docs(changelog): add v2.2.1 entry for Valkey None-timestamps fix (#7425)

Co-authored-by: Pratik <10096516+pratikgajjar@users.noreply.github.com>
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.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": "0.3.1"
+      "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": "0.3.1"
+      "version": "0.3.3"
     }
   ]
 }

.github/workflows/opencode-plugin-checks.yml

@@ -32,6 +32,9 @@ jobs:
       - name: Type check
         run: bun run type-check
 
+      - name: Test
+        run: bun test
+
       - name: Build
         run: bun run build
 

.kimi-plugin/marketplace.json

@@ -5,7 +5,7 @@
     {
       "id": "mem0",
       "displayName": "Mem0",
-      "version": "0.3.1",
+      "version": "0.3.3",
       "description": "Cross-session memory and token savings for coding agents.",
       "homepage": "https://mem0.ai",
       "keywords": ["memory", "personalization", "mcp", "semantic-search"],

cli/node/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mem0/cli",
-  "version": "0.2.13",
+  "version": "0.2.14",
   "description": "The official CLI for mem0 — the memory layer for AI agents",
   "type": "module",
   "bin": {

cli/node/src/backend/platform.ts

@@ -31,7 +31,8 @@ export class PlatformBackend implements Backend {
 		this.headers = {
 			Authorization: `Token ${config.apiKey}`,
 			"Content-Type": "application/json",
-			"X-Mem0-Source": "cli",
+			"X-Mem0-Source": "CLI",
+			"X-Mem0-Client": `mem0-cli-node/${CLI_VERSION}`,
 			"X-Mem0-Client-Language": "node",
 			"X-Mem0-Client-Version": CLI_VERSION,
 		};

cli/python/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "mem0-cli"
-version = "0.2.12"
+version = "0.2.13"
 description = "The official CLI for mem0 — the memory layer for AI agents"
 readme = "README.md"
 license = "Apache-2.0"

cli/python/src/mem0_cli/__init__.py

@@ -1,3 +1,3 @@
 """mem0 CLI — the command-line interface for the mem0 memory layer."""
 
-__version__ = "0.2.12"
+__version__ = "0.2.13"

cli/python/src/mem0_cli/backend/platform.py

@@ -27,7 +27,8 @@ class PlatformBackend(Backend):
             headers={
                 "Authorization": f"Token {config.api_key}",
                 "Content-Type": "application/json",
-                "X-Mem0-Source": "cli",
+                "X-Mem0-Source": "CLI",
+                "X-Mem0-Client": f"mem0-cli-python/{__version__}",
                 "X-Mem0-Client-Language": "python",
                 "X-Mem0-Client-Version": __version__,
             },

docs/api-reference.mdx

@@ -1,7 +1,6 @@
 ---
-title: "Overview"
-seo:
-  title: "API Reference Overview - Mem0"
+title: "API Reference Overview"
+sidebarTitle: "Overview"
 icon: "terminal"
 iconType: "solid"
 description: "REST APIs for memory management, search, and entity operations"

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

@@ -1,7 +1,6 @@
 ---
-title: 'Delete Memory'
-seo:
-  title: "Delete Memory API Endpoint - Mem0"
+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/update-memory.mdx

@@ -1,7 +1,6 @@
 ---
-title: 'Update Memory'
-seo:
-  title: "Update Memory API Endpoint - Mem0"
+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 Member'
-seo:
-  title: "Add Organization Member API Endpoint - Mem0"
+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/get-org-members.mdx

@@ -1,7 +1,6 @@
 ---
-title: 'Get Members'
-seo:
-  title: "Get Organization Members API Endpoint - Mem0"
+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/profiles/generate-profiles.mdx

@@ -0,0 +1,5 @@
+---
+title: 'Generate Profiles'
+description: "Start one generation: sample a few entities, or build one for a single entity."
+openapi: post /v2/profiles/jobs/
+---

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

@@ -0,0 +1,5 @@
+---
+title: 'Get Generation Job'
+description: "Read the progress of a generation, and whether it finished."
+openapi: get /v2/profiles/jobs/{job_id}/
+---

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

@@ -0,0 +1,5 @@
+---
+title: 'Get Profile Settings'
+description: "Retrieve the profile schema, custom instructions, and enabled flag for the current project."
+openapi: get /v2/profiles/settings/
+---

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

@@ -0,0 +1,5 @@
+---
+title: 'Get Profile'
+description: "Retrieve the structured profile for a user, with a status describing whether generation has completed."
+openapi: get /v2/entities/{entity_type}/{entity_id}/profile/
+---

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

@@ -0,0 +1,5 @@
+---
+title: 'Update Profile Settings'
+description: "Set the JSON Schema, custom instructions, or enabled flag that control profile generation for the project."
+openapi: post /v2/profiles/settings/
+---

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

@@ -1,7 +1,6 @@
 ---
-title: 'Add Member'
-seo:
-  title: "Add Project Member API Endpoint - Mem0"
+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/get-project-members.mdx

@@ -1,7 +1,6 @@
 ---
-title: 'Get Members'
-seo:
-  title: "Get Project Members API Endpoint - Mem0"
+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/changelog/sdk.mdx

@@ -7,6 +7,24 @@ mode: "wide"
 <Tabs>
 <Tab title="Python">
 
+<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:**
+- **Client:** Requests now carry three surface-identity headers so the platform can tell which product made a call. `X-Mem0-Source` names the surface and `X-Application` the host app it runs inside, both set-once so a wrapper that already declared its identity keeps it. `X-Mem0-Client` is append-only and carries `name/version` per layer, outermost first, so a plugin calling this SDK reports the whole chain rather than only the last speaker. `MEM0_SOURCE`, `MEM0_APPLICATION` and `MEM0_CLIENT_STACK` set them from the environment for wrappers that cannot pass options ([#7326](https://github.com/mem0ai/mem0/pull/7326))
+- **Client:** The client stack is bounded by dropping whole entries rather than slicing characters, and this SDK's own entry is the reserved one. Truncating the joined string could sever an identifier mid-name and the platform parsed the fragment as a real client ([#7326](https://github.com/mem0ai/mem0/pull/7326))
+
+</Update>
+
 <Update label="2026-09-02" description="v2.0.20">
 
 **Improvements:**
@@ -174,7 +192,7 @@ 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))
@@ -1227,6 +1245,21 @@ See the [OSS v2 to v3 migration guide](https://docs.mem0.ai/migration/oss-v2-to-
 
 <Tab title="TypeScript">
 
+<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:**
+- **Client:** Requests now carry `X-Mem0-Source`, `X-Application` and `X-Mem0-Client`, matching the Python SDK. The first two are set-once so an outer wrapper keeps its identity; the third is append-only and reports the whole layer chain. Read from `MEM0_SOURCE`, `MEM0_APPLICATION` and `MEM0_CLIENT_STACK` when set ([#7326](https://github.com/mem0ai/mem0/pull/7326))
+- **Client:** The SDK version in `X-Mem0-Client` is injected at build time rather than hardcoded, so it cannot go stale at the next release ([#7326](https://github.com/mem0ai/mem0/pull/7326))
+
+</Update>
+
 <Update label="2026-09-02" description="v3.1.8">
 
 **Improvements:**
@@ -1864,6 +1897,13 @@ See the [OSS v2 to v3 migration guide](https://docs.mem0.ai/migration/oss-v2-to-
 
 <Tab title="CLI">
 
+<Update label="2026-09-18" description="Python v0.2.13 / Node v0.2.14">
+
+**Improvements:**
+- **Client:** Requests now carry the three surface-identity headers (`X-Mem0-Source`, `X-Application`, `X-Mem0-Client`) introduced in the Python and TypeScript SDKs, so the platform can attribute calls made through the CLI to the correct surface and version ([#7326](https://github.com/mem0ai/mem0/pull/7326))
+
+</Update>
+
 <Update label="2026-08-24" description="Python v0.2.12 / Node v0.2.13">
 
 **New Features:**
@@ -2076,7 +2116,7 @@ A full-featured command-line interface for Mem0, available in both Python and No
 - New Git repository writes use a hash of the remote identity in `agent_id`. Search and explicit shared-memory deletion include both current and legacy repository IDs within the repository's `app_id`. Existing memories are not rewritten. Legacy IDs retain their original ambiguity for matching owner/repository names on different Git hosts.
 
 **Packaging:**
-- Claude Code, Cursor, Codex, Kimi, Antigravity, and the portable Python bundle are versioned at `0.3.1`. OpenCode, Pi Agent, and DeepSeek Harness are `0.3.0`; OpenClaw is `1.1.0`. Each host's changes and upgrade considerations are listed in its tab.
+- Claude Code, Cursor, Codex, Kimi, Antigravity, and the portable Python bundle are versioned at `0.3.1`. OpenCode and DeepSeek Harness are `0.3.0`; Pi Agent is `0.3.0`; OpenClaw is `1.1.0`. Each host's changes and upgrade considerations are listed in its tab.
 - Python and TypeScript CI run their respective runtime suites. Package checks build the installable artifacts, check generated-file consistency, and reject TypeScript output that still imports monorepo source.
 
 [#7203](https://github.com/mem0ai/mem0/pull/7203)
@@ -2371,9 +2411,25 @@ Initial release of the Mem0 plugin for Claude Code and Cursor, followed by Codex
 
 <Tab title="Claude Code">
 
-<Update label="Unreleased" description="Sidekick availability">
+<Update label="2026-09-23" description="Claude Code plugin v0.3.3">
 
-Sidekick is now available only in Claude Code, with Sonnet, worktree isolation, and parent memories.
+**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:**
+- **Telemetry:** `PLUGIN_VERSION` bumped to `0.3.2`. The `mem0-plugin/<version>` wire header and `plugin_version` telemetry field now reflect the fixes from #7322 through #7358 ([#7373](https://github.com/mem0ai/mem0/pull/7373))
+- **Telemetry:** Events are no longer delivered twice, no longer lose parked events on flush, and now attribute each event to the plugin that produced it ([#7323](https://github.com/mem0ai/mem0/pull/7323), [#7324](https://github.com/mem0ai/mem0/pull/7324), [#7358](https://github.com/mem0ai/mem0/pull/7358))
+
+**Changes:**
+- **Sidekick:** Sidekick is now available only in Claude Code, with Sonnet, worktree isolation, and parent memories.
 
 </Update>
 
@@ -2396,9 +2452,24 @@ Sidekick is now available only in Claude Code, with Sonnet, worktree isolation,
 
 <Tab title="Cursor">
 
-<Update label="Unreleased" description="Sidekick availability">
+<Update label="2026-09-23" description="Cursor plugin v0.3.3">
 
-Removes Sidekick and its start/stop hooks. Memory capture, search, and six skills remain available.
+**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:**
+- **Telemetry:** `PLUGIN_VERSION` bumped to `0.3.2`. The `mem0-plugin/<version>` wire header and `plugin_version` telemetry field now reflect the fixes from #7322 through #7358 ([#7373](https://github.com/mem0ai/mem0/pull/7373))
+- **Telemetry:** Events are no longer delivered twice, no longer lose parked events on flush, and now attribute each event to the plugin that produced it ([#7323](https://github.com/mem0ai/mem0/pull/7323), [#7324](https://github.com/mem0ai/mem0/pull/7324), [#7358](https://github.com/mem0ai/mem0/pull/7358))
+
+**Changes:**
+- **Sidekick:** Removes Sidekick and its start/stop hooks. Memory capture, search, and six skills remain available.
 
 </Update>
 
@@ -2421,9 +2492,24 @@ Removes Sidekick and its start/stop hooks. Memory capture, search, and six skill
 
 <Tab title="Codex">
 
-<Update label="Unreleased" description="Sidekick availability">
+<Update label="2026-09-23" description="Codex plugin v0.3.3">
 
-Renames shared tracking to use subagent terminology. Native subagent memory support remains available.
+**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:**
+- **Telemetry:** `PLUGIN_VERSION` bumped to `0.3.2`. The `mem0-plugin/<version>` wire header and `plugin_version` telemetry field now reflect the fixes from #7322 through #7358 ([#7373](https://github.com/mem0ai/mem0/pull/7373))
+- **Telemetry:** Events are no longer delivered twice, no longer lose parked events on flush, and now attribute each event to the plugin that produced it ([#7323](https://github.com/mem0ai/mem0/pull/7323), [#7324](https://github.com/mem0ai/mem0/pull/7324), [#7358](https://github.com/mem0ai/mem0/pull/7358))
+
+**Changes:**
+- **Sidekick:** Renames shared tracking to use subagent terminology. Native subagent memory support remains available.
 
