Merge remote-tracking branch 'origin/main' into mintlify/af82bda4

Co-authored-by: rupak-eng <rupak-eng@users.noreply.github.com>

.claude-plugin/marketplace.json

@@ -12,7 +12,7 @@
       "name": "mem0",
       "source": "./integrations/claude-code-plugin",
       "description": "Cross-session memory and token savings for coding agents.",
-      "version": "0.3.2"
+      "version": "0.3.3"
     }
   ]
 }

.cursor-plugin/marketplace.json

@@ -12,7 +12,7 @@
       "name": "mem0",
       "source": "./integrations/cursor-plugin",
       "description": "Cross-session memory and token savings for coding agents.",
-      "version": "0.3.2"
+      "version": "0.3.3"
     }
   ]
 }

.kimi-plugin/marketplace.json

@@ -5,7 +5,7 @@
     {
       "id": "mem0",
       "displayName": "Mem0",
-      "version": "0.3.2",
+      "version": "0.3.3",
       "description": "Cross-session memory and token savings for coding agents.",
       "homepage": "https://mem0.ai",
       "keywords": ["memory", "personalization", "mcp", "semantic-search"],

docs/api-reference.mdx

@@ -3,7 +3,7 @@ title: "API Reference Overview"
 sidebarTitle: "Overview"
 icon: "terminal"
 iconType: "solid"
-description: "REST APIs for memory management, search, and entity operations"
+description: "Explore the Mem0 REST API for adding, searching, updating, and deleting memories, plus managing users, organizations, projects, and webhooks."
 ---
 
 ## Mem0 REST API

docs/api-reference/dream/dream-preview.mdx

@@ -1,5 +1,4 @@
 ---
 title: "Preview Dream Scope"
-description: "A no-write preview of the scope Dream synthesis would analyze for a project."
 openapi: "post /api/v1/orgs/organizations/{org_id}/projects/{project_id}/dream/preview/"
 ---

docs/api-reference/dream/get-dream-activity.mdx

@@ -1,5 +1,4 @@
 ---
 title: "Get Dream Activity"
-description: "Supersede/merge activity feed for a project, newest first (keyset-paginated)."
 openapi: "get /api/v1/orgs/organizations/{org_id}/projects/{project_id}/dream/activity/"
 ---

docs/api-reference/dream/get-dream-config.mdx

@@ -1,5 +1,4 @@
 ---
 title: "Get Dream Configuration"
-description: "Retrieve a project's Dream (memory synthesis) configuration and plan entitlements."
 openapi: "get /api/v1/orgs/organizations/{org_id}/projects/{project_id}/dream/config/"
 ---

docs/api-reference/dream/get-dream-memory-sources.mdx

@@ -1,5 +1,4 @@
 ---
 title: "Get a Synthesized Memory's Sources"
-description: "The source memories a synthesized (pattern) memory was distilled from."
 openapi: "get /api/v1/orgs/organizations/{org_id}/projects/{project_id}/dream/memory/{memory_id}/sources/"
 ---

docs/api-reference/dream/get-dream-run-memories.mdx

@@ -1,5 +1,4 @@
 ---
 title: "Get Memories in a Dream Run"
-description: "Keyset page of the synthesized memories within a single synthesis run."
 openapi: "get /api/v1/orgs/organizations/{org_id}/projects/{project_id}/dream/runs/{run_id}/memories/"
 ---

docs/api-reference/dream/get-dream-runs.mdx

@@ -1,5 +1,4 @@
 ---
 title: "Get Dream Synthesis Runs"
-description: "Synthesis activity grouped per run, newest first (keyset-paginated)."
 openapi: "get /api/v1/orgs/organizations/{org_id}/projects/{project_id}/dream/runs/"
 ---

docs/api-reference/dream/get-dream-stats.mdx

@@ -1,5 +1,4 @@
 ---
 title: "Get Dream Stats"
-description: "Lifecycle and synthesis counts for a project, plus reflection freshness."
 openapi: "get /api/v1/orgs/organizations/{org_id}/projects/{project_id}/dream/stats/"
 ---

docs/api-reference/dream/update-dream-config.mdx

@@ -1,5 +1,4 @@
 ---
 title: "Update Dream Configuration"
-description: "Enable or disable Synthesis (reflection) for a project, or change the reflection mode."
 openapi: "patch /api/v1/orgs/organizations/{org_id}/projects/{project_id}/dream/config/"
 ---

docs/api-reference/entities/delete-user.mdx

@@ -1,5 +1,5 @@
 ---
 title: 'Delete User'
-description: "Remove a user entity from the Mem0 platform by entity type and ID using the DELETE endpoint."
+description: "Send a DELETE request to /v2/entities/{entity_type}/{entity_id}/ to permanently remove a user entity from your Mem0 project by ID."
 openapi: delete /v2/entities/{entity_type}/{entity_id}/
 ---

docs/api-reference/entities/get-users.mdx

@@ -1,5 +1,5 @@
 ---
 title: 'Get Users'
-description: "Retrieve a list of all user entities stored in the Mem0 platform using the GET endpoint."
+description: "Send a GET /v1/entities request to list every user entity in your Mem0 project, returning IDs, names, created_at, and metadata fields."
 openapi: get /v1/entities/
 ---

docs/api-reference/events/get-event.mdx

@@ -1,6 +1,6 @@
 ---
 title: 'Get Event'
-description: "Retrieve details of a specific event by ID, including status and payload for async memory operations."
+description: "Fetch details of a specific Mem0 event by its event_id, including status, payload, and completion info for async memory operations."
 openapi: get /v1/event/{event_id}/
 ---
 

docs/api-reference/events/get-events.mdx

@@ -1,6 +1,6 @@
 ---
 title: 'Get Events'
-description: "List recent events for your organization and project, useful for dashboards, alerting, and audit logging."
+description: "List recent events for your Mem0 organization and project to power dashboards, alerting on FAILED writes, and audit logging pipelines."
 openapi: get /v1/events/
 ---
 

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

@@ -1,6 +1,5 @@
 ---
 title: Add Memories
-description: "Add facts, messages, or metadata to a user memory store with async processing and event tracking via the V3 additive pipeline."
 openapi: post /v3/memories/add/
 ---
 

docs/api-reference/memory/batch-delete.mdx

@@ -1,5 +1,4 @@
 ---
 title: 'Batch Delete Memories'
-description: "Delete multiple memories in a single batch request using the Mem0 API DELETE endpoint."
 openapi: delete /v1/batch/
 ---

docs/api-reference/memory/batch-update.mdx

@@ -1,5 +1,4 @@
 ---
 title: 'Batch Update Memories'
-description: "Update multiple memories in a single batch request using the Mem0 API PUT endpoint."
 openapi: put /v1/batch/
----
+---

docs/api-reference/memory/create-memory-export.mdx

@@ -1,6 +1,5 @@
 ---
 title: 'Create Memory Export'
-description: "Submit an export job to create a structured memory export using a customizable Pydantic schema and filters."
 openapi: post /v1/exports/
 ---
 

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

@@ -1,5 +1,4 @@
 ---
 title: 'Delete Memories'
-description: "Delete all memories matching specified filters from the Mem0 memory store using the DELETE endpoint."
 openapi: delete /v1/memories/
 ---

docs/api-reference/memory/delete-memory.mdx

@@ -1,6 +1,5 @@
 ---
 title: "Delete Memory API Endpoint"
 sidebarTitle: "Delete Memory"
-description: "Delete a single memory by its unique memory ID from the Mem0 platform using the DELETE endpoint."
 openapi: delete /v1/memories/{memory_id}/
 ---

docs/api-reference/memory/feedback.mdx

@@ -1,5 +1,4 @@
 ---
 title: 'Feedback'
-description: "Submit positive or negative feedback on memory results to help improve memory accuracy and relevance."
 openapi: post /v1/feedback/
 ---

