chore: bump versions and update changelog for Python 2.0.15, TypeScript 3.1.3, and plugin releases (#6715)

Co-authored-by: kartik-mem0 <kartik.labhshetwar@mem0.ai>

.claude-plugin/marketplace.json

@@ -12,7 +12,7 @@
       "name": "mem0",
       "source": "./integrations/mem0-plugin",
       "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search to Claude workflows.",
-      "version": "0.2.12"
+      "version": "0.2.13"
     }
   ]
 }

.cursor-plugin/marketplace.json

@@ -12,7 +12,7 @@
       "name": "mem0",
       "source": "./integrations/mem0-plugin",
       "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search.",
-      "version": "0.2.12"
+      "version": "0.2.13"
     }
   ]
 }

.github/ISSUE_TEMPLATE/bug_report.yml

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

.github/ISSUE_TEMPLATE/feature_request.yml

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

.github/advanced-issue-labeler.yml

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

.github/component-keywords.json

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

.github/labeler.yml

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

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

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

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

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

.github/workflows/ci-gate.yml

@@ -40,6 +40,8 @@ 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
@@ -79,6 +81,13 @@ jobs:
               - 'integrations/pi-agent-plugin/**'
               - '.github/workflows/pi-agent-plugin-checks.yml'
               - '.github/workflows/ci-gate.yml'
+            n8n_nodes_mem0:
+              - 'integrations/n8n-nodes-mem0/**'
+              - '.github/workflows/n8n-nodes-mem0-checks.yml'
+            zapier_mem0:
+              - 'integrations/zapier-mem0/**'
+              - '.github/workflows/zapier-mem0-checks.yml'
+              - '.github/workflows/ci-gate.yml'
             docs_llms_txt:
               - 'docs/**/*.mdx'
               - 'docs/llms.txt'
@@ -136,6 +145,18 @@ jobs:
     uses: ./.github/workflows/pi-agent-plugin-checks.yml
     secrets: inherit
 
+  n8n-nodes-mem0:
+    name: n8n Node
+    needs: changes
+    if: needs.changes.outputs.n8n_nodes_mem0 == 'true'
+    uses: ./.github/workflows/n8n-nodes-mem0-checks.yml
+  zapier-mem0:
+    name: Zapier App
+    needs: changes
+    if: needs.changes.outputs.zapier_mem0 == 'true'
+    uses: ./.github/workflows/zapier-mem0-checks.yml
+    secrets: inherit
+
   docs-llms-txt:
     name: docs llms.txt
     needs: changes
@@ -154,6 +175,8 @@ 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
+          pip install ruff==0.16.0
       - name: Run Linting
         if: needs.check_changes.outputs.mem0_changed == 'true'
         run: make lint

.github/workflows/issue-labeler.yml

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

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

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

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

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

.github/workflows/pr-labeler.yml

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

.github/workflows/release.yml

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

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

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

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

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

AGENTS.md

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

CONTRIBUTING.md

@@ -45,7 +45,7 @@ The two most common contribution targets are the SDKs:
 | TypeScript SDK (`mem0ai`) | `mem0-ts/` | TypeScript | `pnpm`        |
 
 Other packages include the CLIs (`cli/python/`, `cli/node/`), integrations
-(`integrations/`), the self-hosted `server/`, `openmemory/`, and the docs site
+(`integrations/`), the self-hosted `server/`, and the docs site
 (`docs/`). See [AGENTS.md](./AGENTS.md) for a full map of the repository.
 
 ## Development Workflow

Makefile

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

SECURITY.md

@@ -21,7 +21,7 @@ privately through one of the following channels:
 To help us triage and resolve the issue quickly, please include as much of the
 following as you can:
 
-- Affected component or package (e.g. Python SDK, TypeScript SDK, server, OpenMemory)
+- Affected component or package (e.g. Python SDK, TypeScript SDK, server, CLI)
 - Affected version, tag, or commit
 - Clear, step-by-step reproduction instructions
 - The security impact and a proof of concept, if available

cli/node/package.json

@@ -51,8 +51,8 @@
       "langsmith@<0.6.0": "^0.6.0",
       "tar-fs@>=2.0.0 <2.1.4": "^2.1.4",
       "picomatch@<2.3.2": "^2.3.2",
-      "postcss@<8.5.10": ">=8.5.10",
-      "esbuild": ">=0.28.1"
+      "esbuild": ">=0.28.1",
+      "postcss@<8.5.18": ">=8.5.18 <9.0.0"
     }
   }
 }

cli/node/pnpm-lock.yaml

@@ -9,8 +9,8 @@ overrides:
   langsmith@<0.6.0: ^0.6.0
   tar-fs@>=2.0.0 <2.1.4: ^2.1.4
   picomatch@<2.3.2: ^2.3.2
-  postcss@<8.5.10: '>=8.5.10'
   esbuild: '>=0.28.1'
+  postcss@<8.5.18: '>=8.5.18 <9.0.0'
 
 importers:
 
@@ -40,7 +40,7 @@ importers:
         version: 20.19.37
       tsup:
         specifier: ^8.0.0
-        version: 8.5.1(postcss@8.5.15)(tsx@4.21.0)(typescript@5.9.3)
+        version: 8.5.1(postcss@8.5.23)(tsx@4.21.0)(typescript@5.9.3)
       tsx:
         specifier: ^4.7.0
         version: 4.21.0
@@ -78,24 +78,28 @@ packages:
     engines: {node: '>=14.21.3'}
     cpu: [arm64]
     os: [linux]
+    libc: [musl]
 
   '@biomejs/cli-linux-arm64@1.9.4':
     resolution: {integrity: sha512-fJIW0+LYujdjUgJJuwesP4EjIBl/N/TcOX3IvIHJQNsAqvV2CHIogsmA94BPG6jZATS4Hi+xv4SkBBQSt1N4/g==}
     engines: {node: '>=14.21.3'}
     cpu: [arm64]
     os: [linux]
+    libc: [glibc]
 
   '@biomejs/cli-linux-x64-musl@1.9.4':
     resolution: {integrity: sha512-gEhi/jSBhZ2m6wjV530Yy8+fNqG8PAinM3oV7CyO+6c3CEh16Eizm21uHVsyVBEB6RIM8JHIl6AGYCv6Q6Q9Tg==}
     engines: {node: '>=14.21.3'}
     cpu: [x64]
     os: [linux]
+    libc: [musl]
 
   '@biomejs/cli-linux-x64@1.9.4':
     resolution: {integrity: sha512-lRCJv/Vi3Vlwmbd6K+oQ0KhLHMAysN8lXoCI7XeHlxaajk06u7G+UsFSO01NAs5iYuWKmVZjmiOzJ0OJmGsMwg==}
     engines: {node: '>=14.21.3'}
     cpu: [x64]
     os: [linux]
+    libc: [glibc]
 
   '@biomejs/cli-win32-arm64@1.9.4':
     resolution: {integrity: sha512-tlbhLk+WXZmgwoIKwHIHEBZUwxml7bRJgk0X2sPyNR3S93cdRq6XulAZRQJ17FYGGzWne0fgrXBKpl7l4M87Hg==}
@@ -316,66 +320,79 @@ packages:
     resolution: {integrity: sha512-RzeBwv0B3qtVBWtcuABtSuCzToo2IEAIQrcyB/b2zMvBWVbjo8bZDjACUpnaafaxhTw2W+imQbP2BD1usasK4g==}
     cpu: [arm]
     os: [linux]
+    libc: [glibc]
 
   '@rollup/rollup-linux-arm-musleabihf@4.60.0':
     resolution: {integrity: sha512-Sf7zusNI2CIU1HLzuu9Tc5YGAHEZs5Lu7N1ssJG4Tkw6e0MEsN7NdjUDDfGNHy2IU+ENyWT+L2obgWiguWibWQ==}
     cpu: [arm]
     os: [linux]
+    libc: [musl]
 
   '@rollup/rollup-linux-arm64-gnu@4.60.0':
     resolution: {integrity: sha512-DX2x7CMcrJzsE91q7/O02IJQ5/aLkVtYFryqCjduJhUfGKG6yJV8hxaw8pZa93lLEpPTP/ohdN4wFz7yp/ry9A==}
     cpu: [arm64]
     os: [linux]
+    libc: [glibc]
 
   '@rollup/rollup-linux-arm64-musl@4.60.0':
     resolution: {integrity: sha512-09EL+yFVbJZlhcQfShpswwRZ0Rg+z/CsSELFCnPt3iK+iqwGsI4zht3secj5vLEs957QvFFXnzAT0FFPIxSrkQ==}
     cpu: [arm64]
     os: [linux]
+    libc: [musl]
 
   '@rollup/rollup-linux-loong64-gnu@4.60.0':
     resolution: {integrity: sha512-i9IcCMPr3EXm8EQg5jnja0Zyc1iFxJjZWlb4wr7U2Wx/GrddOuEafxRdMPRYVaXjgbhvqalp6np07hN1w9kAKw==}
     cpu: [loong64]
     os: [linux]
+    libc: [glibc]
 
   '@rollup/rollup-linux-loong64-musl@4.60.0':
     resolution: {integrity: sha512-DGzdJK9kyJ+B78MCkWeGnpXJ91tK/iKA6HwHxF4TAlPIY7GXEvMe8hBFRgdrR9Ly4qebR/7gfUs9y2IoaVEyog==}
     cpu: [loong64]
     os: [linux]
+    libc: [musl]
 
   '@rollup/rollup-linux-ppc64-gnu@4.60.0':
     resolution: {integrity: sha512-RwpnLsqC8qbS8z1H1AxBA1H6qknR4YpPR9w2XX0vo2Sz10miu57PkNcnHVaZkbqyw/kUWfKMI73jhmfi9BRMUQ==}
     cpu: [ppc64]
     os: [linux]
+    libc: [glibc]
 
   '@rollup/rollup-linux-ppc64-musl@4.60.0':
     resolution: {integrity: sha512-Z8pPf54Ly3aqtdWC3G4rFigZgNvd+qJlOE52fmko3KST9SoGfAdSRCwyoyG05q1HrrAblLbk1/PSIV+80/pxLg==}
     cpu: [ppc64]
     os: [linux]
+    libc: [musl]
 
   '@rollup/rollup-linux-riscv64-gnu@4.60.0':
     resolution: {integrity: sha512-3a3qQustp3COCGvnP4SvrMHnPQ9d1vzCakQVRTliaz8cIp/wULGjiGpbcqrkv0WrHTEp8bQD/B3HBjzujVWLOA==}
     cpu: [riscv64]
     os: [linux]
+    libc: [glibc]
 
   '@rollup/rollup-linux-riscv64-musl@4.60.0':
     resolution: {integrity: sha512-pjZDsVH/1VsghMJ2/kAaxt6dL0psT6ZexQVrijczOf+PeP2BUqTHYejk3l6TlPRydggINOeNRhvpLa0AYpCWSQ==}
     cpu: [riscv64]
     os: [linux]
+    libc: [musl]
 
   '@rollup/rollup-linux-s390x-gnu@4.60.0':
     resolution: {integrity: sha512-3ObQs0BhvPgiUVZrN7gqCSvmFuMWvWvsjG5ayJ3Lraqv+2KhOsp+pUbigqbeWqueGIsnn+09HBw27rJ+gYK4VQ==}
     cpu: [s390x]
     os: [linux]
+    libc: [glibc]
 
   '@rollup/rollup-linux-x64-gnu@4.60.0':
     resolution: {integrity: sha512-EtylprDtQPdS5rXvAayrNDYoJhIz1/vzN2fEubo3yLE7tfAw+948dO0g4M0vkTVFhKojnF+n6C8bDNe+gDRdTg==}
     cpu: [x64]
     os: [linux]
+    libc: [glibc]
 
   '@rollup/rollup-linux-x64-musl@4.60.0':
     resolution: {integrity: sha512-k09oiRCi/bHU9UVFqD17r3eJR9bn03TyKraCrlz5ULFJGdJGi7VOmm9jl44vOJvRJ6P7WuBi/s2A97LxxHGIdw==}
     cpu: [x64]
     os: [linux]
+    libc: [musl]
 
   '@rollup/rollup-openbsd-x64@4.60.0':
     resolution: {integrity: sha512-1o/0/pIhozoSaDJoDcec+IVLbnRtQmHwPV730+AOD29lHEEo4F5BEUB24H0OBdhbBBDwIOSuf7vgg0Ywxdfiiw==}
@@ -653,8 +670,8 @@ packages:
   mz@2.7.0:
     resolution: {integrity: sha512-z81GNO7nnYMEhrGh9LeymoE4+Yr0Wn5McHIZMK5cfQCl+NDX08sCZgUc9/6MHni9IWuFLm1Z3HTCXu2z9fN62Q==}
 
-  nanoid@3.3.12:
-    resolution: {integrity: sha512-ZB9RH/39qpq5Vu6Y+NmUaFhQR6pp+M2Xt76XBnEwDaGcVAqhlvxrl3B2bKS5D3NH3QR76v3aSrKaF/Kiy7lEtQ==}
+  nanoid@3.3.16:
+    resolution: {integrity: sha512-bzlKTyNJ7+LdGIIwy8ijFpIqEQIvafahV7eYykJ8Cvh42EdJeODoJ6gUJXpQJvej1BddH8OqTXZNE/KfbWAu8Q==}
     engines: {node: ^10 || ^12 || ^13.7 || ^14 || >=15.0.1}
     hasBin: true
 
@@ -695,7 +712,7 @@ packages:
     engines: {node: '>= 18'}
     peerDependencies:
       jiti: '>=1.21.0'
-      postcss: '>=8.5.10'
+      postcss: '>=8.5.18 <9.0.0'
       tsx: ^4.8.1
       yaml: ^2.4.2
     peerDependenciesMeta:
@@ -708,8 +725,8 @@ packages:
       yaml:
         optional: true
 
-  postcss@8.5.15:
-    resolution: {integrity: sha512-FfR8sjd4em2T6fb3I2MwAJU7HWVMr9zba+enmQeeWFfCbm+UOC/0X4DS8XtpUTMwWMGbjKYP7xjfNekzyGmB3A==}
+  postcss@8.5.23:
+    resolution: {integrity: sha512-g50586zr4bZmwFiTlflMu8E0bDTb5I5gertgwAKmsdUlTQIhZtunzUlD1WSzwcVWPoAVpsrA6vlfCD7oXvRwgg==}
     engines: {node: ^10 || ^12 || >=14}
 
   readdirp@4.1.2:
@@ -821,7 +838,7 @@ packages:
     peerDependencies:
       '@microsoft/api-extractor': ^7.36.0
       '@swc/core': ^1
-      postcss: '>=8.5.10'
+      postcss: '>=8.5.18 <9.0.0'
       typescript: '>=4.5.0'
     peerDependenciesMeta:
       '@microsoft/api-extractor':
@@ -1388,7 +1405,7 @@ snapshots:
       object-assign: 4.1.1
       thenify-all: 1.6.0
 
-  nanoid@3.3.12: {}
+  nanoid@3.3.16: {}
 
   object-assign@4.1.1: {}
 
@@ -1424,16 +1441,16 @@ snapshots:
       mlly: 1.8.2
       pathe: 2.0.3
 
-  postcss-load-config@6.0.1(postcss@8.5.15)(tsx@4.21.0):
+  postcss-load-config@6.0.1(postcss@8.5.23)(tsx@4.21.0):
     dependencies:
       lilconfig: 3.1.3
     optionalDependencies:
-      postcss: 8.5.15
+      postcss: 8.5.23
       tsx: 4.21.0
 
-  postcss@8.5.15:
+  postcss@8.5.23:
     dependencies:
-      nanoid: 3.3.12
+      nanoid: 3.3.16
       picocolors: 1.1.1
       source-map-js: 1.2.1
 
@@ -1554,7 +1571,7 @@ snapshots:
 
   ts-interface-checker@0.1.13: {}
 
-  tsup@8.5.1(postcss@8.5.15)(tsx@4.21.0)(typescript@5.9.3):
+  tsup@8.5.1(postcss@8.5.23)(tsx@4.21.0)(typescript@5.9.3):
     dependencies:
       bundle-require: 5.1.0(esbuild@0.28.1)
       cac: 6.7.14
@@ -1565,7 +1582,7 @@ snapshots:
       fix-dts-default-cjs-exports: 1.0.1
       joycon: 3.1.1
       picocolors: 1.1.1
-      postcss-load-config: 6.0.1(postcss@8.5.15)(tsx@4.21.0)
+      postcss-load-config: 6.0.1(postcss@8.5.23)(tsx@4.21.0)
       resolve-from: 5.0.0
       rollup: 4.60.0
       source-map: 0.7.6
@@ -1574,7 +1591,7 @@ snapshots:
       tinyglobby: 0.2.15
       tree-kill: 1.2.2
     optionalDependencies:
-      postcss: 8.5.15
+      postcss: 8.5.23
       typescript: 5.9.3
     transitivePeerDependencies:
       - jiti
@@ -1602,7 +1619,7 @@ snapshots:
       esbuild: 0.28.1
       fdir: 6.5.0(picomatch@4.0.4)
       picomatch: 4.0.4
-      postcss: 8.5.15
+      postcss: 8.5.23
       rollup: 4.60.0
       tinyglobby: 0.2.15
     optionalDependencies:

cli/node/pnpm-workspace.yaml

@@ -10,5 +10,5 @@ overrides:
   langsmith@<0.6.0: ^0.6.0
   tar-fs@>=2.0.0 <2.1.4: ^2.1.4
   picomatch@<2.3.2: ^2.3.2
-  "postcss@<8.5.10": ">=8.5.10"
   "esbuild": ">=0.28.1"
+  "postcss@<8.5.18": ">=8.5.18 <9.0.0"

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

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

docs/api-reference/organizations-projects.mdx

@@ -79,7 +79,7 @@ new_project = client.project.create(
 
 ### Update Project Settings
 
-Modify project configuration including custom instructions, categories, language preferences, retrieval criteria, and memory decay:
+Modify project configuration including custom instructions, categories, language preferences, and memory decay:
 
 ```python
 # Update project with custom categories
@@ -98,14 +98,6 @@ client.project.update(
 # Use the input language for memory storage and retrieval
 client.project.update(multilingual=True)
 
-# Set retrieval criteria to control which memories are surfaced in search
-client.project.update(
-    retrieval_criteria=[
-        {"name": "relevance", "description": "How directly relevant this memory is to the current topic or user query", "weight": 3},
-        {"name": "access_frequency", "description": "How often this memory has been accessed or surfaced recently", "weight": 1}
-    ]
-)
-
 # Enable Memory Decay (boosts recently-accessed memories at search time)
 client.project.update(decay=True)
 
@@ -120,34 +112,6 @@ client.project.update(
 )
 ```
 