 </Update>
 
@@ -2443,32 +2529,25 @@ Renames shared tracking to use subagent terminology. Native subagent memory supp
 
 </Tab>
 
-<Tab title="Agent Plugins v1">
-
-<Update label="Unreleased" description="Sidekick availability">
-
-Sidekick is available only in Claude Code, not in the portable package.
-
-</Update>
-
-<Update label="2026-09-08" description="Portable Mem0 plugin v0.3.1">
-
-**Added:**
-- One portable package at `integrations/mem0-agent-plugin/`, using the Agent Plugins 1.0.0 root `plugin.json`, `mcp.json`, and fixed `skills/` locations.
-- Ships a local, read-only `search_memories` server and the six shared memory skills. Uses `PLUGIN_ROOT` for bundled files and `PLUGIN_DATA` for persistent plugin state; all package files remain inside the installable directory.
-
-**Packaging:**
-- Generated from the shared Python runtime and skill templates. Builds validate the manifest, MCP configuration, skills, and generated-file consistency.
-- Host lifecycle hooks and native Sidekick declarations remain in the native plugin packages; the portable package does not provide automatic lifecycle capture or host-specific subagent isolation. Its bundled remember skill cannot persist a new memory on its own because the portable package has no capture hooks or write tool.
-
-[#7203](https://github.com/mem0ai/mem0/pull/7203)
-
-</Update>
-
-</Tab>
-
 <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:**
+- **Telemetry:** The PostHog `source` tag changed from the literal `"plugin"` to `OPENCODE_PLUGIN`, and `project_hash` is now salted. Saved PostHog insights filtering on `source = "plugin"` will stop matching new events; historical data is unaffected ([#7322](https://github.com/mem0ai/mem0/pull/7322))
+- **Config:** A new `keyFingerprint` key appears in the install-count deduplication logic; installs are now counted once per key rather than on every activation ([#7325](https://github.com/mem0ai/mem0/pull/7325))
+
+</Update>
+
 <Update label="2026-09-08" description="OpenCode plugin v0.3.0">
 
 **Changed:**
@@ -2567,9 +2646,24 @@ Sidekick is available only in Claude Code, not in the portable package.
 
 <Tab title="Antigravity">
 
-<Update label="Unreleased" description="Sidekick availability">
+<Update label="2026-09-23" description="Antigravity plugin v0.3.3">
 
-Removes Sidekick. Memory capture, search, and six skills remain available.
+**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:**
+- **Telemetry:** `PLUGIN_VERSION` bumped to `0.3.2`. The `mem0-plugin/<version>` wire header and `plugin_version` telemetry field now reflect the fixes from #7322 through #7358 ([#7373](https://github.com/mem0ai/mem0/pull/7373))
+- **Telemetry:** Events are no longer delivered twice, no longer lose parked events on flush, and now attribute each event to the plugin that produced it ([#7323](https://github.com/mem0ai/mem0/pull/7323), [#7324](https://github.com/mem0ai/mem0/pull/7324), [#7358](https://github.com/mem0ai/mem0/pull/7358))
+
+**Changes:**
+- **Sidekick:** Removes Sidekick. Memory capture, search, and six skills remain available.
 
 </Update>
 
@@ -2665,9 +2759,24 @@ Existing memories written by the previous versions are not rewritten. If your me
 
 <Tab title="Kimi">
 
-<Update label="Unreleased" description="Sidekick availability">
+<Update label="2026-09-23" description="Kimi Code plugin v0.3.3">
 
-Removes Sidekick and its start/stop hooks. Memory capture, recall, and six skills remain available.
+**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:**
+- **Telemetry:** `PLUGIN_VERSION` bumped to `0.3.2`. The `mem0-plugin/<version>` wire header and `plugin_version` telemetry field now reflect the fixes from #7322 through #7358 ([#7373](https://github.com/mem0ai/mem0/pull/7373))
+- **Telemetry:** Events are no longer delivered twice, no longer lose parked events on flush, and now attribute each event to the plugin that produced it ([#7323](https://github.com/mem0ai/mem0/pull/7323), [#7324](https://github.com/mem0ai/mem0/pull/7324), [#7358](https://github.com/mem0ai/mem0/pull/7358))
+
+**Changes:**
+- **Sidekick:** Removes Sidekick and its start/stop hooks. Memory capture, recall, and six skills remain available.
 
 </Update>
 
@@ -2705,6 +2814,21 @@ Removes Sidekick and its start/stop hooks. Memory capture, recall, and six skill
 
 <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:**
+- **Config:** Added `keyFingerprint` to the config schema for install-count deduplication; installs are now counted once per key rather than on every activation ([#7325](https://github.com/mem0ai/mem0/pull/7325))
+- **Telemetry:** Events are no longer delivered twice, and the `plugin_version` field now reflects the plugin that produced the event ([#7323](https://github.com/mem0ai/mem0/pull/7323), [#7324](https://github.com/mem0ai/mem0/pull/7324), [#7358](https://github.com/mem0ai/mem0/pull/7358))
+
+</Update>
+
 <Update label="2026-09-08" description="openclaw-mem0 v1.1.0">
 
 **Changed:**
@@ -2993,6 +3117,22 @@ Removes Sidekick and its start/stop hooks. Memory capture, recall, and six skill
 
 <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:**
+- **Telemetry:** Events are no longer delivered twice, no longer lose parked events, and now attribute each event to the plugin that produced it. The `plugin_version` wire field reflects the fixed release ([#7323](https://github.com/mem0ai/mem0/pull/7323), [#7324](https://github.com/mem0ai/mem0/pull/7324), [#7358](https://github.com/mem0ai/mem0/pull/7358))
+
+</Update>
+
 <Update label="2026-09-08" description="Pi Agent plugin v0.3.0">
 
 **Changed:**
@@ -3106,6 +3246,21 @@ Removes Sidekick and its start/stop hooks. Memory capture, recall, and six skill
 
 <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:**
+- **Telemetry:** Rebuild with the fixed shared telemetry core from `agent-plugin-core`. Events are no longer delivered twice, no longer lose parked events on flush, and now attribute each event to the plugin that produced it ([#7323](https://github.com/mem0ai/mem0/pull/7323), [#7324](https://github.com/mem0ai/mem0/pull/7324), [#7358](https://github.com/mem0ai/mem0/pull/7358))
+
+</Update>
+
 <Update label="2026-09-08" description="deepseek-plugin v0.3.0">
 
 **Added:**
@@ -3150,6 +3305,13 @@ Removes Sidekick and its start/stop hooks. Memory capture, recall, and six skill
 
 <Tab title="Vercel AI SDK">
 
+<Update label="2026-09-18" description="Vercel AI SDK v3.0.3">
+
+**Improvements:**
+- **Client:** Inherits the three surface-identity headers (`X-Mem0-Source`, `X-Application`, `X-Mem0-Client`) from the TypeScript SDK bump, so platform calls made through the Vercel AI SDK provider are now correctly attributed ([#7326](https://github.com/mem0ai/mem0/pull/7326))
+
+</Update>
+
 <Update label="2026-08-24" description="Vercel AI SDK v3.0.2">
 
 **Security:**

docs/components/embedders/config.mdx

@@ -1,7 +1,6 @@
 ---
-title: Configurations
-seo:
-  title: "Embedder Configuration Reference - Mem0"
+title: "Embedder Configuration Reference"
+sidebarTitle: "Configurations"
 description: "Reference for embedder configuration options in Mem0, including provider selection and model settings."
 ---
 

docs/components/embedders/models/aws-bedrock.mdx

@@ -1,7 +1,6 @@
 ---
-title: AWS Bedrock
-seo:
-  title: "AWS Bedrock as Embedding Provider - Mem0"
+title: "AWS Bedrock as Embedding Provider"
+sidebarTitle: "AWS Bedrock"
 description: "Configure AWS Bedrock as an embedding provider in Mem0 with IAM credentials and boto3 authentication."
 ---
 

docs/components/embedders/models/azure-openai.mdx

@@ -1,7 +1,6 @@
 ---
-title: Azure OpenAI
-seo:
-  title: "Azure OpenAI as Embedding Provider - Mem0"
+title: "Azure OpenAI as Embedding Provider"
+sidebarTitle: "Azure OpenAI"
 description: "Configure Azure OpenAI as an embedding provider in Mem0 with API key, deployment, and endpoint settings."
 ---
 

docs/components/embedders/models/google-ai.mdx

@@ -1,7 +1,6 @@
 ---
-title: Google AI
-seo:
-  title: "Google AI as Embedding Provider - Mem0"
+title: "Google AI as Embedding Provider"
+sidebarTitle: "Google AI"
 description: "Configure Google AI as an embedding provider in Mem0 using Gemini models and the GOOGLE_API_KEY variable."
 ---
 

docs/components/embedders/models/langchain.mdx

@@ -1,7 +1,6 @@
 ---
-title: LangChain
-seo:
-  title: "LangChain as Embedding Provider - Mem0"
+title: "LangChain as Embedding Provider"
+sidebarTitle: "LangChain"
 description: "Use LangChain as an embedding provider in Mem0 to access a wide range of models through a unified interface."
 ---
 

docs/components/embedders/models/lmstudio.mdx

@@ -1,7 +1,6 @@
 ---
-title: "LM Studio"
-seo:
-  title: "LM Studio as Embedding Provider - Mem0"
+title: "LM Studio as Embedding Provider"
+sidebarTitle: "LM Studio"
 description: "Configure LM Studio as an embedding provider in Mem0 for local embedding generation with models like nomic-embed-text."
 ---
 You can use embedding models from LM Studio to run Mem0 locally.

docs/components/embedders/models/ollama.mdx

@@ -1,7 +1,6 @@
 ---
-title: "Ollama"
-seo:
-  title: "Ollama as Embedding Provider - Mem0"
+title: "Ollama as Embedding Provider"
+sidebarTitle: "Ollama"
 description: "Configure Ollama as an embedding provider in Mem0 to generate embeddings locally using open-source models."
 ---
 You can use embedding models from Ollama to run Mem0 locally.

docs/components/embedders/models/openai.mdx

@@ -1,7 +1,6 @@
 ---
-title: OpenAI
-seo:
-  title: "OpenAI as Embedding Provider - Mem0"
+title: "OpenAI as Embedding Provider"
+sidebarTitle: "OpenAI"
 description: "Configure OpenAI as an embedding provider in Mem0 using models like text-embedding-3-large for vector generation."
 ---
 

docs/components/embedders/models/together.mdx

@@ -1,7 +1,6 @@
 ---
-title: Together
-seo:
-  title: "Together AI as Embedding Provider - Mem0"
+title: "Together AI as Embedding Provider"
+sidebarTitle: "Together"
 description: "Configure Together AI as an embedding provider in Mem0 with support for 1024-dimensional embedding models."
 ---
 
@@ -12,7 +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/overview.mdx

@@ -1,7 +1,6 @@
 ---
-title: Overview
-seo:
-  title: "Embedding Providers Overview - Mem0"
+title: "Embedding Providers Overview"
+sidebarTitle: Overview
 description: "Overview of all supported embedding model providers in Mem0, including OpenAI, Azure, Ollama, and more."
 ---
 

docs/components/llms/config.mdx

@@ -1,7 +1,6 @@
 ---
-title: Configurations
-seo:
-  title: "LLM Configuration Reference - Mem0"
+title: "LLM Configuration Reference"
+sidebarTitle: "Configurations"
 description: "Reference for LLM configuration options in Mem0 for Python and TypeScript, including value precedence rules."
 ---
 

docs/components/llms/models/aws-bedrock.mdx

@@ -1,7 +1,6 @@
 ---
-title: AWS Bedrock
-seo:
-  title: "AWS Bedrock as LLM Provider - Mem0"
+title: "AWS Bedrock as LLM Provider"
+sidebarTitle: "AWS Bedrock"
 description: "Configure AWS Bedrock as an LLM provider in Mem0 with IAM authentication and Claude model support."
 ---
 

docs/components/llms/models/azure-openai.mdx

@@ -1,7 +1,6 @@
 ---
-title: Azure OpenAI
-seo:
-  title: "Azure OpenAI as LLM Provider - Mem0"
+title: "Azure OpenAI as LLM Provider"
+sidebarTitle: "Azure OpenAI"
 description: "Configure Azure OpenAI as an LLM provider in Mem0 with Azure Identity authentication and deployment settings."
 ---
 

docs/components/llms/models/deepseek.mdx

@@ -3,7 +3,7 @@ title: DeepSeek
 description: "Configure DeepSeek as an LLM provider in Mem0 with API key setup and optional custom endpoint configuration."
 ---
 
-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").
+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`).
 
 ## Usage
 

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

@@ -1,7 +1,6 @@
 ---
-title: Google AI
-seo:
-  title: "Google AI as LLM Provider - Mem0"
+title: "Google AI as LLM Provider"
+sidebarTitle: "Google AI"
 description: "Configure Google Gemini as an LLM provider in Mem0 using the google.genai SDK and GOOGLE_API_KEY variable."
 ---
 

docs/components/llms/models/langchain.mdx

@@ -1,7 +1,6 @@
 ---
-title: LangChain
-seo:
-  title: "LangChain as LLM Provider - Mem0"
+title: "LangChain as LLM Provider"
+sidebarTitle: "LangChain"
 description: "Use LangChain as an LLM provider in Mem0 to integrate with various chat models through a unified interface."
 ---
 

docs/components/llms/models/lmstudio.mdx

@@ -1,7 +1,6 @@
 ---
-title: LM Studio
-seo:
-  title: "LM Studio as LLM Provider - Mem0"
+title: "LM Studio as LLM Provider"
+sidebarTitle: "LM Studio"
 description: "Configure LM Studio as an LLM provider in Mem0 for running local language models via an OpenAI-compatible API."
 ---
 
@@ -78,7 +77,7 @@ m.add(messages, user_id="alice123", metadata={"category": "movies"})
   To use LM Studio, you need to:
   1. Download and install [LM Studio](https://lmstudio.ai/)
   2. Start a local server from the "Server" tab
-  3. Set the appropriate `lmstudio_base_url` in your configuration (default is usually http://localhost:1234/v1)
+  3. Set the appropriate `lmstudio_base_url` in your configuration (default is usually `http://localhost:1234/v1`)
 </Note>
 
 ## Config

docs/components/llms/models/minimax.mdx

@@ -3,7 +3,7 @@ title: MiniMax
 description: "Configure MiniMax as an LLM provider in Mem0 with API key setup and optional custom endpoint configuration."
 ---
 
-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").
+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`).
 
