docs(n8n,zapier): rewrite both integration pages in the task-first house style

Both pages opened with an endpoint table and left the reader to work out
setup themselves. Restructure them the way the stronger integration pages
read: problem statement, prerequisites with API-key links, a Steps-based
setup with a verification callout, a runnable quickstart, then the field
reference, then troubleshooting.

Every documented field is checked against the source rather than a schema:
the Zapier action input fields come from src/creates and src/searches, and
Wait for Completion is correctly documented as off by default there (it is
on by default in n8n). Adds the n8n self-hosted-only prerequisite and the
Zapier App Directory availability note, both of which users hit first.

.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

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/sdk.mdx

@@ -7,6 +7,35 @@ mode: "wide"
 <Tabs>
 <Tab title="Python">
 
+<Update label="2026-07-25" description="v2.0.14">
+
+**New Features:**
+- **Vector Stores:** Add an Oracle AI Vector Search provider (`oracledb`) with connection pooling, `HNSW`/`IVF` indexes, JSON metadata filtering, and six selectable distance metrics ([#5358](https://github.com/mem0ai/mem0/pull/5358))
+
+**Bug Fixes:**
+- **Vector Stores:** Translate a `"*"` filter value in OpenSearch into an `exists` query for every key, not just identity keys. It was previously ignored or matched literally against the string `"*"`, so a wildcard filter returned nothing ([#6522](https://github.com/mem0ai/mem0/pull/6522))
+- **Vector Stores:** Re-raise errors from OpenSearch `search()` instead of returning `[]`, so a transport, auth, or index misconfiguration surfaces instead of looking like zero matches. `keyword_search()` still degrades on failure, since it is a best-effort BM25 signal ([#6519](https://github.com/mem0ai/mem0/pull/6519))
+- **Vector Stores:** Guard the `text` field in Milvus `update()` behind the `_has_bm25_schema` check, matching `insert()`, so updating a memory in a collection without the BM25 `text`/`sparse` schema no longer fails ([#5705](https://github.com/mem0ai/mem0/pull/5705))
+
+</Update>
+
+<Update label="2026-07-22" description="v2.0.13">
+
+**Bug Fixes:**
+- **Vector Stores:** Fix `reset()` silently leaving stale vectors behind on local (on-disk) Qdrant when the old collection directory could not be removed, for example an open file handle on Windows or NFS ([#6412](https://github.com/mem0ai/mem0/pull/6412))
+- **Core:** Stop `update()` metadata from overwriting or injecting `user_id`, `agent_id`, `run_id`, or `actor_id`. These identity fields are immutable after creation, so passing them in `metadata` can no longer move a memory into a different tenant's scope ([#6278](https://github.com/mem0ai/mem0/pull/6278))
+- **Vector Stores:** Scope Pinecone `delete_col()`/`reset()` to the configured namespace instead of deleting the whole index, so resetting a namespaced Pinecone store no longer wipes out the other namespaces sharing that index ([#6287](https://github.com/mem0ai/mem0/pull/6287))
+- **Vector Stores:** Convert Baidu Mochow's raw L2 distance into a similarity score in `search()` (`1 / (1 + distance)`), so closer matches rank higher instead of lower, matching the Milvus provider and the rest of the `VectorStoreBase` contract ([#6435](https://github.com/mem0ai/mem0/pull/6435))
+- **LLMs:** Read `OPENAI_BASE_URL` (was `OPENAI_API_BASE`) in `OpenAIStructuredLLM`, matching the official OpenAI SDK's environment variable and the rest of the OpenAI-compatible providers ([#6322](https://github.com/mem0ai/mem0/pull/6322))
+
+**Improvements:**
+- **LLMs:** Remove a dead, no-op `api_key` attribute check from `LLMBase.__init__` ([#6460](https://github.com/mem0ai/mem0/pull/6460))
+
+**Changes:**
+- **Client:** Remove the `retrieval_criteria` parameter from `MemoryClient.update_project()`/`AsyncMemoryClient.update_project()` and `Project.update()`/`AsyncProject.update()`. It was accepted and forwarded but never affected retrieval, so removing it is not a behavior change ([#6313](https://github.com/mem0ai/mem0/pull/6313))
+
+</Update>
+
 <Update label="2026-07-13" description="v2.0.12">
 
 **New Features:**
@@ -1128,6 +1157,35 @@ See the [OSS v2 to v3 migration guide](https://docs.mem0.ai/migration/oss-v2-to-
 
 <Tab title="TypeScript">
 
+<Update label="2026-07-25" description="v3.1.2">
+
+**Bug Fixes:**
+- **Vector Stores:** Apply every operator in a Cassandra compound field filter (e.g. `{ age: { gte: 10, lte: 20 } }`) instead of stopping after the first, so the remaining bounds are no longer silently ignored ([#6511](https://github.com/mem0ai/mem0/pull/6511))
+- **Vector Stores:** Stop the Chroma where-clause translator from dropping filter conditions. Same-field ranges (`gte` + `lte`), multi-field conditions inside `$or`, and negated `contains`/`icontains` under `$not` each collapsed to a single clause or vanished, widening the search instead of narrowing it ([#6521](https://github.com/mem0ai/mem0/pull/6521))
+- **Vector Stores:** Skip `"*"` wildcard filter values in Milvus instead of matching them literally, so a filter like `{ user_id: "*" }` no longer returns zero memories ([#6508](https://github.com/mem0ai/mem0/pull/6508))
+- **Vector Stores:** Read `textLemmatized` for BM25 keyword search on Milvus, OpenSearch, and MongoDB, matching the field the memory layer actually writes, so hybrid search on those backends no longer loses the keyword signal ([#6497](https://github.com/mem0ai/mem0/pull/6497))
+- **LLMs:** Forward `responseFormat` to Gemini's `responseMimeType` in `generateResponse()`, so requesting `json_object` returns JSON instead of free-form text ([#6468](https://github.com/mem0ai/mem0/pull/6468))
+- **LLMs:** Find the Anthropic text block by type instead of indexing `content[0]`, so a thinking-enabled model whose `thinking` block comes first no longer throws `Unexpected response type from Anthropic API` ([#6506](https://github.com/mem0ai/mem0/pull/6506))
+
+</Update>
+
+<Update label="2026-07-22" description="v3.1.1">
+
+**New Features:**
+- **Embeddings:** Add an AWS Bedrock embedding provider ([#6185](https://github.com/mem0ai/mem0/pull/6185))
+
+**Bug Fixes:**
+- **Packaging:** Finish the lazy-loading work started in v3.1.0. The remaining LLMs (Anthropic, Google, Groq, LangChain, Mistral, Ollama), embedders (Google, LangChain, Ollama, Vertex AI), vector stores (Azure AI Search, Azure MySQL, Baidu, LangChain, Qdrant, Redis, Supabase, Valkey, Vectorize), and the Supabase history store still imported their SDKs at module load, so importing `mem0ai/oss` required every provider package to be installed ([#6389](https://github.com/mem0ai/mem0/pull/6389))
+- **Vector Stores:** Convert Baidu Mochow's raw L2 distance into a similarity score in `search()` (`1 / (1 + distance)`), so closer matches rank higher instead of lower. A row the backend returns without a score is now left `undefined` instead of being treated as the closest match ([#6485](https://github.com/mem0ai/mem0/pull/6485))
+- **Memory (OSS):** Coerce non-string entity IDs (e.g. a numeric `user_id`) to strings instead of crashing on `.trim()` ([#6263](https://github.com/mem0ai/mem0/pull/6263))
+- **Memory (OSS):** Stop `update()` metadata from overwriting or injecting `user_id`, `agent_id`, `run_id`, or `actor_id` (in either snake_case or camelCase). These identity fields are immutable after creation, so passing them in `metadata` can no longer move a memory into a different tenant's scope ([#6343](https://github.com/mem0ai/mem0/pull/6343))
+- **Vector Stores:** Scope Pinecone `deleteCol()`/`reset()` to the configured namespace instead of deleting the whole index, so resetting a namespaced Pinecone store no longer wipes out the other namespaces sharing that index ([#6287](https://github.com/mem0ai/mem0/pull/6287))
+
+**Changes:**
+- **Client:** Remove the unused `retrievalCriteria` field from `PromptUpdatePayload`. It was accepted and forwarded but never affected retrieval, so removing it is not a behavior change ([#6313](https://github.com/mem0ai/mem0/pull/6313))
+
+</Update>
+
 <Update label="2026-07-13" description="v3.1.0">
 
 The largest provider release for the TypeScript OSS SDK so far: 17 new vector stores, 5 new LLM providers, 4 new embedders, and reranking support. Importing `mem0ai/oss` no longer pulls in any provider SDK, so you only install what you actually configure.
@@ -1825,6 +1883,15 @@ 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 +2142,13 @@ 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 +2222,15 @@ 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:**

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/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/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/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/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/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/llms.txt

@@ -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/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/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/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/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 |

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",

integrations/mem0-plugin/plugin.json

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

integrations/mem0-plugin/scripts/capture_compact_summary.py

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

integrations/mem0-plugin/scripts/capture_session_summary.py

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

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

@@ -8,7 +8,6 @@ Additional platform capabilities beyond core CRUD operations.
 - [Entity Linking](#entity-linking)
 - [Custom Categories](#custom-categories)
 - [Custom Instructions](#custom-instructions)
-- [Criteria Retrieval](#criteria-retrieval)
 - [Feedback Mechanism](#feedback-mechanism)
 - [Memory Export](#memory-export)
 - [Group Chat](#group-chat)
@@ -165,44 +164,6 @@ await client.updateProject({ customInstructions: "Your guidelines here..." });
 
 ---
 
-## Criteria Retrieval
-
-Custom attribute-based memory ranking using LLM-evaluated criteria with weights. Goes beyond semantic similarity to prioritize memories based on domain-specific signals.
-
-### Configuration
-
-```python
-# Define criteria at project level
-retrieval_criteria = [
-    {"name": "joy", "description": "Positive emotions like happiness and excitement", "weight": 3},
-    {"name": "curiosity", "description": "Inquisitiveness and desire to learn", "weight": 2},
-    {"name": "urgency", "description": "Time-sensitive or high-priority items", "weight": 4},
-]
-client.project.update(retrieval_criteria=retrieval_criteria)
-```
-
-```typescript
-await client.updateProject({
-    retrievalCriteria: [
-        { name: 'joy', description: 'Positive emotions', weight: 3 },
-        { name: 'urgency', description: 'Time-sensitive items', weight: 4 },
-    ],
-});
-```
-
-### Usage
-
-Once configured, `client.search()` automatically applies criteria ranking:
-
-```python
-# Criteria-weighted results returned automatically
-results = client.search("Why am I feeling happy?", filters={"user_id": "alice"})
-```
-
-**Best for:** Wellness assistants, tutoring platforms, productivity tools — any app needing intent-aware retrieval.
-
----
-
 ## Feedback Mechanism
 
 Provide feedback on extracted memories to improve system quality over time.