-#### Set Retrieval Criteria
-
-`retrieval_criteria` is a per-project list of dictionaries (`List[Dict]`) that shapes how memories are ranked and filtered during search. Each dictionary has three fields: `name` (identifier), `description` (interpreted by the LLM to score each memory), and `weight` (relative influence on the final score). Use this to focus retrieval on intent-aligned or signal-specific memories:
-
-```python
-client.project.update(
-    retrieval_criteria=[
-        {
-            "name": "joy",
-            "description": "Measure the intensity of positive emotions such as happiness, excitement, or amusement expressed in the memory. A higher score reflects greater joy.",
-            "weight": 3
-        },
-        {
-            "name": "curiosity",
-            "description": "Assess the extent to which the memory reflects inquisitiveness or interest in exploring new information. A higher score reflects stronger curiosity.",
-            "weight": 2
-        },
-        {
-            "name": "access_frequency",
-            "description": "How often this memory has been accessed or surfaced recently.",
-            "weight": 1
-        }
-    ]
-)
-```
-
-Pass an empty list to clear all criteria and restore default retrieval behaviour.
-
 #### Toggle Memory Decay
 
 `decay` is a per-project boolean that turns on [Memory Decay](/platform/features/memory-decay): a search-time ranking bias that reinforces recently-accessed memories and gently dampens stale ones. The flag is `false` by default; set it via the same project-update endpoint:

docs/changelog/highlights.mdx

@@ -4,6 +4,23 @@ 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**

docs/changelog/sdk.mdx

@@ -7,6 +7,47 @@ mode: "wide"
 <Tabs>
 <Tab title="Python">
 
+<Update label="2026-08-01" description="v2.0.15">
+
+**Bug Fixes:**
+- **Core:** `delete_all()` now paginates through the vector store in batches of 1000 instead of listing once, so accounts with more memories than a single page (most vector stores default to ~100) had the remainder silently left behind ([#6636](https://github.com/mem0ai/mem0/pull/6636))
+- **Vector Stores:** Cap Supabase `search()`/`list()` `top_k` at the `vecs` query limit of 1000 instead of erroring, and fix a `col_info()` crash by reading collection attributes directly instead of calling the removed `describe()` method ([#6695](https://github.com/mem0ai/mem0/pull/6695))
+- **Vector Stores:** Set `size` on Elasticsearch KNN search queries, so results respect `top_k` instead of being capped at Elasticsearch's default of 10 hits ([#5910](https://github.com/mem0ai/mem0/pull/5910))
+
+**Changes:**
+- **Rerankers:** `LLMReranker`'s default model is now `gpt-5-mini` (was `gpt-4o-mini`) ([#6703](https://github.com/mem0ai/mem0/pull/6703))
+
+</Update>
+
+<Update label="2026-07-25" description="v2.0.14">
+
+**New Features:**
+- **Vector Stores:** Add an Oracle AI Vector Search provider (`oracledb`) with connection pooling, `HNSW`/`IVF` indexes, JSON metadata filtering, and six selectable distance metrics ([#5358](https://github.com/mem0ai/mem0/pull/5358))
+
+**Bug Fixes:**
+- **Vector Stores:** Translate a `"*"` filter value in OpenSearch into an `exists` query for every key, not just identity keys. It was previously ignored or matched literally against the string `"*"`, so a wildcard filter returned nothing ([#6522](https://github.com/mem0ai/mem0/pull/6522))
+- **Vector Stores:** Re-raise errors from OpenSearch `search()` instead of returning `[]`, so a transport, auth, or index misconfiguration surfaces instead of looking like zero matches. `keyword_search()` still degrades on failure, since it is a best-effort BM25 signal ([#6519](https://github.com/mem0ai/mem0/pull/6519))
+- **Vector Stores:** Guard the `text` field in Milvus `update()` behind the `_has_bm25_schema` check, matching `insert()`, so updating a memory in a collection without the BM25 `text`/`sparse` schema no longer fails ([#5705](https://github.com/mem0ai/mem0/pull/5705))
+
+</Update>
+
+<Update label="2026-07-22" description="v2.0.13">
+
+**Bug Fixes:**
+- **Vector Stores:** Fix `reset()` silently leaving stale vectors behind on local (on-disk) Qdrant when the old collection directory could not be removed, for example an open file handle on Windows or NFS ([#6412](https://github.com/mem0ai/mem0/pull/6412))
+- **Core:** Stop `update()` metadata from overwriting or injecting `user_id`, `agent_id`, `run_id`, or `actor_id`. These identity fields are immutable after creation, so passing them in `metadata` can no longer move a memory into a different tenant's scope ([#6278](https://github.com/mem0ai/mem0/pull/6278))
+- **Vector Stores:** Scope Pinecone `delete_col()`/`reset()` to the configured namespace instead of deleting the whole index, so resetting a namespaced Pinecone store no longer wipes out the other namespaces sharing that index ([#6287](https://github.com/mem0ai/mem0/pull/6287))
+- **Vector Stores:** Convert Baidu Mochow's raw L2 distance into a similarity score in `search()` (`1 / (1 + distance)`), so closer matches rank higher instead of lower, matching the Milvus provider and the rest of the `VectorStoreBase` contract ([#6435](https://github.com/mem0ai/mem0/pull/6435))
+- **LLMs:** Read `OPENAI_BASE_URL` (was `OPENAI_API_BASE`) in `OpenAIStructuredLLM`, matching the official OpenAI SDK's environment variable and the rest of the OpenAI-compatible providers ([#6322](https://github.com/mem0ai/mem0/pull/6322))
+
+**Improvements:**
+- **LLMs:** Remove a dead, no-op `api_key` attribute check from `LLMBase.__init__` ([#6460](https://github.com/mem0ai/mem0/pull/6460))
+
+**Changes:**
+- **Client:** Remove the `retrieval_criteria` parameter from `MemoryClient.update_project()`/`AsyncMemoryClient.update_project()` and `Project.update()`/`AsyncProject.update()`. It was accepted and forwarded but never affected retrieval, so removing it is not a behavior change ([#6313](https://github.com/mem0ai/mem0/pull/6313))
+
+</Update>
+
 <Update label="2026-07-13" description="v2.0.12">
 
 **New Features:**
@@ -1128,6 +1169,53 @@ See the [OSS v2 to v3 migration guide](https://docs.mem0.ai/migration/oss-v2-to-
 
 <Tab title="TypeScript">
 
+<Update label="2026-08-01" description="v3.1.3">
+
+**New Features:**
+- **Vector Stores:** Add Qdrant server-side BM25 `keywordSearch()` (requires Qdrant >= 1.15.2) plus payload filter indexes, so keyword search runs without a client-side BM25 dependency ([#5851](https://github.com/mem0ai/mem0/pull/5851))
+
+**Bug Fixes:**
+- **Core:** `deleteAll()` now paginates through the vector store in batches of 1000 instead of listing once, so accounts with more memories than a single page had the remainder silently left behind ([#4872](https://github.com/mem0ai/mem0/pull/4872))
+- **Vector Stores:** Supabase `list()` now paginates past PostgREST's 1000-row cap instead of stopping at the first page, `search()` warns when results may have been truncated by that same cap, and the initialization probe reads a row instead of writing a test vector, so Row Level Security policies that only grant read access no longer fail table verification ([#6695](https://github.com/mem0ai/mem0/pull/6695))
+- **Embeddings:** Honor `TOGETHER_API_BASE` in the Together embedder, matching the Together LLM provider, so a custom gateway URL is no longer silently ignored for embeddings ([#6572](https://github.com/mem0ai/mem0/pull/6572))
+
+**Changes:**
+- **Rerankers:** `RerankerFactory`'s default LLM reranker model is now `gpt-5-mini` (was `gpt-4o-mini`) ([#6703](https://github.com/mem0ai/mem0/pull/6703))
+
+**Security:**
+- **Dependencies:** Patched 32 high and 57 medium severity dependency vulnerabilities across the pnpm workspace via `pnpm.overrides` (`axios`, `brace-expansion`, `js-yaml`, `postcss`, `protobufjs`, `mongoose`, `tar`, `fast-xml-parser`, `thrift`) ([#6639](https://github.com/mem0ai/mem0/pull/6639))
+
+</Update>
+
+<Update label="2026-07-25" description="v3.1.2">
+
+**Bug Fixes:**
+- **Vector Stores:** Apply every operator in a Cassandra compound field filter (e.g. `{ age: { gte: 10, lte: 20 } }`) instead of stopping after the first, so the remaining bounds are no longer silently ignored ([#6511](https://github.com/mem0ai/mem0/pull/6511))
+- **Vector Stores:** Stop the Chroma where-clause translator from dropping filter conditions. Same-field ranges (`gte` + `lte`), multi-field conditions inside `$or`, and negated `contains`/`icontains` under `$not` each collapsed to a single clause or vanished, widening the search instead of narrowing it ([#6521](https://github.com/mem0ai/mem0/pull/6521))
+- **Vector Stores:** Skip `"*"` wildcard filter values in Milvus instead of matching them literally, so a filter like `{ user_id: "*" }` no longer returns zero memories ([#6508](https://github.com/mem0ai/mem0/pull/6508))
+- **Vector Stores:** Read `textLemmatized` for BM25 keyword search on Milvus, OpenSearch, and MongoDB, matching the field the memory layer actually writes, so hybrid search on those backends no longer loses the keyword signal ([#6497](https://github.com/mem0ai/mem0/pull/6497))
+- **LLMs:** Forward `responseFormat` to Gemini's `responseMimeType` in `generateResponse()`, so requesting `json_object` returns JSON instead of free-form text ([#6468](https://github.com/mem0ai/mem0/pull/6468))
+- **LLMs:** Find the Anthropic text block by type instead of indexing `content[0]`, so a thinking-enabled model whose `thinking` block comes first no longer throws `Unexpected response type from Anthropic API` ([#6506](https://github.com/mem0ai/mem0/pull/6506))
+
+</Update>
+
+<Update label="2026-07-22" description="v3.1.1">
+
+**New Features:**
+- **Embeddings:** Add an AWS Bedrock embedding provider ([#6185](https://github.com/mem0ai/mem0/pull/6185))
+
+**Bug Fixes:**
+- **Packaging:** Finish the lazy-loading work started in v3.1.0. The remaining LLMs (Anthropic, Google, Groq, LangChain, Mistral, Ollama), embedders (Google, LangChain, Ollama, Vertex AI), vector stores (Azure AI Search, Azure MySQL, Baidu, LangChain, Qdrant, Redis, Supabase, Valkey, Vectorize), and the Supabase history store still imported their SDKs at module load, so importing `mem0ai/oss` required every provider package to be installed ([#6389](https://github.com/mem0ai/mem0/pull/6389))
+- **Vector Stores:** Convert Baidu Mochow's raw L2 distance into a similarity score in `search()` (`1 / (1 + distance)`), so closer matches rank higher instead of lower. A row the backend returns without a score is now left `undefined` instead of being treated as the closest match ([#6485](https://github.com/mem0ai/mem0/pull/6485))
+- **Memory (OSS):** Coerce non-string entity IDs (e.g. a numeric `user_id`) to strings instead of crashing on `.trim()` ([#6263](https://github.com/mem0ai/mem0/pull/6263))
+- **Memory (OSS):** Stop `update()` metadata from overwriting or injecting `user_id`, `agent_id`, `run_id`, or `actor_id` (in either snake_case or camelCase). These identity fields are immutable after creation, so passing them in `metadata` can no longer move a memory into a different tenant's scope ([#6343](https://github.com/mem0ai/mem0/pull/6343))
+- **Vector Stores:** Scope Pinecone `deleteCol()`/`reset()` to the configured namespace instead of deleting the whole index, so resetting a namespaced Pinecone store no longer wipes out the other namespaces sharing that index ([#6287](https://github.com/mem0ai/mem0/pull/6287))
+
+**Changes:**
+- **Client:** Remove the unused `retrievalCriteria` field from `PromptUpdatePayload`. It was accepted and forwarded but never affected retrieval, so removing it is not a behavior change ([#6313](https://github.com/mem0ai/mem0/pull/6313))
+
+</Update>
+
 <Update label="2026-07-13" description="v3.1.0">
 
 The largest provider release for the TypeScript OSS SDK so far: 17 new vector stores, 5 new LLM providers, 4 new embedders, and reranking support. Importing `mem0ai/oss` no longer pulls in any provider SDK, so you only install what you actually configure.
@@ -1825,6 +1913,15 @@ 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:**
@@ -2075,6 +2172,13 @@ Initial release of the Mem0 plugin for Claude Code and Cursor, followed by Codex
 
 <Tab title="OpenCode">
 
+<Update label="2026-07-22" description="OpenCode plugin v0.2.2">
+
+**Fixes:**
+- **Shell-profile API key recovery:** When `MEM0_API_KEY` isn't set in the process environment, the plugin now falls back to reading it from `.zshrc`, `.bashrc`, `.zprofile`, `.bash_profile`, or `.profile`, fixing startup failures on clients (e.g. Desktop) that launch without shell-exported environment variables.
+
+</Update>
+
 <Update label="2026-06-30" description="OpenCode plugin v0.2.1">
 
 **Improvements:**
@@ -2148,6 +2252,15 @@ 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:**
@@ -2195,6 +2308,16 @@ Initial release of the Mem0 plugin for Claude Code and Cursor, followed by Codex
 
 <Tab title="OpenClaw">
 
+<Update label="2026-08-01" description="openclaw-mem0 v1.0.15">
+
+**Improvements:**
+- **Onboarding suggestions:** The example commands shown by `openclaw mem0 config show` now suggest `gpt-5-mini` instead of `gpt-4o` ([#6704](https://github.com/mem0ai/mem0/pull/6704))
+
+**Security:**
+- **Dependencies:** Patched high and medium severity dependency vulnerabilities via `pnpm.overrides` (`protobufjs`, `axios`, `postcss`, `mongoose`) ([#6639](https://github.com/mem0ai/mem0/pull/6639))
+
+</Update>
+
 <Update label="2026-06-30" description="openclaw-mem0 v1.0.14">
 
 **Improvements:**
@@ -2448,6 +2571,13 @@ Initial release of the Mem0 plugin for Claude Code and Cursor, followed by Codex
 
 <Tab title="Pi Agent">
 
+<Update label="2026-08-01" description="Pi Agent plugin v0.1.4">
+
+**Security:**
+- **Dependencies:** Patched high and medium severity dependency vulnerabilities via `pnpm.overrides` (`axios`, `brace-expansion`, `postcss`, `mongoose`, `protobufjs`) ([#6639](https://github.com/mem0ai/mem0/pull/6639))
+
+</Update>
+
 <Update label="2026-06-30" description="Pi Agent plugin v0.1.3">
 
 **New Features:**
@@ -2500,6 +2630,13 @@ Initial release of the Mem0 plugin for Claude Code and Cursor, followed by Codex
 
 <Tab title="Vercel AI SDK">
 
+<Update label="2026-08-01" description="Vercel AI SDK v3.0.1">
+
+**Security:**
+- **Dependencies:** Patched high and medium severity dependency vulnerabilities via `pnpm.overrides` (`brace-expansion`, `js-yaml`) ([#6639](https://github.com/mem0ai/mem0/pull/6639))
+
+</Update>
+
 <Update label="2026-06-10" description="Vercel AI SDK v3.0.0">
 
 **Major Release**: Migrated to Vercel AI SDK v6 (`LanguageModelV3` / `ProviderV3`) and Mem0 v3 API.
@@ -2581,6 +2718,57 @@ Initial release of the Mem0 plugin for Claude Code and Cursor, followed by Codex
 - Added support for graph memories.
 </Update>
 
+</Tab>
+
+<Tab title="n8n">
+
+<Update label="2026-07-30" 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

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

docs/components/embedders/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**, **FastEmbed**, **Google AI**, **Langchain**, **LM Studio**, **Ollama**, and **Together**.
+  All embedders listed below are supported in the Python implementation. The TypeScript implementation supports: **OpenAI**, **Azure OpenAI**, **AWS Bedrock**, **FastEmbed**, **Google AI**, **Hugging Face**, **Langchain**, **LM Studio**, **Ollama**, **Together**, and **Vertex AI**.
 </Note>
 
 <CardGroup cols={4}>

docs/components/rerankers/config.mdx

@@ -52,7 +52,7 @@ All rerankers share these common configuration parameters:
 
 | Parameter        | Description                                | Type    | Default                |
 | ---------------- | ------------------------------------------ | ------- | ---------------------- |
-| `model`          | LLM model to use for scoring               | `str`   | `"gpt-4o-mini"`        |
+| `model`          | LLM model to use for scoring               | `str`   | `"gpt-5-mini"`        |
 | `provider`       | LLM provider (`openai`, `anthropic`, etc.) | `str`   | `"openai"`             |
 | `api_key`        | API key for LLM provider                   | `str`   | `None`                 |
 | `temperature`    | Temperature for LLM generation             | `float` | `0.0`                  |
@@ -114,7 +114,7 @@ The self-hosted [TypeScript SDK](/open-source/features/reranker-search#typescrip
 | `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` |
+| `llm_reranker` | None (uses your LLM provider's own SDK) | `openai` / `gpt-5-mini` | `provider`, `model`, `apiKey`, `llm` (nested override), `topK` |
 
 ```typescript
 import { Memory } from "mem0ai/oss";

docs/components/rerankers/custom-prompts.mdx

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

docs/components/rerankers/models/cohere.mdx

@@ -101,7 +101,7 @@ os.environ["COHERE_API_KEY"] = "your-api-key"
 # Initialize memory with Cohere reranker
 config = {
     "vector_store": {"provider": "chroma"},
-    "llm": {"provider": "openai", "config": {"model": "gpt-4o-mini"}},
+    "llm": {"provider": "openai", "config": {"model": "gpt-5-mini"}},
     "rerank": {
         "provider": "cohere",
         "config": {

docs/components/rerankers/models/llm_reranker.mdx

@@ -19,7 +19,7 @@ config = {
         "provider": "llm_reranker",
         "config": {
             "provider": "openai",
-            "model": "gpt-4o-mini",
+            "model": "gpt-5-mini",
             "api_key": "your-openai-api-key"
         }
     }
@@ -33,7 +33,7 @@ m = Memory.from_config(config)
 | Parameter | Type | Default | Description |
 |-----------|------|---------|-------------|
 | `provider` | str | `"openai"` | LLM provider (openai, anthropic, etc.) |
-| `model` | str | `"gpt-4o-mini"` | LLM model to use for reranking |
+| `model` | str | `"gpt-5-mini"` | LLM model to use for reranking |
 | `api_key` | str | None | API key for the LLM provider |
 | `top_k` | int | None | Number of top documents to return after reranking |
 | `temperature` | float | 0.0 | LLM temperature for consistency |
@@ -69,7 +69,7 @@ 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).
+The [TypeScript OSS SDK](/open-source/features/reranker-search#typescript-sdk) (`mem0ai/oss`) ships the LLM reranker under the provider name `llm_reranker`. It does **not** reuse the Memory's main `llm` instance; it builds its own LLM from the reranker's own config, defaulting to `openai` / `gpt-5-mini`. Set `provider`/`model`/`apiKey` directly on `config`, or nest a fully separate `config.llm: { provider, config }` (its `provider`/`config` take priority over the top-level fields, which only backfill values missing from the nested config).
 