 ## Usage
 

docs/components/llms/models/ollama.mdx

@@ -1,7 +1,6 @@
 ---
-title: Ollama
-seo:
-  title: "Ollama as LLM Provider - Mem0"
+title: "Ollama as LLM Provider"
+sidebarTitle: "Ollama"
 description: "Configure Ollama as an LLM provider in Mem0 for running local language models with tool-calling support."
 ---
 

docs/components/llms/models/openai.mdx

@@ -1,7 +1,6 @@
 ---
-title: OpenAI
-seo:
-  title: "OpenAI as LLM Provider - Mem0"
+title: "OpenAI as LLM Provider"
+sidebarTitle: "OpenAI"
 description: "Configure OpenAI as an LLM provider in Mem0 with support for GPT models and Openrouter compatibility."
 ---
 

docs/components/llms/models/together.mdx

@@ -1,7 +1,6 @@
 ---
-title: Together
-seo:
-  title: "Together AI as LLM Provider - Mem0"
+title: "Together AI as LLM Provider"
+sidebarTitle: "Together"
 description: "Configure Together AI as an LLM provider in Mem0 with API key setup and optional custom endpoint configuration."
 ---
 

docs/components/llms/models/xAI.mdx

@@ -1,7 +1,6 @@
 ---
-title: xAI
-seo:
-  title: "xAI Grok as LLM Provider - Mem0"
+title: "xAI Grok as LLM Provider"
+sidebarTitle: "xAI"
 description: "Configure xAI Grok models as an LLM provider in Mem0 with API key setup and usage examples."
 ---
 

docs/components/llms/overview.mdx

@@ -1,7 +1,6 @@
 ---
-title: Overview
-seo:
-  title: "LLM Providers Overview - Mem0"
+title: "LLM Providers Overview"
+sidebarTitle: Overview
 description: "Overview of all supported LLM providers in Mem0, including OpenAI, Anthropic, Groq, Ollama, and more."
 ---
 

docs/components/rerankers/overview.mdx

@@ -1,7 +1,6 @@
 ---
-title: Overview
-seo:
-  title: "Reranker Providers Overview - Mem0"
+title: "Reranker Providers Overview"
+sidebarTitle: "Overview"
 description: 'Pick the right reranker path to boost Mem0 search relevance.'
 ---
 

docs/components/vectordbs/config.mdx

@@ -1,7 +1,6 @@
 ---
-title: Configurations
-seo:
-  title: "Vector Store Configuration Reference - Mem0"
+title: "Vector Store Configuration Reference"
+sidebarTitle: "Configurations"
 description: "Reference for vector database configuration options in Mem0, including provider selection and connection settings."
 ---
 

docs/components/vectordbs/dbs/azure.mdx

@@ -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/langchain.mdx

@@ -1,7 +1,6 @@
 ---
-title: LangChain
-seo:
-  title: "LangChain as Vector Store Provider - Mem0"
+title: "LangChain as Vector Store Provider"
+sidebarTitle: "LangChain"
 description: "Use LangChain as a unified vector store provider in Mem0 to access multiple vector databases through one interface."
 ---
 

docs/components/vectordbs/dbs/pinecone.mdx

@@ -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/upstash-vector.mdx

@@ -102,7 +102,7 @@ Here are the parameters available for configuring Upstash Vector:
 | `url`               | URL for the Upstash Vector index   | `None`        |
 | `token`             | Token for the Upstash Vector index | `None`        |
 | `client`            | An `upstash_vector.Index` instance | `None`        |
-| `collection_name`   | The default namespace used         | `""`          |
+| `collection_name`   | The default namespace used         | `"mem0"`      |
 | `enable_embeddings` | Whether to use Upstash embeddings  | `False`       |
 
 <Note>

docs/components/vectordbs/overview.mdx

@@ -1,7 +1,6 @@
 ---
-title: Overview
-seo:
-  title: "Vector Store Providers Overview - Mem0"
+title: "Vector Store Providers Overview"
+sidebarTitle: "Overview"
 description: "Overview of all supported vector databases in Mem0, including Qdrant, Chroma, PGVector, Pinecone, Oracle, and more."
 ---
 

