Merge remote-tracking branch 'origin/main' into feat/zapier-mem0

chore(zapier): align package with @mem0 scope and repo conventions
Rename to @mem0/zapier, switch to Apache-2.0 to match the repo and the other integrations, drop private so it can publish, and add the pnpm security overrides plus pnpm-workspace.yaml that every other integration under integrations/ already carries.
2026-07-29 19:59:58 +05:30 · 2026-07-29 16:53:06 +05:30 · 2026-07-29 15:10:32 +05:30 · 2026-07-29 00:07:35 -07:00 · 2026-07-28 23:57:37 -07:00 · 2026-07-25 17:51:48 +05:30
272 changed files with 7249 additions and 21976 deletions
@@ -17,10 +17,6 @@
      "claude code", "opencode", "pi agent", "mem0-plugin",
      "cursor plugin", "codex plugin", "editor plugin"
    ],
-    "openmemory": [
-      "openmemory", "open memory", "localhost:8765", "localhost:3000",
-      "openmemory ui", "openmemory/api", "openmemory/ui"
-    ],
    "cli": ["mem0-cli", "@mem0/cli", "npx mem0", "command line"],
    "vector-store": [
      "pgvector", "pinecone", "chroma", "chromadb", "weaviate",
@@ -23,10 +23,6 @@ rest-api:
  - changed-files:
      - any-glob-to-any-file: 'server/**'

-openmemory:
-  - changed-files:
-      - any-glob-to-any-file: 'openmemory/**'
-
 integrations:
  - changed-files:
      - any-glob-to-any-file: 'integrations/**'
@@ -35,12 +35,6 @@ const cases = [
    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: 3444,
-    title: "Fix: Openmemory run.sh non-existent vector-store route",
-    body: "### 🐛 Describe the bug\n\n# Vector_store not implemented\nThere is many references to ` ${NEXT_PUBLIC_API_URL}/api/v1/config/mem0/vector_store` in lines 280, 293, 306, 319,  332, 345, 358, and 371. \n```bash\ncurl -fsS -X PUT \"${NEXT_PUBLIC_API_URL}/api/v1/config/mem0/vector_store\" # Line 280 and for each vector store\n```\nBut the api route is not implemented in `api/app/routers/config.py`.\n# Suggested solution\nI would implement `vector_store` route or remove and use `update_configuration` for all config updates. Also Create class with all config keys for vector_store",
-    expected: ["openmemory"],
-  },
  {
    number: 6252,
    title: "cursor: on_file_read_cursor.sh ignores auto_search / MEM0_AUTO_SEARCH",
@@ -40,6 +40,7 @@ jobs:
      openclaw: ${{ steps.filter.outputs.openclaw }}
      opencode_plugin: ${{ steps.filter.outputs.opencode_plugin }}
      pi_agent_plugin: ${{ steps.filter.outputs.pi_agent_plugin }}
+      zapier_mem0: ${{ steps.filter.outputs.zapier_mem0 }}
      docs_llms_txt: ${{ steps.filter.outputs.docs_llms_txt }}
    steps:
      - uses: dorny/paths-filter@v3
@@ -79,6 +80,10 @@ jobs:
              - 'integrations/pi-agent-plugin/**'
              - '.github/workflows/pi-agent-plugin-checks.yml'
              - '.github/workflows/ci-gate.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 +141,13 @@ jobs:
    uses: ./.github/workflows/pi-agent-plugin-checks.yml
    secrets: inherit

+  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 +166,7 @@ jobs:
      - openclaw
      - opencode-plugin
      - pi-agent-plugin
+      - zapier-mem0
      - docs-llms-txt
    if: always()
    runs-on: ubuntu-latest
@@ -106,7 +106,7 @@ jobs:
        run: |
          pip install --upgrade pip
          pip install -e ".[test,graph,vector_stores,llms,extras]"
-          pip install ruff
+          pip install ruff==0.16.0
      - name: Run Linting
        if: needs.check_changes.outputs.mem0_changed == 'true'
        run: make lint
@@ -27,7 +27,7 @@ jobs:
          script: |
            const allowed = new Set([
              'sdk-python', 'sdk-typescript', 'vector-store', 'plugin',
-              'rest-api', 'openmemory', 'documentation', 'ci', 'cli', 'integrations',
+              'rest-api', 'documentation', 'ci', 'cli', 'integrations',
            ]);
            const umbrella = { plugin: 'integrations' };
            const { repository } = await github.graphql(
@@ -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
@@ -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
@@ -27,8 +27,8 @@ This is a **polyglot monorepo** containing Python and TypeScript packages, CLIs,
 | `integrations/openclaw/` | `@mem0/openclaw-mem0` — OpenClaw plugin for Claude Code / AI editors |
 | `integrations/pi-agent-plugin/` | `@mem0/pi-agent-plugin` — Pi Agent plugin |
 | `integrations/vercel-ai-sdk/` | `@mem0/vercel-ai-provider` — Vercel AI SDK memory provider |
+| `integrations/zapier-mem0/` | `zapier-mem0` — 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 +62,7 @@ integrations/openclaw/ ──▶ mem0ai (npm)
 - **Node.js**: v18+ (v20 or v22 recommended)
 - **pnpm**: v10+ (`npm install -g pnpm@10`) — used for all TypeScript packages
 - **Hatch**: Python build/environment tool (`pip install hatch`)
- **Docker**: Required for `server/` and `openmemory/` development
+- **Docker**: Required for `server/` development

 ### Initial Setup

@@ -214,28 +214,6 @@ 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 +309,6 @@ 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 +359,6 @@ 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 +407,7 @@ 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 |
+| 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.
@@ -455,6 +432,7 @@ Publishing is routed through a single entry point: **`release.yml` (Release Rout
 - 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
@@ -585,7 +563,7 @@ 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
@@ -608,5 +586,4 @@ 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/`.
@@ -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
@@ -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

@@ -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
@@ -7,6 +7,18 @@ 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:**
@@ -1145,6 +1157,18 @@ 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:**
@@ -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 |
@@ -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}},
+)
+```
@@ -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>
@@ -207,6 +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",
@@ -348,6 +349,7 @@
                    "pages": [
                      "integrations/dify",
                      "integrations/flowise",
+                      "integrations/zapier",
                      "integrations/langchain-tools",
                      "integrations/agentops",
                      "integrations/respan",
@@ -633,7 +635,7 @@
    },
    {
      "source": "/platform/features/expiration-date",
-      "destination": "/"
+      "destination": "/platform/features/memory-expiration"
    },
    {
      "source": "/cookbooks/essentials/memory-expiration-short-and-long-term",
@@ -1234,6 +1236,10 @@
    {
      "source": "/platform/features/criteria-retrieval",
      "destination": "/platform/features/advanced-retrieval"
+    },
+    {
+      "source": "/integrations/keywords",
+      "destination": "/integrations/respan"
    }
  ]
 }
@@ -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>
@@ -0,0 +1,61 @@
+---
+title: Zapier
+description: "Add, search, and manage Mem0 memories from any Zap using the Mem0 Zapier app, no code required."
+---
+
+The [Mem0](https://mem0.ai) Zapier app lets you add, search, list, and delete long-term memories from any [Zapier](https://zapier.com) workflow. Wire "remember" and "recall" into thousands of apps without writing code.
+
+## Overview
+
+The app wraps the hosted Mem0 REST API and exposes four actions:
+
+| Type | Action | 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/` |
+
+## Authentication
+
+The app uses API key authentication. Grab a key from the <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-zapier" rel="nofollow">Mem0 API Key dashboard</a> and paste it when connecting your Mem0 account in Zapier. The key is sent as `Authorization: Token <key>` on every request.
+
+## Actions
+
+### Add Memory
+
+Extracts and stores memories from a message. Fields:
+
+- **Content** (required): the message text to extract memories from.
+- **Role**: `user`, `assistant`, or `system`.
+- **User ID / Agent ID / Run ID**: entity the memory belongs to.
+- **Metadata (JSON)**: optional structured metadata.
+- **Infer**: run LLM extraction (default) or store the message verbatim.
+- **Wait for Completion**: off by default. Extraction is asynchronous, so the action returns immediately with an event ID. Turn this on to poll until extraction finishes and return the resulting memories. Extraction can take longer than a single Zapier step is allowed to run, so a timeout here does not mean the add failed; it usually still completes on the server.
+
+### Search Memories
+
+Semantic search over stored memories. Provide a **Query**, a **User ID** (required, the API needs an entity filter), and an optional **Limit**.
+
+### Get Memories
+
+Lists stored memories for a user. Provide a **User ID** (required), a **Limit** (page size), and a **Page** (1-based) to page through larger result sets.
+
+### Delete Memory
+
+Deletes a single memory by its **Memory ID**.
+
+## Choosing a `userId`
+
+The `user_id` is a stable string you choose to identify whose memories these are. It is not looked up in the dashboard. Common choices are your app's internal user ID, an email, or a UUID. Use the same value across Add, Search, and Get so recall works.
+
+<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" />
@@ -281,6 +281,7 @@ 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.
+- [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.
@@ -416,7 +417,6 @@ 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

@@ -472,6 +472,7 @@ 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.
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-</svg>
@@ -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".

@@ -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",
@@ -0,0 +1,9 @@
+node_modules/
+package-lock.json
+.env
+.zapierapprc
+build/
+dist/
+coverage/
+*.log
+.DS_Store
@@ -0,0 +1,201 @@
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+   Copyright [2023] [Taranjeet Singh]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
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@@ -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
@@ -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"
+    }
+  }
+}
@@ -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"
@@ -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',
+};
@@ -0,0 +1,168 @@
+import type { ZObject, Bundle, AddResponse, EventResponse } from '../types';
+import { captureEvent } from '../telemetry';
+
+const POLL_INTERVAL_MS = 1500;
+// Bounded so the poll budget stays under Zapier's per-step execution timeout.
+const MAX_POLL_ATTEMPTS = 12;
+
+// 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' },
+	},
+};
@@ -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' },
+	},
+};
@@ -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;
@@ -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;
+};
@@ -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' },
+	},
+};
@@ -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' },
+	},
+};
@@ -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',
+				$process_person_profile: false,
+				...properties,
+			},
+		};
+		void fetch(POSTHOG_HOST, {
+			method: 'POST',
+			headers: { 'Content-Type': 'application/json' },
+			body: JSON.stringify(payload),
+		}).catch(() => {
+			/* swallow — telemetry must never surface to the user */
+		});
+	} catch {
+		/* swallow — telemetry must never break the action */
+	}
+}
@@ -0,0 +1,43 @@
+import type { ZObject, Bundle } from 'zapier-platform-core';
+
+export type { ZObject, Bundle };
+
+/** Mutable outgoing request, as seen by the beforeRequest middleware. */
+export interface MutableRequest {
+	url?: string;
+	headers?: Record<string, string>;
+	[key: string]: unknown;
+}
+
+/** Response as seen by the afterResponse middleware. */
+export interface ZResponse {
+	status: number;
+	data?: Record<string, unknown> | unknown[] | null;
+	content?: string;
+	[key: string]: unknown;
+}
+
+/** Async add response from POST /v3/memories/add/. */
+export interface AddResponse {
+	event_id?: string;
+	status?: string;
+	error?: string;
+	message?: string;
+	results?: unknown[];
+	[key: string]: unknown;
+}
+
+/** GET /v1/event/{id}/ response. */
+export interface EventResponse {
+	status?: string;
+	error?: string;
+	message?: string;
+	[key: string]: unknown;
+}
+
+/** A stored memory object returned by search / list. */
+export interface Memory {
+	id?: string;
+	memory?: string;
+	[key: string]: unknown;
+}
@@ -0,0 +1,96 @@
+import * as zapier from 'zapier-platform-core';
+import App from '../src';
+
+const appTester = zapier.createAppTester(App as any);
+
+const authData = {
+	apiKey: process.env.MEM0_API_KEY,
+	baseUrl: process.env.MEM0_BASE_URL || 'https://api.mem0.ai',
+};
+
+const userId = `zapier-e2e-${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();
+	});
+});
@@ -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';
@@ -0,0 +1,164 @@
+// 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('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');
+	});
+});
@@ -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"]
+}
@@ -1,6 +1,6 @@
 {
  "name": "mem0ai",
-  "version": "3.1.1",
+  "version": "3.1.2",
  "description": "The Memory Layer For Your AI Apps",
  "main": "./dist/index.js",
  "module": "./dist/index.mjs",
@@ -100,12 +100,16 @@ export class AnthropicLLM implements LLM {
      return { content, role: "assistant", toolCalls };
    }

-    const firstBlock = response.content[0];
-    if (firstBlock.type === "text") {
-      return firstBlock.text;
-    } else {
-      throw new Error("Unexpected response type from Anthropic API");
+    // Thinking-enabled responses put a thinking block before the text block,
+    // and a response can carry no text block at all, so find the text block
+    // like the tools branch above instead of indexing content[0]. Mirrors the
+    // Python provider (#6481).
+    for (const block of response.content) {
+      if (block.type === "text") {
+        return block.text;
+      }
    }
+    return "";
  }

  async generateChat(messages: Message[]): Promise<LLMResponse> {
@@ -59,6 +59,15 @@ export class GoogleLLM implements LLM {
      ];
    }

+    // Honor a requested JSON response format (parity with the Python SDK's
+    // mem0/llms/gemini.py). Gemini's structured output is opt-in via
+    // responseMimeType — without it the model is never told to emit JSON, so
+    // callers passing json_object silently get free-form text and depend on a
+    // fragile markdown-fence strip downstream.
+    if (responseFormat?.type === "json_object") {
+      config.responseMimeType = "application/json";
+    }
+
    const completion = await this.google.models.generateContent({
      contents,
      model: this.model,
@@ -440,4 +440,52 @@ describe("generateWhereClause", () => {
      ChromaDB.generateWhereClause({ $not: [{ age: { gt: 18 } }] }),
    ).toEqual({ age: { $lte: 18 } });
  });
+
+  // Regression tests for the three where-clause translation bugs fixed in
+  // Python by #6452 and tracked for the TS SDK in #6513. ChromaDB allows
+  // exactly one operator or field per dict level.
+  it("keeps both bounds of a same-field range as $and-combined clauses", () => {
+    expect(ChromaDB.generateWhereClause({ age: { gte: 18, lte: 65 } })).toEqual(
+      { $and: [{ age: { $gte: 18 } }, { age: { $lte: 65 } }] },
+    );
+  });
+
+  it("keeps same-field ranges inside $or branches", () => {
+    expect(
+      ChromaDB.generateWhereClause({
+        $or: [{ age: { gte: 18, lte: 65 } }, { vip: true }],
+      }),
+    ).toEqual({
+      $or: [
+        { $and: [{ age: { $gte: 18 } }, { age: { $lte: 65 } }] },
+        { vip: { $eq: true } },
+      ],
+    });
+  });
+
+  it("wraps multi-field conditions inside $or in $and", () => {
+    expect(
+      ChromaDB.generateWhereClause({
+        $or: [{ age: { gte: 18 }, vip: true }, { city: "sh" }],
+      }),
+    ).toEqual({
+      $or: [
+        { $and: [{ age: { $gte: 18 } }, { vip: { $eq: true } }] },
+        { city: { $eq: "sh" } },
+      ],
+    });
+  });
+
+  it("negates $not contains/icontains instead of dropping the clause", () => {
+    expect(
+      ChromaDB.generateWhereClause({
+        $not: [{ title: { contains: "draft" } }],
+      }),
+    ).toEqual({ title: { $ne: "draft" } });
+    expect(
+      ChromaDB.generateWhereClause({
+        $not: [{ title: { icontains: "draft" } }],
+      }),
+    ).toEqual({ title: { $ne: "draft" } });
+  });
 });
@@ -267,6 +267,36 @@ describe("Milvus vector store (TS OSS SDK)", () => {
    });
  });

+  it("skips a wildcard '*' filter value and keeps the rest", async () => {
+    const client = new FakeMilvusClient({ existing: ["mem0"] });
+    client.searchResponse = { results: [] };
+    const store = makeStore(client, { metricType: "COSINE" });
+    await store.initialize();
+
+    // "*" means match-any: it must be dropped, not emitted as `== "*"` (which
+    // matches nothing), leaving only the real agent_id clause.
+    await store.search([0.1, 0.2, 0.3], 5, {
+      user_id: "*",
+      agent_id: "a1",
+    });
+
+    const searchCall = client.calls.find((c) => c.method === "search")!;
+    expect(searchCall.args.filter).toBe('(metadata["agent_id"] == "a1")');
+  });
+
+  it("omits the filter entirely when every value is a wildcard", async () => {
+    const client = new FakeMilvusClient({ existing: ["mem0"] });
+    const store = makeStore(client);
+    await store.initialize();
+
+    await store.list({ user_id: "*" });
+
+    // All clauses dropped, so list() falls back to its match-all "" filter
+    // rather than a literal `(metadata["user_id"] == "*")` that matches nothing.
+    const queryCall = client.calls.filter((c) => c.method === "query").pop()!;
+    expect(queryCall.args.filter).toBe("");
+  });
+
  it("normalises L2 distances into a 0..1 similarity score", async () => {
    const client = new FakeMilvusClient({ existing: ["mem0"] });
    client.searchResponse = {
@@ -432,12 +462,18 @@ describe("Milvus vector store (TS OSS SDK)", () => {
      ["a"],
      [{ data: "hello world", text_lemmatized: "hello world lemma" }],
    );
+    await store.insert(
+      [[0.2, 0.3, 0.4]],
+      ["c"],
+      [{ data: "hello world", textLemmatized: "hello world camel" }],
+    );
    // Falls back to raw data when there is no lemmatized text.
    await store.insert([[0.4, 0.5, 0.6]], ["b"], [{ data: "just data" }]);

    const insertCalls = client.calls.filter((c) => c.method === "insert");
    expect(insertCalls[0].args.data[0].text).toBe("hello world lemma");
-    expect(insertCalls[1].args.data[0].text).toBe("just data");
+    expect(insertCalls[1].args.data[0].text).toBe("hello world camel");
+    expect(insertCalls[2].args.data[0].text).toBe("just data");
  });

  it("writes the BM25 text field on update for a BM25 collection", async () => {
@@ -5,6 +5,7 @@ const mockFindOne = jest.fn();
 const mockUpdateOne = jest.fn();
 const mockListSearchIndexes = jest.fn();
 const mockCreateSearchIndex = jest.fn();
+const mockDropSearchIndex = jest.fn();
 const mockDrop = jest.fn();
 const mockToArray = jest.fn();
 const mockLimit = jest.fn().mockReturnThis();
@@ -26,6 +27,7 @@ const mockCollection = {
  updateOne: mockUpdateOne,
  listSearchIndexes: mockListSearchIndexes,
  createSearchIndex: mockCreateSearchIndex,
+  dropSearchIndex: mockDropSearchIndex,
  drop: mockDrop,
  find: mockFind,
  aggregate: mockAggregate,
@@ -74,6 +76,25 @@ describe("MongoDB Vector Store", () => {
    await store.close();
  });

+  const expectedTextSearchIndexDefinition = {
+    name: "test_col_text_search_index",
+    definition: {
+      mappings: {
+        dynamic: false,
+        fields: {
+          payload: {
+            type: "document",
+            fields: {
+              data: { type: "string" },
+              textLemmatized: { type: "string" },
+              text_lemmatized: { type: "string" },
+            },
+          },
+        },
+      },
+    },
+  };
+
  it("should initialize client and check/create collection and indexes", async () => {
    await store.initialize();

@@ -84,6 +105,83 @@ describe("MongoDB Vector Store", () => {
    });
    expect(mockCollection.deleteOne).toHaveBeenCalledWith({ _id: 0 });
    expect(mockCreateSearchIndex).toHaveBeenCalledTimes(2);
+    expect(mockCreateSearchIndex).toHaveBeenCalledWith(
+      expectedTextSearchIndexDefinition,
+    );
+  });
+
+  it("should drop and recreate a stale text search index on upgrade", async () => {
+    mockListCollections.mockReturnValue({
+      toArray: jest.fn().mockResolvedValue([{ name: "test_col" }]),
+    });
+    mockListSearchIndexes.mockReturnValue({
+      toArray: jest.fn().mockResolvedValue([
+        { name: "test_col_vector_index" },
+        {
+          name: "test_col_text_search_index",
+          definition: {
+            mappings: {
+              dynamic: false,
+              fields: {
+                payload: {
+                  type: "document",
+                  fields: {
+                    data: { type: "string" },
+                    text_lemmatized: { type: "string" },
+                  },
+                },
+              },
+            },
+          },
+        },
+      ]),
+    });
+
+    await store.initialize();
+
+    expect(mockDropSearchIndex).toHaveBeenCalledWith(
+      "test_col_text_search_index",
+    );
+    expect(mockCreateSearchIndex).toHaveBeenCalledTimes(1);
+    expect(mockCreateSearchIndex).toHaveBeenCalledWith(
+      expectedTextSearchIndexDefinition,
+    );
+  });
+
+  it("should not recreate a text search index that already has textLemmatized", async () => {
+    mockListCollections.mockReturnValue({
+      toArray: jest.fn().mockResolvedValue([{ name: "test_col" }]),
+    });
+    mockListSearchIndexes.mockReturnValue({
+      toArray: jest.fn().mockResolvedValue([
+        { name: "test_col_vector_index" },
+        {
+          name: "test_col_text_search_index",
+          latestDefinition: expectedTextSearchIndexDefinition.definition,
+        },
+      ]),
+    });
+
+    await store.initialize();
+
+    expect(mockDropSearchIndex).not.toHaveBeenCalled();
+    expect(mockCreateSearchIndex).not.toHaveBeenCalled();
+  });
+
+  it("should map payload.textLemmatized in the text search index", async () => {
+    await store.initialize();
+
+    const textIndexCall = mockCreateSearchIndex.mock.calls.find(
+      ([arg]: any[]) => arg.name === "test_col_text_search_index",
+    );
+    expect(textIndexCall).toBeDefined();
+    expect(textIndexCall![0].definition.mappings.fields.payload.fields).toEqual(
+      {
+        data: { type: "string" },
+        text_lemmatized: { type: "string" },
+        textLemmatized: { type: "string" },
+      },
+    );
  });

  it("should insert documents correctly", async () => {
@@ -197,7 +295,11 @@ describe("MongoDB Vector Store", () => {
          index: "test_col_text_search_index",
          text: {
            query: "test",
-            path: ["payload.data", "payload.text_lemmatized"],
+            path: [
+              "payload.data",
+              "payload.text_lemmatized",
+              "payload.textLemmatized",
+            ],
          },
        },
      },
@@ -455,44 +455,64 @@ export class CassandraDB implements VectorStore {
      return value.includes(payloadValue);
    }

+    // Every operator present in a compound condition must hold (AND), so check
+    // them all instead of returning on the first match. Returning early meant a
+    // range like { gte: 10, lte: 20 } only applied `gte`. Mirrors the databricks
+    // store's matcher.
+    let sawOperator = false;
+
    if ("eq" in value) {
-      return payloadValue === value.eq;
+      sawOperator = true;
+      if (payloadValue !== value.eq) return false;
    }
    if ("ne" in value) {
-      return payloadValue !== value.ne;
+      sawOperator = true;
+      if (payloadValue === value.ne) return false;
    }
    if ("gt" in value) {
-      return payloadValue > value.gt;
+      sawOperator = true;
+      if (!(payloadValue > value.gt)) return false;
    }
    if ("gte" in value) {
-      return payloadValue >= value.gte;
+      sawOperator = true;
+      if (!(payloadValue >= value.gte)) return false;
    }
    if ("lt" in value) {
-      return payloadValue < value.lt;
+      sawOperator = true;
+      if (!(payloadValue < value.lt)) return false;
    }
    if ("lte" in value) {
-      return payloadValue <= value.lte;
+      sawOperator = true;
+      if (!(payloadValue <= value.lte)) return false;
    }
    if ("in" in value) {
-      return Array.isArray(value.in) && value.in.includes(payloadValue);
+      sawOperator = true;
+      if (!Array.isArray(value.in) || !value.in.includes(payloadValue))
+        return false;
    }
    if ("nin" in value) {
-      return !Array.isArray(value.nin) || !value.nin.includes(payloadValue);
+      sawOperator = true;
+      if (Array.isArray(value.nin) && value.nin.includes(payloadValue))
+        return false;
    }
    if ("contains" in value) {
-      return (
-        typeof payloadValue === "string" &&
-        payloadValue.includes(value.contains)
-      );
+      sawOperator = true;
+      if (
+        typeof payloadValue !== "string" ||
+        !payloadValue.includes(value.contains)
+      )
+        return false;
    }
    if ("icontains" in value) {
-      return (
-        typeof payloadValue === "string" &&
-        payloadValue.toLowerCase().includes(value.icontains.toLowerCase())
-      );
+      sawOperator = true;
+      if (
+        typeof payloadValue !== "string" ||
+        !payloadValue.toLowerCase().includes(value.icontains.toLowerCase())
+      )
+        return false;
    }

-    return payloadValue === value;
+    return sawOperator ? true : payloadValue === value;
  }

  private filterVector(
@@ -273,14 +273,14 @@ export class ChromaDB implements VectorStore {
  private static convertCondition(
    key: string,
    value: any,
-  ): Record<string, any> | null {
+  ): Array<Record<string, any>> {
    // Wildcard - ChromaDB has no direct wildcard, so skip this filter.
    if (value === "*") {
-      return null;
+      return [];
    }

    if (Array.isArray(value)) {
-      return { [key]: { $in: value } };
+      return [{ [key]: { $in: value } }];
    }

    if (value !== null && typeof value === "object") {
@@ -294,19 +294,31 @@ export class ChromaDB implements VectorStore {
        in: "$in",
        nin: "$nin",
      };
-      const condition: Record<string, any> = {};
-      for (const [op, val] of Object.entries(value)) {
-        if (op in opMap) {
-          condition[key] = { [opMap[op]]: val };
-        } else {
-          // contains/icontains and unknown operators fall back to equality.
-          condition[key] = { $eq: val };
-        }
-      }
-      return condition;
+      // ChromaDB allows exactly one operator per field expression, so each
+      // operator becomes its own clause (combined with $and by the caller).
+      // Previously each operator overwrote the last, silently dropping range
+      // bounds. contains/icontains and unknown operators fall back to
+      // equality.
+      return Object.entries(value).map(([op, val]) => ({
+        [key]: { [opMap[op] ?? "$eq"]: val },
+      }));
    }

-    return { [key]: { $eq: value } };
+    return [{ [key]: { $eq: value } }];
+  }
+
+  /** Combine clauses under a logical operator, unwrapping singletons. */
+  private static combineClauses(
+    clauses: Array<Record<string, any>>,
+    operator: "$and" | "$or",
+  ): Record<string, any> | null {
+    if (clauses.length === 0) {
+      return null;
+    }
+    if (clauses.length === 1) {
+      return clauses[0];
+    }
+    return { [operator]: clauses };
  }