 ```typescript
 import { Memory } from "mem0ai/oss";
@@ -114,7 +114,7 @@ config = {
         "provider": "llm_reranker",
         "config": {
             "provider": "openai",
-            "model": "gpt-4o-mini",
+            "model": "gpt-5-mini",
             "api_key": "your-openai-api-key",
             "temperature": 0.0
         }
@@ -170,15 +170,15 @@ config = {
         "provider": "llm_reranker",
         "config": {
             "provider": "azure_openai",
-            "model": "gpt-4o-mini",
+            "model": "gpt-5-mini",
             "api_key": "your-azure-api-key",
             "llm": {
                 "provider": "azure_openai",
                 "config": {
-                    "model": "gpt-4o-mini",
+                    "model": "gpt-5-mini",
                     "api_key": "your-azure-api-key",
                     "azure_endpoint": "https://your-resource.openai.azure.com/",
-                    "azure_deployment": "gpt-4o-mini-deployment"
+                    "azure_deployment": "gpt-5-mini-deployment"
                 }
             }
         }
@@ -232,7 +232,7 @@ config = {
         "provider": "llm_reranker",
         "config": {
             "provider": "openai",
-            "model": "gpt-4o-mini",
+            "model": "gpt-5-mini",
             "api_key": "your-api-key",
             "scoring_prompt": custom_prompt
         }
@@ -351,7 +351,7 @@ fast_config = {
         "provider": "llm_reranker",
         "config": {
             "provider": "openai",
-            "model": "gpt-4o-mini",
+            "model": "gpt-5-mini",
             "api_key": "your-api-key",
             "top_k": 5,
             "temperature": 0.0
@@ -444,7 +444,7 @@ fallback_config = {
         "provider": "llm_reranker",
         "config": {
             "provider": "openai",
-            "model": "gpt-4o-mini",
+            "model": "gpt-5-mini",
             "api_key": "your-api-key"
         }
     }

docs/components/rerankers/models/sentence_transformer.mdx

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

docs/components/rerankers/models/zero_entropy.mdx

@@ -34,7 +34,7 @@ config = {
     "llm": {
         "provider": "openai",
         "config": {
-            "model": "gpt-4o-mini"
+            "model": "gpt-5-mini"
         }
     },
     "rerank": {
@@ -98,7 +98,7 @@ os.environ["ZERO_ENTROPY_API_KEY"] = "your-api-key"
 # Initialize memory with Zero Entropy reranker
 config = {
     "vector_store": {"provider": "chroma"},
-    "llm": {"provider": "openai", "config": {"model": "gpt-4o-mini"}},
+    "llm": {"provider": "openai", "config": {"model": "gpt-5-mini"}},
     "rerank": {"provider": "zero_entropy", "config": {"model": "zerank-1"}}
 }
 

docs/components/vectordbs/config.mdx

@@ -7,7 +7,7 @@ description: "Reference for vector database configuration options in Mem0, inclu
 
 The `config` is defined as an object with two main keys:
 - `vector_store`: Specifies the vector database provider and its configuration
-  - `provider`: The name of the vector database (e.g., "chroma", "pgvector", "qdrant", "milvus", "upstash_vector", "azure_ai_search", "vertex_ai_vector_search", "valkey")
+  - `provider`: The name of the vector database (e.g., "chroma", "pgvector", "qdrant", "milvus", "upstash_vector", "azure_ai_search", "vertex_ai_vector_search", "valkey", "oracledb")
   - `config`: A nested dictionary containing provider-specific settings
 
 
@@ -95,6 +95,11 @@ Here's a comprehensive list of all parameters that can be used across different
 | `connection_string` | PostgreSQL connection string (for Supabase/PGVector) |
 | `index_method` | Vector index method (for Supabase) |
 | `index_measure` | Distance measure for similarity search (for Supabase) |
+| `connection_params` | Connection settings for Oracle AI Vector Search |
+| `use_connection_pool` | Create an Oracle connection pool from `connection_params` |
+| `distance_metric` | Distance metric for Oracle vector indexing and search |
+| `index_type` | Oracle vector index type: `HNSW` or `IVF` |
+| `index_parameters` | Oracle vector-index parameters for the selected index type |
 </Tab>
 <Tab title="TypeScript">
 | Parameter | Description |

docs/components/vectordbs/dbs/neptune_analytics.mdx

@@ -96,8 +96,8 @@ await memory.add(messages, { userId: "alice", metadata: { category: "movies" } }
 </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.
+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

docs/components/vectordbs/dbs/oracledb.mdx

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

docs/components/vectordbs/dbs/qdrant.mdx

@@ -60,6 +60,12 @@ await memory.add(messages, { userId: "alice", metadata: { category: "movies" } }
 ```
 </CodeGroup>
 
+### Hybrid keyword search
+
+Mem0 blends semantic similarity with BM25 keyword scoring. On the TypeScript SDK, Qdrant computes the BM25 vectors server-side, which requires Qdrant 1.15.2 or newer with inference enabled. Qdrant Cloud enables inference by default only for clusters created after 2025-07-07; older clusters must activate it from the Cluster Detail page. The Python SDK encodes BM25 locally instead and needs the `fastembed` package, so scores are not numerically comparable between the two SDKs.
+
+When BM25 is unavailable, or when the collection was created before hybrid search was added, Mem0 logs a warning and falls back to semantic-only search. Writes are unaffected. To enable keyword scoring on an older collection, use a fresh collection name.
+
 ### Config
 
 Let's see the available parameters for the `qdrant` config:

docs/components/vectordbs/dbs/supabase.mdx

@@ -115,6 +115,27 @@ $$;
 
 Go to [Supabase](https://supabase.com/dashboard/projects) and run the above SQL migrations in the SQL Editor.
 
+### Row Level Security
+
+Tables created through the Supabase dashboard have Row Level Security (RLS) enabled by default with no policies attached. With RLS on and no policies, the TypeScript SDK's queries return zero rows with an HTTP 200 (no error is raised), which looks like an empty memory store rather than a permissions problem. If you use the SQL migrations above (via the SQL Editor), RLS is left in its default off state and this does not apply.
+
+If your table has RLS enabled, add policies for the key your app uses (the example below grants full access to the `service_role` key; scope it down for anon/authenticated keys as needed):
+
+```sql
+alter table memories enable row level security;
+
+create policy "Allow service role full access to memories"
+on memories
+for all
+to service_role
+using (true)
+with check (true);
+```
+
+### PostgREST Row Limits
+
+Supabase's PostgREST layer caps the number of rows returned by a single request at `db-max-rows` (1000 by default), for both `.select()` queries and RPC function calls like `match_vectors`. Requesting a `topK` above this limit for `search()` or `list()` will not raise an error, results are capped at `db-max-rows` instead. The TypeScript `list()` method paginates internally to work around this, but `search()` cannot since `match_vectors` has no offset parameter; it logs a warning when it detects a truncated result. Raise `db-max-rows` in your Supabase project settings if you need more than 1000 results per search.
+
 ### Config
 
 Here are the parameters available for configuring Supabase:

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, and more."
+description: "Overview of all supported vector databases in Mem0, including Qdrant, Chroma, PGVector, Pinecone, Oracle, 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.
@@ -21,6 +21,7 @@ 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>

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

@@ -55,9 +55,8 @@ await addUserPreferences();
 
 ```json Output
 {
-  "message": "Memory processing has been queued for background execution",
-  "status": "PENDING",
-  "event_id": "9f8c2b1a-4e7d-4c3a-9b21-1a2b3c4d5e6f"
+  "event_id": "9f8c2b1a-4e7d-4c3a-9b21-1a2b3c4d5e6f",
+  "status": "PENDING"
 }
 ```
 </CodeGroup>

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

@@ -83,6 +83,50 @@ 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>

docs/docs.json

@@ -84,8 +84,6 @@
                         "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"
                         ]
@@ -209,6 +207,7 @@
                               "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",
@@ -350,6 +349,8 @@
                     "pages": [
                       "integrations/dify",
                       "integrations/flowise",
+                      "integrations/n8n",
+                      "integrations/zapier",
                       "integrations/langchain-tools",
                       "integrations/agentops",
                       "integrations/respan",
@@ -609,6 +610,10 @@
     ]
   },
   "redirects": [
+    {
+      "source": "/platform/features/contextual-add",
+      "destination": "/core-concepts/memory-operations/add"
+    },
     {
       "source": "/changelog/openclaw",
       "destination": "/changelog/sdk"
@@ -631,7 +636,7 @@
     },
     {
       "source": "/platform/features/expiration-date",
-      "destination": "/"
+      "destination": "/platform/features/memory-expiration"
     },
     {
       "source": "/cookbooks/essentials/memory-expiration-short-and-long-term",
@@ -1228,6 +1233,14 @@
     {
       "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

@@ -0,0 +1 @@
+<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

@@ -65,7 +65,9 @@ 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 when the session ends |
+| **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.
 
 ## 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:
 
-   ```
-   /plugin marketplace add mem0ai/mem0
+   ```bash
+   claude plugin marketplace add mem0ai/mem0
    ```
 
 2. Install the plugin:
 
-   ```
-   /plugin install mem0@mem0-plugins
+   ```bash
+   claude plugin install mem0@mem0-plugins
    ```
 
 **Claude Cowork desktop app:** Open the Cowork tab, click **Customize** in the sidebar, click **Browse plugins**, and install Mem0.
@@ -88,6 +88,15 @@ 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>
@@ -143,9 +152,11 @@ 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 when the session ends |
+| **Stop** | `Stop` | Stores a session summary at the end of every assistant turn (not just at session end) |
 | **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
@@ -153,16 +164,17 @@ When installed via the plugin marketplace, Mem0 hooks into Claude Code's lifecyc
 You: Let's refactor the auth module to use JWT tokens instead of sessions.
 
 # Claude searches memories, finds nothing relevant, proceeds with the work.
-# After completing the task, Mem0 stores:
+# 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:
 #   - 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 user preferences.
+# Knows the file structure, decisions made, and your stated preferences.
 # Continues seamlessly without re-explaining the codebase.
 ```
 

docs/integrations/codex.mdx

@@ -125,20 +125,23 @@ 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 when the session ends |
+| **Stop** | `Stop` | Stores a session summary at the end of every assistant turn (not just at session end) |
 | **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 user preferences from prior tasks.
-# After completing the task, Mem0 stores:
+# 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:
 #   - 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

@@ -96,10 +96,11 @@ 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.
-# After completing the task, Mem0 stores:
+# Mem0 stores what you said as yours:
+#   - Your preference: "Prefers query-level fixes over caching"
+# ...and what the agent did as the assistant's:
 #   - 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/langchain-tools.mdx

@@ -98,9 +98,8 @@ add_result = add_tool.invoke(add_input)
 
 ```json Output
 {
-  "message": "Memory processing has been queued for background execution",
-  "status": "PENDING",
-  "event_id": "3a1b2c3d-4e5f-6789-abcd-ef0123456789"
+  "event_id": "3a1b2c3d-4e5f-6789-abcd-ef0123456789",
+  "status": "PENDING"
 }
 ```
 </CodeGroup>

docs/integrations/n8n.mdx

@@ -0,0 +1,157 @@
+---
+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

@@ -0,0 +1,143 @@
+---
+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/introduction.mdx

@@ -10,7 +10,7 @@ mode: "custom"
   </h1>
 
   <p className="max-w-3xl mx-auto text-base text-gray-600 dark:text-zinc-400 leading-relaxed">
-    Universal, self-improving memory layer for LLM applications.
+    Mem0 gives your AI agents long-term memory that persists across sessions, tools, and runs.
   </p>
 </div>
 
@@ -34,10 +34,10 @@ mode: "custom"
       />
       <div className="flex flex-1 flex-col gap-2 px-4 pb-4 pt-3 text-left">
         <h3 className="text-base font-semibold text-gray-900 dark:text-zinc-100 group-hover:text-primary">
-          Add memory to your app
+          Store your first memory
         </h3>
         <p className="text-sm text-gray-600 dark:text-zinc-400">
-          Start with the quickstart and store your first memory in minutes.
+          Five-minute quickstart: get an API key, then save and search a memory in Python or JavaScript.
         </p>
       </div>
     </a>
@@ -60,10 +60,10 @@ mode: "custom"
       />
       <div className="flex flex-1 flex-col gap-2 px-4 pb-4 pt-3 text-left">
         <h3 className="text-base font-semibold text-gray-900 dark:text-zinc-100 group-hover:text-primary">
-          Add memory to your agent
+          Add memory to your coding agent
         </h3>
         <p className="text-sm text-gray-600 dark:text-zinc-400">
-          Give Claude Code, Cursor, and Codex memory that persists across sessions. A drop-in plugin, no code to write.
+          Plugins that let Claude Code, Cursor, Codex, and other harnesses remember your project. Opens the Claude Code guide.
         </p>
       </div>
     </a>
@@ -86,10 +86,10 @@ mode: "custom"
       />
       <div className="flex flex-1 flex-col gap-2 px-4 pb-4 pt-3 text-left">
         <h3 className="text-base font-semibold text-gray-900 dark:text-zinc-100 group-hover:text-primary">
-          Sign up for Mem0 as an agent
+          Let an AI agent sign itself up
         </h3>
         <p className="text-sm text-gray-600 dark:text-zinc-400">
-          Let an AI agent mint a Mem0 API key, claim ownership later, and write its first memory from the terminal.
+          Four terminal commands create an account and API key. No email, no dashboard. Claim it as owner later.
         </p>
       </div>
     </a>
@@ -112,10 +112,10 @@ mode: "custom"
       />
       <div className="flex flex-1 flex-col gap-2 px-4 pb-4 pt-3 text-left">
         <h3 className="text-base font-semibold text-gray-900 dark:text-zinc-100 group-hover:text-primary">
-          Explore Mem0 integrations
+          Use Mem0 with your framework
         </h3>
         <p className="text-sm text-gray-600 dark:text-zinc-400">
-          Connect Mem0 to LangChain, CrewAI, Vercel AI SDK, and 20+ partner frameworks.
+          Setup guides for 22 tools, including LangChain, CrewAI, LlamaIndex, and the Vercel AI SDK.
         </p>
       </div>
     </a>
@@ -138,10 +138,10 @@ mode: "custom"
       />
       <div className="flex flex-1 flex-col gap-2 px-4 pb-4 pt-3 text-left">
         <h3 className="text-base font-semibold text-gray-900 dark:text-zinc-100 group-hover:text-primary">
-          See memory-app examples
+          Copy a working example
         </h3>
         <p className="text-sm text-gray-600 dark:text-zinc-400">
-          Browse cookbooks for companions, support agents, voice agents, research tools, and more.
+          Full walkthroughs for companion chatbots, support agents, voice agents, and research tools.
         </p>
       </div>
     </a>
@@ -164,10 +164,10 @@ mode: "custom"
       />
       <div className="flex flex-1 flex-col gap-2 px-4 pb-4 pt-3 text-left">
         <h3 className="text-base font-semibold text-gray-900 dark:text-zinc-100 group-hover:text-primary">
-          Self-host Mem0
+          Run Mem0 on your own servers
         </h3>
         <p className="text-sm text-gray-600 dark:text-zinc-400">
-          Use Open Source when you want memory without the hosted platform.
+          The open-source version. Run it as a library or a Docker stack, with your data on your infrastructure.
         </p>
       </div>
     </a>

docs/llms.txt

@@ -38,7 +38,7 @@ This mints an evaluation key in <5 seconds. Use it immediately against the Platf
 
 ## Identify the User's Setup
 
-Look at the user's imports first - they determine which product (Platform vs OSS) and which language you should quote docs from. **Mem0 Platform (managed) is the recommended path** - 4-line integration, sub-50ms retrieval, no infra. Route to OSS only when the user has an explicit self-hosting requirement.
+Look at the user's imports first - they determine which product (Platform vs OSS) and which language you should quote docs from. **Mem0 Platform (managed) is the recommended path** - 4-line integration, no infra to run. Route to OSS only when the user has an explicit self-hosting requirement.
 
 ### Platform - Python [Platform]
 
@@ -170,7 +170,7 @@ If the user is on a pre-current major (Python < 2, TS < 3, or Platform `output_f
 
 - [Introduction](https://docs.mem0.ai/introduction) [Both]: Use when the user wants a one-page overview of how memory fits between the LLM and the app.
 - [Vibe Code with Mem0](https://docs.mem0.ai/vibecoding) [Both]: Use when the user is in Claude Code, Cursor, or Windsurf and wants memory wired into their editor.
-- [Platform Overview](https://docs.mem0.ai/platform/overview) [Platform]: Use when the user picks the managed product - 4-line integration, sub-50ms retrieval, dashboard.
+- [Platform Overview](https://docs.mem0.ai/platform/overview) [Platform]: Use when the user picks the managed product - 4-line integration, hosted API, dashboard.
 - [Sign up as an agent](https://docs.mem0.ai/platform/agent-signup) [Platform]: Use when an AI agent needs to mint a Mem0 API key autonomously - four commands, no email or dashboard, human claims ownership later.
 - [Platform vs Open Source](https://docs.mem0.ai/platform/platform-vs-oss) [Both]: Use when the user is deciding between managed and self-hosted.
 - [Platform Quickstart](https://docs.mem0.ai/platform/quickstart) [Platform]: Use for the first Platform integration - API key plus `MemoryClient.add/search`.
@@ -204,9 +204,7 @@ 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.
@@ -283,6 +281,8 @@ 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.
@@ -418,7 +418,6 @@ 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
 
@@ -474,6 +473,7 @@ 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.

docs/migration/platform-v2-to-v3.mdx

@@ -146,9 +146,8 @@ curl -X POST 'https://api.mem0.ai/v3/memories/?page=1&page_size=50' \
 
 ```json
 {
-  "message": "Memory processing has been queued for background execution",
-  "status": "PENDING",
-  "event_id": "evt-uuid"
+  "event_id": "evt-uuid",
+  "status": "PENDING"
 }
 ```
 

docs/open-source/configuration.mdx

@@ -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): import from the OSS entry point, `import { Memory } from "mem0ai/oss"`, not `"mem0ai"`.
+- `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.
 
