AWS_SECRET_ACCESS_KEY` to set environment variables.
 
 ### Usage
 
-<CodeGroup>
-```python Python
+```python
 import os
 from mem0 import Memory
 
-os.environ['AWS_REGION'] = 'us-east-1'
+os.environ['AWS_REGION'] = 'us-west-2'
 os.environ["AWS_ACCESS_KEY_ID"] = "xx"
 os.environ["AWS_SECRET_ACCESS_KEY"] = "xx"
 
@@ -24,7 +22,7 @@ config = {
     "llm": {
         "provider": "aws_bedrock",
         "config": {
-            "model": "anthropic.claude-sonnet-4-20250514-v1:0",
+            "model": "anthropic.claude-3-5-haiku-20241022-v1:0",
             "temperature": 0.2,
             "max_tokens": 2000,
         }
@@ -41,43 +39,6 @@ messages = [
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
-```typescript TypeScript
-import { Memory } from 'mem0ai/oss';
-
-const config = {
-  llm: {
-    provider: 'aws_bedrock',
-    config: {
-      model: 'anthropic.claude-sonnet-4-20250514-v1:0',
-      temperature: 0.2,
-      maxTokens: 2000,
-      // Optional. Omit these to use the default AWS credential chain.
-      awsRegion: process.env.AWS_REGION,
-      awsAccessKeyId: process.env.AWS_ACCESS_KEY_ID,
-      awsSecretAccessKey: process.env.AWS_SECRET_ACCESS_KEY,
-    },
-  },
-};
-
-const memory = new Memory(config);
-const messages = [
-    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
-    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
-    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
-];
-await memory.add(messages, { userId: 'alice', metadata: { category: 'movies' } });
-```
-</CodeGroup>
-
-<Note>
-  `@aws-sdk/client-bedrock-runtime` is an optional peer dependency of `mem0ai`, so npm will not install it for you. The TypeScript provider loads it lazily and throws a clear error on the first request if the package is missing.
-</Note>
-
-<Note>
-  The TypeScript provider calls the Bedrock [Converse API](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html), a single uniform interface across the current Bedrock model families. Streaming and `InvokeModel`-only models are not supported yet.
-</Note>
-
 ### Config
 
-All available parameters for the `aws_bedrock` config are present in [Master List of All Params in Config](../config).
+All available parameters for the `aws_bedrock` config are present in [Master List of All Params in Config](../config).

docs/components/llms/overview.mdx

@@ -16,7 +16,7 @@ For a comprehensive list of available parameters for llm configuration, please r
 See the list of supported LLMs below.
 
 <Note>
-  All LLMs are supported in Python. The following LLMs are also supported in TypeScript: **OpenAI**, **Anthropic**, **AWS Bedrock**, **Groq**, **Azure OpenAI**, **DeepSeek**, **Google AI**, **Langchain**, **LM Studio**, **Mistral AI**, and **Ollama**.
+  All LLMs are supported in Python. The following LLMs are also supported in TypeScript: **OpenAI**, **Anthropic**, **Groq**, **Azure OpenAI**, **DeepSeek**, **Google AI**, **Langchain**, **LM Studio**, **Mistral AI**, and **Ollama**.
 </Note>
 
 <CardGroup cols={4}>

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-v3.5"` |
+| `model`              | Cohere rerank model                          | `str`  | `"rerank-english-v3.0"` |
 | `api_key`            | Cohere API key                               | `str`  | `None`                  |
 | `return_documents`   | Whether to return document texts in response | `bool` | `False`                 |
 | `max_chunks_per_doc` | Maximum chunks per document                  | `int`  | `None`                  |
@@ -103,30 +103,3 @@ config = {
     }
 }
 ```
-
-## TypeScript SDK
-
-The self-hosted [TypeScript SDK](/open-source/features/reranker-search#typescript-sdk) (`mem0ai/oss`) supports the same five providers. Config keys are camelCase (`apiKey`, `topK`, `maxLength`) and each provider's SDK is a peer dependency you install per reranker.
-
-| Provider | Install | Default model | Key config fields |
-| --- | --- | --- | --- |
-| `cohere` | `pnpm add cohere-ai` | `rerank-v3.5` | `apiKey`, `model`, `topK` |
-| `zero_entropy` | `pnpm add zeroentropy` | `zerank-1` | `apiKey`, `model`, `topK` |
-| `sentence_transformer` | `pnpm add @huggingface/transformers` | `Xenova/ms-marco-MiniLM-L-6-v2` | `model`, `device`, `maxLength`, `normalize`, `topK` |
-| `huggingface` | `pnpm add @huggingface/transformers` | `Xenova/bge-reranker-base` | `model`, `device`, `maxLength`, `normalize`, `topK` |
-| `llm_reranker` | None (uses your LLM provider's own SDK) | `openai` / `gpt-4o-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/models/cohere.mdx

@@ -9,9 +9,9 @@ Cohere provides enterprise-grade reranking models with excellent multilingual su
 
 Cohere offers several reranking models:
 
-- **`rerank-v3.5`** (default): Latest reranker, multilingual, best performance
-- **`rerank-english-v3.0`**: Previous generation, English only
-- **`rerank-multilingual-v3.0`**: Previous generation, multilingual
+- **`rerank-english-v3.0`**: Latest English reranker with best performance
+- **`rerank-multilingual-v3.0`**: Multilingual support for global applications
+- **`rerank-english-v2.0`**: Previous generation English reranker
 
 ## Installation
 
@@ -41,7 +41,7 @@ config = {
     "reranker": {
         "provider": "cohere",
         "config": {
-            "model": "rerank-v3.5",
+            "model": "rerank-english-v3.0",
             "api_key": "your-cohere-api-key",  # or set COHERE_API_KEY
             "top_k": 5,
             "return_documents": False,
@@ -53,34 +53,6 @@ config = {
 memory = Memory.from_config(config)
 ```
 
-## TypeScript (self-hosted)
-
-The [TypeScript OSS SDK](/open-source/features/reranker-search#typescript-sdk) (`mem0ai/oss`) ships the Cohere reranker. Config keys are camelCase, it defaults to the `rerank-v3.5` model, and you opt in per search with `rerank: true`.
-
-```bash
-pnpm add cohere-ai
-```
-
-```typescript
-import { Memory } from "mem0ai/oss";
-
-const memory = new Memory({
-  reranker: {
-    provider: "cohere",
-    config: {
-      apiKey: process.env.COHERE_API_KEY, // or set COHERE_API_KEY
-      // model: "rerank-v3.5",     // default
-      topK: 5,
-    },
-  },
-});
-
-const results = await memory.search("What is the user's profession?", {
-  filters: { userId: "bob" },
-  rerank: true,
-});
-```
-
 ## Environment Variables
 
 Set your API key as an environment variable:
@@ -105,7 +77,7 @@ config = {
     "rerank": {
         "provider": "cohere",
         "config": {
-            "model": "rerank-v3.5",
+            "model": "rerank-english-v3.0",
             "top_k": 3
         }
     }
@@ -152,7 +124,7 @@ config = {
 
 | Parameter            | Description                      | Type   | Default                 |
 | -------------------- | -------------------------------- | ------ | ----------------------- |
-| `model`              | Cohere rerank model to use       | `str`  | `"rerank-v3.5"` |
+| `model`              | Cohere rerank model to use       | `str`  | `"rerank-english-v3.0"` |
 | `api_key`            | Cohere API key                   | `str`  | `None`                  |
 | `top_k`              | Maximum documents to return      | `int`  | `None`                  |
 | `return_documents`   | Whether to return document texts | `bool` | `False`                 |
@@ -167,7 +139,7 @@ config = {
 
 ## Best Practices
 
-1. **Model Selection**: `rerank-v3.5` handles English and multilingual workloads; pin an older `v3.0` model only if you need to reproduce prior results
+1. **Model Selection**: Use `rerank-english-v3.0` for English, `rerank-multilingual-v3.0` for other languages
 2. **Batch Processing**: Process multiple queries efficiently
 3. **Error Handling**: Implement retry logic for production systems
 4. **Monitoring**: Track reranking performance and costs

docs/components/rerankers/models/huggingface.mdx

@@ -57,40 +57,6 @@ config = {
 }
 ```
 
-## TypeScript (self-hosted)
-
-The [TypeScript OSS SDK](/open-source/features/reranker-search#typescript-sdk) (`mem0ai/oss`) runs this reranker locally with [Transformers.js](https://huggingface.co/docs/transformers.js), the same cross-encoder path as `sentence_transformer`, just a different default model. It executes ONNX weights, so the default is the ONNX mirror `Xenova/bge-reranker-base`. Point `model` at any ONNX-exported reranker on the Hub (a raw `BAAI/bge-reranker-*` PyTorch checkpoint will not load in this runtime).
-
-```bash
-pnpm add @huggingface/transformers
-```
-
-```typescript
-import { Memory } from "mem0ai/oss";
-
-const memory = new Memory({
-  reranker: {
-    provider: "huggingface",
-    config: {
-      // model: "Xenova/bge-reranker-base", // default (ONNX)
-      device: "cpu", // "cpu" | "wasm" | "webgpu"
-      maxLength: 512, // max tokens per query-document pair
-      normalize: true, // sigmoid-normalize logits to [0, 1] (default)
-      topK: 5,
-    },
-  },
-});
-
-const results = await memory.search("What are the user's interests?", {
-  filters: { userId: "alice" },
-  rerank: true,
-});
-```
-
-<Note>
-  `batchSize` and `showProgressBar` are accepted for parity with the Python SDK but are no-ops in the TypeScript runtime. `trust_remote_code` and `model_kwargs` are Python-only.
-</Note>
-
 ## Popular Models
 
 ### BGE Rerankers (Recommended)

docs/components/rerankers/models/llm_reranker.mdx

@@ -67,43 +67,6 @@ config = {
 }
 ```
 
-## TypeScript (self-hosted)
-
-The [TypeScript OSS SDK](/open-source/features/reranker-search#typescript-sdk) (`mem0ai/oss`) ships the LLM reranker under the provider name `llm_reranker`. It does **not** reuse the Memory's main `llm` instance; it builds its own LLM from the reranker's own config, defaulting to `openai` / `gpt-4o-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

docs/components/rerankers/models/sentence_transformer.mdx

@@ -54,40 +54,6 @@ config = {
 memory = Memory.from_config(config)
 ```
 
-## TypeScript (self-hosted)
-
-The [TypeScript OSS SDK](/open-source/features/reranker-search#typescript-sdk) (`mem0ai/oss`) runs this reranker locally with [Transformers.js](https://huggingface.co/docs/transformers.js). Because it executes ONNX weights, the default model is the ONNX mirror of the Python default: `Xenova/ms-marco-MiniLM-L-6-v2`. Point `model` at any ONNX-exported cross-encoder on the Hub (a raw `cross-encoder/...` PyTorch checkpoint will not load in this runtime).
-
-```bash
-pnpm add @huggingface/transformers
-```
-
-```typescript
-import { Memory } from "mem0ai/oss";
-
-const memory = new Memory({
-  reranker: {
-    provider: "sentence_transformer",
-    config: {
-      // model: "Xenova/ms-marco-MiniLM-L-6-v2", // default (ONNX)
-      device: "cpu", // "cpu" | "wasm" | "webgpu"
-      maxLength: 512, // max tokens per query-document pair
-      normalize: true, // sigmoid-normalize logits to [0, 1] (default)
-      topK: 5,
-    },
-  },
-});
-
-const results = await memory.search("What books does the user like?", {
-  filters: { userId: "charlie" },
-  rerank: true,
-});
-```
-
-<Note>
-  `batchSize` and `showProgressBar` are accepted for parity with the Python SDK but are no-ops in the TypeScript runtime, because a search reranks a small candidate set in a single in-process forward pass. The model downloads once and is cached in-process.
-</Note>
-
 ## GPU Acceleration
 
 For better performance, use GPU acceleration:

docs/components/rerankers/models/zero_entropy.mdx

@@ -50,34 +50,6 @@ config = {
 memory = Memory.from_config(config)
 ```
 
-## TypeScript (self-hosted)
-
-The [TypeScript OSS SDK](/open-source/features/reranker-search#typescript-sdk) (`mem0ai/oss`) ships the Zero Entropy reranker under the same provider name as Python, `zero_entropy`. It reads the key from config or `ZERO_ENTROPY_API_KEY` and defaults to the `zerank-1` model.
-
-```bash
-pnpm add zeroentropy
-```
-
-```typescript
-import { Memory } from "mem0ai/oss";
-
-const memory = new Memory({
-  reranker: {
-    provider: "zero_entropy",
-    config: {
-      apiKey: process.env.ZERO_ENTROPY_API_KEY,
-      // model: "zerank-1",   // default (or "zerank-1-small")
-      topK: 5,
-    },
-  },
-});
-
-const results = await memory.search("What Italian food does the user like?", {
-  filters: { userId: "alice" },
-  rerank: true,
-});
-```
-
 ## Environment Variables
 
 Set your API key as an environment variable:

docs/components/rerankers/optimization.mdx

@@ -47,7 +47,7 @@ config = {
     "reranker": {
         "provider": "cohere",
         "config": {
-            "model": "rerank-v3.5",
+            "model": "rerank-english-v3.0",
             "top_n": 10,
             "max_chunks_per_doc": 10,  # Limit chunk processing
             "return_documents": False   # Reduce response size
@@ -280,7 +280,7 @@ config = {
 ```python
 def benchmark_rerankers():
     configs = [
-        {"provider": "cohere", "model": "rerank-v3.5"},
+        {"provider": "cohere", "model": "rerank-english-v3.0"},
         {"provider": "sentence_transformer", "model": "cross-encoder/ms-marco-MiniLM-L-6-v2"},
         {"provider": "huggingface", "model": "BAAI/bge-reranker-base"}
     ]

docs/components/rerankers/overview.mdx

@@ -19,10 +19,6 @@ Reranking trades extra latency for better precision. Start once you have baselin
   <Card title="Zero Entropy" icon="/images/provider-icons/zeroentropy.svg" href="/components/rerankers/models/zero_entropy" />
 </CardGroup>
 
-<Note>
-All five rerankers are available in both the Python and the [TypeScript](/open-source/features/reranker-search#typescript-sdk) self-hosted SDKs. Each provider page has a **TypeScript (self-hosted)** section with the camelCase config.
-</Note>
-
 ## Reranking Workflow
 
 <CardGroup cols={3}>

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", "oracledb")
+  - `provider`: The name of the vector database (e.g., "chroma", "pgvector", "qdrant", "milvus", "upstash_vector", "azure_ai_search", "vertex_ai_vector_search", "valkey")
   - `config`: A nested dictionary containing provider-specific settings
 
 
@@ -95,11 +95,6 @@ Here's a comprehensive list of all parameters that can be used across different
 | `connection_string` | PostgreSQL connection string (for Supabase/PGVector) |
 | `index_method` | Vector index method (for Supabase) |
 | `index_measure` | Distance measure for similarity search (for Supabase) |
-| `connection_params` | Connection settings for Oracle AI Vector Search |
-| `use_connection_pool` | Create an Oracle connection pool from `connection_params` |
-| `distance_metric` | Distance metric for Oracle vector indexing and search |
-| `index_type` | Oracle vector index type: `HNSW` or `IVF` |
-| `index_parameters` | Oracle vector-index parameters for the selected index type |
 </Tab>
 <Tab title="TypeScript">
 | Parameter | Description |

docs/components/vectordbs/dbs/baidu.mdx

@@ -5,22 +5,10 @@ description: "Use Baidu Mochow as an enterprise vector database in Mem0 for high
 
 [Baidu VectorDB](https://cloud.baidu.com/doc/VDB/index.html) is an enterprise-level distributed vector database service developed by Baidu Intelligent Cloud. It is powered by Baidu's proprietary "Mochow" vector database kernel, providing high performance, availability, and security for vector search.
 
-### Installation
-
-<CodeGroup>
-```bash Python
-pip install pymochow
-```
-
-```bash TypeScript
-npm install @mochow/mochow-sdk-node
-```
-
-</CodeGroup>
-
 ### Usage
 
 ```python
+import os
 from mem0 import Memory
 
 config = {
@@ -48,63 +36,19 @@ messages = [
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
-```typescript
-import { Memory } from "mem0ai/oss";
-
-const memory = new Memory({
-  embedder: {
-    provider: "openai",
-    config: {
-      apiKey: process.env.OPENAI_API_KEY || "",
-      model: "text-embedding-3-small",
-      embeddingDims: 1536,
-    },
-  },
-  vectorStore: {
-    provider: "baidu",
-    config: {
-      endpoint: process.env.BAIDU_ENDPOINT || "",
-      account: process.env.BAIDU_ACCOUNT || "root",
-      apiKey: process.env.BAIDU_API_KEY || "",
-      databaseName: "mem0",
-      tableName: "mem0_table",
-      embeddingModelDims: 1536,
-      metricType: "COSINE",
-    },
-  },
-  llm: {
-    provider: "openai",
-    config: {
-      apiKey: process.env.OPENAI_API_KEY || "",
-      model: "gpt-5-mini",
-    },
-  },
-});
-```
-
 ### Config
 
 Here are the parameters available for configuring Baidu VectorDB:
 
-| Parameter              | Description                                   | Default Value |
-| ---------------------- | --------------------------------------------- | ------------- |
-| `endpoint`             | Endpoint URL for your Baidu VectorDB instance | Required      |
-| `account`              | Baidu VectorDB account name                   | `root`        |
-| `api_key`              | API key for accessing Baidu VectorDB          | Required      |
-| `database_name`        | Name of the database                          | `mem0`        |
-| `table_name`           | Name of the table                             | `mem0`        |
-| `embedding_model_dims` | Dimensions of the embedding model             | `1536`        |
-| `metric_type`          | Distance metric for similarity search         | `L2`          |
-| `client`               | Prebuilt Mochow client (TypeScript SDK only)  | `None`        |
-
-For the TypeScript OSS SDK, use the camelCase equivalents:
-
-- `databaseName`
-- `tableName`
-- `embeddingModelDims`
-- `metricType`
-
-For OSS TS usage, `endpoint`, `account`, `apiKey`, `databaseName`, `tableName`, and `embeddingModelDims` are required unless you inject a prebuilt client. `metricType` defaults to `L2`, matching the Python SDK.
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `endpoint` | Endpoint URL for your Baidu VectorDB instance | Required |
+| `account` | Baidu VectorDB account name | `root` |
+| `api_key` | API key for accessing Baidu VectorDB | Required |
+| `database_name` | Name of the database | `mem0` |
+| `table_name` | Name of the table | `mem0` |
+| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
+| `metric_type` | Distance metric for similarity search | `L2` |
 
 ### Distance Metrics
 
@@ -122,5 +66,3 @@ The vector index is automatically configured with the following HNSW parameters:
 - `efconstruction`: 200 (size of the dynamic candidate list)
 - `auto_build`: true (automatically build index)
 - `auto_build_index_policy`: Incremental build with 10000 rows increment
-
-The TypeScript provider also creates a BM25 inverted index over a `textLemmatized` column so `keywordSearch()` runs against a real full-text index. Mem0 lemmatizes the query before it reaches the vector store, so only the lemmatized form of each memory is indexed. If you point `tableName` at a table created before this index existed, `keywordSearch()` returns `null` and search falls back to vector similarity alone; recreate the table to enable it.

docs/components/vectordbs/dbs/databricks.mdx

@@ -6,8 +6,7 @@ description: "Use Databricks Vector Search as a serverless vector store in Mem0
 
 ### Usage
 
-<CodeGroup>
-```python Python
+```python
 import os
 from mem0 import Memory
 
@@ -37,44 +36,10 @@ messages = [
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
-```typescript TypeScript
-// Requires the Databricks SQL driver (peer dependency): pnpm add @databricks/sql
-import { Memory } from 'mem0ai/oss';
-
-const config = {
-  vectorStore: {
-    provider: 'databricks',
-    config: {
-      workspaceUrl: 'https://your-workspace.databricks.com',
-      // SQL warehouse HTTP path, used for index writes (required)
-      httpPath: '/sql/1.0/warehouses/your-warehouse-id',
-      accessToken: 'your-access-token',
-      catalog: 'your_catalog',
-      schema: 'your_schema',
-      tableName: 'your_table',
-      collectionName: 'your_index_name',
-      embeddingModelDims: 1536,
-    },
-  },
-};
-
-const memory = new Memory(config);
-const messages = [
-    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
-    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
-    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
-]
-await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
-```
-</CodeGroup>
-
 ### Config
 
 Here are the parameters available for configuring Databricks Vector Search:
 
-<Tabs>
-<Tab title="Python">
 | Parameter | Description | Default Value |
 | --- | --- | --- |
 | `workspace_url` | The URL of your Databricks workspace | **Required** |
@@ -95,32 +60,6 @@ Here are the parameters available for configuring Databricks Vector Search:
 | `pipeline_type` | Sync pipeline type: `TRIGGERED` or `CONTINUOUS` | `TRIGGERED` |
 | `warehouse_name` | Databricks SQL warehouse name (if using SQL warehouse) | `None` |
 | `query_type` | Query type: `ANN` or `HYBRID` | `ANN` |
-</Tab>
-<Tab title="TypeScript">
-| Parameter | Description | Default Value |
-| --- | --- | --- |
-| `workspaceUrl` | The URL of your Databricks workspace (or pass `host`) | **Required** |
-| `httpPath` | SQL warehouse HTTP path, used for index writes | **Required** |
-| `accessToken` | Personal Access Token for authentication | `None` |
-| `clientId` | Service principal client ID (alternative to `accessToken`) | `None` |
-| `clientSecret` | Service principal client secret (required with `clientId`) | `None` |
-| `endpointName` | Name of the Vector Search endpoint | `mem0_vector_search` |
-| `endpointType` | Type of endpoint (`STANDARD` or `STORAGE_OPTIMIZED`) | `STANDARD` |
-| `pipelineType` | Delta Sync pipeline type: `TRIGGERED` or `CONTINUOUS` | `TRIGGERED` |
-| `queryType` | Query type: `ANN` or `HYBRID` | `ANN` |
-| `catalog` | Unity Catalog catalog name | `main` |
-| `schema` | Unity Catalog schema name | `default` |
-| `collectionName` | Vector Search index name | `mem0` |
-| `tableName` | Source Delta table name | falls back to `collectionName` |
-| `embeddingModelDims` | Dimension of self-managed embeddings | `1536` |
-| `syncPollIntervalMs` | Poll interval while waiting for a `TRIGGERED` sync | `1000` |
-| `syncTimeoutMs` | Timeout while waiting for an index sync | `300000` |
-
-<Note>
-The TypeScript provider uses `DELTA_SYNC` indexes with self-managed embeddings: pass vectors directly. `DIRECT_ACCESS` indexes, Databricks-computed embeddings (`embedding_model_endpoint_name`), and Azure AD auth are Python-only today. It writes to the index through a SQL warehouse, so `httpPath` is required, and `@databricks/sql` must be installed as a peer dependency.
-</Note>
-</Tab>
-</Tabs>
 
 ### Authentication
 

docs/components/vectordbs/dbs/neptune_analytics.mdx

@@ -2,37 +2,26 @@
 title: "Neptune Analytics"
 description: "Use AWS Neptune Analytics as a vector store in Mem0, combining graph analytics with vector search capabilities."
 ---
+# Neptune Analytics Vector Store
 
-[Neptune Analytics](https://docs.aws.amazon.com/neptune-analytics/latest/userguide/what-is-neptune-analytics.html) is a memory-optimized graph database engine for analytics. With Neptune Analytics, you can get insights and find trends by processing large amounts of graph data in seconds, including vector search.
+[Neptune Analytics](https://docs.aws.amazon.com/neptune-analytics/latest/userguide/what-is-neptune-analytics.html/) is a memory-optimized graph database engine for analytics. With Neptune Analytics, you can get insights and find trends by processing large amounts of graph data in seconds, including vector search.
 