  /**
@@ -337,66 +349,55 @@ export class ChromaDB implements VectorStore {
      if (key === "$or" || key === "OR") {
        const orConditions: any[] = [];
        for (const condition of value as any[]) {
-          const built: Record<string, any> = {};
+          const subClauses: Array<Record<string, any>> = [];
          for (const [subKey, subValue] of Object.entries(condition)) {
-            const converted = ChromaDB.convertCondition(subKey, subValue);
-            if (converted) Object.assign(built, converted);
+            subClauses.push(...ChromaDB.convertCondition(subKey, subValue));
          }
-          if (Object.keys(built).length > 0) orConditions.push(built);
-        }
-        if (orConditions.length > 1) {
-          processed.push({ $or: orConditions });
-        } else if (orConditions.length === 1) {
-          processed.push(orConditions[0]);
+          // Multi-field conditions must be wrapped in $and — ChromaDB rejects
+          // flat objects with more than one field per level.
+          const combined = ChromaDB.combineClauses(subClauses, "$and");
+          if (combined) orConditions.push(combined);
        }
+        const combinedOr = ChromaDB.combineClauses(orConditions, "$or");
+        if (combinedOr) processed.push(combinedOr);
      } else if (key === "$not" || key === "NOT") {
        // De Morgan: NOT(a AND b) is (NOT a) OR (NOT b), so the negated fields
        // within one condition are combined with $or, and separate conditions
        // are combined with $and. This mirrors the Python SDK's ChromaDB port.
        const negatedPerGroup: any[] = [];
        for (const condition of value as any[]) {
-          const negatedFields: any[] = [];
+          const negatedFields: Array<Record<string, any>> = [];
          for (const [subKey, subValue] of Object.entries(condition)) {
            if (subValue !== null && typeof subValue === "object") {
              for (const [op, val] of Object.entries(subValue as any)) {
-                const neg = negateOp[op];
-                if (neg) {
-                  const converted = ChromaDB.convertCondition(subKey, {
-                    [neg]: val,
-                  });
-                  if (converted) negatedFields.push(converted);
-                }
+                // Unknown operators mirror the positive-path equality
+                // fallback as inequality (previously they were silently
+                // dropped, which could erase the entire NOT clause).
+                const neg = negateOp[op] ?? "ne";
+                negatedFields.push(
+                  ...ChromaDB.convertCondition(subKey, { [neg]: val }),
+                );
              }
            } else {
-              const converted = ChromaDB.convertCondition(subKey, {
-                ne: subValue,
-              });
-              if (converted) negatedFields.push(converted);
+              negatedFields.push(
+                ...ChromaDB.convertCondition(subKey, { ne: subValue }),
+              );
            }
          }
-          if (negatedFields.length > 1) {
-            negatedPerGroup.push({ $or: negatedFields });
-          } else if (negatedFields.length === 1) {
-            negatedPerGroup.push(negatedFields[0]);
-          }
-        }
-        if (negatedPerGroup.length > 1) {
-          processed.push({ $and: negatedPerGroup });
-        } else if (negatedPerGroup.length === 1) {
-          processed.push(negatedPerGroup[0]);
+          const combined = ChromaDB.combineClauses(negatedFields, "$or");
+          if (combined) negatedPerGroup.push(combined);
        }
+        const combinedNot = ChromaDB.combineClauses(negatedPerGroup, "$and");
+        if (combinedNot) processed.push(combinedNot);
      } else {
-        const converted = ChromaDB.convertCondition(key, value);
-        if (converted) processed.push(converted);
+        const combined = ChromaDB.combineClauses(
+          ChromaDB.convertCondition(key, value),
+          "$and",
+        );
+        if (combined) processed.push(combined);
      }
    }

-    if (processed.length === 0) {
-      return undefined;
-    }
-    if (processed.length === 1) {
-      return processed[0];
-    }
-    return { $and: processed };
+    return ChromaDB.combineClauses(processed, "$and") ?? undefined;
  }
 }
@@ -235,6 +235,13 @@ export class Milvus implements VectorStore {
      if (!Milvus.SAFE_FILTER_KEY.test(key)) {
        throw new Error(`Invalid filter key: ${JSON.stringify(key)}`);
      }
+      if (value === "*") {
+        // Wildcard - match any value. Milvus has no direct wildcard, so skip
+        // the clause rather than emitting a literal `== "*"` that matches
+        // nothing. Mirrors the Python provider (#6187) and the chroma/pinecone
+        // stores.
+        continue;
+      }
      if (typeof value === "string") {
        // Escape backslashes before quotes so a value can't break out of the
        // string literal (order matters, exactly as in the Python provider).
@@ -252,13 +259,13 @@ export class Milvus implements VectorStore {
  }

  /**
-   * Text fed to the BM25 sparse index for a payload. Prefers the lemmatized
-   * text, falls back to the raw memory `data`, and truncates to the VarChar
-   * limit (mirrors the Python provider).
+   * Text fed to the BM25 sparse index for a payload. Prefers `textLemmatized`,
+   * then `text_lemmatized`, then raw `data`; truncates to the VarChar limit.
   */
  private bm25Text(payload?: Record<string, any>): string {
    if (!payload) return "";
-    const raw = payload.text_lemmatized || payload.data || "";
+    const raw =
+      payload.textLemmatized || payload.text_lemmatized || payload.data || "";
    return String(raw).slice(0, 65535);
  }

@@ -62,6 +62,46 @@ export class MongoDB implements VectorStore {
    return this._initPromise;
  }

+  private textSearchIndexDefinition(textIndexName: string) {
+    return {
+      name: textIndexName,
+      definition: {
+        mappings: {
+          dynamic: false,
+          fields: {
+            payload: {
+              type: "document",
+              fields: {
+                data: { type: "string" },
+                textLemmatized: { type: "string" },
+                text_lemmatized: { type: "string" },
+              },
+            },
+          },
+        },
+      },
+    };
+  }
+
+  private textSearchIndexMappingIsCurrent(
+    index: Record<string, unknown>,
+  ): boolean {
+    const definition =
+      (index.latestDefinition as Record<string, unknown> | undefined) ??
+      (index.definition as Record<string, unknown> | undefined);
+    const payloadFields = (
+      (definition?.mappings as Record<string, unknown> | undefined)?.fields as
+        | Record<string, unknown>
+        | undefined
+    )?.payload as Record<string, unknown> | undefined;
+    const fields =
+      (payloadFields?.fields as Record<string, unknown> | undefined) ?? {};
+    const textLemmatized = fields.textLemmatized as
+      | { type?: string }
+      | undefined;
+    return textLemmatized?.type === "string";
+  }
+
  private async _doInitialize(): Promise<void> {
    await this.ensureClient();
    try {
@@ -111,32 +151,24 @@ export class MongoDB implements VectorStore {
      // Create Text Search Index for keywordSearch
      const textIndexName = `${this.collectionName}_text_search_index`;
      try {
-        let foundTextIndex = false;
+        let existingTextIndex: Record<string, unknown> | null = null;
        try {
          const indexes = await this.collection.listSearchIndexes().toArray();
-          foundTextIndex = indexes.some((idx) => idx.name === textIndexName);
+          existingTextIndex =
+            (indexes.find((idx) => idx.name === textIndexName) as
+              | Record<string, unknown>
+              | undefined) ?? null;
        } catch (e) {
          // ignore
        }

-        if (!foundTextIndex) {
-          await this.collection.createSearchIndex({
-            name: textIndexName,
-            definition: {
-              mappings: {
-                dynamic: false,
-                fields: {
-                  payload: {
-                    type: "document",
-                    fields: {
-                      data: { type: "string" },
-                      text_lemmatized: { type: "string" },
-                    },
-                  },
-                },
-              },
-            },
-          });
+        const textSearchIndex = this.textSearchIndexDefinition(textIndexName);
+
+        if (!existingTextIndex) {
+          await this.collection.createSearchIndex(textSearchIndex);
+        } else if (!this.textSearchIndexMappingIsCurrent(existingTextIndex)) {
+          await this.collection.dropSearchIndex(textIndexName);
+          await this.collection.createSearchIndex(textSearchIndex);
        }
      } catch (e: any) {
        console.warn(
@@ -286,7 +318,11 @@ export class MongoDB implements VectorStore {
            index: textIndexName,
            text: {
              query: query,
-              path: ["payload.data", "payload.text_lemmatized"],
+              path: [
+                "payload.data",
+                "payload.text_lemmatized",
+                "payload.textLemmatized",
+              ],
            },
          },
        },
@@ -276,6 +276,7 @@ export class OpenSearchDB implements VectorStore {
      should: [
        { match: { "payload.data": query } },
        { match: { "payload.text_lemmatized": query } },
+        { match: { "payload.textLemmatized": query } },
      ],
      minimum_should_match: 1,
    };
@@ -72,6 +72,37 @@ describe("AnthropicLLM (unit)", () => {
    expect(callArgs.tool_choice).toBeUndefined();
  });

+  // Regression: thinking-enabled models emit a thinking block before the text
+  // block. Indexing content[0] threw "Unexpected response type"; the text block
+  // must be found by type instead (TS parity with #6481).
+  it("returns the text block when a thinking block precedes it (no tools)", async () => {
+    mockCreate.mockResolvedValueOnce({
+      content: [
+        { type: "thinking", thinking: "Let me reason about this." },
+        { type: "text", text: '{"facts": ["fact1"]}' },
+      ],
+    });
+
+    const llm = new AnthropicLLM({ apiKey: "test-key" });
+    const result = await llm.generateResponse([
+      { role: "user", content: "Hello" },
+    ]);
+
+    expect(result).toBe('{"facts": ["fact1"]}');
+  });
+
+  // A response carrying no text block at all must resolve to "" rather than throw.
+  it("returns an empty string when no text block is present (no tools)", async () => {
+    mockCreate.mockResolvedValueOnce({
+      content: [{ type: "thinking", thinking: "Thinking only." }],
+    });
+
+    const llm = new AnthropicLLM({ apiKey: "test-key" });
+    await expect(
+      llm.generateResponse([{ role: "user", content: "Hello" }]),
+    ).resolves.toBe("");
+  });
+
  // Bug #1 regression: bare string "auto" must NOT be sent; object form required
  it("forwards tool_choice as { type: 'auto' } (not bare string) when tools are provided", async () => {
    mockCreate.mockResolvedValueOnce({
@@ -0,0 +1,63 @@
+/// <reference types="jest" />
+/**
+ * Cassandra vector store — filter matching unit tests.
+ *
+ * Cassandra has no server-side metadata filter, so search()/list() scan rows
+ * and apply filters in-app via matchFieldCondition(). These tests drive that
+ * matcher through the public list() API with an injected fake client.
+ */
+import { CassandraDB } from "../src/vector_stores/cassandra";
+
+type Row = { id: string; payload: Record<string, any> };
+
+// Minimal fake driver: CREATE statements during initialize() return nothing;
+// a SELECT returns the seeded rows in one page (pageState undefined => stop).
+function fakeClient(rows: Row[]) {
+  return {
+    async connect() {},
+    async execute(query: string) {
+      if (/^\s*SELECT/i.test(query)) {
+        return { rows, pageState: undefined };
+      }
+      return { rows: [], pageState: undefined };
+    },
+    async shutdown() {},
+  };
+}
+
+function makeStore(rows: Row[]) {
+  return new CassandraDB({
+    keyspace: "mem0",
+    collectionName: "mem0",
+    dimension: 3,
+    client: fakeClient(rows) as any,
+  } as any);
+}
+
+describe("CassandraDB filter matching", () => {
+  const rows: Row[] = [
+    { id: "a", payload: { data: "a", age: 5 } },
+    { id: "b", payload: { data: "b", age: 15 } },
+    { id: "c", payload: { data: "c", age: 25 } },
+  ];
+
+  it("applies every operator in a compound range filter (not just the first)", async () => {
+    const store = makeStore(rows);
+    // age in [10, 20]: only "b" (15) qualifies. The old matcher returned on the
+    // first operator (gte), so "c" (25) leaked through because lte was ignored.
+    const [results] = await store.list({ age: { gte: 10, lte: 20 } });
+    expect(results.map((r) => r.id)).toEqual(["b"]);
+  });
+
+  it("still matches a single-operator filter", async () => {
+    const store = makeStore(rows);
+    const [results] = await store.list({ age: { gte: 15 } });
+    expect(results.map((r) => r.id).sort()).toEqual(["b", "c"]);
+  });
+
+  it("treats a plain equality filter as before", async () => {
+    const store = makeStore(rows);
+    const [results] = await store.list({ age: 15 });
+    expect(results.map((r) => r.id)).toEqual(["b"]);
+  });
+});
@@ -185,6 +185,37 @@ describe("GoogleLLM (unit)", () => {
    expect(response.toolCalls[1].name).toBe("add_graph_memory");
  });

+  // Regression: generateResponse accepted a responseFormat argument but never
+  // forwarded it, so Gemini was never told to emit JSON (parity with the Python
+  // SDK's mem0/llms/gemini.py, which sets response_mime_type/response_schema).
+  it("forwards json_object responseFormat as responseMimeType", async () => {
+    mockGenerateContent.mockResolvedValueOnce({
+      text: '{"facts": ["fact1"]}',
+      functionCalls: null,
+    });
+
+    const llm = new GoogleLLM({ apiKey: "test-key" });
+    await llm.generateResponse([{ role: "user", content: "Extract facts" }], {
+      type: "json_object",
+    });
+
+    const callArgs = mockGenerateContent.mock.calls[0][0];
+    expect(callArgs.config.responseMimeType).toBe("application/json");
+  });
+
+  it("does not set responseMimeType when no responseFormat is given", async () => {
+    mockGenerateContent.mockResolvedValueOnce({
+      text: "plain text",
+      functionCalls: null,
+    });
+
+    const llm = new GoogleLLM({ apiKey: "test-key" });
+    await llm.generateResponse([{ role: "user", content: "Hello" }]);
+
+    const callArgs = mockGenerateContent.mock.calls[0][0];
+    expect(callArgs.config.responseMimeType).toBeUndefined();
+  });
+
  it("formats generateChat messages and joins Gemini response parts", async () => {
    mockGenerateContent.mockResolvedValueOnce({
      candidates: [
@@ -153,4 +153,26 @@ describe("OpenSearchDB", () => {

    await expect(store.get("missing")).resolves.toBeNull();
  });
+
+  it("keywordSearch queries lemmatized payload fields", async () => {
+    const client = createClient();
+    const store = await createStore(client);
+
+    await store.keywordSearch("stud french", 5, { user_id: "alice" });
+
+    const searchCall = client.search.mock.calls.find(
+      ([arg]: any[]) => arg.index === collectionName,
+    );
+    expect(searchCall).toBeDefined();
+    const should = searchCall![0].body.query.bool.should;
+    expect(should).toContainEqual({
+      match: { "payload.textLemmatized": "stud french" },
+    });
+    expect(should).toContainEqual({
+      match: { "payload.text_lemmatized": "stud french" },
+    });
+    expect(should).toContainEqual({
+      match: { "payload.data": "stud french" },
+    });
+  });
 });
@@ -0,0 +1,113 @@
+"""Pydantic configuration for the Oracle AI Vector Search integration."""
+
+import re
+from typing import Any, Dict, Literal, Optional
+
+from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
+
+
+def _quote_identifier(name: str) -> str:
+    name = name.strip()
+    reg = r'^(?:"[^"]+"|[^".]+)(?:\.(?:"[^"]+"|[^".]+))*$'
+    pattern_validate = re.compile(reg)
+
+    if not pattern_validate.match(name):
+        raise ValueError(f"Identifier name {name} is not valid.")
+
+    pattern_match = r'"([^"]+)"|([^".]+)'
+    groups = re.findall(pattern_match, name)
+    groups = [m[0] or m[1] for m in groups]
+    groups = [f'"{g}"' for g in groups]
+
+    return ".".join(groups)
+
+
+class HnswParams(BaseModel):
+    model_config = ConfigDict(extra="forbid", strict=True)
+
+    neighbors: Optional[int] = Field(None, ge=2, le=2048)
+    efconstruction: Optional[int] = Field(None, ge=1, le=65535)
+
+
+class IvfParams(BaseModel):
+    model_config = ConfigDict(extra="forbid", strict=True)
+
+    neighbor_partitions: Optional[int] = Field(None, alias="neighbor partitions", ge=1, le=10_000_000)
+    samples_per_partition: Optional[int] = Field(None, ge=1)
+    min_vectors_per_partition: Optional[int] = Field(None, ge=0)
+
+
+class OracleAIVectorSearchConfig(BaseModel):
+    """Configuration required to connect to an Oracle database with vector search enabled."""
+
+    connection_params: Optional[dict] = Field(None, description="Database connection parameters, including auth.")
+    use_connection_pool: bool = Field(
+        True,
+        description="Create a ConnectionPool instead of a single Connection when no client is provided",
+    )
+
+    client: Optional[Any] = Field(
+        None, description="Oracle Connection or ConnectionPool (overrides connection string and individual parameters)"
+    )
+
+    collection_name: str = Field("mem0", description="Default name for the collection")
+    embedding_model_dims: int = Field(1536, description="Dimension of the embedding vectors")
+    distance_metric: Literal["EUCLIDEAN", "EUCLIDEAN_SQUARED", "COSINE", "DOT", "HAMMING", "MANHATTAN"] = Field(
+        "COSINE",
+        description="Similarity metric: EUCLIDEAN, EUCLIDEAN_SQUARED, COSINE, DOT, HAMMING or MANHATTAN. Defaults to COSINE",
+    )
+
+    do_create_index: Optional[bool] = Field(True, description="Optional whether to create index")
+    index_type: Literal["HNSW", "IVF"] = Field("HNSW", description="Optional index type, HNSW or IVF")
+    index_name: Optional[str] = Field(None, description="Optional custom name for the vector index")
+    index_parameters: Optional[dict] = Field(
+        None,
+        description="Optional structured CREATE VECTOR INDEX parameters",
+    )
+    index_accuracy: Optional[int] = Field(None, description="Optional index accuracy")
+
+    @field_validator("distance_metric", "index_type", mode="before")
+    @classmethod
+    def _normalize_uppercase(cls, value: Any) -> Any:
+        return value.upper() if isinstance(value, str) else value
+
+    @model_validator(mode="after")
+    def _validate_model(self):
+        """Normalise attributes and validate identifiers/metrics."""
+
+        if not self.connection_params and not self.client:
+            raise ValueError("Must provide at least one of `connection_params` and `client`")
+
+        if self.index_name is None:
+            self.index_name = f"{self.collection_name}_VEC_IDX"
+
+        self.index_name = _quote_identifier(self.index_name)
+        self.collection_name = _quote_identifier(self.collection_name)
+
+        if self.index_parameters is not None:
+            parameter_model = HnswParams if self.index_type == "HNSW" else IvfParams
+            self.index_parameters = parameter_model.model_validate(self.index_parameters).model_dump(
+                by_alias=True,
+                exclude_none=True,
+            )
+
+        if self.index_accuracy and not (0 < self.index_accuracy <= 100):
+            raise ValueError("`index_accuracy` must be between 1 and 100")
+
+        if not (0 < self.embedding_model_dims):
+            raise ValueError("`embedding_model_dims` must be bigger than 0")
+
+        return self
+
+    @model_validator(mode="before")
+    @classmethod
+    def validate_extra_fields(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+        allowed_fields = set(cls.model_fields.keys())
+        extra_fields = set(values.keys()) - allowed_fields
+        if extra_fields:
+            raise ValueError(
+                "Extra fields not allowed: {}. Please input only the following fields: {}".format(
+                    ", ".join(sorted(extra_fields)), ", ".join(sorted(allowed_fields))
+                )
+            )
+        return values
@@ -201,6 +201,7 @@ class VectorStoreFactory:
        "cassandra": "mem0.vector_stores.cassandra.CassandraDB",
        "neptune": "mem0.vector_stores.neptune_analytics.NeptuneAnalyticsVector",
        "turbopuffer": "mem0.vector_stores.turbopuffer.TurbopufferDB",
+        "oracledb": "mem0.vector_stores.oracledb.OracleAIVectorSearch",
    }

    @classmethod
@@ -254,110 +254,111 @@ class ChromaDB(VectorStoreBase):
    def _generate_where_clause(where: dict[str, any]) -> dict[str, any]:
        """
        Generate a properly formatted where clause for ChromaDB.
-        
+
+        ChromaDB's where grammar allows exactly one field or one logical
+        operator per dict level, so multiple operators on the same field and
+        multi-field conditions must be combined with an explicit ``$and``.
+
        Args:
            where (dict[str, any]): The filter conditions.
-            
+
        Returns:
            dict[str, any]: Properly formatted where clause for ChromaDB.
        """
        if where is None:
            return None
-        
-        def convert_condition(key: str, value: any) -> dict:
-            """Convert universal filter format to ChromaDB format."""
+
+        op_map = {
+            "eq": "$eq",
+            "ne": "$ne",
+            "gt": "$gt",
+            "gte": "$gte",
+            "lt": "$lt",
+            "lte": "$lte",
+            "in": "$in",
+            "nin": "$nin",
+        }
+        # Negation of each operator. contains/icontains fall back to equality
+        # on the positive path (ChromaDB has no substring match), so their
+        # negation falls back to inequality for consistency.
+        negate_map = {
+            "eq": "$ne",
+            "ne": "$eq",
+            "gt": "$lte",
+            "gte": "$lt",
+            "lt": "$gte",
+            "lte": "$gt",
+            "in": "$nin",
+            "nin": "$in",
+        }
+
+        def convert_condition(key: str, value: any) -> list:
+            """Convert one field condition to a list of single-field ChromaDB clauses."""
            if value == "*":
                # Wildcard - match any value (ChromaDB doesn't have direct wildcard, so we skip this filter)
+                return []
+            if isinstance(value, dict):
+                # One clause per operator: ChromaDB rejects field expressions
+                # with more than one operator, so a range like
+                # {"gte": 18, "lte": 65} must become two clauses combined
+                # with $and by the caller (previously each operator
+                # overwrote the last, silently dropping bounds).
+                # contains/icontains and unknown operators fall back to equality.
+                return [{key: {op_map.get(op, "$eq"): val}} for op, val in value.items()]
+            # Simple equality
+            return [{key: {"$eq": value}}]
+
+        def combine(clauses: list, operator: str):
+            """Combine clauses under a logical operator, unwrapping singletons."""
+            if not clauses:
                return None
-            elif isinstance(value, dict):
-                # Handle comparison operators
-                chroma_condition = {}
-                for op, val in value.items():
-                    if op == "eq":
-                        chroma_condition[key] = {"$eq": val}
-                    elif op == "ne":
-                        chroma_condition[key] = {"$ne": val}
-                    elif op == "gt":
-                        chroma_condition[key] = {"$gt": val}
-                    elif op == "gte":
-                        chroma_condition[key] = {"$gte": val}
-                    elif op == "lt":
-                        chroma_condition[key] = {"$lt": val}
-                    elif op == "lte":
-                        chroma_condition[key] = {"$lte": val}
-                    elif op == "in":
-                        chroma_condition[key] = {"$in": val}
-                    elif op == "nin":
-                        chroma_condition[key] = {"$nin": val}
-                    elif op in ["contains", "icontains"]:
-                        # ChromaDB doesn't support contains, fallback to equality
-                        chroma_condition[key] = {"$eq": val}
-                    else:
-                        # Unknown operator, treat as equality
-                        chroma_condition[key] = {"$eq": val}
-                return chroma_condition
-            else:
-                # Simple equality
-                return {key: {"$eq": value}}
-        
+            if len(clauses) == 1:
+                return clauses[0]
+            return {operator: clauses}
+
        processed_filters = []
-        
+
        for key, value in where.items():
            if key == "$or":
-                # Handle OR conditions
                or_conditions = []
                for condition in value:
-                    or_condition = {}
+                    sub_clauses = []
                    for sub_key, sub_value in condition.items():
-                        converted = convert_condition(sub_key, sub_value)
-                        if converted:
-                            or_condition.update(converted)
-                    if or_condition:
-                        or_conditions.append(or_condition)
-                
-                if len(or_conditions) > 1:
-                    processed_filters.append({"$or": or_conditions})
-                elif len(or_conditions) == 1:
-                    processed_filters.append(or_conditions[0])
-            
+                        sub_clauses.extend(convert_condition(sub_key, sub_value))
+                    combined = combine(sub_clauses, "$and")
+                    if combined:
+                        or_conditions.append(combined)
+                combined_or = combine(or_conditions, "$or")
+                if combined_or:
+                    processed_filters.append(combined_or)
+
            elif key == "$not":
-                negate_op = {
-                    "eq": "$ne", "ne": "$eq",
-                    "gt": "$lte", "gte": "$lt",
-                    "lt": "$gte", "lte": "$gt",
-                    "in": "$nin", "nin": "$in",
-                }
                negated_per_group = []
                for condition in value:
                    negated_fields = []
                    for sub_key, sub_value in condition.items():
                        if isinstance(sub_value, dict):
                            for op, val in sub_value.items():
-                                neg = negate_op.get(op)
-                                if neg:
-                                    negated_fields.append({sub_key: {neg: val}})
+                                # Unknown operators mirror the positive-path
+                                # equality fallback as inequality (previously
+                                # they were silently dropped, which could
+                                # erase the entire NOT clause).
+                                negated_fields.append({sub_key: {negate_map.get(op, "$ne"): val}})
                        else:
                            negated_fields.append({sub_key: {"$ne": sub_value}})
-                    if len(negated_fields) > 1:
-                        negated_per_group.append({"$or": negated_fields})
-                    elif len(negated_fields) == 1:
-                        negated_per_group.append(negated_fields[0])
+                    # NOT(a AND b) == (NOT a) OR (NOT b)
+                    combined = combine(negated_fields, "$or")
+                    if combined:
+                        negated_per_group.append(combined)
+                combined_not = combine(negated_per_group, "$and")
+                if combined_not:
+                    processed_filters.append(combined_not)

-                if len(negated_per_group) > 1:
-                    processed_filters.append({"$and": negated_per_group})
-                elif len(negated_per_group) == 1:
-                    processed_filters.append(negated_per_group[0])
-                
            else:
                # Regular condition
-                converted = convert_condition(key, value)
-                if converted:
-                    processed_filters.append(converted)
-        
+                combined = combine(convert_condition(key, value), "$and")
+                if combined:
+                    processed_filters.append(combined)
+
        # Return appropriate format based on number of conditions
-        if len(processed_filters) == 0:
-            return None
-        elif len(processed_filters) == 1:
-            return processed_filters[0]
-        else:
-            return {"$and": processed_filters}
+        return combine(processed_filters, "$and")
@@ -35,6 +35,7 @@ class VectorStoreConfig(BaseModel):
        "langchain": "LangchainConfig",
        "s3_vectors": "S3VectorsConfig",
        "turbopuffer": "TurbopufferConfig",
+        "oracledb": "OracleAIVectorSearchConfig",
    }

    @model_validator(mode="after")
@@ -298,10 +298,12 @@ class MilvusDB(VectorStoreBase):
            if payload is None:
                payload = existing[0].get("metadata")