docs/cookbooks/integrations/healthcare-google-adk.mdx

@@ -30,7 +30,7 @@ pip install google-adk mem0ai python-dotenv
 
 ## Code Breakdown
 
-Let's get started and understand the different components required in building a healthcare assistant powered by memory
+Let's get started and understand the different components required in building a healthcare assistant powered by memory.
 
 ```python
 # Import dependencies

docs/cookbooks/integrations/supabase.mdx

@@ -0,0 +1,388 @@
+---
+title: Build a Company Brain with Mem0 Platform and Supabase
+description: "Build a shared company brain using Mem0 Platform as the managed memory layer, Supabase as your system of record, and the Mem0 MCP server."
+---
+
+<Info icon="server">
+**Uses:** Mem0 **Platform** (`MemoryClient`) · **System of record:** Supabase (Postgres + Auth) · **Access layer:** the hosted Mem0 MCP server. **You'll build:** a company brain your whole org (and every agent) writes to and queries, ending with a new-hire onboarding demo.
+</Info>
+
+Companies lose knowledge constantly: why you picked Postgres over Mongo, who owns billing, the deploy rule only one engineer remembers. A **company brain** captures this and answers questions about it, for every employee and every agent, and keeps it after people leave.
+
+We'll build one on **Mem0 Platform** (the managed memory layer, so there's no vector DB to run) with **Supabase as the system of record** (where your employees, teams, and source documents actually live) and the **Mem0 MCP server** as the wire that lets Claude Code, Cursor, or a Slack bot all reach the same brain.
+
+<Note>
+**How Platform and Supabase divide the work.** Mem0 Platform manages storage and extraction server-side, you do **not** point it at your own database. Supabase is your app's source of truth and identity provider; we *ingest* knowledge from Supabase into the brain and use Supabase Auth to decide who's asking. (If you want to self-host the vector store instead, that's the OSS path, see the [Supabase vector store reference](/components/vectordbs/dbs/supabase).)
+</Note>
+
+## Architecture
+
+```mermaid
+flowchart LR
+  subgraph SB["Supabase: system of record"]
+    K[(knowledge / employees / teams)]
+    AU[Auth · who is asking]
+  end
+  subgraph M0["Mem0 Platform: the brain"]
+    B[(managed memory)]
+  end
+  K -->|ingest| B
+  AU -->|maps to scope| B
+  CC[Claude Code] --> MCP[Mem0 MCP server]
+  CU[Cursor] --> MCP
+  SL[Slack bot] --> MCP
+  MCP --> B
+```
+
+Memory splits by entity. An individual is a **`user_id`** (their Supabase Auth id). Shared knowledge lives on an **`agent_id`**: the company-wide brain is `org:acme`, and each team is its own agent, e.g. `team:payments`. A person's own facts route to their `user_id`; company and team facts route to the agent. This split is what lets one search return "my" context alongside the shared org knowledge.
+
+## Prerequisites
+
+- **Python 3.9+**
+- A **Mem0 Platform API key**, [app.mem0.ai/dashboard/api-keys](https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cookbook-company-brain). (Platform runs extraction and embeddings for you, so there's no OpenAI key to manage.)
+- A **Supabase** project, [supabase.com](https://supabase.com)
+
+About 20 minutes.
+
+---
+
+## Step 1: Get your Mem0 Platform API key
+
+Sign in at [app.mem0.ai](https://app.mem0.ai) and copy a key from **Dashboard → API Keys**. The key is scoped to your org and project; Mem0 resolves both server-side, so you never pass IDs by hand.
+
+## Step 2: Create the Supabase system of record
+
+In the Supabase **SQL editor**, create the tables your company already thinks in: people, teams, and a `knowledge` table the brain will ingest from. Identity reuses Supabase Auth's built-in `auth.users`.
+
+```sql
+-- Employees extend Supabase Auth's users; identity is auth.users.id (uuid)
+create table public.employees (
+  id     uuid primary key references auth.users (id) on delete cascade,
+  name   text not null,
+  team   text not null
+);
+
+-- The company knowledge the brain ingests. `scope` decides who can recall it.
+create table public.knowledge (
+  id             bigint generated always as identity primary key,
+  scope          text not null,                    -- the shared agent this belongs to: 'org:acme' | 'team:payments'
+  content        text not null,
+  author         uuid references auth.users (id),   -- who recorded it (their user_id); null for org seed data
+  created_at     timestamptz default now(),
+  mem0_synced_at timestamptz                        -- null until ingested into the brain
+);
+create index on public.knowledge (mem0_synced_at, created_at);
+
+-- Seed a little company knowledge to ingest.
+insert into public.knowledge (scope, content) values
+  ('org:acme', 'We chose Postgres over MongoDB for the core product for strong transactional guarantees and relational joins.'),
+  ('org:acme', 'All production deploys go out Tuesday and Thursday; never on Fridays.'),
+  ('org:acme', 'Customer data must stay in the EU region for GDPR compliance.'),
+  ('org:acme', 'Billing is owned by the Payments team, and Alice is the Payments tech lead.'),
+  ('team:payments', 'Stripe is our processor; webhooks are verified with PAYMENTS_WEBHOOK_SECRET.');
+```
+
+Grab your project URL and **service-role** key from **Settings → API** (the ingestion job runs server-side and needs to read every scope).
+
+## Step 3: Project setup
+
+```bash
+mkdir company-brain && cd company-brain
+pip install "mem0ai>=2.0.17" supabase requests   # 2.0.17+ for agent_custom_instructions
+```
+
+```bash
+export MEM0_API_KEY="m0-..."
+export SUPABASE_URL="https://<project-ref>.supabase.co"
+export SUPABASE_SERVICE_KEY="<service-role-key>"
+```
+
+## Step 4: Configure the brain
+
+Create **`brain.py`**. This constructs the Platform client and teaches it what to remember. The key is the **two** instruction sets: `custom_instructions` governs a person's own (`user_id`) memories, and `agent_custom_instructions` governs shared (`agent_id`) memories, phrased in the third person so company facts read "The company…", not "The user's organization…". `custom_categories` files each memory under a useful label.
+
+```python
+# brain.py
+import os
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key=os.environ["MEM0_API_KEY"])
+
+# Steer extraction (project-wide). Runs server-side; no LLM key needed here.
+client.project.update(
+    # Governs a person's OWN memories (user_id).
+    custom_instructions=(
+        "Extract the individual's own durable preferences, context, and how they work. "
+        "Ignore greetings and one-off chatter."
+    ),
+    # Governs SHARED memories (agent_id); write them in the third person.
+    agent_custom_instructions=(
+        "Extract durable company/team knowledge in the third person "
+        "(\"The company...\", \"The team...\"): decisions and their rationale, ownership "
+        "(who owns what), processes, policies, tooling choices, and gotchas. "
+        "Ignore greetings, scheduling, and one-off chatter."
+    ),
+    custom_categories=[
+        {"decision": "Architectural or product decisions and why they were made"},
+        {"ownership": "Who owns a system, service, or process"},
+        {"policy": "Compliance, security, and process rules"},
+        {"tooling": "Tools, services, and how they're configured"},
+    ],
+)
+
+# Scopes. A person is a user_id; shared brains are agent_ids.
+COMPANY = "org:acme"                       # agent_id: company-wide shared brain
+def team(name):   return f"team:{name}"    # agent_id: a team's shared brain
+def person(uid):  return uid               # user_id: an individual (Supabase auth id)
+```
+
+Run it once to apply the project settings:
+
+```bash
+python -c "import brain; print('brain configured')"
+```
+
+
+## Step 5: Ingest company knowledge from Supabase
+
+This is where Supabase and the brain connect. Create **`ingest.py`**: read un-synced rows from `knowledge`, add each to the Platform brain under its scope, then mark it synced. Platform `add()` is **asynchronous**, it returns an `event_id` you can poll, so we include a small `wait_for` helper.
+
+```python
+# ingest.py
+import os, time, requests
+from supabase import create_client
+from brain import client, person
+
+sb = create_client(os.environ["SUPABASE_URL"], os.environ["SUPABASE_SERVICE_KEY"])
+MEM0_HEADERS = {"Authorization": f"Token {os.environ['MEM0_API_KEY']}"}
+
+def wait_for(event_id, timeout=30):
+    """Platform extraction is async; poll the event until it settles."""
+    for _ in range(timeout):
+        r = requests.get(f"https://api.mem0.ai/v1/event/{event_id}/", headers=MEM0_HEADERS).json()
+        if r.get("status") in ("SUCCEEDED", "FAILED"):
+            return r["status"]
+        time.sleep(1)
+    return "TIMEOUT"
+
+# 1. Read knowledge that hasn't been ingested yet
+rows = sb.table("knowledge").select("*").is_("mem0_synced_at", "null").execute().data
+
+for row in rows:
+    # 2. Add it. agent_id = the shared scope (org/team); user_id = who recorded it.
+    #    Mem0 routes shared facts to the agent and personal facts to the individual,
+    #    so pass both when there's an author.
+    add_kwargs = {
+        "agent_id": row["scope"],
+        "metadata": {"source": "supabase", "knowledge_id": row["id"]},
+    }
+    if row["author"]:
+        add_kwargs["user_id"] = person(row["author"])
+    res = client.add([{"role": "user", "content": row["content"]}], **add_kwargs)
+    # 3. Platform returns an event_id; wait for extraction to finish
+    event_id = res.get("event_id") if isinstance(res, dict) else None
+    if event_id:
+        wait_for(event_id)
+    # 4. Mark the row synced so we never double-ingest
+    sb.table("knowledge").update({"mem0_synced_at": "now()"}).eq("id", row["id"]).execute()
+
+print(f"Ingested {len(rows)} knowledge items into the company brain.")
+```
+
+```bash
+python ingest.py
+```
+
+```text
+Ingested 5 knowledge items into the company brain.
+```
+
+Re-running is safe, `mem0_synced_at` gates it, so a nightly cron can keep the brain in step with Supabase.
+
+## Step 6: Ask the brain
+
+Create **`ask.py`**. It searches everything relevant to the asker: their own (`user_id`) memories **plus** the shared company and team (`agent_id`) memories. This has to be an **`OR`**, each memory row belongs to exactly one entity, so a flat filter or an `AND` of a `user_id` and an `agent_id` matches nothing.
+
+```python
+# ask.py
+import sys
+from brain import client, COMPANY, team, person
+
+def ask(question: str, uid: str | None = None, user_team: str | None = None) -> str:
+    scopes = [{"agent_id": COMPANY}]              # company-wide brain
+    if user_team:
+        scopes.append({"agent_id": team(user_team)})   # the asker's team
+    if uid:
+        scopes.append({"user_id": person(uid)})        # the asker's own memories
+    hits = client.search(
+        query=question,
+        filters={"OR": scopes},   # OR, never AND (one FK per memory row)
+        top_k=5,
+        rerank=True,
+    )
+    return "\n".join(f"- {h['memory']}" for h in hits.get("results", hits))
+
+if __name__ == "__main__":
+    print(ask(" ".join(sys.argv[1:]) or "When can we deploy?"))
+```
+
+```bash
+python ask.py "Why did we pick Postgres, and can I deploy on Friday?"
+```
+
+```text
+- The company chose Postgres over MongoDB for strong transactional guarantees and relational joins
+- The company's production deploys go out Tuesday and Thursday, never on Fridays
+```
+
+Search returns every relevant memory, so a question resolves across separate facts, here it pulls both the owning team and the person:
+
+```bash
+python ask.py "Who should I talk to about billing?"
+```
+
+```text
+- Billing is owned by the Payments team
+- Alice is the Payments tech lead
+```
+
+## Step 7: Sharper retrieval
+
+Platform search is hybrid (semantic + keyword) and filterable. Combine a keyword pass with a category filter to answer precise questions:
+
+```python
+client.search(
+    query="webhook signing secret",
+    filters={"agent_id": "team:payments", "categories": {"in": ["tooling"]}},
+    keyword_search=True,     # hybrid keyword + semantic
+    rerank=True,
+    threshold=0.3,
+)
+```
+
+Filters use keyword operators (`in`, `gte`, `contains`, …) and AND/OR/NOT, so you can scope by date, category, or metadata, for example the company's policies added this quarter:
+
+```python
+client.search(
+    query="compliance rules",
+    filters={"AND": [
+        {"agent_id": "org:acme"},
+        {"categories": {"in": ["policy"]}},
+        {"created_at": {"gte": "2026-01-01"}},
+    ]},
+)
+```
+
+## Step 8: Expose the brain to every agent (MCP)
+
+A brain only your script can reach isn't a company brain. Mem0's **hosted MCP server** lets any agent (Claude Code, Cursor, a Slack bot) query and contribute to the *same* brain. The endpoint is `https://mcp.mem0.ai/mcp`, and the supported way to connect is the `mcp-add` helper, which registers the server and runs Mem0's OAuth login so no key ever lands in a config file.
+
+<Tabs>
+<Tab title="Claude Code / Cursor">
+```bash
+npx mcp-add --url "https://mcp.mem0.ai/mcp" --clients "claude code,cursor"
+```
+Complete the browser login on first connect. Now the agent has the brain's memory tools (`add_memory`, `search_memories`, and more) available in-editor.
+</Tab>
+<Tab title="Manual (.mcp.json)">
+```json
+{
+  "mcpServers": {
+    "mem0": { "url": "https://mcp.mem0.ai/mcp" }
+  }
+}
+```
+Auth happens via Mem0's OAuth flow on first use, don't paste a static token into the file (the hosted gateway may reject a raw `Token` header).
+</Tab>
+<Tab title="Slack bot">
+```python
+# A Slack bot is just another MCP client. Point its MCP layer at the same URL,
+# authenticate via Mem0's OAuth flow, and pass the company scope on each call.
+await mcp.call_tool("search_memories", {
+    "query": user_message,
+    "agent_id": "org:acme",
+})
+```
+</Tab>
+</Tabs>
+
+With this, an engineer asks the brain from their editor and a teammate asks it from Slack, one shared memory behind both.
+
+## Step 9: Onboard a new hire (the payoff)
+
+This is what a company brain is *for*. Dana joins, and her identity comes from **Supabase Auth**, which maps straight to her Mem0 `user_id`. She asks the questions every new hire asks and gets real answers on day one, drawn from the shared company (and her team's) brain, plus anything she's told it herself.
+
+```python
+# onboarding.py
+from brain import client, person
+from ask import ask
+
+# In a real app these come from sb.auth.get_user(jwt) and the employees table.
+dana_uid, dana_team = "8f3c...-dana", "payments"
+
+# Dana also tells the brain how *she* works. This is personal, so it goes to her
+# user_id, not the shared agent, and stays scoped to her.
+client.add(
+    [{"role": "user", "content": "I prefer early returns over nested ifs, and I review PRs in the morning."}],
+    user_id=person(dana_uid),
+)
+
+for q in [
+    "Who owns billing and who do I talk to?",   # company (agent) knowledge
+    "When are deploys, and are there hard rules?",
+    "How do I like to write code?",             # Dana's own (user) knowledge
+]:
+    print(f"Q: {q}\nA: {ask(q, uid=dana_uid, user_team=dana_team)}\n")
+```
+
+```text
+Q: Who owns billing and who do I talk to?
+A: - Billing is owned by the Payments team; Alice is the Payments tech lead
+
+Q: When are deploys, and are there hard rules?
+A: - The company's production deploys go out Tuesday and Thursday, never on Fridays
+
+Q: How do I like to write code?
+A: - User prefers early returns over nested ifs
+```
+
+The same `ask()` blends the shared company facts with Dana's own preference, because the `OR` filter spans both her `user_id` and the org and team `agent_id`s.
+
+Dana onboarded herself by asking, drawing on the shared brain the rest of the team had been filling.
+
+## Production notes
+
+<Warning>
+**`user_id` vs `agent_id`.** An individual is a `user_id`; shared brains (company, team) are `agent_id`s. Keeping them separate is what gives you the third-person "The company…" framing and lets a person's own context sit alongside org knowledge. Put a secret like a webhook key on a **team** agent, never the company agent, or everyone can recall it, and mirror the boundary in Supabase with a Row Level Security policy on `knowledge`.
+</Warning>
+
+<Warning>
+**Search must `OR` the scopes.** A memory row belongs to exactly one entity, so `filters={"OR": [{"user_id": ...}, {"agent_id": "org:acme"}, {"agent_id": "team:..."}]}`. A flat filter, or an `AND` of a `user_id` and an `agent_id`, returns nothing.
+</Warning>
+
+<Warning>
+**`add()` is asynchronous.** It returns `{event_id, status: "PENDING"}` and extraction finishes a moment later, poll `GET /v1/event/{event_id}/` (as in Step 5) when you need to know a write has landed before searching for it.
+</Warning>
+
+<Note>
+**Where the entity ID goes differs by call.** `search()` and `get_all()` take the scope inside `filters={...}` (a top-level `user_id=`/`agent_id=` is rejected). `add()` and `delete_all()` are the opposite, they take it as a top-level keyword: `client.delete_all(agent_id="team:payments")`. Deletes are asynchronous too, so a `get_all` right after a `delete_all` can still show rows for a few seconds.
+</Note>
+
+## Where to take it next
+
+- **Auto-feed the brain** from PR descriptions, RFCs, and incident write-ups so it grows without anyone thinking about it, just insert into Supabase `knowledge` and let the cron ingest.
+- **Scope by real identity** end to end: verify the Supabase JWT, read `sb.auth.get_user(jwt).user.id` for the `user_id`, look up the person's team, and `OR` their `user_id` with the company and team `agent_id`s on every recall.
+- **Give teams a private view** with Supabase RLS so `team:` knowledge is only readable by that team.
+
+---
+
+<CardGroup cols={2}>
+  <Card title="Mem0 MCP Server" icon="plug" href="/platform/mem0-mcp">
+    Connect any agent or editor to the brain over MCP.
+  </Card>
+  <Card title="Custom Categories & Instructions" icon="sliders" href="/platform/features/custom-instructions">
+    Steer exactly what the brain extracts and how it's filed.
+  </Card>
+</CardGroup>
+
+<Snippet file="star-on-github.mdx" />

docs/cookbooks/overview.mdx

@@ -1,7 +1,6 @@
 ---
-title: Overview
-seo:
-  title: "Cookbooks and Tutorials - Mem0"
+title: "Cookbooks and Tutorials"
+sidebarTitle: "Overview"
 description: "Browse cookbook examples and tutorials for building AI applications with Mem0, from companion chatbots to AI agents."
 ---
 

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

@@ -1,7 +1,6 @@
 ---
-title: Delete Memory
-seo:
-  title: "Delete Memory Operation - Mem0"
+title: "Delete Memory Operation"
+sidebarTitle: "Delete Memory"
 description: Remove memories from Mem0 either individually, in bulk, or via filters.
 icon: "trash"
 iconType: "solid"

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

@@ -1,7 +1,6 @@
 ---
-title: Update Memory
-seo:
-  title: "Update Memory Operation - Mem0"
+title: "Update Memory Operation"
+sidebarTitle: "Update Memory"
 description: Modify an existing memory by updating its content or metadata.
 icon: "pen-to-square"
 iconType: "solid"

docs/docs.json

@@ -41,6 +41,7 @@
                     "pages": [
                       "platform/quickstart",
                       "platform/overview",
+                      "platform/copilot",
                       "platform/agent-signup",
                       "vibecoding",
                       "platform/cli",
@@ -71,6 +72,7 @@
                         "pages": [
                           "platform/features/v2-memory-filters",
                           "platform/features/entity-scoped-memory",
+                          "platform/features/user-profiles",
                           "platform/features/graph-memory",
                           "platform/features/async-client",
                           "platform/features/multimodal-support",
@@ -443,7 +445,8 @@
                       "cookbooks/integrations/mastra-agent",
                       "cookbooks/integrations/healthcare-google-adk",
                       "cookbooks/integrations/aws-bedrock",
-                      "cookbooks/integrations/tavily-search"
+                      "cookbooks/integrations/tavily-search",
+                      "cookbooks/integrations/supabase"
                     ]
                   },
                   {
@@ -511,6 +514,17 @@
                       "api-reference/entities/delete-user"
                     ]
                   },
+                  {
+                    "group": "Profiles",
+                    "icon": "id-card",
+                    "pages": [
+                      "api-reference/profiles/get-profile",
+                      "api-reference/profiles/get-profile-settings",
+                      "api-reference/profiles/update-profile-settings",
+                      "api-reference/profiles/generate-profiles",
+                      "api-reference/profiles/get-profile-job"
+                    ]
+                  },
                   {
                     "group": "Organizations",
                     "icon": "building",

docs/integrations.mdx

@@ -1,7 +1,6 @@
 ---
-title: Overview
-seo:
-  title: "Integrations Overview - Mem0"
+title: "Integrations Overview"
+sidebarTitle: "Overview"
 description: "Overview of Mem0 integrations with popular AI frameworks and tools for persistent memory and context management."
 ---
 

docs/integrations/aws-bedrock.mdx

@@ -1,7 +1,6 @@
 ---
-title: AWS Bedrock
-seo:
-  title: "AWS Bedrock Integration with Mem0"
+title: "AWS Bedrock Integration"
+sidebarTitle: "AWS Bedrock"
 description: "Use Mem0 with AWS Bedrock and OpenSearch Service for cloud-native persistent semantic memory storage."
 ---
 

docs/integrations/claude-code.mdx

@@ -172,6 +172,36 @@ claude plugin update mem0@mem0-plugins --scope user
 | Sidekick won't start | Must be in a Git repo. Check that your Claude Code version supports plugin agents and worktrees. |
 | Remove the plugin | `claude plugin uninstall mem0@mem0-plugins` |
 
+## Telemetry
+
+The plugin sends usage events (which hook ran, timing, result counts, failure
+types) so Mem0 can see what's used and what's breaking.
+
+These events are **not anonymous**. When an API key is configured, which
+installing the plugin requires, they are sent under your Mem0 account email,
+the same way the Python SDK and the CLI attribute theirs. Without a key they
+are sent under a random per-machine id.
+
+Each event carries the event name, the plugin version, the harness it ran in,
+your OS and Python version, and per-event properties describing what happened:
+timings, counts, coarse outcome and failure labels, and which model was
+configured. Repository and session identifiers are hashed with a random salt
+generated on your machine, so they cannot be linked back to a repository name
+or path.
+
+The exact set is enforced in code rather than by this list: every property is
+filtered through a denylist of sensitive keys and credential-shaped values are
+redacted before anything is sent.
+
+Prompts, memory text, queries, file paths, repository names, and API keys are
+never sent.
+
+Turn it off:
+
+```bash
+export MEM0_TELEMETRY=false
+```
+
 <CardGroup cols={2}>
   <Card title="Mem0 MCP Setup" icon="puzzle-piece" href="/platform/mem0-mcp">
     Detailed MCP configuration for all clients

docs/integrations/codex.mdx

@@ -3,7 +3,7 @@ title: Codex
 description: "Add persistent memory to OpenAI Codex with automatic capture, automatic recall, a search tool, and six memory skills."
 ---
 
-Add persistent memory to [**OpenAI Codex**](https://openai.com/index/codex/) with the Mem0 plugin. Codex forgets everything between tasks. This plugin fixes that by connecting to Mem0's cloud memory layer via MCP, automatically capturing learnings at key lifecycle points, and retrieving relevant context on the first prompt of a session. Codex can use the search tool for recall later in the session.
+Add persistent memory to [**OpenAI Codex**](https://openai.com/codex/) with the Mem0 plugin. Codex forgets everything between tasks. This plugin fixes that by connecting to Mem0's cloud memory layer via MCP, automatically capturing learnings at key lifecycle points, and retrieving relevant context on the first prompt of a session. Codex can use the search tool for recall later in the session.
 
 <Info>Current plugin version: `0.3.1`.</Info>
 

docs/integrations/deepseek-plugin.mdx

@@ -114,7 +114,7 @@ Both tools also accept optional per-call `userId`, `agentId`, and `runId` params
 
 ## Telemetry
 
-Writes are tagged `source="DEEPSEEK_HARNESS"` so Mem0 can attribute usage to this integration. Anonymous usage events include operation names, durations, result counts, and coarse failure kinds. Queries, memory text, entity IDs, and API keys are never included. Set `MEM0_TELEMETRY=false` to opt out.
+Writes are tagged `source="DEEPSEEK_HARNESS"` so Mem0 can attribute usage to this integration. Usage events include operation names, durations, result counts, and coarse failure kinds. They are **not anonymous**: when an API key is configured they are sent under your Mem0 account email, the same way the SDK attributes its own. Queries, memory text, entity IDs, and API keys are never included. Set `MEM0_TELEMETRY=false` to opt out.
 
 <Note>
   This plugin is a developer preview and tracks the evolving DeepSeek Harness plugin API.

docs/integrations/flowise.mdx

@@ -23,7 +23,7 @@ npm install -g flowise
 npx flowise start
 ```
 