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

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

integrations/mem0-plugin/tests/test_message_roles.py

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

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

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

integrations/n8n-nodes-mem0/.gitignore

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

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

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

integrations/n8n-nodes-mem0/LICENSE

@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
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integrations/n8n-nodes-mem0/README.md

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

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

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

integrations/n8n-nodes-mem0/credentials/mem0.svg

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integrations/n8n-nodes-mem0/gulpfile.js

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

integrations/n8n-nodes-mem0/index.js

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

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

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

integrations/n8n-nodes-mem0/nodes/Mem0/Mem0.node.json

@@ -0,0 +1,21 @@
+{
+	"node": "@mem0/n8n-nodes-mem0.mem0",
+	"nodeVersion": "1.0",
+	"codexVersion": "1.0",
+	"categories": ["AI"],
+	"subcategories": {
+		"AI": ["Memory"]
+	},
+	"resources": {
+		"primaryDocumentation": [
+			{
+				"url": "https://docs.mem0.ai/integrations/n8n"
+			}
+		],
+		"credentialDocumentation": [
+			{
+				"url": "https://docs.mem0.ai/integrations/n8n"
+			}
+		]
+	}
+}

integrations/n8n-nodes-mem0/nodes/Mem0/Mem0.node.ts

@@ -0,0 +1,626 @@
+import {
+	IExecuteFunctions,
+	IDataObject,
+	IHttpRequestMethods,
+	IHttpRequestOptions,
+	INodeExecutionData,
+	INodeType,
+	INodeTypeDescription,
+	JsonObject,
+	NodeApiError,
+	NodeConnectionTypes,
+	NodeOperationError,
+	sleep,
+} from 'n8n-workflow';
+
+// Poll settings for asynchronous (infer=true) memory addition.
+const POLL_INTERVAL_MS = 1500;
+const MAX_POLL_ATTEMPTS = 40; // ~60s ceiling
+
+export class Mem0 implements INodeType {
+	description: INodeTypeDescription = {
+		displayName: 'Mem0',
+		name: 'mem0',
+		icon: 'file:mem0.svg',
+		group: ['transform'],
+		version: 1,
+		subtitle: '={{$parameter["operation"] + ": " + $parameter["resource"]}}',
+		description: 'Add, search, and manage long-term memories with Mem0',
+		defaults: {
+			name: 'Mem0',
+		},
+		// Makes the node available to the AI Agent (Tools Agent) node.
+		usableAsTool: true,
+		inputs: [NodeConnectionTypes.Main],
+		outputs: [NodeConnectionTypes.Main],
+		credentials: [
+			{
+				name: 'mem0Api',
+				required: true,
+			},
+		],
+		properties: [
+			{
+				displayName: 'Resource',
+				name: 'resource',
+				type: 'options',
+				noDataExpression: true,
+				options: [{ name: 'Memory', value: 'memory' }],
+				default: 'memory',
+			},
+			{
+				displayName: 'Operation',
+				name: 'operation',
+				type: 'options',
+				noDataExpression: true,
+				displayOptions: { show: { resource: ['memory'] } },
+				options: [
+					{
+						name: 'Add',
+						value: 'add',
+						action: 'Add a memory',
+						description: 'Extract and store memories from messages',
+					},
+					{
+						name: 'Delete',
+						value: 'delete',
+						action: 'Delete a memory',
+						description: 'Delete a single memory by ID',
+					},
+					{
+						name: 'Get',
+						value: 'get',
+						action: 'Get a memory',
+						description: 'Retrieve a single memory by ID',
+					},
+					{
+						name: 'Get Many',
+						value: 'getAll',
+						action: 'Get many memories',
+						description: 'List stored memories for an entity',
+					},
+					{
+						name: 'Search',
+						value: 'search',
+						action: 'Search memories',
+						description: 'Semantic search over stored memories',
+					},
+					{
+						name: 'Update',
+						value: 'update',
+						action: 'Update a memory',
+						description: 'Update the text or metadata of a memory',
+					},
+				],
+				default: 'add',
+			},
+
+			// ---- Add ---------------------------------------------------------
+			{
+				displayName: 'Messages',
+				name: 'messages',
+				placeholder: 'Add Message',
+				type: 'fixedCollection',
+				typeOptions: { multipleValues: true },
+				displayOptions: { show: { resource: ['memory'], operation: ['add'] } },
+				default: {},
+				description: 'The conversation messages to extract memories from',
+				options: [
+					{
+						name: 'message',
+						displayName: 'Message',
+						values: [
+							{
+								displayName: 'Role',
+								name: 'role',
+								type: 'options',
+								options: [
+									{ name: 'User', value: 'user' },
+									{ name: 'Assistant', value: 'assistant' },
+									{ name: 'System', value: 'system' },
+								],
+								default: 'user',
+							},
+							{
+								displayName: 'Content',
+								name: 'content',
+								type: 'string',
+								typeOptions: { rows: 2 },
+								default: '',
+							},
+						],
+					},
+				],
+			},
+			{
+				displayName: 'User ID',
+				name: 'userId',
+				type: 'string',
+				default: '',
+				displayOptions: { show: { resource: ['memory'], operation: ['add'] } },
+				description: 'Associate the memories with this user',
+			},
+			{
+				displayName: 'Wait for Completion',
+				name: 'waitForCompletion',
+				type: 'boolean',
+				default: true,
+				displayOptions: { show: { resource: ['memory'], operation: ['add'] } },
+				description:
+					'Whether to poll until memory extraction finishes and return the resulting memories. Turn off to return immediately with the event ID.',
+			},
+			{
+				displayName: 'Additional Fields',
+				name: 'addFields',
+				type: 'collection',
+				placeholder: 'Add Field',
+				default: {},
+				displayOptions: { show: { resource: ['memory'], operation: ['add'] } },
+				options: [
+					{
+						displayName: 'Agent ID',
+						name: 'agent_id',
+						type: 'string',
+						default: '',
+					},
+					{
+						displayName: 'App ID',
+						name: 'app_id',
+						type: 'string',
+						default: '',
+					},
+					{
+						displayName: 'Custom Categories',
+						name: 'custom_categories',
+						type: 'json',
+						default: '',
+						description:
+							'Optional taxonomy for categorising extracted memories, as a JSON array of {category: description} objects',
+					},
+					{
+						displayName: 'Custom Instructions',
+						name: 'custom_instructions',
+						type: 'string',
+						typeOptions: { rows: 3 },
+						default: '',
+						description:
+							'Optional instructions that steer what the extractor keeps or ignores',
+					},
+					{
+						displayName: 'Excludes',
+						name: 'excludes',
+						type: 'string',
+						default: '',
+						description: 'Optional: skip memories matching this description',
+					},
+					{
+						displayName: 'Includes',
+						name: 'includes',
+						type: 'string',
+						default: '',
+						description: 'Optional: only extract memories matching this description',
+					},
+					{
+						displayName: 'Infer',
+						name: 'infer',
+						type: 'boolean',
+						default: true,
+						description:
+							'Whether to run LLM extraction over the messages. Turn off to store them verbatim. ' +
+							'This controls extraction only — use "Wait for Completion" to control whether the node waits.',
+					},
+					{
+						displayName: 'Metadata (JSON)',
+						name: 'metadata',
+						type: 'json',
+						default: '',
+					},
+					{
+						displayName: 'Run ID',
+						name: 'run_id',
+						type: 'string',
+						default: '',
+					},
+				],
+			},
+
+			// ---- Search ------------------------------------------------------
+			{
+				displayName: 'Query',
+				name: 'query',
+				type: 'string',
+				default: '',
+				required: true,
+				displayOptions: { show: { resource: ['memory'], operation: ['search'] } },
+				description: 'What to recall from memory',
+			},
+			{
+				displayName: 'User ID',
+				name: 'userId',
+				type: 'string',
+				default: '',
+				displayOptions: { show: { resource: ['memory'], operation: ['search'] } },
+				description:
+					'Restrict the search to this user. Supply at least one of User ID, Agent ID, App ID, or Run ID.',
+			},
+			{
+				displayName: 'Agent ID',
+				name: 'agentId',
+				type: 'string',
+				default: '',
+				displayOptions: { show: { resource: ['memory'], operation: ['search'] } },
+				description: 'Restrict the search to memories scoped to this agent',
+			},
+			{
+				displayName: 'App ID',
+				name: 'appId',
+				type: 'string',
+				default: '',
+				displayOptions: { show: { resource: ['memory'], operation: ['search'] } },
+				description: 'Restrict the search to memories scoped to this app or project',
+			},
+			{
+				displayName: 'Run ID',
+				name: 'runId',
+				type: 'string',
+				default: '',
+				displayOptions: { show: { resource: ['memory'], operation: ['search'] } },
+				description: 'Restrict the search to memories scoped to this session or run',
+			},
+			{
+				displayName: 'Limit',
+				name: 'limit',
+				type: 'number',
+				typeOptions: { minValue: 1 },
+				default: 50,
+				displayOptions: { show: { resource: ['memory'], operation: ['search'] } },
+				description: 'Max number of results to return',
+			},
+
+			// ---- Get Many ----------------------------------------------------
+			{
+				displayName: 'User ID',
+				name: 'userId',
+				type: 'string',
+				default: '',
+				displayOptions: { show: { resource: ['memory'], operation: ['getAll'] } },
+				description:
+					'Restrict the listing to this user. Supply at least one of User ID, Agent ID, App ID, or Run ID.',
+			},
+			{
+				displayName: 'Agent ID',
+				name: 'agentId',
+				type: 'string',
+				default: '',
+				displayOptions: { show: { resource: ['memory'], operation: ['getAll'] } },
+				description: 'Restrict the listing to memories scoped to this agent',
+			},
+			{
+				displayName: 'App ID',
+				name: 'appId',
+				type: 'string',
+				default: '',
+				displayOptions: { show: { resource: ['memory'], operation: ['getAll'] } },
+				description: 'Restrict the listing to memories scoped to this app or project',
+			},
+			{
+				displayName: 'Run ID',
+				name: 'runId',
+				type: 'string',
+				default: '',
+				displayOptions: { show: { resource: ['memory'], operation: ['getAll'] } },
+				description: 'Restrict the listing to memories scoped to this session or run',
+			},
+			{
+				displayName: 'Return All',
+				name: 'returnAll',
+				type: 'boolean',
+				default: false,
+				displayOptions: { show: { resource: ['memory'], operation: ['getAll'] } },
+				description: 'Whether to return all results or only up to a given limit',
+			},
+			{
+				displayName: 'Page',
+				name: 'page',
+				type: 'number',
+				typeOptions: { minValue: 1 },
+				default: 1,
+				displayOptions: {
+					show: { resource: ['memory'], operation: ['getAll'], returnAll: [false] },
+				},
+			},
+			{
+				displayName: 'Page Size',
+				name: 'pageSize',
+				type: 'number',
+				typeOptions: { minValue: 1 },
+				default: 50,
+				displayOptions: { show: { resource: ['memory'], operation: ['getAll'] } },
+			},
+
+			// ---- Get / Update / Delete (by ID) -------------------------------
+			{
+				displayName: 'Memory ID',
+				name: 'memoryId',
+				type: 'string',
+				default: '',
+				required: true,
+				displayOptions: {
+					show: { resource: ['memory'], operation: ['get', 'update', 'delete'] },
+				},
+			},
+			{
+				displayName: 'Text',
+				name: 'text',
+				type: 'string',
+				default: '',
+				displayOptions: { show: { resource: ['memory'], operation: ['update'] } },
+				description: 'The new memory text',
+			},
+			{
+				displayName: 'Metadata (JSON)',
+				name: 'metadata',
+				type: 'json',
+				default: '',
+				displayOptions: { show: { resource: ['memory'], operation: ['update'] } },
+			},
+		],
+	};
+
+	async execute(this: IExecuteFunctions): Promise<INodeExecutionData[][]> {
+		const items = this.getInputData();
+		const returnData: INodeExecutionData[] = [];
+		const credentials = await this.getCredentials('mem0Api');
+		const baseUrl = (credentials.baseUrl as string) || 'https://api.mem0.ai';
+
+		const request = async (
+			method: IHttpRequestMethods,
+			url: string,
+			body?: IDataObject,
+			qs?: IDataObject,
+		): Promise<IDataObject> => {
+			const options: IHttpRequestOptions = {
+				method,
+				url: `${baseUrl}${url}`,
+				json: true,
+				...(body ? { body } : {}),
+				// First-party usage attribution: the backend reads `source` (same as OpenClaw).
+				qs: { source: 'N8N', ...(qs ?? {}) },
+			};
+			return (await this.helpers.httpRequestWithAuthentication.call(
+				this,
+				'mem0Api',
+				options,
+			)) as IDataObject;
+		};
+
+		for (let i = 0; i < items.length; i++) {
+			try {
+				const operation = this.getNodeParameter('operation', i) as string;
+				let responseData: IDataObject | IDataObject[] = {};
+
+				if (operation === 'add') {
+					const messagesUi = this.getNodeParameter('messages.message', i, []) as IDataObject[];
+					if (!messagesUi.length) {
+						throw new NodeOperationError(this.getNode(), 'At least one message is required', {
+							itemIndex: i,
+						});
+					}
+					const addFields = this.getNodeParameter('addFields', i, {}) as IDataObject;
+					const body: IDataObject = {
+						messages: messagesUi.map((m) => ({ role: m.role, content: m.content })),
+						infer: addFields.infer !== undefined ? addFields.infer : true,
+					};
+					const userId = this.getNodeParameter('userId', i, '') as string;
+					if (userId) body.user_id = userId;
+					if (addFields.agent_id) body.agent_id = addFields.agent_id;
+					if (addFields.app_id) body.app_id = addFields.app_id;
+					if (addFields.run_id) body.run_id = addFields.run_id;
+					if (addFields.metadata) {
+						try {
+							body.metadata =
+								typeof addFields.metadata === 'string'
+									? JSON.parse(addFields.metadata as string)
+									: addFields.metadata;
+						} catch {
+							throw new NodeOperationError(this.getNode(), 'Invalid JSON in "Metadata" field', {
+								itemIndex: i,
+							});
+						}
+					}
+
+					// Custom extraction controls (optional): steer what the API extracts.
+					if (addFields.custom_instructions) {
+						body.custom_instructions = addFields.custom_instructions;
+					}
+					if (addFields.custom_categories) {
+						try {
+							body.custom_categories =
+								typeof addFields.custom_categories === 'string'
+									? JSON.parse(addFields.custom_categories as string)
+									: addFields.custom_categories;
+						} catch {
+							throw new NodeOperationError(
+								this.getNode(),
+								'Invalid JSON in "Custom Categories" field',
+								{ itemIndex: i },
+							);
+						}
+					}
+
+					if (addFields.includes) body.includes = addFields.includes;
+					if (addFields.excludes) body.excludes = addFields.excludes;
+
+					// API requires at least one entity id — fail clearly instead of a raw 4xx.
+					if (!body.user_id && !body.agent_id && !body.run_id && !body.app_id) {
+						throw new NodeOperationError(
+							this.getNode(),
+							'Add requires at least one of User ID, Agent ID, Run ID, or App ID',
+							{ itemIndex: i },
+						);
+					}
+
+					const addResp = await request('POST', '/v3/memories/add/', body);
+					const waitForCompletion = this.getNodeParameter('waitForCompletion', i, true) as boolean;
+					const addStatus = addResp.status as string | undefined;
+					const isTerminal = addStatus === 'SUCCEEDED' || addStatus === 'FAILED';
+
+					// Add returns {event_id, status:PENDING|RUNNING}; poll until terminal when asked to wait.
+					if (waitForCompletion && addResp.event_id && !isTerminal) {
+						responseData = await pollEvent(request, addResp.event_id as string, this, i);
+					} else if (addStatus === 'FAILED') {
+						throw new NodeOperationError(
+							this.getNode(),
+							`Mem0 memory add failed: ${(addResp.message as string) || 'unknown error'}`,
+							{ itemIndex: i },
+						);
+					} else {
+						// If the response is already terminal, unwrap results; otherwise return as-is.
+						responseData = Array.isArray(addResp.results)
+							? (addResp.results as IDataObject[])
+							: addResp;
+					}
+				} else if (operation === 'search') {
+					const body: IDataObject = {
+						query: this.getNodeParameter('query', i) as string,
+						output_format: 'v1.1',
+						top_k: this.getNodeParameter('limit', i, 50) as number,
+					};
+					body.filters = buildEntityFilters(
+						{
+							user_id: this.getNodeParameter('userId', i, '') as string,
+							agent_id: this.getNodeParameter('agentId', i, '') as string,
+							app_id: this.getNodeParameter('appId', i, '') as string,
+							run_id: this.getNodeParameter('runId', i, '') as string,
+						},
+						this,
+						i,
+					);
+					const resp = await request('POST', '/v3/memories/search/', body);
+					responseData = Array.isArray(resp.results) ? (resp.results as IDataObject[]) : [];
+				} else if (operation === 'getAll') {
+					const returnAll = this.getNodeParameter('returnAll', i, false) as boolean;
+					const pageSize = this.getNodeParameter('pageSize', i, 50) as number;
+					const body: IDataObject = {
+						filters: buildEntityFilters(
+							{
+								user_id: this.getNodeParameter('userId', i, '') as string,
+								agent_id: this.getNodeParameter('agentId', i, '') as string,
+								app_id: this.getNodeParameter('appId', i, '') as string,
+								run_id: this.getNodeParameter('runId', i, '') as string,
+							},
+							this,
+							i,
+						),
+					};
+					if (returnAll) {
+						// Page through until a short/empty page or no `next` (hard-capped for safety).
+						const all: IDataObject[] = [];
+						for (let page = 1; page <= 10000; page++) {
+							const resp = await request('POST', '/v3/memories/', body, { page, page_size: pageSize });
+							const results = Array.isArray(resp.results) ? (resp.results as IDataObject[]) : [];
+							all.push(...results);
+							if (results.length < pageSize || !resp.next) break;
+						}
+						responseData = all;
+					} else {
+						const page = this.getNodeParameter('page', i, 1) as number;
+						const resp = await request('POST', '/v3/memories/', body, { page, page_size: pageSize });
+						responseData = Array.isArray(resp.results) ? (resp.results as IDataObject[]) : [];
+					}
+				} else if (operation === 'get') {
+					const memoryId = this.getNodeParameter('memoryId', i) as string;
+					responseData = await request('GET', `/v1/memories/${encodeURIComponent(memoryId)}/`);
+				} else if (operation === 'update') {
+					const memoryId = this.getNodeParameter('memoryId', i) as string;
+					const body: IDataObject = {};
+					const text = this.getNodeParameter('text', i, '') as string;
+					const metadata = this.getNodeParameter('metadata', i, '') as string;
+					if (text) body.text = text;
+					if (metadata) {
+						try {
+							body.metadata = typeof metadata === 'string' ? JSON.parse(metadata) : metadata;
+						} catch {
+							throw new NodeOperationError(this.getNode(), 'Invalid JSON in "Metadata" field', {
+								itemIndex: i,
+							});
+						}
+					}
+					if (Object.keys(body).length === 0) {
+						throw new NodeOperationError(this.getNode(), 'Provide text or metadata to update', {
+							itemIndex: i,
+						});
+					}
+					responseData = await request('PUT', `/v1/memories/${encodeURIComponent(memoryId)}/`, body);
+				} else if (operation === 'delete') {
+					const memoryId = this.getNodeParameter('memoryId', i) as string;
+					responseData = await request('DELETE', `/v1/memories/${encodeURIComponent(memoryId)}/`);
+				}
+
+				const arr = Array.isArray(responseData) ? responseData : [responseData];
+				for (const entry of arr) {
+					returnData.push({ json: entry, pairedItem: { item: i } });
+				}
+			} catch (error) {
+				if (this.continueOnFail()) {
+					returnData.push({ json: { error: (error as Error).message }, pairedItem: { item: i } });
+					continue;
+				}
+				throw new NodeApiError(this.getNode(), error as JsonObject, { itemIndex: i });
+			}
+		}
+
+		return [returnData];
+	}
+}
+
+function buildEntityFilters(
+	ids: Record<string, string>,
+	ctx: IExecuteFunctions,
+	itemIndex: number,
+): IDataObject {
+	const clauses: IDataObject[] = Object.entries(ids)
+		.filter(([, value]) => value)
+		.map(([key, value]) => ({ [key]: value }));
+
+	if (clauses.length === 0) {
+		throw new NodeOperationError(
+			ctx.getNode(),
+			'Provide at least one of User ID, Agent ID, App ID, or Run ID',
+			{ itemIndex },
+		);
+	}
+
+	return clauses.length === 1 ? clauses[0] : { OR: clauses };
+}
+
+// Polls GET /v1/event/{id}/ until the memory-addition event resolves.
+async function pollEvent(
+	request: (m: IHttpRequestMethods, u: string) => Promise<IDataObject>,
+	eventId: string,
+	ctx: IExecuteFunctions,
+	itemIndex: number,
+): Promise<IDataObject | IDataObject[]> {
+	for (let attempt = 0; attempt < MAX_POLL_ATTEMPTS; attempt++) {
+		const event = await request('GET', `/v1/event/${encodeURIComponent(eventId)}/`);
+		const status = event.status as string;
+		if (status === 'SUCCEEDED') {
+			// Match the shape of search/getAll (a clean array); fall back to the envelope.
+			return Array.isArray(event.results) ? (event.results as IDataObject[]) : event;
+		}
+		if (status === 'FAILED') {
+			const reason = (event.error as string) || (event.message as string) || 'unknown error';
+			throw new NodeOperationError(
+				ctx.getNode(),
+				`Mem0 memory event ${eventId} failed: ${reason}`,
+				{ itemIndex },
+			);
+		}
+		await sleep(POLL_INTERVAL_MS);
+	}
+	throw new NodeOperationError(
+		ctx.getNode(),
+		`Timed out waiting for memory event ${eventId} to complete`,
+		{ itemIndex },
+	);
+}