-        text = ""
-        if payload:
-            text = (payload.get("text_lemmatized") or payload.get("data", ""))[:65535]
-        schema = {"id": vector_id, "vectors": vector, "metadata": payload, "text": text}
+        schema = {"id": vector_id, "vectors": vector, "metadata": payload}
+        if self._has_bm25_schema:
+            text = ""
+            if payload:
+                text = (payload.get("text_lemmatized") or payload.get("data", ""))[:65535]
+            schema["text"] = text
        self.client.upsert(collection_name=self.collection_name, data=schema)

    def get(self, vector_id) -> Optional[OutputData]:
@@ -16,6 +16,7 @@ from mem0.vector_stores.base import VectorStoreBase
 logger = logging.getLogger(__name__)

 _SAFE_FILTER_KEY = re.compile(r"^[a-zA-Z_][a-zA-Z0-9_.]*$")
+_IDENTITY_FILTER_KEYS = ("user_id", "agent_id", "run_id")


 def _validate_filter(key: str, value) -> None:
@@ -28,6 +29,29 @@ def _validate_filter(key: str, value) -> None:
        )


+def _build_filter_clauses(filters):
+    """Build term clauses from every filter key, not just the identity keys."""
+    filter_clauses = []
+    for key, value in (filters or {}).items():
+        if value is None:
+            continue
+        if value == "*":
+            # "Any value" wildcard (a documented Platform pattern): match
+            # documents where the field exists — as opensearch.ts already
+            # does for every key — instead of a literal, near-always-empty
+            # term match on the string "*".
+            _validate_filter(key, value)
+            filter_clauses.append({"exists": {"field": f"payload.{key}"}})
+            continue
+        if key not in _IDENTITY_FILTER_KEYS and not isinstance(value, (str, int, float, bool)):
+            logger.debug(f"Ignoring non-scalar filter value for key {key!r}")
+            continue
+        _validate_filter(key, value)
+        field = f"payload.{key}.keyword" if isinstance(value, str) else f"payload.{key}"
+        filter_clauses.append({"term": {field: value}})
+    return filter_clauses
+
+
 class OutputData(BaseModel):
    id: str
    score: float
@@ -204,13 +228,7 @@ class OpenSearchDB(VectorStoreBase):
        query_body = {"size": top_k * 2, "query": None}

        # Prepare filter conditions if applicable
-        filter_clauses = []
-        if filters:
-            for key in ["user_id", "run_id", "agent_id"]:
-                value = filters.get(key)
-                if value:
-                    _validate_filter(key, value)
-                    filter_clauses.append({"term": {f"payload.{key}.keyword": value}})
+        filter_clauses = _build_filter_clauses(filters)

        # Combine knn with filters if needed
        if filter_clauses:
@@ -230,7 +248,7 @@ class OpenSearchDB(VectorStoreBase):
            return results
        except Exception as e:
            logger.error(f"Error during search: {e}", exc_info=True)
-            return []
+            raise

    def keyword_search(self, query, top_k=5, filters=None):
        """Search for memories using BM25 keyword matching.
@@ -255,13 +273,7 @@ class OpenSearchDB(VectorStoreBase):
        }

        # Apply filters consistently with the existing search() method
-        filter_clauses = []
-        if filters:
-            for key in ["user_id", "run_id", "agent_id"]:
-                value = filters.get(key)
-                if value:
-                    _validate_filter(key, value)
-                    filter_clauses.append({"term": {f"payload.{key}.keyword": value}})
+        filter_clauses = _build_filter_clauses(filters)

        if filter_clauses:
            bool_query["filter"] = filter_clauses
@@ -281,8 +293,12 @@ class OpenSearchDB(VectorStoreBase):
            ]
            return results
        except Exception as e:
-            logger.error(f"Error during keyword search: {e}")
-            return []
+            # Do NOT re-raise here: keyword_search() is a best-effort helper that
+            # search() may call to augment semantic results. Raising would crash
+            # the whole search() call on a keyword-only failure (regression per
+            # maintainer review on #6519). Log with exc_info and degrade to None.
+            logger.error(f"Error during keyword search: {e}", exc_info=True)
+            return None

    def delete(self, vector_id: str) -> None:
        """Delete a vector by custom ID."""
@@ -370,13 +386,7 @@ class OpenSearchDB(VectorStoreBase):
            """List all memories with optional filters."""
            query: Dict = {"query": {"match_all": {}}}

-            filter_clauses = []
-            if filters:
-                for key in ["user_id", "run_id", "agent_id"]:
-                    value = filters.get(key)
-                    if value:
-                        _validate_filter(key, value)
-                        filter_clauses.append({"term": {f"payload.{key}.keyword": value}})
+            filter_clauses = _build_filter_clauses(filters)

            if filter_clauses:
                query["query"] = {"bool": {"filter": filter_clauses}}
@@ -0,0 +1,592 @@
+"""Oracle AI Vector Search vector store integration for mem0."""
+
+import array
+import json
+import logging
+import math
+import re
+import uuid
+from contextlib import contextmanager
+from typing import Any, Dict, List, Optional
+
+try:
+    import oracledb
+except ImportError as exc:  # pragma: no cover - dependency guard
+    raise ImportError("Oracle AI Vector Search requires the 'oracledb' package.") from exc
+
+from pydantic import BaseModel
+
+from mem0.configs.vector_stores.oracledb import OracleAIVectorSearchConfig
+from mem0.vector_stores.base import VectorStoreBase
+
+logger = logging.getLogger(__name__)
+
+
+class OutputData(BaseModel):
+    """Standard output structure returned from vector operations."""
+
+    id: Optional[str]
+    score: Optional[float]
+    payload: Optional[Dict[str, Any]]
+
+
+# Allow letters, digits, underscore, dot, brackets, comma, *, space (for 'to')
+METADATA_PATTERN = re.compile(r"[a-zA-Z0-9_\.\[\],\s\*]+")
+
+
+def _validate_metadata_key(metadata_key: str) -> None:
+    if not METADATA_PATTERN.fullmatch(metadata_key):
+        raise ValueError(
+            f"Invalid metadata key '{metadata_key}'. "
+            "Only letters, numbers, underscores, nesting via '.', "
+            "and array wildcards '[*]' are allowed."
+        )
+
+
+_SCORE_FROM_DISTANCE = {
+    "COSINE": lambda d: max(0.0, min(1.0, 1.0 - d)),
+    "EUCLIDEAN": lambda d: 1.0 / (1.0 + max(0.0, d)),
+    "EUCLIDEAN_SQUARED": lambda d: 1.0 / (1.0 + math.sqrt(max(0.0, d))),
+    "HAMMING": lambda d: 1.0 / (1.0 + max(0.0, d)),
+    "MANHATTAN": lambda d: 1.0 / (1.0 + max(0.0, d)),
+    "DOT": lambda d: -d,
+}
+
+
+def _convert_distance_to_score(distance: float, metric: str) -> float:
+    try:
+        return _SCORE_FROM_DISTANCE[metric.upper()](distance)
+    except KeyError:
+        raise ValueError(f"Unsupported distance metric: {metric}") from None
+
+
+_FIELD_OPERATORS = {"eq", "ne", "gt", "gte", "lt", "lte", "in", "nin", "contains", "icontains"}
+_COMPARISON_OPERATORS = {
+    "eq": "==",
+    "ne": "!=",
+    "gt": ">",
+    "gte": ">=",
+    "lt": "<",
+    "lte": "<=",
+}
+_LOGICAL_OPERATORS = {
+    "$and": "and",
+    "$or": "or",
+    "$not": "not",
+    "AND": "and",
+    "OR": "or",
+    "NOT": "not",
+}
+
+
+def _json_path(metadata_key: str) -> str:
+    _validate_metadata_key(metadata_key)
+
+    path_parts: List[str] = []
+    for part in metadata_key.split("."):
+        if part.endswith("[*]"):
+            path_parts.append(f'."{part[:-3]}"[*]')
+        else:
+            path_parts.append(f'."{part}"')
+    return "".join(path_parts)
+
+
+def _bind_filter_value(value: Any, params: Dict[str, Any]) -> tuple[str, str]:
+    param = f"f_{len(params)}"
+    params[param] = value
+    return f"${param}", f':{param} AS "{param}"'
+
+
+def _json_exists(json_path: str, predicate: str, passings: List[str]) -> str:
+    passing_clause = f" PASSING {', '.join(passings)}" if passings else ""
+    return f"JSON_EXISTS(payload, '${json_path}?({predicate})'{passing_clause})"
+
+
+def _validate_scalar_operand(operator: str, value: Any) -> None:
+    if isinstance(value, (dict, list, tuple, set)):
+        raise ValueError(f"Oracle filter operator {operator!r} requires a scalar value")
+
+
+def _build_field_condition(metadata_key: str, value: Any, params: Dict[str, Any]) -> str:
+    json_path = _json_path(metadata_key)
+
+    if value == "*":
+        return f"JSON_EXISTS(payload, '${json_path}')"
+
+    if not isinstance(value, dict):
+        _validate_scalar_operand("eq", value)
+        if value is None:
+            return _json_exists(json_path, "@ == null", [])
+        variable, passing = _bind_filter_value(value, params)
+        return _json_exists(json_path, f"@ == {variable}", [passing])
+
+    if not value:
+        raise ValueError(f"Operator filter for field {metadata_key!r} must not be empty")
+
+    unsupported = set(value) - _FIELD_OPERATORS
+    if unsupported:
+        raise ValueError(
+            f"Unsupported Oracle filter operator(s) for field {metadata_key!r}: "
+            f"{', '.join(sorted(map(str, unsupported)))}"
+        )
+
+    predicates: List[str] = []
+    passings: List[str] = []
+    additional_clauses: List[str] = []
+
+    for operator, operand in value.items():
+        if operator in _COMPARISON_OPERATORS:
+            _validate_scalar_operand(operator, operand)
+            if operand is None:
+                if operator not in {"eq", "ne"}:
+                    raise ValueError(f"Oracle filter operator {operator!r} does not support null")
+                predicates.append(f"@ {_COMPARISON_OPERATORS[operator]} null")
+                continue
+            variable, passing = _bind_filter_value(operand, params)
+            predicates.append(f"@ {_COMPARISON_OPERATORS[operator]} {variable}")
+            passings.append(passing)
+            continue
+
+        if operator in {"in", "nin"}:
+            if not isinstance(operand, (list, tuple)) or not operand:
+                raise ValueError(f"Oracle filter operator {operator!r} requires a non-empty list")
+
+            variables: List[str] = []
+            list_passings: List[str] = []
+            for item in operand:
+                _validate_scalar_operand(operator, item)
+                if item is None:
+                    variables.append("null")
+                    continue
+                variable, passing = _bind_filter_value(item, params)
+                variables.append(variable)
+                list_passings.append(passing)
+
+            membership = _json_exists(json_path, f"@ in ({', '.join(variables)})", list_passings)
+            if operator == "in":
+                additional_clauses.append(membership)
+            else:
+                additional_clauses.append(f"NOT ({membership})")
+            continue
+
+        if not isinstance(operand, str):
+            raise ValueError(f"Oracle filter operator {operator!r} requires a string value")
+
+        if operator == "contains":
+            variable, passing = _bind_filter_value(operand, params)
+            predicates.append(f"@ has substring {variable}")
+            passings.append(passing)
+        else:
+            variable, passing = _bind_filter_value(operand.lower(), params)
+            predicates.append(f"@.lower() has substring {variable}")
+            passings.append(passing)
+
+    clauses = list(additional_clauses)
+    if predicates:
+        clauses.insert(0, _json_exists(json_path, " && ".join(predicates), passings))
+
+    if len(clauses) == 1:
+        return clauses[0]
+    return "(" + " AND ".join(clauses) + ")"
+
+
+def _build_filter_group(filters: Dict[str, Any], params: Dict[str, Any]) -> str:
+    if not isinstance(filters, dict) or not filters:
+        raise ValueError("Oracle filter groups must be non-empty dictionaries")
+
+    clauses: List[str] = []
+    for key, value in filters.items():
+        if key in _LOGICAL_OPERATORS:
+            if not isinstance(value, list) or not value:
+                raise ValueError(f"Logical filter operator {key!r} requires a non-empty list")
+
+            nested = [_build_filter_group(condition, params) for condition in value]
+            logical_operator = _LOGICAL_OPERATORS[key]
+            if logical_operator == "not":
+                clauses.append(f"NOT ({' OR '.join(nested)})")
+            else:
+                joiner = " AND " if logical_operator == "and" else " OR "
+                clauses.append("(" + joiner.join(nested) + ")")
+            continue
+
+        if key.startswith("$"):
+            raise ValueError(f"Unsupported Oracle logical filter operator: {key}")
+
+        clauses.append(_build_field_condition(key, value, params))
+
+    if len(clauses) == 1:
+        return clauses[0]
+    return "(" + " AND ".join(clauses) + ")"
+
+
+class OracleAIVectorSearch(VectorStoreBase):
+    """Oracle AI Vector Search backend for mem0."""
+
+    def __init__(self, **kwargs: Any) -> None:
+        self.config = OracleAIVectorSearchConfig(**kwargs)
+        self.collection_name = self.config.collection_name
+
+        if self.config.client:
+            logger.debug("Using Oracle connection pool: %s", self.config.client)
+            self.client = self.config.client
+            self._owns_client = False
+        elif self.config.use_connection_pool:
+            pool_kwargs = {
+                "min": 1,
+                "max": 4,
+            }
+            pool_kwargs.update(self.config.connection_params)
+
+            logger.debug("Creating Oracle connection pool")
+            self.client = oracledb.create_pool(**pool_kwargs)
+            self._owns_client = True
+        else:
+            logger.debug("Creating Oracle connection")
+            self.client = oracledb.connect(**self.config.connection_params)
+            self._owns_client = True
+
+        if not (hasattr(self.client, "thin") and self.client.thin):
+            if oracledb.clientversion()[:2] < (23, 4):
+                raise RuntimeError(
+                    f"Oracle DB client driver version {'.'.join(map(str, oracledb.clientversion()))} "
+                    "not supported, must be >=23.4 for vector support"
+                )
+
+        if isinstance(self.client, oracledb.Connection):
+            db_version = tuple([int(v) for v in self.client.version.split(".")])
+        else:
+            with self.client.acquire() as conn:
+                db_version = tuple([int(v) for v in conn.version.split(".")])
+
+        if db_version < (23, 4):
+            raise ValueError(
+                f"Oracle DB version {'.'.join(map(str, db_version))} not supported, must be >=23.4 for vector support"
+            )
+
+        self.create_col()
+
+    @contextmanager
+    def _get_cursor(self, commit: bool = False):
+        if isinstance(self.client, oracledb.ConnectionPool):
+            with self.client.acquire() as connection:
+                with connection.cursor() as cursor:
+                    try:
+                        yield cursor
+                        if commit:
+                            connection.commit()
+                    except Exception:
+                        connection.rollback()
+                        raise
+        else:
+            with self.client.cursor() as cursor:
+                try:
+                    yield cursor
+                    if commit:
+                        self.client.commit()
+                except Exception:
+                    self.client.rollback()
+                    raise
+
+    # Utility helpers --------------------------------------------------
+    @staticmethod
+    def _load_payload(value: Any) -> Dict[str, Any]:
+        if value is None:
+            return {}
+        if isinstance(value, dict):
+            return value
+        if hasattr(value, "read"):
+            value = value.read()
+        if isinstance(value, bytes):
+            value = value.decode("utf-8")
+        try:
+            return json.loads(value)
+        except json.JSONDecodeError:
+            logger.debug("Failed to decode payload JSON")
+            raise
+
+    @staticmethod
+    def _catalog_name(name: str) -> str:
+        return name.replace('"', "")
+
+    def _create_index_ddl(self) -> str:
+        accuracy_str = ""
+        if self.config.index_accuracy:
+            accuracy_str = f"WITH TARGET ACCURACY {self.config.index_accuracy}"
+
+        parameters = self._index_parameters()
+        parameters_str = f"PARAMETERS ({parameters})" if parameters else ""
+
+        distance_metric = self.config.distance_metric
+
+        create_index = (
+            f"CREATE VECTOR INDEX IF NOT EXISTS {self.config.index_name} ON {self.collection_name} (vector) "
+            f"ORGANIZATION {'INMEMORY NEIGHBOR GRAPH' if self.config.index_type == 'HNSW' else 'NEIGHBOR PARTITIONS'}"
+            f" DISTANCE {distance_metric} {accuracy_str} {parameters_str}"
+        )
+
+        return create_index
+
+    def _index_parameters(self) -> str:
+        index_parameters = self.config.index_parameters
+        if not index_parameters:
+            return ""
+
+        parameters = [f"type {self.config.index_type}"]
+        parameters.extend(f"{key} {value}" for key, value in index_parameters.items())
+
+        return ", ".join(parameters)
+
+    # Vector store API -------------------------------------------------
+    def create_col(self) -> None:
+        """
+        Create a new collection (table in Oracle).
+        Will also initialize vector search index if specified.
+        """
+        with self._get_cursor(commit=True) as cursor:
+            cursor.execute(
+                f"""
+                CREATE TABLE IF NOT EXISTS {self.collection_name} (
+                    id VARCHAR2(36) PRIMARY KEY,
+                    vector VECTOR({self.config.embedding_model_dims}),
+                    payload JSON
+                )
+                """
+            )
+
+            if self.config.do_create_index:
+                ddl = self._create_index_ddl()
+                cursor.execute(ddl)
+
+    def insert(
+        self,
+        vectors: List[List[float]],
+        payloads: Optional[List[Dict[str, Any]]] = None,
+        ids: Optional[List[str]] = None,
+    ) -> None:
+        logger.info(f"Inserting {len(vectors)} vectors into collection {self.collection_name}")
+
+        if payloads is not None and len(payloads) != len(vectors):
+            raise ValueError(f"Payload count must match vector count. Expected {len(vectors)} got {len(payloads)}.")
+        if ids is not None and len(ids) != len(vectors):
+            raise ValueError(f"ID count must match vector count. Expected {len(vectors)} got {len(ids)}.")
+
+        ids = ids or [str(uuid.uuid4()) for _ in vectors]
+        data = [
+            {"id": _id, "vector": array.array("f", vector), "payload": payload}
+            for vector, payload, _id in zip(vectors, payloads or [{}] * len(vectors), ids)
+        ]
+
+        with self._get_cursor(commit=True) as cursor:
+            cursor.setinputsizes(
+                vector=oracledb.DB_TYPE_VECTOR,
+                payload=oracledb.DB_TYPE_JSON,
+            )
+            cursor.executemany(
+                f"INSERT INTO {self.collection_name} (id, vector, payload) VALUES (:id, :vector, :payload)", data
+            )
+
+    def search(
+        self,
+        query: str,
+        vectors: List[float],
+        top_k: int = 5,
+        filters: Optional[Dict[str, Any]] = None,
+    ) -> List[OutputData]:
+        """
+        Search for similar vectors using the vector search index.
+
+        Args:
+            query (str): Query string
+            vectors (List[float]): Query vector.
+            top_k (int, optional): Number of results to return. Defaults to 5.
+            filters (Dict, optional): Filters to apply to the search.
+
+        Returns:
+            List[OutputData]: Search results.
+        """
+        filter_clause, params = self._build_filters(filters)
+
+        distance_metric = self.config.distance_metric
+
+        sql = (
+            f"SELECT id, payload, VECTOR_DISTANCE(vector, :query_vec, {distance_metric}) distance "
+            f"FROM {self.collection_name} {filter_clause} ORDER BY distance FETCH APPROX FIRST :limit ROWS ONLY"
+        )
+
+        with self._get_cursor() as cursor:
+            cursor.execute(sql, query_vec=array.array("f", vectors), limit=top_k, **params)
+            rows = cursor.fetchall()
+
+        return [
+            OutputData(
+                id=row[0],
+                payload=self._load_payload(row[1]),
+                score=_convert_distance_to_score(float(row[2]), distance_metric),
+            )
+            for row in rows
+        ]
+
+    def _build_filters(self, filters: Optional[Dict[str, Any]]) -> tuple[str, Dict[str, Any]]:
+        if not filters:
+            return "", {}
+
+        params: Dict[str, Any] = {}
+        return "WHERE " + _build_filter_group(filters, params), params
+
+    def delete(self, vector_id: str) -> None:
+        """
+        Delete a vector by ID.
+
+        Args:
+            vector_id (str): ID of the vector to delete.
+        """
+        with self._get_cursor(commit=True) as cursor:
+            cursor.execute(f"DELETE FROM {self.collection_name} WHERE id = :id", id=vector_id)
+
+    def update(
+        self,
+        vector_id: str,
+        vector: Optional[List[float]] = None,
+        payload: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """
+        Update a vector and its payload.
+
+        Args:
+            vector_id (str): ID of the vector to update.
+            vector (List[float], optional): Updated vector.
+            payload (Dict, optional): Updated payload.
+        """
+        if vector is None and payload is None:
+            return
+
+        with self._get_cursor(commit=True) as cursor:
+            sets, params = [], {"vector_id": vector_id}
+            if vector is not None:
+                sets.append("vector = :vector")
+                params["vector"] = array.array("f", vector)
+                cursor.setinputsizes(vector=oracledb.DB_TYPE_VECTOR)
+            if payload is not None:
+                sets.append("payload = :payload")
+                params["payload"] = payload
+                cursor.setinputsizes(payload=oracledb.DB_TYPE_JSON)
+            cursor.execute(f"UPDATE {self.collection_name} SET {', '.join(sets)} WHERE id = :vector_id", params)
+
+    def get(self, vector_id: str) -> Optional[OutputData]:
+        """
+        Retrieve a vector by ID.
+
+        Args:
+            vector_id (str): ID of the vector to retrieve.
+
+        Returns:
+            OutputData: Retrieved vector.
+        """
+        with self._get_cursor() as cursor:
+            cursor.execute(
+                f"SELECT id, payload FROM {self.collection_name} WHERE id = :vector_id",
+                vector_id=vector_id,
+            )
+            row = cursor.fetchone()
+        if row is None:
+            return None
+        return OutputData(id=row[0], score=None, payload=self._load_payload(row[1]))
+
+    def list_cols(self) -> List[str]:
+        """
+        List all collections.
+
+        Returns:
+            List[str]: List of collection names.
+        """
+        with self._get_cursor() as cursor:
+            cursor.execute("SELECT table_name FROM user_tables")
+            tables = [row[0] for row in cursor.fetchall()]
+        return tables
+
+    def delete_col(self) -> None:
+        """Delete a collection."""
+        with self._get_cursor(commit=True) as cursor:
+            cursor.execute(f"DROP TABLE {self.collection_name} PURGE")
+
+    def col_info(self) -> Dict[str, Any]:
+        """
+        Get information about a collection.
+
+        Returns:
+            Dict[str, Any]: Collection information.
+        """
+        owner, table_name = self._split_collection_name()
+
+        sql = f"""
+        SELECT
+            table_name,
+            (SELECT COUNT(*) FROM {self.collection_name}) AS row_count,
+            (SELECT
+                ROUND(SUM(bytes) / 1024 / 1024, 2) || ' MB'
+            FROM user_segments
+            WHERE segment_name = :table_name
+            AND segment_type = 'TABLE'
+            ) AS total_size
+        FROM all_tables
+        WHERE table_name = :table_name
+        AND owner = NVL(:owner, USER)
+        """
+
+        with self._get_cursor() as cursor:
+            cursor.execute(sql, table_name=table_name, owner=owner)
+            result = cursor.fetchone()
+
+        if result is None:
+            raise ValueError(f"Collection {self.collection_name} not found")
+
+        return {"name": result[0], "count": result[1], "size": result[2]}
+
+    def _split_collection_name(self) -> tuple[Optional[str], str]:
+        """Split the quoted collection name into its optional owner and table parts."""
+        segments = re.findall(r'"([^"]+)"', self.collection_name)
+        if len(segments) > 1:
+            return segments[-2], segments[-1]
+        return None, segments[-1]
+
+    def list(self, filters: Optional[Dict[str, Any]] = None, top_k: Optional[int] = 100) -> List[List[OutputData]]:
+        """
+        List all vectors in a collection.
+
+        Args:
+            filters (Dict, optional): Filters to apply to the list.
+            top_k (int, optional): Number of vectors to return. Defaults to 100.
+
+        Returns:
+            List[List[OutputData]]: A single-element list holding the list of vectors.
+        """
+        filter_clause, params = self._build_filters(filters)
+
+        limit_clause = ""
+        if top_k is not None:
+            limit_clause = " FETCH FIRST :limit ROWS ONLY"
+            params["limit"] = top_k
+
+        sql = f"SELECT id, payload FROM {self.collection_name} {filter_clause} {limit_clause}"
+
+        with self._get_cursor() as cursor:
+            cursor.execute(sql, **params)
+            rows = cursor.fetchall()
+
+        return [[OutputData(id=row[0], score=None, payload=self._load_payload(row[1])) for row in rows]]
+
+    def reset(self) -> None:
+        """Reset the index by deleting and recreating it."""
+        logger.warning("Resetting collection %s", self.collection_name)
+        self.delete_col()
+        self.create_col()
+
+    def __del__(self) -> None:
+        """
+        Close the database connection pool when the object is deleted.
+        """
+        try:
+            if getattr(self, "_owns_client", False):
+                self.client.close()
+        except Exception:
+            pass
@@ -459,13 +459,13 @@ class PGVector(VectorStoreBase):
        self._ensure_collection()
        with self._get_cursor() as cur:
            cur.execute(
-                sql.SQL("SELECT id, vector, payload FROM {} WHERE id = %s").format(self._col()),
+                sql.SQL("SELECT id, payload FROM {} WHERE id = %s").format(self._col()),
                (vector_id,),
            )
            result = cur.fetchone()
            if not result:
                return None
-            return OutputData(id=str(result[0]), score=None, payload=result[2])
+            return OutputData(id=str(result[0]), score=None, payload=result[1])

    def list_cols(self) -> List[str]:
        """
@@ -528,7 +528,7 @@ class PGVector(VectorStoreBase):
        with self._get_cursor() as cur:
            cur.execute(
                sql.SQL("""
-                SELECT id, vector, payload
+                SELECT id, payload
                FROM {}
                {}
                LIMIT %s
@@ -536,7 +536,7 @@ class PGVector(VectorStoreBase):
                (*filter_params, top_k),
            )
            results = cur.fetchall()
-        return [[OutputData(id=str(r[0]), score=None, payload=r[2]) for r in results]]
+        return [[OutputData(id=str(r[0]), score=None, payload=r[1]) for r in results]]