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

@@ -1,6 +1,5 @@
 ---
 title: "Get Memories"
-description: "Retrieve memories with paginated results and advanced filtering using logical operators like AND, OR, NOT, and comparison queries."
 openapi: post /v3/memories/
 ---
 

docs/api-reference/memory/get-memory-export.mdx

@@ -1,7 +1,6 @@
 ---
 title: 'Get Memory Export'
-description: "Retrieve the latest structured memory export after submitting an export job, with optional entity filters."
 openapi: post /v1/exports/get
 ---
 
-Retrieve the latest structured memory export after submitting an export job. You can filter the export by `user_id`, `agent_id`, `app_id`, `run_id`, `created_at`, or `updated_at` to get the most recent export matching your filters.
+Retrieve the latest structured memory export after submitting an export job. You can filter the export by `user_id`, `agent_id`, `app_id`, `run_id`, `created_at`, or `updated_at` to get the most recent export matching your filters.

docs/api-reference/memory/get-memory.mdx

@@ -1,5 +1,4 @@
 ---
 title: 'Get Memory'
-description: "Retrieve a single memory by its unique memory ID from the Mem0 platform using the GET endpoint."
 openapi: get /v1/memories/{memory_id}/
----
+---

docs/api-reference/memory/history-memory.mdx

@@ -1,5 +1,4 @@
 ---
 title: 'Memory History'
-description: "Retrieve the full change history of a specific memory to track how it has evolved over time."
 openapi: get /v1/memories/{memory_id}/history/
----
+---

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

@@ -1,6 +1,5 @@
 ---
 title: 'Search Memories'
-description: "Search memories with hybrid retrieval (semantic + BM25 + entity matching) and advanced filtering using logical and comparison operators."
 openapi: post /v3/memories/search/
 ---
 

docs/api-reference/memory/update-memory.mdx

@@ -1,7 +1,6 @@
 ---
 title: "Update Memory API Endpoint"
 sidebarTitle: "Update Memory"
-description: "Update the content, metadata, timestamp, or expiration date of a single memory by its unique ID using the PUT endpoint."
 openapi: put /v1/memories/{memory_id}/
 ---
 

docs/api-reference/organization/add-org-member.mdx

@@ -1,7 +1,6 @@
 ---
 title: "Add Organization Member API Endpoint"
 sidebarTitle: "Add Member"
-description: "Add a new member to an organization with a specified role such as READER or OWNER access level."
 openapi: post /api/v1/orgs/organizations/{org_id}/members/
 ---
 

docs/api-reference/organization/create-org.mdx

@@ -1,5 +1,4 @@
 ---
 title: 'Create Organization'
-description: "Create a new organization on the Mem0 platform to manage projects, members, and memory resources."
 openapi: post /api/v1/orgs/organizations/
----
+---

docs/api-reference/organization/delete-org.mdx

@@ -1,5 +1,4 @@
 ---
 title: 'Delete Organization'
-description: "Permanently delete an organization and its associated resources from the Mem0 platform."
 openapi: delete /api/v1/orgs/organizations/{org_id}/
----
+---

docs/api-reference/organization/get-org-members.mdx

@@ -1,6 +1,5 @@
 ---
 title: "Get Organization Members API Endpoint"
 sidebarTitle: "Get Members"
-description: "Retrieve a list of all members belonging to a specific organization on the Mem0 platform."
 openapi: get /api/v1/orgs/organizations/{org_id}/members/
 ---

docs/api-reference/organization/get-org.mdx

@@ -1,5 +1,4 @@
 ---
 title: 'Get Organization'
-description: "Retrieve details of a specific organization by its ID from the Mem0 platform using the GET endpoint."
 openapi: get /api/v1/orgs/organizations/{org_id}/
----
+---

docs/api-reference/organization/get-orgs.mdx

@@ -1,5 +1,5 @@
 ---
 title: 'Get Organizations'
-description: "Retrieve a list of all organizations associated with your Mem0 account using the GET endpoint."
+description: "Call GET /api/v1/orgs/organizations to retrieve every organization tied to your Mem0 account, including IDs, names, and role metadata."
 openapi: get /api/v1/orgs/organizations/
 ---

docs/api-reference/organization/remove-org-member.mdx

@@ -1,5 +1,5 @@
 ---
 title: "Remove Organization Member"
-description: "Remove a member from an organization to revoke their access to its projects and resources."
+description: "Send a DELETE request to remove a member from a Mem0 organization and revoke their access to all of its projects, memories, and resources."
 openapi: "delete /api/v1/orgs/organizations/{org_id}/members/"
 ---

docs/api-reference/organization/update-org-member.mdx

@@ -1,5 +1,4 @@
 ---
 title: "Update Organization Member"
-description: "Update an existing member's role within an organization to change their permissions and access level."
 openapi: "put /api/v1/orgs/organizations/{org_id}/members/"
 ---

docs/api-reference/organizations-projects.mdx

@@ -1,7 +1,7 @@
 ---
 title: Organizations & Projects
 icon: "building"
-description: "Manage multi-tenant applications with organization and project APIs"
+description: "Manage multi-tenant Mem0 apps with the organizations and projects APIs, covering isolation, access control, API keys, and team collaboration."
 ---
 
 ## Overview

docs/api-reference/profiles/generate-profiles.mdx

@@ -0,0 +1,4 @@
+---
+title: 'Generate Profiles'
+openapi: post /v2/profiles/jobs/
+---

docs/api-reference/profiles/get-profile-job.mdx

@@ -0,0 +1,4 @@
+---
+title: 'Get Generation Job'
+openapi: get /v2/profiles/jobs/{job_id}/
+---

docs/api-reference/profiles/get-profile-settings.mdx

@@ -0,0 +1,4 @@
+---
+title: 'Get Profile Settings'
+openapi: get /v2/profiles/settings/
+---

docs/api-reference/profiles/get-profile.mdx

@@ -0,0 +1,4 @@
+---
+title: 'Get Profile'
+openapi: get /v2/entities/{entity_type}/{entity_id}/profile/
+---

docs/api-reference/profiles/update-profile-settings.mdx

@@ -0,0 +1,4 @@
+---
+title: 'Update Profile Settings'
+openapi: post /v2/profiles/settings/
+---

docs/api-reference/project/add-project-member.mdx

@@ -1,7 +1,6 @@
 ---
 title: "Add Project Member API Endpoint"
 sidebarTitle: "Add Member"
-description: "Add a new member to a project with a specified role such as READER or OWNER access level."
 openapi: post /api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/
 ---
 

docs/api-reference/project/create-project.mdx

@@ -1,5 +1,4 @@
 ---
 title: 'Create Project'
-description: "Create a new project within an organization on the Mem0 platform to isolate memory resources."
 openapi: post /api/v1/orgs/organizations/{org_id}/projects/
----
+---

docs/api-reference/project/delete-project.mdx

@@ -1,5 +1,4 @@
 ---
 title: 'Delete Project'
-description: "Permanently delete a project and its associated data from the Mem0 platform by project ID."
 openapi: delete /api/v1/orgs/organizations/{org_id}/projects/{project_id}/
----
+---

docs/api-reference/project/get-project-members.mdx

@@ -1,6 +1,5 @@
 ---
 title: "Get Project Members API Endpoint"
 sidebarTitle: "Get Members"
-description: "Retrieve a list of all members belonging to a specific project on the Mem0 platform."
 openapi: get /api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/
 ---

docs/api-reference/project/get-project.mdx

@@ -1,5 +1,4 @@
 ---
 title: 'Get Project'
-description: "Retrieve details of a specific project by its organization and project ID using the GET endpoint."
 openapi: get /api/v1/orgs/organizations/{org_id}/projects/{project_id}/
----
+---