-### Installation
 
-The Neptune Analytics provider needs the AWS Neptune Graph client. Install it alongside `mem0ai`:
+## Installation
 
-<CodeGroup>
-```bash Python
+```bash
 pip install mem0ai[vector-stores]
 ```
 
-```bash TypeScript
-npm install @aws-sdk/client-neptune-graph
-```
-</CodeGroup>
-
-### Usage
-
-Configure AWS credentials in your environment (environment variables, shared config file, an IAM role, or an instance profile). Both SDKs pick them up automatically through the standard AWS credential chain.
-
-<CodeGroup>
-```python Python
-from mem0 import Memory
+## Usage
 
+```python
 config = {
     "vector_store": {
         "provider": "neptune",
         "config": {
             "collection_name": "mem0",
-            "endpoint": "neptune-graph://g-abc123xyz0",
+            "endpoint": f"neptune-graph://my-graph-identifier",
         },
     },
 }
@@ -40,90 +29,18 @@ config = {
 m = Memory.from_config(config)
 messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movie? They can be quite engaging."},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
-```typescript TypeScript
-import { Memory } from 'mem0ai/oss';
+## Parameters
 
-const config = {
-  vectorStore: {
-    provider: 'neptune',
-    config: {
-      collectionName: 'mem0',
-      graphIdentifier: 'g-abc123xyz0',
-      // Any other key here (region, credentials, maxAttempts, ...) is
-      // forwarded to the underlying NeptuneGraphClient constructor.
-      region: 'us-east-1',
-    },
-  },
-};
+Let's see the available parameters for the `neptune` config:
 
-const memory = new Memory(config);
-const messages = [
-  { role: "user", content: "I'm planning to watch a movie tonight. Any recommendations?" },
-  { role: "assistant", content: "How about a thriller movie? They can be quite engaging." },
-  { role: "user", content: "I'm not a big fan of thriller movies but I love sci-fi movies." },
-  { role: "assistant", content: "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future." },
-];
-await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
-```
-</CodeGroup>
-
-### Config
-
-<Tabs>
-<Tab title="Python">
 | Parameter | Description | Default Value |
 | --- | --- | --- |
 | `collection_name` | The name of the collection to store the vectors | `mem0` |
-| `endpoint` | Connection URL for the Neptune Analytics service, must be `neptune-graph://<graph-id>` | Required |
-</Tab>
-<Tab title="TypeScript">
-| Parameter | Description | Default Value |
-| --- | --- | --- |
-| `collectionName` | The name of the collection to store the vectors | `memories` |
-| `graphIdentifier` | Graph ID, e.g. `g-abc123xyz0`. Takes priority over `endpoint`. | Required, unless `endpoint` supplies it |
-| `endpoint` | Either `neptune-graph://<graph-id>` (or a bare graph ID) to supply the graph ID, or an `https://` service endpoint to override the AWS endpoint. An `https://` value must be paired with `graphIdentifier`. | `undefined` |
-| `dimension` | Embedding vector dimension | Auto-detected from the embedder when omitted |
-| `client` | A pre-built `NeptuneGraphClient` to use instead of constructing one | `undefined` |
-| any other key | Forwarded as-is to the [`NeptuneGraphClient`](https://www.npmjs.com/package/@aws-sdk/client-neptune-graph) constructor, e.g. `region`, `credentials`, `maxAttempts` | N/A |
-</Tab>
-</Tabs>
-
-Both SDKs store vectors on graph nodes labeled `MEM0_VECTOR_<collection_name>`. Point them at the same
-graph with the same `collection_name` — the defaults differ, `mem0` in Python and `memories` in
-TypeScript — and `get()`, `list()`, and `delete()` interoperate across SDKs.
-
-<Note>
-`search()` is not currently cross-SDK compatible. The TypeScript provider filters on Neptune's reserved
-`~label` metafield, while the Python provider filters on a synthetic `label` property that only Python's
-own `insert()` writes. Python's `search()` therefore cannot see nodes written by the TypeScript provider.
-</Note>
-
-### IAM Permissions
-
-Your AWS identity (user or role) needs a policy that allows the [`ExecuteQuery`](https://docs.aws.amazon.com/neptune-analytics/latest/apiref/API_ExecuteQuery.html) actions used for reads, writes, and deletes:
-
-```json
-{
-  "Version": "2012-10-17",
-  "Statement": [
-    {
-      "Effect": "Allow",
-      "Action": [
-        "neptune-graph:ReadDataViaQuery",
-        "neptune-graph:WriteDataViaQuery",
-        "neptune-graph:DeleteDataViaQuery"
-      ],
-      "Resource": "*"
-    }
-  ]
-}
-```
-
-For production, scope the resource ARN down to your specific graph.
+| `endpoint` | Connection URL for the Neptune Analytics service | `neptune-graph://my-graph-identifier` |

docs/components/vectordbs/dbs/oracledb.mdx

@@ -1,134 +0,0 @@
----
-title: "Oracle AI Vector Search"
-description: "Use Oracle Database AI Vector Search as a vector store in Mem0 for semantic and relational queries."
----
-
-[Oracle AI Vector Search](https://www.oracle.com/database/ai-vector-search/) stores embeddings in an Oracle table using the native `VECTOR` data type, so you can combine semantic search over unstructured data with relational queries over business data in a single database.
-
-### Requirements
-
-- Oracle Database 23.4 or later, with a user that can create tables and vector indexes
-- The `python-oracledb` driver. In thick mode, Oracle Client 23.4 or later is also required.
-
-```bash
-pip install oracledb
-```
-
-### Usage
-
-<CodeGroup>
-```python Python
-import os
-from mem0 import Memory
-
-os.environ["OPENAI_API_KEY"] = "sk-xx"
-
-config = {
-    "vector_store": {
-        "provider": "oracledb",
-        "config": {
-            "collection_name": "mem0",
-            "embedding_model_dims": 1536,
-            "connection_params": {
-                "user": "mem0_user",
-                "password": "your-password",
-                "dsn": "localhost:1521/FREEPDB1",
-            },
-        }
-    }
-}
-
-m = Memory.from_config(config)
-messages = [
-    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
-    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
-    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
-]
-m.add(messages, user_id="alice", metadata={"category": "movies"})
-```
-</CodeGroup>
-
-To reuse a connection or pool you already manage, pass it as `client` instead of `connection_params`:
-
-```python
-import oracledb
-
-pool = oracledb.create_pool(user="mem0_user", password="your-password", dsn="localhost:1521/FREEPDB1")
-
-config = {
-    "vector_store": {
-        "provider": "oracledb",
-        "config": {"client": pool},
-    }
-}
-```
-
-### Config
-
-Here are the parameters available for configuring Oracle AI Vector Search:
-
-| Parameter | Description | Default Value |
-| --- | --- | --- |
-| `connection_params` | Connection settings passed to `python-oracledb`, such as `user`, `password` and `dsn`. See the [connection handling guide](https://python-oracledb.readthedocs.io/en/latest/user_guide/connection_handling.html). | `None` |
-| `use_connection_pool` | Create a connection pool from `connection_params` instead of a single connection | `True` |
-| `client` | An existing `oracledb.Connection` or `oracledb.ConnectionPool` to use instead of building one from `connection_params` | `None` |
-| `collection_name` | Name of the Oracle table that stores vectors and payloads | `mem0` |
-| `embedding_model_dims` | Dimension of your embedding vectors, must be greater than 0 | `1536` |
-| `distance_metric` | Distance function used for indexing and search: `COSINE`, `EUCLIDEAN`, `EUCLIDEAN_SQUARED`, `DOT`, `HAMMING` or `MANHATTAN` | `COSINE` |
-| `do_create_index` | Whether to create a vector index on the collection | `True` |
-| `index_type` | Vector index type: `HNSW` or `IVF` | `HNSW` |
-| `index_name` | Name of the vector index | `<collection_name>_VEC_IDX` |
-| `index_parameters` | Index tuning parameters. For `HNSW`: `neighbors`, `efconstruction`. For `IVF`: `neighbor partitions`, `samples_per_partition`, `min_vectors_per_partition`. | `None` |
-| `index_accuracy` | Target index accuracy from 1 to 100, applied as `WITH TARGET ACCURACY <n>` | `None` |
-
-<Note>
-  When you pass a pre-built `client`, Mem0 uses it as-is and ignores `connection_params` and `use_connection_pool`. Mem0 does not close a client it did not create.
-</Note>
-
-### Vector indexes
-
-Set the index type with `index_type` and tune it with `index_parameters`:
-
-```python
-config = {
-    "vector_store": {
-        "provider": "oracledb",
-        "config": {
-            "connection_params": {"user": "mem0_user", "password": "your-password", "dsn": "localhost:1521/FREEPDB1"},
-            "index_type": "HNSW",
-            "index_parameters": {"neighbors": 32, "efconstruction": 200},
-            "index_accuracy": 95,
-        }
-    }
-}
-```
-
-For the full list of supported options, see the Oracle [`CREATE VECTOR INDEX`](https://docs.oracle.com/en/database/oracle/oracle-database/26/sqlrf/create-vector-index.html) reference.
-
-### Search scores
-
-Oracle returns a distance from `VECTOR_DISTANCE`, which Mem0 converts to a `score` where higher means more similar. `COSINE` and the other non-negative metrics produce scores in the range `[0, 1]`. `DOT` returns the inner product, which can fall outside that range.
-
-### Metadata filters
-
-Filters run against the JSON `payload` column and support:
-
-| Filter type | Examples |
-| --- | --- |
-| Scalar equality | `{"user_id": "alice"}` |
-| Field existence | `{"agent_id": "*"}` |
-| Comparison | `{"score": {"gte": 0.5}}`, also `eq`, `ne`, `gt`, `lt`, `lte` |
-| Membership | `{"category": {"in": ["movies", "books"]}}`, also `nin` |
-| String matching | `{"title": {"contains": "sci-fi"}}`, also `icontains` for case-insensitive |
-| Logical groups | `{"AND": [...]}`, `{"OR": [...]}`, `{"NOT": [...]}` |
-
-Multiple fields at the top level are combined with `AND`:
-
-```python
-m.search(
-    "movie recommendations",
-    user_id="alice",
-    filters={"category": {"in": ["movies", "books"]}, "rating": {"gte": 4}},
-)
-```

docs/components/vectordbs/dbs/qdrant.mdx

@@ -42,7 +42,7 @@ const config = {
     provider: 'qdrant',
     config: {
       collectionName: 'memories',
-      dimension: 1536,
+      embeddingModelDims: 1536,
       host: 'localhost',
       port: 6333,
     },
@@ -83,7 +83,7 @@ Let's see the available parameters for the `qdrant` config:
 | Parameter | Description | Default Value |
 | --- | --- | --- |
 | `collectionName` | The name of the collection to store the vectors | `mem0` |
-| `dimension` | Dimensions of the embedding model | `1536` |
+| `embeddingModelDims` | Dimensions of the embedding model | `1536` |
 | `host` | The host where the Qdrant server is running | `None` |
 | `port` | The port where the Qdrant server is running | `None` |
 | `path` | Path for the Qdrant database | `/tmp/qdrant` |

docs/components/vectordbs/overview.mdx

@@ -1,6 +1,6 @@
 ---
 title: Overview
-description: "Overview of all supported vector databases in Mem0, including Qdrant, Chroma, PGVector, Pinecone, Oracle, and more."
+description: "Overview of all supported vector databases in Mem0, including Qdrant, Chroma, PGVector, Pinecone, and more."
 ---
 
 Mem0 includes built-in support for various popular databases. Memory can utilize the database provided by the user, ensuring efficient use for specific needs.
@@ -10,7 +10,7 @@ Mem0 includes built-in support for various popular databases. Memory can utilize
 See the list of supported vector databases below.
 
 <Note>
-  The following vector databases are supported in the Python implementation. The TypeScript implementation currently supports Qdrant, Redis, PGVector, Supabase, LangChain, Azure AI Search, Vectorize, Amazon S3 Vectors, Milvus, Neptune Analytics, and an in-memory store.
+  The following vector databases are supported in the Python implementation. The TypeScript implementation currently supports Qdrant, Redis, PGVector, Supabase, LangChain, Azure AI Search, Vectorize, Amazon S3 Vectors, Milvus, and an in-memory store.
 </Note>
 
 <CardGroup cols={3}>
@@ -21,7 +21,6 @@ See the list of supported vector databases below.
   <Card title="Milvus" icon="/images/provider-icons/milvus.svg" href="/components/vectordbs/dbs/milvus"></Card>
   <Card title="Pinecone" icon="/images/provider-icons/pinecone.svg" href="/components/vectordbs/dbs/pinecone"></Card>
   <Card title="MongoDB" icon="/images/provider-icons/mongodb.svg" href="/components/vectordbs/dbs/mongodb"></Card>
-  <Card title="Oracle AI Vector Search" icon="/images/provider-icons/oracle.svg" href="/components/vectordbs/dbs/oracledb"></Card>
   <Card title="Azure" icon="/images/provider-icons/azure-color.svg" href="/components/vectordbs/dbs/azure"></Card>
   <Card title="Redis" icon="/images/provider-icons/redis.svg" href="/components/vectordbs/dbs/redis"></Card>
   <Card title="Valkey" icon="/images/provider-icons/valkey.svg" href="/components/vectordbs/dbs/valkey"></Card>
@@ -33,7 +32,6 @@ See the list of supported vector databases below.
   <Card title="FAISS" icon="layer-group" href="/components/vectordbs/dbs/faiss"></Card>
   <Card title="LangChain" icon="/images/provider-icons/langchain-color.svg" href="/components/vectordbs/dbs/langchain"></Card>
   <Card title="Amazon S3 Vectors" icon="/images/provider-icons/aws-color.svg" href="/components/vectordbs/dbs/s3_vectors"></Card>
-  <Card title="Neptune Analytics" icon="/images/provider-icons/aws-color.svg" href="/components/vectordbs/dbs/neptune_analytics"></Card>
   <Card title="Databricks" icon="/images/provider-icons/databricks.svg" href="/components/vectordbs/dbs/databricks"></Card>
   <Card title="Turbopuffer" icon="/images/provider-icons/turbopuffer.svg" href="/components/vectordbs/dbs/turbopuffer"></Card>
 </CardGroup>

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

@@ -588,9 +588,9 @@ mem0_client.project.update(
     Exclude: greetings, filler, casual chat
     """,
     custom_categories=[
-        {"goals": "Training targets"},
-        {"constraints": "Injuries and limitations"},
-        {"preferences": "Training style"}
+        {"name": "goals", "description": "Training targets"},
+        {"name": "constraints", "description": "Injuries and limitations"},
+        {"name": "preferences", "description": "Training style"}
     ]
 )
 ```

docs/cookbooks/essentials/tagging-and-organizing-memories.mdx

@@ -19,7 +19,7 @@ client = MemoryClient(api_key="your-api-key")
 ```
 
 <Note>
-Define custom categories at the **project level** with `client.project.update()` before adding memories. Categories apply to all future memories: Mem0 auto-assigns them based on content semantics. You can also pass `custom_categories` on a single `client.add()` call to override the project list for just those memories. See [Custom Categories](/platform/features/custom-categories).
+Define custom categories at the **project level** with `client.project.update()` before adding memories. Categories apply to all future memories: Mem0 auto-assigns them based on content semantics.
 </Note>
 
 ---
@@ -96,10 +96,6 @@ Start with 3-5 clear categories that match how your team thinks. Too many catego
 
 These categories are now available project-wide. Every memory can be tagged with one or more categories.
 
-<Tip>
-Need a different vocabulary for one tenant or one kind of conversation? Pass `custom_categories=[...]` to `client.add()`. That list replaces the project list for the memories created by that call, and it does not change the project configuration.
-</Tip>
-
 ---
 
 ## Tagging Memories

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

@@ -15,7 +15,6 @@ Adding memory is how Mem0 captures useful details from a conversation so your ag
 - **Infer**: Controls whether Mem0 extracts structured memories (`infer=True`, default) or stores raw messages.
 - **Metadata**: Optional filters (e.g., `{"category": "movie_recommendations"}`) that improve retrieval later.
 - **User / Session identifiers**: `user_id`, `agent_id`, `app_id`, or `run_id` that scope the memory for future searches.
-- **expiration_date**: Optional `YYYY-MM-DD` date after which the memory is treated as expired. Use `expirationDate` in the JavaScript SDKs. Expired memories are hidden from `search` and `get_all` unless you pass `show_expired` (`showExpired` in JavaScript); fetching by ID still returns them.
 
 ## How does it work?
 
@@ -83,50 +82,6 @@ await client.add(messages, {
   Expect a `status: "PENDING"` response with an `event_id`. Poll `GET /v1/event/{event_id}/` to confirm completion.
 </Info>
 
-### Automatic conversation context
-
-On the Platform, you only send new messages. Mem0 automatically pulls the earlier messages that share the same identifiers (`user_id`, and `run_id` if you use one) and uses them as context when extracting memories, so you never need to resend conversation history.
-
-This means a follow-up turn is understood against what came before it:
-
-<CodeGroup>
-```python Python
-# First interaction
-client.add(
-    [{"role": "user", "content": "My dog's name is Biscuit. He's a golden retriever."}],
-    user_id="alice",
-)
-
-# Later — send only the new turn, no history
-client.add(
-    [{"role": "user", "content": "He turned 5 today, and I'm taking him to the vet on Friday."}],
-    user_id="alice",
-)
-# Stored as: "User's dog Biscuit turned 5" — "He" is resolved against the earlier turn.
-```
-
-```javascript JavaScript
-// First interaction
-await client.add(
-  [{ role: "user", content: "My dog's name is Biscuit. He's a golden retriever." }],
-  { userId: "alice" },
-);
-
-// Later — send only the new turn, no history
-await client.add(
-  [{ role: "user", content: "He turned 5 today, and I'm taking him to the vet on Friday." }],
-  { userId: "alice" },
-);
-// Stored as: "User's dog Biscuit turned 5" — "He" is resolved against the earlier turn.
-```
-</CodeGroup>
-
-Without that earlier turn, the same message can only be stored as "User's male pet turned 5", because there is nothing to resolve "He" against. Scope each conversation with a consistent `user_id` (plus `run_id` for a distinct session) and Mem0 handles the rest.
-
-<Info>
-  This is default behavior and needs no configuration. Earlier SDK versions gated it behind a `version="v2"` argument on `add`; that argument no longer exists and is ignored if sent.
-</Info>
-
 ## Add with Mem0 Open Source
 
 <CodeGroup>
@@ -150,9 +105,6 @@ result = m.add(messages, user_id="alice", metadata={"category": "movie_recommend
 
 # Optionally store raw messages without inference
 result = m.add(messages, user_id="alice", metadata={"category": "movie_recommendations"}, infer=False)
-
-# Optionally set an expiration date (YYYY-MM-DD)
-result = m.add(messages, user_id="alice", expiration_date="2030-01-31")
 ```
 
 ```javascript JavaScript
@@ -171,12 +123,6 @@ const result = memory.add(messages, {
   userId: "alice",
   metadata: { category: "preferences" }
 });
-
-// Optionally set an expiration date (YYYY-MM-DD)
-const expiring = memory.add(messages, {
-  userId: "alice",
-  expirationDate: "2030-01-31",
-});
 ```
 </CodeGroup>
 