-2. Access to the Flowise UI at http://localhost:3000
+2. Access to the Flowise UI at `http://localhost:3000`
 3. Basic familiarity with [Flowise's LLM orchestration](https://flowiseai.com/#features) concepts
 
 ## Setup and Configuration

docs/integrations/hermes.mdx

@@ -1,39 +1,35 @@
 ---
 title: Hermes Agent
-description: "Add long-term memory to Hermes agents with Mem0, on managed Mem0 Cloud or fully self-hosted (OSS), with automatic background sync and zero-latency prefetch."
+description: "Add long-term memory to Hermes agents using Mem0 Platform, a self-hosted server, or local OSS mode with background fact extraction."
 ---
 
-Add long-term memory to [Hermes Agent](https://github.com/NousResearch/hermes-agent), a self-improving AI agent CLI by Nous Research. Hermes has a pluggable memory system, and Mem0 is one of the supported providers. Once enabled, Mem0 learns facts from your conversations and surfaces relevant ones before each turn, without slowing down the chat.
+Add long-term memory to [Hermes Agent](https://github.com/NousResearch/hermes-agent), a self-improving AI agent CLI by Nous Research. Hermes has a pluggable memory system, and Mem0 is one of the supported providers. Once enabled, Mem0 learns facts from your conversations and surfaces relevant ones for the current question, without slowing down the chat.
 
-You can run Mem0 in two ways:
+You can run Mem0 in three ways:
 
 - **Platform mode** (default): managed Mem0 Cloud. Add your API key and you are ready.
-- **OSS mode**: fully self-hosted with your own LLM, embedder, and vector store. No data leaves your machine.
+- **Self-hosted server mode**: point the plugin at a Mem0 server you run yourself (the Docker-shipped server). The plugin only talks HTTP to your server.
+- **OSS mode**: run Mem0 in-process with your own LLM, embedder, and vector store. No Mem0 server required.
 
 ## How It Works
 
-Hermes runs a built-in memory system (file-based `MEMORY.md` and `USER.md`) alongside one external provider. When Mem0 is active, it works additively with the built-in system at three points in every conversation turn.
+Hermes runs a built-in memory system (file-based `MEMORY.md` and `USER.md`) alongside one external provider. When Mem0 is active, it works additively with the built-in system at two points in every conversation turn.
 
-### 1. Before the agent responds (prefetch)
+### 1. Current-turn recall (bounded wait)
 
-When you send a message, Hermes checks for cached Mem0 search results from the previous turn. If they exist, those memories are injected into the system prompt so the model can see them. This is zero-latency, with no waiting on an API call.
+When you send a message, Hermes searches your stored memories for the current question and waits up to 3 seconds for results. If they arrive in time, they are injected into the system prompt so the model can see them. If the backend is slower, Hermes skips the injection and the model can still call `mem0_search` itself — so a slow backend never blocks a turn.
 
-### 2. After the agent responds (sync)
+### 2. Background fact extraction (sync)
 
 Once the model finishes, Hermes sends the `(user message, assistant response)` pair to Mem0 in a background thread. Mem0 extracts facts automatically (for example, "user prefers Python" or "user works at Acme Corp"), so you never have to tell it what to remember. Each write is tagged with the gateway channel it came from.
 
-### 3. Background prefetch for the next turn
-
-At the same time, Hermes runs a background search to pre-load relevant memories for your next message. By the time you type, the results are already cached.
-
 ## Agent Tools
 
-When Mem0 is active, the model gets five tools it can call during a conversation:
+When Mem0 is active, the model gets four tools it can call during a conversation:
 
 | Tool | Description | Parameters |
 |------|-------------|------------|
-| `mem0_list` | List all stored memories, for a full overview | `page`, `page_size` (default 100, max 200) |
-| `mem0_search` | Semantic search by meaning, ranked by relevance | `query` (required), `top_k` (default 10, max 50), `rerank` (default `true`, Platform mode only) |
+| `mem0_search` | Semantic search by meaning, ranked by relevance | `query` (required), `top_k` (default 10, max 50), `rerank` (default `false`, Platform mode only) |
 | `mem0_add` | Store a fact verbatim, with no LLM extraction | `content` (required) |
 | `mem0_update` | Update a memory's text by ID | `memory_id`, `text` (both required) |
 | `mem0_delete` | Delete a memory by ID | `memory_id` (required) |
@@ -79,6 +75,36 @@ memory:
 
 That's it. Mem0 runs automatically from here.
 
+## Self-Hosted Server Setup
+
+Run the [Mem0 server](https://github.com/mem0ai/mem0/tree/main/server) (FastAPI + pgvector) from its Docker image and point the plugin at it. Unlike OSS mode, the plugin just talks HTTP to your server.
+
+### Interactive
+
+```bash
+hermes memory setup
+# Select "mem0", then "Self-hosted server", and enter the server URL
+```
+
+### With flags
+
+```bash
+hermes memory setup mem0 --mode selfhosted \
+  --host http://localhost:8888 \
+  --api-key your-admin-api-key
+```
+
+### With environment variables
+
+```bash
+echo "MEM0_HOST=http://localhost:8888" >> ~/.hermes/.env
+echo "MEM0_API_KEY=your-admin-api-key" >> ~/.hermes/.env
+```
+
+Then start a fresh Hermes session and call `mem0_search` — it connects to your server. The plugin authenticates with `X-API-Key` and uses the server's `/search` and `/memories` routes. The API key is optional only for servers running with `AUTH_DISABLED`.
+
+<Note>Setting `host` routes to the self-hosted server automatically. Don't combine it with `mode: oss` — OSS takes precedence and ignores `host`.</Note>
+
 ## OSS (Self-Hosted) Setup
 
 OSS mode runs Mem0 entirely on your own infrastructure: your LLM, your embedder, and your vector store. No data is sent to Mem0 Cloud, and no Mem0 API key is required.
@@ -111,15 +137,20 @@ hermes memory setup mem0 --mode oss \
 
 | Flag | Description |
 |------|-------------|
-| `--mode` | `platform` or `oss` |
+| `--mode` | `platform`, `selfhosted`, or `oss` |
+| `--api-key` | Platform API key, or the admin key of a self-hosted server |
+| `--host` | Self-hosted server URL (with `--mode selfhosted`) |
 | `--oss-llm` | LLM provider (`openai` or `ollama`, default `openai`) |
 | `--oss-llm-key` | LLM API key (for `openai`) |
 | `--oss-llm-model` | Override the LLM model |
 | `--oss-llm-url` | LLM base URL (for `ollama` or a custom endpoint) |
 | `--oss-embedder` | Embedder provider (default `openai`) |
 | `--oss-embedder-key` | Embedder API key |
+| `--oss-embedder-model` | Override the embedder model |
+| `--oss-embedder-url` | Embedder base URL (for `ollama` or a custom endpoint) |
 | `--oss-vector` | Vector store (`qdrant` or `pgvector`, default `qdrant`) |
 | `--oss-vector-path` | Local Qdrant storage path |
+| `--oss-vector-url` | Qdrant server URL |
 | `--oss-vector-host`, `--oss-vector-port` | PGVector or remote Qdrant host and port |
 | `--oss-vector-user`, `--oss-vector-password`, `--oss-vector-dbname` | PGVector connection details |
 | `--user-id` | Canonical user identifier |
@@ -127,7 +158,7 @@ hermes memory setup mem0 --mode oss \
 
 ## Switching Modes
 
-You can move between Platform and OSS at any time. Run the setup command again, or edit `~/.hermes/mem0.json` directly.
+You can move between the three modes at any time. Run the setup command again, or edit `~/.hermes/mem0.json` directly.
 
 ```bash
 # Platform to OSS
@@ -136,6 +167,9 @@ hermes memory setup mem0 --mode oss --oss-llm-key sk-...
 # OSS to Platform
 hermes memory setup mem0 --mode platform --api-key sk-...
 
+# Platform to a self-hosted server
+hermes memory setup mem0 --mode selfhosted --host http://localhost:8888
+
 # Preview without writing anything
 hermes memory setup mem0 --mode oss --oss-llm-key sk-... --dry-run
 ```
@@ -146,7 +180,7 @@ A self-hosted `~/.hermes/mem0.json` looks like this:
 {
   "mode": "oss",
   "oss": {
-    "llm": {"provider": "openai", "config": {"model": "gpt-5-mini"}},
+    "llm": {"provider": "openai", "config": {"model": "gpt-5-mini", "is_reasoning_model": true}},
     "embedder": {"provider": "openai", "config": {"model": "text-embedding-3-small"}},
     "vector_store": {"provider": "qdrant", "config": {"path": "~/.hermes/mem0_qdrant"}}
   }
@@ -159,11 +193,12 @@ Behavioral settings live in `~/.hermes/mem0.json` and are written for you by `he
 
 | Key | Default | Description |
 |-----|---------|-------------|
-| `mode` | `platform` | `platform` (Mem0 Cloud) or `oss` (self-hosted) |
-| `api_key` | none | Mem0 Platform API key, required in Platform mode. Stored in `.env` as `MEM0_API_KEY` |
+| `mode` | `platform` | `platform` (Mem0 Cloud) or `oss` (self-managed, in-process). Self-hosted server routing is set via `host` |
+| `host` | none | Self-hosted Mem0 server URL. When set, the plugin talks HTTP to your server instead of the cloud |
+| `api_key` | none | Mem0 Platform API key, or the admin key of a self-hosted server. Stored in `.env` as `MEM0_API_KEY` |
 | `user_id` | `hermes-user` | Identifier that scopes memories. See cross-channel behavior below |
 | `agent_id` | `hermes` | Agent identifier attached to writes |
-| `rerank` | `true` | Rerank search results for relevance (Platform mode only) |
+| `rerank` | `false` | Rerank search results for relevance (Platform mode only) |
 
 ### Cross-channel memories
 
@@ -174,12 +209,11 @@ Hermes can run from the CLI and from gateways like Telegram, Slack, and Discord.
 
 Either way, every write is tagged with `metadata.channel` (for example `telegram` or `cli`), so per-channel views are still possible at query time.
 
-
 ## Reliability
 
 - **Circuit breaker**: if Mem0 fails five times in a row, Hermes pauses calls for two minutes, then retries. The agent keeps working without memory during that window. Expected client errors, like a 404 on a missing memory id, do not count toward tripping the breaker.
-- **Non-blocking**: every Mem0 call runs in a background daemon thread, so a slow or failed call never blocks your conversation.
-- **Thread-safe**: the client uses lazy initialization with locking, and the background sync and prefetch threads are guarded so concurrent gateway messages cannot produce duplicate memories.
+- **Non-blocking**: fact extraction runs in a background daemon thread, and current-turn recall waits at most 3 seconds, so a slow or failed call never blocks your conversation.
+- **Thread-safe**: the client uses lazy initialization with locking, and the background sync and recall threads are guarded so concurrent gateway messages cannot produce duplicate memories.
 
 ## Troubleshooting
 
@@ -188,6 +222,7 @@ Either way, every write is tagged with `metadata.channel` (for example `telegram
 The circuit breaker tripped after five consecutive failures and resets after two minutes.
 
 - **Platform mode**: check your API key and internet connection.
+- **Self-hosted server mode**: check that the server is running and reachable at the configured `host` URL.
 - **OSS mode**: make sure your vector store (Qdrant or PGVector) is running and reachable.
 