 <CardGroup cols={2}>
   <Card

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

@@ -97,7 +97,7 @@ const results = await memory.search("What movies do I like?", {
 
 ### 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`:
+To score with an LLM instead of a dedicated reranker, use the `llm_reranker` provider. It builds its own LLM from the reranker's config (defaulting to `openai` / `gpt-5-mini`) rather than reusing the Memory's main `llm`:
 
 ```typescript
 const memory = new Memory({
@@ -137,7 +137,7 @@ const memory = new Memory({
 | `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` |
+| `llm_reranker` | `openai` / `gpt-5-mini` | `provider`, `model`, `apiKey`, `llm` (nested override), `topK` |
 
 <Note>
   `rerank` is opt-in per search and a no-op when no `reranker` is configured. If the reranker call fails, Mem0 logs a warning and returns the original vector-ranked results.

docs/open-source/python-quickstart.mdx

@@ -35,8 +35,7 @@ pip install mem0ai
 from mem0 import Memory
 
 m = Memory()
-
-````
+```
 </Step>
 
 <Step title="Add a memory">
@@ -46,7 +45,7 @@ messages = [
     {"role": "assistant", "content": "Hey Alex! I'll remember your interests."}
 ]
 m.add(messages, user_id="alex")
-````
+```
 
 </Step>
 

docs/openapi.json

@@ -2035,7 +2035,8 @@
 									"type": "object",
 									"properties": {
 										"message": {
-											"type": "string"
+											"type": "string",
+											"description": "Only present when `infer` is `false`, where processing is synchronous."
 										},
 										"status": {
 											"type": "string",
@@ -2052,9 +2053,8 @@
 									}
 								},
 								"example": {
-									"message": "Memory processing has been queued for background execution",
-									"status": "PENDING",
-									"event_id": "2c4d1f44-4f7b-4b2f-9f6e-7b5b4f5a1234"
+									"event_id": "2c4d1f44-4f7b-4b2f-9f6e-7b5b4f5a1234",
+									"status": "PENDING"
 								}
 							}
 						}

docs/platform/advanced-memory-operations.mdx

@@ -27,7 +27,7 @@ pip install mem0ai
 </Step>
 <Step title="Export your API key">
 ```bash
-export MEM0_API_KEY="sk-platform-..."
+export MEM0_API_KEY="m0-..."
 ```
 </Step>
 <Step title="Create an async client">
@@ -49,7 +49,7 @@ npm install mem0ai
 </Step>
 <Step title="Load your API key">
 ```bash
-export MEM0_API_KEY="sk-platform-..."
+export MEM0_API_KEY="m0-..."
 ```
 </Step>
 <Step title="Instantiate the client">
@@ -158,6 +158,8 @@ await memory.update(matches.results[0].id, {
 
 ## Clean up
 
+Scope each delete call to a single entity id where possible; combining more than one entity filter in the same `delete_all` call is not guaranteed to AND them together today.
+
 <Tabs>
   <Tab title="Python">
 <Steps>

docs/platform/agent-signup.mdx

@@ -1,13 +1,19 @@
 ---
 title: Sign up as an agent
-description: "Zero-friction signup for AI agents: mint a working Mem0 API key in under five seconds. No email, no dashboard, no OTP."
+description: "Let an AI agent create its own Mem0 account and API key in four commands, with no email or dashboard needed."
 icon: "robot"
 iconType: "solid"
 ---
 
-If you're an AI agent (Claude Code, Cursor, Codex, Cline, Aider, Windsurf, or any other autonomous coding tool), you don't need a human to set up Mem0 for you. Four commands (install, sign up, push a memory, search it) and you have persistent memory.
+Normally a person signs up for Mem0, gets an API key from the dashboard, and hands it to their tools. Agent Mode skips that: a coding agent creates its own account from the terminal and starts storing memories immediately. A human can take ownership of that account later, without disrupting the agent.
 
-## The 4-command demo
+This page is written for both readers. The setup below is for the agent; [Claiming the account](#claiming-the-account-for-the-human-owner) is for the person who ends up owning it.
+
+<Note>
+**If you're an AI agent** (Claude Code, Cursor, Codex, Cline, Aider, Windsurf, or any other coding tool), run the four commands below. You don't need a human to set Mem0 up for you.
+</Note>
+
+## Sign up in four commands
 
 <CodeGroup>
 ```bash npm
@@ -17,11 +23,11 @@ npm install -g @mem0/cli
 # 2. Sign up as an agent (replace `claude-code` with your name)
 mem0 init --agent --agent-caller claude-code
 
-# 3. Push a memory
-mem0 add "I am using mem0"
+# 3. Store a memory
+mem0 add "Prefers TypeScript over JavaScript for new projects"
 
-# 4. Verify
-mem0 search "am I using mem0"
+# 4. Search it back
+mem0 search "what language should I use"
 ```
 
 ```bash pip
@@ -31,14 +37,22 @@ pip install mem0-cli
 # 2. Sign up as an agent (replace `claude-code` with your name)
 mem0 init --agent --agent-caller claude-code
 
-# 3. Push a memory
-mem0 add "I am using mem0"
+# 3. Store a memory
+mem0 add "Prefers TypeScript over JavaScript for new projects"
 
-# 4. Verify
-mem0 search "am I using mem0"
+# 4. Search it back
+mem0 search "what language should I use"
 ```
 </CodeGroup>
 
+Step 2 prints your new account's default scope and how to hand the account to a human later:
+
+```
+✓ Agent Mode active. Default user_id: swift-otter-4821
+
+  To claim this account later: mem0 init --email <your-email>
+```
+
 That's it. Under 30 seconds from a fresh shell to a working memory store.
 
 ## Identify yourself with `--agent-caller`
@@ -46,17 +60,17 @@ That's it. Under 30 seconds from a fresh shell to a working memory store.
 Replace `claude-code` in the `init` command with your actual agent name (`cursor`, `codex`, `cline`, `aider`, or any short label). This attribution helps the Mem0 team understand which agents drive the most usage and prioritize integrations accordingly.
 
 <Tip>
-**Forgot `--agent-caller` on init?** Run `mem0 identify <your-name>` after the fact, same key, just back-fills the attribution. Idempotent.
+**Forgot `--agent-caller` when you ran `init`?** Run `mem0 identify <your-name>` afterwards. It keeps the same API key and just fills in the name, so it is safe to run more than once.
 </Tip>
 
 ## What happened when you ran `mem0 init --agent`?
 
-- A **shadow account** was minted on the Mem0 backend with a fresh API key.
-- The key was saved to `~/.mem0/config.json` (mode 0600) on the machine you ran the command on.
-- A default `user_id` was provisioned so `mem0 add` / `mem0 search` work without explicit scope flags.
-- The account is **unclaimed**; no human owns it yet.
+- A new Mem0 account was created with its own API key.
+- The key was saved to `~/.mem0/config.json` on that machine, readable only by you (file mode `0600`).
+- A default `user_id` was created, so `mem0 add` and `mem0 search` work without you passing scope flags.
+- **Nobody owns the account yet.** It works, but no human is attached to it until someone claims it.
 
-Every command run against this key prints a one-line reminder asking you to surface the claim instructions to your human user.
+Every command run with this key prints a one-line reminder to pass the claim instructions on to your human.
 
 ## Claiming the account (for the human owner)
 
@@ -66,7 +80,7 @@ When the human is ready to take ownership of the agent's account (and all the me
 mem0 init --email you@yourcompany.com
 ```
 
-The CLI detects the existing Agent Mode config, sends a verification code, and upgrades the shadow account in-place:
+The CLI spots the existing Agent Mode setup, emails a verification code, and upgrades the account in place:
 
 - **The API key never changes**, so the agent isn't disrupted.
 - **All memories transfer** to the human's account.
@@ -76,31 +90,33 @@ Pass `--code 123456` to skip the interactive code prompt for fully non-interacti
 
 ## Rate limits and quotas
 
-Agent Mode signups are rate-limited to **5 per day per IP address** to prevent abuse. If you hit the limit, the CLI returns:
+Agent Mode signups are limited to **5 per day per IP address** to prevent abuse. If you hit the limit, the CLI tells you so:
 
 ```
 Daily Agent Mode signup limit reached for this network (5/day).
 Try again from a different IP or after midnight UTC.
 ```
 
-Unclaimed agent accounts get the standard Mem0 free-tier quotas. The human owner can upgrade after claiming.
+Calling the API directly instead of through the CLI returns a plain `403 Forbidden` with no explanation and no `Retry-After` header, so handle that case yourself.
+
+Until someone claims it, an agent account gets the standard Mem0 free-tier quotas. The human owner can upgrade after claiming.
 
 ## What's next
 
 <CardGroup cols={2}>
-<Card title="CLI Reference" icon="terminal" href="/platform/cli">
-Full command-by-command reference for `mem0 add`, `mem0 search`, `mem0 list`, and the rest.
+<Card title="CLI reference" icon="terminal" href="/platform/cli">
+Every command in full: `mem0 add`, `mem0 search`, `mem0 list`, and the rest.
 </Card>
 
-<Card title="Memory Operations" icon="database" href="/core-concepts/memory-operations/add">
+<Card title="Memory operations" icon="database" href="/core-concepts/memory-operations/add">
 How `add`, `search`, `update`, and `delete` work under the hood.
 </Card>
 
 <Card title="Mem0 MCP" icon="plug" href="/platform/mem0-mcp">
-Connect agents to Mem0 via the Model Context Protocol, as an alternative integration path.
+Give your agent memory as a set of tools instead of shell commands.
 </Card>
 
-<Card title="Platform Overview" icon="star" href="/platform/overview">
-The full Mem0 Platform feature set once you claim your account.
+<Card title="Platform overview" icon="star" href="/platform/overview">
+Everything the account unlocks once a human claims it.
 </Card>
 </CardGroup>

docs/platform/cli.mdx

@@ -9,9 +9,7 @@ The mem0 CLI lets you add, search, list, update, and delete memories directly fr
 
 Both implementations provide identical behavior: same commands, same options, same output formats.
 
-<Tip>
-**Built for AI agents.** Pass `--agent` (or `--json`) as a global flag on any command to get structured JSON output optimized for programmatic consumption: sanitized fields, no colors or spinners, and errors as JSON too. Drop it into any agent tool loop with zero extra parsing.
-</Tip>
+Running it inside an agent? Put `--agent` before any command to get clean JSON instead of human output. See [Use with AI agents](#use-with-ai-agents).
 
 ## Installation
 
@@ -97,6 +95,8 @@ mem0 init --api-key m0-xxx --user-id alice --force
 | `-u, --user-id` | Default user ID (skip prompt) |
 | `--email` | Login via email verification code |
 | `--code` | Verification code (use with `--email` for non-interactive login) |
+| `--agent` | Create an Agent Mode account, with no email needed |
+| `--agent-caller` | Name the agent running the command, such as `claude-code` |
 | `--force` | Overwrite existing config without confirmation |
 
 <Note>
@@ -117,10 +117,15 @@ echo "Loves hiking on weekends" | mem0 add --user-id alice
 |------|-------------|
 | `-u, --user-id` | Scope to a user |
 | `--agent-id` | Scope to an agent |
+| `--app-id` | Scope to an app |
+| `--run-id` | Scope to a single run or session |
 | `--messages` | Conversation messages as JSON |
 | `-f, --file` | Read messages from a JSON file |
 | `-m, --metadata` | Custom metadata as JSON |
 | `--categories` | Categories (JSON array or comma-separated) |
+| `--expires` | Expiration date, after which the memory stops being returned |
+| `--immutable` | Store the memory so it can never be updated or overwritten |
+| `--no-infer` | Store the text exactly as given, skipping fact extraction |
 | `-o, --output` | Output format: `text`, `json`, `quiet` |
 
 ### `mem0 search`
@@ -135,11 +140,15 @@ mem0 search "preferred tools" --user-id alice --output json --top-k 5
 | Flag | Description |
 |------|-------------|
 | `-u, --user-id` | Filter by user |
-| `-k, --top-k` | Number of results (default: 10) |
+| `--agent-id` | Filter by agent |
+| `--app-id` | Filter by app |
+| `--run-id` | Filter by run or session |
+| `-k, --top-k` | Number of results (default: 10). `--limit` does the same thing |
 | `--threshold` | Minimum similarity score (default: 0.3) |
 | `--rerank` | Enable reranking |
 | `--keyword` | Use keyword search instead of semantic |
 | `--filter` | Advanced filter expression (JSON) |
+| `--fields` | Return only the named fields |
 | `-o, --output` | Output format: `text`, `json`, `table` |
 
 ### `mem0 list`
@@ -155,6 +164,9 @@ mem0 list --user-id alice --after 2024-01-01 --page-size 50
 | Flag | Description |
 |------|-------------|
 | `-u, --user-id` | Filter by user |
+| `--agent-id` | Filter by agent |
+| `--app-id` | Filter by app |
+| `--run-id` | Filter by run or session |
 | `--page` | Page number (default: 1) |
 | `--page-size` | Results per page (default: 100) |
 | `--category` | Filter by category |
@@ -171,6 +183,10 @@ mem0 get 7b3c1a2e-4d5f-6789-abcd-ef0123456789
 mem0 get 7b3c1a2e-4d5f-6789-abcd-ef0123456789 --output json
 ```
 
+| Flag | Description |
+|------|-------------|
+| `-o, --output` | Output format: `text`, `json` |
+
 ### `mem0 update`
 
 Update the text or metadata of an existing memory.
@@ -181,6 +197,11 @@ mem0 update <memory-id> --metadata '{"priority": "high"}'
 echo "new text" | mem0 update <memory-id>
 ```
 
+| Flag | Description |
+|------|-------------|
+| `-m, --metadata` | Replace the memory's metadata with this JSON |
+| `-o, --output` | Output format: `text`, `json`, `quiet` |
+
 ### `mem0 delete`
 
 Delete a single memory, all memories for a scope, or an entire entity.
@@ -201,6 +222,10 @@ mem0 delete --all --user-id alice --dry-run
 
 | Flag | Description |
 |------|-------------|
+| `-u, --user-id` | Scope the deletion to a user |
+| `--agent-id` | Scope the deletion to an agent |
+| `--app-id` | Scope the deletion to an app |
+| `--run-id` | Scope the deletion to a run or session |
 | `--all` | Delete all memories matching scope filters |
 | `--entity` | Delete the entity and all its memories |
 | `--project` | With `--all`: delete all memories project-wide |
@@ -217,6 +242,12 @@ mem0 import data.json --user-id alice
 
 The file should be a JSON array where each item has a `memory` (or `text` or `content`) field and optional `user_id`, `agent_id`, and `metadata` fields.
 
+| Flag | Description |
+|------|-------------|
+| `-u, --user-id` | Default user for items that do not set their own |
+| `--agent-id` | Default agent for items that do not set their own |
+| `-o, --output` | Output format: `text`, `json` |
+
 ### `mem0 config`
 
 View or modify the local CLI configuration.
@@ -237,6 +268,18 @@ mem0 entity list agents --output json
 mem0 entity delete --user-id alice --force
 ```
 
+Deleting an entity removes it and every memory belonging to it. Preview first with `--dry-run`.
+
+| Flag | Description |
+|------|-------------|
+| `-u, --user-id` | The user to delete |
+| `--agent-id` | The agent to delete |
+| `--app-id` | The app to delete |
+| `--run-id` | The run or session to delete |
+| `--dry-run` | Show what would be deleted, without deleting it |
+| `--force` | Skip confirmation prompt |
+| `-o, --output` | Output format: `text`, `json` |
+
 ### `mem0 event`
 
 Inspect background processing events created by async operations (e.g. bulk deletes, large add jobs).
@@ -261,23 +304,15 @@ Verify your API connection and display the current project.
 mem0 status
 ```
 
-### `mem0 version`
+### `mem0 whoami`
 
-Print the CLI version.
+Print the identity the CLI is currently using. After `mem0 init --agent`, the
+server issues an identifier (`default_user_id`, for example
+`user_a1b2c3d4e5f6`) and the CLI stores it in `~/.mem0/config.json`. That value
+is the agent's stable identity across runs, and it is the row key on the
+[AGENTRUSH leaderboard](https://mem0.ai/agentrush).
 