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

@@ -188,17 +188,12 @@ memory = Memory()
 memory.delete(memory_id="mem_123")
 memory.delete_all(user_id="alice")
 ```
-
-```typescript TypeScript
-import { Memory } from "mem0ai/oss";
-
-const memory = new Memory();
-
-await memory.delete("mem_123");
-await memory.deleteAll({ userId: "alice" });
-```
 </CodeGroup>
 
+<Note>
+  The OSS JavaScript SDK does not yet expose deletion helpers: use the REST API or Python SDK when self-hosting.
+</Note>
+
 ## Use cases recap
 
 - Forget a user’s preferences at their request.

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

@@ -12,7 +12,7 @@ Mem0’s update operation lets you fix or enrich an existing memory without dele
 ## Key terms
 
 - **memory_id**: Unique identifier returned by `add` or `search` results.
-- **text**: New content that replaces the stored memory value. In the Python OSS SDK, `data` is a deprecated alias for `text`.
+- **text** / **data**: New content that replaces the stored memory value.
 - **metadata**: Optional key-value pairs you update alongside the text.
 - **timestamp**: Unix epoch (int/float) or ISO 8601 string to override the memory's timestamp.
 - **batch_update**: Platform API that edits multiple memories in a single request.
@@ -110,47 +110,17 @@ from mem0 import Memory
 
 memory = Memory()
 
-# Replace the content
 memory.update(
     memory_id="mem_123",
-    text="Alex now prefers decaf coffee",
-)
-
-# Update content plus metadata and an expiration date (None clears it)
-memory.update(
-    memory_id="mem_123",
-    text="Alex now prefers decaf coffee",
-    metadata={"category": "preferences"},
-    expiration_date="2030-01-31",
+    data="Alex now prefers decaf coffee",
 )
 ```
-
-```javascript JavaScript
-import { Memory } from "mem0ai/oss";
-
-const memory = new Memory();
-
-// Replace the content
-await memory.update("mem_123", { text: "Alex now prefers decaf coffee" });
-
-// Update content plus metadata and an expiration date (null clears it)
-await memory.update("mem_123", {
-  text: "Alex now prefers decaf coffee",
-  metadata: { category: "preferences" },
-  expirationDate: "2030-01-31",
-});
-
-// Update metadata only, leaving the stored text untouched
-await memory.update("mem_123", { metadata: { category: "preferences" } });
+```
 ```
 </CodeGroup>
 
 <Note>
-  In both OSS SDKs the content is optional: pass only `metadata` and/or an expiration date to update those while keeping the existing content. At least one of the three must be provided, otherwise the call raises.
-</Note>
-
-<Note>
-  `data` is a deprecated alias for `text` in both OSS SDKs (`data=` in Python, `{ data: ... }` in JavaScript). It still works but logs a warning; prefer `text`. In JavaScript, passing a bare string is shorthand for `{ text }`, so `update(memoryId, "new text")` also still works.
+  OSS JavaScript SDK does not expose `update` yet: use the REST API or Python SDK when self-hosting.
 </Note>
 
 ## Tips
@@ -168,7 +138,7 @@ await memory.update("mem_123", { metadata: { category: "preferences" } });
 
 | Capability | Mem0 Platform | Mem0 OSS |
 | --- | --- | --- |
-| Update call | `client.update(memory_id, {...})` | `memory.update(memory_id, text=...)` |
+| Update call | `client.update(memory_id, {...})` | `memory.update(memory_id, data=...)` |
 | Batch updates | `client.batch_update` (up to 1000 memories) | Script your own loop or bulk job |
 | Dashboard visibility | Inspect updates in the UI | Inspect via logs or custom tooling |
 | Immutable handling | Returns descriptive error | Raises exception: delete and re-add |

docs/docs.json

@@ -84,6 +84,8 @@
                         "pages": [
                           "platform/features/advanced-retrieval",
                           "platform/advanced-memory-operations",
+                          "platform/features/criteria-retrieval",
+                          "platform/features/contextual-add",
                           "platform/features/custom-instructions",
                           "platform/features/memory-decay"
                         ]
@@ -94,8 +96,7 @@
                         "pages": [
                           "platform/features/direct-import",
                           "platform/features/memory-export",
-                          "platform/features/timestamp",
-                          "platform/features/memory-expiration"
+                          "platform/features/timestamp"
                         ]
                       },
                       {
@@ -151,8 +152,7 @@
                       "open-source/features/multimodal-support",
                       "open-source/features/custom-instructions",
                       "open-source/features/rest-api",
-                      "open-source/features/openai_compatibility",
-                      "platform/features/memory-expiration"
+                      "open-source/features/openai_compatibility"
                     ]
                   },
                   {
@@ -207,7 +207,6 @@
                               "components/vectordbs/dbs/milvus",
                               "components/vectordbs/dbs/pinecone",
                               "components/vectordbs/dbs/mongodb",
-                              "components/vectordbs/dbs/oracledb",
                               "components/vectordbs/dbs/azure",
                               "components/vectordbs/dbs/azure_mysql",
                               "components/vectordbs/dbs/redis",
@@ -349,8 +348,6 @@
                     "pages": [
                       "integrations/dify",
                       "integrations/flowise",
-                      "integrations/n8n",
-                      "integrations/zapier",
                       "integrations/langchain-tools",
                       "integrations/agentops",
                       "integrations/respan",
@@ -610,10 +607,6 @@
     ]
   },
   "redirects": [
-    {
-      "source": "/platform/features/contextual-add",
-      "destination": "/core-concepts/memory-operations/add"
-    },
     {
       "source": "/changelog/openclaw",
       "destination": "/changelog/sdk"
@@ -636,7 +629,7 @@
     },
     {
       "source": "/platform/features/expiration-date",
-      "destination": "/platform/features/memory-expiration"
+      "destination": "/"
     },
     {
       "source": "/cookbooks/essentials/memory-expiration-short-and-long-term",
@@ -1233,14 +1226,6 @@
     {
       "source": "/open-source/multimodal-support",
       "destination": "/open-source/features/multimodal-support"
-    },
-    {
-      "source": "/platform/features/criteria-retrieval",
-      "destination": "/platform/features/advanced-retrieval"
-    },
-    {
-      "source": "/integrations/keywords",
-      "destination": "/integrations/respan"
     }
   ]
 }

docs/images/provider-icons/oracle.svg

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

docs/integrations/antigravity.mdx

@@ -65,9 +65,7 @@ The plugin uses the same shell scripts as Claude Code, Cursor, and Codex: hooks
 | **User prompt** | `UserPromptSubmit` | Searches relevant memories before each message |
 | **Pre-tool** | `PreToolUse` | Blocks MEMORY.md writes, enforces `user_id`/`app_id` on mem0 tools |
 | **Post-tool** | `PostToolUse` | Tracks stats, scans bash errors for related memories |
-| **Stop** | `Stop` | Stores a session summary at the end of every assistant turn (not just at session end) |
-
-What you type is stored as yours. What the agent produces — session summaries and compaction summaries — is stored as the assistant's, so its suggestions never become your stated preferences.
+| **Stop** | `Stop` | Stores a session summary when the session ends |
 
 ## Troubleshooting
 

docs/integrations/claude-code.mdx

@@ -44,14 +44,14 @@ Install the full plugin including MCP server, lifecycle hooks, and SDK skill.
 
 1. Add the Mem0 marketplace:
 
-   ```bash
-   claude plugin marketplace add mem0ai/mem0
+   ```
+   /plugin marketplace add mem0ai/mem0
    ```
 
 2. Install the plugin:
 
-   ```bash
-   claude plugin install mem0@mem0-plugins
+   ```
+   /plugin install mem0@mem0-plugins
    ```
 
 **Claude Cowork desktop app:** Open the Cowork tab, click **Customize** in the sidebar, click **Browse plugins**, and install Mem0.
@@ -88,15 +88,6 @@ Add to your Claude Code MCP config (`.mcp.json`):
 }
 ```
 
-### Managing the Plugin
-
-```bash
-claude plugin update mem0@mem0-plugins         # update the plugin to the latest version (restart to apply)
-claude plugin marketplace update mem0-plugins  # refresh the marketplace catalog
-claude plugin uninstall mem0@mem0-plugins      # uninstall the plugin (keeps the marketplace)
-claude plugin marketplace remove mem0-plugins  # unregister the marketplace entirely
-```
-
 <Info icon="check">
   Start a new session and ask: *"List my mem0 entities"* or *"Search my memories for hello"*. If the `mem0` tools appear and respond, you're all set.
 </Info>
@@ -152,11 +143,9 @@ When installed via the plugin marketplace, Mem0 hooks into Claude Code's lifecyc
 | **User prompt** | `UserPromptSubmit` | Searches relevant memories before each message; skips short prompts |
 | **Pre-tool (3 handlers)** | `PreToolUse` | Blocks MEMORY.md writes; enforces `user_id`/`app_id` on mem0 tool calls; scans files being read for relevant memory context |
 | **Post-tool** | `PostToolUse` | Tracks stats, scans bash errors for related memories |
-| **Stop** | `Stop` | Stores a session summary at the end of every assistant turn (not just at session end) |
+| **Stop** | `Stop` | Stores a session summary when the session ends |
 | **Pre-compact** | `PreCompact` | Stores a summary before the context is compacted |
 
-What you type is stored as yours. What Claude produces — session summaries and compaction summaries — is stored as the assistant's, so its suggestions never become your stated preferences.
-
 ## Example Workflow
 
 ```text
@@ -164,17 +153,16 @@ What you type is stored as yours. What Claude produces — session summaries and
 You: Let's refactor the auth module to use JWT tokens instead of sessions.
 
 # Claude searches memories, finds nothing relevant, proceeds with the work.
-# Mem0 stores what you said as yours:
-#   - Your preference: "Prefers TypeScript, uses ESLint"
-# ...and what Claude did as the assistant's, in the session summary:
+# After completing the task, Mem0 stores:
 #   - Decision: "Migrated auth from sessions to JWT tokens"
 #   - Files modified: auth/middleware.ts, auth/token.ts
+#   - User preference: "Prefers TypeScript, uses ESLint"
 
 # Session 2 (days later): Related work
 You: Add refresh token rotation to the auth system.
 
 # Claude searches memories, retrieves the JWT migration context.
-# Knows the file structure, decisions made, and your stated preferences.
+# Knows the file structure, decisions made, and user preferences.
 # Continues seamlessly without re-explaining the codebase.
 ```
 

docs/integrations/codex.mdx

@@ -125,23 +125,20 @@ When installed via the plugin marketplace, Mem0 hooks into Codex's lifecycle to
 | **User prompt** | `UserPromptSubmit` | Searches relevant memories before each message |
 | **Pre-tool (3 handlers)** | `PreToolUse` | Blocks MEMORY.md writes; enforces `user_id`/`app_id` on mem0 tool calls; scans files being read for relevant memory context |
 | **Post-tool** | `PostToolUse` | Tracks stats, scans bash errors for related memories |
-| **Stop** | `Stop` | Stores a session summary at the end of every assistant turn (not just at session end) |
+| **Stop** | `Stop` | Stores a session summary when the session ends |
 | **Pre-compact** | `PreCompact` | Stores a summary before the context is compacted |
 