integrations/n8n-nodes-mem0/nodes/Mem0/mem0.svg

@@ -0,0 +1,19 @@
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+</svg>

integrations/n8n-nodes-mem0/package.json

@@ -0,0 +1,79 @@
+{
+  "name": "@mem0/n8n-nodes-mem0",
+  "version": "0.1.0",
+  "description": "n8n community node for Mem0 — the memory layer for AI agents. Add, search, get, update, and delete long-term memories.",
+  "keywords": [
+    "n8n-community-node-package",
+    "mem0",
+    "memory",
+    "ai",
+    "agents",
+    "llm"
+  ],
+  "license": "Apache-2.0",
+  "homepage": "https://mem0.ai",
+  "author": {
+    "name": "Mem0",
+    "email": "founders@mem0.ai"
+  },
+  "repository": {
+    "type": "git",
+    "url": "https://github.com/mem0ai/mem0",
+    "directory": "integrations/n8n-nodes-mem0"
+  },
+  "engines": {
+    "node": ">=20.15"
+  },
+  "main": "index.js",
+  "publishConfig": {
+    "access": "public"
+  },
+  "scripts": {
+    "build": "npx rimraf dist && tsc && gulp build:icons",
+    "dev": "tsc --watch",
+    "format": "prettier nodes credentials --write",
+    "lint": "eslint nodes credentials package.json",
+    "lintfix": "eslint nodes credentials package.json --fix",
+    "test": "jest",
+    "prepublishOnly": "npm run build && npm run lint"
+  },
+  "files": [
+    "dist"
+  ],
+  "n8n": {
+    "n8nNodesApiVersion": 1,
+    "credentials": [
+      "dist/credentials/Mem0Api.credentials.js"
+    ],
+    "nodes": [
+      "dist/nodes/Mem0/Mem0.node.js"
+    ]
+  },
+  "devDependencies": {
+    "@types/jest": "^29.5.14",
+    "@types/node": "^20.0.0",
+    "@typescript-eslint/parser": "^8.0.0",
+    "eslint": "^8.57.0",
+    "jest": "^29.7.0",
+    "eslint-plugin-n8n-nodes-base": "^1.16.3",
+    "gulp": "^5.0.0",
+    "n8n-workflow": "*",
+    "prettier": "^3.3.0",
+    "rimraf": "^5.0.0",
+    "ts-jest": "^29.2.5",
+    "typescript": "^5.5.0"
+  },
+  "peerDependencies": {
+    "n8n-workflow": "*"
+  },
+  "pnpm": {
+    "overrides": {
+      "form-data@<4.0.6": ">=4.0.6",
+      "uuid@<11.1.1": ">=11.1.1 <12.0.0",
+      "lodash@<=4.17.23": ">=4.18.0",
+      "brace-expansion@<1.1.16": ">=1.1.16 <2.0.0",
+      "brace-expansion@>=2.0.0 <2.1.2": ">=2.1.2 <3.0.0",
+      "brace-expansion@>=5.0.0 <5.0.8": ">=5.0.8"
+    }
+  }
+}