    def __del__(self) -> None:
        """
@@ -1,14 +0,0 @@
-*.db
-.env*
-!.env.example
-!.env.dev
-!ui/lib
-.venv/
-__pycache__
-.DS_Store
-node_modules/
-*.log
-api/.openmemory*
-**/.next
-.openmemory/
-ui/package-lock.json
@@ -1,70 +0,0 @@
-# Contributing to OpenMemory
-
-We are a team of developers passionate about the future of AI and open-source software. With years of experience in both fields, we believe in the power of community-driven development and are excited to build tools that make AI more accessible and personalized.
-
-## Ways to Contribute
-
-We welcome all forms of contributions:
- Bug reports and feature requests through GitHub Issues
- Documentation improvements
- Code contributions
- Testing and feedback
- Community support and discussions
-
-## Development Workflow
-
-1. Fork the repository
-2. Create your feature branch (`git checkout -b openmemory/feature/amazing-feature`)
-3. Commit your changes (`git commit -m 'Add some amazing feature'`)
-4. Push to the branch (`git push origin openmemory/feature/amazing-feature`)
-5. Open a Pull Request
-
-## Development Setup
-
-### Backend Setup
-
-```bash
-# Copy environment file and edit file to update OPENAI_API_KEY and other secrets
-make env
-
-# Build the containers
-make build
-
-# Start the services
-make up
-```
-
-### Frontend Setup
-
-The frontend is a React application. To start the frontend:
-
-```bash
-# Install dependencies and start the development server
-make ui-dev
-```
-
-### Prerequisites
- Docker and Docker Compose
- Python 3.9+ (for backend development)
- Node.js (for frontend development)
- OpenAI API Key (for LLM interactions)
-
-### Getting Started
-Follow the setup instructions in the README.md file to set up your development environment.
-
-## Code Standards
-
-We value:
- Clean, well-documented code
- Thoughtful discussions about features and improvements
- Respectful and constructive feedback
- A welcoming environment for all contributors
-
-## Pull Request Process
-
-1. Ensure your code follows the project's coding standards
-2. Update documentation as needed
-3. Include tests for new features
-4. Make sure all tests pass before submitting
-
-Join us in building the future of AI memory management! Your contributions help make OpenMemory better for everyone.
@@ -1,52 +0,0 @@
-.PHONY: help up down logs shell migrate test test-clean env ui-install ui-start ui-dev ui-build ui-dev-start
-
-NEXT_PUBLIC_USER_ID=$(USER)
-NEXT_PUBLIC_API_URL=http://localhost:8765
-
-# Default target
-help:
-	@echo "Available commands:"
-	@echo "  make env       - Copy .env.example to .env"
-	@echo "  make up        - Start the containers"
-	@echo "  make down      - Stop the containers"
-	@echo "  make logs      - Show container logs"
-	@echo "  make shell     - Open a shell in the api container"
-	@echo "  make migrate   - Run database migrations"
-	@echo "  make test      - Run tests in a new container"
-	@echo "  make test-clean - Run tests and clean up volumes"
-	@echo "  make ui-install - Install frontend dependencies"
-	@echo "  make ui-start  - Start the frontend development server"
-	@echo "  make ui-dev    - Install dependencies and start the frontend in dev mode"
-	@echo "  make ui        - Install dependencies and start the frontend in production mode"
-
-env:
-	cd api && cp .env.example .env
-	cd ui && cp .env.example .env
-
-build:
-	docker compose build
-
-up:
-	NEXT_PUBLIC_USER_ID=$(USER) NEXT_PUBLIC_API_URL=$(NEXT_PUBLIC_API_URL) docker compose up
-
-down:
-	docker compose down -v
-	rm -f api/openmemory.db
-
-logs:
-	docker compose logs -f
-
-shell:
-	docker compose exec api bash
-
-upgrade:
-	docker compose exec api alembic upgrade head
-
-migrate:
-	docker compose exec api alembic upgrade head
-
-downgrade:
-	docker compose exec api alembic downgrade -1
-
-ui-dev:
-	cd ui && NEXT_PUBLIC_USER_ID=$(USER) NEXT_PUBLIC_API_URL=$(NEXT_PUBLIC_API_URL) pnpm install && pnpm dev
@@ -1,168 +0,0 @@
-# OpenMemory
-
-> **⚠️ Sunsetting Notice:** OpenMemory is being sunset. For local self-hosted memory with a dashboard, please use the [Mem0 self-hosted server](https://docs.mem0.ai/open-source/overview) instead. Get started with `cd server && make bootstrap`. See the [self-hosted docs](https://docs.mem0.ai/open-source/setup) for configuration details.
-
-OpenMemory is your personal memory layer for LLMs - private, portable, and open-source. Your memories live locally, giving you complete control over your data. Build AI applications with personalized memories while keeping your data secure.
-
-![OpenMemory](https://github.com/user-attachments/assets/3c701757-ad82-4afa-bfbe-e049c2b4320b)
-
-## Easy Setup
-
-### Prerequisites
- Docker
- OpenAI API Key
-
-You can quickly run OpenMemory by running the following command:
-
-```bash
-curl -sL https://raw.githubusercontent.com/mem0ai/mem0/main/openmemory/run.sh | bash
-```
-
-You should set the `OPENAI_API_KEY` as a global environment variable:
-
-```bash
-export OPENAI_API_KEY=your_api_key
-```
-
-You can also set the `OPENAI_API_KEY` as a parameter to the script:
-
-```bash
-curl -sL https://raw.githubusercontent.com/mem0ai/mem0/main/openmemory/run.sh | OPENAI_API_KEY=your_api_key bash
-```
-
-## Prerequisites
-
- Docker and Docker Compose
- Python 3.9+ (for backend development)
- Node.js (for frontend development)
- OpenAI API Key (required for LLM interactions, run `cp api/.env.example api/.env` then change **OPENAI_API_KEY** to yours)
-
-## Quickstart
-
-### 1. Set Up Environment Variables
-
-Before running the project, you need to configure environment variables for both the API and the UI.
-
-You can do this in one of the following ways:
-
- **Manually**:  
-  Create a `.env` file in each of the following directories:
-  - `/api/.env`
-  - `/ui/.env`
-
- **Using `.env.example` files**:  
-  Copy and rename the example files:
-
-  ```bash
-  cp api/.env.example api/.env
-  cp ui/.env.example ui/.env
-  ```
-
- - **Using Makefile** (if supported):  
-    Run:
-  
-   ```bash
-   make env
-   ```
- #### Example `/api/.env`
-
-```env
-OPENAI_API_KEY=sk-xxx
-USER=<user-id> # The User Id you want to associate the memories with
-```
-
- #### LLM Configuration (optional)
-
-By default, OpenMemory uses OpenAI (`gpt-4o-mini`) for the LLM and embedder. You can configure a different provider using these environment variables in `/api/.env`:
-
-| Variable | Description | Default |
-|---|---|---|
-| `LLM_PROVIDER` | LLM provider (`openai`, `ollama`, `anthropic`, `groq`, `together`, `deepseek`, etc.) | `openai` |
-| `LLM_MODEL` | Model name for the LLM provider | `gpt-4o-mini` (OpenAI) / `llama3.1:latest` (Ollama) |
-| `LLM_API_KEY` | API key for the LLM provider | `OPENAI_API_KEY` env var |
-| `LLM_BASE_URL` | Custom base URL for the LLM API | Provider default |
-| `OLLAMA_BASE_URL` | Ollama-specific base URL (takes precedence over `LLM_BASE_URL` for Ollama) | `http://localhost:11434` |
-| `EMBEDDER_PROVIDER` | Embedder provider (defaults to `ollama` when LLM is Ollama, otherwise `openai`) | `openai` |
-| `EMBEDDER_MODEL` | Model name for the embedder | `text-embedding-3-small` (OpenAI) / `nomic-embed-text` (Ollama) |
-| `EMBEDDER_API_KEY` | API key for the embedder provider | `OPENAI_API_KEY` env var |
-| `EMBEDDER_BASE_URL` | Custom base URL for the embedder API | Provider default |
-
-**Example: Using Ollama (fully local)**
-```env
-LLM_PROVIDER=ollama
-LLM_MODEL=llama3.1:latest
-EMBEDDER_PROVIDER=ollama
-EMBEDDER_MODEL=nomic-embed-text
-OLLAMA_BASE_URL=http://localhost:11434
-```
-
-**Example: Using Anthropic**
-```env
-LLM_PROVIDER=anthropic
-LLM_MODEL=claude-sonnet-4-20250514
-LLM_API_KEY=sk-ant-xxx
-```
- #### Example `/ui/.env`
-
-```env
-NEXT_PUBLIC_API_URL=http://localhost:8765
-NEXT_PUBLIC_USER_ID=<user-id> # Same as the user id for environment variable in api
-```
-
-### 2. Build and Run the Project
-You can run the project using the following two commands:
-```bash
-make build # builds the mcp server and ui
-make up  # runs openmemory mcp server and ui
-```
-
-After running these commands, you will have:
- OpenMemory MCP server running at: http://localhost:8765 (API documentation available at http://localhost:8765/docs)
- OpenMemory UI running at: http://localhost:3000
-
-#### UI not working on `localhost:3000`?
-
-If the UI does not start properly on [http://localhost:3000](http://localhost:3000), try running it manually:
-
-```bash
-cd ui
-pnpm install
-pnpm dev
-```
-
-### MCP Client Setup
-
-Use the following one step command to configure OpenMemory Local MCP to a client. The general command format is as follows:
-
-```bash
-npx @openmemory/install local http://localhost:8765/mcp/<client-name>/sse/<user-id> --client <client-name>
-```
-
-Replace `<client-name>` with the desired client name and `<user-id>` with the value specified in your environment variables.
-
-
-## Project Structure
-
- `api/` - Backend APIs + MCP server
- `ui/` - Frontend React application
-
-## Contributing
-
-We are a team of developers passionate about the future of AI and open-source software. With years of experience in both fields, we believe in the power of community-driven development and are excited to build tools that make AI more accessible and personalized.
-
-We welcome all forms of contributions:
- Bug reports and feature requests
- Documentation improvements
- Code contributions
- Testing and feedback
- Community support
-
-How to contribute:
-
-1. Fork the repository
-2. Create your feature branch (`git checkout -b openmemory/feature/amazing-feature`)
-3. Commit your changes (`git commit -m 'Add some amazing feature'`)
-4. Push to the branch (`git push origin openmemory/feature/amazing-feature`)
-5. Open a Pull Request
-
-Join us in building the future of AI memory management! Your contributions help make OpenMemory better for everyone.
@@ -1,23 +0,0 @@
-# Ignore all .env files
-**/.env
-**/.env.*
-
-# Ignore all database files
-**/*.db
-**/*.sqlite
-**/*.sqlite3
-
-# Ignore logs
-**/*.log
-
-# Ignore runtime data
-**/node_modules
-**/__pycache__
-**/.pytest_cache
-**/.coverage
-**/coverage
-
-# Ignore Docker runtime files
-**/.dockerignore
-**/Dockerfile
-**/docker-compose*.yml 
@@ -1,15 +0,0 @@
-OPENAI_API_KEY=sk-xxx
-USER=user
-
-# LLM Configuration (optional - defaults to openai/gpt-4o-mini)
-# LLM_PROVIDER=ollama
-# LLM_MODEL=llama3.1:latest
-# LLM_API_KEY=
-# LLM_BASE_URL=
-# OLLAMA_BASE_URL=http://localhost:11434
-
-# Embedder Configuration (optional - defaults to openai/text-embedding-3-small)
-# EMBEDDER_PROVIDER=ollama
-# EMBEDDER_MODEL=nomic-embed-text
-# EMBEDDER_API_KEY=
-# EMBEDDER_BASE_URL=
@@ -1 +0,0 @@
-3.12
@@ -1,14 +0,0 @@
-FROM python:3.12-slim
-
-LABEL org.opencontainers.image.name="mem0/openmemory-mcp"
-
-WORKDIR /usr/src/openmemory
-
-COPY requirements.txt .
-RUN pip install -r requirements.txt
-
-COPY config.json .
-COPY . .
-
-EXPOSE 8765
-CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8765"]
@@ -1,60 +0,0 @@
-# OpenMemory API
-
-This directory contains the backend API for OpenMemory, built with FastAPI and SQLAlchemy. This also runs the Mem0 MCP Server that you can use with MCP clients to remember things.
-
-## Quick Start with Docker (Recommended)
-
-The easiest way to get started is using Docker. Make sure you have Docker and Docker Compose installed.
-
-1. Build the containers:
-```bash
-make build
-```
-
-2. Create `.env` file:
-```bash
-make env
-```
-
-Once you run this command, edit the file `api/.env` and enter the `OPENAI_API_KEY`.
-
-3. Start the services:
-```bash
-make up
-```
-
-The API will be available at `http://localhost:8765`
-
-### Common Docker Commands
-
- View logs: `make logs`
- Open shell in container: `make shell`
- Run database migrations: `make migrate`
- Run tests: `make test`
- Run tests and clean up: `make test-clean`
- Stop containers: `make down`
-
-## API Documentation
-
-Once the server is running, you can access the API documentation at:
- Swagger UI: `http://localhost:8765/docs`
- ReDoc: `http://localhost:8765/redoc`
-
-## Project Structure
-
- `app/`: Main application code
-  - `models.py`: Database models
-  - `database.py`: Database configuration
-  - `routers/`: API route handlers
- `migrations/`: Database migration files
- `tests/`: Test files
- `alembic/`: Alembic migration configuration
- `main.py`: Application entry point
-
-## Development Guidelines
-
- Follow PEP 8 style guide
- Use type hints
- Write tests for new features
- Update documentation when making changes
- Run migrations for database changes
@@ -1,114 +0,0 @@
-# A generic, single database configuration.
-
-[alembic]
-# path to migration scripts
-# Use forward slashes (/) also on windows to provide an os agnostic path
-script_location = alembic
-
-# template used to generate migration file names; The default value is %%(rev)s_%%(slug)s
-# Uncomment the line below if you want the files to be prepended with date and time
-# see https://alembic.sqlalchemy.org/en/latest/tutorial.html#editing-the-ini-file
-# for all available tokens
-# file_template = %%(year)d_%%(month).2d_%%(day).2d_%%(hour).2d%%(minute).2d-%%(rev)s_%%(slug)s
-
-# sys.path path, will be prepended to sys.path if present.
-# defaults to the current working directory.
-prepend_sys_path = .
-
-# timezone to use when rendering the date within the migration file
-# as well as the filename.
-# If specified, requires the python-dateutil library that can be
-# installed by adding `alembic[tz]` to the pip requirements
-# timezone =
-
-# max length of characters to apply to the "slug" field
-# truncate_slug_length = 40
-
-# set to 'true' to run the environment during
-# the 'revision' command, regardless of autogenerate
-# revision_environment = false
-
-# set to 'true' to allow .pyc and .pyo files without
-# a source .py file to be detected as revisions in the
-# versions/ directory
-# sourceless = false
-
-# version location specification; This defaults
-# to alembic/versions.  When using multiple version
-# directories, initial revisions must be specified with --version-path.
-# The path separator used here should be the separator specified by "version_path_separator" below.
-# version_locations = %(here)s/bar:%(here)s/bat:alembic/versions
-
-# version path separator; As mentioned above, this is the character used to split
-# version_locations. The default within new alembic.ini files is "os", which uses os.pathsep.
-# If this key is omitted entirely, it falls back to the legacy behavior of splitting on spaces and/or colons.
-# Valid values for version_path_separator are:
-#
-# version_path_separator = :
-# version_path_separator = ;
-# version_path_separator = space
-version_path_separator = os  # Use os.pathsep. Default configuration used for new projects.
-
-# set to 'true' to search source files recursively
-# in each "version_locations" directory
-# new in Alembic version 1.10
-# recursive_version_locations = false
-
-# the output encoding used when revision files
-# are written from script.py.mako
-# output_encoding = utf-8
-
-sqlalchemy.url = sqlite:///./openmemory.db
-
-
-[post_write_hooks]
-# post_write_hooks defines scripts or Python functions that are run
-# on newly generated revision scripts.  See the documentation for further
-# detail and examples
-
-# format using "black" - use the console_scripts runner, against the "black" entrypoint
-# hooks = black
-# black.type = console_scripts
-# black.entrypoint = black
-# black.options = -l 79 REVISION_SCRIPT_FILENAME
-
-# lint with attempts to fix using "ruff" - use the exec runner, execute a binary
-# hooks = ruff
-# ruff.type = exec
-# ruff.executable = %(here)s/.venv/bin/ruff
-# ruff.options = check --fix REVISION_SCRIPT_FILENAME
-
-# Logging configuration
-[loggers]
-keys = root,sqlalchemy,alembic
-
-[handlers]
-keys = console
-
-[formatters]
-keys = generic
-
-[logger_root]
-level = WARN
-handlers = console
-qualname =
-
-[logger_sqlalchemy]
-level = WARN
-handlers =
-qualname = sqlalchemy.engine
-
-[logger_alembic]
-level = INFO
-handlers =
-qualname = alembic
-
-[handler_console]
-class = StreamHandler
-args = (sys.stderr,)
-level = NOTSET
-formatter = generic
-
-[formatter_generic]
-format = %(levelname)-5.5s [%(name)s] %(message)s
-datefmt = %H:%M:%S
@@ -1 +0,0 @@
-Generic single-database configuration.
@@ -1,88 +0,0 @@
-import os
-import sys
-from logging.config import fileConfig
-
-from alembic import context
-from dotenv import load_dotenv
-from sqlalchemy import engine_from_config, pool
-
-# Add the parent directory to the Python path
-sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-
-# Load environment variables
-load_dotenv()
-
-# Import your models here - moved after path setup
-from app.database import Base  # noqa: E402
-
-# this is the Alembic Config object, which provides
-# access to the values within the .ini file in use.
-config = context.config
-
-# Interpret the config file for Python logging.
-# This line sets up loggers basically.
-if config.config_file_name is not None:
-    fileConfig(config.config_file_name)
-
-# add your model's MetaData object here
-# for 'autogenerate' support
-target_metadata = Base.metadata
-
-# other values from the config, defined by the needs of env.py,
-# can be acquired:
-# my_important_option = config.get_main_option("my_important_option")
-# ... etc.
-
-
-def run_migrations_offline() -> None:
-    """Run migrations in 'offline' mode.
-
-    This configures the context with just a URL
-    and not an Engine, though an Engine is acceptable
-    here as well.  By skipping the Engine creation
-    we don't even need a DBAPI to be available.
-
-    Calls to context.execute() here emit the given string to the
-    script output.
-
-    """
-    url = os.getenv("DATABASE_URL", "sqlite:///./openmemory.db")
-    context.configure(
-        url=url,
-        target_metadata=target_metadata,
-        literal_binds=True,
-        dialect_opts={"paramstyle": "named"},
-    )
-
-    with context.begin_transaction():
-        context.run_migrations()
-
-
-def run_migrations_online() -> None:
-    """Run migrations in 'online' mode.
-
-    In this scenario we need to create an Engine
-    and associate a connection with the context.
-
-    """
-    configuration = config.get_section(config.config_ini_section)
-    configuration["sqlalchemy.url"] = os.getenv("DATABASE_URL", "sqlite:///./openmemory.db")
-    connectable = engine_from_config(
-        configuration,
-        prefix="sqlalchemy.",
-        poolclass=pool.NullPool,
-    )
-
-    with connectable.connect() as connection:
-        context.configure(
-            connection=connection, target_metadata=target_metadata
-        )
-
-        with context.begin_transaction():
-            context.run_migrations()
-
-
-if context.is_offline_mode():
-    run_migrations_offline()
-else:
-    run_migrations_online()
@@ -1,28 +0,0 @@
-"""${message}
-
-Revision ID: ${up_revision}
-Revises: ${down_revision | comma,n}
-Create Date: ${create_date}
-
-"""
-from typing import Sequence, Union
-
-from alembic import op
-import sqlalchemy as sa
-${imports if imports else ""}
-
-# revision identifiers, used by Alembic.
-revision: str = ${repr(up_revision)}
-down_revision: Union[str, None] = ${repr(down_revision)}
-branch_labels: Union[str, Sequence[str], None] = ${repr(branch_labels)}
-depends_on: Union[str, Sequence[str], None] = ${repr(depends_on)}
-
-
-def upgrade() -> None:
-    """Upgrade schema."""
-    ${upgrades if upgrades else "pass"}
-
-
-def downgrade() -> None:
-    """Downgrade schema."""
-    ${downgrades if downgrades else "pass"}
@@ -1,225 +0,0 @@
-"""Initial migration
-
-Revision ID: 0b53c747049a
-Revises: 
-Create Date: 2025-04-19 00:59:56.244203
-
-"""
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision: str = '0b53c747049a'
-down_revision: Union[str, None] = None
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
-    """Upgrade schema."""
-    # ### commands auto generated by Alembic - please adjust! ###
-    op.create_table('access_controls',
-    sa.Column('id', sa.UUID(), nullable=False),
-    sa.Column('subject_type', sa.String(), nullable=False),
-    sa.Column('subject_id', sa.UUID(), nullable=True),
-    sa.Column('object_type', sa.String(), nullable=False),
-    sa.Column('object_id', sa.UUID(), nullable=True),
-    sa.Column('effect', sa.String(), nullable=False),
-    sa.Column('created_at', sa.DateTime(), nullable=True),
-    sa.PrimaryKeyConstraint('id')
-    )
-    op.create_index('idx_access_object', 'access_controls', ['object_type', 'object_id'], unique=False)
-    op.create_index('idx_access_subject', 'access_controls', ['subject_type', 'subject_id'], unique=False)
-    op.create_index(op.f('ix_access_controls_created_at'), 'access_controls', ['created_at'], unique=False)
-    op.create_index(op.f('ix_access_controls_effect'), 'access_controls', ['effect'], unique=False)
-    op.create_index(op.f('ix_access_controls_object_id'), 'access_controls', ['object_id'], unique=False)
-    op.create_index(op.f('ix_access_controls_object_type'), 'access_controls', ['object_type'], unique=False)
-    op.create_index(op.f('ix_access_controls_subject_id'), 'access_controls', ['subject_id'], unique=False)
-    op.create_index(op.f('ix_access_controls_subject_type'), 'access_controls', ['subject_type'], unique=False)
-    op.create_table('archive_policies',
-    sa.Column('id', sa.UUID(), nullable=False),
-    sa.Column('criteria_type', sa.String(), nullable=False),
-    sa.Column('criteria_id', sa.UUID(), nullable=True),
-    sa.Column('days_to_archive', sa.Integer(), nullable=False),
-    sa.Column('created_at', sa.DateTime(), nullable=True),
-    sa.PrimaryKeyConstraint('id')
-    )
-    op.create_index('idx_policy_criteria', 'archive_policies', ['criteria_type', 'criteria_id'], unique=False)
-    op.create_index(op.f('ix_archive_policies_created_at'), 'archive_policies', ['created_at'], unique=False)
-    op.create_index(op.f('ix_archive_policies_criteria_id'), 'archive_policies', ['criteria_id'], unique=False)
-    op.create_index(op.f('ix_archive_policies_criteria_type'), 'archive_policies', ['criteria_type'], unique=False)
-    op.create_table('categories',
-    sa.Column('id', sa.UUID(), nullable=False),
-    sa.Column('name', sa.String(), nullable=False),
-    sa.Column('description', sa.String(), nullable=True),
-    sa.Column('created_at', sa.DateTime(), nullable=True),
-    sa.Column('updated_at', sa.DateTime(), nullable=True),
-    sa.PrimaryKeyConstraint('id')
-    )
-    op.create_index(op.f('ix_categories_created_at'), 'categories', ['created_at'], unique=False)
-    op.create_index(op.f('ix_categories_name'), 'categories', ['name'], unique=True)
-    op.create_table('users',
-    sa.Column('id', sa.UUID(), nullable=False),
-    sa.Column('user_id', sa.String(), nullable=False),
-    sa.Column('name', sa.String(), nullable=True),
-    sa.Column('email', sa.String(), nullable=True),
-    sa.Column('metadata', sa.JSON(), nullable=True),
-    sa.Column('created_at', sa.DateTime(), nullable=True),
-    sa.Column('updated_at', sa.DateTime(), nullable=True),
-    sa.PrimaryKeyConstraint('id')
-    )
-    op.create_index(op.f('ix_users_created_at'), 'users', ['created_at'], unique=False)
-    op.create_index(op.f('ix_users_email'), 'users', ['email'], unique=True)
-    op.create_index(op.f('ix_users_name'), 'users', ['name'], unique=False)
-    op.create_index(op.f('ix_users_user_id'), 'users', ['user_id'], unique=True)
-    op.create_table('apps',
-    sa.Column('id', sa.UUID(), nullable=False),
-    sa.Column('owner_id', sa.UUID(), nullable=False),
-    sa.Column('name', sa.String(), nullable=False),
-    sa.Column('description', sa.String(), nullable=True),
-    sa.Column('metadata', sa.JSON(), nullable=True),
-    sa.Column('is_active', sa.Boolean(), nullable=True),
-    sa.Column('created_at', sa.DateTime(), nullable=True),
-    sa.Column('updated_at', sa.DateTime(), nullable=True),
-    sa.ForeignKeyConstraint(['owner_id'], ['users.id'], ),
-    sa.PrimaryKeyConstraint('id')
-    )
-    op.create_index(op.f('ix_apps_created_at'), 'apps', ['created_at'], unique=False)
-    op.create_index(op.f('ix_apps_is_active'), 'apps', ['is_active'], unique=False)
-    op.create_index(op.f('ix_apps_name'), 'apps', ['name'], unique=True)
-    op.create_index(op.f('ix_apps_owner_id'), 'apps', ['owner_id'], unique=False)
-    op.create_table('memories',
-    sa.Column('id', sa.UUID(), nullable=False),
-    sa.Column('user_id', sa.UUID(), nullable=False),
-    sa.Column('app_id', sa.UUID(), nullable=False),
-    sa.Column('content', sa.String(), nullable=False),
-    sa.Column('vector', sa.String(), nullable=True),
-    sa.Column('metadata', sa.JSON(), nullable=True),
-    sa.Column('state', sa.Enum('active', 'paused', 'archived', 'deleted', name='memorystate'), nullable=True),
-    sa.Column('created_at', sa.DateTime(), nullable=True),
-    sa.Column('updated_at', sa.DateTime(), nullable=True),
-    sa.Column('archived_at', sa.DateTime(), nullable=True),
-    sa.Column('deleted_at', sa.DateTime(), nullable=True),
-    sa.ForeignKeyConstraint(['app_id'], ['apps.id'], ),
-    sa.ForeignKeyConstraint(['user_id'], ['users.id'], ),
-    sa.PrimaryKeyConstraint('id')
-    )
-    op.create_index('idx_memory_app_state', 'memories', ['app_id', 'state'], unique=False)
-    op.create_index('idx_memory_user_app', 'memories', ['user_id', 'app_id'], unique=False)
-    op.create_index('idx_memory_user_state', 'memories', ['user_id', 'state'], unique=False)
-    op.create_index(op.f('ix_memories_app_id'), 'memories', ['app_id'], unique=False)
-    op.create_index(op.f('ix_memories_archived_at'), 'memories', ['archived_at'], unique=False)
-    op.create_index(op.f('ix_memories_created_at'), 'memories', ['created_at'], unique=False)
-    op.create_index(op.f('ix_memories_deleted_at'), 'memories', ['deleted_at'], unique=False)
-    op.create_index(op.f('ix_memories_state'), 'memories', ['state'], unique=False)
-    op.create_index(op.f('ix_memories_user_id'), 'memories', ['user_id'], unique=False)
-    op.create_table('memory_access_logs',
-    sa.Column('id', sa.UUID(), nullable=False),
-    sa.Column('memory_id', sa.UUID(), nullable=False),
-    sa.Column('app_id', sa.UUID(), nullable=False),
-    sa.Column('accessed_at', sa.DateTime(), nullable=True),
-    sa.Column('access_type', sa.String(), nullable=False),
-    sa.Column('metadata', sa.JSON(), nullable=True),
-    sa.ForeignKeyConstraint(['app_id'], ['apps.id'], ),
-    sa.ForeignKeyConstraint(['memory_id'], ['memories.id'], ),
-    sa.PrimaryKeyConstraint('id')
-    )
-    op.create_index('idx_access_app_time', 'memory_access_logs', ['app_id', 'accessed_at'], unique=False)
-    op.create_index('idx_access_memory_time', 'memory_access_logs', ['memory_id', 'accessed_at'], unique=False)
-    op.create_index(op.f('ix_memory_access_logs_access_type'), 'memory_access_logs', ['access_type'], unique=False)
-    op.create_index(op.f('ix_memory_access_logs_accessed_at'), 'memory_access_logs', ['accessed_at'], unique=False)
-    op.create_index(op.f('ix_memory_access_logs_app_id'), 'memory_access_logs', ['app_id'], unique=False)
-    op.create_index(op.f('ix_memory_access_logs_memory_id'), 'memory_access_logs', ['memory_id'], unique=False)
-    op.create_table('memory_categories',
-    sa.Column('memory_id', sa.UUID(), nullable=False),
-    sa.Column('category_id', sa.UUID(), nullable=False),
-    sa.ForeignKeyConstraint(['category_id'], ['categories.id'], ),
-    sa.ForeignKeyConstraint(['memory_id'], ['memories.id'], ),
-    sa.PrimaryKeyConstraint('memory_id', 'category_id')
-    )
-    op.create_index('idx_memory_category', 'memory_categories', ['memory_id', 'category_id'], unique=False)
-    op.create_index(op.f('ix_memory_categories_category_id'), 'memory_categories', ['category_id'], unique=False)
-    op.create_index(op.f('ix_memory_categories_memory_id'), 'memory_categories', ['memory_id'], unique=False)
-    op.create_table('memory_status_history',
-    sa.Column('id', sa.UUID(), nullable=False),
-    sa.Column('memory_id', sa.UUID(), nullable=False),
-    sa.Column('changed_by', sa.UUID(), nullable=False),
-    sa.Column('old_state', sa.Enum('active', 'paused', 'archived', 'deleted', name='memorystate'), nullable=False),