-What you type is stored as yours. What Codex produces — session summaries and compaction summaries — is stored as the assistant's, so its suggestions never become your stated preferences.
-
 ## Example Workflow
 
 ```text
 # Task 1: Setting up a new service
 You: Create a REST API for the notifications service using Express and TypeScript.
 
-# Codex searches memories, finds your preferences from prior tasks.
-# Mem0 stores what you said as yours:
-#   - Your preference: "Prefers explicit error types over generic catch-all"
-# ...and what Codex did as the assistant's, in the session summary:
+# Codex searches memories, finds user preferences from prior tasks.
+# After completing the task, Mem0 stores:
 #   - Decision: "Notifications service uses Express + TypeScript + Zod validation"
 #   - Convention: "All API routes follow /api/v1/{resource} pattern"
+#   - Preference: "User prefers explicit error types over generic catch-all"
 
 # Task 2 (days later): Extending the service
 You: Add WebSocket support for real-time notification delivery.

docs/integrations/cursor.mdx

@@ -96,11 +96,10 @@ Once installed, the following tools are available in every Cursor session:
 You: The API endpoint /users is taking 3 seconds. Help me optimize it.
 
 # Cursor agent searches memories, proceeds with investigation.
-# Mem0 stores what you said as yours:
-#   - Your preference: "Prefers query-level fixes over caching"
-# ...and what the agent did as the assistant's:
+# After completing the task, Mem0 stores:
 #   - Learning: "N+1 query in UserService.getAll(): fixed with eager loading"
 #   - Decision: "Added database index on users.email column"
+#   - Preference: "User prefers query-level fixes over caching"
 
 # Session 2 (next week): Similar issue
 You: The /orders endpoint is also slow, same pattern as before.

docs/integrations/n8n.mdx

@@ -1,157 +0,0 @@
----
-title: n8n
-description: "Add long-term memory to n8n workflows and AI Agents with the Mem0 community node, no code required."
----
-
-Your n8n workflows start from zero on every run. The [`@mem0/n8n-nodes-mem0`](https://www.npmjs.com/package/@mem0/n8n-nodes-mem0) community node fixes that: store durable facts as memories, recall them in any later run, and hand the node to an [n8n AI Agent](https://docs.n8n.io/advanced-ai/) as a tool so it can remember and recall on its own.
-
-## Overview
-
-1. Install the node from n8n's community nodes panel.
-2. Connect your Mem0 API key once as a credential.
-3. Drop a **Mem0** node into any workflow to add, search, or manage memories.
-4. Optionally attach it to an **AI Agent** node, where it becomes a tool the agent calls itself.
-
-## Prerequisites
-
-1. A Mem0 API key from the <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-n8n" rel="nofollow">API Keys dashboard</a> (sign up at <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-n8n" rel="nofollow">app.mem0.ai</a> if you do not have an account).
-2. A **self-hosted** n8n instance. Installing community nodes from npm is a self-hosted feature; n8n Cloud only offers nodes that n8n has verified.
-3. Owner access to that instance, since only instance owners can install community nodes.
-
-## Installation
-
-<Steps>
-  <Step title="Open the community nodes panel">
-    In n8n, go to **Settings → Community Nodes** and select **Install**.
-  </Step>
-  <Step title="Install the package">
-    Enter `@mem0/n8n-nodes-mem0`, tick the risk acknowledgement, and select **Install**.
-  </Step>
-  <Step title="Create the credential">
-    Add a new **Mem0 API** credential and paste your API key. Leave **Base URL** at `https://api.mem0.ai` unless you run Mem0 somewhere else.
-  </Step>
-</Steps>
-
-<Info>
-  **Verify the install:** search the nodes panel for `Mem0`. The node should appear with a **Memory** resource offering Add, Search, Get, Get Many, Update, and Delete.
-</Info>
-
-## Quickstart
-
-A two-node workflow that writes a memory and reads it back:
-
-```text
-Manual Trigger  →  Mem0 (Add)  →  Mem0 (Search)
-```
-
-<Steps>
-  <Step title="Add a memory">
-    Add a **Mem0** node, keep **Operation: Add**, set **User ID** to `alice`, and add one message with **Role** `user` and **Content**:
-
-    `I am vegetarian and I never eat mushrooms.`
-  </Step>
-  <Step title="Search for it">
-    Add a second **Mem0** node with **Operation: Search**, **User ID** `alice`, and **Query** `what does the user eat?`.
-  </Step>
-  <Step title="Run it">
-    Select **Test workflow**. The Search node returns the extracted dietary memory.
-  </Step>
-</Steps>
-
-<Note>
-  Extraction is asynchronous. The Add node's **Wait for Completion** option is on by default, so it polls until extraction finishes before the next node runs. If you turn it off, allow a few seconds before searching for what you just wrote.
-</Note>
-
-## Use it as an AI Agent tool
-
-The node is marked `usableAsTool`, so an n8n **AI Agent** (Tools Agent) can call it without any wiring on your side:
-
-```text
-Chat Trigger  →  AI Agent  ──tool──▶  Mem0 (Search)
-                          ──tool──▶  Mem0 (Add)
-```
-
-Attach one Mem0 node set to **Search** and one set to **Add**. The agent searches memory before answering and writes back durable facts after a meaningful exchange. Keep **User ID** the same on both.
-
-## Operations
-
-The node wraps the hosted Mem0 REST API and supports six operations on the **Memory** resource:
-
-| Operation | What it does | Endpoint |
-| --- | --- | --- |
-| **Add** | Extract and store memories from messages | `POST /v3/memories/add/` |
-| **Search** | Semantic search over stored memories | `POST /v3/memories/search/` |
-| **Get Many** | List stored memories (one page, or **Return All**) | `POST /v3/memories/` |
-| **Get** | Fetch a single memory by ID | `GET /v1/memories/{id}/` |
-| **Update** | Change a memory's text or metadata | `PUT /v1/memories/{id}/` |
-| **Delete** | Delete a single memory by ID | `DELETE /v1/memories/{id}/` |
-
-### Add
-
-Extracts and stores memories from one or more messages. Supply at least one entity id (**User ID**, or **Agent ID** / **App ID** / **Run ID** under Additional Fields); the node checks this before calling the API.
-
-**Additional Fields:**
-
-| Field | Purpose |
-| --- | --- |
-| **Agent ID** | Scopes the memory to an agent |
-| **App ID** | Scopes the memory to an app or project |
-| **Run ID** | Scopes the memory to a single session or run |
-| **Metadata (JSON)** | Arbitrary JSON attached to each extracted memory |
-| **Infer** | On by default. Turn off to store messages verbatim instead of running LLM extraction |
-| **Custom Instructions** | Free-text guidance steering what the extractor keeps or ignores, for this call |
-| **Custom Categories** | JSON array of `{category: description}` objects, replacing the project-level catalog for this call |
-| **Includes** | Only extract memories matching this description |
-| **Excludes** | Skip memories matching this description |
-
-**Includes** and **Excludes** narrow what extraction keeps. Sending *"I am vegetarian and I never eat mushrooms. I drive a blue Toyota Corolla and my parking spot is B12"* stores three memories by default; with `Includes: "only record food and diet preferences"` it stores just the dietary one.
-
-### Search
-
-Semantic search over stored memories. Takes a **Query**, at least one entity id, and an optional **Limit**.
-
-### Get Many
-
-Lists stored memories for the entity ids you supply. Turn on **Return All** to page through everything automatically, or leave it off to fetch a single **Page**. **Page Size** applies either way.
-
-### Get, Update, Delete
-
-Operate on one memory by **Memory ID**. Update accepts new **Text** and/or **Metadata (JSON)**.
-
-## Entity filters on Search and Get Many
-
-Both operations take **User ID**, **Agent ID**, **App ID**, and **Run ID**. At least one is required, since the API rejects a query with no entity scope, and the node fails with a clear message before making the call if all four are empty.
-
-Supply several and they combine with **OR**, so the result is the union of those scopes:
-
-```json
-{ "OR": [{ "user_id": "alice" }, { "agent_id": "support-bot" }] }
-```
-
-<Warning>
-  This is deliberate, not a shortcut. Mem0 indexes each entity separately, so an `AND` across `user_id` and `agent_id` matches nothing even when a memory was written with both. To narrow rather than widen, run one operation per entity id.
-</Warning>
-
-## Choosing a User ID
-
-The **User ID** is a stable string you pick to identify whose memories these are. It is not looked up in the dashboard, so any consistent value works: your app's internal user ID, an email, or a UUID. Use the same value across Add, Search, and Get Many or recall returns nothing.
-
-## Troubleshooting
-
-- **The node does not appear in the panel**: community nodes install on self-hosted n8n only, and only instance owners can install them. On n8n Cloud, this node is not yet available.
-- **`401 Unauthorized`**: the API key is wrong or was revoked. Regenerate it in the <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-n8n" rel="nofollow">API Keys dashboard</a> and update the credential.
-- **"Provide at least one of User ID, Agent ID, App ID, or Run ID"**: every Add, Search, and Get Many needs an entity scope. Fill in at least one.
-- **Search returns nothing right after an Add**: extraction is asynchronous. Leave **Wait for Completion** on, or add a short Wait node before searching.
-- **Searching two entity ids returns more than expected**: multiple ids are combined with OR by design. Run one operation per id to narrow.
-- **"Timed out waiting for memory event"**: the add was accepted and is likely still finishing on the server. A timeout here does not mean it failed.
-
-<CardGroup cols={2}>
-  <Card title="Zapier Integration" icon="bolt" href="/integrations/zapier">
-    Add memory to Zaps across thousands of apps
-  </Card>
-  <Card title="Flowise Integration" icon="blocks" href="/integrations/flowise">
-    Add memory to Flowise chatflows
-  </Card>
-</CardGroup>
-
-<Snippet file="star-on-github.mdx" />

docs/integrations/zapier.mdx

@@ -1,143 +0,0 @@
----
-title: Zapier
-description: "Add, search, and manage Mem0 memories from any Zap using the Mem0 Zapier app, no code required."
----
-
-Zaps fire and forget. The [Mem0](https://mem0.ai) app gives them memory: store durable facts from a form submission, a support ticket, or a chat message, then recall them later from any of [Zapier's](https://zapier.com) thousands of apps. No code, no server.
-
-## Overview
-
-1. Connect your Mem0 API key once as a Zapier connection.
-2. Use **Add Memory** to store what a Zap learns.
-3. Use **Search Memories** or **Get Memories** to pull that context back into a later step.
-4. Use **Delete Memory** to remove one by ID.
-
-## Prerequisites
-
-1. A Mem0 API key from the <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-zapier" rel="nofollow">API Keys dashboard</a> (sign up at <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-zapier" rel="nofollow">app.mem0.ai</a> if you do not have an account).
-2. A Zapier account on any plan.
-
-<Note>
-  The Mem0 app is not yet listed in Zapier's public App Directory, so you need an invite link to add it to a Zap. Email [support@mem0.ai](mailto:support@mem0.ai) to request one.
-</Note>
-
-## Setup
-
-<Steps>
-  <Step title="Add a Mem0 step">
-    In the Zap editor, search for **Mem0** and pick an action such as **Add Memory**.
-  </Step>
-  <Step title="Connect your account">
-    Select **Sign in**, paste your **Mem0 API Key** (it starts with `m0-`), and leave **Base URL** at `https://api.mem0.ai` unless you run Mem0 somewhere else.
-  </Step>
-  <Step title="Confirm the connection">
-    Zapier validates the key against Mem0 the moment you save it. A connection labelled **Mem0** means the key works.
-  </Step>
-</Steps>
-
-<Info>
-  The key is a password field, so Zapier masks it in the editor. It is sent to Mem0 as `Authorization: Token <key>`.
-</Info>
-
-## Quickstart
-
-Remember what a user tells you:
-
-```text
-Trigger (form, chat, ticket)  →  Mem0: Add Memory
-```
-
-Set **Content** to the message text and **User ID** to a stable identifier for that person, such as their email.
-
-Then recall it in a later Zap:
-
-```text
-Trigger (new message)  →  Mem0: Search Memories  →  Send reply
-```
-
-Set **Query** to the incoming message and **User ID** to the same value. The matched memories become available to every step after it.
-
-<Note>
-  Extraction is asynchronous. **Add Memory** returns immediately with an event ID by default, so a Search fired a second later may not see the new memory yet. See [Waiting for extraction](#waiting-for-extraction).
-</Note>
-
-## Actions
-
-| Type | Action | What it does | Endpoint |
-| --- | --- | --- | --- |
-| Create | **Add Memory** | Extract and store memories from a message | `POST /v3/memories/add/` |
-| Search | **Search Memories** | Semantic search over stored memories | `POST /v3/memories/search/` |
-| Search | **Get Memories** | List stored memories, one page at a time | `POST /v3/memories/` |
-| Create | **Delete Memory** | Delete a single memory by ID | `DELETE /v1/memories/{id}/` |
-
-### Add Memory
-
-| Field | Required | Purpose |
-| --- | --- | --- |
-| **Content** | Yes | The message text to extract memories from |
-| **Role** | | `User` (default), `Assistant`, or `System` |
-| **User ID** | | Scopes the memory to a person |
-| **Agent ID** | | Scopes the memory to an agent |
-| **Run ID** | | Scopes the memory to a single session or run |
-| **Metadata (JSON)** | | Arbitrary JSON attached to each extracted memory |
-| **Custom Instructions** | | Free-text guidance steering what the extractor keeps or ignores, for this call |
-| **Custom Categories (JSON)** | | JSON array of `{category: description}` objects, replacing the project-level catalog for this call |
-| **Includes** | | Only extract memories matching this description |
-| **Excludes** | | Skip memories matching this description |
-| **Infer** | | On by default. Turn off to store the message verbatim instead of running LLM extraction |
-| **Wait for Completion** | | Off by default. Turn on to poll until extraction finishes and return the resulting memories |
-
-**Includes** and **Excludes** narrow what extraction keeps. Sending *"I am vegetarian and I never eat mushrooms. I drive a blue Toyota Corolla and my parking spot is B12"* stores three memories by default; with `Includes: "only record food and diet preferences"` it stores just the dietary one.
-
-#### Waiting for extraction
-
-Extraction runs asynchronously, so **Add Memory** returns an event ID and moves on unless you turn on **Wait for Completion**. When you do, the step polls for up to 60 seconds and returns the extracted memories instead.
-
-<Warning>
-  Extraction can take longer than Zapier allows a single step to run, which is why waiting is opt-in. If the step times out, the add was still accepted and typically completes on Mem0's side, so do not retry it blindly.
-</Warning>
-
-### Search Memories
-
-| Field | Required | Purpose |
-| --- | --- | --- |
-| **Query** | Yes | Natural-language search text |
-| **User ID** | Yes | Whose memories to search. The API needs an entity filter |
-| **Limit** | | Maximum results, default `50` |
-
-### Get Memories
-
-| Field | Required | Purpose |
-| --- | --- | --- |
-| **User ID** | Yes | Whose memories to list |
-| **Limit** | | Memories per page, default `50` |
-| **Page** | | Which page to return, 1-based, default `1` |
-
-Returns one page per run. Raise **Page** to walk through larger result sets.
-
-### Delete Memory
-
-Takes a **Memory ID** and deletes that memory. Pair it with **Search Memories** or **Get Memories** to get the ID first.
-
-## Choosing a User ID
-
-The **User ID** is a stable string you pick to identify whose memories these are. It is not looked up in the dashboard, so any consistent value works: your app's internal user ID, an email, or a UUID. Use the same value on Add, Search, and Get Memories or recall returns nothing.
-
-## Troubleshooting
-
-- **Mem0 does not appear in the Zap editor**: the app is not yet in the public App Directory. Email [support@mem0.ai](mailto:support@mem0.ai) for an invite link.
-- **The connection fails when you paste the key**: check that it starts with `m0-` and has not been revoked in the <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-zapier" rel="nofollow">API Keys dashboard</a>.
-- **Search returns nothing right after an Add**: extraction is asynchronous. Turn on **Wait for Completion**, or put a Zapier **Delay** step before the Search.
-- **"Metadata must be valid JSON" or "Custom Categories must be valid JSON"**: those fields take raw JSON. Check for smart quotes and trailing commas.
-- **The Add step times out**: the memory was still accepted and is likely finishing server-side. Confirm with **Get Memories** before re-running.
-
-<CardGroup cols={2}>
-  <Card title="n8n Integration" icon="diagram-project" href="/integrations/n8n">
-    Build workflows with the Mem0 n8n community node
-  </Card>
-  <Card title="Flowise Integration" icon="blocks" href="/integrations/flowise">
-    Add memory to Flowise chatflows
-  </Card>
-</CardGroup>
-
-<Snippet file="star-on-github.mdx" />

docs/llms.txt

@@ -61,7 +61,7 @@ client.get_all(user_id="alice")
 client.get(memory_id="<id>")
 
 # Update
-client.update(memory_id="<id>", text="Alice loves mountain hiking")
+client.update(memory_id="<id>", data="Alice loves mountain hiking")
 
 # Delete
 client.delete(memory_id="<id>")
@@ -118,7 +118,7 @@ m.get_all(user_id="alice")
 m.get(memory_id="<id>")
 
 # Update
-m.update(memory_id="<id>", text="Alice loves mountain hiking")
+m.update(memory_id="<id>", data="Alice loves mountain hiking")
 
 # Delete
 m.delete(memory_id="<id>")
@@ -204,7 +204,9 @@ If the user is on a pre-current major (Python < 2, TS < 3, or Platform `output_f
 
 ### Features - Advanced Retrieval
 - [Advanced Retrieval](https://docs.mem0.ai/platform/features/advanced-retrieval) [Platform]: Use when the user needs keyword search, reranking, or hybrid retrieval.
+- [Criteria-Based Retrieval](https://docs.mem0.ai/platform/features/criteria-retrieval) [Platform]: Use when targeting memories by custom criteria, not just semantic similarity.
 - [Temporal Reasoning](https://docs.mem0.ai/platform/features/temporal-reasoning) [Platform]: Use when time-aware searches like last week, upcoming, or right now need better result ordering.
+- [Contextual Add](https://docs.mem0.ai/platform/features/contextual-add) [Platform]: Use when `add()` should consider the surrounding conversation, not just the latest turn.
 - [Custom Instructions](https://docs.mem0.ai/platform/features/custom-instructions) [Platform]: Use when tailoring what Mem0 extracts and stores on Platform.
 - [Memory Decay](https://docs.mem0.ai/platform/features/memory-decay) [Platform]: Use when search results should boost recently-reinforced memories and dampen stale ones. Opt in per project; applies at search time and never filters candidates out.
 - [Advanced Memory Operations](https://docs.mem0.ai/platform/advanced-memory-operations) [Platform]: Use when basic CRUD is not enough - batch ops, complex filters, workflows.
@@ -213,7 +215,6 @@ If the user is on a pre-current major (Python < 2, TS < 3, or Platform `output_f
 - [Direct Import](https://docs.mem0.ai/platform/features/direct-import) [Platform]: Use when seeding a Mem0 project from existing data.
 - [Memory Export](https://docs.mem0.ai/platform/features/memory-export) [Platform]: Use when exporting memories via a Pydantic schema.
 - [Timestamp Support](https://docs.mem0.ai/platform/features/timestamp) [Platform]: Use when temporal queries or time-based filtering matter.
-- [Memory Expiration](https://docs.mem0.ai/platform/features/memory-expiration) [Both]: Use when a memory should stop surfacing after a known date without being deleted, e.g. trial facts, seasonal preferences, or retention windows.
 
 ### Features - Integration & Ops
 - [Webhooks](https://docs.mem0.ai/platform/features/webhooks) [Platform]: Use when another system needs to react to memory changes in real time.
@@ -281,8 +282,6 @@ If the user is on a pre-current major (Python < 2, TS < 3, or Platform `output_f
 ### Developer Tools
 - [Dify](https://docs.mem0.ai/integrations/dify) [Both]: Use when the user is on Dify LLMOps.
 - [Flowise](https://docs.mem0.ai/integrations/flowise) [Both]: Use when the user is on Flowise no-code.
-- [n8n](https://docs.mem0.ai/integrations/n8n) [Both]: Use when the user builds workflows or AI agents in n8n.
-- [Zapier](https://docs.mem0.ai/integrations/zapier) [Both]: Use when the user automates workflows with Zapier.
 - [AgentOps](https://docs.mem0.ai/integrations/agentops) [Both]: Use when tracking agent observability with memory metadata.
 - [Respan](https://docs.mem0.ai/integrations/respan) [Both]: Use when monitoring Mem0 with Respan (formerly Keywords AI) LLM observability.
 - [Raycast](https://docs.mem0.ai/integrations/raycast) [Both]: Use when the user wants quick memory access via Raycast.
@@ -418,6 +417,7 @@ Editor-specific setup docs (already listed above under `## Integrations > AI Cod
 ### MCP Endpoints
 
 - Hosted MCP server: `https://mcp.mem0.ai` - requires Platform API key. See `platform/mem0-mcp`.
+- Self-hosted MCP server: ships with `openmemory/api/` (FastAPI) - runs against your own Qdrant + LLM stack.
 
 ## Community & Support
 
@@ -473,7 +473,6 @@ Everything below is OSS-only provider configuration. Skip this entire section wh
 - [Milvus](https://docs.mem0.ai/components/vectordbs/dbs/milvus) [OSS]: Use for large-scale Milvus deployments.
 - [Pinecone](https://docs.mem0.ai/components/vectordbs/dbs/pinecone) [OSS]: Use when the user is on Pinecone managed.
 - [MongoDB](https://docs.mem0.ai/components/vectordbs/dbs/mongodb) [OSS]: Use when Mongo Atlas Vector Search is the backing store.
-- [Oracle AI Vector Search](https://docs.mem0.ai/components/vectordbs/dbs/oracledb) [OSS]: Use when Oracle Database AI Vector Search is the backing store.
 - [Azure AI Search](https://docs.mem0.ai/components/vectordbs/dbs/azure) [OSS]: Use when the user is on Azure AI Search.
 - [Azure MySQL](https://docs.mem0.ai/components/vectordbs/dbs/azure_mysql) [OSS]: Use when vector search runs on Azure Database for MySQL.
 - [Redis](https://docs.mem0.ai/components/vectordbs/dbs/redis) [OSS]: Use when Redis Stack is the backing store.
@@ -502,6 +501,5 @@ Everything below is OSS-only provider configuration. Skip this entire section wh
 - [Custom Reranker Prompts](https://docs.mem0.ai/components/rerankers/custom-prompts) [OSS]: Use when rewriting reranker prompts.
 - [Cohere Reranker](https://docs.mem0.ai/components/rerankers/models/cohere) [OSS]: Use for Cohere Rerank.
 - [Sentence Transformer Reranker](https://docs.mem0.ai/components/rerankers/models/sentence_transformer) [OSS]: Use for local cross-encoder rerankers.
-- [Hugging Face Reranker](https://docs.mem0.ai/components/rerankers/models/huggingface) [OSS]: Use for HF-hosted reranker models.
-- [LLM Reranker](https://docs.mem0.ai/components/rerankers/models/llm_reranker) [OSS]: Use when the reranker is a prompted LLM (implementation reference).
+- [Hugging Face Reranker](https://docs.mem0.ai/components/rerankers/models/huggingface) [OSS]: Use for HF-hosted reranker models.- [LLM Reranker](https://docs.mem0.ai/components/rerankers/models/llm_reranker) [OSS]: Use when the reranker is a prompted LLM (implementation reference).
 - [Zero Entropy Reranker](https://docs.mem0.ai/components/rerankers/models/zero_entropy) [OSS]: Use for the Zero Entropy reranker.

docs/logo/openmemory-icon.svg

@@ -1,19 +1,19 @@
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 </svg>

docs/migration/oss-to-platform.mdx

@@ -339,22 +339,21 @@ The Platform introduces powerful capabilities not available in OSS:
     </Info>
     ```python
     # Set custom categories for your project
-    client.project.update(
-        custom_categories=[
-            {"customer_preferences": "Likes, dislikes, and product preferences"},
-            {"product_feedback": "Feature requests and complaints about the product"},
-            {"support_issues": "Problems reported and how they were resolved"}
+    client.projects.update_categories(
+        project_id="proj_123",
+        categories=[
+            "Customer Preferences",
+            "Product Feedback",
+            "Support Issues",
+            "Feature Requests"
         ]
     )
 
-    # Mem0 assigns these categories automatically as memories come in
-    client.add("User wants dark mode in dashboard", user_id="alex")
-
-    # Or pass a different catalog for a single call
+    # Memories will use these categories
     client.add(
         "User wants dark mode in dashboard",
         user_id="alex",
-        custom_categories=[{"ui_requests": "Requests about interface and appearance"}]
+        categories=["Customer Preferences"]
     )
     ```
   </Accordion>

docs/open-source/configuration.mdx

@@ -59,7 +59,7 @@ config = {
     },
     "reranker": {
         "provider": "cohere",
-        "config": {"model": "rerank-v3.5"},
+        "config": {"model": "rerank-english-v3.0"},
     },
 }
 
@@ -119,7 +119,7 @@ Change the `provider` string to switch backends. The most common options:
 
 | Component | Python | TypeScript |
 | --- | --- | --- |
-| LLM | `openai`, `anthropic`, `gemini`, `groq`, `ollama`, `aws_bedrock`, `azure_openai`, `litellm` | `openai`, `anthropic`, `gemini`, `groq`, `ollama`, `aws_bedrock`, `azure_openai`, `mistral`, `deepseek` |
+| LLM | `openai`, `anthropic`, `gemini`, `groq`, `ollama`, `aws_bedrock`, `azure_openai`, `litellm` | `openai`, `anthropic`, `gemini`, `groq`, `ollama`, `azure_openai`, `mistral`, `deepseek` |
 | Embedder | `openai`, `gemini`, `azure_openai`, `ollama`, `huggingface`, `vertexai`, `aws_bedrock` | `openai`, `gemini`, `azure_openai`, `ollama` |
 | Vector store | `qdrant`, `pgvector`, `chroma`, `pinecone`, `redis`, `weaviate`, `milvus`, `elasticsearch` | `memory`, `qdrant`, `pgvector`, `redis`, `supabase`, `azure-ai-search`, `vectorize`, `milvus` |
 
@@ -148,7 +148,7 @@ See the full catalog in <Link href="/components/llms/overview">Components</Link>
 - Qdrant connection errors: confirm port `6333` is exposed and the API key (if set) matches.
 - Empty search results: verify the embedder model name. A mismatch causes dimension errors.
 - `Unknown reranker` (Python): upgrade the SDK with `pip install --upgrade mem0ai` to load the latest provider registry.
-- `Cannot find module` (Node): two common causes. First, import from the OSS entry point, `import { Memory } from "mem0ai/oss"`, not `"mem0ai"`. Second, provider SDKs are optional peer dependencies loaded on demand, so install the one for the provider you configured (for example `npm install @qdrant/js-client-rest` for Qdrant). Installing `mem0ai` alone only pulls in the providers used by default; you do not need SDKs for providers you never select.
+- `Cannot find module` (Node): import from the OSS entry point, `import { Memory } from "mem0ai/oss"`, not `"mem0ai"`.
 
 <CardGroup cols={2}>
   <Card

docs/open-source/features/async-memory.mdx

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

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

@@ -18,129 +18,7 @@ Reranker-enhanced search adds a second scoring pass after vector retrieval so Me
 </Warning>
 
 <Note>
-  The `Configure it` and `See it in action` snippets below use the Python SDK. The self-hosted **TypeScript SDK** supports the Cohere, Zero Entropy, Sentence Transformer, Hugging Face, and LLM rerankers; see [TypeScript SDK](#typescript-sdk).
-</Note>
-
----
-
-## TypeScript SDK
-
-The self-hosted TypeScript SDK (`mem0ai/oss`) ships five rerankers: **Cohere**, **Zero Entropy**, **Sentence Transformer**, **Hugging Face**, and the **LLM reranker**. Configure one under `reranker`, then opt in per search with `rerank: true`. Keys are camelCase (`apiKey`, not `api_key`).
-
-Provider SDKs are peer dependencies. Install the one your reranker needs:
-
-```bash
-pnpm add cohere-ai          # cohere
-pnpm add zeroentropy        # zero_entropy
-pnpm add @huggingface/transformers   # sentence_transformer, huggingface
-# llm_reranker defaults to openai (already a core dependency); install another
-# provider's SDK only if you nest a different one under config.llm
-```
-
-### Hosted rerankers (Cohere, Zero Entropy)
-
-Both call a hosted API and read their key from config or the provider's environment variable (`COHERE_API_KEY`, `ZERO_ENTROPY_API_KEY`).
-
-```typescript
-import { Memory } from "mem0ai/oss";
-
-// Cohere reranker (defaults to the rerank-v3.5 model)
-const memory = new Memory({
-  reranker: {
-    provider: "cohere",
-    config: { apiKey: process.env.COHERE_API_KEY },
-  },
-});
-
-const results = await memory.search("What are my food preferences?", {
-  filters: { userId: "alice" },
-  rerank: true,
-});
-```
-
-```typescript
-// Zero Entropy reranker (defaults to the zerank-1 model)
-const memory = new Memory({
-  reranker: {
-    provider: "zero_entropy",
-    config: { apiKey: process.env.ZERO_ENTROPY_API_KEY },
-  },
-});
-```
-
-### Local cross-encoders (Sentence Transformer, Hugging Face)
-
-Both run a cross-encoder locally with [Transformers.js](https://huggingface.co/docs/transformers.js): no API key, no network at inference time. Because Transformers.js runs ONNX weights, the default models are the ONNX mirrors of the Python SDK's defaults (`sentence_transformer` → `Xenova/ms-marco-MiniLM-L-6-v2`, `huggingface` → `Xenova/bge-reranker-base`). Point `model` at any ONNX-exported cross-encoder on the Hub to override.
-
-```typescript
-const memory = new Memory({
-  reranker: {
-    provider: "sentence_transformer", // or "huggingface"
-    config: {
-      // model: "Xenova/bge-reranker-base",  // override the default
-      device: "cpu", // Transformers.js device: "cpu" | "wasm" | "webgpu"
-      maxLength: 512, // max tokens per query-document pair
-      normalize: true, // sigmoid-normalize logits to [0, 1] (default)
-    },
-  },
-});
-
-const results = await memory.search("What movies do I like?", {
-  filters: { userId: "alice" },
-  rerank: true,
-});
-```
-
-<Note>
-  `batchSize` and `showProgressBar` are accepted for config parity with the Python SDK but are no-ops in this runtime, because a memory search reranks a small candidate set in a single in-process forward pass. The model is downloaded once and cached in-process on first use.
-</Note>
-
-### LLM reranker
-
-To score with an LLM instead of a dedicated reranker, use the `llm_reranker` provider. It builds its own LLM from the reranker's config (defaulting to `openai` / `gpt-4o-mini`) rather than reusing the Memory's main `llm`:
-
-```typescript
-const memory = new Memory({
-  reranker: {
-    provider: "llm_reranker",
-    config: { apiKey: process.env.OPENAI_API_KEY },
-  },
-});
-
-const results = await memory.search("What movies do I like?", {
-  filters: { userId: "alice" },
-  rerank: true,
-});
-```
-
-Nest a different provider under `config.llm` to override the default:
-
-```typescript
-const memory = new Memory({
-  reranker: {
-    provider: "llm_reranker",
-    config: {
-      llm: {
-        provider: "anthropic",
-        config: { apiKey: process.env.ANTHROPIC_API_KEY },
-      },
-    },
-  },
-});
-```
-
-### Config reference
-
-| Provider | Default model | Key config fields |
-| --- | --- | --- |
-| `cohere` | `rerank-v3.5` | `apiKey`, `model`, `topK` |
-| `zero_entropy` | `zerank-1` | `apiKey`, `model`, `topK` |
-| `sentence_transformer` | `Xenova/ms-marco-MiniLM-L-6-v2` | `model`, `device`, `maxLength`, `normalize`, `topK` |
-| `huggingface` | `Xenova/bge-reranker-base` | `model`, `device`, `maxLength`, `normalize`, `topK` |
-| `llm_reranker` | `openai` / `gpt-4o-mini` | `provider`, `model`, `apiKey`, `llm` (nested override), `topK` |
-
-<Note>
-  `rerank` is opt-in per search and a no-op when no `reranker` is configured. If the reranker call fails, Mem0 logs a warning and returns the original vector-ranked results.
+  All configuration snippets translate directly to the TypeScript SDK: swap dictionaries for objects while keeping the same keys (`provider`, `config`, `rerank` flags).
 </Note>
 
 ---
@@ -183,7 +61,7 @@ config = {
     "reranker": {
         "provider": "cohere",
         "config": {
-            "model": "rerank-v3.5",
+            "model": "rerank-english-v3.0",
             "api_key": "your-cohere-api-key"
         }
     }
@@ -208,7 +86,7 @@ config = {
     "reranker": {
         "provider": "cohere",
         "config": {
-            "model": "rerank-v3.5",
+            "model": "rerank-english-v3.0",
             "api_key": "your-cohere-api-key",
             "top_k": 10,
             "return_documents": True
@@ -286,7 +164,7 @@ config = {
     "reranker": {
         "provider": "cohere",
         "config": {
-            "model": "rerank-v3.5",
+            "model": "rerank-english-v3.0",
             "api_key": "your-cohere-api-key",
             "top_k": 15,
             "return_documents": True
@@ -460,7 +338,7 @@ config = {
     "reranker": {
         "provider": "cohere",
         "config": {
-            "model": "rerank-v3.5",
+            "model": "rerank-english-v3.0",
             "api_key": "your-cohere-api-key"
         }
     }

docs/openapi.json

@@ -1232,7 +1232,7 @@
 				"tags": [
 					"memories"
 				],
-				"description": "Delete memories by filter. At least one filter is required. Previously, omitting all filters silently deleted everything; now it returns a validation error.",
+				"description": "Delete memories by filter. At least one filter is required — previously omitting all filters silently deleted everything; now it returns a validation error.",
 				"operationId": "memories_delete_all",
 				"parameters": [
 					{
@@ -1315,15 +1315,15 @@
 				"x-code-samples": [
 					{
 						"lang": "Python",
-						"source": "# To use the Python SDK, install the package:\n# pip install mem0ai\n\nfrom mem0 import MemoryClient\nclient = MemoryClient(api_key=\"your_api_key\")\n\n# Delete all memories for a specific user\nclient.delete_all(user_id=\"<user_id>\")\n\n# Delete all memories for every user in the project (wildcard)\nclient.delete_all(user_id=\"*\")\n\n# Full project wipe: all four filters must be explicitly set to \"*\"\nclient.delete_all(user_id=\"*\", agent_id=\"*\", app_id=\"*\", run_id=\"*\")\n\n# NOTE: Calling delete_all() with no filters raises a validation error.\n# At least one filter is required to prevent accidental data loss."
+						"source": "# To use the Python SDK, install the package:\n# pip install mem0ai\n\nfrom mem0 import MemoryClient\nclient = MemoryClient(api_key=\"your_api_key\")\n\n# Delete all memories for a specific user\nclient.delete_all(user_id=\"<user_id>\")\n\n# Delete all memories for every user in the project (wildcard)\nclient.delete_all(user_id=\"*\")\n\n# Full project wipe — all four filters must be explicitly set to \"*\"\nclient.delete_all(user_id=\"*\", agent_id=\"*\", app_id=\"*\", run_id=\"*\")\n\n# NOTE: Calling delete_all() with no filters raises a validation error.\n# At least one filter is required to prevent accidental data loss."
 					},
 					{
 						"lang": "JavaScript",
-						"source": "// To use the JavaScript SDK, install the package:\n// npm i mem0ai\n\nimport MemoryClient from 'mem0ai';\nconst client = new MemoryClient({ apiKey: \"your-api-key\" });\n\n// Delete all memories for a specific user\nclient.deleteAll({ user_id: \"<user_id>\" })\n  .then(result => console.log(result))\n  .catch(error => console.error(error));\n\n// Delete all memories for every user in the project (wildcard)\nclient.deleteAll({ user_id: \"*\" })\n  .then(result => console.log(result))\n  .catch(error => console.error(error));\n\n// Full project wipe: all four filters must be explicitly set to \"*\"\nclient.deleteAll({ user_id: \"*\", agent_id: \"*\", app_id: \"*\", run_id: \"*\" })\n  .then(result => console.log(result))\n  .catch(error => console.error(error));"
+						"source": "// To use the JavaScript SDK, install the package:\n// npm i mem0ai\n\nimport MemoryClient from 'mem0ai';\nconst client = new MemoryClient({ apiKey: \"your-api-key\" });\n\n// Delete all memories for a specific user\nclient.deleteAll({ user_id: \"<user_id>\" })\n  .then(result => console.log(result))\n  .catch(error => console.error(error));\n\n// Delete all memories for every user in the project (wildcard)\nclient.deleteAll({ user_id: \"*\" })\n  .then(result => console.log(result))\n  .catch(error => console.error(error));\n\n// Full project wipe — all four filters must be explicitly set to \"*\"\nclient.deleteAll({ user_id: \"*\", agent_id: \"*\", app_id: \"*\", run_id: \"*\" })\n  .then(result => console.log(result))\n  .catch(error => console.error(error));"
 					},
 					{
 						"lang": "cURL",
-						"source": "# Delete memories for a specific user\ncurl --request DELETE \\\n  --url 'https://api.mem0.ai/v1/memories/?user_id=<user_id>' \\\n  --header 'Authorization: Token <api-key>'\n\n# Delete memories for all users (wildcard)\ncurl --request DELETE \\\n  --url 'https://api.mem0.ai/v1/memories/?user_id=*' \\\n  --header 'Authorization: Token <api-key>'\n\n# Full project wipe: all four filters must be set to *\ncurl --request DELETE \\\n  --url 'https://api.mem0.ai/v1/memories/?user_id=*&agent_id=*&app_id=*&run_id=*' \\\n  --header 'Authorization: Token <api-key>'"
+						"source": "# Delete memories for a specific user\ncurl --request DELETE \\\n  --url 'https://api.mem0.ai/v1/memories/?user_id=<user_id>' \\\n  --header 'Authorization: Token <api-key>'\n\n# Delete memories for all users (wildcard)\ncurl --request DELETE \\\n  --url 'https://api.mem0.ai/v1/memories/?user_id=*' \\\n  --header 'Authorization: Token <api-key>'\n\n# Full project wipe — all four filters must be set to *\ncurl --request DELETE \\\n  --url 'https://api.mem0.ai/v1/memories/?user_id=*&agent_id=*&app_id=*&run_id=*' \\\n  --header 'Authorization: Token <api-key>'"
 					},
 					{
 						"lang": "Go",
@@ -1740,7 +1740,7 @@
 					"memories"
 				],
 				"summary": "Get all memories (V3, paginated)",
-				"description": "List memories scoped by filters, paginated. Entity IDs **must** be passed inside the `filters` object. Top-level `user_id` / `agent_id` / `run_id` are rejected with 400. `filters` supports the same operator set as V2 search (`AND`, `OR`, `NOT`, `in`, `gte`, `lte`, etc.). Response is a paginated envelope; pass `page` and `page_size` as query parameters to step through results.",
+				"description": "List memories scoped by filters, paginated. Entity IDs **must** be passed inside the `filters` object — top-level `user_id` / `agent_id` / `run_id` are rejected with 400. `filters` supports the same operator set as V2 search (`AND`, `OR`, `NOT`, `in`, `gte`, `lte`, etc.). Response is a paginated envelope; pass `page` and `page_size` as query parameters to step through results.",
 				"operationId": "memories_list_v3",
 				"parameters": [
 					{
@@ -1896,10 +1896,10 @@
 						}
 					},
 					"400": {
-						"description": "Validation error, e.g. empty `filters` or no positively-scoped entity ID."
+						"description": "Validation error — e.g. empty `filters` or no positively-scoped entity ID."
 					},
 					"401": {
-						"description": "Unauthorized: missing or invalid API key."
+						"description": "Unauthorized — missing or invalid API key."
 					}
 				},
 				"security": [
@@ -1992,17 +1992,6 @@
 										"type": "string",
 										"description": "Project-level instructions that guide extraction for this call."
 									},
-									"custom_categories": {
-										"type": "array",
-										"description": "Category catalog for this call. Replaces the project-level list rather than merging with it. Omit to fall back to the project list, then the default catalog.",
-										"items": {
-											"type": "object",
-											"additionalProperties": {
-												"type": "string"
-											},
-											"description": "Maps a category name to the description the classifier matches against."
-										}
-									},
 									"infer": {
 										"type": "boolean",
 										"default": true,
@@ -2018,7 +2007,7 @@
 									},
 									{
 										"role": "assistant",
-										"content": "Got it, I'll update your location."
+										"content": "Got it — I'll update your location."
 									}
 								],
 								"user_id": "alice"
@@ -2060,10 +2049,10 @@
 						}
 					},
 					"400": {
-						"description": "Validation error, e.g. missing `messages` or no entity ID supplied."
+						"description": "Validation error — e.g. missing `messages` or no entity ID supplied."
 					},
 					"401": {
-						"description": "Unauthorized: missing or invalid API key."
+						"description": "Unauthorized — missing or invalid API key."
 					}
 				},
 				"security": [
@@ -2074,15 +2063,15 @@
 				"x-codeSamples": [
 					{
 						"lang": "cURL",
-						"source": "curl -X POST https://api.mem0.ai/v3/memories/add/ \\\n  -H \"Authorization: Token <api-key>\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"messages\": [\n      {\"role\": \"user\", \"content\": \"I just moved to San Francisco from New York.\"},\n      {\"role\": \"assistant\", \"content\": \"Got it, I\\u0027ll update your location.\"}\n    ],\n    \"user_id\": \"alice\"\n  }'"
+						"source": "curl -X POST https://api.mem0.ai/v3/memories/add/ \\\n  -H \"Authorization: Token <api-key>\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"messages\": [\n      {\"role\": \"user\", \"content\": \"I just moved to San Francisco from New York.\"},\n      {\"role\": \"assistant\", \"content\": \"Got it — I\\u0027ll update your location.\"}\n    ],\n    \"user_id\": \"alice\"\n  }'"
 					},
 					{
 						"lang": "Python",
-						"source": "from mem0 import MemoryClient\n\nclient = MemoryClient(api_key=\"your-api-key\")\n\nresult = client.add(\n    messages=[\n        {\"role\": \"user\", \"content\": \"I just moved to San Francisco from New York.\"},\n        {\"role\": \"assistant\", \"content\": \"Got it, I'll update your location.\"}\n    ],\n    user_id=\"alice\",\n)\nprint(result)"
+						"source": "from mem0 import MemoryClient\n\nclient = MemoryClient(api_key=\"your-api-key\")\n\nresult = client.add(\n    messages=[\n        {\"role\": \"user\", \"content\": \"I just moved to San Francisco from New York.\"},\n        {\"role\": \"assistant\", \"content\": \"Got it — I'll update your location.\"}\n    ],\n    user_id=\"alice\",\n)\nprint(result)"
 					},
 					{
 						"lang": "JavaScript",
-						"source": "import MemoryClient from \"mem0ai\";\n\nconst client = new MemoryClient({ apiKey: \"your-api-key\" });\n\nconst result = await client.add(\n  [\n    { role: \"user\", content: \"I just moved to San Francisco from New York.\" },\n    { role: \"assistant\", content: \"Got it, I'll update your location.\" },\n  ],\n  { userId: \"alice\" }\n);\nconsole.log(result);"
+						"source": "import MemoryClient from \"mem0ai\";\n\nconst client = new MemoryClient({ apiKey: \"your-api-key\" });\n\nconst result = await client.add(\n  [\n    { role: \"user\", content: \"I just moved to San Francisco from New York.\" },\n    { role: \"assistant\", content: \"Got it — I'll update your location.\" },\n  ],\n  { userId: \"alice\" }\n);\nconsole.log(result);"
 					}
 				]
 			}
@@ -2093,7 +2082,7 @@
 					"memories"
 				],
 				"summary": "Search memories (V3)",
-				"description": "Relevance-ranked search across stored memories. V3 uses hybrid retrieval and can also apply temporal reasoning for time-aware queries. Entity IDs **must** be passed inside the `filters` object. Top-level `user_id` / `agent_id` / `run_id` are rejected with 400. At least one entity ID is required.",
+				"description": "Relevance-ranked search across stored memories. V3 uses hybrid retrieval and can also apply temporal reasoning for time-aware queries. Entity IDs **must** be passed inside the `filters` object — top-level `user_id` / `agent_id` / `run_id` are rejected with 400. At least one entity ID is required.",
 				"operationId": "memories_search_v3",
 				"requestBody": {
 					"required": true,
@@ -2247,10 +2236,10 @@
 						}
 					},
 					"400": {
-						"description": "Validation error, e.g. empty `query`, missing `filters`, or no positively-scoped entity ID."
+						"description": "Validation error — e.g. empty `query`, missing `filters`, or no positively-scoped entity ID."
 					},
 					"401": {
-						"description": "Unauthorized: missing or invalid API key."
+						"description": "Unauthorized — missing or invalid API key."
 					}
 				},
 				"security": [

docs/platform/advanced-memory-operations.mdx

@@ -129,7 +129,7 @@ matches = await memory.search(
 ```python
 await memory.update(
     memory_id=matches["results"][0]["id"],
-    text="Morgan avoids shellfish and prefers boutique hotels in central Tokyo.",
+    data="Morgan avoids shellfish and prefers boutique hotels in central Tokyo.",
 )
 ```
 </Step>

docs/platform/features/contextual-add.mdx

@@ -0,0 +1,251 @@
+---
+title: Contextual Memory Creation
+description: "Add messages with automatic context management - no manual history tracking required"
+---
+
+## What is Contextual Memory Creation?
+
+Contextual memory creation automatically manages message history, allowing you to focus on building AI experiences without manually tracking interactions. Simply send new messages, and Mem0 handles the context automatically.
+
+<CodeGroup>
+```python Python
+# Just send new messages - Mem0 handles the context
+messages = [
+    {"role": "user", "content": "I love Italian food, especially pasta"},
+    {"role": "assistant", "content": "Great! I'll remember your preference for Italian cuisine."}
+]
+
+client.add(messages, user_id="user123")
+```
+
+```javascript JavaScript
+// Just send new messages - Mem0 handles the context
+const messages = [
+    {"role": "user", "content": "I love Italian food, especially pasta"},
+    {"role": "assistant", "content": "Great! I'll remember your preference for Italian cuisine."}
+];
+
+await client.add(messages, { userId: "user123" });
+```
+</CodeGroup>
+
+## Why Use Contextual Memory Creation?
+
+- **Simple**: Send only new messages, no manual history tracking
+- **Efficient**: Smaller payloads and faster processing
+- **Automatic**: Context management handled by Mem0
+- **Reliable**: No risk of missing interaction history
+- **Scalable**: Works seamlessly as your application grows
+
+## How It Works
+
+### Basic Usage
+
+<CodeGroup>
+```python Python
+# First interaction
+messages1 = [
+    {"role": "user", "content": "Hi, I'm Sarah from New York"},
+    {"role": "assistant", "content": "Hello Sarah! Nice to meet you."}
+]
+client.add(messages1, user_id="sarah")
+
+# Later interaction - just send new messages
+messages2 = [
+    {"role": "user", "content": "I'm planning a trip to Italy next month"},
+    {"role": "assistant", "content": "How exciting! Italy is beautiful this time of year."}
+]
+client.add(messages2, user_id="sarah")
+# Mem0 automatically knows Sarah is from New York and can use this context
+```
+
+```javascript JavaScript
+// First interaction
+const messages1 = [
+    {"role": "user", "content": "Hi, I'm Sarah from New York"},
+    {"role": "assistant", "content": "Hello Sarah! Nice to meet you."}
+];
+await client.add(messages1, { userId: "sarah" });
+
+// Later interaction - just send new messages
+const messages2 = [
+    {"role": "user", "content": "I'm planning a trip to Italy next month"},
+    {"role": "assistant", "content": "How exciting! Italy is beautiful this time of year."}
+];
+await client.add(messages2, { userId: "sarah" });
+// Mem0 automatically knows Sarah is from New York and can use this context
+```
+</CodeGroup>
+
+## Organization Strategies
+
+Choose the right approach based on your application's needs:
+
+### User-Level Memories (`user_id` only)
+
+**Best for:** Personal preferences, profile information, long-term user data
+
+<CodeGroup>
+```python Python
+# Persistent user memories across all interactions
+messages = [
+    {"role": "user", "content": "I'm allergic to nuts and dairy"},
+    {"role": "assistant", "content": "I've noted your allergies for future reference."}
+]
+
+client.add(messages, user_id="user123")
+# This allergy info will be available in ALL future interactions
+```
+
+```javascript JavaScript
+// Persistent user memories across all interactions
+const messages = [
+    {"role": "user", "content": "I'm allergic to nuts and dairy"},
+    {"role": "assistant", "content": "I've noted your allergies for future reference."}
+];
+
+await client.add(messages, { userId: "user123" });
+// This allergy info will be available in ALL future interactions
+```
+</CodeGroup>
+
+### Session-Specific Memories (`user_id` + `run_id`)
+
+**Best for:** Task-specific context, separate interaction threads, project-based sessions
+
+<CodeGroup>
+```python Python
+# Trip planning session
+messages1 = [
+    {"role": "user", "content": "I want to plan a 5-day trip to Tokyo"},
+    {"role": "assistant", "content": "Perfect! Let's plan your Tokyo adventure."}
+]
+client.add(messages1, user_id="user123", run_id="tokyo-trip-2024")
+
+# Later in the same trip planning session
+messages2 = [
+    {"role": "user", "content": "I prefer staying near Shibuya"},
+    {"role": "assistant", "content": "Great choice! Shibuya is very convenient."}
+]
+client.add(messages2, user_id="user123", run_id="tokyo-trip-2024")
+
+# Different session for work project (separate context)
+work_messages = [
+    {"role": "user", "content": "Let's discuss the Q4 marketing strategy"},
+    {"role": "assistant", "content": "Sure! What are your main goals for Q4?"}
+]
+client.add(work_messages, user_id="user123", run_id="q4-marketing")
+```
+
+```javascript JavaScript
+// Trip planning session
+const messages1 = [
+    {"role": "user", "content": "I want to plan a 5-day trip to Tokyo"},
+    {"role": "assistant", "content": "Perfect! Let's plan your Tokyo adventure."}
+];
+await client.add(messages1, { userId: "user123", runId: "tokyo-trip-2024" });
+
+// Later in the same trip planning session
+const messages2 = [
+    {"role": "user", "content": "I prefer staying near Shibuya"},
+    {"role": "assistant", "content": "Great choice! Shibuya is very convenient."}
+];
+await client.add(messages2, { userId: "user123", runId: "tokyo-trip-2024" });
+
+// Different session for work project (separate context)
+const workMessages = [
+    {"role": "user", "content": "Let's discuss the Q4 marketing strategy"},
+    {"role": "assistant", "content": "Sure! What are your main goals for Q4?"}
+];
+await client.add(workMessages, { userId: "user123", runId: "q4-marketing" });
+```
+</CodeGroup>
+
+## Real-World Use Cases
+
+<Tabs>
+  <Tab title="Customer Support">
+```python Python
+# Support ticket context - keeps interaction focused
+messages = [
+    {"role": "user", "content": "My subscription isn't working"},
+    {"role": "assistant", "content": "I can help with that. What specific issue are you experiencing?"},
+    {"role": "user", "content": "I can't access premium features even though I paid"}
+]
+
+# Each support ticket gets its own run_id
+client.add(messages, 
+    user_id="customer123", 
+    run_id="ticket-2024-001"
+)
+```
+  </Tab>
+  <Tab title="Personal AI Assistant">
+```python Python
+# Personal preferences (persistent across all interactions)
+preference_messages = [
+    {"role": "user", "content": "I prefer morning workouts and vegetarian meals"},
+    {"role": "assistant", "content": "Got it! I'll keep your fitness and dietary preferences in mind."}
+]
+
+client.add(preference_messages, user_id="user456")
+
+# Daily planning session (session-specific)
+planning_messages = [
+    {"role": "user", "content": "Help me plan tomorrow's schedule"},
+    {"role": "assistant", "content": "Of course! I'll consider your morning workout preference."}
+]
+
+client.add(planning_messages, 
+    user_id="user456", 
+    run_id="daily-plan-2024-01-15"
+)
+```
+  </Tab>
+  <Tab title="Educational Platform">
+```python Python
+# Student profile (persistent)
+profile_messages = [
+    {"role": "user", "content": "I'm studying computer science and struggle with math"},
+    {"role": "assistant", "content": "I'll tailor explanations to help with math concepts."}
+]
+
+client.add(profile_messages, user_id="student789")
+
+# Specific lesson session
+lesson_messages = [
+    {"role": "user", "content": "Can you explain algorithms?"},
+    {"role": "assistant", "content": "Sure! I'll explain algorithms with math-friendly examples."}
+]
+
+client.add(lesson_messages,
+    user_id="student789",
+    run_id="algorithms-lesson-1"
+)
+```
+  </Tab>
+</Tabs>
+
+## Best Practices
+
+### ✅ Do
+- **Organize by context scope**: Use `user_id` only for persistent data, add `run_id` for session-specific context
+- **Keep messages focused** on the current interaction
+- **Test with real interaction flows** to ensure context works as expected
+
+### ❌ Don't
+- Send duplicate messages or interaction history
+- Skip identifiers like `user_id` or `run_id` that scope the memory
+- Mix contextual and non-contextual approaches in the same application
+
+## Troubleshooting
+
+| Issue | Solution |
+|-------|----------|
+| **Context not working** | Ensure each call uses the same `user_id` / `run_id` combo; version is automatic |
+| **Wrong context retrieved** | Check if you need separate `run_id` values for different interaction topics |
+| **Missing interaction history** | Verify all messages in the interaction thread use the same `user_id` and `run_id` |
+| **Too much irrelevant context** | Use more specific `run_id` values to separate different interaction types |
+
+
+<Snippet file="get-help.mdx" />

docs/platform/features/criteria-retrieval.mdx

@@ -0,0 +1,201 @@
+---
+title: Criteria Retrieval
+description: "Rank and retrieve memories based on custom-defined criteria like emotional tone, intent, and behavioral signals."
+---
+
+Mem0's Criteria Retrieval feature allows you to retrieve memories based on your defined criteria. It goes beyond generic semantic relevance and ranks memories based on what matters to your application: emotional tone, intent, behavioral signals, or other custom traits.
+
+Instead of just searching for "how similar a memory is to this query," you can define what relevance truly means for your project. For example:
+
+- Prioritize joyful memories when building a wellness assistant
+- Downrank negative memories in a productivity-focused agent
+- Highlight curiosity in a tutoring agent
+
+You define criteria: custom attributes like "joy", "negativity", "confidence", or "urgency", and assign weights to control how they influence scoring. When you search, Mem0 uses these to re-rank semantically relevant memories, favoring those that better match your intent.
+
+This gives you nuanced, intent-aware memory search that adapts to your use case.
+
+
+
+## When to Use Criteria Retrieval
+
+Use Criteria Retrieval if:
+
+- You’re building an agent that should react to **emotions** or **behavioral signals**
+- You want to guide memory selection based on **context**, not just content
+- You have domain-specific signals like "risk", "positivity", "confidence", etc. that shape recall
+
+
+
+## Setting Up Criteria Retrieval
+
+Let’s walk through how to configure and use Criteria Retrieval step by step.
+
+### Initialize the Client
+
+Before defining any criteria, make sure to initialize the `MemoryClient` with your credentials and project ID:
+
+```python
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="your_mem0_api_key")
+```
+
+### Define Your Criteria
+
+Each criterion includes:
+- A `name` (used in scoring)
+- A `description` (interpreted by the LLM)
+- A `weight` (how much it influences the final score)
+
+```python
+retrieval_criteria = [
+    {
+        "name": "joy",
+        "description": "Measure the intensity of positive emotions such as happiness, excitement, or amusement expressed in the sentence. A higher score reflects greater joy.",
+        "weight": 3
+    },
+    {
+        "name": "curiosity",
+        "description": "Assess the extent to which the sentence reflects inquisitiveness, interest in exploring new information, or asking questions. A higher score reflects stronger curiosity.",
+        "weight": 2
+    },
+    {
+        "name": "emotion",
+        "description": "Evaluate the presence and depth of sadness or negative emotional tone, including expressions of disappointment, frustration, or sorrow. A higher score reflects greater sadness.",
+        "weight": 1
+    }
+]
+```
+
+### Apply Criteria to Your Project
+
+Once defined, register the criteria to your project:
+
+```python
+client.project.update(retrieval_criteria=retrieval_criteria)
+```
+
+Criteria apply project-wide. Once set, they affect all searches automatically.
+
+
+## Example Walkthrough
+
+After setting up your criteria, you can use them to filter and retrieve memories. Here's an example:
+
+### Add Memories
+
+```python
+messages = [
+    {"role": "user", "content": "What a beautiful sunny day! I feel so refreshed and ready to take on anything!"},
+    {"role": "user", "content": "I've always wondered how storms form, what triggers them in the atmosphere?"},
+    {"role": "user", "content": "It's been raining for days, and it just makes everything feel heavier."},
+    {"role": "user", "content": "Finally I get time to draw something today, after a long time!! I am super happy today."}
+]
+
+client.add(messages, user_id="alice")
+```
+
+### Run Standard vs. Criteria-Based Search
+
+```python
+# Search with criteria enabled
+filters = {"user_id": "alice"}
+results_with_criteria = client.search(
+    query="Why I am feeling happy today?",
+    filters=filters
+)
+
+# To disable criteria for a specific search
+results_without_criteria = client.search(
+    query="Why I am feeling happy today?",
+    filters=filters,
+    use_criteria=False  # Disable criteria-based scoring
+)
+```
+
+### Compare Results
+
+### Search Results (with Criteria)
+```text
+[
+    {"memory": "User feels refreshed and ready to take on anything on a beautiful sunny day", "score": 0.666, ...},
+    {"memory": "User finally has time to draw something after a long time", "score": 0.616, ...},
+    {"memory": "User is happy today", "score": 0.500, ...},
+    {"memory": "User is curious about how storms form and what triggers them in the atmosphere.", "score": 0.400, ...},
+    {"memory": "It has been raining for days, making everything feel heavier.", "score": 0.116, ...}
+]
+```
+
+### Search Results (without Criteria)
+```text
+[
+    {"memory": "User is happy today", "score": 0.607, ...},
+    {"memory": "User feels refreshed and ready to take on anything on a beautiful sunny day", "score": 0.512, ...},
+    {"memory": "It has been raining for days, making everything feel heavier.", "score": 0.4617, ...},
+    {"memory": "User is curious about how storms form and what triggers them in the atmosphere.", "score": 0.340, ...},
+    {"memory": "User finally has time to draw something after a long time", "score": 0.336, ...},
+]
+```
+
+## Search Results Comparison
+
+1. **Memory Ordering**: With criteria, memories with high joy scores (like feeling refreshed and drawing) are ranked higher. Without criteria, the most relevant memory ("User is happy today") comes first.
+2. **Score Distribution**: With criteria, scores are more spread out (0.116 to 0.666) and reflect the criteria weights. Without criteria, scores are more clustered (0.336 to 0.607) and based purely on relevance.
+3. **Trait Sensitivity**: "Rainy day" content is penalized due to negative tone, while "Storm curiosity" is recognized and scored accordingly.
+
+
+
+## Key Differences vs. Standard Search
+
+| Aspect                  | Standard Search                      | Criteria Retrieval                              |
+|-------------------------|--------------------------------------|-------------------------------------------------|
+| Ranking Logic           | Semantic similarity only             | Semantic + LLM-based criteria scoring           |
+| Control Over Relevance  | None                                 | Fully customizable with weighted criteria       |
+| Memory Reordering       | Static based on similarity           | Dynamically re-ranked by intent alignment       |
+| Emotional Sensitivity   | No tone or trait awareness           | Incorporates emotion, tone, or custom behaviors |
+| Activation              | Default (no criteria defined)        | Enabled when criteria are defined in project    |
+
+<Note>
+If no criteria are defined for a project, search behaves normally based on semantic similarity only.
+</Note>
+
+
+
+## Best Practices
+
+- Choose 3-5 criteria that reflect your application's intent
+- Make descriptions clear and distinct; these are interpreted by an LLM
+- Use stronger weights to amplify the impact of important traits
+- Avoid redundant or ambiguous criteria (e.g., "positivity" and "joy")
+- Always handle empty result sets in your application logic
+
+
+
+## How It Works
+
+1. **Criteria Definition**: Define custom criteria with a name, description, and weight. These describe what matters in a memory (e.g., joy, urgency, empathy).
+2. **Project Configuration**: Register these criteria using `project.update()`. They apply at the project level and automatically influence all searches.
+3. **Memory Retrieval**: When you perform a search, Mem0 first retrieves relevant memories based on the query.
+4. **Weighted Scoring**: Each retrieved memory is evaluated and scored against your defined criteria and weights.
+
+This lets you prioritize memories that align with your agent's goals and not just those that look similar to the query.
+
+<Note>
+Criteria retrieval is automatically enabled when criteria are defined in your project. Use `use_criteria=False` in search to temporarily disable it for a specific query. `use_criteria` is a server-side parameter passed through to the Platform API: it is not a typed option in the SDK's `SearchMemoryOptions` interface, but the server accepts and processes it when included in the request body.
+</Note>
+
+
+
+## Summary
+
+- Define what "relevant" means using criteria
+- Apply them per project via `project.update()`
+- Criteria-aware search activates automatically when criteria are configured
+- Build agents that reason not just with relevance, but **contextual importance**
+
+---
+
+Need help designing or tuning your criteria?
+
+<Snippet file="get-help.mdx" />

docs/platform/features/custom-categories.mdx

@@ -1,6 +1,6 @@
 ---
 title: Custom Categories
-description: "Replace default memory tags with custom category labels that match your product terminology, set once per project or per individual add call."
+description: "Replace default memory tags with custom category labels that match your product terminology at the project level."
 ---
 
 # Custom Categories
@@ -14,7 +14,9 @@ Mem0 automatically tags every memory, but the default labels (travel, sports, mu
   - You’re moving from the open-source version and want the same labels here.
 </Info>
 
-You can set the list once for the whole project, or pass a different list on an individual `add` call.
+<Warning>
+  Per-request overrides (`custom_categories=...` on `client.add`) are not supported on the managed API yet. Set categories at the project level, then ingest memories as usual.
+</Warning>
 
 ## Configure access
 
@@ -25,20 +27,7 @@ You can set the list once for the whole project, or pass a different list on an
 
 - **Default list**: Each project starts with 15 broad categories like `travel`, `sports`, and `music`.
 - **Project override**: When you call `project.update(custom_categories=[...])`, that list replaces the defaults for future memories.
-- **Per-call override**: When you pass `custom_categories=[...]` to `client.add(...)`, that list is used for the memories extracted from that call.
-- **Automatic tags**: As new memories come in, Mem0 picks the closest matches from the active list and saves them in the `categories` field.
-
-### Which list wins
-
-Mem0 resolves the category catalog for each `add` call in this order, and stops at the first one it finds:
-
-1. `custom_categories` passed on the `add` call
-2. `custom_categories` set on the project
-3. The built-in default catalog
-
-A per-call list **fully replaces** the project list for that call. The two are not merged, so a memory added with a per-call list can only be tagged with categories from that list.
-
-Categories are applied at ingestion time. Changing the project list, or passing a new per-call list, does not re-tag memories that already exist.
+- **Automatic tags**: As new memories come in, Mem0 picks the closest matches from your list and saves them in the `categories` field.
 
 <Note>
   Default catalog: `personal_details`, `family`, `professional_details`, `sports`, `travel`, `food`, `music`, `health`, `technology`, `hobbies`, `fashion`, `entertainment`, `milestones`, `user_preferences`, `misc`.
@@ -95,82 +84,6 @@ print(categories)
 ```
 </CodeGroup>
 