docs/api-reference/project/get-projects.mdx

@@ -1,5 +1,4 @@
 ---
 title: 'Get Projects'
-description: "Retrieve a list of all projects within an organization on the Mem0 platform using the GET endpoint."
 openapi: get /api/v1/orgs/organizations/{org_id}/projects/
----
+---

docs/api-reference/project/remove-project-member.mdx

@@ -1,5 +1,5 @@
 ---
 title: "Remove Project Member"
-description: "Remove a member from a project to revoke their access to its memories, configuration, and resources."
+description: "Send a DELETE request to remove a member from a Mem0 project and revoke their access to its memories, settings, API keys, and resources."
 openapi: "delete /api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/"
 ---

docs/api-reference/project/update-project-member.mdx

@@ -1,5 +1,4 @@
 ---
 title: "Update Project Member"
-description: "Update an existing member's role within a project to change their permissions and access level."
 openapi: "put /api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/"
 ---

docs/api-reference/project/update-project.mdx

@@ -1,5 +1,4 @@
 ---
 title: "Update Project"
-description: "Update a project's settings, including name, custom instructions, and other configuration options."
 openapi: "patch /api/v1/orgs/organizations/{org_id}/projects/{project_id}/"
 ---

docs/api-reference/webhook/create-webhook.mdx

@@ -1,6 +1,5 @@
 ---
 title: 'Create Webhook'
-description: "Create a new webhook for a project to receive real-time notifications about memory events."
 openapi: post /api/v1/webhooks/projects/{project_id}/
 ---
 

docs/api-reference/webhook/delete-webhook.mdx

@@ -1,5 +1,4 @@
 ---
 title: 'Delete Webhook'
-description: "Delete an existing webhook by its ID to stop receiving notifications for memory events."
 openapi: delete /api/v1/webhooks/{webhook_id}/
 ---

docs/api-reference/webhook/get-webhook.mdx

@@ -1,6 +1,5 @@
 ---
 title: 'Get Webhook'
-description: "Retrieve webhook configuration details for a specific project on the Mem0 platform."
 openapi: get /api/v1/webhooks/projects/{project_id}/
 ---
 

docs/api-reference/webhook/update-webhook.mdx

@@ -1,6 +1,5 @@
 ---
 title: 'Update Webhook'
-description: "Update an existing webhook configuration, such as its URL or event subscriptions, by webhook ID."
 openapi: put /api/v1/webhooks/{webhook_id}/
 ---
 

docs/changelog/highlights.mdx

@@ -1,6 +1,6 @@
 ---
 title: "Highlights"
-description: "Major product launches, headline features, and milestones for Mem0."
+description: "Highlights of major Mem0 product launches, headline features, integrations like n8n and Zapier, and milestones across the platform and SDKs."
 mode: "wide"
 ---
 

docs/changelog/platform.mdx

@@ -1,6 +1,6 @@
 ---
 title: "Platform"
-description: "Release notes for the Mem0 hosted platform: backend, dashboard, billing, and infrastructure changes."
+description: "Release notes for the Mem0 hosted platform covering backend APIs, dashboard, billing, temporal reasoning, search, and infrastructure updates."
 mode: "wide"
 ---
 

docs/changelog/sdk.mdx

@@ -1,12 +1,34 @@
 ---
 title: "SDK & Tools"
-description: "Release notes for the Mem0 Python SDK, TypeScript SDK, Vercel AI SDK, CLI, and editor plugins."
+description: "Release notes for the Mem0 Python SDK, TypeScript SDK, Vercel AI SDK, CLI, and editor plugins, including new vector stores and bug fixes."
 mode: "wide"
 ---
 
 <Tabs>
 <Tab title="Python">
 
+<Update label="2026-09-25" description="v2.2.1">
+
+**Bug Fixes:**
+- **Core:** `add()` (`Memory` and `AsyncMemory`) no longer reports records the vector store rejected as successful `ADD` events. Only records that were actually inserted are written to history, entity-linked, and returned. If none of the extracted memories could be inserted, `add()` now raises `VectorStoreError` instead of returning memories that were never stored ([#7066](https://github.com/mem0ai/mem0/pull/7066))
+- **Core:** Restore the context-manager protocol on `Memory` (`with Memory() as m:`) and `AsyncMemory` (`async with AsyncMemory() as m:`), which closes the instance on exit ([#7354](https://github.com/mem0ai/mem0/pull/7354))
+- **LLMs:** The AWS Bedrock Anthropic path now returns the first Converse content block that carries text instead of always reading `content[0]`. Claude reasoning models can emit a `reasoningContent` block before the text block, which previously made the call fail with a `KeyError` ([#6369](https://github.com/mem0ai/mem0/pull/6369))
+- **Vector Stores:** Turbopuffer filters now apply every operator (`eq`, `ne`, `gt`, `gte`, `lt`, `lte`, `in`, `nin`). Only `gte` and `lte` were read before, so any other operator was silently dropped and the query returned unfiltered results. An unsupported operator now raises `ValueError` ([#6564](https://github.com/mem0ai/mem0/pull/6564))
+- **Vector Stores:** Turbopuffer `search()` scores now respect `distance_metric`. With `euclidean_squared`, the unbounded squared distance maps to `1 / (1 + distance)` instead of `1 - distance`, which went negative and inverted ranking for any distance above 1 ([#6559](https://github.com/mem0ai/mem0/pull/6559))
+- **Vector Stores:** S3 Vectors `search()` scores are now metric-aware. With `euclidean`, the distance maps to `1 / (1 + distance)` instead of `max(0, 1 - distance)`, which collapsed most scores to 0 ([#6547](https://github.com/mem0ai/mem0/pull/6547))
+
+</Update>
+
+<Update label="2026-09-23" description="v2.2.0">
+
+**New Features:**
+- **Client:** Add User Profiles to `MemoryClient` and `AsyncMemoryClient`: `get_profile()`, `generate_profile()`, `get_profile_settings()`, `update_profile_settings()`, `sample_profiles()`, and `get_profile_job()`. A profile is a structured, always-current JSON summary of one user, shaped by a JSON Schema you configure per project and filled by an LLM from that user's memories. Generation is asynchronous. Every job POST carries an `Idempotency-Key`; to retry a lost request without starting a second job, pass the same `idempotency_key` on each attempt ([#7340](https://github.com/mem0ai/mem0/pull/7340))
+
+**Bug Fixes:**
+- **Vector Stores:** Guard against `None` timestamps in the Valkey vector store's `insert()` and `update()` paths. `created_at` and `updated_at` fields that were present in the payload but set to `None` previously passed the `"created_at" not in payload` / `"updated_at" in payload` checks and raised `TypeError` when `datetime.fromisoformat()` received `None`. Both paths now use `.get()` with a truthiness check so `None` values fall through to the default, matching the Redis provider's behavior ([#6993](https://github.com/mem0ai/mem0/pull/6993))
+
+</Update>
+
 <Update label="2026-09-18" description="v2.1.0">
 
 **Improvements:**
@@ -182,7 +204,7 @@ mode: "wide"
 <Update label="2026-06-24" description="v2.0.8">
 
 **New Features:**
-- **Embeddings:** Add native `embed_batch` to five embedders: LM Studio, Together, HuggingFace, Vertex AI, and Google GenAI: for batched embedding requests ([#5609](https://github.com/mem0ai/mem0/pull/5609))
+- **Embeddings:** Add native `embed_batch` to five embedders for batched embedding requests: LM Studio, Together, HuggingFace, Vertex AI, and Google GenAI ([#5609](https://github.com/mem0ai/mem0/pull/5609))
 
 **Bug Fixes:**
 - **Core:** Guard against malformed `image_url` entries in `parse_vision_messages` to prevent crashes ([#5631](https://github.com/mem0ai/mem0/pull/5631))
@@ -972,7 +994,7 @@ See the [OSS v2 to v3 migration guide](https://docs.mem0.ai/migration/oss-v2-to-
 **New Features:**
 - **OpenMemory:** Added OpenMemory support
 - **Neo4j:** Added weights to Neo4j model
-- **AWS:** Added support for Opsearch Serverless
+- **AWS:** Added support for OpenSearch Serverless
 - **Examples:** Added ElizaOS Example
 