-    sa.Column('new_state', sa.Enum('active', 'paused', 'archived', 'deleted', name='memorystate'), nullable=False),
-    sa.Column('changed_at', sa.DateTime(), nullable=True),
-    sa.ForeignKeyConstraint(['changed_by'], ['users.id'], ),
-    sa.ForeignKeyConstraint(['memory_id'], ['memories.id'], ),
-    sa.PrimaryKeyConstraint('id')
-    )
-    op.create_index('idx_history_memory_state', 'memory_status_history', ['memory_id', 'new_state'], unique=False)
-    op.create_index('idx_history_user_time', 'memory_status_history', ['changed_by', 'changed_at'], unique=False)
-    op.create_index(op.f('ix_memory_status_history_changed_at'), 'memory_status_history', ['changed_at'], unique=False)
-    op.create_index(op.f('ix_memory_status_history_changed_by'), 'memory_status_history', ['changed_by'], unique=False)
-    op.create_index(op.f('ix_memory_status_history_memory_id'), 'memory_status_history', ['memory_id'], unique=False)
-    op.create_index(op.f('ix_memory_status_history_new_state'), 'memory_status_history', ['new_state'], unique=False)
-    op.create_index(op.f('ix_memory_status_history_old_state'), 'memory_status_history', ['old_state'], unique=False)
-    # ### end Alembic commands ###
-
-
-def downgrade() -> None:
-    """Downgrade schema."""
-    # ### commands auto generated by Alembic - please adjust! ###
-    op.drop_index(op.f('ix_memory_status_history_old_state'), table_name='memory_status_history')
-    op.drop_index(op.f('ix_memory_status_history_new_state'), table_name='memory_status_history')
-    op.drop_index(op.f('ix_memory_status_history_memory_id'), table_name='memory_status_history')
-    op.drop_index(op.f('ix_memory_status_history_changed_by'), table_name='memory_status_history')
-    op.drop_index(op.f('ix_memory_status_history_changed_at'), table_name='memory_status_history')
-    op.drop_index('idx_history_user_time', table_name='memory_status_history')
-    op.drop_index('idx_history_memory_state', table_name='memory_status_history')
-    op.drop_table('memory_status_history')
-    op.drop_index(op.f('ix_memory_categories_memory_id'), table_name='memory_categories')
-    op.drop_index(op.f('ix_memory_categories_category_id'), table_name='memory_categories')
-    op.drop_index('idx_memory_category', table_name='memory_categories')
-    op.drop_table('memory_categories')
-    op.drop_index(op.f('ix_memory_access_logs_memory_id'), table_name='memory_access_logs')
-    op.drop_index(op.f('ix_memory_access_logs_app_id'), table_name='memory_access_logs')
-    op.drop_index(op.f('ix_memory_access_logs_accessed_at'), table_name='memory_access_logs')
-    op.drop_index(op.f('ix_memory_access_logs_access_type'), table_name='memory_access_logs')
-    op.drop_index('idx_access_memory_time', table_name='memory_access_logs')
-    op.drop_index('idx_access_app_time', table_name='memory_access_logs')
-    op.drop_table('memory_access_logs')
-    op.drop_index(op.f('ix_memories_user_id'), table_name='memories')
-    op.drop_index(op.f('ix_memories_state'), table_name='memories')
-    op.drop_index(op.f('ix_memories_deleted_at'), table_name='memories')
-    op.drop_index(op.f('ix_memories_created_at'), table_name='memories')
-    op.drop_index(op.f('ix_memories_archived_at'), table_name='memories')
-    op.drop_index(op.f('ix_memories_app_id'), table_name='memories')
-    op.drop_index('idx_memory_user_state', table_name='memories')
-    op.drop_index('idx_memory_user_app', table_name='memories')
-    op.drop_index('idx_memory_app_state', table_name='memories')
-    op.drop_table('memories')
-    op.drop_index(op.f('ix_apps_owner_id'), table_name='apps')
-    op.drop_index(op.f('ix_apps_name'), table_name='apps')
-    op.drop_index(op.f('ix_apps_is_active'), table_name='apps')
-    op.drop_index(op.f('ix_apps_created_at'), table_name='apps')
-    op.drop_table('apps')
-    op.drop_index(op.f('ix_users_user_id'), table_name='users')
-    op.drop_index(op.f('ix_users_name'), table_name='users')
-    op.drop_index(op.f('ix_users_email'), table_name='users')
-    op.drop_index(op.f('ix_users_created_at'), table_name='users')
-    op.drop_table('users')
-    op.drop_index(op.f('ix_categories_name'), table_name='categories')
-    op.drop_index(op.f('ix_categories_created_at'), table_name='categories')
-    op.drop_table('categories')
-    op.drop_index(op.f('ix_archive_policies_criteria_type'), table_name='archive_policies')
-    op.drop_index(op.f('ix_archive_policies_criteria_id'), table_name='archive_policies')
-    op.drop_index(op.f('ix_archive_policies_created_at'), table_name='archive_policies')
-    op.drop_index('idx_policy_criteria', table_name='archive_policies')
-    op.drop_table('archive_policies')
-    op.drop_index(op.f('ix_access_controls_subject_type'), table_name='access_controls')
-    op.drop_index(op.f('ix_access_controls_subject_id'), table_name='access_controls')
-    op.drop_index(op.f('ix_access_controls_object_type'), table_name='access_controls')
-    op.drop_index(op.f('ix_access_controls_object_id'), table_name='access_controls')
-    op.drop_index(op.f('ix_access_controls_effect'), table_name='access_controls')
-    op.drop_index(op.f('ix_access_controls_created_at'), table_name='access_controls')
-    op.drop_index('idx_access_subject', table_name='access_controls')
-    op.drop_index('idx_access_object', table_name='access_controls')
-    op.drop_table('access_controls')
-    # ### end Alembic commands ###
@@ -1,40 +0,0 @@
-"""add_config_table
-
-Revision ID: add_config_table
-Revises: 0b53c747049a
-Create Date: 2023-06-01 10:00:00.000000
-
-"""
-import uuid
-
-import sqlalchemy as sa
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision = 'add_config_table'
-down_revision = '0b53c747049a'
-branch_labels = None
-depends_on = None
-
-
-def upgrade():
-    # Create configs table if it doesn't exist
-    op.create_table(
-        'configs',
-        sa.Column('id', sa.UUID(), nullable=False, default=lambda: uuid.uuid4()),
-        sa.Column('key', sa.String(), nullable=False),
-        sa.Column('value', sa.JSON(), nullable=False),
-        sa.Column('created_at', sa.DateTime(), nullable=True),
-        sa.Column('updated_at', sa.DateTime(), nullable=True),
-        sa.PrimaryKeyConstraint('id'),
-        sa.UniqueConstraint('key')
-    )
-    
-    # Create index for key lookups
-    op.create_index('idx_configs_key', 'configs', ['key'])
-
-
-def downgrade():
-    # Drop the configs table
-    op.drop_index('idx_configs_key', 'configs')
-    op.drop_table('configs') 
@@ -1,34 +0,0 @@
-"""remove_global_unique_constraint_on_app_name_add_composite_unique
-
-Revision ID: afd00efbd06b
-Revises: add_config_table
-Create Date: 2025-06-04 01:59:41.637440
-
-"""
-from typing import Sequence, Union
-
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision: str = 'afd00efbd06b'
-down_revision: Union[str, None] = 'add_config_table'
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
-    """Upgrade schema."""
-    # ### commands auto generated by Alembic - please adjust! ###
-    op.drop_index('ix_apps_name', table_name='apps')
-    op.create_index(op.f('ix_apps_name'), 'apps', ['name'], unique=False)
-    op.create_index('idx_app_owner_name', 'apps', ['owner_id', 'name'], unique=True)
-    # ### end Alembic commands ###
-
-
-def downgrade() -> None:
-    """Downgrade schema."""
-    # ### commands auto generated by Alembic - please adjust! ###
-    op.drop_index('idx_app_owner_name', table_name='apps')
-    op.drop_index(op.f('ix_apps_name'), table_name='apps')
-    op.create_index('ix_apps_name', 'apps', ['name'], unique=True)
-    # ### end Alembic commands ###
@@ -1 +0,0 @@
-# This file makes the app directory a Python package
@@ -1,4 +0,0 @@
-import os
-
-USER_ID = os.getenv("USER", "default_user")
-DEFAULT_APP_ID = "openmemory"
@@ -1,30 +0,0 @@
-import os
-
-from dotenv import load_dotenv
-from sqlalchemy import create_engine
-from sqlalchemy.orm import declarative_base, sessionmaker
-
-# load .env file (make sure you have DATABASE_URL set)
-load_dotenv()
-
-DATABASE_URL = os.getenv("DATABASE_URL", "sqlite:///./openmemory.db")
-if not DATABASE_URL:
-    raise RuntimeError("DATABASE_URL is not set in environment")
-
-# SQLAlchemy engine & session
-engine = create_engine(
-    DATABASE_URL,
-    connect_args={"check_same_thread": False}  # Needed for SQLite
-)
-SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
-
-# Base class for models
-Base = declarative_base()
-
-# Dependency for FastAPI
-def get_db():
-    db = SessionLocal()
-    try:
-        yield db
-    finally:
-        db.close()
@@ -1,574 +0,0 @@
-"""
-MCP Server for OpenMemory with resilient memory client handling.
-
-This module implements an MCP (Model Context Protocol) server that provides
-memory operations for OpenMemory. The memory client is initialized lazily
-to prevent server crashes when external dependencies (like Ollama) are
-unavailable. If the memory client cannot be initialized, the server will
-continue running with limited functionality and appropriate error messages.
-
-Key features:
- Lazy memory client initialization
- Graceful error handling for unavailable dependencies
- Fallback to database-only mode when vector store is unavailable
- Proper logging for debugging connection issues
- Environment variable parsing for API keys
-"""
-
-import contextvars
-import datetime
-import json
-import logging
-import uuid
-
-import anyio
-
-from app.database import SessionLocal
-from app.models import Memory, MemoryAccessLog, MemoryState, MemoryStatusHistory
-from app.utils.db import get_user_and_app
-from app.utils.memory import get_memory_client
-from app.utils.permissions import check_memory_access_permissions
-from dotenv import load_dotenv
-from fastapi import FastAPI, Request
-from fastapi.routing import APIRouter
-from mcp.server.fastmcp import FastMCP
-from mcp.server.sse import SseServerTransport
-from mcp.server.streamable_http import StreamableHTTPServerTransport
-from starlette.responses import Response
-
-# Load environment variables
-load_dotenv()
-
-# Initialize MCP
-mcp = FastMCP("mem0-mcp-server")
-
-# Don't initialize memory client at import time - do it lazily when needed
-def get_memory_client_safe():
-    """Get memory client with error handling. Returns None if client cannot be initialized."""
-    try:
-        return get_memory_client()
-    except Exception as e:
-        logging.warning(f"Failed to get memory client: {e}")
-        return None
-
-# Context variables for user_id and client_name
-user_id_var: contextvars.ContextVar[str] = contextvars.ContextVar("user_id")
-client_name_var: contextvars.ContextVar[str] = contextvars.ContextVar("client_name")
-
-# Create a router for MCP endpoints
-mcp_router = APIRouter(prefix="/mcp")
-
-# Initialize SSE transport
-sse = SseServerTransport("/mcp/messages/")
-
-@mcp.tool(description="Add a new memory. This method is called everytime the user informs anything about themselves, their preferences, or anything that has any relevant information which can be useful in the future conversation. This can also be called when the user asks you to remember something. Set infer to False to store the memory verbatim without LLM fact extraction.")
-async def add_memories(text: str, infer: bool = True) -> str:
-    uid = user_id_var.get(None)
-    client_name = client_name_var.get(None)
-
-    if not uid:
-        return "Error: user_id not provided"
-    if not client_name:
-        return "Error: client_name not provided"
-
-    # Get memory client safely
-    memory_client = get_memory_client_safe()
-    if not memory_client:
-        return "Error: Memory system is currently unavailable. Please try again later."
-
-    try:
-        db = SessionLocal()
-        try:
-            # Get or create user and app
-            user, app = get_user_and_app(db, user_id=uid, app_id=client_name)
-
-            # Check if app is active
-            if not app.is_active:
-                return f"Error: App {app.name} is currently paused on OpenMemory. Cannot create new memories."
-
-            response = memory_client.add(text,
-                                         user_id=uid,
-                                         metadata={
-                                            "source_app": "openmemory",
-                                            "mcp_client": client_name,
-                                         },
-                                         infer=infer)
-
-            # Process the response and update database
-            if isinstance(response, dict) and 'results' in response:
-                for result in response['results']:
-                    memory_id = uuid.UUID(result['id'])
-                    memory = db.query(Memory).filter(Memory.id == memory_id).first()
-
-                    if result['event'] == 'ADD':
-                        if not memory:
-                            memory = Memory(
-                                id=memory_id,
-                                user_id=user.id,
-                                app_id=app.id,
-                                content=result['memory'],
-                                state=MemoryState.active
-                            )
-                            db.add(memory)
-                        else:
-                            memory.state = MemoryState.active
-                            memory.content = result['memory']
-
-                        # Create history entry
-                        history = MemoryStatusHistory(
-                            memory_id=memory_id,
-                            changed_by=user.id,
-                            old_state=MemoryState.deleted if memory else None,
-                            new_state=MemoryState.active
-                        )
-                        db.add(history)
-
-                    elif result['event'] == 'DELETE':
-                        if memory:
-                            memory.state = MemoryState.deleted
-                            memory.deleted_at = datetime.datetime.now(datetime.UTC)
-                            # Create history entry
-                            history = MemoryStatusHistory(
-                                memory_id=memory_id,
-                                changed_by=user.id,
-                                old_state=MemoryState.active,
-                                new_state=MemoryState.deleted
-                            )
-                            db.add(history)
-
-                db.commit()
-
-            return json.dumps(response)
-        finally:
-            db.close()
-    except Exception as e:
-        logging.exception(f"Error adding to memory: {e}")
-        return f"Error adding to memory: {e}"
-
-
-@mcp.tool(description="Search through stored memories. This method is called EVERYTIME the user asks anything.")
-async def search_memory(query: str) -> str:
-    uid = user_id_var.get(None)
-    client_name = client_name_var.get(None)
-    if not uid:
-        return "Error: user_id not provided"
-    if not client_name:
-        return "Error: client_name not provided"
-
-    # Get memory client safely
-    memory_client = get_memory_client_safe()
-    if not memory_client:
-        return "Error: Memory system is currently unavailable. Please try again later."
-
-    try:
-        db = SessionLocal()
-        try:
-            # Get or create user and app
-            user, app = get_user_and_app(db, user_id=uid, app_id=client_name)
-
-            # Get accessible memory IDs based on ACL
-            user_memories = db.query(Memory).filter(Memory.user_id == user.id).all()
-            accessible_memory_ids = [memory.id for memory in user_memories if check_memory_access_permissions(db, memory, app.id)]
-
-            filters = {
-                "user_id": uid
-            }
-
-            embeddings = memory_client.embedding_model.embed(query, "search")
-
-            hits = memory_client.vector_store.search(
-                query=query, 
-                vectors=embeddings, 
-                limit=10, 
-                filters=filters,
-            )
-
-            allowed = set(str(mid) for mid in accessible_memory_ids) if accessible_memory_ids else None
-
-            results = []
-            for h in hits:
-                # All vector db search functions return OutputData class
-                id, score, payload = h.id, h.score, h.payload
-                if allowed and (h.id is None or h.id not in allowed):
-                    continue
-                
-                results.append({
-                    "id": id, 
-                    "memory": payload.get("data"), 
-                    "hash": payload.get("hash"),
-                    "created_at": payload.get("created_at"), 
-                    "updated_at": payload.get("updated_at"), 
-                    "score": score,
-                })
-
-            for r in results: 
-                if r.get("id"): 
-                    access_log = MemoryAccessLog(
-                        memory_id=uuid.UUID(r["id"]),
-                        app_id=app.id,
-                        access_type="search",
-                        metadata_={
-                            "query": query,
-                            "score": r.get("score"),
-                            "hash": r.get("hash"),
-                        },
-                    )
-                    db.add(access_log)
-            db.commit()
-
-            return json.dumps({"results": results}, indent=2)
-        finally:
-            db.close()
-    except Exception as e:
-        logging.exception(e)
-        return f"Error searching memory: {e}"
-
-
-@mcp.tool(description="List all memories in the user's memory")
-async def list_memories() -> str:
-    uid = user_id_var.get(None)
-    client_name = client_name_var.get(None)
-    if not uid:
-        return "Error: user_id not provided"
-    if not client_name:
-        return "Error: client_name not provided"
-
-    # Get memory client safely
-    memory_client = get_memory_client_safe()
-    if not memory_client:
-        return "Error: Memory system is currently unavailable. Please try again later."
-
-    try:
-        db = SessionLocal()
-        try:
-            # Get or create user and app
-            user, app = get_user_and_app(db, user_id=uid, app_id=client_name)
-
-            # Get all memories
-            memories = memory_client.get_all(user_id=uid)
-            filtered_memories = []
-
-            # Filter memories based on permissions
-            user_memories = db.query(Memory).filter(Memory.user_id == user.id).all()
-            accessible_memory_ids = [memory.id for memory in user_memories if check_memory_access_permissions(db, memory, app.id)]
-            if isinstance(memories, dict) and 'results' in memories:
-                for memory_data in memories['results']:
-                    if 'id' in memory_data:
-                        memory_id = uuid.UUID(memory_data['id'])
-                        if memory_id in accessible_memory_ids:
-                            # Create access log entry
-                            access_log = MemoryAccessLog(
-                                memory_id=memory_id,
-                                app_id=app.id,
-                                access_type="list",
-                                metadata_={
-                                    "hash": memory_data.get('hash')
-                                }
-                            )
-                            db.add(access_log)
-                            filtered_memories.append(memory_data)
-                db.commit()
-            else:
-                for memory in memories:
-                    memory_id = uuid.UUID(memory['id'])
-                    memory_obj = db.query(Memory).filter(Memory.id == memory_id).first()
-                    if memory_obj and check_memory_access_permissions(db, memory_obj, app.id):
-                        # Create access log entry
-                        access_log = MemoryAccessLog(
-                            memory_id=memory_id,
-                            app_id=app.id,
-                            access_type="list",
-                            metadata_={
-                                "hash": memory.get('hash')
-                            }
-                        )
-                        db.add(access_log)
-                        filtered_memories.append(memory)
-                db.commit()
-            return json.dumps(filtered_memories, indent=2)
-        finally:
-            db.close()
-    except Exception as e:
-        logging.exception(f"Error getting memories: {e}")
-        return f"Error getting memories: {e}"
-
-
-@mcp.tool(description="Delete specific memories by their IDs")
-async def delete_memories(memory_ids: list[str]) -> str:
-    uid = user_id_var.get(None)
-    client_name = client_name_var.get(None)
-    if not uid:
-        return "Error: user_id not provided"
-    if not client_name:
-        return "Error: client_name not provided"
-
-    # Get memory client safely
-    memory_client = get_memory_client_safe()
-    if not memory_client:
-        return "Error: Memory system is currently unavailable. Please try again later."
-
-    try:
-        db = SessionLocal()
-        try:
-            # Get or create user and app
-            user, app = get_user_and_app(db, user_id=uid, app_id=client_name)
-
-            # Convert string IDs to UUIDs and filter accessible ones
-            requested_ids = [uuid.UUID(mid) for mid in memory_ids]
-            user_memories = db.query(Memory).filter(Memory.user_id == user.id).all()
-            accessible_memory_ids = [memory.id for memory in user_memories if check_memory_access_permissions(db, memory, app.id)]
-
-            # Only delete memories that are both requested and accessible
-            ids_to_delete = [mid for mid in requested_ids if mid in accessible_memory_ids]
-
-            if not ids_to_delete:
-                return "Error: No accessible memories found with provided IDs"
-
-            # Delete from vector store
-            for memory_id in ids_to_delete:
-                try:
-                    memory_client.delete(str(memory_id))
-                except Exception as delete_error:
-                    logging.warning(f"Failed to delete memory {memory_id} from vector store: {delete_error}")
-
-            # Update each memory's state and create history entries
-            now = datetime.datetime.now(datetime.UTC)
-            for memory_id in ids_to_delete:
-                memory = db.query(Memory).filter(Memory.id == memory_id).first()
-                if memory:
-                    # Update memory state
-                    memory.state = MemoryState.deleted
-                    memory.deleted_at = now
-
-                    # Create history entry
-                    history = MemoryStatusHistory(
-                        memory_id=memory_id,
-                        changed_by=user.id,
-                        old_state=MemoryState.active,
-                        new_state=MemoryState.deleted
-                    )
-                    db.add(history)
-
-                    # Create access log entry
-                    access_log = MemoryAccessLog(
-                        memory_id=memory_id,
-                        app_id=app.id,
-                        access_type="delete",
-                        metadata_={"operation": "delete_by_id"}
-                    )
-                    db.add(access_log)
-
-            db.commit()
-            return f"Successfully deleted {len(ids_to_delete)} memories"
-        finally:
-            db.close()
-    except Exception as e:
-        logging.exception(f"Error deleting memories: {e}")
-        return f"Error deleting memories: {e}"
-
-
-@mcp.tool(description="Delete all memories in the user's memory")
-async def delete_all_memories() -> str:
-    uid = user_id_var.get(None)
-    client_name = client_name_var.get(None)
-    if not uid:
-        return "Error: user_id not provided"
-    if not client_name:
-        return "Error: client_name not provided"
-
-    # Get memory client safely
-    memory_client = get_memory_client_safe()
-    if not memory_client:
-        return "Error: Memory system is currently unavailable. Please try again later."
-
-    try:
-        db = SessionLocal()
-        try:
-            # Get or create user and app
-            user, app = get_user_and_app(db, user_id=uid, app_id=client_name)
-
-            user_memories = db.query(Memory).filter(Memory.user_id == user.id).all()
-            accessible_memory_ids = [memory.id for memory in user_memories if check_memory_access_permissions(db, memory, app.id)]
-
-            # delete the accessible memories only
-            for memory_id in accessible_memory_ids:
-                try:
-                    memory_client.delete(str(memory_id))
-                except Exception as delete_error:
-                    logging.warning(f"Failed to delete memory {memory_id} from vector store: {delete_error}")
-
-            # Update each memory's state and create history entries
-            now = datetime.datetime.now(datetime.UTC)
-            for memory_id in accessible_memory_ids:
-                memory = db.query(Memory).filter(Memory.id == memory_id).first()
-                # Update memory state
-                memory.state = MemoryState.deleted
-                memory.deleted_at = now
-
-                # Create history entry
-                history = MemoryStatusHistory(
-                    memory_id=memory_id,
-                    changed_by=user.id,
-                    old_state=MemoryState.active,
-                    new_state=MemoryState.deleted
-                )
-                db.add(history)
-
-                # Create access log entry
-                access_log = MemoryAccessLog(
-                    memory_id=memory_id,
-                    app_id=app.id,
-                    access_type="delete_all",
-                    metadata_={"operation": "bulk_delete"}
-                )
-                db.add(access_log)
-
-            db.commit()
-            return "Successfully deleted all memories"
-        finally:
-            db.close()
-    except Exception as e:
-        logging.exception(f"Error deleting memories: {e}")
-        return f"Error deleting memories: {e}"
-
-
-@mcp_router.get("/{client_name}/sse/{user_id}")
-async def handle_sse(request: Request):
-    """Handle SSE connections for a specific user and client"""
-    # Extract user_id and client_name from path parameters
-    uid = request.path_params.get("user_id")
-    user_token = user_id_var.set(uid or "")
-    client_name = request.path_params.get("client_name")
-    client_token = client_name_var.set(client_name or "")
-
-    try:
-        # NOTE: request._send is the raw ASGI `send` callable. Starlette does not
-        # expose it publicly, but the MCP SDK transports require the raw ASGI
-        # interface (scope, receive, send). This is the standard pattern from the
-        # MCP Python SDK examples.
-        async with sse.connect_sse(
-            request.scope,
-            request.receive,
-            request._send,
-        ) as (read_stream, write_stream):
-            await mcp._mcp_server.run(
-                read_stream,
-                write_stream,
-                mcp._mcp_server.create_initialization_options(),
-            )
-    finally:
-        # Clean up context variables
-        user_id_var.reset(user_token)
-        client_name_var.reset(client_token)
-
-
-@mcp_router.post("/messages/")
-async def handle_get_message(request: Request):
-    return await handle_post_message(request)
-
-
-@mcp_router.post("/{client_name}/sse/{user_id}/messages/")
-async def handle_post_message(request: Request):
-    return await handle_post_message(request)
-
-async def handle_post_message(request: Request):
-    """Handle POST messages for SSE"""
-    try:
-        body = await request.body()
-
-        # Create a simple receive function that returns the body
-        async def receive():
-            return {"type": "http.request", "body": body, "more_body": False}
-
-        # Create a simple send function that does nothing