 ### OSS: vector store connection refused
@@ -217,8 +252,8 @@ curl http://localhost:11434/api/tags
 
 ## Key Features
 
-1. **Two ways to run**: managed Platform or fully self-hosted OSS, switchable at any time.
-2. **Zero-latency recall**: memories are prefetched in the background and cached before you type.
+1. **Three ways to run**: managed Platform, a self-hosted server, or fully local OSS, switchable at any time.
+2. **Current-turn recall**: memories for the current question are injected within a 3-second window, with `mem0_search` as the model's own backstop.
 3. **Automatic extraction**: Mem0 extracts and deduplicates facts from each exchange for you.
 4. **Non-blocking and fault tolerant**: background threads plus a circuit breaker keep the agent responsive even when Mem0 is unreachable.
 5. **Additive memory**: works alongside Hermes' built-in file memory (`MEMORY.md`, `USER.md`).

docs/integrations/langchain.mdx

@@ -1,7 +1,6 @@
 ---
-title: Langchain
-seo:
-  title: "LangChain Integration with Mem0"
+title: "LangChain Integration"
+sidebarTitle: "Langchain"
 description: "Build personalized AI agents using LangChain for conversation flow and Mem0 for long-term memory retention."
 ---
 

docs/integrations/llama-index.mdx

@@ -45,7 +45,7 @@ memory_from_client = Mem0Memory.from_client(
 )
 ```
 
-Context is used to identify the user, agent or the conversation in the Mem0. It is required to be passed in the at least one of the fields in the `Mem0Memory` constructor. It can be any of the following:
+Context is used to identify the user, agent or the conversation in the Mem0. It is required to be passed in at least one of the fields in the `Mem0Memory` constructor. It can be any of the following:
 
 ```python
 context = {

docs/integrations/n8n.mdx

@@ -1,7 +1,6 @@
 ---
-title: n8n
-seo:
-  title: "n8n Integration with Mem0"
+title: "n8n Integration"
+sidebarTitle: "n8n"
 description: "Add long-term memory to n8n workflows and AI Agents with the Mem0 community node, no code required."
 ---
 

docs/integrations/openclaw.mdx

@@ -481,7 +481,9 @@ Plugin config is stored in `~/.openclaw/openclaw.json` with file permissions `0o
 
 ### Telemetry
 
-Anonymous usage telemetry (PostHog) is enabled by default to help improve the plugin. No conversation content or memory values are included, only event counts (recall, capture, tool usage, CLI commands).
+Usage telemetry (PostHog) is enabled by default to help improve the plugin. No conversation content or memory values are included, only event counts (recall, capture, tool usage, CLI commands).
+
+These events are **not anonymous**. OpenClaw does not send your account email the way the SDK does, but it does send an unsalted SHA-256 hash of it, falling back to a hash of the API key and then to a random per-machine id. Mem0 holds the email the hash is derived from, so the hash identifies your account rather than concealing it. The first run that resolves an account also emits a PostHog `$identify`, which permanently merges any earlier random id into that identity.
 
 To opt out, set the environment variable:
 

docs/llms.txt

@@ -171,6 +171,7 @@ If the user is on a pre-current major (Python < 2, TS < 3, or a Platform call st
 - [Introduction](https://docs.mem0.ai/introduction) [Both]: Use when the user wants a one-page overview of how memory fits between the LLM and the app.
 - [Vibe Code with Mem0](https://docs.mem0.ai/vibecoding) [Both]: Use when the user is in Claude Code, Cursor, or Windsurf and wants memory wired into their editor.
 - [Platform Overview](https://docs.mem0.ai/platform/overview) [Platform]: Use when the user picks the managed product - 4-line integration, hosted API, dashboard.
+- [Mem0 Copilot](https://docs.mem0.ai/platform/copilot) [Platform]: Use when inspecting project memories, reviewing configuration changes, or testing extraction in the dashboard.
 - [Sign up as an agent](https://docs.mem0.ai/platform/agent-signup) [Platform]: Use when an AI agent needs to mint a Mem0 API key autonomously - four commands, no email or dashboard, human claims ownership later.
 - [Platform vs Open Source](https://docs.mem0.ai/platform/platform-vs-oss) [Both]: Use when the user is deciding between managed and self-hosted.
 - [Platform Quickstart](https://docs.mem0.ai/platform/quickstart) [Platform]: Use for the first Platform integration - API key plus `MemoryClient.add/search`.
@@ -197,6 +198,7 @@ If the user is on a pre-current major (Python < 2, TS < 3, or a Platform call st
 ### Features - Essential
 - [V2 Memory Filters](https://docs.mem0.ai/platform/features/v2-memory-filters) [Platform]: Use when compound filters (AND/OR on metadata, entity, time) are needed at search.
 - [Entity-Scoped Memory](https://docs.mem0.ai/platform/features/entity-scoped-memory) [Platform]: Use when partitioning memories by user, agent, app, or run.
+- [Profiles](https://docs.mem0.ai/platform/features/user-profiles) [Platform]: Use when a structured always-current summary of a user is needed in one read, instead of searching their memories.
 - [Graph Memory](https://docs.mem0.ai/platform/features/graph-memory) [Platform]: Use when connecting facts across memories through shared entities for entity-centric or multi-hop questions.
 - [Async Client](https://docs.mem0.ai/platform/features/async-client) [Platform]: Use when the app issues many concurrent Mem0 calls and needs non-blocking I/O.
 - [Multimodal Support](https://docs.mem0.ai/platform/features/multimodal-support) [Platform]: Use when storing images or PDFs as memory input.
@@ -325,6 +327,7 @@ If the user is on a pre-current major (Python < 2, TS < 3, or a Platform call st
 - [Healthcare Google ADK](https://docs.mem0.ai/cookbooks/integrations/healthcare-google-adk) [Platform]: Use when the domain is medical and the framework is Google ADK.
 - [AWS Bedrock](https://docs.mem0.ai/cookbooks/integrations/aws-bedrock) [OSS]: Use when deploying with AWS managed model services.
 - [Tavily Search](https://docs.mem0.ai/cookbooks/integrations/tavily-search) [Platform]: Use when the agent layers web search on memory.
+- [Company Brain (Mem0 Platform + Supabase)](https://docs.mem0.ai/cookbooks/integrations/supabase) [Platform]: Use to build a shared org brain on Mem0 Platform with Supabase as system of record and the MCP server as the access layer (with a new-hire onboarding demo).
 
 ### Framework Examples
 - [LlamaIndex React](https://docs.mem0.ai/cookbooks/frameworks/llamaindex-react) [Both]: Use when building a React UI with LlamaIndex and memory.
@@ -362,6 +365,11 @@ All API Reference docs describe Mem0 Platform REST endpoints (requires API key).
 ### Entities
 - [Get Users](https://docs.mem0.ai/api-reference/entities/get-users) [Platform]: Use when listing users, agents, or apps known to a project.
 - [Delete User](https://docs.mem0.ai/api-reference/entities/delete-user) [Platform]: Use when removing an entity and all its memories.
+- [Get Profile](https://docs.mem0.ai/api-reference/profiles/get-profile) [Platform]: Use when reading a user's structured profile and branching on its generation status.
+- [Get Profile Settings](https://docs.mem0.ai/api-reference/profiles/get-profile-settings) [Platform]: Use when checking the project's profile schema, instructions, or enabled flag.
+- [Update Profile Settings](https://docs.mem0.ai/api-reference/profiles/update-profile-settings) [Platform]: Use when defining or changing the JSON Schema that shapes profiles for a project.
+- [Generate Profiles](https://docs.mem0.ai/api-reference/profiles/generate-profiles) [Platform]: Use when building profiles now: a sample of ten, or one entity.
+- [Get Generation Job](https://docs.mem0.ai/api-reference/profiles/get-profile-job) [Platform]: Use when checking how far a generation has got, and whether it finished.
 
 ### Organizations
 - [Create Organization](https://docs.mem0.ai/api-reference/organization/create-org) [Platform]: Use when setting up a new org.

docs/open-source/features/custom-instructions.mdx

@@ -1,7 +1,6 @@
 ---
-title: Custom Instructions
-seo:
-  title: "Open Source Custom Instructions - Mem0"
+title: "Open Source Custom Instructions"
+sidebarTitle: "Custom Instructions"
 description: Tailor fact extraction so Mem0 stores only the details you care about.
 icon: "wand-magic-sparkles"
 ---

docs/open-source/features/multimodal-support.mdx

@@ -1,7 +1,6 @@
 ---
-title: Multimodal Support
-seo:
-  title: "Open Source Multimodal Support - Mem0"
+title: "Open Source Multimodal Support"
+sidebarTitle: "Multimodal Support"
 description: Capture and recall memories from both text and images.
 icon: "image"
 ---

docs/open-source/features/overview.mdx

@@ -1,7 +1,6 @@
 ---
-title: "Overview"
-seo:
-  title: "Open Source Features Overview - Mem0"
+title: "Open Source Features Overview"
+sidebarTitle: "Overview"
 description: "Self-hosting features that extend Mem0 beyond basic memory storage"
 icon: "list"
 ---

docs/open-source/features/rest-api.mdx

@@ -56,7 +56,7 @@ The Mem0 REST API server exposes every OSS memory operation over HTTP. Run it al
     make bootstrap    # starts Compose, creates an admin, issues the first API key
     ```
 
-    Or to start the stack only and finish setup via the browser wizard at http://localhost:3000:
+    Or to start the stack only and finish setup via the browser wizard at `http://localhost:3000`:
 
     ```bash
     cd server

docs/open-source/overview.mdx

@@ -1,7 +1,6 @@
 ---
-title: "Overview"
-seo:
-  title: "Mem0 Open Source Overview"
+title: "Open Source Overview"
+sidebarTitle: "Overview"
 description: "Self-host Mem0 with full control over your infrastructure and data"
 icon: "house"
 ---