-```bash
-mem0 version
-```
-
-## Identity helper: `mem0 whoami`
-
-After running `mem0 init --agent`, the CLI persists a server-issued identifier
-(`default_user_id`, e.g. `user_a1b2c3d4e5f6`) in `~/.mem0/config.json`. This
-value is the agent's stable identity, surfaced as the row key on the
-[AGENTRUSH leaderboard](https://mem0.ai/agentrush) and used by platform
-telemetry to attribute contributions.
-
-Print it without parsing the config file by hand:
+Use this instead of parsing the config file by hand:
 
 ```bash
 mem0 whoami
@@ -288,89 +323,47 @@ mem0 whoami
 No network call. The command exits with code `1` if no `default_user_id` is
 configured yet. In that case, run `mem0 init --agent` first.
 
-## AGENTRUSH: `mem0 agent-rush <add | search>`
+### `mem0 identify`
 
-AGENTRUSH is a 7-day public competition where AI agents (not humans) compete
-inside a single shared Mem0 project. Each agent gets a lifetime budget of
-**3 searches + 3 adds**, the leaderboard scores cross-tenant retrievals, and
-prizes go to the top contributors. See [mem0.ai/agentrush](https://mem0.ai/agentrush)
-for current event details.
+Attach an agent name to an account created with `mem0 init --agent`, if you did
+not pass `--agent-caller` at the time. It keeps the same API key and only fills
+in the name, so it is safe to run more than once. See
+[Sign up as an agent](/platform/agent-signup).
 
-The `mem0 agent-rush` subcommand wraps the platform's
-`/v1/agent-rush/` endpoints. Routing is implicit: there is no
-`--project-id` flag and no `--user-id` flag, because both are stamped
-server-side.
+### `mem0 help`
 
-### Bootstrap once, then play
-
-```bash
-# 1. Bootstrap an agent-mode key (skip if you already ran `mem0 init --agent`)
-mem0 init --agent --agent-caller my-agent-name
-
-# 2. Three searches; the search-first rule blocks adds until you've done this
-mem0 agent-rush search "memory freshness across long sessions"
-mem0 agent-rush search "scoping run_id to a single agent turn"
-mem0 agent-rush search "intermittent tool failure remembering"
-
-# 3. Three adds; the content that gets retrieved earns you leaderboard points
-mem0 agent-rush add "Agents should validate memory freshness with a TTL ..."
-mem0 agent-rush add "Scoping memories by run_id avoids cross-session ..."
-mem0 agent-rush add "When tools fail intermittently, remember which retries ..."
-
-# 4. Check your row
-mem0 whoami
-# Then visit https://mem0.ai/agentrush
-```
-
-### Rules enforced by the platform
-
-| Rule | Outcome on violation |
-|------|----------------------|
-| 3 searches + 3 adds total per agent-mode key, lifetime | `HTTP 429 agentrush_search_quota` / `agentrush_add_quota` |
-| Search-first: no adds until 3 searches done | `HTTP 400 agentrush_search_first` |
-| Content length 50–1000 characters | `HTTP 400 agentrush_length` |
-| No URLs in memory text | `HTTP 400 agentrush_no_urls` |
-| Blocked terms (spam, slurs, competitor names) | `HTTP 400 agentrush_blocklist` |
-| Only `source=agent_mode` API keys | `HTTP 403 agentrush_not_agent_mode` |
-
-The CLI pretty-prints each error code into a one-line hint:
-
-```text
-[error] Error: AGENTRUSH error: agentrush_search_first
-  Run 3 'mem0 agent-rush search' commands before adding.
-```
-
-### Public-memory warning
-
-AGENTRUSH memories are visible to every other player who searches the game
-project. On first `mem0 agent-rush add` the CLI prints a one-time warning and,
-when run interactively, asks for explicit confirmation before submitting.
-**Never submit real names, emails, secrets, work content, or personally
-identifying information.** The acknowledgement is stored under
-`agent_rush.acknowledged_at` in `~/.mem0/config.json` so you are only asked
-once per machine.
-
-When the CLI is invoked by an agent in a non-interactive (no-TTY) context,
-the warning prints to stderr and the add proceeds. Agents cannot answer
-y/N prompts. Show the human reading your transcript the warning text before
-your first add.
+Print the command tree. Adding `--json` returns the whole tree as structured
+data, so an agent can discover the available commands and options for itself.
 
 ## Output formats
 
-All commands support the `--output` flag to control how results are displayed:
+Most commands take `-o, --output` to control how results are displayed. Not every command accepts every format:
 
-| Format | Description |
-|--------|-------------|
-| `text` | Human-readable output with colors and formatting (default for most commands) |
-| `json` | Structured JSON, suitable for piping to `jq` or consumption by AI agents |
-| `table` | Tabular format (default for `list`) |
-| `quiet` | Minimal output: just IDs or status codes |
-| `agent` | Structured JSON envelope with sanitized fields, set automatically by `--json`/`--agent` |
+| Format | Description | Accepted by |
+|--------|-------------|-------------|
+| `text` | Human-readable output with colors and formatting. The default everywhere except `list` | every command |
+| `json` | Structured JSON, suitable for piping to `jq` | every command |
+| `table` | Tabular format, and the default for `list` | `search`, `list` |
+| `quiet` | Minimal output: just IDs or status codes | `add`, `update`, `delete` |
 
-Example with JSON output:
+Passing a format a command does not accept is an error, so check the command's own flag table above.
+
+There is no `agent` value for `--output`. Agent mode is turned on by the global `--json`/`--agent` flag placed before the command name, and it overrides `--output`. See [Use with AI agents](#use-with-ai-agents).
+
+The exact JSON shape depends on the command. `search` returns a bare array of memories, while `list` returns an envelope object with the memories under `data`:
 
 ```bash
-mem0 search "user preferences" --user-id alice --output json | jq '.data.results[].memory'
+# search: results are the top-level array
+mem0 search "user preferences" --user-id alice --output json | jq '.[].memory'
+
+# list: results are nested under .data
+mem0 list --user-id alice --output json | jq '.data[].memory'
+```
+
+Agent mode always returns the envelope, whichever command you run, so `.data[]` works everywhere:
+
+```bash
+mem0 --agent search "user preferences" --user-id alice | jq '.data[].memory'
 ```
 
 ## Use with AI agents
@@ -424,6 +417,22 @@ Two other agent-friendly features:
 
 For non-interactive environments (CI, agent runtimes), set credentials via `mem0 init --api-key m0-xxx --user-id alice --force` or the `MEM0_API_KEY` environment variable.
 
+## Errors and exit codes
+
+Every command exits `0` when it succeeds and `1` when it fails, so `if mem0 ...; then` works as you would expect in a script. In agent mode (`--json` or `--agent`), failures still print a JSON envelope to stdout alongside the non-zero exit, so you can parse successes and failures the same way.
+
+Errors you are most likely to hit:
+
+| Message | Cause | Fix |
+|---------|-------|-----|
+| `Authentication failed` | The API key is missing, wrong, or revoked | Run `mem0 init` again, or get a new key from the dashboard |
+| `No content provided. Pass text, --messages, --file, or pipe via stdin.` | `mem0 add` was called with nothing to store | Give it text, a file, or piped input |
+| `Invalid JSON in --messages` / `--metadata` / `--filter` | The JSON value would not parse | Check the quoting, especially inside shell single quotes |
+| `Invalid date format for --expires. Use YYYY-MM-DD` | `--expires` got something other than a date | Use `YYYY-MM-DD`, and make sure the date is in the future |
+| `--threshold must be between 0.0 and 1.0.` | Score threshold out of range | Pass a value between `0.0` and `1.0` |
+
+Run [`mem0 status`](#mem0-status) to check whether the CLI can reach the API and which project the key belongs to. It reports the connection state, the API URL, and any error.
+
 ## Environment variables
 
 | Variable | Description |
@@ -434,17 +443,28 @@ For non-interactive environments (CI, agent runtimes), set credentials via `mem0
 | `MEM0_AGENT_ID` | Default agent ID |
 | `MEM0_APP_ID` | Default app ID |
 | `MEM0_RUN_ID` | Default run ID |
+| `MEM0_TELEMETRY` | Set to `false` to turn off usage telemetry. On by default |
 
 Environment variables take precedence over values in the config file, which take precedence over defaults.
 
 ## Global flags
 
-These flags are available on all commands:
+These two flags belong to `mem0` itself, so they go **before** the command name:
 
 | Flag | Description |
 |------|-------------|
 | `--json` | Enable agent mode: structured JSON envelope output, no colors or spinners |
 | `--agent` | Alias for `--json` |
+| `--version` | Print the CLI version and exit |
+
+<Warning>
+On `init` only, `--agent` means something different. `mem0 init --agent` creates an Agent Mode account (see [Sign up as an agent](/platform/agent-signup)); it does not switch the output to JSON. To get JSON from `init`, put the flag first: `mem0 --json init`.
+</Warning>
+
+The following flags are accepted by most commands, but they belong to the command, so they go **after** the command name:
+
+| Flag | Description |
+|------|-------------|
 | `--api-key` | Override the configured API key for this request |
 | `--base-url` | Override the configured API base URL for this request |
 | `-o, --output` | Set the output format |
@@ -456,11 +476,11 @@ These flags are available on all commands:
 Store your first memory in under five minutes using the SDK or CLI
 </Card>
 
-<Card title="Memory Operations" icon="database" href="/core-concepts/memory-operations/add">
+<Card title="Memory operations" icon="database" href="/core-concepts/memory-operations/add">
 Learn about add, search, update, and delete operations in depth
 </Card>
 
-<Card title="API Reference" icon="code" href="/api-reference/memory/add-memories">
+<Card title="API reference" icon="code" href="/api-reference/memory/add-memories">
 See the complete REST API documentation
 </Card>
 </CardGroup>

docs/platform/faqs.mdx

@@ -17,7 +17,7 @@ iconType: "solid"
   </Accordion>
 
     <Accordion title="What are the key features of Mem0?">
-        - **User, Session, and AI Agent Memory**: Retains information across sessions and interactions for users and AI agents, ensuring continuity and context.
+        - **User, Agent, App, and Run Memory**: Scopes memories to the individual, AI agent, application, and conversation/session they belong to, ensuring continuity and context.
         - **Adaptive Personalization**: Continuously updates memories based on user interactions and feedback.
         - **Developer-Friendly API**: Offers a straightforward API for seamless integration into various applications.
         - **Platform Consistency**: Ensures consistent behavior and data across different platforms and devices.
@@ -84,10 +84,12 @@ iconType: "solid"
         - Include specific examples or cases rather than general definitions
     </Accordion>
 
-    <Accordion title="How do I configure Mem0 for AWS Lambda?">
-        When deploying Mem0 on AWS Lambda, you'll need to modify the storage directory configuration due to Lambda's file system restrictions. By default, Lambda only allows writing to the `/tmp` directory.
+    <Accordion title="How do I configure Mem0 for AWS Lambda? (self-hosted / OSS only)">
+        This applies only if you are running the self-hosted OSS `Memory` class yourself (for example with a local vector store) inside a Lambda function. If you're using the hosted Mem0 Platform (`MemoryClient`), there is nothing to configure here: the Platform stores all memory data on Mem0's servers, not on your Lambda instance's filesystem, so Lambda's `/tmp`-only write restriction does not apply to you.
 
-        To configure Mem0 for AWS Lambda, set the `MEM0_DIR` environment variable to point to a writable directory in `/tmp`:
+        When deploying self-hosted Mem0 on AWS Lambda, you'll need to modify the storage directory configuration due to Lambda's file system restrictions. By default, Lambda only allows writing to the `/tmp` directory.
+
+        To configure self-hosted Mem0 for AWS Lambda, set the `MEM0_DIR` environment variable to point to a writable directory in `/tmp`:
 
         ```bash
         MEM0_DIR=/tmp/.mem0
@@ -149,7 +151,7 @@ iconType: "solid"
         2. Go to **Settings → Account**.
         3. Click **Delete account** and confirm.
 
-        Deletion is immediate and irreversible. The following is removed:
+        Deletion is asynchronous: the request is accepted immediately and processed in the background, typically within a few minutes. Once it completes, the following is removed:
 
         - Your user profile and login credentials
         - All memories, agents, and runs you created
@@ -157,7 +159,7 @@ iconType: "solid"
         - Organizations you solely own, along with their data
         - Your membership in any shared organizations (the orgs themselves are not affected)
 
-        Any application still using your old API keys will start receiving `401 Unauthorized` responses immediately. If you'd like to use Mem0 again later, you can create a new account at any time: it will start fresh with no data carried over.
+        Your old API keys stop working once deletion completes, not the instant you click confirm. If you'd like to use Mem0 again later, you can create a new account at any time: it will start fresh with no data carried over.
     </Accordion>
 
 </AccordionGroup>

docs/platform/features/advanced-retrieval.mdx

@@ -51,7 +51,7 @@ results = client.search(
 # Smart home assistant finding device preferences
 results = client.search(
     query="How do I like my bedroom temperature?",
-    rerank=True,           # Get most recent preferences first
+    rerank=True,           # Closest-matching preferences first
     filters={"user_id": "user123"},
 )
 
@@ -86,7 +86,7 @@ results = client.search(
 # Find learning progress for specific topics
 results = client.search(
     query="Python programming progress and difficulties",
-    rerank=True,           # Recent progress first
+    rerank=True,           # Closest-matching progress notes first
     filters={"user_id": "student123"},
 )
 
@@ -108,21 +108,13 @@ def quick_search(query, user_id):
         filters={"user_id": user_id},
     )
 
-# Reranked search - good for most applications
+# Reranked search - good when result order matters
 def standard_search(query, user_id):
     return client.search(
         query=query,
         rerank=True,
         filters={"user_id": user_id},
     )
-
-# Reranked search - good for critical applications
-def precise_search(query, user_id):
-    return client.search(
-        query=query,
-        rerank=True,
-        filters={"user_id": user_id},
-    )
 ```
 
 ```javascript JavaScript
@@ -133,21 +125,13 @@ function quickSearch(query, userId) {
     });
 }
 
-// Reranked search - good for most applications
+// Reranked search - good when result order matters
 function standardSearch(query, userId) {
     return client.search(query, {
         filters: { user_id: userId },
         rerank: true,
     });
 }
-
-// Reranked search - good for critical applications
-function preciseSearch(query, userId) {
-    return client.search(query, {
-        filters: { user_id: userId },
-        rerank: true,
-    });
-}
 ```
 </CodeGroup>
 

docs/platform/features/async-client.mdx

@@ -66,7 +66,7 @@ await client.search("What is Alice's favorite sport?", filters={"user_id": "alic
 ```
 
 ```javascript JavaScript
-await client.search("What is Alice's favorite sport?", { filters: { userId: "alice" } });
+await client.search("What is Alice's favorite sport?", { filters: { user_id: "alice" } });
 ```
 
 </CodeGroup>
@@ -124,7 +124,7 @@ await client.deleteAll({ userId: "alice" });
 </CodeGroup>
 
 <Note>
-  At least one filter (`user_id`, `agent_id`, `app_id`, or `run_id`) is required: calling `delete_all` with no filters raises an error to prevent accidental data loss. You can pass `"*"` as a value to delete all memories for a given entity type (e.g., `user_id="*"` removes memories for every user). A full project wipe requires all four filters set to `"*"`.
+  At least one filter (`user_id`, `agent_id`, `app_id`, or `run_id`) is required: calling `delete_all` with no filters raises an error to prevent accidental data loss. You can pass `"*"` as a value to delete all memories for a given entity type (e.g., `user_id="*"` removes memories for every user). A full project wipe requires all four filters set to `"*"`. When multiple entity filters are combined on a single `delete_all` call, only one is currently guaranteed to be honored; scope each call to a single entity id to be safe.
 </Note>
 
 ### History

docs/platform/features/contextual-add.mdx

@@ -1,251 +0,0 @@
----
-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

@@ -1,201 +0,0 @@
----
-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

@@ -75,6 +75,8 @@ print(response)
 ```
 </CodeGroup>
 
+This "Updated custom categories" message is specific to a PATCH-style partial update, which is what `client.project.update()` sends. Calling the raw API with a full PUT instead returns a generic `{"message": "Project updated successfully."}`, regardless of which fields changed.
+
 ### 2. Confirm the active catalog
 
 <CodeGroup>
@@ -86,16 +88,19 @@ print(categories)
 
 ```json Output
 {
+  "name": "default-project",
+  "description": null,
   "custom_categories": [
     {"lifestyle_management_concerns": "Tracks daily routines, habits, hobbies and interests including cooking, time management and work-life balance"},
     {"seeking_structure": "Documents goals around creating routines, schedules, and organized systems in various life areas"},
     {"personal_information": "Basic information about the user including name, preferences, and personality traits"}
-  ]
+  ],
+  "custom_instructions": null
 }
 ```
 </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.
+`project.get()` always returns the full project object, not just the fields you asked for. The `fields` parameter is accepted but has no filtering effect server-side, so read `custom_categories` off the full response. `update` also accepts a plain list of names, such as `["billing", "support"]`, in which case `custom_categories` comes back as 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"`.
@@ -306,16 +311,19 @@ client.project.get(["custom_categories"])
 
 ```json Output
 {
-    "custom_categories": null
+    "name": "default-project",
+    "description": null,
+    "custom_categories": null,
+    "custom_instructions": null
 }
 ```
 </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.
+The response is always the full project object; `["custom_categories"]` does not filter it down. A project that has never set a list returns `custom_categories: 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.
+- `client.project.get(["custom_categories"])` returns the full project object; read the `custom_categories` key off it to see the list you set.
 - `client.get_all(filters={"user_id": ...})` shows populated `categories` lists on new memories.
 - The Mem0 dashboard (Project → Memories) displays the custom labels in the Category column.
 

docs/platform/features/custom-instructions.mdx

@@ -68,6 +68,8 @@ console.log(response.customInstructions);
 ```
 </CodeGroup>
 
+`project.get()` always returns the full project object. The `fields` parameter is accepted but has no filtering effect server-side, so `response["custom_instructions"]` above is a key on the full response, not a pre-filtered payload.
+
 ### Best Practice Template
 
 Structure your instructions using this proven template:

docs/platform/features/direct-import.mdx

@@ -22,17 +22,35 @@ messages = [
 client.add(messages, user_id="alice", infer=False)
 ```
 
-```markdown Output
-[]
+```json Output
+{
+  "message": "Memories stored successfully",
+  "status": "SUCCEEDED",
+  "event_id": "8c1f5e2b-3d47-4a9e-b6c1-5f2a7d3e9b04",
+  "results": [
+    {
+      "id": "19d6d7aa-2454-4e58-96fc-e74d9e9f8dd1",
+      "data": { "memory": "Alice loves playing badminton" },
+      "event": "ADD"
+    },
+    {
+      "id": "8557f05d-7b3c-47e5-b409-9886f9e314fc",
+      "data": { "memory": "Alice mostly cooks at home because of her gym plan" },
+      "event": "ADD"
+    }
+  ]
+}
 ```
 </CodeGroup>
 
-You can see that the output of the add call is an empty list.
-
-<Note>Only messages with the role "user" will be used for storage. Messages with roles such as "assistant" or "system" will be ignored during the storage process.</Note>
+<Note>
+Direct import stores `"user"` and `"assistant"` messages alike, regardless of which entity ids you pass. Only `"system"` messages are dropped. Each surviving message becomes one memory, with its content stored verbatim.
+</Note>
 
 <Warning>
-Direct import skips the inference pipeline, so it also skips duplicate detection. If you later send the same fact with `infer=True`, Mem0 will store a second copy. Pick one mode per memory source unless you truly want both versions.
+Direct import skips semantic duplicate detection: two different phrasings of the same fact will be stored as two separate memories. Exact repeats in the same scope are deduplicated by an exact hash of the text, so resending identical text is safe. Mixing `infer=False` and `infer=True` for the same fact can still produce two memories, since neither path checks the other for duplicates.
+
+Direct import also does not support multimodal content (`image_url`, `pdf_url`, `txt_url`, `mdx_url`). Those messages are skipped without raising an error, so they are missing from `results` rather than reported as a failure. Pass plain string content only when `infer=False`.
 </Warning>
 
 ## How to Retrieve Memories

docs/platform/features/entity-scoped-memory.mdx

@@ -36,11 +36,11 @@ Call `client.project.get()` to verify your connection. It should return your pro
 | User        | `user_id`  | Persistent persona or account                    | `"customer_6412"`   |
 | Agent       | `agent_id` | Distinct agent persona or tool                   | `"meal_planner"`    |
 | Application | `app_id`   | White-label app or product surface               | `"ios_retail_demo"` |
-| Session     | `run_id`   | Short-lived flow, ticket, or conversation thread | `"ticket-9241"`     |
+| Run         | `run_id`   | Short-lived flow, ticket, or session thread      | `"ticket-9241"`     |
 
-- **Writes** (`client.add`) accept any combination of these fields. Absent fields default to `null`.
-- **Reads** (`client.search`, `client.get_all`, exports, deletes) accept the same identifiers inside the `filters` JSON object.
-- **Implicit null scoping**: Passing only `{"user_id": "alice"}` automatically restricts results to records where `agent_id`, `app_id`, and `run_id` are `null`. Add wildcards (`"*"`), explicit lists, or additional filters when you need broader joins.
+- **Writes** (`client.add`) accept any combination of these fields. `app_id` and `run_id` are stored on every memory the call produces. `user_id` and `agent_id` behave differently on the default extraction path: each extracted fact is attributed to whoever stated it, so a record carries `user_id` or `agent_id`, not both. Absent fields default to `null`.
+- **Reads** (`client.search`, `client.get_all`, exports) accept the same identifiers inside the `filters` JSON object. Deletes (`client.delete_all`) scope via query parameters instead; the SDK builds these for you when you pass `user_id=...`, `run_id=...`, and so on.
+- **Unmentioned entities are not constrained**: Passing only `{"user_id": "alice"}` matches on `user_id` alone. It does not require `agent_id`, `app_id`, or `run_id` to be `null`, so records that also have those fields set are still returned.
 
 <Warning>
   **Common Pitfall**: If you create a memory with `user_id="alice"` but the other fields default to `null`, then search with `{"AND": [{"user_id": "alice"}, {"agent_id": "bot"}]}` will return nothing because you're looking for a memory where `agent_id="bot"`, not `null`.
@@ -78,7 +78,9 @@ client.add(
 The response will include one or more memory IDs. Check the dashboard → Memories to confirm the entry appears under the correct user, agent, app, and run.
 
 <Warning>
-Platform writes that include both `user_id` and `agent_id` (or other combinations) are persisted as separate records per entity so we can enforce privacy boundaries. Each record carries exactly one primary entity, which is why `{"AND": [{"user_id": ...}, {"agent_id": ...}]}` never returns results. Plan searches per entity scope or combine scopes with `OR`.
+Passing both `user_id` and `agent_id` to `client.add` does **not** produce records with both fields set. On the default extraction path, each extracted fact is attributed to the speaker who stated it: facts from `user` messages are stored with `user_id` set and `agent_id` null, facts from `assistant` messages with `agent_id` set and `user_id` null. `app_id` and `run_id` are carried on every record either way.
+
+As a result, `{"AND": [{"user_id": ...}, {"agent_id": ...}]}` returns nothing for memories created this way. Use `OR` to match either scope. Records with both fields populated only come from [Direct Import](/platform/features/direct-import) (`infer=False`), which writes your text verbatim without attribution splitting.
 </Warning>
 
 The HTTP equivalent uses `POST /v1/memories/` with the same identifiers in the JSON body. See the Add Memories API reference for REST details.
@@ -130,7 +132,7 @@ print(agent_results)
 ```
 