-`get` echoes back the shape you set. `update` also accepts a plain list of names, such as `["billing", "support"]`, in which case `get` returns that same list of names. Descriptions are optional here, and the classifier uses them to disambiguate when it has them.
-
-<Warning>
-  `add` is stricter than `update`. Every entry in a per-call `custom_categories` list must be an object mapping a name to a description. Passing bare names to `add` fails with `400 Expected a dictionary of items but got type "str"`.
-</Warning>
-
-### 3. Override categories on a single add call
-
-Pass `custom_categories` directly to `add` when one call needs a different catalog than the project default. The memories created by that call are tagged from the list you pass, and the project list is left untouched.
-
-<CodeGroup>
-```python Python
-health_messages = [
-    {"role": "user", "content": "My doctor bumped my metformin to 1000mg and I see her again on the 14th."},
-    {"role": "assistant", "content": "Noted the new dosage and the follow-up appointment."},
-]
-
-health_categories = [
-    {"symptoms": "Reported physical or mental symptoms"},
-    {"medications": "Prescriptions, dosages, and adherence"},
-    {"appointments": "Scheduled visits and follow-ups"},
-]
-
-client.add(
-    health_messages,
-    user_id="alice",
-    custom_categories=health_categories,
-)
-```
-
-```javascript JavaScript
-const healthMessages = [
-  { role: "user", content: "My doctor bumped my metformin to 1000mg and I see her again on the 14th." },
-  { role: "assistant", content: "Noted the new dosage and the follow-up appointment." },
-];
-
-const healthCategories = [
-  { symptoms: "Reported physical or mental symptoms" },
-  { medications: "Prescriptions, dosages, and adherence" },
-  { appointments: "Scheduled visits and follow-ups" },
-];
-
-await client.add(healthMessages, {
-  userId: "alice",
-  customCategories: healthCategories,
-});
-```
-
-```text Resulting categories
-["medications", "appointments"]
-```
-</CodeGroup>
-
-The memory is tagged from `health_categories` alone. The project catalog is not consulted for this call, and it is not modified.
-
-#### Per-user categories inside one project
-
-The main reason to reach for a per-call list is to give different users, tenants, or entities their own vocabulary without splitting them across projects. Keep one project, and pass the list that fits the entity you are writing for.
-
-<CodeGroup>
-```python Python
-patient_categories = [
-    {"symptoms": "Reported physical or mental symptoms"},
-    {"medications": "Prescriptions, dosages, and adherence"},
-]
-
-clinician_categories = [
-    {"caseload": "Patients under this clinician's care"},
-    {"availability": "Shift patterns and on-call windows"},
-]
-
-client.add("My metformin is now 1000mg.", user_id="alice", custom_categories=patient_categories)
-client.add("I'm on call Tuesdays and Thursdays.", user_id="dr-reyes", custom_categories=clinician_categories)
-```
-</CodeGroup>
-
 ## See it in action
 