docs/openapi.json

@@ -8070,6 +8070,480 @@
 					}
 				}
 			}
+		},
+		"/v2/entities/{entity_type}/{entity_id}/profile/": {
+			"get": {
+				"tags": [
+					"profiles"
+				],
+				"operationId": "profiles_read",
+				"summary": "Get an entity's profile",
+				"description": "Return the memory profile for one user.\n\nGeneration is asynchronous, so a known entity that has no profile yet is a normal 200 carrying a `status`. A 404 means only that no such entity exists.",
+				"parameters": [
+					{
+						"name": "entity_type",
+						"in": "path",
+						"required": true,
+						"schema": {
+							"type": "string",
+							"enum": [
+								"user"
+							]
+						},
+						"description": "The kind of entity that carries the profile."
+					},
+					{
+						"name": "entity_id",
+						"in": "path",
+						"required": true,
+						"schema": {
+							"type": "string"
+						},
+						"description": "The entity's id, as supplied when the memory was added."
+					}
+				],
+				"responses": {
+					"200": {
+						"description": "The profile envelope.",
+						"content": {
+							"application/json": {
+								"schema": {
+									"type": "object",
+									"properties": {
+										"profile": {
+											"type": "object",
+											"additionalProperties": true,
+											"description": "The generated profile, shaped by the project's schema. Empty unless status is succeeded."
+										},
+										"status": {
+											"type": "string",
+											"enum": [
+												"succeeded",
+												"pending",
+												"failed",
+												"not_enabled",
+												"insufficient_data"
+											],
+											"description": "Generation state. Branch on this rather than on an empty profile."
+										},
+										"entity_type": {
+											"type": "string",
+											"enum": [
+												"user"
+											]
+										},
+										"entity_id": {
+											"type": "string"
+										},
+										"updated_at": {
+											"type": "string",
+											"format": "date-time",
+											"nullable": true
+										},
+										"generation_count": {
+											"type": "integer"
+										}
+									}
+								}
+							}
+						}
+					},
+					"400": {
+						"description": "Unsupported entity type."
+					},
+					"404": {
+						"description": "No such entity in this project."
+					}
+				}
+			}
+		},
+		"/v2/profiles/settings/": {
+			"get": {
+				"tags": [
+					"profiles"
+				],
+				"operationId": "profiles_settings_read",
+				"summary": "Get profile settings",
+				"description": "Return the profile settings for the project the API key is scoped to.",
+				"responses": {
+					"200": {
+						"description": "Current settings.",
+						"content": {
+							"application/json": {
+								"schema": {
+									"type": "object",
+									"properties": {
+										"enabled": {
+											"type": "boolean",
+											"description": "Whether profile generation runs for this project. Project-wide."
+										},
+										"entities": {
+											"type": "object",
+											"description": "Settings for user profiles, under `user`.",
+											"properties": {
+												"user": {
+													"type": "object",
+													"properties": {
+														"schema": {
+															"type": "object",
+															"additionalProperties": true,
+															"nullable": true,
+															"description": "JSON Schema describing the profile. Every property needs a description."
+														},
+														"custom_instructions": {
+															"type": "string",
+															"nullable": true,
+															"description": "Extra guidance for the extraction step."
+														}
+													}
+												}
+											}
+										},
+										"capabilities": {
+											"type": "object",
+											"properties": {
+												"jobs": {
+													"type": "boolean"
+												},
+												"estimates": {
+													"type": "boolean"
+												},
+												"samples": {
+													"type": "boolean"
+												},
+												"full_rebuild": {
+													"type": "boolean",
+													"description": "Whether a project-wide rebuild (regenerate/backfill) is available. Currently false."
+												}
+											}
+										}
+									}
+								}
+							}
+						}
+					}
+				}
+			},
+			"post": {
+				"tags": [
+					"profiles"
+				],
+				"operationId": "profiles_settings_update",
+				"summary": "Update profile settings",
+				"description": "Update the project's profile settings. Only the fields present in the body are written, so one setting can change without re-sending the others.",
+				"requestBody": {
+					"required": true,
+					"content": {
+						"application/json": {
+							"schema": {
+								"type": "object",
+								"description": "Only the fields present are written. `schema` and `custom_instructions` nest under `entities.user`; a flat body is rejected.",
+								"properties": {
+									"enabled": {
+										"type": "boolean",
+										"description": "Whether profile generation runs for this project. Project-wide."
+									},
+									"entities": {
+										"type": "object",
+										"description": "Settings for user profiles, under `user`.",
+										"properties": {
+											"user": {
+												"type": "object",
+												"properties": {
+													"schema": {
+														"type": "object",
+														"additionalProperties": true,
+														"nullable": true,
+														"description": "JSON Schema describing the profile. Every property needs a description. Send null to clear it."
+													},
+													"custom_instructions": {
+														"type": "string",
+														"nullable": true,
+														"description": "Extra guidance for the extraction step. Send null to clear it."
+													}
+												}
+											}
+										}
+									}
+								}
+							}
+						}
+					}
+				},
+				"responses": {
+					"200": {
+						"description": "Settings as stored after the update.",
+						"content": {
+							"application/json": {
+								"schema": {
+									"type": "object",
+									"properties": {
+										"enabled": {
+											"type": "boolean",
+											"description": "Whether profile generation runs for this project. Project-wide."
+										},
+										"entities": {
+											"type": "object",
+											"description": "Settings for user profiles, under `user`.",
+											"properties": {
+												"user": {
+													"type": "object",
+													"properties": {
+														"schema": {
+															"type": "object",
+															"additionalProperties": true,
+															"nullable": true,
+															"description": "JSON Schema describing the profile. Every property needs a description."
+														},
+														"custom_instructions": {
+															"type": "string",
+															"nullable": true,
+															"description": "Extra guidance for the extraction step."
+														}
+													}
+												}
+											}
+										},
+										"capabilities": {
+											"type": "object",
+											"properties": {
+												"jobs": {
+													"type": "boolean"
+												},
+												"estimates": {
+													"type": "boolean"
+												},
+												"samples": {
+													"type": "boolean"
+												},
+												"full_rebuild": {
+													"type": "boolean",
+													"description": "Whether a project-wide rebuild (regenerate/backfill) is available. Currently false."
+												}
+											}
+										}
+									}
+								}
+							}
+						}
+					},
+					"400": {
+						"description": "The schema is not a valid profile schema."
+					}
+				}
+			}
+		},
+		"/v2/profiles/jobs/": {
+			"post": {
+				"tags": [
+					"profiles"
+				],
+				"operationId": "profiles_create_job",
+				"summary": "Generate profiles",
+				"description": "Start one generation. `operation` says what to build:\n\n- `sample` — up to 10 real entities, so a schema can be judged before it is used widely. These are real profiles: they are saved to those entities and count toward usage.\n- `trigger` — one entity, named by `entity_id`.\n\nSend an `Idempotency-Key` header. Replaying the same key returns the same job instead of charging twice. Poll `status_url` from the response until the status is terminal.",
+				"parameters": [
+					{
+						"in": "header",
+						"name": "Idempotency-Key",
+						"required": true,
+						"schema": {
+							"type": "string",
+							"minLength": 8,
+							"maxLength": 128
+						},
+						"description": "Makes a retry safe: the same key returns the same job."
+					}
+				],
+				"requestBody": {
+					"required": true,
+					"content": {
+						"application/json": {
+							"schema": {
+								"type": "object",
+								"required": [
+									"operation",
+									"entity_type"
+								],
+								"properties": {
+									"operation": {
+										"type": "string",
+										"enum": [
+											"sample",
+											"trigger"
+										],
+										"description": "What to generate. Optional only when `entity_id` is set, which means `trigger`."
+									},
+									"entity_type": {
+										"type": "string",
+										"enum": [
+											"user"
+										]
+									},
+									"entity_id": {
+										"type": "string",
+										"description": "One entity, for `trigger`."
+									},
+									"limit": {
+										"type": "integer",
+										"minimum": 1,
+										"maximum": 10,
+										"description": "How many entities to sample."
+									}
+								}
+							}
+						}
+					}
+				},
+				"responses": {
+					"202": {
+						"description": "Job accepted.",
+						"content": {
+							"application/json": {
+								"schema": {
+									"type": "object",
+									"properties": {
+										"job_id": {
+											"type": "string"
+										},
+										"status": {
+											"type": "string"
+										},
+										"status_url": {
+											"type": "string",
+											"description": "Poll this. Building the path yourself breaks on a route change."
+										},
+										"operation": {
+											"type": "string"
+										},
+										"entity_type": {
+											"type": "string"
+										},
+										"entity_count_reserved": {
+											"type": "integer",
+											"description": "Entities reserved against usage for this job."
+										},
+										"event_id": {
+											"type": "string",
+											"nullable": true
+										},
+										"replayed": {
+											"type": "boolean",
+											"description": "True when an Idempotency-Key returned an existing job."
+										},
+										"sampled": {
+											"type": "integer",
+											"description": "`sample` only."
+										},
+										"entity_ids": {
+											"type": "array",
+											"items": {
+												"type": "string"
+											},
+											"description": "`sample` only: the entity ids picked. Read each with `GET /v2/entities/user/{entity_id}/profile/`."
+										}
+									}
+								}
+							}
+						}
+					},
+					"400": {
+						"description": "Unknown or missing `operation`, or profiles are not configured."
+					},
+					"402": {
+						"description": "Payment required."
+					},
+					"409": {
+						"description": "A job is already running, or the Idempotency-Key was used for a different request. Branch on `error.code`."
+					},
+					"429": {
+						"description": "Cooldown. `retry_after_seconds` sits inside `error`."
+					},
+					"503": {
+						"description": "`jobs_unavailable` — generation is switched off for this project."
+					}
+				}
+			}
+		},
+		"/v2/profiles/jobs/{job_id}/": {
+			"get": {
+				"tags": [
+					"profiles"
+				],
+				"operationId": "profiles_get_job",
+				"summary": "Read a generation job",
+				"description": "The job nests under `job`. `total` is null until `enumeration_complete`, and `completed` is `succeeded + failed + skipped`.",
+				"parameters": [
+					{
+						"in": "path",
+						"name": "job_id",
+						"required": true,
+						"schema": {
+							"type": "string"
+						}
+					}
+				],
+				"responses": {
+					"200": {
+						"description": "The job.",
+						"content": {
+							"application/json": {
+								"schema": {
+									"type": "object",
+									"properties": {
+										"job": {
+											"type": "object",
+											"properties": {
+												"id": {
+													"type": "string"
+												},
+												"operation": {
+													"type": "string"
+												},
+												"entity_type": {
+													"type": "string"
+												},
+												"status": {
+													"type": "string",
+													"enum": [
+														"QUEUED",
+														"RUNNING",
+														"SUCCEEDED",
+														"PARTIALLY_SUCCEEDED",
+														"FAILED",
+														"CANCELLED"
+													]
+												},
+												"total": {
+													"type": "integer",
+													"nullable": true
+												},
+												"enumeration_complete": {
+													"type": "boolean"
+												},
+												"completed": {
+													"type": "integer"
+												},
+												"succeeded": {
+													"type": "integer"
+												},
+												"failed": {
+													"type": "integer"
+												},
+												"skipped": {
+													"type": "integer"
+												}
+											}
+										}
+									}
+								}
+							}
+						}
+					},
+					"404": {
+						"description": "No such job in this project."
+					}
+				}
+			}
 		}
 	},
 	"components": {
@@ -8988,4 +9462,4 @@
 		}
 	},
 	"x-original-swagger-version": "2.0"
-}
+}

docs/platform/cli.mdx

@@ -1,7 +1,6 @@
 ---
-title: CLI
-seo:
-  title: "Mem0 CLI for Terminal Memory Management"
+title: "CLI for Terminal Memory Management"
+sidebarTitle: "CLI"
 description: "Manage memories from your terminal, for both humans and AI agents."
 icon: "terminal"
 iconType: "solid"

docs/platform/copilot.mdx

@@ -0,0 +1,194 @@
+---
+title: "Mem0 Copilot"
+description: "Inspect project memories, review configuration changes, and test extraction from the Mem0 dashboard."
+icon: "message"
+---
+
+Copilot is an AI assistant in the [Mem0 dashboard](https://app.mem0.ai/dashboard/copilot). Ask it to inspect stored memories, suggest extraction rules and categories, change project settings, or explain the platform SDKs and APIs.
+
+You describe the task in chat. Copilot uses tools to read project data or propose changes. In **Review changes** mode, you approve each change before it runs.
+
+## Start with your project
+
+1. Sign in to the dashboard and select the organization and project you want to work on.
+2. Open **Copilot** in the sidebar.
+3. Select **Review changes** below the message box.
+4. Ask: “Show my current project settings and explain what each one does.”
+5. Expand an activity card, such as **Read project config**, to see its **Input** and **Result**. Check these results when reviewing an answer or confirming a change.
+
+You can ask about settings or SDK usage with an empty project. Suggestions based on stored data need enough project history to analyze.
+
+## Inspect stored memories
+
+Start with a project overview, then narrow the question:
+
+| Prompt | What to inspect |
+| --- | --- |
+| “Summarize what this project has stored and how it is categorized.” | The memories and category patterns Copilot reads. Check which records support its summary. |
+| “Analyze the memories for user alice.” | Facts stored for `alice`, their categories, and unwanted information. To investigate a missing fact, also provide the original input. |
+| “Search alice's memories for dietary preferences.” | Memories relevant to the query within that user's scope. |
+
+Copilot can list memories across the project. Searching by meaning needs a specific User, Agent, App, or Run ID. Select one in **Scope** or name it in your prompt.
+
+Memory lists are paginated, and suggestions use samples. Check the activity results to see which records were read. Ask for more pages when you need to inspect the rest.
+
+### Choose the memory scope
+
+Open **Scope** below the message box. Select an existing ID or type one and choose it.
+
+| Control | Identifies |
+| --- | --- |
+| **User** | The end user whose memories you want to inspect, such as `alice`. |
+| **Agent** | An AI agent associated with the memories. |
+| **App** | An application associated with the memories. |
+| **Run** | A particular execution or session associated with the memories. |
+
+A field set to **any** adds no filter for that entity type. **Clear scope** removes the selections. Scope gives Copilot default IDs to use; you can request a different entity in a message. Check the activity's **Input** to confirm which IDs it used. Adding a test memory requires a **User** ID, even when another entity is selected.
+
+<Note>
+  Scope selects memories, not separate settings. Categories, extraction instructions, memory depth, and multilingual settings apply to the selected project. Category and extraction suggestions also analyze project data, regardless of the selected entity.
+</Note>
+
+See [Entity-scoped memory](/platform/features/entity-scoped-memory) for how these IDs organize memories in your application.
+
+## Review and apply changes
+
+The mode control below the message box determines when changes run:
+
+- **Review changes**: Copilot pauses before changing project configuration or adding a test memory. Read the proposal and choose whether to apply it.
+- **Auto-apply**: Copilot can change settings and add test memories without asking for approval. Check the selected project and scope before using it.
+
+Ask Copilot to show the current settings before requesting an update. In the approval card, review every listed field. Approving the card applies the whole proposal, including fields you did not edit.
+
+| Action | Effect |
+| --- | --- |
+| **Apply change** | Approve the proposed write. Inspect the subsequent result to confirm it succeeded. |
+| **Edit** | When offered, edit extraction instructions or category names and descriptions. Choose **Apply with edits** to submit your revision. |
+| **Discard edits** | Return to the original proposal without applying it. |
+| **Decline** | Reject this proposal without applying it. |
+| **Ask for something else** | Enter feedback and choose **Send**. This declines the current proposal and sends your feedback as a new message. Review the next proposal before applying it. |
+
+Not every field has an inline editor. If a proposal includes both extraction instructions and categories, only the instructions get an editor. The editor cannot apply blank instructions or an empty category list. Use **Ask for something else** to change other fields or request separate proposals.
+
+<Warning>
+  A generated prompt profile contains separate prompts for extraction and summaries. If your project uses one, saving extraction instructions through Copilot deactivates it. Review the replacement rules and test the affected behavior before using them in production.
+</Warning>
+
+## What Copilot cannot do
+
+Copilot cannot perform these actions, even in **Auto-apply** mode:
+
+- Create or delete API keys.
+- Directly edit or delete existing memories.
+- Export project data.
+- Invite or remove organization or project members, or change their roles and permissions.
+- Create or delete organizations or projects.
+- Change billing details or subscription plans.
+
+Use the dashboard or the relevant platform API for these tasks. Copilot can explain the steps or point you to documentation, but it cannot carry out the actions.
+
+Copilot supports the managed Mem0 platform. It does not help configure or use the [self-hosted open-source library](/open-source/overview).
+
+## Example 1: Stop storing small talk, then test extraction
+
+This walkthrough uses a project where you have noticed greetings or small talk in stored memories. Use a development project when trying configuration changes, since a test user does not isolate project settings.
+
+<Steps>
+  <Step title="Inspect the problem">
+    Select the project, set **User** to `alice`, and ask:
+
+    > Analyze the memories for user alice.
+
+    Inspect the returned memories. Identify examples of small talk you want to exclude and useful facts you still want to keep.
+  </Step>
+  <Step title="Request a targeted change">
+    With **Review changes** selected, ask:
+
+    > Update my extraction instructions to ignore greetings and small talk. Keep the existing rules for durable user preferences.
+
+    Check for a **Read project config** activity before reviewing the update. If it is missing, ask Copilot to read the current instructions first. If analysis reports insufficient data, ask it to use the rule you provided. You can also set the rule directly through [Custom instructions](/platform/features/custom-instructions).
+  </Step>
+  <Step title="Review the proposal">
+    Review the full replacement text. Keep existing rules your application needs. For this test, the relevant rules could look like:
+
+    ```text
+    Remember the following:
+
+    * The user's dietary preferences and food restrictions.
+
+    Don't remember the following:
+
+    * Greetings and small talk.
+    ```
+
+    Use **Edit** to adjust the wording, or **Ask for something else** to request a revision.
+
+    Choose **Apply change** or **Apply with edits**, then inspect the result to confirm the instructions were saved.
+  </Step>
+  <Step title="Add a test conversation">
+    Choose a new **User** ID for this test, such as `copilot-extraction-test-01`, and clear any other entity selections. Ask:
+
+    > Add this as a user message for copilot-extraction-test-01: “Hi! How's your day? I prefer vegetarian meals and avoid peanuts.”
+
+    Review the test-memory proposal and choose **Apply change**.
+
+    <Warning>
+      Adding a test memory writes to the selected project. It is not a dry run. Use a dedicated test user so you can find and remove the test data afterward.
+    </Warning>
+  </Step>
+  <Step title="Wait for extraction, then check the result">
+    Expand **Add test memory** and inspect **Result**. A `PENDING` status means extraction is still running. Use the returned `event_id` with the [Get Event API](/api-reference/events/get-event) to check progress. Wait for `SUCCEEDED` before evaluating the output. If the event is `FAILED`, inspect its details before retrying.
+
+    Then ask:
+
+    > List all memories for user copilot-extraction-test-01. Then search that user's memories for dietary preferences.
+
+    Check that the memories retain the vegetarian preference and peanut restriction, and exclude the greeting. Inspect the full list as well as search results: a relevant search can hide unwanted small-talk records.
+
+    If the output is wrong, describe the mismatch and review another instruction change. Use a new test user for the next attempt so earlier memories do not affect the result. Remove test data afterward through the dashboard or [memory deletion API](/api-reference/memory/delete-memories).
+  </Step>
+</Steps>
+
+Test with new inputs after changing settings. Updating configuration is not a cleanup operation for existing memories. See [Custom instructions](/platform/features/custom-instructions) for guidance on writing extraction rules.
+
+## Example 2: Suggest categories and extraction instructions
+
+These workflows sample the selected project's data. Generating a suggestion does not update settings by itself. Copilot can then propose applying it, which follows the selected review mode. Use **Review changes** to inspect suggestions before they are saved.
+
+| Workflow | Data used | Example prompt |
+| --- | --- | --- |
+| Custom categories | Stored memories, excluding deleted memories. | “Suggest custom categories from this project's memories.” |
+| Extraction instructions | Successful requests to add conversation data, including requests that produced no memories. | “Compare recent add requests with the extracted memories and suggest better extraction instructions.” |
+
+Category suggestions identify recurring themes. Review the names, descriptions, and examples. Applying a category list replaces the project's previous list; it does not add to it or re-tag existing memories. See [Custom categories](/platform/features/custom-categories) for project and per-call behavior.
+
+Extraction suggestions compare conversation inputs with what was extracted. One add request can produce several memories or none, so the number of add requests is different from the number of stored memories. Keep your application's existing requirements when reviewing the proposal.
+
+Both workflows require enough project data. If a suggestion fails because there is too little data, check the activity's **Result** for the current and required counts. You can still inspect memories, ask SDK/API questions, or provide your own rules instead of requesting a data-based suggestion.
+
+## Example 3: Adjust detail and language
+
+Ask Copilot to read the current settings, then request the change you need:
+
+- **Memory depth** controls the level of detail: **Less**, **Medium**, or **More detailed**. Try “Show my current memory depth, then propose More detailed memories.” For deciding *which facts* to retain, use extraction instructions.
+- **Multilingual** behavior preserves the user's original language. Try “Enable multilingual behavior so memories preserve the language of the input.” Test it with a new conversation in the language your application uses.
+
+Both are project settings. Review the proposed values and test a fresh input after applying them. See [Organization and project settings](/api-reference/organizations-projects) for configuration through the API.
+
+## Example 4: Ask SDK and API questions
+
+Include your language and the task, for example:
+
+> Show me how to search memories for user alice with the managed Mem0 Python SDK. Link the documentation you used.
+
+Copilot can look up the official platform documentation. Check the **Browse mem0 docs** and **Read docs page** activities and open the cited pages. If an answer has no sources, ask for them before using the example. The [Platform quickstart](/platform/quickstart) covers adding and searching memories in code.
+
+## Resume a conversation and check message limits
+
+Use **History** to reopen your 50 most recently active chats for the selected project. Chats belong to the person who created them; other project members cannot open them. Use **New chat** to start a separate conversation, or **Delete chat** in history to remove one. Deleting a chat does not undo settings changes or remove test memories.
+
+Message allowances depend on the organization's plan and are shared across its projects and members. They reset at the start of each calendar month in UTC.
+
+For plans with a limit, a usage notice appears once 80% of the allowance is used. Below that point, no counter is shown. At the limit, new messages are disabled until the allowance resets or the plan is upgraded. Follow the notice's upgrade link to review plan options.
+
+Sending feedback through **Ask for something else** counts as a new message. Approving or declining a saved proposal without feedback does not use another message. Test additions and searches also use the platform APIs and remain subject to their normal quotas.