 **Improvements:**
@@ -1235,6 +1257,23 @@ See the [OSS v2 to v3 migration guide](https://docs.mem0.ai/migration/oss-v2-to-
 
 <Tab title="TypeScript">
 
+<Update label="2026-09-25" description="v3.3.1">
+
+**Bug Fixes:**
+- **Config (OSS):** `ConfigManager` no longer injects OpenAI's default `baseURL` and `model` into non-OpenAI LLM providers. The defaults now apply only to `openai` and `openai_structured`, so a DeepSeek, xAI, or other provider config without an explicit `baseURL` or `model` falls back to that provider's own defaults and env vars (`DEEPSEEK_API_BASE`, `XAI_API_BASE`, ...) instead of pointing at OpenAI with an OpenAI model name ([#7350](https://github.com/mem0ai/mem0/pull/7350))
+- **Vector Stores:** Turbopuffer filters now apply every operator (`eq`, `ne`, `gt`, `gte`, `lt`, `lte`, `in`, `nin`). Only the range operators were read before, so `eq`, `ne`, `in`, and `nin` were silently dropped. A `"*"` value now matches anything instead of nothing, an array value is treated as `in`, and an unsupported operator throws ([#6578](https://github.com/mem0ai/mem0/pull/6578))
+- **Vector Stores:** Turbopuffer `search()` with `euclidean_squared` now maps the unbounded squared distance to `1 / (1 + distance)` instead of `1 - distance`, which went negative and inverted ranking for any distance above 1 ([#6580](https://github.com/mem0ai/mem0/pull/6580))
+- **Packaging:** `pg`, `@types/pg`, and `natural` are now optional peer dependencies, and `pg` / `@types/pg` accept caret ranges instead of the exact `8.11.3` / `8.11.0` pins, so installing `mem0ai` no longer pulls in `pg` or conflicts with an app's own `pg` version. The PGVector store now imports `pg` only when it is used, so install it alongside `mem0ai` (`npm install pg`) if you use that store. `@types/jest` moved from peer dependencies to dev dependencies ([#7450](https://github.com/mem0ai/mem0/pull/7450))
+
+</Update>
+
+<Update label="2026-09-23" description="v3.3.0">
+
+**New Features:**
+- **Client:** Add User Profiles to `MemoryClient`: `getProfile()`, `generateProfile()`, `getProfileSettings()`, `updateProfileSettings()`, `sampleProfiles()`, and `getProfileJob()`. A profile is a structured, always-current JSON summary of one user, shaped by a JSON Schema you configure per project and filled by an LLM from that user's memories. Generation is asynchronous. Every job POST carries an `Idempotency-Key`; to retry a lost request without starting a second job, pass the same `idempotencyKey` on each attempt ([#7340](https://github.com/mem0ai/mem0/pull/7340))
+
+</Update>
+
 <Update label="2026-09-18" description="v3.2.0">
 
 **Improvements:**
@@ -2394,6 +2433,17 @@ Initial release of the Mem0 plugin for Claude Code and Cursor, followed by Codex
 
 <Tab title="Claude Code">
 
+<Update label="2026-09-23" description="Claude Code plugin v0.3.3">
+
+**Improvements:**
+- **Search:** The `search_memories` tool description no longer tells the agent to call it before answering anything that could depend on prior context. It now asks for a search before repeating investigation or when earlier decisions, fixes, commands, or results may help, which reduces unnecessary searches ([#7420](https://github.com/mem0ai/mem0/pull/7420))
+- **Sidekick:** Sidekick searches memories when earlier sessions could help, instead of before every answer ([#7420](https://github.com/mem0ai/mem0/pull/7420))
+- **Extraction:** Repository memory instructions are shorter. They no longer ask for a dedicated memory for each command that failed and was then fixed, and no longer carry separate rules against saving personal preferences or memories that only name the repository, branch, or directory ([#7420](https://github.com/mem0ai/mem0/pull/7420))
+- **Search skill:** `/search` no longer describes categories as best-effort labels or asks for a retry without the category ([#7420](https://github.com/mem0ai/mem0/pull/7420))
+- **Packaging:** `PLUGIN_VERSION` bumped to `0.3.3`, so the `mem0-plugin/<version>` wire header and `plugin_version` telemetry field identify builds with these prompts ([#7420](https://github.com/mem0ai/mem0/pull/7420))
+
+</Update>
+
 <Update label="2026-09-18" description="Claude Code plugin v0.3.2">
 
 **Improvements:**
@@ -2424,6 +2474,16 @@ Initial release of the Mem0 plugin for Claude Code and Cursor, followed by Codex
 
 <Tab title="Cursor">
 
+<Update label="2026-09-23" description="Cursor plugin v0.3.3">
+
+**Improvements:**
+- **Search:** The `search_memories` tool description no longer tells the agent to call it before answering anything that could depend on prior context. It now asks for a search before repeating investigation or when earlier decisions, fixes, commands, or results may help, which reduces unnecessary searches ([#7420](https://github.com/mem0ai/mem0/pull/7420))
+- **Extraction:** Repository memory instructions are shorter. They no longer ask for a dedicated memory for each command that failed and was then fixed, and no longer carry separate rules against saving personal preferences or memories that only name the repository, branch, or directory ([#7420](https://github.com/mem0ai/mem0/pull/7420))
+- **Search skill:** `/search` no longer describes categories as best-effort labels or asks for a retry without the category ([#7420](https://github.com/mem0ai/mem0/pull/7420))
+- **Packaging:** `PLUGIN_VERSION` bumped to `0.3.3`, so the `mem0-plugin/<version>` wire header and `plugin_version` telemetry field identify builds with these prompts ([#7420](https://github.com/mem0ai/mem0/pull/7420))
+
+</Update>
+
 <Update label="2026-09-18" description="Cursor plugin v0.3.2">
 
 **Improvements:**
@@ -2454,6 +2514,16 @@ Initial release of the Mem0 plugin for Claude Code and Cursor, followed by Codex
 
 <Tab title="Codex">
 
+<Update label="2026-09-23" description="Codex plugin v0.3.3">
+
+**Improvements:**
+- **Search:** The `search_memories` tool description no longer tells the agent to call it before answering anything that could depend on prior context. It now asks for a search before repeating investigation or when earlier decisions, fixes, commands, or results may help, which reduces unnecessary searches ([#7420](https://github.com/mem0ai/mem0/pull/7420))
+- **Extraction:** Repository memory instructions are shorter. They no longer ask for a dedicated memory for each command that failed and was then fixed, and no longer carry separate rules against saving personal preferences or memories that only name the repository, branch, or directory ([#7420](https://github.com/mem0ai/mem0/pull/7420))
+- **Search skill:** `/search` no longer describes categories as best-effort labels or asks for a retry without the category ([#7420](https://github.com/mem0ai/mem0/pull/7420))
+- **Packaging:** `PLUGIN_VERSION` bumped to `0.3.3`, so the `mem0-plugin/<version>` wire header and `plugin_version` telemetry field identify builds with these prompts ([#7420](https://github.com/mem0ai/mem0/pull/7420))
+
+</Update>
+
 <Update label="2026-09-18" description="Codex plugin v0.3.2">
 
 **Improvements:**
@@ -2483,6 +2553,15 @@ Initial release of the Mem0 plugin for Claude Code and Cursor, followed by Codex
 