-        async def send(message):
-            return {}
-
-        # Call handle_post_message with the correct arguments
-        await sse.handle_post_message(request.scope, receive, send)
-
-        # Return a success response
-        return {"status": "ok"}
-    finally:
-        pass
-
-
-@mcp_router.api_route("/{client_name}/http/{user_id}", methods=["POST", "GET", "DELETE"])
-async def handle_streamable_http(request: Request):
-    """Handle Streamable HTTP connections for a specific user and client.
-
-    Uses the Streamable HTTP transport (MCP spec 2025-03-26+) which replaces
-    the deprecated SSE transport. Runs in stateless mode — each request is
-    handled independently with no persistent session.
-
-    The transport writes its response directly to the ASGI ``send`` callable.
-    We intercept it via ``capture_send`` so we can return a proper ``Response``
-    to FastAPI — otherwise FastAPI would also try to send its own response,
-    causing a "double-response" bug.
-    """
-    uid = request.path_params.get("user_id")
-    user_token = user_id_var.set(uid or "")
-    client_name = request.path_params.get("client_name")
-    client_token = client_name_var.set(client_name or "")
-
-    # Intercept the ASGI messages the transport sends so we can return them
-    # as a single Response to FastAPI.  Without this, FastAPI would attempt to
-    # write its own response after the transport already wrote one.
-    response_started = False
-    response_status = 200
-    response_headers: list[tuple[bytes, bytes]] = []
-    response_body = bytearray()
-
-    async def capture_send(message):
-        nonlocal response_started, response_status
-        if message["type"] == "http.response.start":
-            response_started = True
-            response_status = message["status"]
-            response_headers.extend(message.get("headers", []))
-        elif message["type"] == "http.response.body":
-            response_body.extend(message.get("body", b""))
-
-    try:
-        transport = StreamableHTTPServerTransport(
-            mcp_session_id=None,
-            is_json_response_enabled=True,
-        )
-
-        async with anyio.create_task_group() as tg:
-
-            async def run_server(*, task_status=anyio.TASK_STATUS_IGNORED):
-                async with transport.connect() as (read_stream, write_stream):
-                    task_status.started()
-                    await mcp._mcp_server.run(
-                        read_stream,
-                        write_stream,
-                        mcp._mcp_server.create_initialization_options(),
-                        stateless=True,
-                    )
-
-            await tg.start(run_server)
-            await transport.handle_request(request.scope, request.receive, capture_send)
-            await transport.terminate()
-            tg.cancel_scope.cancel()
-    finally:
-        user_id_var.reset(user_token)
-        client_name_var.reset(client_token)
-
-    if not response_started:
-        return Response(status_code=500, content=b"Transport did not produce a response")
-
-    # Header dict conversion is safe here: the MCP transport in stateless JSON
-    # mode only emits single-valued headers (Content-Type, Content-Length).
-    return Response(
-        content=bytes(response_body),
-        status_code=response_status,
-        headers={k.decode(): v.decode() for k, v in response_headers},
-    )
-
-
-def setup_mcp_server(app: FastAPI):
-    """Setup MCP server with the FastAPI application"""
-    mcp._mcp_server.name = "mem0-mcp-server"
-
-    # Include MCP router in the FastAPI app
-    app.include_router(mcp_router)
@@ -1,243 +0,0 @@
-import datetime
-import enum
-import uuid
-
-import sqlalchemy as sa
-from app.database import Base
-from app.utils.categorization import get_categories_for_memory
-from sqlalchemy import (
-    JSON,
-    UUID,
-    Boolean,
-    Column,
-    DateTime,
-    Enum,
-    ForeignKey,
-    Index,
-    Integer,
-    String,
-    Table,
-    event,
-)
-from sqlalchemy.orm import Session, relationship
-
-
-def get_current_utc_time():
-    """Get current UTC time"""
-    return datetime.datetime.now(datetime.UTC)
-
-
-class MemoryState(enum.Enum):
-    active = "active"
-    paused = "paused"
-    archived = "archived"
-    deleted = "deleted"
-
-
-class User(Base):
-    __tablename__ = "users"
-    id = Column(UUID, primary_key=True, default=lambda: uuid.uuid4())
-    user_id = Column(String, nullable=False, unique=True, index=True)
-    name = Column(String, nullable=True, index=True)
-    email = Column(String, unique=True, nullable=True, index=True)
-    metadata_ = Column('metadata', JSON, default=dict)
-    created_at = Column(DateTime, default=get_current_utc_time, index=True)
-    updated_at = Column(DateTime,
-                        default=get_current_utc_time,
-                        onupdate=get_current_utc_time)
-
-    apps = relationship("App", back_populates="owner")
-    memories = relationship("Memory", back_populates="user")
-
-
-class App(Base):
-    __tablename__ = "apps"
-    id = Column(UUID, primary_key=True, default=lambda: uuid.uuid4())
-    owner_id = Column(UUID, ForeignKey("users.id"), nullable=False, index=True)
-    name = Column(String, nullable=False, index=True)
-    description = Column(String)
-    metadata_ = Column('metadata', JSON, default=dict)
-    is_active = Column(Boolean, default=True, index=True)
-    created_at = Column(DateTime, default=get_current_utc_time, index=True)
-    updated_at = Column(DateTime,
-                        default=get_current_utc_time,
-                        onupdate=get_current_utc_time)
-
-    owner = relationship("User", back_populates="apps")
-    memories = relationship("Memory", back_populates="app")
-
-    __table_args__ = (
-        sa.UniqueConstraint('owner_id', 'name', name='idx_app_owner_name'),
-    )
-
-
-class Config(Base):
-    __tablename__ = "configs"
-    id = Column(UUID, primary_key=True, default=lambda: uuid.uuid4())
-    key = Column(String, unique=True, nullable=False, index=True)
-    value = Column(JSON, nullable=False)
-    created_at = Column(DateTime, default=get_current_utc_time)
-    updated_at = Column(DateTime,
-                        default=get_current_utc_time,
-                        onupdate=get_current_utc_time)
-
-
-class Memory(Base):
-    __tablename__ = "memories"
-    id = Column(UUID, primary_key=True, default=lambda: uuid.uuid4())
-    user_id = Column(UUID, ForeignKey("users.id"), nullable=False, index=True)
-    app_id = Column(UUID, ForeignKey("apps.id"), nullable=False, index=True)
-    content = Column(String, nullable=False)
-    vector = Column(String)
-    metadata_ = Column('metadata', JSON, default=dict)
-    state = Column(Enum(MemoryState), default=MemoryState.active, index=True)
-    created_at = Column(DateTime, default=get_current_utc_time, index=True)
-    updated_at = Column(DateTime,
-                        default=get_current_utc_time,
-                        onupdate=get_current_utc_time)
-    archived_at = Column(DateTime, nullable=True, index=True)
-    deleted_at = Column(DateTime, nullable=True, index=True)
-
-    user = relationship("User", back_populates="memories")
-    app = relationship("App", back_populates="memories")
-    categories = relationship("Category", secondary="memory_categories", back_populates="memories")
-
-    __table_args__ = (
-        Index('idx_memory_user_state', 'user_id', 'state'),
-        Index('idx_memory_app_state', 'app_id', 'state'),
-        Index('idx_memory_user_app', 'user_id', 'app_id'),
-    )
-
-
-class Category(Base):
-    __tablename__ = "categories"
-    id = Column(UUID, primary_key=True, default=lambda: uuid.uuid4())
-    name = Column(String, unique=True, nullable=False, index=True)
-    description = Column(String)
-    created_at = Column(DateTime, default=datetime.datetime.now(datetime.UTC), index=True)
-    updated_at = Column(DateTime,
-                        default=get_current_utc_time,
-                        onupdate=get_current_utc_time)
-
-    memories = relationship("Memory", secondary="memory_categories", back_populates="categories")
-
-memory_categories = Table(
-    "memory_categories", Base.metadata,
-    Column("memory_id", UUID, ForeignKey("memories.id"), primary_key=True, index=True),
-    Column("category_id", UUID, ForeignKey("categories.id"), primary_key=True, index=True),
-    Index('idx_memory_category', 'memory_id', 'category_id')
-)
-
-
-class AccessControl(Base):
-    __tablename__ = "access_controls"
-    id = Column(UUID, primary_key=True, default=lambda: uuid.uuid4())
-    subject_type = Column(String, nullable=False, index=True)
-    subject_id = Column(UUID, nullable=True, index=True)
-    object_type = Column(String, nullable=False, index=True)
-    object_id = Column(UUID, nullable=True, index=True)
-    effect = Column(String, nullable=False, index=True)
-    created_at = Column(DateTime, default=get_current_utc_time, index=True)
-
-    __table_args__ = (
-        Index('idx_access_subject', 'subject_type', 'subject_id'),
-        Index('idx_access_object', 'object_type', 'object_id'),
-    )
-
-
-class ArchivePolicy(Base):
-    __tablename__ = "archive_policies"
-    id = Column(UUID, primary_key=True, default=lambda: uuid.uuid4())
-    criteria_type = Column(String, nullable=False, index=True)
-    criteria_id = Column(UUID, nullable=True, index=True)
-    days_to_archive = Column(Integer, nullable=False)
-    created_at = Column(DateTime, default=get_current_utc_time, index=True)
-
-    __table_args__ = (
-        Index('idx_policy_criteria', 'criteria_type', 'criteria_id'),
-    )
-
-
-class MemoryStatusHistory(Base):
-    __tablename__ = "memory_status_history"
-    id = Column(UUID, primary_key=True, default=lambda: uuid.uuid4())
-    memory_id = Column(UUID, ForeignKey("memories.id"), nullable=False, index=True)
-    changed_by = Column(UUID, ForeignKey("users.id"), nullable=False, index=True)
-    old_state = Column(Enum(MemoryState), nullable=False, index=True)
-    new_state = Column(Enum(MemoryState), nullable=False, index=True)
-    changed_at = Column(DateTime, default=get_current_utc_time, index=True)
-
-    __table_args__ = (
-        Index('idx_history_memory_state', 'memory_id', 'new_state'),
-        Index('idx_history_user_time', 'changed_by', 'changed_at'),
-    )
-
-
-class MemoryAccessLog(Base):
-    __tablename__ = "memory_access_logs"
-    id = Column(UUID, primary_key=True, default=lambda: uuid.uuid4())
-    memory_id = Column(UUID, ForeignKey("memories.id"), nullable=False, index=True)
-    app_id = Column(UUID, ForeignKey("apps.id"), nullable=False, index=True)
-    accessed_at = Column(DateTime, default=get_current_utc_time, index=True)
-    access_type = Column(String, nullable=False, index=True)
-    metadata_ = Column('metadata', JSON, default=dict)
-
-    __table_args__ = (
-        Index('idx_access_memory_time', 'memory_id', 'accessed_at'),
-        Index('idx_access_app_time', 'app_id', 'accessed_at'),
-    )
-
-def categorize_memory(memory: Memory, db: Session) -> None:
-    """Categorize a memory using OpenAI and store the categories in the database."""
-    try:
-        # Get categories from OpenAI
-        categories = get_categories_for_memory(memory.content)
-
-        # Get or create categories in the database
-        for category_name in categories:
-            category = db.query(Category).filter(Category.name == category_name).first()
-            if not category:
-                category = Category(
-                    name=category_name,
-                    description=f"Automatically created category for {category_name}"
-                )
-                db.add(category)
-                db.flush()  # Flush to get the category ID
-
-            # Check if the memory-category association already exists
-            existing = db.execute(
-                memory_categories.select().where(
-                    (memory_categories.c.memory_id == memory.id) &
-                    (memory_categories.c.category_id == category.id)
-                )
-            ).first()
-
-            if not existing:
-                # Create the association
-                db.execute(
-                    memory_categories.insert().values(
-                        memory_id=memory.id,
-                        category_id=category.id
-                    )
-                )
-
-        db.commit()
-    except Exception as e:
-        db.rollback()
-        print(f"Error categorizing memory: {e}")
-
-
-@event.listens_for(Memory, 'after_insert')
-def after_memory_insert(mapper, connection, target):
-    """Trigger categorization after a memory is inserted."""
-    db = Session(bind=connection)
-    categorize_memory(target, db)
-    db.close()
-
-
-@event.listens_for(Memory, 'after_update')
-def after_memory_update(mapper, connection, target):
-    """Trigger categorization after a memory is updated."""
-    db = Session(bind=connection)
-    categorize_memory(target, db)
-    db.close()
@@ -1,7 +0,0 @@
-from .apps import router as apps_router
-from .backup import router as backup_router
-from .config import router as config_router
-from .memories import router as memories_router
-from .stats import router as stats_router
-
-__all__ = ["memories_router", "apps_router", "stats_router", "config_router", "backup_router"]
@@ -1,223 +0,0 @@
-from typing import Optional
-from uuid import UUID
-
-from app.database import get_db
-from app.models import App, Memory, MemoryAccessLog, MemoryState
-from fastapi import APIRouter, Depends, HTTPException, Query
-from sqlalchemy import desc, func
-from sqlalchemy.orm import Session, joinedload
-
-router = APIRouter(prefix="/api/v1/apps", tags=["apps"])
-
-# Helper functions
-def get_app_or_404(db: Session, app_id: UUID) -> App:
-    app = db.query(App).filter(App.id == app_id).first()
-    if not app:
-        raise HTTPException(status_code=404, detail="App not found")
-    return app
-
-# List all apps with filtering
-@router.get("/")
-async def list_apps(
-    name: Optional[str] = None,
-    is_active: Optional[bool] = None,
-    sort_by: str = 'name',
-    sort_direction: str = 'asc',
-    page: int = Query(1, ge=1),
-    page_size: int = Query(10, ge=1, le=100),
-    db: Session = Depends(get_db)
-):
-    # Create a subquery for memory counts
-    memory_counts = db.query(
-        Memory.app_id,
-        func.count(Memory.id).label('memory_count')
-    ).filter(
-        Memory.state.in_([MemoryState.active, MemoryState.paused, MemoryState.archived])
-    ).group_by(Memory.app_id).subquery()
-
-    # Create a subquery for access counts
-    access_counts = db.query(
-        MemoryAccessLog.app_id,
-        func.count(func.distinct(MemoryAccessLog.memory_id)).label('access_count')
-    ).group_by(MemoryAccessLog.app_id).subquery()
-
-    # Base query
-    query = db.query(
-        App,
-        func.coalesce(memory_counts.c.memory_count, 0).label('total_memories_created'),
-        func.coalesce(access_counts.c.access_count, 0).label('total_memories_accessed')
-    )
-
-    # Join with subqueries
-    query = query.outerjoin(
-        memory_counts,
-        App.id == memory_counts.c.app_id
-    ).outerjoin(
-        access_counts,
-        App.id == access_counts.c.app_id
-    )
-
-    if name:
-        query = query.filter(App.name.ilike(f"%{name}%"))
-
-    if is_active is not None:
-        query = query.filter(App.is_active == is_active)
-
-    # Apply sorting
-    if sort_by == 'name':
-        sort_field = App.name
-    elif sort_by == 'memories':
-        sort_field = func.coalesce(memory_counts.c.memory_count, 0)
-    elif sort_by == 'memories_accessed':
-        sort_field = func.coalesce(access_counts.c.access_count, 0)
-    else:
-        sort_field = App.name  # default sort
-
-    if sort_direction == 'desc':
-        query = query.order_by(desc(sort_field))
-    else:
-        query = query.order_by(sort_field)
-
-    total = query.count()
-    apps = query.offset((page - 1) * page_size).limit(page_size).all()
-
-    return {
-        "total": total,
-        "page": page,
-        "page_size": page_size,
-        "apps": [
-            {
-                "id": app[0].id,
-                "name": app[0].name,
-                "is_active": app[0].is_active,
-                "total_memories_created": app[1],
-                "total_memories_accessed": app[2]
-            }
-            for app in apps
-        ]
-    }
-
-# Get app details
-@router.get("/{app_id}")
-async def get_app_details(
-    app_id: UUID,
-    db: Session = Depends(get_db)
-):
-    app = get_app_or_404(db, app_id)
-
-    # Get memory access statistics
-    access_stats = db.query(
-        func.count(MemoryAccessLog.id).label("total_memories_accessed"),
-        func.min(MemoryAccessLog.accessed_at).label("first_accessed"),
-        func.max(MemoryAccessLog.accessed_at).label("last_accessed")
-    ).filter(MemoryAccessLog.app_id == app_id).first()
-
-    return {
-        "is_active": app.is_active,
-        "total_memories_created": db.query(Memory)
-            .filter(Memory.app_id == app_id)
-            .count(),
-        "total_memories_accessed": access_stats.total_memories_accessed or 0,
-        "first_accessed": access_stats.first_accessed,
-        "last_accessed": access_stats.last_accessed
-    }
-
-# List memories created by app
-@router.get("/{app_id}/memories")
-async def list_app_memories(
-    app_id: UUID,
-    page: int = Query(1, ge=1),
-    page_size: int = Query(10, ge=1, le=100),
-    db: Session = Depends(get_db)
-):
-    get_app_or_404(db, app_id)
-    query = db.query(Memory).filter(
-        Memory.app_id == app_id,
-        Memory.state.in_([MemoryState.active, MemoryState.paused, MemoryState.archived])
-    )
-    # Add eager loading for categories
-    query = query.options(joinedload(Memory.categories))
-    total = query.count()
-    memories = query.order_by(Memory.created_at.desc()).offset((page - 1) * page_size).limit(page_size).all()
-
-    return {
-        "total": total,
-        "page": page,
-        "page_size": page_size,
-        "memories": [
-            {
-                "id": memory.id,
-                "content": memory.content,
-                "created_at": memory.created_at,
-                "state": memory.state.value,
-                "app_id": memory.app_id,
-                "categories": [category.name for category in memory.categories],
-                "metadata_": memory.metadata_
-            }
-            for memory in memories
-        ]
-    }
-
-# List memories accessed by app
-@router.get("/{app_id}/accessed")
-async def list_app_accessed_memories(
-    app_id: UUID,
-    page: int = Query(1, ge=1),
-    page_size: int = Query(10, ge=1, le=100),
-    db: Session = Depends(get_db)
-):
-    
-    # Get memories with access counts
-    query = db.query(
-        Memory,
-        func.count(MemoryAccessLog.id).label("access_count")
-    ).join(
-        MemoryAccessLog,
-        Memory.id == MemoryAccessLog.memory_id
-    ).filter(
-        MemoryAccessLog.app_id == app_id
-    ).group_by(
-        Memory.id
-    ).order_by(
-        desc("access_count")
-    )
-
-    # Add eager loading for categories
-    query = query.options(joinedload(Memory.categories))
-
-    total = query.count()
-    results = query.offset((page - 1) * page_size).limit(page_size).all()
-
-    return {
-        "total": total,
-        "page": page,
-        "page_size": page_size,
-        "memories": [
-            {
-                "memory": {
-                    "id": memory.id,
-                    "content": memory.content,
-                    "created_at": memory.created_at,
-                    "state": memory.state.value,
-                    "app_id": memory.app_id,
-                    "app_name": memory.app.name if memory.app else None,
-                    "categories": [category.name for category in memory.categories],
-                    "metadata_": memory.metadata_
-                },
-                "access_count": count
-            }
-            for memory, count in results
-        ]
-    }
-
-
-@router.put("/{app_id}")
-async def update_app_details(
-    app_id: UUID,
-    is_active: bool,
-    db: Session = Depends(get_db)
-):
-    app = get_app_or_404(db, app_id)
-    app.is_active = is_active
-    db.commit()
-    return {"status": "success", "message": "Updated app details successfully"}
@@ -1,499 +0,0 @@
-from datetime import UTC, datetime
-import io 
-import json 
-import gzip 
-import zipfile
-from typing import Optional, List, Dict, Any
-from uuid import UUID
-
-from fastapi import APIRouter, Depends, HTTPException, UploadFile, File, Query, Form
-from fastapi.responses import StreamingResponse
-from pydantic import BaseModel
-from sqlalchemy.orm import Session, joinedload
-from sqlalchemy import and_
-
-from app.database import get_db
-from app.models import (
-    User, App, Memory, MemoryState, Category, memory_categories, 
-    MemoryStatusHistory, AccessControl
-)
-from app.utils.memory import get_memory_client
-
-from uuid import uuid4
-
-router = APIRouter(prefix="/api/v1/backup", tags=["backup"])
-
-class ExportRequest(BaseModel):
-    user_id: str
-    app_id: Optional[UUID] = None
-    from_date: Optional[int] = None
-    to_date: Optional[int] = None
-    include_vectors: bool = True
-
-def _iso(dt: Optional[datetime]) -> Optional[str]: 
-    if isinstance(dt, datetime): 
-        try: 
-            return dt.astimezone(UTC).isoformat()
-        except: 
-            return dt.replace(tzinfo=UTC).isoformat()
-    return None
-
-def _parse_iso(dt: Optional[str]) -> Optional[datetime]:
-    if not dt:
-        return None
-    try:
-        return datetime.fromisoformat(dt)
-    except Exception:
-        try:
-            return datetime.fromisoformat(dt.replace("Z", "+00:00"))
-        except Exception:
-            return None
-
-def _export_sqlite(db: Session, req: ExportRequest) -> Dict[str, Any]: 
-    user = db.query(User).filter(User.user_id == req.user_id).first()
-    if not user: 
-        raise HTTPException(status_code=404, detail="User not found")
-    
-    time_filters = []
-    if req.from_date: 
-        time_filters.append(Memory.created_at >= datetime.fromtimestamp(req.from_date, tz=UTC))
-    if req.to_date: 
-        time_filters.append(Memory.created_at <= datetime.fromtimestamp(req.to_date, tz=UTC))
-
-    mem_q = (
-        db.query(Memory)
-        .options(joinedload(Memory.categories), joinedload(Memory.app))
-        .filter(
-            Memory.user_id == user.id, 
-            *(time_filters or []), 
-            * ( [Memory.app_id == req.app_id] if req.app_id else [] ),
-        )
-    )
-
-    memories = mem_q.all()
-    memory_ids = [m.id for m in memories]
-
-    app_ids = sorted({m.app_id for m in memories if m.app_id})
-    apps = db.query(App).filter(App.id.in_(app_ids)).all() if app_ids else []
-
-    cats = sorted({c for m in memories for c in m.categories}, key = lambda c: str(c.id))
-
-    mc_rows = db.execute(
-        memory_categories.select().where(memory_categories.c.memory_id.in_(memory_ids))
-    ).fetchall() if memory_ids else []
-
-    history = db.query(MemoryStatusHistory).filter(MemoryStatusHistory.memory_id.in_(memory_ids)).all() if memory_ids else []
-
-    acls = db.query(AccessControl).filter(
-        AccessControl.subject_type == "app", 
-        AccessControl.subject_id.in_(app_ids) if app_ids else False
-    ).all() if app_ids else []
-
-    return {
-        "user": {
-            "id": str(user.id), 
-            "user_id": user.user_id, 
-            "name": user.name, 
-            "email": user.email, 
-            "metadata": user.metadata_, 
-            "created_at": _iso(user.created_at), 
-            "updated_at": _iso(user.updated_at)
-        }, 
-        "apps": [
-            {
-                "id": str(a.id), 
-                "owner_id": str(a.owner_id), 
-                "name": a.name, 
-                "description": a.description, 
-                "metadata": a.metadata_, 
-                "is_active": a.is_active, 
-                "created_at": _iso(a.created_at), 
-                "updated_at": _iso(a.updated_at),
-            }
-            for a in apps
-        ], 
-        "categories": [
-            {
-                "id": str(c.id), 
-                "name": c.name, 
-                "description": c.description, 
-                "created_at": _iso(c.created_at), 
-                "updated_at": _iso(c.updated_at), 
-            }
-            for c in cats
-        ], 
-        "memories": [
-            {
-                "id": str(m.id), 
-                "user_id": str(m.user_id), 
-                "app_id": str(m.app_id) if m.app_id else None, 
-                "content": m.content, 
-                "metadata": m.metadata_, 
-                "state": m.state.value,
-                "created_at": _iso(m.created_at), 
-                "updated_at": _iso(m.updated_at), 
-                "archived_at": _iso(m.archived_at), 
-                "deleted_at": _iso(m.deleted_at), 
-                "category_ids": [str(c.id) for c in m.categories], #TODO: figure out a way to add category names simply to this
-            }
-            for m in memories
-        ], 
-        "memory_categories": [
-            {"memory_id": str(r.memory_id), "category_id": str(r.category_id)}
-            for r in mc_rows
-        ], 
-        "status_history": [
-            {
-                "id": str(h.id), 
-                "memory_id": str(h.memory_id), 
-                "changed_by": str(h.changed_by), 
-                "old_state": h.old_state.value, 
-                "new_state": h.new_state.value, 
-                "changed_at": _iso(h.changed_at), 
-            }
-            for h in history
-        ], 
-        "access_controls": [
-            {
-                "id": str(ac.id), 
-                "subject_type": ac.subject_type, 
-                "subject_id": str(ac.subject_id) if ac.subject_id else None, 
-                "object_type": ac.object_type, 
-                "object_id": str(ac.object_id) if ac.object_id else None, 
-                "effect": ac.effect, 
-                "created_at": _iso(ac.created_at), 
-            }
-            for ac in acls
-        ], 
-        "export_meta": {
-            "app_id_filter": str(req.app_id) if req.app_id else None,
-            "from_date": req.from_date,
-            "to_date": req.to_date,
-            "version": "1",
-            "generated_at": datetime.now(UTC).isoformat(),
-        },
-    }
-
-def _export_logical_memories_gz(
-        db: Session, 
-        *, 
-        user_id: str, 
-        app_id: Optional[UUID] = None, 
-        from_date: Optional[int] = None, 
-        to_date: Optional[int] = None
-) -> bytes: 
-    """
-    Export a provider-agnostic backup of memories so they can be restored to any vector DB
-    by re-embedding content. One JSON object per line, gzip-compressed.
-
-    Schema (per line):
-    {
-      "id": "<uuid>",
-      "content": "<text>",
-      "metadata": {...},
-      "created_at": "<iso8601 or null>",
-      "updated_at": "<iso8601 or null>",
-      "state": "active|paused|archived|deleted",
-      "app": "<app name or null>",
-      "categories": ["catA", "catB", ...]
-    }
-    """
-
-    user = db.query(User).filter(User.user_id == user_id).first()
-    if not user: 
-        raise HTTPException(status_code=404, detail="User not found")
-    
-    time_filters = []
-    if from_date: 
-        time_filters.append(Memory.created_at >= datetime.fromtimestamp(from_date, tz=UTC))
-    if to_date: 
-        time_filters.append(Memory.created_at <= datetime.fromtimestamp(to_date, tz=UTC))
-    
-    q = (
-        db.query(Memory)
-        .options(joinedload(Memory.categories), joinedload(Memory.app))
-        .filter(
-            Memory.user_id == user.id,
-            *(time_filters or []),
-        )
-    )
-    if app_id:
-        q = q.filter(Memory.app_id == app_id)
-
-    buf = io.BytesIO()
-    with gzip.GzipFile(fileobj=buf, mode="wb") as gz: 
-        for m in q.all(): 
-            record = {