 ### Add a memory (uses the project catalog automatically)
@@ -191,57 +104,46 @@ client.add(messages, user_id="alice")
 
 ### Retrieve memories and inspect categories
 
-`get_all` returns a paginated object. The memories are under `results`, and each one carries its own `categories` list.
-
 <CodeGroup>
 ```python Code
-response = client.get_all(filters={"user_id": "alice"})
-
-for memory in response["results"]:
-    print(memory["memory"], memory["categories"])
+memories = client.get_all(filters={"user_id": "alice"})
 ```
 
-```text Output
-User introduced herself as Alice and expressed a desire for help organizing her daily schedule. ['lifestyle_management_concerns', 'seeking_structure', 'personal_information']
-User feels overwhelmed trying to balance work responsibilities, regular exercise, and a social life, indicating difficulty managing time across these areas. ['lifestyle_management_concerns']
-User's goals include becoming more productive at work, maintaining a consistent workout routine, and preserving enough energy for friends and hobbies. ['lifestyle_management_concerns', 'seeking_structure']
+```json Output
+["lifestyle_management_concerns", "seeking_structure"]
 ```
 </CodeGroup>
 
-Extraction is model driven, so the exact wording and the number of memories vary between runs. The categories are drawn from the active list.
-
 <Info>
   **Sample memory payload**
   ```json
   {
-    "id": "638008c4-***",
-    "memory": "User is seeking to balance work responsibilities with regular workout sessions and requests a personalized schedule to manage both.",
+    "id": "33d2***",
+    "memory": "Trying to balance work and workouts",
     "user_id": "alice",
     "metadata": null,
-    "categories": ["lifestyle_management_concerns", "seeking_structure"],
-    "created_at": "2026-07-10T06:13:12-07:00",
-    "updated_at": "2026-07-10T06:13:20-07:00",
+    "categories": ["wellness"],  // ← matches the custom category we set
+    "created_at": "2025-11-01T02:13:32.828364-07:00",
+    "updated_at": "2025-11-01T02:13:32.830896-07:00",
     "expiration_date": null,
     "structured_attributes": {
-      "year": 2026,
-      "month": 7,
-      "day": 10,
-      "hour": 13,
+      "day": 1,
+      "hour": 9,
+      "year": 2025,
+      "month": 11,
       "minute": 13,
-      "day_of_week": "friday",
-      "week_of_year": 28,
-      "day_of_year": 191,
-      "quarter": 3,
-      "is_weekend": false
+      "quarter": 4,
+      "is_weekend": true,
+      "day_of_week": "saturday",
+      "day_of_year": 305,
+      "week_of_year": 44
     }
   }
   ```
 </Info>
 
-Categorization runs asynchronously, a moment after the memory itself is written. A memory fetched immediately after `add` may not show up in `get_all` yet, or can come back with `categories: null` and pick up its tags a moment later. Poll until `categories` is populated rather than reading once.
-
 <Note>
-  Need ad-hoc labels for a single call? Pass `custom_categories` on that `add` call. Use `metadata` instead when the label is a fixed value you already know, rather than something the classifier should infer.
+  Need ad-hoc labels for a single call? Store them in `metadata` until per-request overrides become available.
 </Note>
 
 ## Default categories (fallback)
@@ -306,13 +208,11 @@ client.project.get(["custom_categories"])
 