integrations/n8n-nodes-mem0/test/Mem0.node.test.ts

@@ -0,0 +1,211 @@
+// Offline unit tests: they stub IExecuteFunctions and mock the HTTP layer, so
+// they run with no network. They cover the review-flagged paths: the entity-id
+// guard, JSON-parse errors, the poll-timeout loop, plus source attribution and
+// the Return All pagination.
+
+// Make the poll loop instant (pollEvent sleeps between attempts).
+jest.mock('n8n-workflow', () => {
+	const actual = jest.requireActual('n8n-workflow');
+	return { ...actual, sleep: jest.fn().mockResolvedValue(undefined) };
+});
+
+import { Mem0 } from '../nodes/Mem0/Mem0.node';
+
+type HttpImpl = (options: any) => Promise<any>;
+
+function makeCtx(
+	operation: string,
+	params: Record<string, any>,
+	http: HttpImpl,
+	opts: { continueOnFail?: boolean } = {},
+): any {
+	const requests: any[] = [];
+	const node = { name: 'Mem0', type: '@mem0/n8n-nodes-mem0.mem0', typeVersion: 1 };
+	const ctx: any = {
+		getInputData: () => [{ json: {} }],
+		getCredentials: async () => ({ apiKey: 'm0-test', baseUrl: 'https://api.mem0.ai' }),
+		getNodeParameter: (name: string, _i: number, dflt?: any) =>
+			name === 'operation' ? operation : name in params ? params[name] : dflt,
+		getNode: () => node,
+		continueOnFail: () => opts.continueOnFail ?? false,
+		helpers: {
+			httpRequestWithAuthentication: jest.fn(async function (_cred: string, options: any) {
+				requests.push(options);
+				return http(options);
+			}),
+		},
+	};
+	ctx.requests = requests;
+	return ctx;
+}
+
+const run = (ctx: any) => Mem0.prototype.execute.call(ctx);
+
+describe('Mem0 node (offline)', () => {
+	it('reports a clear error when Add has no entity id', async () => {
+		const ctx = makeCtx(
+			'add',
+			{ 'messages.message': [{ role: 'user', content: 'hi' }], addFields: {}, userId: '' },
+			async () => ({}),
+			{ continueOnFail: true },
+		);
+		const out: any = await run(ctx);
+		expect(out[0][0].json.error).toMatch(/at least one of User ID/i);
+	});
+
+	it('forwards app id, includes and excludes on Add', async () => {
+		const ctx = makeCtx(
+			'add',
+			{
+				'messages.message': [{ role: 'user', content: 'hi' }],
+				addFields: {
+					app_id: 'p1',
+					includes: 'only food preferences',
+					excludes: 'nothing about vehicles',
+				},
+				userId: 'u1',
+			},
+			async () => ({}),
+		);
+		await run(ctx);
+		expect(ctx.requests[0].body).toMatchObject({
+			app_id: 'p1',
+			includes: 'only food preferences',
+			excludes: 'nothing about vehicles',
+		});
+	});
+
+	it('accepts an app id alone as the entity scope on Add', async () => {
+		const ctx = makeCtx(
+			'add',
+			{
+				'messages.message': [{ role: 'user', content: 'hi' }],
+				addFields: { app_id: 'p1' },
+				userId: '',
+			},
+			async () => ({}),
+			{ continueOnFail: true },
+		);
+		const out: any = await run(ctx);
+		expect(out[0][0].json.error).toBeUndefined();
+		expect(ctx.requests[0].body.app_id).toBe('p1');
+	});
+
+	it('reports a clear error on invalid JSON in Custom Categories', async () => {
+		const ctx = makeCtx(
+			'add',
+			{
+				'messages.message': [{ role: 'user', content: 'hi' }],
+				addFields: { custom_categories: '{bad' },
+				userId: 'u1',
+			},
+			async () => ({}),
+			{ continueOnFail: true },
+		);
+		const out: any = await run(ctx);
+		expect(out[0][0].json.error).toMatch(/Invalid JSON/i);
+	});
+
+	it('times out when the add event never resolves', async () => {
+		const ctx = makeCtx(
+			'add',
+			{
+				'messages.message': [{ role: 'user', content: 'hi' }],
+				addFields: {},
+				userId: 'u1',
+				waitForCompletion: true,
+			},
+			async (options) => {
+				if (options.url.includes('/v3/memories/add/')) return { event_id: 'e1', status: 'PENDING' };
+				if (options.url.includes('/v1/event/')) return { status: 'PENDING' }; // never terminal
+				return {};
+			},
+			{ continueOnFail: true },
+		);
+		const out: any = await run(ctx);
+		expect(out[0][0].json.error).toMatch(/Timed out waiting for memory event/i);
+	});
+
+	it('tags every request with source=N8N for first-party attribution', async () => {
+		const ctx = makeCtx('search', { query: 'x', userId: 'u1', limit: 5 }, async () => ({ results: [] }));
+		await run(ctx);
+		expect(ctx.requests[0].qs.source).toBe('N8N');
+	});
+
+	it('sends a single entity id as a flat filter', async () => {
+		const ctx = makeCtx('search', { query: 'x', userId: 'u1' }, async () => ({ results: [] }));
+		await run(ctx);
+		expect(ctx.requests[0].body.filters).toEqual({ user_id: 'u1' });
+	});
+
+	it('combines entity ids with OR, never AND (entities are stored separately, so AND matches nothing)', async () => {
+		const ctx = makeCtx(
+			'search',
+			{ query: 'x', userId: 'u1', agentId: 'a1', appId: 'p1', runId: 'r1' },
+			async () => ({ results: [] }),
+		);
+		await run(ctx);
+		expect(ctx.requests[0].body.filters).toEqual({
+			OR: [{ user_id: 'u1' }, { agent_id: 'a1' }, { app_id: 'p1' }, { run_id: 'r1' }],
+		});
+	});
+
+	it.each(['search', 'getAll'])('filters %s by app id alone', async (op) => {
+		const ctx = makeCtx(op, { query: 'x', appId: 'p1' }, async () => ({ results: [] }));
+		await run(ctx);
+		expect(ctx.requests[0].body.filters).toEqual({ app_id: 'p1' });
+	});
+
+	it('filters Get Many by agent id alone', async () => {
+		const ctx = makeCtx('getAll', { agentId: 'a1' }, async () => ({ results: [] }));
+		await run(ctx);
+		expect(ctx.requests[0].body.filters).toEqual({ agent_id: 'a1' });
+	});
+
+	it.each(['search', 'getAll'])('reports a clear error when %s has no entity id', async (op) => {
+		const ctx = makeCtx(op, { query: 'x' }, async () => ({ results: [] }), {
+			continueOnFail: true,
+		});
+		const out: any = await run(ctx);
+		expect(out[0][0].json.error).toMatch(/at least one of User ID/i);
+	});
+
+	it.each([
+		['get', 'GET'],
+		['delete', 'DELETE'],
+	])('escapes the memory id in the %s url', async (op, method) => {
+		const ctx = makeCtx(op, { memoryId: '../v1/entities' }, async () => ({}));
+		await run(ctx);
+		expect(ctx.requests[0].method).toBe(method);
+		expect(ctx.requests[0].url).toBe('https://api.mem0.ai/v1/memories/..%2Fv1%2Fentities/');
+	});
+
+	it('escapes the event id when polling', async () => {
+		const ctx = makeCtx(
+			'add',
+			{
+				'messages.message': [{ role: 'user', content: 'hi' }],
+				addFields: {},
+				userId: 'u1',
+				waitForCompletion: true,
+			},
+			async (options) => {
+				if (options.url.includes('/v3/memories/add/')) return { event_id: 'a b/c' };
+				return { status: 'SUCCEEDED', results: [] };
+			},
+		);
+		await run(ctx);
+		expect(ctx.requests[1].url).toBe('https://api.mem0.ai/v1/event/a%20b%2Fc/');
+	});
+
+	it('Return All pages through until a short page', async () => {
+		let call = 0;
+		const ctx = makeCtx('getAll', { userId: 'u1', returnAll: true, pageSize: 2 }, async () => {
+			call++;
+			if (call === 1) return { results: [{ id: 'a' }, { id: 'b' }], next: 'page2' };
+			return { results: [{ id: 'c' }], next: null }; // short page -> stop
+		});
+		const out: any = await run(ctx);
+		expect(out[0].map((d: any) => d.json.id)).toEqual(['a', 'b', 'c']);
+	});
+});

integrations/n8n-nodes-mem0/tsconfig.json

@@ -0,0 +1,27 @@
+{
+  "compilerOptions": {
+    "strict": true,
+    "module": "commonjs",
+    "moduleResolution": "node",
+    "target": "es2019",
+    "lib": ["es2019", "es2020", "es2022.error"],
+    "removeComments": true,
+    "useUnknownInCatchVariables": false,
+    "forceConsistentCasingInFileNames": true,
+    "noImplicitAny": true,
+    "noImplicitReturns": true,
+    "noUnusedLocals": true,
+    "strictNullChecks": true,
+    "preserveConstEnums": true,
+    "esModuleInterop": true,
+    "isolatedModules": true,
+    "resolveJsonModule": true,
+    "incremental": false,
+    "declaration": false,
+    "sourceMap": true,
+    "skipLibCheck": true,
+    "outDir": "./dist/"
+  },
+  "include": ["credentials/**/*", "nodes/**/*"],
+  "exclude": ["node_modules", "dist"]
+}

integrations/zapier-mem0/.gitignore

@@ -0,0 +1,9 @@
+node_modules/
+package-lock.json
+.env
+.zapierapprc
+build/
+dist/
+coverage/
+*.log
+.DS_Store

integrations/zapier-mem0/LICENSE

@@ -0,0 +1,201 @@
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integrations/zapier-mem0/README.md