-                "id": str(m.id),
-                "content": m.content,
-                "metadata": m.metadata_ or {},
-                "created_at": _iso(m.created_at),
-                "updated_at": _iso(m.updated_at),
-                "state": m.state.value,
-                "app": m.app.name if m.app else None,
-                "categories": [c.name for c in m.categories],
-            }
-            gz.write((json.dumps(record) + "\n").encode("utf-8"))
-    return buf.getvalue()
-
-@router.post("/export")
-async def export_backup(req: ExportRequest, db: Session = Depends(get_db)): 
-    sqlite_payload = _export_sqlite(db=db, req=req)
-    memories_blob = _export_logical_memories_gz(
-        db=db, 
-        user_id=req.user_id, 
-        app_id=req.app_id, 
-        from_date=req.from_date, 
-        to_date=req.to_date,
-
-    )
-
-    #TODO: add vector store specific exports in future for speed 
-
-    zip_buf = io.BytesIO()
-    with zipfile.ZipFile(zip_buf, "w", compression=zipfile.ZIP_DEFLATED) as zf: 
-        zf.writestr("memories.json", json.dumps(sqlite_payload, indent=2))
-        zf.writestr("memories.jsonl.gz", memories_blob)
-        
-    zip_buf.seek(0)
-    return StreamingResponse(
-        zip_buf, 
-        media_type="application/zip", 
-        headers={"Content-Disposition": f'attachment; filename="memories_export_{req.user_id}.zip"'},
-    )
-
-@router.post("/import")
-async def import_backup(
-    file: UploadFile = File(..., description="Zip with memories.json and memories.jsonl.gz"), 
-    user_id: str = Form(..., description="Import memories into this user_id"),
-    mode: str = Query("overwrite"), 
-    db: Session = Depends(get_db)
-): 
-    if not file.filename.endswith(".zip"): 
-        raise HTTPException(status_code=400, detail="Expected a zip file.")
-    
-    if mode not in {"skip", "overwrite"}:
-        raise HTTPException(status_code=400, detail="Invalid mode. Must be 'skip' or 'overwrite'.")
-    
-    user = db.query(User).filter(User.user_id == user_id).first()
-    if not user: 
-        raise HTTPException(status_code=404, detail="User not found")
-
-    content = await file.read()
-    try:
-        with zipfile.ZipFile(io.BytesIO(content), "r") as zf:
-            names = zf.namelist()
-
-            def find_member(filename: str) -> Optional[str]:
-                for name in names:
-                    # Skip directory entries
-                    if name.endswith('/'):
-                        continue
-                    if name.rsplit('/', 1)[-1] == filename:
-                        return name
-                return None
-
-            sqlite_member = find_member("memories.json")
-            if not sqlite_member:
-                raise HTTPException(status_code=400, detail="memories.json missing in zip")
-
-            memories_member = find_member("memories.jsonl.gz")
-
-            sqlite_data = json.loads(zf.read(sqlite_member))
-            memories_blob = zf.read(memories_member) if memories_member else None
-    except Exception:
-        raise HTTPException(status_code=400, detail="Invalid zip file")
-
-    default_app = db.query(App).filter(App.owner_id == user.id, App.name == "openmemory").first()
-    if not default_app: 
-        default_app = App(owner_id=user.id, name="openmemory", is_active=True, metadata_={})
-        db.add(default_app)
-        db.commit()
-        db.refresh(default_app)
-
-    cat_id_map: Dict[str, UUID] = {}
-    for c in sqlite_data.get("categories", []): 
-        cat = db.query(Category).filter(Category.name == c["name"]).first()
-        if not cat: 
-            cat = Category(name=c["name"], description=c.get("description"))
-            db.add(cat)
-            db.commit()
-            db.refresh(cat)
-        cat_id_map[c["id"]] = cat.id
-
-    old_to_new_id: Dict[str, UUID] = {}
-    for m in sqlite_data.get("memories", []): 
-        incoming_id = UUID(m["id"])
-        existing = db.query(Memory).filter(Memory.id == incoming_id).first()
-
-        # Cross-user collision: always mint a new UUID and import as a new memory
-        if existing and existing.user_id != user.id:
-            target_id = uuid4()
-        else:
-            target_id = incoming_id
-
-        old_to_new_id[m["id"]] = target_id
-
-        # Same-user collision + skip mode: leave existing row untouched
-        if existing and (existing.user_id == user.id) and mode == "skip": 
-            continue 
-        
-        # Same-user collision + overwrite mode: treat import as ground truth
-        if existing and (existing.user_id == user.id) and mode == "overwrite": 
-            incoming_state = m.get("state", "active")
-            existing.user_id = user.id 
-            existing.app_id = default_app.id
-            existing.content = m.get("content") or ""
-            existing.metadata_ = m.get("metadata") or {}
-            try: 
-                existing.state = MemoryState(incoming_state)
-            except Exception: 
-                existing.state = MemoryState.active
-            # Update state-related timestamps from import (ground truth)
-            existing.archived_at = _parse_iso(m.get("archived_at"))
-            existing.deleted_at = _parse_iso(m.get("deleted_at"))
-            existing.created_at = _parse_iso(m.get("created_at")) or existing.created_at
-            existing.updated_at = _parse_iso(m.get("updated_at")) or existing.updated_at
-            db.add(existing)
-            db.commit()
-            continue
-
-        new_mem = Memory(
-            id=target_id,
-            user_id=user.id,
-            app_id=default_app.id,
-            content=m.get("content") or "",
-            metadata_=m.get("metadata") or {},
-            state=MemoryState(m.get("state", "active")) if m.get("state") else MemoryState.active,
-            created_at=_parse_iso(m.get("created_at")) or datetime.now(UTC),
-            updated_at=_parse_iso(m.get("updated_at")) or datetime.now(UTC),
-            archived_at=_parse_iso(m.get("archived_at")),
-            deleted_at=_parse_iso(m.get("deleted_at")),
-        )
-        db.add(new_mem)
-        db.commit()
-
-    for link in sqlite_data.get("memory_categories", []): 
-        mid = old_to_new_id.get(link["memory_id"])
-        cid = cat_id_map.get(link["category_id"])
-        if not (mid and cid): 
-            continue
-        exists = db.execute(
-            memory_categories.select().where(
-                (memory_categories.c.memory_id == mid) & (memory_categories.c.category_id == cid)
-            )
-        ).first()
-
-        if not exists: 
-            db.execute(memory_categories.insert().values(memory_id=mid, category_id=cid))
-            db.commit()
-
-    for h in sqlite_data.get("status_history", []): 
-        hid = UUID(h["id"])
-        mem_id = old_to_new_id.get(h["memory_id"], UUID(h["memory_id"]))
-        exists = db.query(MemoryStatusHistory).filter(MemoryStatusHistory.id == hid).first()
-        if exists and mode == "skip":
-            continue
-        rec = exists if exists else MemoryStatusHistory(id=hid)
-        rec.memory_id = mem_id
-        rec.changed_by = user.id
-        try:
-            rec.old_state = MemoryState(h.get("old_state", "active"))
-            rec.new_state = MemoryState(h.get("new_state", "active"))
-        except Exception:
-            rec.old_state = MemoryState.active
-            rec.new_state = MemoryState.active
-        rec.changed_at = _parse_iso(h.get("changed_at")) or datetime.now(UTC)
-        db.add(rec)
-        db.commit()
-
-    memory_client = get_memory_client()
-    vector_store = getattr(memory_client, "vector_store", None) if memory_client else None
-
-    if vector_store and memory_client and hasattr(memory_client, "embedding_model"):
-        def iter_logical_records():
-            if memories_blob:
-                gz_buf = io.BytesIO(memories_blob)
-                with gzip.GzipFile(fileobj=gz_buf, mode="rb") as gz:
-                    for raw in gz:
-                        yield json.loads(raw.decode("utf-8"))
-            else:
-                for m in sqlite_data.get("memories", []):
-                    yield {
-                        "id": m["id"],
-                        "content": m.get("content"),
-                        "metadata": m.get("metadata") or {},
-                        "created_at": m.get("created_at"),
-                        "updated_at": m.get("updated_at"),
-                    }
-
-        for rec in iter_logical_records():
-            old_id = rec["id"]
-            new_id = old_to_new_id.get(old_id, UUID(old_id))
-            content = rec.get("content") or ""
-            metadata = rec.get("metadata") or {}
-            created_at = rec.get("created_at")
-            updated_at = rec.get("updated_at")
-
-            if mode == "skip":
-                try:
-                    get_fn = getattr(vector_store, "get", None)
-                    if callable(get_fn) and vector_store.get(str(new_id)):
-                        continue
-                except Exception:
-                    pass
-
-            payload = dict(metadata)
-            payload["data"] = content
-            if created_at:
-                payload["created_at"] = created_at
-            if updated_at:
-                payload["updated_at"] = updated_at
-            payload["user_id"] = user_id
-            payload.setdefault("source_app", "openmemory")
-
-            try:
-                vec = memory_client.embedding_model.embed(content, "add")
-                vector_store.insert(vectors=[vec], payloads=[payload], ids=[str(new_id)])
-            except Exception as e:
-                print(f"Vector upsert failed for memory {new_id}: {e}")
-                continue
-
-        return {"message": f'Import completed into user "{user_id}"'}
-
-    return {"message": f'Import completed into user "{user_id}"'}
-
-
-    
-            
-        
- 
-
-
-    
-
-    
-
-
-
-
-
-
-    
-
-    
-
-
-
-
-
-
-
-
-
-
- 
-
-
-
-
@@ -1,291 +0,0 @@
-from typing import Any, Dict, Optional
-
-from app.database import get_db
-from app.models import Config as ConfigModel
-from app.utils.memory import reset_memory_client
-from fastapi import APIRouter, Depends, HTTPException
-from pydantic import BaseModel, Field
-from sqlalchemy.orm import Session
-
-router = APIRouter(prefix="/api/v1/config", tags=["config"])
-
-class LLMConfig(BaseModel):
-    model: str = Field(..., description="LLM model name")
-    temperature: float = Field(..., description="Temperature setting for the model")
-    max_tokens: int = Field(..., description="Maximum tokens to generate")
-    api_key: Optional[str] = Field(None, description="API key or 'env:API_KEY' to use environment variable")
-    ollama_base_url: Optional[str] = Field(None, description="Base URL for Ollama server (e.g., http://host.docker.internal:11434)")
-
-class LLMProvider(BaseModel):
-    provider: str = Field(..., description="LLM provider name")
-    config: LLMConfig
-
-class EmbedderConfig(BaseModel):
-    model: str = Field(..., description="Embedder model name")
-    api_key: Optional[str] = Field(None, description="API key or 'env:API_KEY' to use environment variable")
-    ollama_base_url: Optional[str] = Field(None, description="Base URL for Ollama server (e.g., http://host.docker.internal:11434)")
-
-class EmbedderProvider(BaseModel):
-    provider: str = Field(..., description="Embedder provider name")
-    config: EmbedderConfig
-
-class VectorStoreProvider(BaseModel):
-    provider: str = Field(..., description="Vector store provider name")
-    # Below config can vary widely based on the vector store used. Refer https://docs.mem0.ai/components/vectordbs/config
-    config: Dict[str, Any] = Field(..., description="Vector store-specific configuration")
-
-class OpenMemoryConfig(BaseModel):
-    custom_instructions: Optional[str] = Field(None, description="Custom instructions for memory management and fact extraction")
-
-class Mem0Config(BaseModel):
-    llm: Optional[LLMProvider] = None
-    embedder: Optional[EmbedderProvider] = None
-    vector_store: Optional[VectorStoreProvider] = None
-
-class ConfigSchema(BaseModel):
-    openmemory: Optional[OpenMemoryConfig] = None
-    mem0: Optional[Mem0Config] = None
-
-def get_default_configuration():
-    """Get the default configuration with sensible defaults for LLM and embedder."""
-    return {
-        "openmemory": {
-            "custom_instructions": None
-        },
-        "mem0": {
-            "llm": {
-                "provider": "openai",
-                "config": {
-                    "model": "gpt-4o-mini",
-                    "temperature": 0.1,
-                    "max_tokens": 2000,
-                    "api_key": "env:OPENAI_API_KEY"
-                }
-            },
-            "embedder": {
-                "provider": "openai",
-                "config": {
-                    "model": "text-embedding-3-small",
-                    "api_key": "env:OPENAI_API_KEY"
-                }
-            },
-            "vector_store": None
-        }
-    }
-
-def get_config_from_db(db: Session, key: str = "main"):
-    """Get configuration from database."""
-    config = db.query(ConfigModel).filter(ConfigModel.key == key).first()
-    
-    if not config:
-        # Create default config with proper provider configurations
-        default_config = get_default_configuration()
-        db_config = ConfigModel(key=key, value=default_config)
-        db.add(db_config)
-        db.commit()
-        db.refresh(db_config)
-        return default_config
-    
-    # Ensure the config has all required sections with defaults
-    config_value = config.value
-    default_config = get_default_configuration()
-    
-    # Merge with defaults to ensure all required fields exist
-    if "openmemory" not in config_value:
-        config_value["openmemory"] = default_config["openmemory"]
-    
-    if "mem0" not in config_value:
-        config_value["mem0"] = default_config["mem0"]
-    else:
-        # Ensure LLM config exists with defaults
-        if "llm" not in config_value["mem0"] or config_value["mem0"]["llm"] is None:
-            config_value["mem0"]["llm"] = default_config["mem0"]["llm"]
-        
-        # Ensure embedder config exists with defaults
-        if "embedder" not in config_value["mem0"] or config_value["mem0"]["embedder"] is None:
-            config_value["mem0"]["embedder"] = default_config["mem0"]["embedder"]
-        
-        # Ensure vector_store config exists with defaults
-        if "vector_store" not in config_value["mem0"]:
-            config_value["mem0"]["vector_store"] = default_config["mem0"]["vector_store"]
-
-    # Save the updated config back to database if it was modified
-    if config_value != config.value:
-        config.value = config_value
-        db.commit()
-        db.refresh(config)
-    
-    return config_value
-
-def save_config_to_db(db: Session, config: Dict[str, Any], key: str = "main"):
-    """Save configuration to database."""
-    db_config = db.query(ConfigModel).filter(ConfigModel.key == key).first()
-    
-    if db_config:
-        db_config.value = config
-        db_config.updated_at = None  # Will trigger the onupdate to set current time
-    else:
-        db_config = ConfigModel(key=key, value=config)
-        db.add(db_config)
-        
-    db.commit()
-    db.refresh(db_config)
-    return db_config.value
-
-@router.get("/", response_model=ConfigSchema)
-async def get_configuration(db: Session = Depends(get_db)):
-    """Get the current configuration."""
-    config = get_config_from_db(db)
-    return config
-
-@router.put("/", response_model=ConfigSchema)
-async def update_configuration(config: ConfigSchema, db: Session = Depends(get_db)):
-    """Update the configuration."""
-    current_config = get_config_from_db(db)
-    
-    # Convert to dict for processing
-    updated_config = current_config.copy()
-    
-    # Update openmemory settings if provided
-    if config.openmemory is not None:
-        if "openmemory" not in updated_config:
-            updated_config["openmemory"] = {}
-        updated_config["openmemory"].update(config.openmemory.dict(exclude_none=True))
-    
-    # Update mem0 settings
-    updated_config["mem0"] = config.mem0.dict(exclude_none=True)
-    
-
-@router.patch("/", response_model=ConfigSchema)
-async def patch_configuration(config_update: ConfigSchema, db: Session = Depends(get_db)):
-    """Update parts of the configuration."""
-    current_config = get_config_from_db(db)
-
-    def deep_update(source, overrides):
-        for key, value in overrides.items():
-            if isinstance(value, dict) and key in source and isinstance(source[key], dict):
-                source[key] = deep_update(source[key], value)
-            else:
-                source[key] = value
-        return source
-
-    update_data = config_update.dict(exclude_unset=True)
-    updated_config = deep_update(current_config, update_data)
-
-    save_config_to_db(db, updated_config)
-    reset_memory_client()
-    return updated_config
-
-
-@router.post("/reset", response_model=ConfigSchema)
-async def reset_configuration(db: Session = Depends(get_db)):
-    """Reset the configuration to default values."""
-    try:
-        # Get the default configuration with proper provider setups
-        default_config = get_default_configuration()
-        
-        # Save it as the current configuration in the database
-        save_config_to_db(db, default_config)
-        reset_memory_client()
-        return default_config
-    except Exception as e:
-        raise HTTPException(
-            status_code=500, 
-            detail=f"Failed to reset configuration: {str(e)}"
-        )
-
-@router.get("/mem0/llm", response_model=LLMProvider)
-async def get_llm_configuration(db: Session = Depends(get_db)):
-    """Get only the LLM configuration."""
-    config = get_config_from_db(db)
-    llm_config = config.get("mem0", {}).get("llm", {})
-    return llm_config
-
-@router.put("/mem0/llm", response_model=LLMProvider)
-async def update_llm_configuration(llm_config: LLMProvider, db: Session = Depends(get_db)):
-    """Update only the LLM configuration."""
-    current_config = get_config_from_db(db)
-    
-    # Ensure mem0 key exists
-    if "mem0" not in current_config:
-        current_config["mem0"] = {}
-    
-    # Update the LLM configuration
-    current_config["mem0"]["llm"] = llm_config.dict(exclude_none=True)
-    
-    # Save the configuration to database
-    save_config_to_db(db, current_config)
-    reset_memory_client()
-    return current_config["mem0"]["llm"]
-
-@router.get("/mem0/embedder", response_model=EmbedderProvider)
-async def get_embedder_configuration(db: Session = Depends(get_db)):
-    """Get only the Embedder configuration."""
-    config = get_config_from_db(db)
-    embedder_config = config.get("mem0", {}).get("embedder", {})
-    return embedder_config
-
-@router.put("/mem0/embedder", response_model=EmbedderProvider)
-async def update_embedder_configuration(embedder_config: EmbedderProvider, db: Session = Depends(get_db)):
-    """Update only the Embedder configuration."""
-    current_config = get_config_from_db(db)
-    
-    # Ensure mem0 key exists
-    if "mem0" not in current_config:
-        current_config["mem0"] = {}
-    
-    # Update the Embedder configuration
-    current_config["mem0"]["embedder"] = embedder_config.dict(exclude_none=True)
-    
-    # Save the configuration to database
-    save_config_to_db(db, current_config)
-    reset_memory_client()
-    return current_config["mem0"]["embedder"]
-
-@router.get("/mem0/vector_store", response_model=Optional[VectorStoreProvider])
-async def get_vector_store_configuration(db: Session = Depends(get_db)):
-    """Get only the Vector Store configuration."""
-    config = get_config_from_db(db)
-    vector_store_config = config.get("mem0", {}).get("vector_store", None)
-    return vector_store_config
-
-@router.put("/mem0/vector_store", response_model=VectorStoreProvider)
-async def update_vector_store_configuration(vector_store_config: VectorStoreProvider, db: Session = Depends(get_db)):
-    """Update only the Vector Store configuration."""
-    current_config = get_config_from_db(db)
-    
-    # Ensure mem0 key exists
-    if "mem0" not in current_config:
-        current_config["mem0"] = {}
-    
-    # Update the Vector Store configuration
-    current_config["mem0"]["vector_store"] = vector_store_config.dict(exclude_none=True)
-    
-    # Save the configuration to database
-    save_config_to_db(db, current_config)
-    reset_memory_client()
-    return current_config["mem0"]["vector_store"]
-
-@router.get("/openmemory", response_model=OpenMemoryConfig)
-async def get_openmemory_configuration(db: Session = Depends(get_db)):
-    """Get only the OpenMemory configuration."""
-    config = get_config_from_db(db)
-    openmemory_config = config.get("openmemory", {})
-    return openmemory_config
-
-@router.put("/openmemory", response_model=OpenMemoryConfig)
-async def update_openmemory_configuration(openmemory_config: OpenMemoryConfig, db: Session = Depends(get_db)):
-    """Update only the OpenMemory configuration."""
-    current_config = get_config_from_db(db)
-    
-    # Ensure openmemory key exists
-    if "openmemory" not in current_config:
-        current_config["openmemory"] = {}
-    
-    # Update the OpenMemory configuration
-    current_config["openmemory"].update(openmemory_config.dict(exclude_none=True))
-    
-    # Save the configuration to database
-    save_config_to_db(db, current_config)
-    reset_memory_client()
-    return current_config["openmemory"]
@@ -1,694 +0,0 @@
-import logging
-from datetime import UTC, datetime
-from typing import List, Optional, Set
-from uuid import UUID
-
-from app.database import get_db
-from app.models import (
-    AccessControl,
-    App,
-    Category,
-    Memory,
-    MemoryAccessLog,
-    MemoryState,
-    MemoryStatusHistory,
-    User,
-)
-from app.schemas import MemoryResponse
-from app.utils.memory import get_memory_client
-from app.utils.permissions import check_memory_access_permissions
-from fastapi import APIRouter, Depends, HTTPException, Query
-from fastapi_pagination import Page, Params
-from fastapi_pagination.ext.sqlalchemy import paginate as sqlalchemy_paginate
-from pydantic import BaseModel
-from sqlalchemy import func
-from sqlalchemy.orm import Session, joinedload
-
-router = APIRouter(prefix="/api/v1/memories", tags=["memories"])
-
-
-def get_memory_or_404(db: Session, memory_id: UUID) -> Memory:
-    memory = db.query(Memory).filter(Memory.id == memory_id).first()
-    if not memory:
-        raise HTTPException(status_code=404, detail="Memory not found")
-    return memory
-
-
-def update_memory_state(db: Session, memory_id: UUID, new_state: MemoryState, user_id: UUID):
-    memory = get_memory_or_404(db, memory_id)
-    old_state = memory.state
-
-    # Update memory state
-    memory.state = new_state
-    if new_state == MemoryState.archived:
-        memory.archived_at = datetime.now(UTC)
-    elif new_state == MemoryState.deleted:
-        memory.deleted_at = datetime.now(UTC)
-
-    # Record state change
-    history = MemoryStatusHistory(
-        memory_id=memory_id,
-        changed_by=user_id,
-        old_state=old_state,
-        new_state=new_state
-    )
-    db.add(history)
-    db.commit()
-    return memory
-
-
-def get_accessible_memory_ids(db: Session, app_id: UUID) -> Set[UUID]:
-    """
-    Get the set of memory IDs that the app has access to based on app-level ACL rules.
-    Returns all memory IDs if no specific restrictions are found.
-    """
-    # Get app-level access controls
-    app_access = db.query(AccessControl).filter(
-        AccessControl.subject_type == "app",
-        AccessControl.subject_id == app_id,
-        AccessControl.object_type == "memory"
-    ).all()
-
-    # If no app-level rules exist, return None to indicate all memories are accessible
-    if not app_access:
-        return None
-
-    # Initialize sets for allowed and denied memory IDs
-    allowed_memory_ids = set()
-    denied_memory_ids = set()
-
-    # Process app-level rules
-    for rule in app_access:
-        if rule.effect == "allow":
-            if rule.object_id:  # Specific memory access
-                allowed_memory_ids.add(rule.object_id)
-            else:  # All memories access
-                return None  # All memories allowed
-        elif rule.effect == "deny":
-            if rule.object_id:  # Specific memory denied
-                denied_memory_ids.add(rule.object_id)
-            else:  # All memories denied
-                return set()  # No memories accessible
-
-    # Remove denied memories from allowed set
-    if allowed_memory_ids:
-        allowed_memory_ids -= denied_memory_ids
-
-    return allowed_memory_ids
-
-
-# List all memories with filtering
-@router.get("/", response_model=Page[MemoryResponse])
-async def list_memories(
-    user_id: str,
-    app_id: Optional[UUID] = None,
-    from_date: Optional[int] = Query(
-        None,
-        description="Filter memories created after this date (timestamp)",
-        examples=[1718505600]
-    ),
-    to_date: Optional[int] = Query(
-        None,
-        description="Filter memories created before this date (timestamp)",
-        examples=[1718505600]
-    ),
-    categories: Optional[str] = None,
-    params: Params = Depends(),
-    search_query: Optional[str] = None,
-    sort_column: Optional[str] = Query(None, description="Column to sort by (memory, categories, app_name, created_at)"),
-    sort_direction: Optional[str] = Query(None, description="Sort direction (asc or desc)"),
-    db: Session = Depends(get_db)
-):
-    user = db.query(User).filter(User.user_id == user_id).first()
-    if not user:
-        raise HTTPException(status_code=404, detail="User not found")
-
-    # Build base query
-    query = db.query(Memory).filter(
-        Memory.user_id == user.id,
-        Memory.state != MemoryState.deleted,
-        Memory.state != MemoryState.archived,
-        Memory.content.ilike(f"%{search_query}%") if search_query else True
-    )
-
-    # Apply filters
-    if app_id:
-        query = query.filter(Memory.app_id == app_id)
-
-    if from_date:
-        from_datetime = datetime.fromtimestamp(from_date, tz=UTC)
-        query = query.filter(Memory.created_at >= from_datetime)
-
-    if to_date:
-        to_datetime = datetime.fromtimestamp(to_date, tz=UTC)
-        query = query.filter(Memory.created_at <= to_datetime)
-
-    # Add joins for app and categories after filtering
-    query = query.outerjoin(App, Memory.app_id == App.id)
-    query = query.outerjoin(Memory.categories)
-
-    # Apply category filter if provided
-    if categories:
-        category_list = [c.strip() for c in categories.split(",")]
-        query = query.filter(Category.name.in_(category_list))
-
-    # Apply sorting if specified
-    if sort_column:
-        sort_field = getattr(Memory, sort_column, None)
-        if sort_field:
-            query = query.order_by(sort_field.desc()) if sort_direction == "desc" else query.order_by(sort_field.asc())
-
-    # Add eager loading for app and categories
-    query = query.options(
-        joinedload(Memory.app),
-        joinedload(Memory.categories)
-    ).distinct(Memory.id)
-
-    # Get paginated results with transformer
-    return sqlalchemy_paginate(
-        query,
-        params,
-        transformer=lambda items: [
-            MemoryResponse(
-                id=memory.id,
-                content=memory.content,
-                created_at=memory.created_at,
-                state=memory.state.value,
-                app_id=memory.app_id,
-                app_name=memory.app.name if memory.app else None,
-                categories=[category.name for category in memory.categories],
-                metadata_=memory.metadata_
-            )
-            for memory in items
-            if check_memory_access_permissions(db, memory, app_id)
-        ]
-    )
-
-
-# Get all categories
-@router.get("/categories")
-async def get_categories(
-    user_id: str,