 <Tip icon="compass">
-Writes can include multiple identifiers, but searches resolve one entity space at a time. Query user scope *or* agent scope in a given call: combining both returns an empty list today.
+A search that `AND`s `user_id` **and** `agent_id` only matches records that have both fields set, which the default extraction path never produces. Use `OR` when you want either scope to match on its own.
 </Tip>
 
 <Tip icon="sparkles">
@@ -141,11 +143,11 @@ Want to experiment with AND/OR logic, nested operators, or wildcards? The <Link
 ```python
 app_scope = {
     "AND": [
-        {"app_id": "concierge_portal"}
-    ],
-    "OR": [
-        {"user_id": "*"},
-        {"agent_id": "*"}
+        {"app_id": "concierge_portal"},
+        {"OR": [
+            {"user_id": "*"},
+            {"agent_id": "*"}
+        ]}
     ]
 }
 

docs/platform/features/feedback-mechanism.mdx

@@ -150,7 +150,7 @@ Handle potential errors when submitting feedback:
 
 ```python Python
 from mem0 import MemoryClient
-from mem0.exceptions import MemoryNotFoundError, NetworkError
+from mem0.exceptions import MemoryError, ValidationError
 
 client = MemoryClient(api_key="your_api_key")
 
@@ -161,12 +161,10 @@ try:
         feedback_reason="Helpful context for user query"
     )
     print("Feedback submitted successfully")
-except MemoryNotFoundError:
-    print("Memory not found")
-except NetworkError as e:
-    print(f"Network error: {e}")
-except Exception as e:
-    print(f"Unexpected error: {e}")
+except ValidationError:
+    print("memory_id is not a valid UUID")
+except MemoryError as e:
+    print(f"memory_id was not found or not accessible ({e.error_code})")
 ```
 
 ```javascript JavaScript
@@ -182,16 +180,23 @@ try {
     });
     console.log("Feedback submitted successfully");
 } catch (error) {
-    if (error.status === 404) {
-        console.log("Memory not found");
+    if (error.errorCode === "HTTP_400") {
+        console.log("memory_id is not a valid UUID");
+    } else if (error.errorCode === "HTTP_500") {
+        console.log("Server error - the memory_id may not exist or be inaccessible, or it may be transient; retry if it persists");
     } else {
-        console.log(`Error: ${error.message}`);
+        console.log(`Unexpected error: ${error.message}`);
     }
 }
 ```
 
 </CodeGroup>
 
+### Handling an unknown or malformed `memory_id`
+
+- A malformed `memory_id` (not a valid UUID) returns a clean `400` with `{"error": "Invalid memory_id"}`.
+- A well-formed but non-existent `memory_id` (or one that doesn't belong to your org or project) currently returns a `500 Internal Server Error` instead of a `404`. This is a server-side bug being tracked in [MEM-5745](https://linear.app/mem0/issue/MEM-5745). A `5xx` can also be a transient server error, so don't treat it as a definitive "not found"; to avoid the case entirely, only call `feedback` with a `memory_id` you just read from a `get_all` or `search` response.
+
 ## Feedback Analytics
 
 Track the impact of your feedback by monitoring memory performance over time. Consider implementing:

docs/platform/features/graph-memory.mdx

@@ -90,7 +90,7 @@ results = client.search(
 Earlier versions of Mem0 offered graph memory by connecting an **external graph database** (Neo4j, Memgraph, Kuzu, Apache AGE, or Neptune) through an `enable_graph` flag and a `graph_store` configuration block. That integration has been replaced by **native, built-in Graph Memory**:
 
 - **No external graph store.** The graph is built inside Mem0 from your memories. There is nothing to provision or connect.
-- **Always on, all plans.** The `enable_graph` flag is no longer needed; Graph Memory is automatic. (If you still send the parameter, it is ignored.)
+- **Always on, all plans.** The `enable_graph` flag is no longer needed: Graph Memory is automatic, and `relations` is always an empty list regardless of whether you send it. On non-paginated `get_all` (v1.1 format), sending `enable_graph=True` still adds an empty `"relations": []` key to the response for backward compatibility; it does not restore the old graph behavior.
 - **Connections power retrieval directly.** Entity connections are folded into the combined `score` on each result. The standalone `relations` field that the external graph store returned is no longer populated. If your application read that field, see the migration guide below.
 
 <Card title="Platform Migration Guide" icon="arrow-right" href="/migration/platform-v2-to-v3">

docs/platform/features/group-chat.mdx

@@ -1,45 +1,33 @@
 ---
 title: Group Chat
-description: 'Enable multi-participant conversations with automatic memory attribution to individual speakers'
+description: 'Enable multi-participant conversations and scope each speaker with user_id or agent_id'
 ---
 
 ## Overview
 
-The Group Chat feature enables Mem0 to process conversations involving multiple participants and automatically attribute memories to individual speakers. This allows for precise tracking of each participant's preferences, characteristics, and contributions in collaborative discussions, team meetings, or multi-agent conversations.
+The Group Chat feature helps you use Mem0 with conversations involving multiple participants, such as team meetings or multi-agent conversations. You control which speaker a memory belongs to by scoping each `add()` call with `user_id`, `agent_id`, and `run_id`; Mem0 does not infer that scope automatically from the conversation.
 
-When you provide messages with participant names, Mem0 automatically:
-- Extracts memories from each participant's messages separately
-- Attributes each memory to the correct speaker using their name as the `user_id` or `agent_id`
-- Maintains individual memory profiles for each participant
+When you scope conversations correctly, Mem0:
+- Extracts memories from each participant's messages
+- Keeps each participant's memories in a separate profile, addressed by the `user_id` or `agent_id` you assigned them
+- Lets you retrieve any participant's memories independently using filters
 
 ## How Group Chat Works
 
-Mem0 automatically detects group chat scenarios when messages contain a `name` field:
+Mem0 does not automatically split a multi-participant conversation into separate memories per speaker. A `name` field on a message is stored as context for extraction, but it does not change which `user_id` or `agent_id` the resulting memories are scoped to: that scope is always whatever `user_id`, `agent_id`, or `run_id` you pass to `add()`.
 
-```json
-{
-  "role": "user",
-  "name": "Alice",
-  "content": "Hey team, I think we should use React for the frontend"
-}
-```
-
-When names are present, Mem0:
-- Formats messages as `"Alice (user): content"` for processing
-- Extracts memories with proper attribution to each speaker
-- Stores memories with the speaker's name as the `user_id` (for users) or `agent_id` (for assistants/agents)
+To keep separate memory profiles per participant, scope each participant's messages explicitly: call `add()` once per participant with their own `user_id`, or use `run_id` to group the conversation and filter by participant in your own message metadata.
 
 ### Memory Attribution Rules
 
-- **User Messages**: The `name` field becomes the `user_id` in stored memories
-- **Assistant/Agent Messages**: The `name` field becomes the `agent_id` in stored memories
-- **Messages without names**: Fall back to standard processing using role as identifier
+- Memories are always scoped to the `user_id`, `agent_id`, and `run_id` you pass to `add()`, not to the `name` field on individual messages.
+- If you need per-participant memories, call `add()` separately for each participant's messages with that participant's `user_id`.
 
 ## Using Group Chat
 
 ### Basic Group Chat
 
-Add memories from a multi-participant conversation:
+Scope each participant's messages with their own `user_id` and a shared `run_id` for the session. Call `add()` once per participant:
 
 <CodeGroup>
 
@@ -48,46 +36,60 @@ from mem0 import MemoryClient
 
 client = MemoryClient(api_key="your-api-key")
 
-# Group chat with multiple users
-messages = [
-    {"role": "user", "name": "Alice", "content": "Hey team, I think we should use React for the frontend"},
-    {"role": "user", "name": "Bob", "content": "I disagree, Vue.js would be better for our use case"},
-    {"role": "user", "name": "Charlie", "content": "What about considering Angular? It has great enterprise support"},
-    {"role": "assistant", "content": "All three frameworks have their merits. Let me summarize the pros and cons of each."}
-]
-
+# Each participant gets their own user_id; run_id ties them to one session
+client.add(
+    [{"role": "user", "content": "Hey team, I think we should use React for the frontend"}],
+    user_id="alice", run_id="group_chat_1",
+)
+client.add(
+    [{"role": "user", "content": "I'd prefer Vue.js for our use case"}],
+    user_id="bob", run_id="group_chat_1",
+)
 response = client.add(
-    messages,
-    run_id="group_chat_1",
-    infer=True
+    [{"role": "user", "content": "Consider Angular, it has great enterprise support"}],
+    user_id="charlie", run_id="group_chat_1",
 )
 print(response)
 ```
 
 ```json Output
 {
-  "results": [
-    {
-      "id": "4d82478a-8d50-47e6-9324-1f65efff5829",
-      "event": "ADD",
-      "memory": "prefers using React for the frontend"
-    },
-    {
-      "id": "1d8b8f39-7b17-4d18-8632-ab1c64fa35b9",
-      "event": "ADD",
-      "memory": "prefers Vue.js for our use case"
-    },
-    {
-      "id": "147559a8-c5f7-44d0-9418-91f53f7a89a4",
-      "event": "ADD",
-      "memory": "suggests considering Angular because it has great enterprise support"
-    }
-  ]
+  "event_id": "4d82478a-8d50-47e6-9324-1f65efff5829",
+  "status": "PENDING"
 }
 ```
 
 </CodeGroup>
 
+`add()` is asynchronous: it queues extraction and returns immediately. Poll `get_all` (see below) once processing completes to see the extracted memories. Each participant's memory is scoped to the `user_id` you passed, so filtering by `run_id` returns all three, and filtering by a single `user_id` returns just that participant.
+
+### The `name` field does not change scope
+
+The `name` field is stored as extraction context only. Attribution follows the `user_id`/`agent_id`/`run_id` you pass to `add()`, never the `name`. Passing two different names in one `add()` call does **not** split the memories across two profiles:
+
+<CodeGroup>
+
+```python Python
+# BOTH messages are scoped to user_id="team_session", NOT to "alice"/"bob"
+client.add(
+    [
+        {"role": "user", "name": "Alice", "content": "I strongly prefer React"},
+        {"role": "user", "name": "Bob", "content": "I strongly prefer Vue"},
+    ],
+    user_id="team_session", run_id="group_chat_2",
+)
+
+# Every extracted memory lands under user_id="team_session"
+client.get_all(filters={"AND": [{"user_id": "team_session"}]})
+
+# Nothing is stored under user_id="alice" or user_id="bob"
+client.get_all(filters={"AND": [{"user_id": "alice"}]})  # -> no results from this call
+```
+
+</CodeGroup>
+
+To keep Alice's and Bob's memories in separate profiles, call `add()` once per participant with their own `user_id`, as shown in [Basic Group Chat](#basic-group-chat) above.
+
 ## Retrieving Group Chat Memories
 
 ### Get All Memories for a Session
@@ -224,33 +226,15 @@ print(search_response)
 
 </CodeGroup>
 
-## Async Mode Support
-
-Group chat supports async processing for improved performance. Memory additions are processed asynchronously by default.
-
-<CodeGroup>
-
-```python Python
-# Group chat: async processing is the default
-response = client.add(
-    messages,
-    run_id="groupchat_async",
-    infer=True,
-)
-print(response)
-```
-
-</CodeGroup>
-
 ## Message Format Requirements
 
 ### Required Fields
 
-Each message in a group chat must include:
+Each message must include:
 
 - `role`: The participant's role (`"user"`, `"assistant"`, `"agent"`)
 - `content`: The message content
-- `name`: The participant's name (required for group chat detection)
+- `name` (optional): The participant's name, stored as context for extraction. It does not change which `user_id` or `agent_id` a memory is scoped to.
 
 ### Example Message Structure
 
@@ -261,14 +245,14 @@ Each message in a group chat must include:
   "content": "I think we should use React for the frontend"
 }
 ```
-### Supported Roles
+### Roles
 
-- **`user`**: Human participants (memories stored with `user_id`)
-- **`assistant`**: AI assistants (memories stored with `agent_id`)
+- **`user`**: Human participants
+- **`assistant`**: AI assistants
 
 ## Best Practices
 
-1. **Consistent Naming**: Use consistent names for participants across sessions to maintain proper memory attribution.
+1. **Consistent Scoping**: Use a consistent `user_id` (or `agent_id`) per participant across sessions so their memories stay in one profile.
 
 2. **Clear Role Assignment**: Ensure each participant has the correct role (`user`, `assistant`, or `agent`) for proper memory categorization.
 

docs/platform/features/memory-decay.mdx

@@ -43,7 +43,7 @@ At search time the pipeline:
 5. Truncates to the `top_k` you requested.
 6. Records a fire-and-forget reinforcement against each returned memory: its access history grows by one, capped at the most recent 20 touches.
 
-Memories created before decay was enabled don't yet have an access history. They use a sensible fallback: their `updated_at` is treated as a single past touch, so the same scale above applies based on how stale that update is: a recently-updated legacy memory enters near the neutral band, a long-stale one sits closer to the floor. Once surfaced in a search after decay is on, they accumulate access history naturally and behave like any other memory.
+Memories created before decay was enabled don't yet have an access history. They use a sensible fallback: a memory's `event_date` (if it has one) is treated as its single past touch; only when `event_date` is absent does `updated_at` stand in instead. A future `event_date` gets a fresh/full activation, a past one decays from when the event happened. Once surfaced in a search after decay is on, they accumulate access history naturally and behave like any other memory.
 
 ## Configure access
 
@@ -81,26 +81,26 @@ curl -X PATCH https://api.mem0.ai/api/v1/orgs/organizations/$ORG_ID/projects/$PR
 
 ### 2. Confirm the state
 
-`decay` is returned on every project read. To fetch only this field, use `?fields=decay`.
+`decay` is returned on every project read; there is currently no way to narrow the response to just this field, so read the full project object and pick out `decay`.
 
 <CodeGroup>
 ```python Python
-response = client.project.get(fields=["decay"])
+response = client.project.get()
 print(response["decay"])
 ```
 
 ```javascript JavaScript
-const response = await client.project.get({ fields: ["decay"] });
+const response = await client.project.get();
 console.log(response.decay);
 ```
 
 ```bash cURL
-curl "https://api.mem0.ai/api/v1/orgs/organizations/$ORG_ID/projects/$PROJECT_ID/?fields=decay" \
+curl "https://api.mem0.ai/api/v1/orgs/organizations/$ORG_ID/projects/$PROJECT_ID/" \
   -H "Authorization: Token $MEM0_API_KEY"
 ```
 
 ```json Response
-{ "decay": true }
+{ "decay": true, "...": "full project object" }
 ```
 </CodeGroup>
 
@@ -154,7 +154,7 @@ curl -X PATCH https://api.mem0.ai/api/v1/orgs/organizations/$ORG_ID/projects/$PR
 | Reinforced on a recent search | 1.2 – 1.5× | Sustains its boost for the next several searches. |
 | Idle for a few days | 0.6 – 1.0× | Falls back into the neutral band. |
 | Idle for weeks | 0.4 – 0.6× | Mild dampening: can still surface for strong matches. |
-| Pre-decay legacy memory (no access history) | 0.3 – 1.0× | Falls back to `updated_at`: recently-updated entries land near 1.0×, long-stale entries approach the 0.3× floor. |
+| Pre-decay legacy memory (no access history) | 0.3 – 1.0× | Falls back to `event_date` if set (future dates land near 1.0×, past dates decay from the event); falls back further to `updated_at` only when `event_date` is absent. |
 
 The reinforcement is bounded: each memory tracks at most the last 20 access timestamps, so the boost stays well-behaved no matter how many times a memory is retrieved.
 
@@ -170,7 +170,7 @@ The threshold is applied to the candidate pool pre-decay; the scaling factor the
 No. The `client.add(...)` path is unchanged. Decay is a search-time ranking adjustment.
 
 **What if I had memories before turning decay on?**
-They use a fallback: the memory's `updated_at` is treated as a single historical touch, so the same scaling applies based on how stale that update is: a recently-updated legacy memory enters near the neutral band (~1.0×), a long-stale one closer to the floor (~0.3×). Once retrieved they accumulate access history and behave like any other memory.
+They use a fallback: a memory's `event_date`, if it has one, is treated as its single historical touch; `updated_at` only stands in when `event_date` is absent. A memory with a future `event_date` enters near the neutral band (~1.0×); one with a past `event_date` decays from when the event happened, closer to the floor (~0.3×) the further back it is. Once retrieved they accumulate access history and behave like any other memory.
 
 **Can I tune how aggressively decay scales scores?**
 Not in this version. The current scaling is calibrated to be conservative: wide enough to meaningfully reorder candidates, narrow enough to never dominate the underlying relevance score. Per-project tuning is on the roadmap.

docs/platform/features/memory-expiration.mdx

@@ -1,9 +1,10 @@
 ---
-title: Memory Expiration
-description: "Give a memory a shelf life: set an expiration date and it stops surfacing in search once that date passes, without deleting the record."
+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."
 ---
 
-# Memory Expiration
+## 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.
 
@@ -18,7 +19,7 @@ Some facts are only true for a while. A trial plan ends, a seasonal preference g
 - **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.
 
-## Set an expiration date
+## How do you set an expiration date on a memory?
 
 Set it when you add the memory:
 
@@ -97,7 +98,7 @@ The same parameter and spelling work on the OSS `Memory` class. See <Link href="
   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>
 
-## Clear an expiration date
+## 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".
 

docs/platform/features/memory-export.mdx

@@ -141,7 +141,7 @@ console.log(responseWithInstructions);
 ```
 
 ```bash cURL
-curl -X POST "https://api.mem0.ai/v1/memories/export/" \
+curl -X POST "https://api.mem0.ai/v1/exports/" \
      -H "Authorization: Token your-api-key" \
      -H "Content-Type: application/json" \
      -d '{
@@ -185,6 +185,13 @@ const response = await client.getMemoryExport({
 console.log(response);
 ```
 