@@ -0,0 +1,41 @@
+# Zapier integration for Mem0
+
+A [Zapier](https://zapier.com) integration for [Mem0](https://mem0.ai) — the memory layer for AI agents. Add, search, list, and delete long-term memories from any Zap.
+
+Built with the [Zapier Platform CLI](https://docs.zapier.com/platform/quickstart/cli-tutorial).
+
+## Actions
+
+| Type | Name | Endpoint |
+| --- | --- | --- |
+| Create | **Add Memory** | `POST /v3/memories/add/` |
+| Create | **Delete Memory** | `DELETE /v1/memories/{id}/` |
+| Search | **Search Memories** | `POST /v3/memories/search/` |
+| Search | **Get Memories** | `POST /v3/memories/` |
+
+**Add Memory** runs LLM extraction asynchronously and returns immediately with an event ID by default. Turn on **Wait for Completion** to have the action poll until extraction finishes and return the resulting memories — note that extraction can take longer than Zapier allows a single step to run, and a timeout there does **not** mean the add failed (it typically still completes server-side). Set **Infer = false** to store the message verbatim instead of extracting.
+
+**Get Memories** returns one page at a time; use the **Page** and **Limit** fields to page through larger result sets.
+
+## Authentication
+
+Custom (API key) auth. Provide a Mem0 API key from [app.mem0.ai](https://app.mem0.ai) → Settings → API Keys. It is sent as `Authorization: Token <key>`.
+
+## Development
+
+Written in TypeScript; the app compiles to `dist/` (Zapier runs the compiled JS).
+
+```bash
+pnpm install
+pnpm build                      # compile src/ → dist/
+pnpm test:unit                  # offline unit tests (mocked, no network)
+MEM0_API_KEY=m0-... pnpm test   # unit + live E2E against api.mem0.ai
+```
+
+Anonymous usage telemetry is sent to Mem0; opt out with `MEM0_TELEMETRY=false`.
+
+To deploy (maintainers): `pnpm build && zapier push`.
+
+## License
+
+MIT

integrations/zapier-mem0/package.json

@@ -0,0 +1,69 @@
+{
+  "name": "@mem0/zapier",
+  "version": "0.1.0",
+  "description": "Zapier integration for Mem0 — the memory layer for AI agents.",
+  "keywords": [
+    "zapier",
+    "mem0",
+    "memory",
+    "ai",
+    "agents"
+  ],
+  "homepage": "https://mem0.ai",
+  "author": {
+    "name": "Mem0",
+    "email": "founders@mem0.ai"
+  },
+  "repository": {
+    "type": "git",
+    "url": "https://github.com/mem0ai/mem0",
+    "directory": "integrations/zapier-mem0"
+  },
+  "license": "Apache-2.0",
+  "main": "dist/index.js",
+  "scripts": {
+    "build": "tsc",
+    "test": "jest --testTimeout 180000",
+    "test:unit": "jest test/unit.test.ts"
+  },
+  "engines": {
+    "node": ">=18",
+    "npm": ">=5.6.0"
+  },
+  "publishConfig": {
+    "access": "public"
+  },
+  "files": [
+    "dist",
+    "src",
+    "README.md",
+    "LICENSE"
+  ],
+  "jest": {
+    "testEnvironment": "node",
+    "testMatch": ["**/test/**/*.test.ts"],
+    "setupFiles": ["<rootDir>/test/setup.ts"],
+    "transform": {
+      "^.+\\.tsx?$": ["ts-jest", {}]
+    }
+  },
+  "dependencies": {
+    "zapier-platform-core": "19.0.0"
+  },
+  "devDependencies": {
+    "@types/jest": "^29.5.14",
+    "@types/node": "^22.9.0",
+    "jest": "^29.7.0",
+    "ts-jest": "^29.2.5",
+    "typescript": "^5.6.3"
+  },
+  "pnpm": {
+    "overrides": {
+      "form-data@<4.0.6": ">=4.0.6",
+      "uuid@<11.1.1": ">=11.1.1",
+      "esbuild": ">=0.28.1",
+      "undici@<6.27.0": ">=6.27.0 <8.0.0",
+      "undici@>=8.0.0 <8.5.0": ">=8.5.0"
+    }
+  }
+}

integrations/zapier-mem0/pnpm-workspace.yaml

@@ -0,0 +1,9 @@
+packages:
+  - '.'
+
+overrides:
+  "form-data@<4.0.6": ">=4.0.6"
+  "uuid@<11.1.1": ">=11.1.1"
+  "esbuild": ">=0.28.1"
+  "undici@<6.27.0": ">=6.27.0 <8.0.0"
+  "undici@>=8.0.0 <8.5.0": ">=8.5.0"

integrations/zapier-mem0/src/authentication.ts

@@ -0,0 +1,31 @@
+import type { ZObject, Bundle } from './types';
+
+// Custom (API key) authentication for Mem0.
+// The key is sent as "Authorization: Token <apiKey>" (matches Mem0's SDKs).
+const test = (z: ZObject, _bundle: Bundle) => z.request({ url: '/v1/ping/', method: 'GET' });
+
+export default {
+	type: 'custom',
+	test,
+	fields: [
+		{
+			key: 'apiKey',
+			label: 'Mem0 API Key',
+			// `password` so Zapier masks the key in the connection UI (it is a secret).
+			type: 'password',
+			required: true,
+			helpText:
+				'Your Mem0 API key (starts with `m0-`). Create one at [app.mem0.ai](https://app.mem0.ai) → Settings → API Keys.',
+		},
+		{
+			key: 'baseUrl',
+			label: 'Base URL',
+			type: 'string',
+			required: false,
+			default: 'https://api.mem0.ai',
+			helpText: 'Override only for self-hosted or non-default deployments.',
+		},
+	],
+	// Shown on the connection label in the Zap editor.
+	connectionLabel: 'Mem0',
+};

integrations/zapier-mem0/src/creates/add_memory.ts

@@ -0,0 +1,168 @@
+import type { ZObject, Bundle, AddResponse, EventResponse } from '../types';
+import { captureEvent } from '../telemetry';
+
+const POLL_INTERVAL_MS = 1500;
+// Bounded to a 60s poll budget, under Zapier's per-step execution timeout.
+const MAX_POLL_ATTEMPTS = 40;
+
+// Polls GET /v1/event/{id}/ until the async memory-addition event resolves.
+const pollEvent = async (z: ZObject, eventId: string): Promise<EventResponse> => {
+	for (let attempt = 0; attempt < MAX_POLL_ATTEMPTS; attempt++) {
+		const res = await z.request({ url: `/v1/event/${eventId}/`, method: 'GET' });
+		const data = res.data as EventResponse;
+		const status = data && data.status;
+		if (status === 'SUCCEEDED') {
+			return data;
+		}
+		if (status === 'FAILED') {
+			const reason = (data && (data.error || data.message)) || 'unknown error';
+			throw new z.errors.Error(`Mem0 memory event ${eventId} failed: ${reason}`, 'Mem0EventFailed', 400);
+		}
+		await new Promise((resolve) => setTimeout(resolve, POLL_INTERVAL_MS));
+	}
+	throw new z.errors.Error(
+		`Timed out waiting for memory event ${eventId}. The add was accepted and is ` +
+			`likely still completing on the server — a timeout here does not mean it failed.`,
+		'Mem0Timeout',
+		408,
+	);
+};
+
+const perform = async (z: ZObject, bundle: Bundle): Promise<AddResponse | EventResponse> => {
+	// Zapier boolean fields can arrive as the strings 'true'/'false'; coerce
+	// explicitly so "Infer = No" / "Wait = No" are honored.
+	const infer = String(bundle.inputData.infer) !== 'false';
+	// Waiting is opt-in (the poll path can exceed Zapier's step timeout).
+	const wait = String(bundle.inputData.waitForCompletion) === 'true';
+
+	const body: Record<string, unknown> = {
+		messages: [{ role: bundle.inputData.role || 'user', content: bundle.inputData.content }],
+		infer,
+	};
+	if (bundle.inputData.user_id) body.user_id = bundle.inputData.user_id;
+	if (bundle.inputData.agent_id) body.agent_id = bundle.inputData.agent_id;
+	if (bundle.inputData.run_id) body.run_id = bundle.inputData.run_id;
+	if (bundle.inputData.metadata) {
+		try {
+			body.metadata =
+				typeof bundle.inputData.metadata === 'string'
+					? JSON.parse(bundle.inputData.metadata)
+					: bundle.inputData.metadata;
+		} catch {
+			throw new z.errors.Error('Metadata must be valid JSON.', 'InvalidInput', 400);
+		}
+	}
+	// Custom extraction controls (optional): steer what the API extracts.
+	if (bundle.inputData.custom_instructions) {
+		body.custom_instructions = bundle.inputData.custom_instructions;
+	}
+	if (bundle.inputData.custom_categories) {
+		try {
+			body.custom_categories =
+				typeof bundle.inputData.custom_categories === 'string'
+					? JSON.parse(bundle.inputData.custom_categories)
+					: bundle.inputData.custom_categories;
+		} catch {
+			throw new z.errors.Error('Custom Categories must be valid JSON.', 'InvalidInput', 400);
+		}
+	}
+	if (bundle.inputData.includes) body.includes = bundle.inputData.includes;
+	if (bundle.inputData.excludes) body.excludes = bundle.inputData.excludes;
+
+	captureEvent('zapier.add_memory', bundle.authData?.apiKey, { infer, wait });
+
+	const response = await z.request({
+		url: '/v3/memories/add/',
+		method: 'POST',
+		body,
+	});
+
+	// Default to an empty object so an empty/no-content 2xx body can't crash the
+	// `.status` / `.event_id` reads below.
+	const data = (response.data ?? {}) as AddResponse;
+
+	// Add returns {event_id, status:PENDING|RUNNING}; poll only when opted in.
+	if (wait && data.event_id && data.status !== 'SUCCEEDED' && data.status !== 'FAILED') {
+		return pollEvent(z, data.event_id);
+	}
+	if (data.status === 'FAILED') {
+		const reason = data.error || data.message || 'unknown error';
+		throw new z.errors.Error(`Mem0 memory add failed: ${reason}`, 'Mem0EventFailed', 400);
+	}
+	return data;
+};
+
+export default {
+	key: 'add_memory',
+	noun: 'Memory',
+	display: {
+		label: 'Add Memory',
+		description: 'Extract and store memories from a message.',
+	},
+	operation: {
+		perform,
+		inputFields: [
+			{
+				key: 'content',
+				label: 'Content',
+				type: 'text',
+				required: true,
+				helpText: 'The message content to extract memories from.',
+			},
+			{
+				key: 'role',
+				label: 'Role',
+				choices: { user: 'User', assistant: 'Assistant', system: 'System' },
+				default: 'user',
+			},
+			{ key: 'user_id', label: 'User ID', type: 'string' },
+			{ key: 'agent_id', label: 'Agent ID', type: 'string' },
+			{ key: 'run_id', label: 'Run ID', type: 'string' },
+			{ key: 'metadata', label: 'Metadata (JSON)', type: 'string' },
+			{
+				key: 'custom_instructions',
+				label: 'Custom Instructions',
+				type: 'text',
+				helpText: 'Optional instructions that steer what the extractor keeps or ignores.',
+			},
+			{
+				key: 'custom_categories',
+				label: 'Custom Categories (JSON)',
+				type: 'string',
+				helpText:
+					'Optional taxonomy for categorising memories, as a JSON array of {category: description} objects.',
+			},
+			{
+				key: 'includes',
+				label: 'Includes',
+				type: 'text',
+				helpText: 'Optional: only extract memories matching this description.',
+			},
+			{
+				key: 'excludes',
+				label: 'Excludes',
+				type: 'text',
+				helpText: 'Optional: skip memories matching this description.',
+			},
+			{
+				key: 'infer',
+				label: 'Infer',
+				type: 'boolean',
+				default: 'true',
+				helpText: 'Run LLM extraction over the message. Turn off to store it verbatim.',
+			},
+			{
+				key: 'waitForCompletion',
+				label: 'Wait for Completion',
+				type: 'boolean',
+				default: 'false',
+				helpText:
+					'Poll until extraction finishes and return the resulting memories. ' +
+					'Leave off (default) to return immediately with an event ID — extraction can take ' +
+					'longer than Zapier allows this step to run, and a timeout does not mean the add failed.',
+			},
+		],
+		// Default (no-wait) returns the accepted event; the wait path returns the resolved event.
+		sample: { event_id: '00000000-0000-0000-0000-000000000000', status: 'PENDING' },
+	},
+};