-    db: Session = Depends(get_db)
-):
-    user = db.query(User).filter(User.user_id == user_id).first()
-    if not user:
-        raise HTTPException(status_code=404, detail="User not found")
-
-    # Get unique categories associated with the user's memories
-    # Get all memories
-    memories = db.query(Memory).filter(Memory.user_id == user.id, Memory.state != MemoryState.deleted, Memory.state != MemoryState.archived).all()
-    # Get all categories from memories
-    categories = [category for memory in memories for category in memory.categories]
-    # Get unique categories
-    unique_categories = list(set(categories))
-
-    return {
-        "categories": unique_categories,
-        "total": len(unique_categories)
-    }
-
-
-class CreateMemoryRequest(BaseModel):
-    user_id: str
-    text: str
-    metadata: dict = {}
-    infer: bool = True
-    app: str = "openmemory"
-
-
-# Create new memory
-@router.post("/")
-async def create_memory(
-    request: CreateMemoryRequest,
-    db: Session = Depends(get_db)
-):
-    user = db.query(User).filter(User.user_id == request.user_id).first()
-    if not user:
-        raise HTTPException(status_code=404, detail="User not found")
-    # Get or create app
-    app_obj = db.query(App).filter(App.name == request.app,
-                                   App.owner_id == user.id).first()
-    if not app_obj:
-        app_obj = App(name=request.app, owner_id=user.id)
-        db.add(app_obj)
-        db.commit()
-        db.refresh(app_obj)
-
-    # Check if app is active
-    if not app_obj.is_active:
-        raise HTTPException(status_code=403, detail=f"App {request.app} is currently paused on OpenMemory. Cannot create new memories.")
-
-    # Log what we're about to do
-    logging.info(f"Creating memory for user_id: {request.user_id} with app: {request.app}")
-    
-    # Try to get memory client safely
-    try:
-        memory_client = get_memory_client()
-        if not memory_client:
-            raise Exception("Memory client is not available")
-    except Exception as client_error:
-        logging.warning(f"Memory client unavailable: {client_error}. Creating memory in database only.")
-        # Return a json response with the error
-        return {
-            "error": str(client_error)
-        }
-
-    # Try to save to Qdrant via memory_client
-    try:
-        qdrant_response = memory_client.add(
-            request.text,
-            user_id=request.user_id,  # Use string user_id to match search
-            metadata={
-                "source_app": "openmemory",
-                "mcp_client": request.app,
-            },
-            infer=request.infer
-        )
-        
-        # Log the response for debugging
-        logging.info(f"Qdrant response: {qdrant_response}")
-        
-        # Process Qdrant response
-        if isinstance(qdrant_response, dict) and 'results' in qdrant_response:
-            created_memories = []
-            
-            for result in qdrant_response['results']:
-                if result['event'] == 'ADD':
-                    # Get the Qdrant-generated ID
-                    memory_id = UUID(result['id'])
-                    
-                    # Check if memory already exists
-                    existing_memory = db.query(Memory).filter(Memory.id == memory_id).first()
-                    
-                    if existing_memory:
-                        # Update existing memory
-                        existing_memory.state = MemoryState.active
-                        existing_memory.content = result['memory']
-                        memory = existing_memory
-                    else:
-                        # Create memory with the EXACT SAME ID from Qdrant
-                        memory = Memory(
-                            id=memory_id,  # Use the same ID that Qdrant generated
-                            user_id=user.id,
-                            app_id=app_obj.id,
-                            content=result['memory'],
-                            metadata_=request.metadata,
-                            state=MemoryState.active
-                        )
-                        db.add(memory)
-                    
-                    # Create history entry
-                    history = MemoryStatusHistory(
-                        memory_id=memory_id,
-                        changed_by=user.id,
-                        old_state=MemoryState.deleted if existing_memory else MemoryState.deleted,
-                        new_state=MemoryState.active
-                    )
-                    db.add(history)
-                    
-                    created_memories.append(memory)
-            
-            # Commit all changes at once
-            if created_memories:
-                db.commit()
-                for memory in created_memories:
-                    db.refresh(memory)
-                
-                # Return the first memory (for API compatibility)
-                # but all memories are now saved to the database
-                return created_memories[0]
-    except Exception as qdrant_error:
-        logging.warning(f"Qdrant operation failed: {qdrant_error}.")
-        # Return a json response with the error
-        return {
-            "error": str(qdrant_error)
-        }
-
-
-
-
-# Get memory by ID
-@router.get("/{memory_id}")
-async def get_memory(
-    memory_id: UUID,
-    db: Session = Depends(get_db)
-):
-    memory = get_memory_or_404(db, memory_id)
-    return {
-        "id": memory.id,
-        "text": memory.content,
-        "created_at": int(memory.created_at.timestamp()),
-        "state": memory.state.value,
-        "app_id": memory.app_id,
-        "app_name": memory.app.name if memory.app else None,
-        "categories": [category.name for category in memory.categories],
-        "metadata_": memory.metadata_
-    }
-
-
-class DeleteMemoriesRequest(BaseModel):
-    memory_ids: List[UUID]
-    user_id: str
-
-# Delete multiple memories
-@router.delete("/")
-async def delete_memories(
-    request: DeleteMemoriesRequest,
-    db: Session = Depends(get_db)
-):
-    user = db.query(User).filter(User.user_id == request.user_id).first()
-    if not user:
-        raise HTTPException(status_code=404, detail="User not found")
-
-    # Get memory client to delete from vector store
-    try:
-        memory_client = get_memory_client()
-        if not memory_client:
-            raise HTTPException(
-                status_code=503,
-                detail="Memory client is not available"
-            )
-    except HTTPException:
-        raise
-    except Exception as client_error:
-        logging.error(f"Memory client initialization failed: {client_error}")
-        raise HTTPException(
-            status_code=503,
-            detail=f"Memory service unavailable: {str(client_error)}"
-        )
-
-    # Delete from vector store then mark as deleted in database
-    for memory_id in request.memory_ids:
-        try:
-            memory_client.delete(str(memory_id))
-        except Exception as delete_error:
-            logging.warning(f"Failed to delete memory {memory_id} from vector store: {delete_error}")
-
-        update_memory_state(db, memory_id, MemoryState.deleted, user.id)
-
-    return {"message": f"Successfully deleted {len(request.memory_ids)} memories"}
-
-
-# Archive memories
-@router.post("/actions/archive")
-async def archive_memories(
-    memory_ids: List[UUID],
-    user_id: UUID,
-    db: Session = Depends(get_db)
-):
-    for memory_id in memory_ids:
-        update_memory_state(db, memory_id, MemoryState.archived, user_id)
-    return {"message": f"Successfully archived {len(memory_ids)} memories"}
-
-
-class PauseMemoriesRequest(BaseModel):
-    memory_ids: Optional[List[UUID]] = None
-    category_ids: Optional[List[UUID]] = None
-    app_id: Optional[UUID] = None
-    all_for_app: bool = False
-    global_pause: bool = False
-    state: Optional[MemoryState] = None
-    user_id: str
-
-# Pause access to memories
-@router.post("/actions/pause")
-async def pause_memories(
-    request: PauseMemoriesRequest,
-    db: Session = Depends(get_db)
-):
-    
-    global_pause = request.global_pause
-    all_for_app = request.all_for_app
-    app_id = request.app_id
-    memory_ids = request.memory_ids
-    category_ids = request.category_ids
-    state = request.state or MemoryState.paused
-
-    user = db.query(User).filter(User.user_id == request.user_id).first()
-    if not user:
-        raise HTTPException(status_code=404, detail="User not found")
-    
-    user_id = user.id
-    
-    if global_pause:
-        # Pause all memories
-        memories = db.query(Memory).filter(
-            Memory.state != MemoryState.deleted,
-            Memory.state != MemoryState.archived
-        ).all()
-        for memory in memories:
-            update_memory_state(db, memory.id, state, user_id)
-        return {"message": "Successfully paused all memories"}
-
-    if app_id:
-        # Pause all memories for an app
-        memories = db.query(Memory).filter(
-            Memory.app_id == app_id,
-            Memory.user_id == user.id,
-            Memory.state != MemoryState.deleted,
-            Memory.state != MemoryState.archived
-        ).all()
-        for memory in memories:
-            update_memory_state(db, memory.id, state, user_id)
-        return {"message": f"Successfully paused all memories for app {app_id}"}
-    
-    if all_for_app and memory_ids:
-        # Pause all memories for an app
-        memories = db.query(Memory).filter(
-            Memory.user_id == user.id,
-            Memory.state != MemoryState.deleted,
-            Memory.id.in_(memory_ids)
-        ).all()
-        for memory in memories:
-            update_memory_state(db, memory.id, state, user_id)
-        return {"message": "Successfully paused all memories"}
-
-    if memory_ids:
-        # Pause specific memories
-        for memory_id in memory_ids:
-            update_memory_state(db, memory_id, state, user_id)
-        return {"message": f"Successfully paused {len(memory_ids)} memories"}
-
-    if category_ids:
-        # Pause memories by category
-        memories = db.query(Memory).join(Memory.categories).filter(
-            Category.id.in_(category_ids),
-            Memory.state != MemoryState.deleted,
-            Memory.state != MemoryState.archived
-        ).all()
-        for memory in memories:
-            update_memory_state(db, memory.id, state, user_id)
-        return {"message": f"Successfully paused memories in {len(category_ids)} categories"}
-
-    raise HTTPException(status_code=400, detail="Invalid pause request parameters")
-
-
-# Get memory access logs
-@router.get("/{memory_id}/access-log")
-async def get_memory_access_log(
-    memory_id: UUID,
-    page: int = Query(1, ge=1),
-    page_size: int = Query(10, ge=1, le=100),
-    db: Session = Depends(get_db)
-):
-    query = db.query(MemoryAccessLog).filter(MemoryAccessLog.memory_id == memory_id)
-    total = query.count()
-    logs = query.order_by(MemoryAccessLog.accessed_at.desc()).offset((page - 1) * page_size).limit(page_size).all()
-
-    # Get app name
-    for log in logs:
-        app = db.query(App).filter(App.id == log.app_id).first()
-        log.app_name = app.name if app else None
-
-    return {
-        "total": total,
-        "page": page,
-        "page_size": page_size,
-        "logs": logs
-    }
-
-
-class UpdateMemoryRequest(BaseModel):
-    memory_content: str
-    user_id: str
-
-# Update a memory
-@router.put("/{memory_id}")
-async def update_memory(
-    memory_id: UUID,
-    request: UpdateMemoryRequest,
-    db: Session = Depends(get_db)
-):
-    user = db.query(User).filter(User.user_id == request.user_id).first()
-    if not user:
-        raise HTTPException(status_code=404, detail="User not found")
-    memory = get_memory_or_404(db, memory_id)
-    memory.content = request.memory_content
-    db.commit()
-    db.refresh(memory)
-    return memory
-
-class FilterMemoriesRequest(BaseModel):
-    user_id: str
-    page: int = 1
-    size: int = 10
-    search_query: Optional[str] = None
-    app_ids: Optional[List[UUID]] = None
-    category_ids: Optional[List[UUID]] = None
-    sort_column: Optional[str] = None
-    sort_direction: Optional[str] = None
-    from_date: Optional[int] = None
-    to_date: Optional[int] = None
-    show_archived: Optional[bool] = False
-
-@router.post("/filter", response_model=Page[MemoryResponse])
-async def filter_memories(
-    request: FilterMemoriesRequest,
-    db: Session = Depends(get_db)
-):
-    user = db.query(User).filter(User.user_id == request.user_id).first()
-    if not user:
-        raise HTTPException(status_code=404, detail="User not found")
-
-    # Build base query
-    query = db.query(Memory).filter(
-        Memory.user_id == user.id,
-        Memory.state != MemoryState.deleted,
-    )
-
-    # Filter archived memories based on show_archived parameter
-    if not request.show_archived:
-        query = query.filter(Memory.state != MemoryState.archived)
-
-    # Apply search filter
-    if request.search_query:
-        query = query.filter(Memory.content.ilike(f"%{request.search_query}%"))
-
-    # Apply app filter
-    if request.app_ids:
-        query = query.filter(Memory.app_id.in_(request.app_ids))
-
-    # Add joins for app and categories
-    query = query.outerjoin(App, Memory.app_id == App.id)
-
-    # Apply category filter
-    if request.category_ids:
-        query = query.join(Memory.categories).filter(Category.id.in_(request.category_ids))
-    else:
-        query = query.outerjoin(Memory.categories)
-
-    # Apply date filters
-    if request.from_date:
-        from_datetime = datetime.fromtimestamp(request.from_date, tz=UTC)
-        query = query.filter(Memory.created_at >= from_datetime)
-
-    if request.to_date:
-        to_datetime = datetime.fromtimestamp(request.to_date, tz=UTC)
-        query = query.filter(Memory.created_at <= to_datetime)
-
-    # Apply sorting
-    if request.sort_column and request.sort_direction:
-        sort_direction = request.sort_direction.lower()
-        if sort_direction not in ['asc', 'desc']:
-            raise HTTPException(status_code=400, detail="Invalid sort direction")
-
-        sort_mapping = {
-            'memory': Memory.content,
-            'app_name': App.name,
-            'created_at': Memory.created_at
-        }
-
-        if request.sort_column not in sort_mapping:
-            raise HTTPException(status_code=400, detail="Invalid sort column")
-
-        sort_field = sort_mapping[request.sort_column]
-        if sort_direction == 'desc':
-            query = query.order_by(sort_field.desc())
-        else:
-            query = query.order_by(sort_field.asc())
-    else:
-        # Default sorting
-        query = query.order_by(Memory.created_at.desc())
-
-    # Add eager loading for categories and make the query distinct
-    query = query.options(
-        joinedload(Memory.categories)
-    ).distinct(Memory.id)
-
-    # Use fastapi-pagination's paginate function
-    return sqlalchemy_paginate(
-        query,
-        Params(page=request.page, size=request.size),
-        transformer=lambda items: [
-            MemoryResponse(
-                id=memory.id,
-                content=memory.content,
-                created_at=memory.created_at,
-                state=memory.state.value,
-                app_id=memory.app_id,
-                app_name=memory.app.name if memory.app else None,
-                categories=[category.name for category in memory.categories],
-                metadata_=memory.metadata_
-            )
-            for memory in items
-        ]
-    )
-
-
-@router.get("/{memory_id}/related", response_model=Page[MemoryResponse])
-async def get_related_memories(
-    memory_id: UUID,
-    user_id: str,
-    params: Params = Depends(),
-    db: Session = Depends(get_db)
-):
-    # Validate user
-    user = db.query(User).filter(User.user_id == user_id).first()
-    if not user:
-        raise HTTPException(status_code=404, detail="User not found")
-    
-    # Get the source memory
-    memory = get_memory_or_404(db, memory_id)
-    
-    # Extract category IDs from the source memory
-    category_ids = [category.id for category in memory.categories]
-    
-    if not category_ids:
-        return Page.create([], total=0, params=params)
-    
-    # Build query for related memories
-    query = db.query(Memory).distinct(Memory.id).filter(
-        Memory.user_id == user.id,
-        Memory.id != memory_id,
-        Memory.state != MemoryState.deleted
-    ).join(Memory.categories).filter(
-        Category.id.in_(category_ids)
-    ).options(
-        joinedload(Memory.categories),
-        joinedload(Memory.app)
-    ).order_by(
-        func.count(Category.id).desc(),
-        Memory.created_at.desc()
-    ).group_by(Memory.id)
-    
-    # ⚡ Force page size to be 5
-    params = Params(page=params.page, size=5)
-    
-    return sqlalchemy_paginate(
-        query,
-        params,
-        transformer=lambda items: [
-            MemoryResponse(
-                id=memory.id,
-                content=memory.content,
-                created_at=memory.created_at,
-                state=memory.state.value,
-                app_id=memory.app_id,
-                app_name=memory.app.name if memory.app else None,
-                categories=[category.name for category in memory.categories],
-                metadata_=memory.metadata_
-            )
-            for memory in items
-        ]
-    )
@@ -1,29 +0,0 @@
-from app.database import get_db
-from app.models import App, Memory, MemoryState, User
-from fastapi import APIRouter, Depends, HTTPException
-from sqlalchemy.orm import Session
-
-router = APIRouter(prefix="/api/v1/stats", tags=["stats"])
-
-@router.get("/")
-async def get_profile(
-    user_id: str,
-    db: Session = Depends(get_db)
-):
-    user = db.query(User).filter(User.user_id == user_id).first()
-    if not user:
-        raise HTTPException(status_code=404, detail="User not found")
-    
-    # Get total number of memories
-    total_memories = db.query(Memory).filter(Memory.user_id == user.id, Memory.state != MemoryState.deleted).count()
-
-    # Get total number of apps
-    apps = db.query(App).filter(App.owner == user)
-    total_apps = apps.count()
-
-    return {
-        "total_memories": total_memories,
-        "total_apps": total_apps,
-        "apps": apps.all()
-    }
-
@@ -1,65 +0,0 @@
-from datetime import datetime
-from typing import List, Optional
-from uuid import UUID
-
-from pydantic import BaseModel, ConfigDict, Field, validator
-
-
-class MemoryBase(BaseModel):
-    content: str
-    metadata_: Optional[dict] = Field(default_factory=dict)
-
-class MemoryCreate(MemoryBase):
-    user_id: UUID
-    app_id: UUID
-
-
-class Category(BaseModel):
-    name: str
-
-
-class App(BaseModel):
-    id: UUID
-    name: str
-
-
-class Memory(MemoryBase):
-    id: UUID
-    user_id: UUID
-    app_id: UUID
-    created_at: datetime
-    updated_at: Optional[datetime] = None
-    state: str
-    categories: Optional[List[Category]] = None
-    app: App
-
-    model_config = ConfigDict(from_attributes=True)
-
-class MemoryUpdate(BaseModel):
-    content: Optional[str] = None
-    metadata_: Optional[dict] = None
-    state: Optional[str] = None
-
-
-class MemoryResponse(BaseModel):
-    id: UUID
-    content: str
-    created_at: int
-    state: str
-    app_id: UUID
-    app_name: str
-    categories: List[str]
-    metadata_: Optional[dict] = None
-
-    @validator('created_at', pre=True)
-    def convert_to_epoch(cls, v):
-        if isinstance(v, datetime):
-            return int(v.timestamp())
-        return v
-
-class PaginatedMemoryResponse(BaseModel):
-    items: List[MemoryResponse]
-    total: int
-    page: int
-    size: int
-    pages: int
@@ -1,43 +0,0 @@
-import logging
-from typing import List
-
-from app.utils.prompts import MEMORY_CATEGORIZATION_PROMPT
-from dotenv import load_dotenv
-from openai import OpenAI
-from pydantic import BaseModel
-from tenacity import retry, stop_after_attempt, wait_exponential
-
-load_dotenv()
-openai_client = OpenAI()
-
-
-class MemoryCategories(BaseModel):
-    categories: List[str]
-
-
-@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=15))
-def get_categories_for_memory(memory: str) -> List[str]:
-    try:
-        messages = [
-            {"role": "system", "content": MEMORY_CATEGORIZATION_PROMPT},
-            {"role": "user", "content": memory}
-        ]
-
-        # Let OpenAI handle the pydantic parsing directly
-        completion = openai_client.beta.chat.completions.parse(
-            model="gpt-4o-mini",
-            messages=messages,
-            response_format=MemoryCategories,
-            temperature=0
-        )
-
-        parsed: MemoryCategories = completion.choices[0].message.parsed
-        return [cat.strip().lower() for cat in parsed.categories]
-
-    except Exception as e:
-        logging.error(f"[ERROR] Failed to get categories: {e}")
-        try:
-            logging.debug(f"[DEBUG] Raw response: {completion.choices[0].message.content}")
-        except Exception as debug_e:
-            logging.debug(f"[DEBUG] Could not extract raw response: {debug_e}")
-        raise
@@ -1,33 +0,0 @@
-from typing import Tuple
-
-from app.models import App, User
-from sqlalchemy.orm import Session
-
-
-def get_or_create_user(db: Session, user_id: str) -> User:
-    """Get or create a user with the given user_id"""
-    user = db.query(User).filter(User.user_id == user_id).first()
-    if not user:
-        user = User(user_id=user_id)
-        db.add(user)
-        db.commit()
-        db.refresh(user)
-    return user
-
-
-def get_or_create_app(db: Session, user: User, app_id: str) -> App:
-    """Get or create an app for the given user"""
-    app = db.query(App).filter(App.owner_id == user.id, App.name == app_id).first()
-    if not app:
-        app = App(owner_id=user.id, name=app_id)
-        db.add(app)
-        db.commit()
-        db.refresh(app)
-    return app
-
-
-def get_user_and_app(db: Session, user_id: str, app_id: str) -> Tuple[User, App]:
-    """Get or create both user and their app"""
-    user = get_or_create_user(db, user_id)
-    app = get_or_create_app(db, user, app_id)
-    return user, app
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
kartik-mem0	fe20b2ea8a	Merge remote-tracking branch 'origin/main' into feat/zapier-mem0	2026-07-29 19:59:58 +05:30
kartik-mem0	2e4f11afe1	chore(zapier): align package with @mem0 scope and repo conventions Rename to @mem0/zapier, switch to Apache-2.0 to match the repo and the other integrations, drop private so it can publish, and add the pnpm security overrides plus pnpm-workspace.yaml that every other integration under integrations/ already carries.	2026-07-29 16:53:06 +05:30
KartikandGitHub	ea2ee07586	chore: remove OpenMemory from the monorepo (#6530 )	2026-07-29 15:10:32 +05:30
KartikandGitHub	540d23d610	docs: serve an unconditioned favicon at the docs domain root (#6649 )	2026-07-29 00:07:35 -07:00
KartikandGitHub	3274390f82	docs: redirect /integrations/keywords to /integrations/respan (#6648 )	2026-07-28 23:57:37 -07:00
KartikandGitHub	b357a5a1b0	chore(release): Python SDK v2.0.14, TypeScript SDK v3.1.2 (#6589 )	2026-07-25 17:51:48 +05:30
Hrushikesh YadavandGitHub	d653b63fac	fix(milvus): guard text field in update() with _has_bm25_schema check (#5705 )	2026-07-24 18:27:54 +05:30
Abhay SinghandGitHub	cc4671579f	fix(ts-oss/cassandra): apply every operator in a compound field filter (#6511 )	2026-07-24 18:26:10 +05:30
Bartok GitHub yashwanth123	01afdde3e7	salvage: fix(opensearch) re-raise search errors (credit @yashwanth123 #6477 ) (#6519 ) Co-authored-by: yashwanth123 <yashwanth123@users.noreply.github.com>	2026-07-24 18:19:26 +05:30
Elif Sema Balcioglu GitHub kartik-mem0	d6d89c987b	Add Oracle Vector Store Integration (#5358 ) Co-authored-by: kartik-mem0 <kartik.labhshetwar@mem0.ai>	2026-07-23 21:30:01 +05:30
KartikandGitHub	c2150e8f1a	docs: SEO and AEO updates for the memory expiration page (#6535 )	2026-07-23 20:46:29 +05:30
microblueyandGitHub	19c7bb84a2	fix(ts-oss/chroma): stop dropping filter conditions in where-clause translation (#6521 )	2026-07-23 20:07:19 +05:30
Abhishek ChauhanandGitHub	e6281ab724	fix(ts-oss): forward responseFormat to Gemini in generateResponse (#6468 )	2026-07-23 19:48:33 +05:30
Rod BoevandGitHub	a71d7bdbe3	fix(dashboard): clear the LLM API key on provider change (#6475 )	2026-07-23 19:44:54 +05:30
microblueyandGitHub	56ec7d20f1	fix(vector_stores/opensearch): translate the '*' wildcard to an exists query for every key (#6522 )	2026-07-23 19:41:09 +05:30
Himanshu-Sangshetti	de39f294dd	fix(zapier): address review feedback (masked key, ES imports) + test hygiene - authentication: API key field is now type 'password' so Zapier masks it in the connection UI instead of showing it in cleartext. - index: use ES imports for the platform version and package version instead of require(), matching the repo's TypeScript rule. dist stays flat; both versions resolve at runtime. - AGENTS.md: add the Key Directories row for integrations/zapier-mem0. - tests: disable telemetry during test runs so CI never fires real PostHog events, which also removes a lingering open handle after the suite. Verified: build + zapier validate (0 failures) + 16 tests (13 unit, 3 live E2E).	2026-07-23 18:33:11 +05:30
Himanshu-Sangshetti	1bd9910382	fix(zapier): harden null-body handling and error messages From an independent review pass: - Guard search / get / add against a null or empty 2xx body (previously an uncaught TypeError on .results / .status); return [] or an empty object. - Stringify non-string API error detail so a thrown message never renders as "[object Object]". - Correct the Add sample to the default no-wait shape {event_id, status}. - Add a regression unit test for the null-body path. Verified: build + zapier validate + 16 tests (13 unit, 3 live E2E), plus a live check of the v3-add to /v1/event poll cross-version path.	2026-07-23 11:49:25 +05:30
Himanshu-Sangshetti	9b1e2ef604	feat(integrations): add Zapier app for Mem0 A Zapier Platform CLI app (TypeScript) wrapping the hosted Mem0 v3 REST API: Add / Search / Get Memories / Delete actions with custom API-key auth. - Written in TypeScript, compiles to dist/; offline unit tests (mocked z.request) plus live E2E; `zapier validate` clean - Add supports custom_instructions, custom_categories, includes, excludes; Wait-for-Completion defaults off (extraction can exceed Zapier's 30s step limit) - Middleware surfaces non-2xx as errors (no silent empty results) - Anonymous opt-out telemetry (MEM0_TELEMETRY=false) - Docs page + docs.json/llms.txt entry, ci-gate wiring, AGENTS.md rows Split out of #6057 per review (separate PRs per integration).	2026-07-23 03:05:53 +05:30
Abhay SinghandGitHub	ca2abca2b8	fix(ts-oss/milvus): skip '*' wildcard filter values instead of matching literally (#6508 )	2026-07-23 00:41:45 +05:30
Abhay SinghandGitHub	0e582adc6c	fix(ts-oss/anthropic): find the text block in no-tools responses (#6506 )	2026-07-23 00:40:40 +05:30
Clement Antony GitHub kartik-mem0	9caffeaa7b	fix(ts-sdk): use textLemmatized for BM25 keyword search on Milvus, OpenSearch, and MongoDB (#6497 ) Co-authored-by: kartik-mem0 <kartik.labhshetwar@mem0.ai>	2026-07-23 00:39:18 +05:30
microbluey GitHub kartik-mem0	a58e0586ad	fix(vector_stores/opensearch): honor all filter keys instead of a hardcoded identity-key list (#6454 ) Co-authored-by: kartik-mem0 <kartik.labhshetwar@mem0.ai>	2026-07-23 00:24:16 +05:30
microblueyandGitHub	a9cb4bb644	fix(vector_stores/chroma): stop dropping filter conditions in where-clause translation (#6452 )	2026-07-23 00:22:10 +05:30
Gyubin SonandGitHub	7bf84b8d38	fix: drop unused vector column from pgvector get() and list() queries (#6483 )	2026-07-23 00:11:40 +05:30
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				`<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>`
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				`# This file makes the app directory a Python package`