 <Tab title="OpenCode">
 
+<Update label="2026-09-23" description="OpenCode plugin v0.4.1">
+
+**Improvements:**
+- **Search:** The `search_memories` tool description no longer asks the agent to search proactively or to run several searches for multi-part questions. It now asks for a search before repeating investigation or when earlier decisions, fixes, commands, or results may help, the same wording as the other coding-agent plugins ([#7420](https://github.com/mem0ai/mem0/pull/7420))
+- **Session context:** Removed two system-context lines that told the agent to run 2 parallel searches before responding and 2-4 parallel searches for non-trivial tasks ([#7420](https://github.com/mem0ai/mem0/pull/7420))
+- **Skills:** `/mem0-search` and `/mem0-context-loader` make one `search_memories` call instead of 2 and 2-4 parallel calls. `/mem0-context-loader` now uses the search skill description from the other coding-agent plugins instead of asking to load at every new task or context switch ([#7420](https://github.com/mem0ai/mem0/pull/7420))
+
+</Update>
+
 <Update label="2026-09-18" description="OpenCode plugin v0.4.0">
 
 **Changes:**
@@ -2589,6 +2668,16 @@ Initial release of the Mem0 plugin for Claude Code and Cursor, followed by Codex
 
 <Tab title="Antigravity">
 
+<Update label="2026-09-23" description="Antigravity plugin v0.3.3">
+
+**Improvements:**
+- **Search:** The `search_memories` tool description no longer tells the agent to call it before answering anything that could depend on prior context. It now asks for a search before repeating investigation or when earlier decisions, fixes, commands, or results may help, which reduces unnecessary searches ([#7420](https://github.com/mem0ai/mem0/pull/7420))
+- **Extraction:** Repository memory instructions are shorter. They no longer ask for a dedicated memory for each command that failed and was then fixed, and no longer carry separate rules against saving personal preferences or memories that only name the repository, branch, or directory ([#7420](https://github.com/mem0ai/mem0/pull/7420))
+- **Search skill:** `/search` no longer describes categories as best-effort labels or asks for a retry without the category ([#7420](https://github.com/mem0ai/mem0/pull/7420))
+- **Packaging:** `PLUGIN_VERSION` bumped to `0.3.3`, so the `mem0-plugin/<version>` wire header and `plugin_version` telemetry field identify builds with these prompts ([#7420](https://github.com/mem0ai/mem0/pull/7420))
+
+</Update>
+
 <Update label="2026-09-18" description="Antigravity plugin v0.3.2">
 
 **Improvements:**
@@ -2692,6 +2781,16 @@ Existing memories written by the previous versions are not rewritten. If your me
 
 <Tab title="Kimi">
 
+<Update label="2026-09-23" description="Kimi Code plugin v0.3.3">
+
+**Improvements:**
+- **Search:** The `search_memories` tool description no longer tells the agent to call it before answering anything that could depend on prior context. It now asks for a search before repeating investigation or when earlier decisions, fixes, commands, or results may help, which reduces unnecessary searches ([#7420](https://github.com/mem0ai/mem0/pull/7420))
+- **Extraction:** Repository memory instructions are shorter. They no longer ask for a dedicated memory for each command that failed and was then fixed, and no longer carry separate rules against saving personal preferences or memories that only name the repository, branch, or directory ([#7420](https://github.com/mem0ai/mem0/pull/7420))
+- **Search skill:** `/search` no longer describes categories as best-effort labels or asks for a retry without the category ([#7420](https://github.com/mem0ai/mem0/pull/7420))
+- **Packaging:** `PLUGIN_VERSION` bumped to `0.3.3`, so the `mem0-plugin/<version>` wire header and `plugin_version` telemetry field identify builds with these prompts ([#7420](https://github.com/mem0ai/mem0/pull/7420))
+
+</Update>
+
 <Update label="2026-09-18" description="Kimi Code plugin v0.3.2">
 
 **Improvements:**
@@ -2737,6 +2836,13 @@ Existing memories written by the previous versions are not rewritten. If your me
 
 <Tab title="OpenClaw">
 
+<Update label="2026-09-23" description="openclaw-mem0 v1.2.1">
+
+**Improvements:**
+- **Search:** The `memory_search` tool description no longer asks the agent to search proactively or to run several searches for multi-part questions. It now asks for a search before repeating investigation or when earlier decisions, fixes, commands, or results may help. Recall strategies (`smart`, `always`, `manual`) are unchanged ([#7420](https://github.com/mem0ai/mem0/pull/7420))
+
+</Update>
+
 <Update label="2026-09-18" description="openclaw-mem0 v1.2.0">
 
 **Changes:**
@@ -3033,6 +3139,15 @@ Existing memories written by the previous versions are not rewritten. If your me
 
 <Tab title="Pi Agent">
 
+<Update label="2026-09-23" description="Pi Agent plugin v0.3.2">
+
+**Improvements:**
+- **Search:** The memory policy, `mem0_memory` tool description, and prompt guidelines no longer ask the agent to search before answering anything that may depend on earlier context or to run several searches per question. They now ask for a search before repeating investigation or when earlier decisions, fixes, commands, or results may help ([#7420](https://github.com/mem0ai/mem0/pull/7420))
+- **Skills:** `context-loader` makes one search instead of 2-4 parallel searches, and uses the search skill description from the other coding-agent plugins ([#7420](https://github.com/mem0ai/mem0/pull/7420))
+- **Automatic recall:** Injected memories are introduced as "Mem0 found these relevant memories from earlier work in this repository:", the same heading as the Python plugins. The old heading called them a shallow first pass and told the agent to search again ([#7420](https://github.com/mem0ai/mem0/pull/7420))
+
+</Update>
+
 <Update label="2026-09-18" description="Pi Agent plugin v0.3.1">
 
 **Improvements:**
@@ -3153,6 +3268,14 @@ Existing memories written by the previous versions are not rewritten. If your me
 
 <Tab title="DeepSeek Harness">
 
+<Update label="2026-09-23" description="deepseek-plugin v0.3.2">
+
+**Improvements:**
+- **Search:** The `search_memory` tool description no longer asks the agent to search proactively before answering anything that may depend on earlier context. It now asks for a search before repeating investigation or when earlier decisions, fixes, commands, or results may help ([#7420](https://github.com/mem0ai/mem0/pull/7420))
+- **Automatic recall:** Injected memories are introduced as "Mem0 found these relevant memories from earlier work:". The old heading called them a shallow first pass and told the agent to search again with `mem0_memory`, a tool DeepSeek does not have ([#7420](https://github.com/mem0ai/mem0/pull/7420))
+
+</Update>
+
 <Update label="2026-09-18" description="deepseek-plugin v0.3.1">
 
 **Improvements:**

docs/components/embedders/models/fastembed.mdx

@@ -1,6 +1,6 @@
 ---
 title: "FastEmbed"
-description: "Configure FastEmbed as an embedding provider in Mem0 to generate embeddings locally using ONNX-based models without a GPU."
+description: "Run FastEmbed inside Mem0 to generate ONNX-based text embeddings locally on CPU without API keys, using models like gte-large or bge-small."
 ---
 
 You can use FastEmbed to run embedding models locally in Mem0. FastEmbed is an ONNX-based embedding library that runs efficiently on CPU without requiring a GPU or an external API key.

docs/components/embedders/models/huggingface.mdx

@@ -1,6 +1,6 @@
 ---
 title: Hugging Face
-description: "Configure Hugging Face as an embedding provider in Mem0 for local embedding generation with open-source models."
+description: "Use Hugging Face embeddings in Mem0 via local sentence-transformers or a Text Embeddings Inference (TEI) endpoint for fast, self-hosted vectors."
 ---
 
 You can use embedding models from Huggingface to run Mem0 locally.

docs/components/embedders/models/together.mdx

@@ -11,7 +11,7 @@ To use Together embedding models, set the `TOGETHER_API_KEY` environment variabl
 <Note> The `embedding_model_dims` parameter for `vector_store` should be set to `1024` for Together embedder. </Note>
 