integrations/zapier-mem0/src/creates/delete_memory.ts

@@ -0,0 +1,24 @@
+import type { ZObject, Bundle } from '../types';
+
+const perform = async (z: ZObject, bundle: Bundle) => {
+	// Trailing slash required (Django APPEND_SLASH); id encoded so a stray slash can't mistarget the path.
+	const response = await z.request({
+		url: `/v1/memories/${encodeURIComponent(String(bundle.inputData.memory_id))}/`,
+		method: 'DELETE',
+	});
+	return response.data || { message: 'Deleted', memory_id: bundle.inputData.memory_id };
+};
+
+export default {
+	key: 'delete_memory',
+	noun: 'Memory',
+	display: {
+		label: 'Delete Memory',
+		description: 'Delete a single memory by its ID.',
+	},
+	operation: {
+		perform,
+		inputFields: [{ key: 'memory_id', label: 'Memory ID', type: 'string', required: true }],
+		sample: { message: 'Memory deleted successfully' },
+	},
+};

integrations/zapier-mem0/src/index.ts

@@ -0,0 +1,33 @@
+import { version as platformVersion } from 'zapier-platform-core';
+import authentication from './authentication';
+import { includeApiKey, handleBadResponses } from './middleware';
+import addMemory from './creates/add_memory';
+import deleteMemory from './creates/delete_memory';
+import searchMemories from './searches/search_memories';
+import getMemories from './searches/get_memories';
+import pkg from '../package.json';
+
+const app = {
+	version: pkg.version,
+	platformVersion,
+
+	authentication,
+
+	beforeRequest: [includeApiKey],
+	afterResponse: [handleBadResponses],
+
+	creates: {
+		[addMemory.key]: addMemory,
+		[deleteMemory.key]: deleteMemory,
+	},
+
+	searches: {
+		[searchMemories.key]: searchMemories,
+		[getMemories.key]: getMemories,
+	},
+
+	resources: {},
+	triggers: {},
+};
+
+export = app;

integrations/zapier-mem0/src/middleware.ts

@@ -0,0 +1,50 @@
+import type { ZObject, Bundle, MutableRequest, ZResponse } from './types';
+
+// Prepend the configured base URL and inject the auth header on every request.
+export const includeApiKey = (
+	request: MutableRequest,
+	_z: ZObject,
+	bundle: Bundle,
+): MutableRequest => {
+	if (bundle.authData && bundle.authData.apiKey) {
+		request.headers = request.headers || {};
+		request.headers.Authorization = `Token ${bundle.authData.apiKey}`;
+	}
+	// Resolve relative URLs against the configured base URL.
+	if (request.url && request.url.startsWith('/')) {
+		const base = (bundle.authData && bundle.authData.baseUrl) || 'https://api.mem0.ai';
+		request.url = `${base.replace(/\/$/, '')}${request.url}`;
+	}
+	return request;
+};
+
+// Surface HTTP failures as errors. z.request does NOT throw on non-2xx by
+// default, so without this a 4xx/5xx would flow downstream as a fake success
+// (empty search results / error body returned as a created memory).
+export const handleBadResponses = (
+	response: ZResponse,
+	z: ZObject,
+	_bundle: Bundle,
+): ZResponse => {
+	if (response.status === 401 || response.status === 403) {
+		throw new z.errors.Error(
+			'The Mem0 API key you supplied is invalid or lacks access.',
+			'AuthenticationError',
+			response.status,
+		);
+	}
+	if (response.status >= 400) {
+		const data = (response.data as Record<string, unknown>) || {};
+		const raw =
+			data.detail || data.error || data.message || response.content || 'unknown error';
+		// DRF sometimes returns detail as an object/array; stringify so the
+		// thrown message never renders as "[object Object]".
+		const detail = typeof raw === 'string' ? raw : JSON.stringify(raw);
+		throw new z.errors.Error(
+			`Mem0 API request failed (HTTP ${response.status}): ${detail}`,
+			'Mem0ApiError',
+			response.status,
+		);
+	}
+	return response;
+};

integrations/zapier-mem0/src/searches/get_memories.ts

@@ -0,0 +1,52 @@
+import type { ZObject, Bundle, Memory } from '../types';
+import { captureEvent } from '../telemetry';
+
+const perform = async (z: ZObject, bundle: Bundle): Promise<Memory[]> => {
+	const body: Record<string, unknown> = {};
+	if (bundle.inputData.user_id) body.filters = { user_id: bundle.inputData.user_id };
+
+	captureEvent('zapier.get_memories', bundle.authData?.apiKey);
+
+	const response = await z.request({
+		url: '/v3/memories/',
+		method: 'POST',
+		params: {
+			page: Math.max(1, Math.floor(Number(bundle.inputData.page) || 1)),
+			page_size: Math.max(1, Math.floor(Number(bundle.inputData.limit) || 50)),
+		},
+		body,
+	});
+	// Guard against a null/empty body; always return an array.
+	const data = response.data as Memory[] | { results?: Memory[] } | null;
+	return Array.isArray(data) ? data : data?.results ?? [];
+};
+
+export default {
+	key: 'get_memories',
+	noun: 'Memory',
+	display: {
+		label: 'Get Memories',
+		description: 'List stored memories for a user.',
+	},
+	operation: {
+		perform,
+		inputFields: [
+			{ key: 'user_id', label: 'User ID', type: 'string', required: true },
+			{
+				key: 'limit',
+				label: 'Limit',
+				type: 'integer',
+				default: '50',
+				helpText: 'Max memories per page. Use Page to page through larger result sets.',
+			},
+			{
+				key: 'page',
+				label: 'Page',
+				type: 'integer',
+				default: '1',
+				helpText: 'Which page of results to return (1-based).',
+			},
+		],
+		sample: { id: '00000000-0000-0000-0000-000000000000', memory: 'User loves hiking' },
+	},
+};

integrations/zapier-mem0/src/searches/search_memories.ts

@@ -0,0 +1,40 @@
+import type { ZObject, Bundle, Memory } from '../types';
+import { captureEvent } from '../telemetry';
+
+const perform = async (z: ZObject, bundle: Bundle): Promise<Memory[]> => {
+	const body: Record<string, unknown> = {
+		query: bundle.inputData.query,
+		output_format: 'v1.1',
+		top_k: Math.max(1, Math.floor(Number(bundle.inputData.limit) || 50)),
+	};
+	if (bundle.inputData.user_id) body.filters = { user_id: bundle.inputData.user_id };
+
+	captureEvent('zapier.search_memories', bundle.authData?.apiKey);
+
+	const response = await z.request({
+		url: '/v3/memories/search/',
+		method: 'POST',
+		body,
+	});
+	// Searches must return an array; guard against a null/empty body.
+	const data = response.data as Memory[] | { results?: Memory[] } | null;
+	return Array.isArray(data) ? data : data?.results ?? [];
+};
+
+export default {
+	key: 'search_memories',
+	noun: 'Memory',
+	display: {
+		label: 'Search Memories',
+		description: 'Semantic search over stored memories.',
+	},
+	operation: {
+		perform,
+		inputFields: [
+			{ key: 'query', label: 'Query', type: 'string', required: true },
+			{ key: 'user_id', label: 'User ID', type: 'string', required: true },
+			{ key: 'limit', label: 'Limit', type: 'integer', default: '50' },
+		],
+		sample: { id: '00000000-0000-0000-0000-000000000000', memory: 'User loves hiking' },
+	},
+};

integrations/zapier-mem0/src/telemetry.ts

@@ -0,0 +1,39 @@
+import { createHash } from 'crypto';
+
+// Anonymous usage telemetry via PostHog, mirroring the Mem0 CLI / plugins.
+// Fire-and-forget: never awaited, never throws, so it can neither slow down
+// nor break an action. Opt out with MEM0_TELEMETRY=false.
+const POSTHOG_API_KEY = 'phc_hgJkUVJFYtmaJqrvf6CYN67TIQ8yhXAkWzUn9AMU4yX';
+const POSTHOG_HOST = 'https://us.i.posthog.com/i/v0/e/';
+
+export function captureEvent(
+	event: string,
+	apiKey: string | undefined,
+	properties: Record<string, unknown> = {},
+): void {
+	if (process.env.MEM0_TELEMETRY === 'false') return;
+	try {
+		// Hash the API key so events are attributable to one account without
+		// ever transmitting the key itself.
+		const distinctId = apiKey ? createHash('md5').update(apiKey).digest('hex') : 'zapier-anon';
+		const payload = {
+			api_key: POSTHOG_API_KEY,
+			event,
+			distinct_id: distinctId,
+			properties: {
+				source: 'ZAPIER',
+				$process_person_profile: false,
+				...properties,
+			},
+		};
+		void fetch(POSTHOG_HOST, {
+			method: 'POST',
+			headers: { 'Content-Type': 'application/json' },
+			body: JSON.stringify(payload),
+		}).catch(() => {
+			/* swallow — telemetry must never surface to the user */
+		});
+	} catch {
+		/* swallow — telemetry must never break the action */
+	}
+}

integrations/zapier-mem0/src/types.ts

@@ -0,0 +1,43 @@
+import type { ZObject, Bundle } from 'zapier-platform-core';
+
+export type { ZObject, Bundle };
+
+/** Mutable outgoing request, as seen by the beforeRequest middleware. */
+export interface MutableRequest {
+	url?: string;
+	headers?: Record<string, string>;
+	[key: string]: unknown;
+}
+
+/** Response as seen by the afterResponse middleware. */
+export interface ZResponse {
+	status: number;
+	data?: Record<string, unknown> | unknown[] | null;
+	content?: string;
+	[key: string]: unknown;
+}
+
+/** Async add response from POST /v3/memories/add/. */
+export interface AddResponse {
+	event_id?: string;
+	status?: string;
+	error?: string;
+	message?: string;
+	results?: unknown[];
+	[key: string]: unknown;
+}
+
+/** GET /v1/event/{id}/ response. */
+export interface EventResponse {
+	status?: string;
+	error?: string;
+	message?: string;
+	[key: string]: unknown;
+}
+
+/** A stored memory object returned by search / list. */
+export interface Memory {
+	id?: string;
+	memory?: string;
+	[key: string]: unknown;
+}