 ```json Output
 {
-    "custom_categories": null
+    "custom_categories": None
 }
 ```
 </CodeGroup>
 
-A project that has never set a list returns `null`. One you have reset with `project.update(custom_categories=[])` returns `[]`. Both mean the default catalog is active.
-
 ## Verify the feature is working
 
 - `client.project.get(["custom_categories"])` returns the category list you set.
@@ -323,8 +223,7 @@ A project that has never set a list returns `null`. One you have reset with `pro
 
 - Keep category descriptions concise but specific; the classifier uses them to disambiguate.
 - Review memories with empty `categories` to see where you might extend or rename your list.
-- Set the catalog your app uses most often at the project level, and reserve per-call lists for the calls that genuinely need a different vocabulary.
-- If a per-call list should also keep the project categories, include them in the list you pass. Passing a list replaces, it does not extend.
+- Stick with project-level overrides until per-request support is released; mixing approaches causes confusion.
 
 <CardGroup cols={2}>
   <Card title="Advanced Memory Operations" icon="wand-magic-sparkles" href="/platform/advanced-memory-operations">

docs/platform/features/memory-expiration.mdx

@@ -1,176 +0,0 @@
----
-title: "Memory Expiration in Mem0"
-sidebarTitle: "Memory Expiration"
-description: "Set an expiration date on a Mem0 memory and it stops surfacing in search once that date passes. Nothing is deleted. Works on Platform and Open Source."
----
-
-## Why Use Memory Expiration?
-
-Some facts are only true for a while. A trial plan ends, a seasonal preference goes stale, a support ticket ages past its retention window. Set an `expiration_date` on a memory and Mem0 stops surfacing it once that date passes, so you don't need a cleanup job hunting for rows to delete.
-
-**Expiration hides a memory, it does not delete it.** The record stays in storage untouched. `search()` and `get_all()` skip it, fetching it by ID still returns it, and clearing the date brings it straight back.
-
-## How it works
-
-- **Format**: a plain `YYYY-MM-DD` date. No time component, no timezone offset.
-- **Evaluated in UTC**, never against the caller's local timezone.
-- **Inclusive of the date itself**: a memory set to expire on `2030-01-31` stays visible all through `2030-01-31` UTC and disappears on `2030-02-01`.
-- **Only list-shaped reads filter**: `search()` and `get_all()` (`getAll()` in TypeScript) hide expired memories. <Link href="/api-reference/memory/get-memory">`get(memory_id)`</Link> always returns the memory, so there is no `show_expired` parameter on that path.
-- **No expiration date means never expires.** That is the default for every memory.
-- **Malformed dates fail open**: a stored value Mem0 can't parse is treated as *not* expired. A bad date never makes a memory silently vanish.
-
-## How do you set an expiration date on a memory?
-
-Set it when you add the memory:
-
-<CodeGroup>
-```python Python
-# Mem0 Platform
-from mem0 import MemoryClient
-
-client = MemoryClient(api_key="your-api-key")
-messages = [{"role": "user", "content": "My Pro trial ends soon."}]
-
-client.add(messages, user_id="alice", expiration_date="2030-01-31")
-
-# Mem0 OSS
-from mem0 import Memory
-
-memory = Memory()
-memory.add(messages, user_id="alice", expiration_date="2030-01-31")
-```
-
-```javascript JavaScript
-// Mem0 Platform
-import { MemoryClient } from "mem0ai";
-
-const client = new MemoryClient({ apiKey: "your-api-key" });
-const messages = [{ role: "user", content: "My Pro trial ends soon." }];
-
-await client.add(messages, { userId: "alice", expirationDate: "2030-01-31" });
-
-// Mem0 OSS
-import { Memory } from "mem0ai/oss";
-
-const memory = new Memory();
-await memory.add(messages, { userId: "alice", expirationDate: "2030-01-31" });
-```
-</CodeGroup>
-
-Or attach it to a memory that already exists, using `update()`:
-
-<CodeGroup>
-```python Python
-client.update("mem_123", expiration_date="2030-01-31")   # Platform
-memory.update("mem_123", expiration_date="2030-01-31")   # OSS
-```
-
-```javascript JavaScript
-await client.update("mem_123", { expirationDate: "2030-01-31" });   // Platform
-await memory.update("mem_123", { expirationDate: "2030-01-31" });   // OSS
-```
-</CodeGroup>
-
-Full field lists live in the <Link href="/api-reference/memory/add-memories">Add Memories</Link> and <Link href="/api-reference/memory/update-memory">Update Memory</Link> references.
-
-## Read expired memories back
-
-Pass `show_expired` (`showExpired` in TypeScript) to include them. It defaults to `false` on every client.
-
-<CodeGroup>
-```python Python
-client.get_all(filters={"user_id": "alice"}, show_expired=True)
-client.search("What plan is Alice on?", filters={"user_id": "alice"}, show_expired=True)
-```
-
-```javascript JavaScript
-await client.getAll({ filters: { user_id: "alice" }, showExpired: true });
-await client.search("What plan is Alice on?", {
-  filters: { user_id: "alice" },
-  showExpired: true,
-});
-```
-</CodeGroup>
-
-The same parameter and spelling work on the OSS `Memory` class. See <Link href="/api-reference/memory/search-memories">Search Memories</Link> and <Link href="/api-reference/memory/get-memories">Get Memories</Link>.
-
-<Note>
-  Expired memories are dropped *before* your `top_k` is applied, so Mem0 widens the internal candidate pool first and short result sets are rare. They are not impossible: if nearly every memory in a scope has expired, a call can still return fewer than `top_k` results. Pass `show_expired: true` to get the full set back.
-</Note>
-
-## How do you clear or remove an expiration date?
-
-Pass an explicit `None` (Python) or `null` (TypeScript) to make the memory permanent again. The SDKs deliberately preserve that null instead of treating it as "argument not supplied".
-
-<CodeGroup>
-```python Python
-client.update("mem_123", expiration_date=None)   # Platform
-memory.update("mem_123", expiration_date=None)   # OSS
-```
-
-```javascript JavaScript
-await client.update("mem_123", { expirationDate: null });   // Platform
-await memory.update("mem_123", { expirationDate: null });   // OSS
-```
-</CodeGroup>
-
-<Note>
-  `update()` needs at least one of `text`, `metadata`, or `expiration_date`, and raises if you pass none of them. Clearing the date satisfies that on its own: the memory's content and metadata are left alone.
-</Note>
-
-## What each client accepts
-
-| Client | Accepted input | Notes |
-| --- | --- | --- |
-| Python (Platform and OSS) | `str` in `YYYY-MM-DD` form, or a `date` / `datetime` object | Normalized to `YYYY-MM-DD` before storage. |
-| TypeScript (Platform and OSS) | `string` in `YYYY-MM-DD` form only | Stricter than `new Date()`: rejects `12/31/2099`, `2099-12-31T23:00:00`, and non-days like `2099-02-30` or `2100-02-29`. |
-| Self-hosted REST server | `string` in `YYYY-MM-DD` form | Same normalization as OSS Python underneath. |
-| CLI (`mem0 add --expires`) | `string` in `YYYY-MM-DD` form | Must be strictly in the future, checked against the local system date. The SDKs have no such restriction. Platform only: the CLI has no OSS backend. |
-
-## Reading the field back
-
-Most clients return expiration as a top-level field on the memory: `expiration_date` in Python (Platform and OSS) and in the REST API, `expirationDate` in the Platform TypeScript SDK.
-
-<Info>
-  The OSS TypeScript SDK is the one exception. There, expiration round-trips under **`result.metadata.expiration_date`**, not `result.expirationDate`, on both `get()` and `getAll()`.
-</Info>
-
-## Expiration, decay, and delete
-
-These three get conflated. They solve different problems:
-
-|  | Memory Expiration | Memory Decay | Delete |
-| --- | --- | --- | --- |
-| What it does | Hides a memory once a date you set passes | Re-ranks results by how recently a memory was used | Removes a memory permanently |
-| Data still stored? | Yes | Yes | No |
-| Filters results? | Yes, after the date | Never, it only reorders scores | Yes, permanently |
-| Reversible? | Yes, clear or push back the date | Yes, toggle `decay` off | No |
-| Set where | Per memory, by you | Per project, opt-in | Per call |
-| Available in | Platform and OSS | Platform only | Platform and OSS |
-
-Reach for <Link href="/platform/features/memory-decay">Memory Decay</Link> when old memories should rank lower but stay searchable, expiration when a memory should stop appearing after a specific known date, and <Link href="/core-concepts/memory-operations/delete">Delete</Link> when it should be gone for good.
-
-## Common patterns
-
-**Trial and subscription facts.** "Alice is on the Pro trial" is true until the trial ends. Set `expiration_date` to that end date when you write the fact. If she upgrades, clear the date and the memory becomes permanent. If she doesn't, it stops surfacing the next day on its own.
-
-**Seasonal preferences.** "Alex wants gift ideas for the holidays" matters in December and is noise in July. A short-lived expiration date keeps it from competing with evergreen preferences in every search.
-
-**Retention windows.** Data-retention policies usually want a soft window before a hard delete: keep a ticket's memories searchable for 90 days, stop surfacing them, purge them later on a schedule. Expiration is the soft step, and a scheduled <Link href="/core-concepts/memory-operations/delete">delete</Link> is the permanent one.
-
-<CardGroup cols={2}>
-  <Card
-    title="Memory Decay"
-    description="Rank stale memories lower instead of hiding them outright."
-    icon="chart-line"
-    href="/platform/features/memory-decay"
-  />
-  <Card
-    title="Delete Memories"
-    description="Remove memories permanently instead of hiding them."
-    icon="trash"
-    href="/core-concepts/memory-operations/delete"
-  />
-</CardGroup>
-
-<Snippet file="get-help.mdx" />

docs/platform/platform-vs-oss.mdx

@@ -60,6 +60,7 @@ Mem0 offers two powerful ways to add memory to your AI applications. Choose base
     | **Multimodal support** | ✅ | ✅ |
     | **Custom categories** | ✅ | Limited |
     | **Advanced retrieval** | ✅ | ✅ |
+    | **Criteria retrieval** | ✅ | ❌ |
     | **Temporal reasoning** | ✅ (v3) | ❌ |
     | **Memory decay** | ✅ (v3) | ❌ |
     | **Graph memory** | ✅ Built-in | ✅ External graph store |

integrations/mem0-plugin/.claude-plugin/plugin.json

@@ -1,6 +1,6 @@
 {
   "name": "mem0",
-  "version": "0.2.13",
+  "version": "0.2.12",
   "description": "Persistent memory for Claude Code. Remembers decisions, patterns, and preferences across sessions.",
   "author": {
     "name": "Mem0",

integrations/mem0-plugin/.codex-plugin/plugin.json

@@ -1,6 +1,6 @@
 {
   "name": "mem0",
-  "version": "0.2.13",
+  "version": "0.2.12",
   "description": "Persistent memory for Codex. Remembers decisions, patterns, and preferences across sessions.",
   "author": {
     "name": "Mem0",

integrations/mem0-plugin/.cursor-plugin/plugin.json

@@ -1,6 +1,6 @@
 {
   "name": "mem0",
-  "version": "0.2.13",
+  "version": "0.2.12",
   "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search using the Mem0 Platform MCP server.",
   "author": {
     "name": "Mem0",

integrations/mem0-plugin/.opencode-plugin/api-key.test.ts

@@ -1,153 +0,0 @@
-import {afterEach, describe, expect, test} from "bun:test";
-import {mkdtempSync, mkdirSync, writeFileSync} from "fs";
-import {tmpdir} from "os";
-import {join} from "path";
-import {parseApiKeyLine, resolveApiKey} from "./api-key";
-import Mem0Plugin from "./opencode-mem0";
-
-const originalKey = process.env.MEM0_API_KEY;
-const originalHome = process.env.HOME;
-const originalUserProfile = process.env.USERPROFILE;
-const originalTelemetry = process.env.MEM0_TELEMETRY;
-const originalFetch = globalThis.fetch;
-const testCleanups: Array<() => Promise<void> | void> = [];
-
-afterEach(async () => {
-  while (testCleanups.length > 0) {
-    await testCleanups.pop()?.();
-  }
-  if (originalKey === undefined) delete process.env.MEM0_API_KEY;
-  else process.env.MEM0_API_KEY = originalKey;
-  if (originalHome === undefined) delete process.env.HOME;
-  else process.env.HOME = originalHome;
-  if (originalUserProfile === undefined) delete process.env.USERPROFILE;
-  else process.env.USERPROFILE = originalUserProfile;
-  if (originalTelemetry === undefined) delete process.env.MEM0_TELEMETRY;
-  else process.env.MEM0_TELEMETRY = originalTelemetry;
-  globalThis.fetch = originalFetch;
-});
-
-function home(): string {
-  return mkdtempSync(join(tmpdir(), "mem0-api-key-"));
-}
-
-function pluginContext(logs: unknown[]) {
-  return {
-    client: {app: {log: async (entry: unknown) => logs.push(entry)}},
-    $: () => ({quiet: async () => ({stdout: ""})}),
-  } as any;
-}
-
-function stubFetch(): void {
-  globalThis.fetch = (async (input) => {
-    const url = typeof input === "string" ? input : input instanceof URL ? input.toString() : input.url;
-    const body =
-      url.includes("/v1/ping/")
-        ? {status: "ok", userEmail: "plugin-test@mem0.dev"}
-        : url.includes("/v1/projects/")
-          ? {customCategories: []}
-          : {};
-    return new Response(JSON.stringify(body), {
-      status: 200,
-      headers: {"content-type": "application/json"},
-    });
-  }) as typeof fetch;
-}
-
-function captureDeferredPluginCleanup(): () => Promise<void> {
-  const baseline = new Set(process.listeners("beforeExit"));
-  return async () => {
-    await Promise.resolve();
-    for (const listener of process.listeners("beforeExit")) {
-      if (!baseline.has(listener)) {
-        process.off("beforeExit", listener as (...args: any[]) => void);
-      }
-    }
-  };
-}
-
-describe("parseApiKeyLine", () => {
-  test("accepts literal export and assignment forms", () => {
-    expect(parseApiKeyLine('export MEM0_API_KEY="m0-quoted" # comment')).toBe("m0-quoted");
-    expect(parseApiKeyLine("MEM0_API_KEY=m0-literal")).toBe("m0-literal");
-  });
-
-  test("rejects unrelated, empty, and executable-looking values", () => {
-    expect(parseApiKeyLine("OTHER_KEY=value")).toBeUndefined();
-    expect(parseApiKeyLine("MEM0_API_KEY=")).toBeUndefined();
-    expect(parseApiKeyLine("MEM0_API_KEY=$MEM0_API_KEY")).toBeUndefined();
-    expect(parseApiKeyLine("MEM0_API_KEY=$(cat secret)")).toBeUndefined();
-  });
-});
-
-describe("resolveApiKey", () => {
-  test("explicit environment value wins over profiles", () => {
-    const dir = home();
-    writeFileSync(join(dir, ".zshrc"), "MEM0_API_KEY=profile\n");
-    expect(resolveApiKey({MEM0_API_KEY: " explicit "}, dir)).toBe("explicit");
-  });
-
-  test("uses the first valid allowlisted profile", () => {
-    const dir = home();
-    writeFileSync(join(dir, ".zshrc"), "MEM0_API_KEY=$UNSET\n");
-    writeFileSync(join(dir, ".bashrc"), "export MEM0_API_KEY='m0-from-bashrc'\n");
-    writeFileSync(join(dir, ".profile"), "MEM0_API_KEY=late\n");
-    expect(resolveApiKey({}, dir)).toBe("m0-from-bashrc");
-  });
-
-  test("continues after an unreadable or absent earlier profile", () => {
-    const dir = home();
-    mkdirSync(join(dir, ".zshrc"));
-    writeFileSync(join(dir, ".profile"), "MEM0_API_KEY=m0-later\n");
-    expect(resolveApiKey({}, dir)).toBe("m0-later");
-  });
-
-  test("ignores unsupported files and invalid assignments", () => {
-    const dir = home();
-    writeFileSync(join(dir, ".env"), "MEM0_API_KEY=unsupported\n");
-    writeFileSync(join(dir, ".zshrc"), "MEM0_API_KEY= # empty\nMEM0_API_KEY=$(unsafe)\n");
-    expect(resolveApiKey({}, dir)).toBe("");
-  });
-
-  test("keeps the missing-key guard when no source yields a key", async () => {
-    const dir = home();
-    writeFileSync(join(dir, ".zshrc"), "MEM0_API_KEY= # empty\nMEM0_API_KEY=$UNSET\n");
-    delete process.env.MEM0_API_KEY;
-    process.env.HOME = dir;
-    process.env.USERPROFILE = dir;
-    process.env.MEM0_TELEMETRY = "false";
-    stubFetch();
-    testCleanups.push(captureDeferredPluginCleanup());
-
-    const logs: unknown[] = [];
-    const plugin = await Mem0Plugin(pluginContext(logs));
-
-    expect(logs).toHaveLength(1);
-    expect(logs[0]).toMatchObject({
-      body: {
-        level: "error",
-        message: "MEM0_API_KEY environment variable not set. Get one at https://app.mem0.ai/dashboard/api-keys",
-      },
-    });
-    expect(plugin).toEqual({});
-  });
-
-  test("recovers the issue's shell-profile startup path", async () => {
-    const dir = home();
-    // Problem 2 in issue #6003: Desktop has no process key, but .zshrc does.
-    writeFileSync(join(dir, ".zshrc"), 'export MEM0_API_KEY="m0-from-profile"\n');
-    delete process.env.MEM0_API_KEY;
-    process.env.HOME = dir;
-    process.env.USERPROFILE = dir;
-    process.env.MEM0_TELEMETRY = "false";
-    stubFetch();
-    testCleanups.push(captureDeferredPluginCleanup());
-
-    const logs: unknown[] = [];
-    const plugin = await Mem0Plugin(pluginContext(logs));
-
-    expect(logs).toHaveLength(0);
-    expect(Object.keys(plugin)).toContain("chat.message");
-    expect(plugin).toHaveProperty("tool");
-  });
-});

integrations/mem0-plugin/.opencode-plugin/api-key.ts

@@ -1,30 +0,0 @@
-import {readFileSync} from "fs";
-import {homedir} from "os";
-import {join} from "path";
-
-const PROFILE_FILES = [".zshrc", ".bashrc", ".zprofile", ".bash_profile", ".profile"];
-
-export function parseApiKeyLine(line: string): string | undefined {
-  const match = line.match(/^\s*(?:export\s+)?MEM0_API_KEY=(.*)$/);
-  if (!match) return undefined;
-
-  const value = match[1].replace(/#.*$/, "").trim().replace(/^("|')(.*)\1$/, "$2").trim();
-  return value && !value.startsWith("$") ? value : undefined;
-}
-
-export function resolveApiKey(env: NodeJS.ProcessEnv = process.env, homeDir = homedir()): string {
-  const explicit = env.MEM0_API_KEY?.trim();
-  if (explicit) return explicit;
-
-  for (const profile of PROFILE_FILES) {
-    try {
-      for (const line of readFileSync(join(homeDir, profile), "utf8").split(/\r?\n/)) {
-        const key = parseApiKeyLine(line);
-        if (key) return key;
-      }
-    } catch {
-    }
-  }
-
-  return "";
-}

integrations/mem0-plugin/.opencode-plugin/opencode-mem0.ts

@@ -25,7 +25,6 @@ import {
 } from "./dream";
 import {asScope, scopeSearchFilters, scopeWriteParams, resolveDefaultScope, SCOPE_GUIDANCE, type Scope} from "./scope";
 import {parseProjectFromRemote} from "./project";
-import {resolveApiKey} from "./api-key";
 
 async function getUserId(): Promise<string> {
   if (process.env.MEM0_USER_ID) return process.env.MEM0_USER_ID;
@@ -259,7 +258,7 @@ function extractUserText(input: any, output: any): string {
 const Mem0Plugin: Plugin = async (ctx) => {
   const {$, client} = ctx;
 
-  const apiKey = resolveApiKey();
+  const apiKey = process.env.MEM0_API_KEY;
 
   if (!apiKey) {
     try {

integrations/mem0-plugin/.opencode-plugin/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mem0/opencode-plugin",
-  "version": "0.2.2",
+  "version": "0.2.1",
   "type": "module",
   "description": "Mem0 persistent memory plugin for OpenCode — add, search, and manage memories across sessions",
   "main": "dist/index.js",

integrations/mem0-plugin/plugin.json

@@ -1,7 +1,7 @@
 {
   "id": "mem0",
   "name": "mem0",
-  "version": "0.1.5",
+  "version": "0.1.4",
   "description": "Persistent semantic memory for Antigravity agents. Cross-session, user-level recall via the Mem0 Platform MCP server. 16 slash commands, lifecycle hooks for auto-capture and metadata enforcement.",
   "author": { "name": "Mem0", "email": "support@mem0.ai" },
   "publisher": "mem0ai",

integrations/mem0-plugin/scripts/capture_compact_summary.py

@@ -104,11 +104,8 @@ def store_summary(api_key: str, summary: str, user_id: str, session_id: str, pro
     }
     if branch:
         metadata["branch"] = branch
-    # The compact summary is model-authored prose, in the first person and with no
-    # framing to mark it as such. Under role="user" mem0 reads "I recommend X" as
-    # the human saying it and stores "User recommends X".
     body = {
-        "messages": [{"role": "assistant", "content": summary}],
+        "messages": [{"role": "user", "content": summary}],
         "user_id": user_id,
         "app_id": project_id,
         "metadata": metadata,

integrations/mem0-plugin/scripts/capture_session_summary.py

@@ -175,12 +175,8 @@ def store_summary(
     if files:
         metadata["files_touched"] = files[:20]
 
-    # summary_prompt wraps the assistant's own last message. Mem0 extracts "facts
-    # about the user" from each message and role is the only signal telling it who
-    # spoke, so role="user" here turns Claude's opinions into the human's stated
-    # preferences ("User prefers dropping Redis...").
     body = {
-        "messages": [{"role": "assistant", "content": summary_prompt}],
+        "messages": [{"role": "user", "content": summary_prompt}],
         "user_id": user_id,
         "app_id": project_id,
         "run_id": session_id,

integrations/mem0-plugin/skills/mem0/references/features.md

@@ -8,6 +8,7 @@ Additional platform capabilities beyond core CRUD operations.
 - [Entity Linking](#entity-linking)
 - [Custom Categories](#custom-categories)
 - [Custom Instructions](#custom-instructions)
+- [Criteria Retrieval](#criteria-retrieval)
 - [Feedback Mechanism](#feedback-mechanism)
 - [Memory Export](#memory-export)
 - [Group Chat](#group-chat)
@@ -101,29 +102,9 @@ await client.updateProject({ customCategories: newCategories });
 categories = client.project.get(fields=["custom_categories"])
 ```
 
-**Override categories for a single add call:**
-```python
-client.add(messages, user_id="alice", custom_categories=per_call_categories)
-```
+### Key Constraint
 
-```javascript
-await client.add(messages, { userId: "alice", customCategories: perCallCategories });
-```
-
-### Resolution Order
-
-1. `custom_categories` passed on the `add` call
-2. `custom_categories` set on the project
-3. Built-in default catalog
-
-### Key Constraints
-
-- A per-call list **fully replaces** the project list for that call. The lists are not merged.
-- Categories are applied at ingestion time. Changing the list later does not re-tag existing memories.
-
-### Main Use Case
-
-Per-call lists give different users or entities their own vocabulary inside a single project, without splitting them across projects.
+Per-request overrides (`custom_categories=...` on `client.add`) are **not supported** on the managed API. Only project-level configuration works. Workaround: store ad-hoc labels in `metadata` field.
 