 <Warning>
-**Breaking default change.** The default Together embedding model is now `intfloat/multilingual-e5-large-instruct` (**1024-dim**), replacing the previous default `togethercomputer/m2-bert-80M-8k-retrieval` (**768-dim**). If you created a self-hosted vector store with the old default, its collection is 768-dim and will reject the new 1024-dim vectors **recreate/reindex the collection at 1024 dimensions** after upgrading. To defer the change, pin the previous values explicitly (`model="togethercomputer/m2-bert-80M-8k-retrieval"`, `embedding_dims=768`) note Together no longer lists this model among its recommended embeddings, so reindexing at 1024 is the durable path.
+**Breaking default change.** The default Together embedding model is now `intfloat/multilingual-e5-large-instruct` (**1024-dim**), replacing the previous default `togethercomputer/m2-bert-80M-8k-retrieval` (**768-dim**). If you created a self-hosted vector store with the old default, its collection is 768-dim and will reject the new 1024-dim vectors — **recreate/reindex the collection at 1024 dimensions** after upgrading. To defer the change, pin the previous values explicitly (`model="togethercomputer/m2-bert-80M-8k-retrieval"`, `embedding_dims=768`) — note Together no longer lists this model among its recommended embeddings, so reindexing at 1024 is the durable path.
 </Warning>
 
 <CodeGroup>

docs/components/embedders/models/vertexai.mdx

@@ -1,6 +1,6 @@
 ---
 title: "Vertex AI"
-description: "Configure Google Cloud Vertex AI as an embedding provider in Mem0 with support for task-specific embedding types."
+description: "Use Google Cloud Vertex AI embeddings in Mem0 with ADC authentication, gemini-embedding-001, and task-specific types for add, update, and search."
 ---
 ### Vertex AI
 

docs/components/llms/models/anthropic.mdx

@@ -1,6 +1,6 @@
 ---
 title: Anthropic
-description: "Configure Anthropic Claude models as the LLM provider in Mem0 with API key setup and usage examples."
+description: "Use Anthropic Claude models such as claude-sonnet-4-6 as your Mem0 LLM with the ANTHROPIC_API_KEY, plus temperature and max token controls."
 ---
 
 

docs/components/llms/models/deepseek.mdx

@@ -1,6 +1,6 @@
 ---
 title: DeepSeek
-description: "Configure DeepSeek as an LLM provider in Mem0 with API key setup and optional custom endpoint configuration."
+description: "Use DeepSeek chat models such as deepseek-chat as a Mem0 LLM with the DEEPSEEK_API_KEY and an optional DEEPSEEK_API_BASE custom endpoint URL."
 ---
 
 To use DeepSeek LLM models, you have to set the `DEEPSEEK_API_KEY` environment variable. You can also optionally set `DEEPSEEK_API_BASE` if you need to use a different API endpoint (defaults to `https://api.deepseek.com`).

docs/components/llms/models/groq.mdx

@@ -1,6 +1,6 @@
 ---
 title: Groq
-description: "Configure Groq as an LLM provider in Mem0 for high-speed inference using LPU-powered language models."
+description: "Use Groq LPU-hosted models such as llama-3.3-70b-versatile as a Mem0 LLM for very low-latency inference with the GROQ_API_KEY environment variable."
 ---
 
 [Groq](https://groq.com/) is the creator of the world's first Language Processing Unit (LPU), providing exceptional speed performance for AI workloads running on their LPU Inference Engine.

docs/components/llms/models/litellm.mdx

@@ -1,6 +1,6 @@
 ---
 title: "LiteLLM"
-description: "Use LiteLLM as an LLM provider in Mem0 to access over 100 language models through a unified interface."
+description: "Route Mem0 through LiteLLM to reach 100+ providers via one API in Python, or a LiteLLM proxy at LITELLM_API_BASE from the TypeScript SDK."
 ---
 [Litellm](https://litellm.vercel.app/docs/) is compatible with over 100 large language models (LLMs), all using a standardized input/output format. You can explore the [available models](https://litellm.vercel.app/docs/providers) to use with Litellm. Ensure you set the `API_KEY` for the model you choose to use.
 

docs/components/llms/models/minimax.mdx

@@ -1,6 +1,6 @@
 ---
 title: MiniMax
-description: "Configure MiniMax as an LLM provider in Mem0 with API key setup and optional custom endpoint configuration."
+description: "Use MiniMax chat models like MiniMax-M2.7 as a Mem0 LLM with the MINIMAX_API_KEY and an optional MINIMAX_API_BASE custom endpoint override."
 ---
 
 To use MiniMax LLM models, you have to set the `MINIMAX_API_KEY` environment variable. You can also optionally set `MINIMAX_API_BASE` if you need to use a different API endpoint (defaults to `https://api.minimax.io/v1`).

docs/components/llms/models/mistral-ai.mdx

@@ -1,6 +1,6 @@
 ---
 title: Mistral AI
-description: "Configure Mistral AI as an LLM provider in Mem0 using the litellm integration and Mixtral model family."
+description: "Use Mistral AI models such as open-mixtral-8x7b as a Mem0 LLM through the litellm provider with the MISTRAL_API_KEY environment variable."
 ---
 
 To use mistral's models, please obtain the Mistral AI api key from their [console](https://console.mistral.ai/). Set the `MISTRAL_API_KEY` environment variable to use the model as given below in the example.

docs/components/llms/models/sarvam.mdx

@@ -1,6 +1,6 @@
 ---
 title: Sarvam AI
-description: "Configure Sarvam AI as an LLM provider in Mem0, specializing in Indian language support with the Sarvam-M model."
+description: "Use Sarvam AI models like sarvam-m as a Mem0 LLM for Indian language conversations, with reasoning_effort, frequency_penalty, and seed controls."
 ---
 
 **Sarvam AI** is an Indian AI company developing language models with a focus on Indian languages and cultural context. Their latest model **Sarvam-M** is designed to understand and generate content in multiple Indian languages while maintaining high performance in English.

docs/components/llms/models/vllm.mdx

@@ -1,6 +1,6 @@
 ---
 title: vLLM
-description: "Configure vLLM as an LLM provider in Mem0 for high-performance local inference with GPU-optimized serving."
+description: "Serve local models like Qwen2.5-32B-Instruct through a vLLM server and connect Mem0 to it via VLLM_BASE_URL for fast, GPU-optimized inference."
 ---
 
 [vLLM](https://docs.vllm.ai/) is a high-performance inference engine for large language models that provides significant performance improvements for local inference. It's designed to maximize throughput and memory efficiency for serving LLMs.

docs/components/rerankers/config.mdx

@@ -1,6 +1,6 @@
 ---
 title: Config
-description: "Reference for shared and provider-specific reranker configuration options in Mem0, including top_k and API key settings."
+description: "Reference for Mem0 reranker configuration covering Cohere, Zero Entropy, sentence-transformer, HuggingFace, and LLM-based providers with top_k limits."
 ---
 
 ## Common Configuration Parameters

docs/components/rerankers/custom-prompts.mdx

@@ -1,6 +1,6 @@
 ---
 title: Custom Prompts
-description: "Customize the LLM reranker prompt template in Mem0 to control how search results are ranked and scored."
+description: "Write custom LLM reranker scoring prompts in Mem0 using query and document variables to tune relevance ranking for your specific domain or use case."
 ---
 
 When using LLM rerankers, you can customize the prompts used for ranking to better suit your specific use case and domain.

docs/components/rerankers/models/cohere.mdx

@@ -1,6 +1,6 @@
 ---
 title: Cohere
-description: "Configure Cohere as a reranker in Mem0 with support for English and multilingual reranking models."
+description: "Use Cohere rerank-v3.5 or older v3.0 models as a Mem0 reranker for English and multilingual search relevance with COHERE_API_KEY authentication."
 ---
 
 Cohere provides enterprise-grade reranking models with excellent multilingual support and production-ready performance.