integrations/zapier-mem0/test/mem0.test.ts

@@ -0,0 +1,96 @@
+import * as zapier from 'zapier-platform-core';
+import App from '../src';
+
+const appTester = zapier.createAppTester(App as any);
+
+const authData = {
+	apiKey: process.env.MEM0_API_KEY,
+	baseUrl: process.env.MEM0_BASE_URL || 'https://api.mem0.ai',
+};
+
+const userId = `zapier-e2e-${Date.now()}`;
+
+// Retry an async op until `done` is satisfied or attempts run out. Extraction is
+// async, so the default Add returns before the memory is searchable.
+const until = async <T>(
+	fn: () => Promise<T>,
+	done: (r: T) => boolean,
+	{ attempts = 30, delayMs = 2000 } = {},
+): Promise<T> => {
+	let last: T = undefined as unknown as T;
+	for (let i = 0; i < attempts; i++) {
+		last = await fn();
+		if (done(last)) return last;
+		await new Promise((resolve) => setTimeout(resolve, delayMs));
+	}
+	return last;
+};
+
+// The E2E suite hits the live Mem0 API, so it only runs when MEM0_API_KEY is
+// set (locally / with a secret). In CI without a key it is skipped, not failed.
+const describeE2E = authData.apiKey ? describe : describe.skip;
+
+describeE2E('Mem0 Zapier integration (E2E)', () => {
+	it('authentication.test succeeds', async () => {
+		const res: any = await appTester((App as any).authentication.test, { authData });
+		expect(res.status).toBe(200);
+	});
+
+	it('adds, searches, lists, and deletes a memory', async () => {
+		// Add via the default path: returns immediately with an event id.
+		const added: any = await appTester((App as any).creates.add_memory.operation.perform, {
+			authData,
+			inputData: {
+				content: 'I love hiking in the Alps and my favorite food is sushi',
+				user_id: userId,
+			},
+		});
+		expect(added.event_id).toBeDefined();
+
+		// Extraction is async; retry search until the memory is indexed.
+		// Budget generously — live extraction can occasionally exceed a minute.
+		const found: any[] = await until(
+			() =>
+				appTester((App as any).searches.search_memories.operation.perform, {
+					authData,
+					inputData: { query: 'outdoor activities', user_id: userId, limit: 5 },
+				}),
+			(r: any[]) => Array.isArray(r) && r.length > 0,
+			{ attempts: 60, delayMs: 2000 },
+		);
+		expect(Array.isArray(found)).toBe(true);
+		expect(found.length).toBeGreaterThan(0);
+
+		// Get all
+		const all: any[] = await appTester((App as any).searches.get_memories.operation.perform, {
+			authData,
+			inputData: { user_id: userId },
+		});
+		expect(Array.isArray(all)).toBe(true);
+		expect(all.length).toBeGreaterThan(0);
+
+		// Cleanup: delete every memory we created
+		for (const mem of all) {
+			await appTester((App as any).creates.delete_memory.operation.perform, {
+				authData,
+				inputData: { memory_id: mem.id },
+			});
+		}
+
+		const afterDelete: any[] = await appTester(
+			(App as any).searches.get_memories.operation.perform,
+			{ authData, inputData: { user_id: userId } },
+		);
+		expect(afterDelete.length).toBe(0);
+	});
+
+	it('surfaces API errors instead of returning an empty array (search needs a filter)', async () => {
+		// filters is required by the API; omitting it must throw, not return [].
+		await expect(
+			appTester((App as any).searches.search_memories.operation.perform, {
+				authData,
+				inputData: { query: 'anything' },
+			}),
+		).rejects.toThrow();
+	});
+});

integrations/zapier-mem0/test/setup.ts

@@ -0,0 +1,3 @@
+// Disable telemetry during tests so runs never fire real PostHog events
+// (and so the fire-and-forget fetch cannot leave an open handle after tests).
+process.env.MEM0_TELEMETRY = 'false';

integrations/zapier-mem0/test/unit.test.ts

@@ -0,0 +1,183 @@
+// Offline unit tests: they mock `z.request`, so they run unconditionally in CI
+// (unlike the live E2E suite in mem0.test.ts, gated on MEM0_API_KEY). They cover
+// what `zapier validate` can't: boolean coercion, URL join, metadata, array shapes.
+
+import addMemory from '../src/creates/add_memory';
+import deleteMemory from '../src/creates/delete_memory';
+import searchMemories from '../src/searches/search_memories';
+import getMemories from '../src/searches/get_memories';
+import { includeApiKey } from '../src/middleware';
+
+// Minimal `z` stub: hands back queued responses and records every request.
+const makeZ = (responses: any[] = []): any => {
+	const queue = [...responses];
+	const requests: any[] = [];
+	return {
+		requests,
+		request: async (opts: any) => {
+			requests.push(opts);
+			const next = queue.shift();
+			return next !== undefined ? next : { data: {} };
+		},
+		errors: {
+			Error: class Mem0Error extends Error {
+				name: string;
+				status?: number;
+				constructor(message: string, name?: string, status?: number) {
+					super(message);
+					this.name = name || 'Error';
+					this.status = status;
+				}
+			},
+		},
+	};
+};
+
+describe('add_memory (offline)', () => {
+	it('coerces infer="false" to a boolean and does not poll by default', async () => {
+		const z = makeZ([{ data: { event_id: 'e1', status: 'PENDING' } }]);
+		const res = await addMemory.operation.perform(z, {
+			inputData: { content: 'hi', user_id: 'u1', infer: 'false' },
+		} as any);
+		// waitForCompletion defaults off -> a single request (the add), no poll.
+		expect(z.requests).toHaveLength(1);
+		expect(z.requests[0].body.infer).toBe(false);
+		expect((res as any).status).toBe('PENDING');
+	});
+
+	it('polls the event only when waitForCompletion="true"', async () => {
+		const z = makeZ([
+			{ data: { event_id: 'e1', status: 'PENDING' } },
+			{ data: { status: 'SUCCEEDED', results: [{ id: 'm1' }] } },
+		]);
+		const res = await addMemory.operation.perform(z, {
+			inputData: { content: 'hi', user_id: 'u1', waitForCompletion: 'true' },
+		} as any);
+		expect(z.requests).toHaveLength(2);
+		expect(z.requests[1].url).toBe('/v1/event/e1/');
+		expect((res as any).status).toBe('SUCCEEDED');
+	});
+
+	it('keeps polling past the old 12-attempt budget when the API is slow', async () => {
+		jest.useFakeTimers();
+		const pendingPolls = Array.from({ length: 20 }, () => ({ data: { status: 'PENDING' } }));
+		const z = makeZ([
+			{ data: { event_id: 'e1', status: 'PENDING' } },
+			...pendingPolls,
+			{ data: { status: 'SUCCEEDED', results: [{ id: 'm1' }] } },
+		]);
+		const resultPromise = addMemory.operation.perform(z, {
+			inputData: { content: 'hi', user_id: 'u1', waitForCompletion: 'true' },
+		} as any);
+		for (let i = 0; i < pendingPolls.length; i++) {
+			await jest.advanceTimersByTimeAsync(1500);
+		}
+		const res = await resultPromise;
+		expect((res as any).status).toBe('SUCCEEDED');
+		jest.useRealTimers();
+	});
+
+	it('throws a clear error on invalid JSON metadata', async () => {
+		const z = makeZ();
+		await expect(
+			addMemory.operation.perform(z, {
+				inputData: { content: 'hi', user_id: 'u1', metadata: '{not json' },
+			} as any),
+		).rejects.toThrow('Metadata must be valid JSON.');
+	});
+
+	it('forwards custom_instructions and parses custom_categories JSON', async () => {
+		const z = makeZ([{ data: { event_id: 'e1', status: 'PENDING' } }]);
+		await addMemory.operation.perform(z, {
+			inputData: {
+				content: 'hi',
+				user_id: 'u1',
+				custom_instructions: 'keep durable facts',
+				custom_categories: '[{"work":"job related"}]',
+			},
+		} as any);
+		expect(z.requests[0].body.custom_instructions).toBe('keep durable facts');
+		expect(z.requests[0].body.custom_categories).toEqual([{ work: 'job related' }]);
+	});
+
+	it('throws a clear error on invalid Custom Categories JSON', async () => {
+		const z = makeZ();
+		await expect(
+			addMemory.operation.perform(z, {
+				inputData: { content: 'hi', user_id: 'u1', custom_categories: '{bad' },
+			} as any),
+		).rejects.toThrow('Custom Categories must be valid JSON.');
+	});
+
+	it('forwards includes and excludes when provided', async () => {
+		const z = makeZ([{ data: { event_id: 'e1', status: 'PENDING' } }]);
+		await addMemory.operation.perform(z, {
+			inputData: { content: 'hi', user_id: 'u1', includes: 'work facts', excludes: 'small talk' },
+		} as any);
+		expect(z.requests[0].body.includes).toBe('work facts');
+		expect(z.requests[0].body.excludes).toBe('small talk');
+	});
+});
+
+describe('search / get array-shape enforcement (offline)', () => {
+	it('search unwraps an object {results:[...]} into an array', async () => {
+		const z = makeZ([{ data: { results: [{ id: 'm1' }] } }]);
+		const res = await searchMemories.operation.perform(z, {
+			inputData: { query: 'x', user_id: 'u1' },
+		} as any);
+		expect(Array.isArray(res)).toBe(true);
+		expect(res).toHaveLength(1);
+	});
+
+	it('get_memories returns [] when the API returns neither array nor results', async () => {
+		const z = makeZ([{ data: {} }]);
+		const res = await getMemories.operation.perform(z, { inputData: { user_id: 'u1' } } as any);
+		expect(Array.isArray(res)).toBe(true);
+		expect(res).toHaveLength(0);
+	});
+
+	it('get_memories forwards page and page_size as numbers', async () => {
+		const z = makeZ([{ data: { results: [] } }]);
+		await getMemories.operation.perform(z, {
+			inputData: { user_id: 'u1', page: '2', limit: '10' },
+		} as any);
+		expect(z.requests[0].params).toEqual({ page: 2, page_size: 10 });
+	});
+
+	it('search and get_memories return [] on a null/empty body (no crash)', async () => {
+		const zSearch = makeZ([{ data: null }]);
+		const found = await searchMemories.operation.perform(zSearch, {
+			inputData: { query: 'x', user_id: 'u1' },
+		} as any);
+		expect(found).toEqual([]);
+
+		const zGet = makeZ([{ data: null }]);
+		const all = await getMemories.operation.perform(zGet, { inputData: { user_id: 'u1' } } as any);
+		expect(all).toEqual([]);
+	});
+});
+
+describe('delete_memory (offline)', () => {
+	it('encodes the memory id in the URL path', async () => {
+		const z = makeZ([{ data: {} }]);
+		await deleteMemory.operation.perform(z, { inputData: { memory_id: 'a/b c' } } as any);
+		expect(z.requests[0].url).toBe('/v1/memories/a%2Fb%20c/');
+	});
+});
+
+describe('includeApiKey middleware (offline)', () => {
+	it('prepends the base URL and injects the auth header', () => {
+		const req = includeApiKey({ url: '/v3/memories/' }, null as any, {
+			authData: { apiKey: 'k', baseUrl: 'https://api.mem0.ai/' },
+		} as any);
+		expect(req.url).toBe('https://api.mem0.ai/v3/memories/');
+		expect(req.headers!.Authorization).toBe('Token k');
+	});
+
+	it('leaves absolute URLs untouched', () => {
+		const req = includeApiKey({ url: 'https://other.example/x' }, null as any, {
+			authData: { apiKey: 'k' },
+		} as any);
+		expect(req.url).toBe('https://other.example/x');
+	});
+});

integrations/zapier-mem0/tsconfig.json

@@ -0,0 +1,19 @@
+{
+  "compilerOptions": {
+    "target": "ES2020",
+    "module": "commonjs",
+    "moduleResolution": "node",
+    "outDir": "dist",
+    "rootDir": "src",
+    "strict": true,
+    "esModuleInterop": true,
+    "resolveJsonModule": true,
+    "isolatedModules": true,
+    "declaration": false,
+    "sourceMap": true,
+    "skipLibCheck": true,
+    "forceConsistentCasingInFileNames": true
+  },
+  "include": ["src"],
+  "exclude": ["node_modules", "dist", "test"]
+}