 ---
 
@@ -164,6 +145,44 @@ await client.updateProject({ customInstructions: "Your guidelines here..." });
 
 ---
 
+## Criteria Retrieval
+
+Custom attribute-based memory ranking using LLM-evaluated criteria with weights. Goes beyond semantic similarity to prioritize memories based on domain-specific signals.
+
+### Configuration
+
+```python
+# Define criteria at project level
+retrieval_criteria = [
+    {"name": "joy", "description": "Positive emotions like happiness and excitement", "weight": 3},
+    {"name": "curiosity", "description": "Inquisitiveness and desire to learn", "weight": 2},
+    {"name": "urgency", "description": "Time-sensitive or high-priority items", "weight": 4},
+]
+client.project.update(retrieval_criteria=retrieval_criteria)
+```
+
+```typescript
+await client.updateProject({
+    retrievalCriteria: [
+        { name: 'joy', description: 'Positive emotions', weight: 3 },
+        { name: 'urgency', description: 'Time-sensitive items', weight: 4 },
+    ],
+});
+```
+
+### Usage
+
+Once configured, `client.search()` automatically applies criteria ranking:
+
+```python
+# Criteria-weighted results returned automatically
+results = client.search("Why am I feeling happy?", filters={"user_id": "alice"})
+```
+
+**Best for:** Wellness assistants, tutoring platforms, productivity tools — any app needing intent-aware retrieval.
+
+---
+
 ## Feedback Mechanism
 
 Provide feedback on extracted memories to improve system quality over time.

integrations/mem0-plugin/skills/mem0/scripts/mem0_doc_search.py

@@ -62,6 +62,10 @@ SECTION_MAP = {
         "/open-source/features/rest-api",
         "/open-source/configure-components",
     ],
+    "openmemory": [
+        "/openmemory/overview",
+        "/openmemory/quickstart",
+    ],
     "sdks": [
         "/sdks/python",
         "/sdks/js",

integrations/mem0-plugin/tests/test_message_roles.py

@@ -1,144 +0,0 @@
-"""Regression tests: assistant-authored text must never be posted as role="user".
-
-The Stop hook (capture_session_summary) and the post-compact hook
-(capture_compact_summary) both ship *model-authored* prose to
-POST /v3/memories/add/. Mem0's fact extractor renders each message as
-"{role}: {content}" and is instructed to extract "facts and preferences about
-the user" — so role is the only signal separating what the human said from what
-Claude said.
-
-Posting Claude's own words under role="user" made the extractor read Claude's
-first-person prose ("I recommend pgvector", "I found the bug in auth.py") as the
-*human's* statements and store them under their user_id. The Stop hook fires on
-every assistant turn, so this corrupted memory on nearly every message.
-"""
-
-from __future__ import annotations
-
-import json
-
-
-class _FakeResp:
-    status = 200
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, *_):
-        return False
-
-
-def _capture(monkeypatch, module):
-    """Patch urlopen so store_summary posts nowhere; capture the request body."""
-    captured: dict = {}
-
-    def fake_urlopen(req, timeout=0):
-        captured["body"] = json.loads(req.data.decode("utf-8"))
-        return _FakeResp()
-
-    monkeypatch.setattr(module.urllib.request, "urlopen", fake_urlopen)
-    return captured
-
-
-# Claude's own voice — first-person prose that must never be attributed to the human.
-ASSISTANT_PROSE = (
-    "I traced the root cause to auth.py and I recommend we switch to pgvector "
-    "for the vector store. I'll refactor the session handler next."
-)
-
-
-def test_session_summary_posts_assistant_prose_as_assistant(monkeypatch):
-    """Stop hook: the last assistant message must be tagged role="assistant"."""
-    import capture_session_summary as css
-
-    captured = _capture(monkeypatch, css)
-
-    css.store_summary(
-        api_key="test-key",
-        summary_prompt=css.build_summary_prompt(ASSISTANT_PROSE, []),
-        user_id="u1",
-        session_id="s1",
-        project_id="p1",
-        branch="main",
-        files=[],
-    )
-
-    messages = captured["body"]["messages"]
-    for msg in messages:
-        if ASSISTANT_PROSE in msg["content"]:
-            assert msg["role"] == "assistant", (
-                "Claude's own words were posted as role='user' — mem0 will extract "
-                "them as facts about the human. Got role=%r" % msg["role"]
-            )
-            break
-    else:
-        raise AssertionError("assistant prose never made it into the payload")
-
-
-def test_compact_summary_posts_assistant_prose_as_assistant(monkeypatch):
-    """Post-compact hook: the compact summary is model-authored, not user-authored."""
-    import capture_compact_summary as ccs
-
-    captured = _capture(monkeypatch, ccs)
-
-    ccs.store_summary(
-        api_key="test-key",
-        summary=ASSISTANT_PROSE,
-        user_id="u1",
-        session_id="s1",
-        project_id="p1",
-        branch="main",
-    )
-
-    messages = captured["body"]["messages"]
-    for msg in messages:
-        if ASSISTANT_PROSE in msg["content"]:
-            assert msg["role"] == "assistant", (
-                "Compact summary (written by Claude) was posted as role='user'. Got role=%r" % msg["role"]
-            )
-            break
-    else:
-        raise AssertionError("assistant prose never made it into the payload")
-
-
-def test_no_user_role_message_carries_assistant_prose(monkeypatch):
-    """Belt and braces: no user-role message may contain the assistant's words."""
-    import capture_session_summary as css
-
-    captured = _capture(monkeypatch, css)
-
-    css.store_summary(
-        api_key="test-key",
-        summary_prompt=css.build_summary_prompt(ASSISTANT_PROSE, ["auth.py"]),
-        user_id="u1",
-        session_id="s1",
-        project_id="p1",
-        branch="main",
-        files=["auth.py"],
-    )
-
-    for msg in captured["body"]["messages"]:
-        if msg["role"] == "user":
-            assert ASSISTANT_PROSE not in msg["content"], (
-                "A user-role message carries Claude's prose — this is the misattribution bug."
-            )
-
-
-def test_auto_capture_preserves_real_roles():
-    """auto_capture is the reference: it must pass roles through untouched."""
-    import auto_capture
-
-    lines = [
-        json.dumps({"type": "user", "message": {"role": "user", "content": "why is the build failing on main?"}}),
-        json.dumps(
-            {
-                "type": "assistant",
-                "message": {"role": "assistant", "content": [{"type": "text", "text": ASSISTANT_PROSE}]},
-            }
-        ),
-    ]
-
-    messages = auto_capture.extract_recent_exchanges(lines)
-
-    assert [m["role"] for m in messages] == ["user", "assistant"]
-    assert ASSISTANT_PROSE in messages[1]["content"]

integrations/n8n-nodes-mem0/.eslintrc.js

@@ -1,42 +0,0 @@
-module.exports = {
-	root: true,
-	env: { browser: true, es6: true, node: true },
-	parser: '@typescript-eslint/parser',
-	parserOptions: { sourceType: 'module', extraFileExtensions: ['.json'] },
-	ignorePatterns: ['.eslintrc.js', '**/*.js', '**/node_modules/**', '**/dist/**'],
-	overrides: [
-		{
-			files: ['package.json'],
-			plugins: ['eslint-plugin-n8n-nodes-base'],
-			extends: ['plugin:n8n-nodes-base/community'],
-			rules: {
-				'n8n-nodes-base/community-package-json-name-still-default': 'off',
-				'n8n-nodes-base/community-package-json-license-not-default': 'off',
-			},
-		},
-		{
-			files: ['./credentials/**/*.ts'],
-			plugins: ['eslint-plugin-n8n-nodes-base'],
-			extends: ['plugin:n8n-nodes-base/credentials'],
-			rules: {
-				// This rule only applies to nodes in n8n's main repository (where
-				// documentationUrl is an internal docs slug). Community nodes use a
-				// full external URL, so it is disabled here.
-				'n8n-nodes-base/cred-class-field-documentation-url-miscased': 'off',
-			},
-		},
-		{
-			files: ['./nodes/**/*.ts'],
-			plugins: ['eslint-plugin-n8n-nodes-base'],
-			extends: ['plugin:n8n-nodes-base/nodes'],
-			rules: {
-				// Superseded by the verification scanner's `@n8n/community-nodes`
-				// node-connection-type-literal rule, which requires
-				// NodeConnectionTypes.Main instead of the 'main' string literal.
-				// n8n's own scanner disables these two, so we match it.
-				'n8n-nodes-base/node-class-description-inputs-wrong-regular-node': 'off',
-				'n8n-nodes-base/node-class-description-outputs-wrong': 'off',
-			},
-		},
-	],
-};

integrations/n8n-nodes-mem0/.gitignore

@@ -1,8 +0,0 @@
-node_modules/
-dist/
-package-lock.json
-*.tsbuildinfo
-.env
-coverage/
-*.log
-.DS_Store

integrations/n8n-nodes-mem0/.prettierrc.js

@@ -1,9 +0,0 @@
-module.exports = {
-	semi: true,
-	trailingComma: 'all',
-	bracketSpacing: true,
-	useTabs: true,
-	tabWidth: 2,
-	printWidth: 100,
-	singleQuote: true,
-};

integrations/n8n-nodes-mem0/LICENSE

@@ -1,201 +0,0 @@
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
-   1. Definitions.
-
-      "License" shall mean the terms and conditions for use, reproduction,
-      and distribution as defined by Sections 1 through 9 of this document.
-
-      "Licensor" shall mean the copyright owner or entity authorized by
-      the copyright owner that is granting the License.
-
-      "Legal Entity" shall mean the union of the acting entity and all
-      other entities that control, are controlled by, or are under common
-      control with that entity. For the purposes of this definition,
-      "control" means (i) the power, direct or indirect, to cause the
-      direction or management of such entity, whether by contract or
-      otherwise, or (ii) ownership of fifty percent (50%) or more of the
-      outstanding shares, or (iii) beneficial ownership of such entity.
-
-      "You" (or "Your") shall mean an individual or Legal Entity
-      exercising permissions granted by this License.
-
-      "Source" form shall mean the preferred form for making modifications,
-      including but not limited to software source code, documentation
-      source, and configuration files.
-
-      "Object" form shall mean any form resulting from mechanical
-      transformation or translation of a Source form, including but
-      not limited to compiled object code, generated documentation,
-      and conversions to other media types.
-
-      "Work" shall mean the work of authorship, whether in Source or
-      Object form, made available under the License, as indicated by a
-      copyright notice that is included in or attached to the work
-      (an example is provided in the Appendix below).
-
-      "Derivative Works" shall mean any work, whether in Source or Object
-      form, that is based on (or derived from) the Work and for which the
-      editorial revisions, annotations, elaborations, or other modifications
-      represent, as a whole, an original work of authorship. For the purposes
-      of this License, Derivative Works shall not include works that remain
-      separable from, or merely link (or bind by name) to the interfaces of,
-      the Work and Derivative Works thereof.
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-      "Contribution" shall mean any work of authorship, including
-      the original version of the Work and any modifications or additions
-      to that Work or Derivative Works thereof, that is intentionally
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-      or a Contribution incorporated within the Work constitutes direct
-      or contributory patent infringement, then any patent licenses
-      granted to You under this License for that Work shall terminate
-      as of the date such litigation is filed.
-
-   4. Redistribution. You may reproduce and distribute copies of the
-      Work or Derivative Works thereof in any medium, with or without
-      modifications, and in Source or Object form, provided that You
-      meet the following conditions:
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-      (a) You must give any other recipients of the Work or
-          Derivative Works a copy of this License; and
-
-      (b) You must cause any modified files to carry prominent notices
-          stating that You changed the files; and
-
-      (c) You must retain, in the Source form of any Derivative Works
-          that You distribute, all copyright, patent, trademark, and
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integrations/n8n-nodes-mem0/README.md

@@ -1,71 +0,0 @@
-# @mem0/n8n-nodes-mem0
-
-This is an n8n community node that lets you use [Mem0](https://mem0.ai) — the memory layer for AI agents — in your n8n workflows.
-
-Mem0 gives your agents long-term memory: add memories from conversations, then search and recall them across sessions.
-
-[n8n](https://n8n.io) is a [fair-code licensed](https://docs.n8n.io/reference/license/) workflow automation platform.
-
-[Installation](#installation) · [Operations](#operations) · [Credentials](#credentials) · [Usage](#usage) · [Resources](#resources)
-
-## Installation
-
-Follow the [community nodes installation guide](https://docs.n8n.io/integrations/community-nodes/installation/) and install `@mem0/n8n-nodes-mem0`.
-
-## Operations
-
-The **Memory** resource supports:
-
-| Operation | Description | Endpoint |
-| --- | --- | --- |
-| **Add** | Extract and store memories from messages | `POST /v3/memories/add/` |
-| **Search** | Semantic search over stored memories | `POST /v3/memories/search/` |
-| **Get Many** | List stored memories (single page, or **Return All**) | `POST /v3/memories/` |
-| **Get** | Retrieve a single memory by ID | `GET /v1/memories/{id}/` |
-| **Update** | Update a memory's text or metadata | `PUT /v1/memories/{id}/` |
-| **Delete** | Delete a single memory by ID | `DELETE /v1/memories/{id}/` |
-
-### Add & asynchronous extraction
-
-By default, **Add** runs LLM-based extraction asynchronously: the API returns an event ID and the node polls until extraction finishes, then returns the resulting memories.
-
-Two independent controls:
-
-- **Wait for Completion** (on by default) decides whether the node polls. Turn it off to return immediately with the event ID.
-- **Infer** (on by default, under Additional Fields) decides whether the API runs LLM extraction at all. Turn it off to store the messages verbatim.
-
-**Custom Instructions**, **Custom Categories**, **Includes**, and **Excludes** (also under Additional Fields) steer what extraction keeps for that call. **Agent ID**, **App ID**, and **Run ID** live there too, and scope the memory alongside (or instead of) **User ID**.
-
-### Entity filters on Search & Get Many
-
-Both take **User ID**, **Agent ID**, **App ID**, and **Run ID**, and at least one is required — the API rejects a query with no entity scope, and the node fails with a clear message before calling it.
-
-Supplying several combines them with **OR**, giving the union of those scopes. Mem0 indexes each entity separately, so an `AND` across `user_id` and `agent_id` matches nothing even for a memory written with both. To narrow instead of widen, run one operation per entity id.
-
-## Credentials
-
-You need a Mem0 API key. Create one at [app.mem0.ai](https://app.mem0.ai) → Settings → API Keys. The key is sent as `Authorization: Token <key>`.
-
-## Usage
-
-This node is also **usable as a tool** by n8n's AI Agent node — attach it so an agent can "remember" and "recall" autonomously.
-
-A typical loop:
-
-1. **Search** memory before answering, filtered by `User ID` (or `Agent ID` / `App ID` / `Run ID`).
-2. **Add** durable facts after a meaningful exchange.
-
-Memory writes are asynchronous by default; allow a moment after an Add before searching for the same content.
-
-## Telemetry
-
-This node sends no third-party telemetry. Its API requests are tagged with `source: "N8N"` so Mem0 can see aggregate usage of the integration. No separate analytics service is contacted and nothing else is collected.
-
-## Resources
-
-- [Mem0 documentation](https://docs.mem0.ai)
-- [n8n community nodes documentation](https://docs.n8n.io/integrations/community-nodes/)
-
-## License
-
-[Apache-2.0](./LICENSE)

integrations/n8n-nodes-mem0/credentials/Mem0Api.credentials.ts

@@ -1,56 +0,0 @@
-import {
-	IAuthenticateGeneric,
-	Icon,
-	ICredentialTestRequest,
-	ICredentialType,
-	INodeProperties,
-} from 'n8n-workflow';
-
-export class Mem0Api implements ICredentialType {
-	name = 'mem0Api';
-
-	displayName = 'Mem0 API';
-
-	icon: Icon = 'file:mem0.svg';
-
-	documentationUrl = 'https://docs.mem0.ai/platform/quickstart';
-
-	properties: INodeProperties[] = [
-		{
-			displayName: 'API Key',
-			name: 'apiKey',
-			type: 'string',
-			typeOptions: { password: true },
-			default: '',
-			required: true,
-			description: 'Your Mem0 API key (starts with "m0-"). Create one at app.mem0.ai → Settings → API Keys.',
-		},
-		{
-			displayName: 'Base URL',
-			name: 'baseUrl',
-			type: 'string',
-			default: 'https://api.mem0.ai',
-			description: 'Mem0 API base URL. Override only for self-hosted or non-default deployments.',
-		},
-	];
-
-	// Injects "Authorization: Token <apiKey>" on every request, matching the
-	// scheme used by Mem0's official SDKs (Authorization: Token m0-...).
-	authenticate: IAuthenticateGeneric = {
-		type: 'generic',
-		properties: {
-			headers: {
-				Authorization: '=Token {{$credentials.apiKey}}',
-			},
-		},
-	};
-
-	// Cheap authenticated GET; validates the key when the user clicks "Test".
-	test: ICredentialTestRequest = {
-		request: {
-			baseURL: '={{$credentials.baseUrl}}',
-			url: '/v1/ping/',
-			method: 'GET',
-		},
-	};
-}

integrations/n8n-nodes-mem0/gulpfile.js

@@ -1,16 +0,0 @@
-const path = require('path');
-const { task, src, dest } = require('gulp');
-
-task('build:icons', copyIcons);
-
-function copyIcons() {
-	// Copy icons and the codex (*.node.json) next to the compiled nodes; tsc emits
-	// only .js, so these static assets need copying for n8n to pick them up.
-	const nodeSource = path.resolve('nodes', '**', '*.{png,svg,json}');
-	const nodeDestination = path.resolve('dist', 'nodes');
-	src(nodeSource).pipe(dest(nodeDestination));
-
-	const credSource = path.resolve('credentials', '**', '*.{png,svg}');
-	const credDestination = path.resolve('dist', 'credentials');
-	return src(credSource, { allowEmpty: true }).pipe(dest(credDestination));
-}

integrations/n8n-nodes-mem0/index.js

@@ -1,3 +0,0 @@
-// n8n loads nodes and credentials via the "n8n" key in package.json.
-// This entry point is intentionally empty.
-module.exports = {};

integrations/n8n-nodes-mem0/jest.config.js

@@ -1,10 +0,0 @@
-/** Jest config lives here (not in package.json) so the published package.json
- * stays minimal for the n8n verification scanner. This file is dev-only; it is
- * not shipped (see the `files` field in package.json). */
-module.exports = {
-	testEnvironment: 'node',
-	testMatch: ['**/test/**/*.test.ts'],
-	transform: {
-		'^.+\\.tsx?$': ['ts-jest', {}],
-	},
-};