docs/components/rerankers/models/huggingface.mdx

@@ -1,6 +1,6 @@
 ---
 title: Hugging Face Reranker
-description: 'Access thousands of reranking models from Hugging Face Hub'
+description: 'Access thousands of Hugging Face Hub reranking models in Mem0, including BAAI BGE cross-encoders, to boost memory search relevance and precision.'
 ---
 
 ## Overview

docs/components/rerankers/models/llm-reranker.mdx

@@ -1,6 +1,6 @@
 ---
 title: LLM Reranker
-description: 'Use any language model as a reranker with custom prompts'
+description: 'Use any Mem0-supported LLM as a reranker with custom prompts to score and rank memories by query relevance for flexible, tunable retrieval.'
 ---
 
 ## Overview

docs/components/rerankers/models/sentence-transformer.mdx

@@ -1,6 +1,6 @@
 ---
 title: Sentence Transformer
-description: 'Local reranking with HuggingFace cross-encoder models'
+description: 'Run local reranking in Mem0 with Sentence Transformers cross-encoder models like ms-marco-MiniLM for privacy-focused, on-premises deployments.'
 ---
 
 Sentence Transformer reranker provides local reranking using HuggingFace cross-encoder models, perfect for privacy-focused deployments where you want to keep data on-premises.

docs/components/rerankers/models/zero-entropy.mdx

@@ -1,6 +1,6 @@
 ---
 title: Zero Entropy
-description: "Configure Zero Entropy neural reranking models in Mem0 with zerank-1 and zerank-1-small support."
+description: "Configure Zero Entropy neural reranking in Mem0 with the zerank-1 and zerank-1-small models for fast, high-relevance search over stored memories."
 ---
 
 [Zero Entropy](https://www.zeroentropy.dev) provides neural reranking models that significantly improve search relevance with fast performance.

docs/components/rerankers/optimization.mdx

@@ -1,6 +1,6 @@
 ---
 title: Performance Optimization
-description: "Best practices for optimizing reranker performance in Mem0, covering candidate sizing, batching, and tuning."
+description: "Best practices for tuning reranker performance in Mem0, covering candidate set sizing, batching, model selection, and latency versus precision tradeoffs."
 ---
 
 Optimizing reranker performance is crucial for maintaining fast search response times while improving result quality. This guide covers best practices for different reranker types.

docs/components/rerankers/overview.mdx

@@ -1,7 +1,7 @@
 ---
 title: "Reranker Providers Overview"
 sidebarTitle: "Overview"
-description: 'Pick the right reranker path to boost Mem0 search relevance.'
+description: "Compare Mem0 reranker providers (Cohere, Hugging Face, Sentence Transformers, LLM reranker, Zero Entropy) and boost vector search relevance."
 ---
 
 Mem0 rerankers rescore vector search hits so your agents surface the most relevant memories. Use this hub to decide when reranking helps, configure a provider, and fine-tune performance.

docs/components/vectordbs/dbs/azure-mysql.mdx

@@ -1,6 +1,6 @@
 ---
 title: Azure MySQL
-description: "Use Azure Database for MySQL as a vector store in Mem0 with JSON-based vector storage for semantic search."
+description: "Use Azure Database for MySQL as a vector store in Mem0 with JSON-based vector storage for semantic memory search on a managed MySQL backend."
 ---
 
 [Azure Database for MySQL](https://azure.microsoft.com/products/mysql) is a fully managed relational database service that provides enterprise-grade reliability and security. It supports JSON-based vector storage for semantic search capabilities in AI applications.

docs/components/vectordbs/dbs/azure.mdx

@@ -1,6 +1,6 @@
 ---
 title: Azure AI Search
-description: "Use Azure AI Search as a vector store in Mem0 for managed vector search with service name and API key setup."
+description: "Configure Azure AI Search as a managed vector store in Mem0 using your service name, API key, and index for secure, scalable memory retrieval."
 ---
 
 [Azure AI Search](https://learn.microsoft.com/azure/search/search-what-is-azure-search/) (formerly known as "Azure Cognitive Search") provides secure information retrieval at scale over user-owned content in traditional and generative AI search applications.
@@ -95,7 +95,7 @@ Uses the identity from Azure PowerShell (`Connect-AzAccount`).
 7. **Azure Developer CLI Credential:**
 Uses the session from Azure Developer CLI (`azd auth login`).
 
-<Note> If an API is provided, it will be used for authentication over an Azure Identity </Note>
+<Note> If an API key is provided, it will be used for authentication over an Azure Identity </Note>
 To enable Role-Based Access Control (RBAC) for Azure AI Search, follow these steps:
 
 1. In the Azure Portal, navigate to your **Azure AI Search** service.

docs/components/vectordbs/dbs/baidu.mdx

@@ -1,6 +1,6 @@
 ---
 title: Baidu VectorDB (Mochow)
-description: "Use Baidu Mochow as an enterprise vector database in Mem0 for high-performance vector storage and retrieval."
+description: "Use Baidu VectorDB (Mochow) as an enterprise-grade vector store in Mem0 for high-performance, distributed vector storage and semantic retrieval."
 ---
 
 [Baidu VectorDB](https://cloud.baidu.com/doc/VDB/index.html) is an enterprise-level distributed vector database service developed by Baidu Intelligent Cloud. It is powered by Baidu's proprietary "Mochow" vector database kernel, providing high performance, availability, and security for vector search.

docs/components/vectordbs/dbs/cassandra.mdx

@@ -1,6 +1,6 @@
 ---
 title: Apache Cassandra
-description: "Use Apache Cassandra as a distributed vector store in Mem0 with semantic search over large-scale datasets."
+description: "Use Apache Cassandra as a distributed NoSQL vector store in Mem0 for scalable, fault-tolerant semantic memory search over large-scale datasets."
 ---
 
 [Apache Cassandra](https://cassandra.apache.org/) is a highly scalable, distributed NoSQL database designed for handling large amounts of data across many commodity servers with no single point of failure. It supports vector storage for semantic search capabilities in AI applications and can scale to massive datasets with linear performance improvements.

docs/components/vectordbs/dbs/chroma.mdx

@@ -1,6 +1,6 @@
 ---
 title: "Chroma"
-description: "Use Chroma as a vector database in Mem0 for local or cloud-hosted vector storage with built-in embedding support."
+description: "Use Chroma as an AI-native vector database in Mem0 for local or cloud-hosted embedding storage, quick prototyping, and simple LLM app development."
 ---
 [Chroma](https://www.trychroma.com/) is an AI-native open-source vector database that simplifies building LLM apps by providing tools for storing, embedding, and searching embeddings with a focus on simplicity and speed. It supports both local deployment and cloud hosting through ChromaDB Cloud.
 

docs/components/vectordbs/dbs/databricks.mdx

@@ -1,6 +1,6 @@
 ---
 title: "Databricks"
-description: "Use Databricks Vector Search as a serverless vector store in Mem0 with auto-updating indexes from Delta tables."
+description: "Use Databricks Vector Search as a serverless vector store in Mem0 with auto-updating indexes synced from Delta tables and Unity Catalog governance."
 ---
 [Databricks Vector Search](https://docs.databricks.com/en/generative-ai/vector-search.html) is a serverless similarity search engine that allows you to store a vector representation of your data, including metadata, in a vector database. With Vector Search, you can create auto-updating vector search indexes from Delta tables managed by Unity Catalog and query them with a simple API to return the most similar vectors.
 

docs/components/vectordbs/dbs/elasticsearch.mdx

@@ -1,6 +1,6 @@
 ---
 title: "Elasticsearch"
-description: "Use Elasticsearch as a vector database in Mem0 for distributed vector search using dense vectors and k-NN queries."
+description: "Use Elasticsearch as a distributed vector database in Mem0 with dense vectors and k-NN queries for hybrid semantic and keyword memory search."
 ---
 [Elasticsearch](https://www.elastic.co/) is a distributed, RESTful search and analytics engine that can efficiently store and search vector data using dense vectors and k-NN search.
 