mem0-ts/package.json

@@ -1,6 +1,6 @@
 {
   "name": "mem0ai",
-  "version": "3.1.0",
+  "version": "3.1.2",
   "description": "The Memory Layer For Your AI Apps",
   "main": "./dist/index.js",
   "module": "./dist/index.mjs",
@@ -151,6 +151,42 @@
     "@aws-sdk/client-bedrock-runtime": ">=3.0.0 <3.968.0"
   },
   "peerDependenciesMeta": {
+    "@qdrant/js-client-rest": {
+      "optional": true
+    },
+    "redis": {
+      "optional": true
+    },
+    "@supabase/supabase-js": {
+      "optional": true
+    },
+    "cloudflare": {
+      "optional": true
+    },
+    "@azure/search-documents": {
+      "optional": true
+    },
+    "@azure/identity": {
+      "optional": true
+    },
+    "@langchain/core": {
+      "optional": true
+    },
+    "@anthropic-ai/sdk": {
+      "optional": true
+    },
+    "@google/genai": {
+      "optional": true
+    },
+    "groq-sdk": {
+      "optional": true
+    },
+    "@mistralai/mistralai": {
+      "optional": true
+    },
+    "ollama": {
+      "optional": true
+    },
     "mysql2": {
       "optional": true
     },

mem0-ts/src/client/mem0.types.ts

@@ -59,7 +59,6 @@ export interface ProjectOptions {
 export interface PromptUpdatePayload {
   customInstructions?: string;
   customCategories?: custom_categories[];
-  retrievalCriteria?: any[];
   version?: string;
   memoryDepth?: string | null;
   usecaseSetting?: string | number;

mem0-ts/src/oss/src/embeddings/aws_bedrock.ts

@@ -0,0 +1,292 @@
+import { Embedder } from "./base";
+import { EmbeddingConfig } from "../types";
+
+const DEFAULT_MODEL = "amazon.titan-embed-text-v1";
+const DEFAULT_REGION = "us-west-2";
+
+// Cohere's Bedrock embed API rejects an InvokeModel call carrying more than 96
+// texts, so `embedBatch` chunks at that boundary.
+const COHERE_MAX_BATCH = 96;
+
+// Titan has no server-side batch endpoint -- one InvokeModel call per text --
+// so without a cap a large embedBatch() would fan out one request per text.
+// Bounds concurrency the same way COHERE_MAX_BATCH bounds the Cohere path.
+const TITAN_MAX_CONCURRENCY = 4;
+
+// Cohere wants to know whether a text is being embedded for storage or for a
+// retrieval query; embedding a search query in document mode silently
+// degrades retrieval. Titan ignores this and has no equivalent parameter.
+const COHERE_INPUT_TYPES: Record<"add" | "update" | "search", string> = {
+  add: "search_document",
+  update: "search_document",
+  search: "search_query",
+};
+
+type BedrockRuntimeModule = typeof import("@aws-sdk/client-bedrock-runtime");
+
+interface BedrockCredentials {
+  accessKeyId: string;
+  secretAccessKey: string;
+  sessionToken?: string;
+}
+
+interface BedrockEmbeddingResponse {
+  // Titan returns a single vector. Cohere v3 returns a flat array of vectors;
+  // Cohere v4, when `embedding_types` is requested, nests it as `{ float }`.
+  embedding?: number[];
+  embeddings?: number[][] | { float?: number[][] };
+}
+
+/**
+ * Runs `fn` over `items` with at most `limit` calls in flight at once,
+ * returning results in input order regardless of completion order.
+ */
+async function mapWithConcurrencyLimit<T, R>(
+  items: T[],
+  limit: number,
+  fn: (item: T) => Promise<R>,
+): Promise<R[]> {
+  const results: R[] = new Array(items.length);
+  let next = 0;
+
+  async function worker(): Promise<void> {
+    while (next < items.length) {
+      const index = next++;
+      results[index] = await fn(items[index]);
+    }
+  }
+
+  await Promise.all(
+    Array.from({ length: Math.min(limit, items.length) }, worker),
+  );
+  return results;
+}
+
+/**
+ * AWS Bedrock embedder, mirroring `mem0/embeddings/aws_bedrock.py`.
+ *
+ * Supports the Amazon Titan and Cohere embedding model families. The
+ * `@aws-sdk/client-bedrock-runtime` dependency is lazily imported so the
+ * package stays optional: importing this module never forces the SDK to be
+ * installed until a Bedrock embedder actually embeds something.
+ */
+export class AWSBedrockEmbedder implements Embedder {
+  private readonly model: string;
+  private readonly region: string;
+  private readonly embeddingDims?: number;
+  private readonly credentials?: BedrockCredentials;
+  private clientPromise?: Promise<{
+    sdk: BedrockRuntimeModule;
+    client: { send: (command: any) => Promise<{ body?: Uint8Array }> };
+  }>;
+
+  constructor(config: EmbeddingConfig) {
+    this.model = config.model || DEFAULT_MODEL;
+    this.region = config.awsRegion || process.env.AWS_REGION || DEFAULT_REGION;
+    this.embeddingDims = config.embeddingDims;
+
+    const hasKeyPair = Boolean(
+      config.awsAccessKeyId && config.awsSecretAccessKey,
+    );
+    const hasAnyCredential = Boolean(
+      config.awsAccessKeyId ||
+      config.awsSecretAccessKey ||
+      config.awsSessionToken,
+    );
+
+    // Partially configured credentials would silently fall back to the default
+    // chain, embedding under an identity the caller never chose.
+    if (hasAnyCredential && !hasKeyPair) {
+      throw new Error(
+        "AWS Bedrock requires both awsAccessKeyId and awsSecretAccessKey when any explicit credential is configured. " +
+          "Omit all credential fields to use the AWS default credential chain.",
+      );
+    }
+
+    // Leaving `credentials` unset lets the AWS SDK resolve them from its
+    // default chain: environment, shared config, SSO, or the instance role.
+    if (hasKeyPair) {
+      this.credentials = {
+        accessKeyId: config.awsAccessKeyId!,
+        secretAccessKey: config.awsSecretAccessKey!,
+        ...(config.awsSessionToken && { sessionToken: config.awsSessionToken }),
+      };
+    }
+  }
+
+  private async loadSdk(): Promise<BedrockRuntimeModule> {
+    try {
+      return await import("@aws-sdk/client-bedrock-runtime");
+    } catch (error) {
+      // Only a genuine module-resolution failure gets the friendly install
+      // hint. Node's native ESM loader raises ERR_MODULE_NOT_FOUND; Jest's
+      // and bundlers' CJS-style resolvers raise MODULE_NOT_FOUND. Anything
+      // else (e.g. the package is installed but throws while loading, such
+      // as on a Node version older than the SDK's own engines requirement)
+      // rethrows unchanged instead of being misreported as "not installed".
+      const code = (error as { code?: string } | undefined)?.code;
+      if (code === "ERR_MODULE_NOT_FOUND" || code === "MODULE_NOT_FOUND") {
+        throw Object.assign(
+          new Error(
+            "The '@aws-sdk/client-bedrock-runtime' package is required to use the AWS Bedrock embedder. " +
+              "Install it with: npm install @aws-sdk/client-bedrock-runtime",
+          ),
+          { cause: error },
+        );
+      }
+      throw error;
+    }
+  }
+
+  private async createClient(): Promise<{
+    sdk: BedrockRuntimeModule;
+    client: { send: (command: any) => Promise<{ body?: Uint8Array }> };
+  }> {
+    const sdk = await this.loadSdk();
+    return {
+      sdk,
+      client: new sdk.BedrockRuntimeClient({
+        region: this.region,
+        ...(this.credentials && { credentials: this.credentials }),
+      }),
+    };
+  }
+
+  private getClient() {
+    // Memoized so concurrent embed() calls share one client instead of each
+    // racing to build their own. Cleared on rejection so a transient failure
+    // (e.g. a network blip while resolving credentials) doesn't permanently
+    // disable Bedrock for the rest of this embedder's lifetime.
+    if (!this.clientPromise) {
+      this.clientPromise = this.createClient().catch((err) => {
+        this.clientPromise = undefined;
+        throw err;
+      });
+    }
+    return this.clientPromise;
+  }
+
+  private isCohereModel(): boolean {
+    return this.model.startsWith("cohere.");
+  }
+
+  private isCohereV4Model(): boolean {
+    return this.model.includes("embed-v4");
+  }
+
+  private buildRequestBody(
+    texts: string[],
+    memoryAction?: "add" | "update" | "search",
+  ): Record<string, unknown> {
+    if (this.isCohereModel()) {
+      const body: Record<string, unknown> = {
+        texts,
+        input_type: memoryAction
+          ? COHERE_INPUT_TYPES[memoryAction]
+          : "search_document",
+      };
+
+      // Only Embed v4 understands embedding_types / output_dimension; v3
+      // rejects unknown fields, so they're guarded to the v4 model family.
+      if (this.isCohereV4Model()) {
+        body.embedding_types = ["float"];
+        if (this.embeddingDims !== undefined) {
+          body.output_dimension = this.embeddingDims;
+        }
+      }
+      return body;
+    }
+
+    // Titan accepts one text per call. Only Titan Text Embeddings V2 supports
+    // a caller-chosen output size (256/512/1024), so the field is guarded the
+    // same way the Python provider guards it.
+    return {
+      inputText: texts[0],
+      ...(this.embeddingDims !== undefined &&
+        this.model.includes("titan-embed-text-v2") && {
+          dimensions: this.embeddingDims,
+        }),
+    };
+  }
+
+  private async invoke(
+    texts: string[],
+    memoryAction?: "add" | "update" | "search",
+  ): Promise<number[][]> {
+    const { sdk, client } = await this.getClient();
+
+    let payload: BedrockEmbeddingResponse;
+    try {
+      const response = await client.send(
+        new sdk.InvokeModelCommand({
+          modelId: this.model,
+          contentType: "application/json",
+          accept: "application/json",
+          body: new TextEncoder().encode(
+            JSON.stringify(this.buildRequestBody(texts, memoryAction)),
+          ),
+        }),
+      );
+      payload = JSON.parse(new TextDecoder().decode(response.body));
+    } catch (error) {
+      const message = error instanceof Error ? error.message : String(error);
+      throw new Error(
+        `Error getting embedding from AWS Bedrock model ${this.model}: ${message}`,
+      );
+    }
+
+    // Validated outside the try so this message is not re-wrapped by the catch.
+    // Cohere v3 replies with a flat `embeddings` array; v4 (when
+    // embedding_types is requested) nests it under `.float`.
+    const embeddings = this.isCohereModel()
+      ? Array.isArray(payload.embeddings)
+        ? payload.embeddings
+        : payload.embeddings?.float
+      : payload.embedding && [payload.embedding];
+
+    // `[]` is truthy, so a lone zero-length vector must be checked for
+    // explicitly -- otherwise it passes the length check and hands the
+    // caller an empty embedding instead of an error.
+    if (
+      !embeddings ||
+      embeddings.length !== texts.length ||
+      embeddings.some((embedding) => embedding.length === 0)
+    ) {
+      throw new Error(
+        `AWS Bedrock model ${this.model} returned no embedding for one or more inputs`,
+      );
+    }
+    return embeddings;
+  }
+
+  async embed(
+    text: string,
+    memoryAction?: "add" | "update" | "search",
+  ): Promise<number[]> {
+    return (await this.invoke([text], memoryAction))[0];
+  }
+
+  async embedBatch(
+    texts: string[],
+    memoryAction?: "add" | "update" | "search",
+  ): Promise<number[][]> {
+    if (texts.length === 0) return [];
+
+    if (!this.isCohereModel()) {
+      return mapWithConcurrencyLimit(texts, TITAN_MAX_CONCURRENCY, (text) =>
+        this.embed(text, memoryAction),
+      );
+    }
+
+    const embeddings: number[][] = [];
+    for (let i = 0; i < texts.length; i += COHERE_MAX_BATCH) {
+      embeddings.push(
+        ...(await this.invoke(
+          texts.slice(i, i + COHERE_MAX_BATCH),
+          memoryAction,
+        )),
+      );
+    }
+    return embeddings;
+  }
+}