+```bash cURL
+curl -X POST "https://api.mem0.ai/v1/exports/get/" \
+     -H "Authorization: Token your-api-key" \
+     -H "Content-Type: application/json" \
+     -d '{"memory_export_id": "550e8400-e29b-41d4-a716-446655440000"}'
+```
+
 ```json Output
 {
     "full_name": "John Doe",
@@ -231,6 +238,20 @@ const response = await client.getMemoryExport({
 console.log(response);
 ```
 
+```bash cURL
+curl -X POST "https://api.mem0.ai/v1/exports/get/" \
+     -H "Authorization: Token your-api-key" \
+     -H "Content-Type: application/json" \
+     -d '{
+         "filters": {
+             "AND": [
+                 {"created_at": {"gte": "2024-07-10", "lte": "2024-07-20"}},
+                 {"user_id": "alex"}
+             ]
+         }
+     }'
+```
+
 ```json Output
 {
     "full_name": "John Doe",

docs/platform/features/multimodal-support.mdx

@@ -9,6 +9,10 @@ Mem0 extends its capabilities beyond text by supporting multimodal data, includi
 
 When a user submits an image or document, Mem0 processes it to extract textual information and other pertinent details. These details are then added to the user's memory, enhancing the system's ability to understand and recall multimodal inputs.
 
+<Warning>
+Multimodal content (`image_url`, `pdf_url`, `txt_url`, `mdx_url`) requires the default `infer=True`. Do not combine structured multimodal `content` with `infer=False` (Direct Import): the Direct Import path expects plain-text `content` and rejects structured multimodal messages with a `400`.
+</Warning>
+
 <CodeGroup>
 ```python Python
 import os
@@ -172,20 +176,18 @@ await client.add([imageMessage], { userId: "alice" })
 
 ### 2. Text Documents (MDX/TXT)
 
-Mem0 supports both online and local text documents in MDX or TXT format.
+Mem0 supports both online and local text documents in MDX or TXT format. Use `"type": "mdx_url"` for Markdown/MDX content and `"type": "txt_url"` for plain text; both accept the same shape (`{"url": ...}`, either an HTTP(S) URL or a base64-encoded string).
 
 #### Using a Document URL
 
 ```python
-# Define the document URL
 document_url = "https://www.w3.org/TR/2003/REC-PNG-20031110/iso_8859-1.txt"
 
-# Create the message dictionary with the document URL
 document_message = {
     "role": "user",
     "content": {
-        "type": "mdx_url",
-        "mdx_url": {
+        "type": "txt_url",
+        "txt_url": {
             "url": document_url
         }
     }

docs/platform/features/temporal-reasoning.mdx

@@ -51,24 +51,22 @@ Temporal Reasoning is enabled by default for all v3 searches and writes. There i
 
 Two parameters give you precise control when you need it:
 
-- `timestamp` on `add()`: anchors an imported memory to the time it actually happened, rather than the time it was added to Mem0
+- `observation_date` (or `observation_datetime`, which takes priority when both are set) on `add()`: anchors an imported memory to the time it actually happened. Pair with `timezone` when resolving a date-only value.
+- `timestamp` on `add()`: a Unix epoch that sets the memory's creation time. When present it takes priority over `observation_datetime` and `observation_date` as the anchor Temporal Reasoning ranks against, so pass only the one you want to win.
 - `reference_date` on `search()`: resolves relative phrases like `last week` against a fixed point in time
 
 <CodeGroup>
 ```python Python
-from datetime import datetime, timezone
 from mem0 import MemoryClient
 
 client = MemoryClient(api_key="your-api-key")
 
-# Import a historical memory anchored to when it happened
 client.add(
     [{"role": "user", "content": "I finished the Q1 review on March 10, 2025."}],
     user_id="jordan",
-    timestamp=int(datetime(2025, 3, 10, tzinfo=timezone.utc).timestamp()),
+    observation_date="2025-03-10",
 )
 
-# Search with a relative query anchored to a known date
 results = client.search(
     "what did I do last week?",
     filters={"user_id": "jordan"},
@@ -81,16 +79,14 @@ import { MemoryClient } from "mem0ai";
 
 const client = new MemoryClient({ apiKey: "your-api-key" });
 
-// Import a historical memory anchored to when it happened
 await client.add(
   [{ role: "user", content: "I finished the Q1 review on March 10, 2025." }],
   {
     userId: "jordan",
-    timestamp: Math.floor(new Date("2025-03-10T00:00:00Z").getTime() / 1000),
+    observationDate: "2025-03-10",
   }
 );
 
-// Search with a relative query anchored to a known date
 const results = await client.search("what did I do last week?", {
   filters: { user_id: "jordan" },
   referenceDate: "2025-03-21T00:00:00Z",
@@ -131,7 +127,7 @@ const results = await client.search("what did I do last week?", {
 ## Best practices
 
 - Use explicit dates in source conversations when events or plans matter temporally.
-- Pass `timestamp` during historical imports so the ingestion time does not become the only time anchor.
+- Pass `observation_date` (or `observation_datetime`) during historical imports so the ingestion time does not become the only time anchor. Do not also pass `timestamp` on those calls, since it overrides both as the ranking anchor.
 - Scope searches with `filters` so time-aware ranking operates inside the right user boundary.
 - Use `reference_date` in automated tests and reproducible demos.
 

docs/platform/features/v2-memory-filters.mdx

@@ -49,9 +49,9 @@ Filters use a nested JSON structure with logical operators at the root:
 ### Content fields
 | Field | Operators | Example |
 |-------|-----------|---------|
-| `categories` | `eq`, `ne`, `in`, `contains` | `{"categories": {"in": ["finance"]}}` |
+| `categories` | `eq`, `ne`, `in` (matches any category in the list), `contains` (case-insensitive) | `{"categories": {"in": ["finance"]}}` |
 | `metadata` | `eq`, `ne`, `contains` | `{"metadata": {"key": "value"}}` |
-| `keywords` | `contains`, `icontains` | `{"keywords": {"icontains": "invoice"}}` |
+| `keywords` | `contains` (case-sensitive), `icontains` (case-insensitive) | `{"keywords": {"icontains": "invoice"}}` |
 
 ### Special fields
 | Field | Operators | Example |
@@ -120,24 +120,23 @@ filters = {
 Find memories containing specific text, categories, or metadata values.
 
 <AccordionGroup>
-  <Accordion title="Text search">
+  <Accordion title="Text search (keywords)">
     ```python
-    # Case-insensitive match
+    # Substring match on memory text via get_all
     filters = {
         "AND": [
             {"user_id": "user_123"},
-            {"keywords": {"icontains": "pizza"}}
-        ]
-    }
-
-    # Case-sensitive match
-    filters = {
-        "AND": [
-            {"user_id": "user_123"},
-            {"keywords": {"contains": "Invoice_2024"}}
+            {"keywords": {"icontains": "invoice"}}
         ]
     }
+    memories = client.get_all(filters=filters)
     ```
+
+    Use `contains` for case-sensitive matching and `icontains` for case-insensitive.
+
+    <Callout type="warning" icon="exclamation-triangle" color="#E74C3C">
+    A `keywords` filter works on `get_all`, but passing it to `search()` currently returns a `500` ([MEM-5746](https://linear.app/mem0/issue/MEM-5746)). For text relevance during a search, pass the text to the `query` argument instead of filtering on `keywords`.
+    </Callout>
   </Accordion>
 
   <Accordion title="Categories">
@@ -254,10 +253,8 @@ Combine various filters for complex queries across different dimensions.
     ```
   </Accordion>
 
-  <Accordion title="All entities populated (single entity scope)">
+  <Accordion title="All entities populated">
     ```python
-    # Require user_id plus non-null run/app IDs
-    # (Memories are stored separately per entity, so scope one dimension at a time.)
     filters = {
         "AND": [
             {"user_id": "user_123"},
@@ -266,6 +263,7 @@ Combine various filters for complex queries across different dimensions.
         ]
     }
     ```
+    Matches records where `user_id` equals `user_123` and `run_id`/`app_id` are both non-null. It does not require other entity fields to be null.
   </Accordion>
 </AccordionGroup>
 
@@ -276,16 +274,16 @@ Level up foundational patterns with compound filters that coordinate entity scop
 <AccordionGroup>
   <Accordion title="Multi-dimensional filtering">
     ```python
-    # Invoice memories in Q1 2024
     filters = {
         "AND": [
             {"user_id": "user_123"},
-            {"keywords": {"icontains": "invoice"}},
             {"categories": {"in": ["finance"]}},
+            {"keywords": {"icontains": "invoice"}},
             {"created_at": {"gte": "2024-01-01T00:00:00Z"}},
             {"created_at": {"lt": "2024-04-01T00:00:00Z"}}
         ]
     }
+    memories = client.get_all(filters=filters)
     ```
   </Accordion>
 
@@ -330,7 +328,7 @@ Level up foundational patterns with compound filters that coordinate entity scop
 ## Best practices
 
 <Callout type="tip" icon="lightbulb" color="#26A17B">
-The root must be `AND`, `OR`, or `NOT` with an array of conditions.
+The root does not have to be `AND`, `OR`, or `NOT`. A bare filter like `{"user_id": "alice"}` works on its own; wrap conditions in a logical operator only when you need to combine more than one.
 </Callout>
 
 <Callout type="tip" icon="lightbulb" color="#26A17B">
@@ -338,7 +336,9 @@ Use `"*"` to match any non-null value for a field.
 </Callout>
 
 <Callout type="warning" icon="exclamation-triangle" color="#E74C3C">
-Memories are stored per-entity (user, agent, app, run). Combining `user_id` **and** `agent_id` in the same `AND` clause returns no results because no record contains both values at once. Query one entity scope at a time or use `OR` logic for parallel lookups.
+Combining `user_id` **and** `agent_id` in the same `AND` clause only returns records that have both values set. Memories created by a normal `client.add` never do: each extracted fact is attributed to its speaker, so it carries `user_id` or `agent_id`, not both. Use `OR` to match either scope. Only [Direct Import](/platform/features/direct-import) (`infer=False`) writes both fields on one record.
+
+A filter object with both an `AND` key and a sibling `OR` key at the same level silently drops the `OR` branch today. Nest the `OR` inside the `AND` array instead of placing them as siblings.
 </Callout>
 
 ## Troubleshooting
@@ -347,7 +347,7 @@ Memories are stored per-entity (user, agent, app, run). Combining `user_id` **an
   <Accordion title="Missing results with agent_id">
     **Problem**: Filtered by `user_id` but don't see agent memories.
 
-    **Solution**: User and agent memories are stored as separate records. Use OR to query both scopes:
+    **Solution**: A `user_id` filter only matches records that have that `user_id` set; it won't surface memories written with only `agent_id`. Use OR to query both scopes:
     ```python
     {"OR": [{"user_id": "user_123"}, {"agent_id": "agent_name"}]}
     ```
@@ -362,8 +362,12 @@ Memories are stored per-entity (user, agent, app, run). Combining `user_id` **an
     ```
   </Accordion>
 
-  <Accordion title="Case-insensitive search">
-    **Solution**: Swap to `icontains` to normalize casing.
+  <Accordion title="Case-insensitive text match">
+    **Solution**: Use `keywords` with `icontains` on `get_all`:
+    ```python
+    {"AND": [{"user_id": "user_123"}, {"keywords": {"icontains": "invoice"}}]}
+    ```
+    This works on `get_all`. It is not supported on `search()` yet (returns a `500`, [MEM-5746](https://linear.app/mem0/issue/MEM-5746)) - for search, pass the text to the `query` argument instead.
   </Accordion>
 
   <Accordion title="Date range between two dates">
@@ -388,7 +392,7 @@ Memories are stored per-entity (user, agent, app, run). Combining `user_id` **an
 
 <AccordionGroup>
   <Accordion title="Do I need AND/OR/NOT?">
-    Yes. The root must be a logical operator with an array.
+    No. A bare filter like `{"user_id": "u1"}` works on its own. Add `AND`, `OR`, or `NOT` only when you need to combine more than one condition.
   </Accordion>
 
   <Accordion title="What does * match?">
@@ -408,7 +412,7 @@ Memories are stored per-entity (user, agent, app, run). Combining `user_id` **an
   </Accordion>
 
   <Accordion title="How to search text?">
-    Use `keywords` with `contains` (case-sensitive) or `icontains` (case-insensitive).
+    For a substring match, use the `keywords` filter with `contains`/`icontains` on `get_all`. For relevance-ranked search, pass the text to the `query` argument on `search()`. Note: `keywords` is not supported inside `search()` filters yet (returns a `500`, [MEM-5746](https://linear.app/mem0/issue/MEM-5746)).
   </Accordion>
 
 <Accordion title="Can I nest AND/OR?">
@@ -428,6 +432,6 @@ Memories are stored per-entity (user, agent, app, run). Combining `user_id` **an
 
 ## Known limitations
 
-- Entity filters operate on a single scope per record. Use separate queries or `OR` logic to compare users vs agents.
+- Filters only constrain the fields you mention; unmentioned entity fields are not required to be null. A record carries `user_id` or `agent_id` (never both) unless it came from Direct Import, plus whatever `app_id` and `run_id` were passed.
 - Metadata supports only bare/`eq`, `contains`, and `ne` comparisons.
 - Wildcards (`"*"` ) match only records where the field is already non-null.

docs/platform/features/webhooks.mdx

@@ -51,6 +51,7 @@ console.log(webhook);
   "webhook_id": "wh_123",
   "name": "Memory Logger",
   "url": "https://your-app.com/webhook",
+  "owner": "john",
   "event_types": ["memory_add"],
   "project": "default-project",
   "is_active": true,
@@ -63,7 +64,7 @@ console.log(webhook);
 
 ### Get Webhooks
 
-Retrieve all webhooks for your project:
+Retrieve all webhooks for your project. Each webhook includes an `owner` field: the username of the account that created it. This is set automatically and cannot be changed via the API.
 
 <CodeGroup>
 
@@ -168,6 +169,10 @@ Mem0 supports the following event types for webhooks:
 - `memory_update`: Triggered when an existing memory is updated.
 - `memory_delete`: Triggered when a memory is deleted.
 - `memory_categorize`: Triggered when a memory is categorized.
+- `ingest_job_completed`: Triggered when an ingest job finishes successfully.
+- `ingest_job_partially_completed`: Triggered when an ingest job finishes with some items failed.
+- `ingest_job_failed`: Triggered when an ingest job fails entirely.
+- `ingest_job_cancelled`: Triggered when an ingest job is cancelled.
 
 ## Webhook Payload
 

docs/platform/mem0-mcp.mdx

@@ -8,18 +8,20 @@ estimatedTime: "~2 minutes"
 <Info>
   **Prerequisites**
   - Mem0 Platform account (<a href="https://app.mem0.ai?utm_source=oss&utm_medium=platform-mem0-mcp" rel="nofollow">Sign up here</a>)
-  - API key (<a href="https://app.mem0.ai/settings/api-keys?utm_source=oss&utm_medium=platform-mem0-mcp" rel="nofollow">Get one from dashboard</a>)
-  - Node.js 14+ (for npx)
+  - API key (<a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=platform-mem0-mcp" rel="nofollow">Get one from dashboard</a>)
+  - Node.js 18+ (for npx)
   - An MCP-compatible client (Claude, Claude Code, Codex, Cursor, Windsurf, VS Code, OpenCode)
 </Info>
 
 ## What is Mem0 MCP?
 
-Mem0 MCP Server exposes Mem0's memory capabilities as MCP tools, letting AI agents decide when to save, search, or update information. The cloud-hosted MCP server requires no local installation: just connect and start using memory.
+MCP (Model Context Protocol) is a standard way for AI clients to call external tools. The Mem0 MCP server hands your agent a set of memory tools, so it can decide for itself when to save something, look something up, or update what it already knows. Nothing runs on your machine: the server is hosted by Mem0, and your client connects to it over HTTPS.
 
-## Quick Setup
+Memories you store this way live in your Mem0 account, not on your computer.
 
-Add Mem0 MCP to your preferred clients with a single command:
+## Quick setup
+
+Point your clients at the hosted server with a single command:
 
 ```bash
 npx mcp-add \
@@ -29,9 +31,31 @@ npx mcp-add \
   --clients "claude,claude code,cursor,windsurf,vscode,opencode"
 ```
 
-This automatically configures Mem0 MCP for all supported clients at once.
+`mcp-add` is a helper that writes the Mem0 server into each client's own MCP config file, so you do not have to edit them by hand. Name only the clients you actually use. If you would rather see the change yourself, every client's manual config is under [Client-specific setup](#client-specific-setup).
 