docs/components/vectordbs/dbs/faiss.mdx

@@ -1,6 +1,6 @@
 ---
 title: "FAISS"
-description: "Use Facebook FAISS as a high-performance vector store in Mem0, optimized for memory usage and fast similarity search."
+description: "Use Meta FAISS as a high-performance local vector store in Mem0, optimized for memory usage and fast in-process similarity search over dense vectors."
 ---
 [FAISS](https://github.com/facebookresearch/faiss) is a library for efficient similarity search and clustering of dense vectors. It is designed to work with large-scale datasets and provides a high-performance search engine for vector data. FAISS is optimized for memory usage and search speed, making it an excellent choice for production environments.
 

docs/components/vectordbs/dbs/milvus.mdx

@@ -1,6 +1,6 @@
 ---
 title: "Milvus"
-description: "Use Milvus as an open-source vector database in Mem0, scalable from local development to production workloads."
+description: "Use Milvus as an open-source vector database in Mem0, scaling from Jupyter notebook demos to production similarity search across billions of vectors."
 ---
 [Milvus](https://milvus.io/) is an open-source vector database that suits AI applications of every size, from running a demo chatbot in a Jupyter notebook to building web-scale search that serves billions of users.
 

docs/components/vectordbs/dbs/mongodb.mdx

@@ -1,6 +1,6 @@
 ---
 title: "MongoDB"
-description: "Use MongoDB as a vector database in Mem0 with built-in vector search for high-dimensional similarity queries."
+description: "Use MongoDB Atlas as a vector database in Mem0 with native vector search for high-dimensional similarity queries alongside your document data."
 ---
 
 # MongoDB

docs/components/vectordbs/dbs/neon.mdx

@@ -1,6 +1,6 @@
 ---
 title: "Neon"
-description: "Use Neon as a vector store in Mem0, powered by PostgreSQL and pgvector."
+description: "Use Neon serverless Postgres with the pgvector extension as a vector store in Mem0 for autoscaling, branchable memory storage with a Postgres URL."
 ---
 
 Use [Neon](https://neon.com/) as a vector store in Mem0, powered by PostgreSQL and the

docs/components/vectordbs/dbs/neptune-analytics.mdx

@@ -1,6 +1,6 @@
 ---
 title: "Neptune Analytics"
-description: "Use AWS Neptune Analytics as a vector store in Mem0, combining graph analytics with vector search capabilities."
+description: "Use AWS Neptune Analytics as a memory-optimized vector store in Mem0, combining graph analytics with vector search for connected data insights."
 ---
 
 [Neptune Analytics](https://docs.aws.amazon.com/neptune-analytics/latest/userguide/what-is-neptune-analytics.html) is a memory-optimized graph database engine for analytics. With Neptune Analytics, you can get insights and find trends by processing large amounts of graph data in seconds, including vector search.

docs/components/vectordbs/dbs/opensearch.mdx

@@ -1,6 +1,6 @@
 ---
 title: "OpenSearch"
-description: "Use OpenSearch as a vector database in Mem0 with k-NN search support via AWS OpenSearch Service serverless collections."
+description: "Use OpenSearch as a vector database in Mem0 with k-NN search over dense embeddings, including AWS OpenSearch Service serverless collections."
 ---
 [OpenSearch](https://opensearch.org/) is an enterprise-grade search and observability suite that brings order to unstructured data at scale. OpenSearch supports k-NN (k-Nearest Neighbors) and allows you to store and retrieve high-dimensional vector embeddings efficiently.
 

docs/components/vectordbs/dbs/oracledb.mdx

@@ -1,6 +1,6 @@
 ---
 title: "Oracle AI Vector Search"
-description: "Use Oracle Database AI Vector Search as a vector store in Mem0 for semantic and relational queries."
+description: "Use Oracle Database AI Vector Search as a vector store in Mem0 with the native VECTOR type to combine semantic memory search and relational queries."
 ---
 
 {/* Copyright (c) 2026, Oracle and/or its affiliates. */}

docs/components/vectordbs/dbs/pgvector.mdx

@@ -1,10 +1,16 @@
 ---
 title: "pgvector"
-description: "Use pgvector as a vector store in Mem0 for PostgreSQL-based vector similarity search with open-source simplicity."
+description: "Use the pgvector extension as a vector store in Mem0 for PostgreSQL-based similarity search with open-source simplicity and standard SQL tooling."
 ---
 
 [pgvector](https://github.com/pgvector/pgvector) is an open-source vector similarity search extension for Postgres. After connecting to Postgres, run `CREATE EXTENSION IF NOT EXISTS vector;` to create the vector extension.
 
+The TypeScript SDK loads the `pg` driver only when you use this store, so install it alongside `mem0ai`:
+
+```bash
+npm install pg
+```
+
 ### Usage
 
 <CodeGroup>

docs/components/vectordbs/dbs/pinecone.mdx

@@ -1,6 +1,6 @@
 ---
 title: "Pinecone"
-description: "Use Pinecone as a fully managed vector database in Mem0 with serverless deployment and namespace-based multi-tenancy."
+description: "Use Pinecone as a fully managed vector database in Mem0 with serverless indexes and namespace-based multi-tenancy for low-latency semantic search."
 ---
 [Pinecone](https://www.pinecone.io/) is a fully managed vector database designed for machine learning applications, offering high performance vector search with low latency at scale. It's particularly well-suited for semantic search, recommendation systems, and other AI-powered applications.
 
@@ -94,6 +94,7 @@ Here are the parameters available for configuring Pinecone:
 | `hybrid_search` | Whether to enable hybrid search | `False` |
 | `metric` | Distance metric for vector similarity | `"cosine"` |
 | `batch_size` | Batch size for operations | `100` |
+| `extra_params` | Additional keyword arguments passed to the `Pinecone` client constructor. Ignored when `client` is supplied. | `None` |
 | `namespace` | Namespace for the collection, useful for multi-tenancy. | `None` |
 </Tab>
 <Tab title="TypeScript">

docs/components/vectordbs/dbs/qdrant.mdx

@@ -1,6 +1,6 @@
 ---
 title: "Qdrant"
-description: "Use Qdrant as an open-source vector search engine in Mem0 for high-performance similarity search at scale."
+description: "Use Qdrant as an open-source vector search engine in Mem0 for high-performance, filterable similarity search at scale, self-hosted or on Qdrant Cloud."
 ---
 [Qdrant](https://qdrant.tech/) is an open-source vector search engine. It is designed to work with large-scale datasets and provides a high-performance search engine for vector data.
 

docs/components/vectordbs/dbs/redis.mdx

@@ -1,6 +1,6 @@
 ---
 title: "Redis"
-description: "Use Redis as a real-time vector database in Mem0 for fast vector search using Redis Stack and redisvl."
+description: "Use Redis as a real-time vector database in Mem0 for fast in-memory similarity search using Redis Stack, the RediSearch module, and the redisvl client."
 ---
 [Redis](https://redis.io/) is a scalable, real-time database that can store, search, and analyze vector data.
 

docs/components/vectordbs/dbs/s3-vectors.mdx

@@ -1,6 +1,6 @@
 ---
 title: Amazon S3 Vectors
-description: "Use Amazon S3 Vectors as a cost-optimized vector storage service in Mem0 with AWS credential authentication."
+description: "Use Amazon S3 Vectors as a cost-optimized vector storage service in Mem0 with AWS credential auth, sub-second queries, and S3-level durability."
 ---
 
 [Amazon S3 Vectors](https://aws.amazon.com/s3/features/vectors/) is a purpose-built, cost-optimized vector storage and query service for semantic search and AI applications. It provides S3-level elasticity and durability with sub-second query performance.