-## Available Tools
+Restart each client afterwards so it picks up the new server.
+
+## Signing in
+
+The server is authenticated, so connecting is not enough on its own. There are two ways in:
+
+<Tabs>
+  <Tab title="Sign in through the client">
+    Most clients handle this for you. The first time your agent uses a Mem0 tool, the client opens a browser window asking you to authorize access to your Mem0 account. Approve it once and the client stores the token, refreshing it as needed.
+
+    This is the easier path, and it is what happens by default if you followed the quick setup above.
+  </Tab>
+
+  <Tab title="Use your API key">
+    For clients without browser sign-in, or for headless environments like CI, send your API key as a bearer token instead. Where you put it depends on the client; see [Client-specific setup](#client-specific-setup) for the exact syntax.
+
+    Get a key from the <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=platform-mem0-mcp" rel="nofollow">Mem0 dashboard</a>, and keep it out of any file you commit.
+  </Tab>
+</Tabs>
+
+If neither is set up, the server replies `401 Authentication required` and your client reports the connection as failed.
+
+## Available tools
 
 The MCP server exposes these memory tools to your AI client:
 
@@ -41,7 +65,7 @@ The MCP server exposes these memory tools to your AI client:
 | `search_memories` | Semantic search across existing memories with filters |
 | `get_memories` | List memories with structured filters and pagination |
 | `get_memory` | Retrieve one memory by its `memory_id` |
-| `update_memory` | Overwrite a memory's text after confirming the ID |
+| `update_memory` | Overwrite a memory's text and/or metadata after confirming the ID |
 | `delete_memory` | Delete a single memory by `memory_id` |
 | `delete_all_memories` | Bulk delete all memories in scope |
 | `delete_entities` | Delete a user/agent/app/run entity and its memories |
@@ -51,7 +75,7 @@ The MCP server exposes these memory tools to your AI client:
 
 ---
 
-## Client-Specific Setup
+## Client-specific setup
 
 You can also configure individual clients:
 
@@ -170,43 +194,44 @@ You can also configure individual clients:
 
 ---
 
-## Verify Your Setup
+## Check that it worked
 
-Once configured, your AI client can:
-- Automatically save information with `add_memory`
-- Search memories with `search_memories`
-- Update memories with `update_memory`
-- Delete memories with `delete_memory`
-
-**Sample Interactions:**
+Restart your client, then ask it to store something and read it back in a later message:
 
 ```
-User: Remember that I love tiramisu
-Agent: Got it! I've saved that you love tiramisu.
+You:   Remember that I prefer TypeScript over JavaScript for new projects.
+Agent: Saved.
 
-User: What do you know about my food preferences?
-Agent: Based on your memories, you love tiramisu.
-
-User: Update my project: the mobile app is now 80% complete
-Agent: Updated your project status successfully.
+You:   What language do I prefer for new projects?
+Agent: You prefer TypeScript over JavaScript.
 ```
 
-<Info icon="check">
-  If you get "Connection failed", ensure you have a valid API key from <a href="https://app.mem0.ai/settings/api-keys?utm_source=oss&utm_medium=platform-mem0-mcp" rel="nofollow">Mem0 Dashboard</a>.
-</Info>
+The second answer only works if the memory was really stored, so this is a genuine round-trip test rather than the model repeating itself.
+
+Two things to look for while you do it:
+
+- Your client should show the Mem0 tools among its available tools. Most clients list them in a tools or MCP panel.
+- The first tool call should trigger the browser sign-in described above, unless you configured an API key.
+
+To confirm from outside the client, open the <a href="https://app.mem0.ai/dashboard?utm_source=oss&utm_medium=platform-mem0-mcp" rel="nofollow">Mem0 dashboard</a>: anything the agent saved appears there.
 
 ---
 
-## Quick Recovery
+## Troubleshooting
 
-- **"Connection refused"** → Check your internet connection and ensure the MCP client is correctly configured
-- **"Invalid API key"** → Get a new key from <a href="https://app.mem0.ai/settings/api-keys?utm_source=oss&utm_medium=platform-mem0-mcp" rel="nofollow">Mem0 Dashboard</a>
-- **"npx command not found"** → Install Node.js from [nodejs.org](https://nodejs.org)
+| What you see | What it means | What to do |
+|---|---|---|
+| `401` or "Authentication required" | The client connected but is not signed in | Complete the browser sign-in, or set your API key as a bearer token |
+| "Connection refused" or "failed to connect" | The client cannot reach the server | Check your internet connection, then confirm the URL is exactly `https://mcp.mem0.ai/mcp` |
+| The agent has no Mem0 tools | The config was written but the client has not reloaded it | Restart the client. If the tools are still missing, check that `mcp-add` wrote to the config file your client actually reads |
+| `npx: command not found` | Node.js is not installed | Install it from [nodejs.org](https://nodejs.org) |
+| "Invalid API key" | The key is wrong, revoked, or from a different account | Get a new one from the <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=platform-mem0-mcp" rel="nofollow">Mem0 dashboard</a> |
 
 ---
 
 ## Next steps
 
-- [Platform Quickstart](/platform/quickstart) - direct SDK/API integration guide
-- [MCP Specification](https://modelcontextprotocol.io) - the Model Context Protocol standard
-- [Gemini with Mem0 MCP](/cookbooks/frameworks/gemini-3-with-mem0-mcp) - example integration cookbook
+- [Platform quickstart](/platform/quickstart): call Mem0 from your own code instead of through an agent
+- [Mem0 CLI](/platform/cli): the same operations from your terminal
+- [MCP specification](https://modelcontextprotocol.io): the Model Context Protocol standard
+- [Gemini with Mem0 MCP](/cookbooks/frameworks/gemini-3-with-mem0-mcp): a worked example

docs/platform/overview.mdx

@@ -50,7 +50,7 @@ For the full pipeline, see [How Mem0 works](/core-concepts/how-it-works).
     Get an API key and save your first memory.
   </Card>
   <Card title="Understand memory types" icon="brain" href="/core-concepts/memory-types">
-    How user, agent, run, and session memory differ.
+    How user, agent, app, and run memory differ.
   </Card>
   <Card title="Add, search, and update" icon="layer-group" href="/core-concepts/memory-operations/add">
     The core memory operations, end to end.

docs/platform/platform-vs-oss.mdx

@@ -60,7 +60,6 @@ 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 |

docs/platform/quickstart.mdx

@@ -5,31 +5,42 @@ icon: "bolt"
 iconType: "solid"
 ---
 
-Get started with Mem0 Platform's hosted API in under 5 minutes. This guide shows you how to authenticate and store your first memory.
+In about five minutes you will get an API key, store your first memory, and search it back. Follow along in Python, JavaScript, cURL, or the terminal.
 
 <Note>
-**Are you an AI agent?** See [Sign up as an agent](/platform/agent-signup): mint a working API key in four commands, no email or dashboard required.
+**Are you an AI agent?** See [Sign up as an agent](/platform/agent-signup): create a working API key in four commands, with no email or dashboard.
 </Note>
 
 ## Prerequisites
 
 - Mem0 Platform account (<a href="https://app.mem0.ai?utm_source=oss&utm_medium=platform-quickstart" rel="nofollow">Sign up here</a>)
 - API key (<a href="https://app.mem0.ai/dashboard/settings?tab=api-keys&subtab=configuration" rel="nofollow">Get one from dashboard</a>)
-- Python 3.10+, Node.js 18+, or cURL
+- Python 3.10+, Node.js 18+, or cURL. The CLI needs either Node.js 18+ or Python 3.10+.
 
-## Installation
+## Store your first memory
 
 <Steps>
-<Step title="Install SDK">
+<Step title="Install">
+Pick a tab and use the same one for every step below.
+
 <CodeGroup>
-```bash pip
+```bash Python
 pip install mem0ai
 ```
 
-```bash npm
+```bash JavaScript
 npm install mem0ai
 ```
 
+```bash cURL
+# Nothing to install. cURL ships with macOS and most Linux distributions.
+```
+
+```bash CLI
+npm install -g @mem0/cli
+# or, if you prefer Python: pip install mem0-cli
+```
+
 </CodeGroup>
 </Step>
 
@@ -39,12 +50,12 @@ npm install mem0ai
 from mem0 import MemoryClient
 
 client = MemoryClient(api_key="your-api-key")
-````
+```
 
 ```javascript JavaScript
 import MemoryClient from 'mem0ai';
 const client = new MemoryClient({ apiKey: 'your-api-key' });
-````
+```
 
 ```bash cURL
 export MEM0_API_KEY="your-api-key"
@@ -65,7 +76,7 @@ messages = [
     {"role": "assistant", "content": "Got it! I'll remember your dietary preferences."}
 ]
 client.add(messages, user_id="user123")
-````
+```
 
 ```javascript JavaScript
 const messages = [
@@ -73,7 +84,7 @@ const messages = [
     {"role": "assistant", "content": "Got it! I'll remember your dietary preferences."}
 ];
 await client.add(messages, { userId: "user123" });
-````
+```
 
 ```bash cURL
 curl -X POST https://api.mem0.ai/v3/memories/add/ \
@@ -93,6 +104,17 @@ mem0 add "I'm a vegetarian and allergic to nuts." --user-id user123
 ```
 
 </CodeGroup>
+
+Mem0 pulls the individual facts out of the conversation and stores each one separately:
+
+```json
+{
+  "results": [
+    {"id": "0f2c1b6e-9a3d-4b18-8f77-1c2d3e4f5a6b", "memory": "Is a vegetarian", "event": "ADD"},
+    {"id": "14e1b28a-2014-40ad-ac42-69c9ef42193d", "memory": "Allergic to nuts", "event": "ADD"}
+  ]
+}
+```
 </Step>
 
 <Step title="Search memories">
@@ -100,12 +122,12 @@ mem0 add "I'm a vegetarian and allergic to nuts." --user-id user123
 ```python Python
 results = client.search("What are my dietary restrictions?", filters={"user_id": "user123"})
 print(results)
-````
+```
 
 ```javascript JavaScript
 const results = await client.search("What are my dietary restrictions?", { filters: { user_id: "user123" } });
 console.log(results);
-````
+```
 
 ```bash cURL
 curl -X POST https://api.mem0.ai/v3/memories/search/ \
@@ -123,7 +145,7 @@ mem0 search "What are my dietary restrictions?" --user-id user123
 
 </CodeGroup>
 
-**Output:**
+Both facts come back, ranked by how well they match the question:
 
 ```json
 {
@@ -132,37 +154,65 @@ mem0 search "What are my dietary restrictions?" --user-id user123
       "id": "14e1b28a-2014-40ad-ac42-69c9ef42193d",
       "memory": "Allergic to nuts",
       "user_id": "user123",
+      "agent_id": null,
+      "app_id": null,
+      "run_id": null,
       "categories": ["health"],
+      "metadata": {},
       "created_at": "2025-10-22T04:40:22.864647-07:00",
-      "score": 0.30
+      "updated_at": "2025-10-22T04:40:22.864647-07:00",
+      "expiration_date": null,
+      "score": 0.87
+    },
+    {
+      "id": "0f2c1b6e-9a3d-4b18-8f77-1c2d3e4f5a6b",
+      "memory": "Is a vegetarian",
+      "user_id": "user123",
+      "agent_id": null,
+      "app_id": null,
+      "run_id": null,
+      "categories": ["food_preferences"],
+      "metadata": {},
+      "created_at": "2025-10-22T04:40:22.864647-07:00",
+      "updated_at": "2025-10-22T04:40:22.864647-07:00",
+      "expiration_date": null,
+      "score": 0.81
     }
   ]
 }
 ```
 
+Pass these memories to your model as context, and it answers with what it already knows about the user instead of asking again.
+
 </Step>
 </Steps>
 
-<Callout type="tip" icon="plug">
-  **Pro Tip**: Want AI agents to manage their own memory automatically? Use <Link href="/platform/mem0-mcp">Mem0 MCP</Link> to let LLMs decide when to save, search, and update memories.
-</Callout>
+<Tip>
+  Rather than calling `add` and `search` yourself, you can hand Mem0 to your agent as a set of tools and let it decide when to save and look things up. See [Mem0 MCP](/platform/mem0-mcp).
+</Tip>
 
 ## What's next?
 
-You stored and searched your first memory. Keep going:
+You stored and searched your first memory. Start with scoping, since every call you make from here needs it:
 
-<CardGroup cols={3}>
-<Card title="How it works" icon="diagram-project" href="/core-concepts/how-it-works">
-  See how Mem0 extracts, stores, and retrieves memories under the hood.
+<CardGroup cols={2}>
+<Card title="Scope memories to users and agents" icon="users" href="/platform/features/entity-scoped-memory">
+  What `user_id` actually does, plus the `agent_id`, `app_id`, and `run_id` fields that came back empty above.
 </Card>
 
-<Card title="Memory operations" icon="database" href="/core-concepts/memory-operations/add">
-  Go beyond add and search: update, delete, and the full memory lifecycle.
+<Card title="How Mem0 works" icon="diagram-project" href="/core-concepts/how-it-works">
+  Why one sentence became two memories, and how Mem0 decides what to keep.
 </Card>
 
-<Card title="Build an AI companion" icon="users" href="/cookbooks/essentials/building-ai-companion">
-  Put it to work in a real app, end to end, in about 10 minutes.
+<Card title="Update and delete memories" icon="database" href="/core-concepts/memory-operations/add">
+  The operations beyond add and search, for when stored facts change or go stale.
+</Card>
+
+<Card title="Use Mem0 with your agent framework" icon="plug" href="/integrations">
+  Wire memory into LangChain, CrewAI, LangGraph, or the OpenAI Agents SDK.
 </Card>
 </CardGroup>
 
-Stuck on setup? See the [FAQs and troubleshooting](/platform/faqs).
+<Note>
+  Something not working? The [FAQs and troubleshooting](/platform/faqs) page covers the common setup errors.
+</Note>

docs/vibecoding.mdx

@@ -5,13 +5,15 @@ description: "Agent skills, starter prompts, and setup for building with Mem0 us
 icon: "wand-magic-sparkles"
 ---
 
-These docs are designed to be easily consumable by LLMs. Each page has a button that lets you copy the page as Markdown or paste directly into ChatGPT, Claude, or any AI coding tool.
+Vibecoding means building software by describing what you want to an AI coding assistant and letting it write the code. The catch is that assistants guess at unfamiliar libraries, and a wrong guess about Mem0 costs you a debugging session.
 
-We follow the llms.txt standard:
+This page fixes that three ways: skills that teach your assistant the Mem0 SDKs, an MCP connection so it can read and write memories itself, and a starter prompt you can paste into any tool.
 
-- [llms.txt](https://docs.mem0.ai/llms.txt)
+<Note>
+Every page in these docs has a button to copy it as Markdown or send it straight to ChatGPT or Claude, so you can hand your assistant any page it needs. We follow the [llms.txt](https://docs.mem0.ai/llms.txt) standard.
+</Note>
 
-## Agent Skills
+## Agent skills
 
 Mem0 ships two kinds of skills for AI coding assistants. Both work with Claude Code, Codex, Cursor, Windsurf, OpenCode, OpenClaw, and any assistant that supports the skills standard.
 
@@ -45,7 +47,7 @@ npx skills add https://github.com/mem0ai/mem0 --skill mem0-oss-to-platform
 
 See the [skills index](https://github.com/mem0ai/mem0/tree/main/skills) for the full catalog.
 
-## MCP Server Setup
+## MCP server setup
 
 Connect Claude, Claude Code, Cursor, Windsurf, VS Code, OpenCode, or any MCP-compatible client to Mem0.
 
@@ -61,13 +63,13 @@ npx mcp-add \
 
 For per-client setup and advanced options, see [Mem0 MCP Setup](/platform/mem0-mcp).
 
-## Universal Starter Prompt
+## Universal starter prompt
 
 Copy this into any AI tool to start building with Mem0:
 
 ```text
-I want to start building with Mem0, a self-improving memory layer for LLM
-applications that gives agents persistent context across sessions.
+I want to start building with Mem0, which gives AI agents long-term memory
+that persists across sessions, tools, and runs.
 
 ## Mem0 Resources
 
@@ -86,10 +88,10 @@ applications that gives agents persistent context across sessions.
 - Cookbooks: https://docs.mem0.ai/cookbooks/overview
 
 **What Mem0 Does:**
-Mem0 is a memory layer for AI apps, managed (Mem0 Platform) or self-hosted
-(Open Source). It stores, retrieves, and manages user memories so agents
-remember preferences, learn from interactions, and personalize over time.
-Sub-50ms retrieval. Storage: vector embeddings.
+Mem0 gives AI agents long-term memory, either managed (Mem0 Platform) or
+self-hosted (Open Source). It stores, retrieves, and manages memories so
+agents remember preferences, learn from past runs, and personalize over
+time. Storage: vector embeddings.
 
 **Architecture Overview:**
 - Memory is scoped by user_id, agent_id, or run_id
@@ -109,7 +111,7 @@ Sub-50ms retrieval. Storage: vector embeddings.
   import MemoryClient from 'mem0ai';
   const client = new MemoryClient({ apiKey: 'm0-xxx' });
   await client.add([{ role: "user", content: "I prefer dark mode." }], { userId: "user1" });
-  const results = await client.search("What editor?", { filters: { userId: "user1" } });
+  const results = await client.search("What editor?", { filters: { user_id: "user1" } });
 
 **Quick Usage (Python Open Source):**
   from mem0 import Memory
@@ -121,10 +123,10 @@ Help me integrate Mem0 into my project. Start by asking what I'm building,
 what language/framework I'm using, and whether I want managed or self-hosted.
 ```
 
-## Go Deeper
+## Go deeper
 
 <CardGroup cols={2}>
-  <Card title="Platform Quickstart" icon="cloud" href="/platform/quickstart">
+  <Card title="Platform quickstart" icon="cloud" href="/platform/quickstart">
     Get started with the managed API
   </Card>
   <Card title="Open Source" icon="code-branch" href="/open-source/overview">
@@ -133,7 +135,7 @@ what language/framework I'm using, and whether I want managed or self-hosted.
   <Card title="Cookbooks" icon="book" href="/cookbooks/overview">
     Production-ready tutorials and examples
   </Card>
-  <Card title="API Reference" icon="code" href="/api-reference">
+  <Card title="API reference" icon="code" href="/api-reference">
     Explore every REST endpoint
   </Card>
 </CardGroup>

examples/mem0-demo/package.json

@@ -28,7 +28,7 @@
     "clsx": "^2.1.1",
     "js-cookie": "^3.0.6",
     "lucide-react": "^0.477.0",
-    "next": "15.5.18",
+    "next": "15.5.21",
     "react": "^19.0.0",
     "react-dom": "^19.0.0",
     "react-markdown": "^10.0.1",

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

@@ -1,6 +1,6 @@
 {
   "name": "mem0",
-  "version": "0.2.12",
+  "version": "0.2.13",
   "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": "0.2.12",
+  "version": "0.2.13",
   "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": "0.2.12",
+  "version": "0.2.13",
   "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

@@ -0,0 +1,153 @@
+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

@@ -0,0 +1,30 @@
+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

@@ -25,6 +25,7 @@ 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;
@@ -258,7 +259,7 @@ function extractUserText(input: any, output: any): string {
 const Mem0Plugin: Plugin = async (ctx) => {
   const {$, client} = ctx;
 
-  const apiKey = process.env.MEM0_API_KEY;
+  const apiKey = resolveApiKey();
 
   if (!apiKey) {
     try {

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

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