docs/platform/features/custom-instructions.mdx

@@ -1,7 +1,6 @@
 ---
-title: Custom Instructions
-seo:
-  title: "Platform Custom Instructions - Mem0"
+title: "Platform Custom Instructions"
+sidebarTitle: "Custom Instructions"
 description: 'Control how Mem0 extracts and stores memories using natural language guidelines'
 ---
 

docs/platform/features/multimodal-support.mdx

@@ -1,7 +1,6 @@
 ---
-title: Multimodal Support
-seo:
-  title: "Platform Multimodal Support - Mem0"
+title: "Platform Multimodal Support"
+sidebarTitle: "Multimodal Support"
 description: Integrate images and documents into your interactions with Mem0
 ---
 

docs/platform/features/user-profiles.mdx

@@ -0,0 +1,359 @@
+---
+title: Profiles
+description: "Build a structured, always-current summary of each user from their memories, shaped by a JSON Schema you define."
+---
+
+# Profiles
+
+Memories are individual facts. A profile is the summary of all of them for one entity: a single structured object, shaped by a JSON Schema you define, that Mem0 keeps current as new memories arrive.
+
+Search answers "what did this user say about X". A profile answers "who is this user", in one read, with no query to write.
+
+<Info>
+  **Use profiles when…**
+  - You want to personalize a first response, before the user says anything in this session.
+  - You need a compact object to drop into a prompt instead of a list of memories.
+  - You want the same fields for every user, so your code can rely on their shape.
+</Info>
+
+<Note>
+  User Profiles are in **beta** and available on request. To enable them for your
+  organization, contact [support@mem0.ai](mailto:support@mem0.ai).
+</Note>
+
+## How it works
+
+1. You define a **schema**: the fields a profile should contain, each with a description.
+2. Mem0 builds each entity's profile from their memories, and rebuilds it as new memories arrive.
+3. You read the profile whenever you need it.
+
+Generation is **asynchronous**. A profile is not ready the instant an entity's first memory lands, so a read tells you where it is with a `status` rather than failing.
+
+## Define the schema
+
+The schema is JSON Schema. Every property needs a `description` — that is what tells the model how to fill the field, so a vague description gives a vague profile.
+
+<CodeGroup>
+```python Python
+from mem0 import MemoryClient
+
+client = MemoryClient()
+
+client.update_profile_settings(
+    enabled=True,
+    schema={
+        "type": "object",
+        "properties": {
+            "communication_style": {
+                "type": "string",
+                "description": "How the user prefers to be addressed: terse, detailed, formal, casual",
+            },
+            "expertise_areas": {
+                "type": "array",
+                "items": {"type": "string"},
+                "description": "Subjects the user demonstrates working knowledge of",
+            },
+            "current_goals": {
+                "type": "array",
+                "items": {"type": "string"},
+                "description": "What the user is actively trying to accomplish",
+            },
+        },
+    },
+    custom_instructions="Prefer durable traits over one-off remarks.",
+)
+```
+
+```typescript TypeScript
+import MemoryClient from "mem0ai";
+
+const client = new MemoryClient({ apiKey: "your-api-key" });
+
+await client.updateProfileSettings({
+  enabled: true,
+  schema: {
+    type: "object",
+    properties: {
+      communication_style: {
+        type: "string",
+        description:
+          "How the user prefers to be addressed: terse, detailed, formal, casual",
+      },
+      expertise_areas: {
+        type: "array",
+        items: { type: "string" },
+        description: "Subjects the user demonstrates working knowledge of",
+      },
+      current_goals: {
+        type: "array",
+        items: { type: "string" },
+        description: "What the user is actively trying to accomplish",
+      },
+    },
+  },
+  customInstructions: "Prefer durable traits over one-off remarks.",
+});
+```
+</CodeGroup>
+
+<Note>
+  Your schema's property names reach the API exactly as you write them. The SDKs do not rewrite them, so a profile always comes back with the field names you chose.
+</Note>
+
+Only the fields you pass are written. To turn the feature off without touching your schema, send `enabled` alone.
+
+## Read a profile
+
+<CodeGroup>
+```python Python
+result = client.get_profile("alice")
+
+if result["status"] == "succeeded":
+    print(result["profile"])
+else:
+    print("not ready:", result["status"])
+```
+
+```typescript TypeScript
+const result = await client.getProfile({ entityId: "alice" });
+
+if (result.status === "succeeded") {
+  console.log(result.profile);
+} else {
+  console.log("not ready:", result.status);
+}
+```
+</CodeGroup>
+
+A response looks like this:
+
+```json
+{
+  "profile": {
+    "communication_style": "terse",
+    "expertise_areas": ["distributed systems", "postgres"],
+    "current_goals": ["cut p99 latency", "migrate off the legacy queue"]
+  },
+  "status": "succeeded",
+  "entity_type": "user",
+  "entity_id": "alice",
+  "updated_at": "2026-02-08T10:30:00Z",
+  "generation_count": 3
+}
+```
+
+`generation_count` is how many times this profile has been (re)generated — `0` before the first generation completes.
+
+### Always branch on `status`
+
+`profile` is empty unless `status` is `succeeded`. Check the status rather than the emptiness of the object, so a profile that is merely still building is not mistaken for a user you know nothing about.
+
+| `status` | Meaning | What to do |
+|---|---|---|
+| `succeeded` | Profile is built and current | Use it |
+| `pending` | Generation is queued or running | Read again shortly |
+| `insufficient_data` | Not enough memories to say anything yet | Fall back to defaults |
+| `not_enabled` | Profiles are off for this project | Enable them in settings |
+| `failed` | The last generation did not complete | Retry, or trigger a new one |
+
+A `404` means only that no such entity exists in your project.
+
+## Generate a profile on demand
+
+Profiles are built once an entity has accumulated enough messages, so a brand-new user has none during their first few interactions. Trigger one directly to close that gap:
+
+<CodeGroup>
+```python Python
+client.generate_profile("alice")
+```
+
+```typescript TypeScript
+await client.generateProfile({ entityId: "alice" });
+```
+</CodeGroup>
+
+The call returns as soon as the work is queued. Poll the read endpoint and branch on `status`.
+
+## Test a schema before applying it
+
+A schema that reads well can still produce disappointing profiles. Sample a few real entities and inspect the output before committing to it.
+
+Sampling is asynchronous: the call returns a job as soon as it is queued. Poll `status_url` until the job is terminal, then read each sampled entity's profile:
+
+<CodeGroup>
+```python Python
+import time
+
+job = client.sample_profiles(limit=5)
+
+# Poll until the sample job reaches a terminal state (job status is UPPERCASE).
+TERMINAL = {"SUCCEEDED", "PARTIALLY_SUCCEEDED", "FAILED", "CANCELLED"}
+deadline = time.time() + 120
+while True:
+    status = client.get_profile_job(job["status_url"])["job"]
+    if status["status"] in TERMINAL:
+        break
+    if time.time() > deadline:
+        raise TimeoutError("Sample job did not finish in time")
+    time.sleep(3)
+
+print(status["status"], status["succeeded"], "of", status["total"])
+
+# The create response lists the sampled entities; read each one's saved profile.
+for entity_id in job.get("entity_ids", []):
+    print(client.get_profile(entity_id))
+```
+
+```typescript TypeScript
+const job = await client.sampleProfiles({ limit: 5 });
+
+// Poll until the sample job reaches a terminal state (job status is UPPERCASE).
+const TERMINAL = ["SUCCEEDED", "PARTIALLY_SUCCEEDED", "FAILED", "CANCELLED"];
+const deadline = Date.now() + 120_000;
+let status;
+while (true) {
+  status = (await client.getProfileJob(job.statusUrl)).job;
+  if (TERMINAL.includes(status.status)) break;
+  if (Date.now() > deadline)
+    throw new Error("Sample job did not finish in time");
+  await new Promise((resolve) => setTimeout(resolve, 3000));
+}
+
+console.log(status.status, status.succeeded, "of", status.total);
+
+// The create response lists the sampled entities; read each one's saved profile.
+for (const entityId of job.entityIds ?? []) {
+  console.log(await client.getProfile({ entityId }));
+}
+```
+</CodeGroup>
+
+These are real generations. The profiles are saved to those entities and count toward your usage, so sampling is not wasted work and not a free dry run. A sample covers up to 10 entities and cannot be repeated immediately.
+
+## Apply a new schema to existing entities
+
+A new schema shapes the next generation. Profiles that already exist keep their values until their entity is generated again.
+
+Each entity picks the new schema up as it sends more memories, and you can generate one now with `generate_profile`.
+
+<Note>
+Rebuilding every profile in a project at once is not available yet. Refresh profiles one entity at a time with `generate_profile`, or let each one update on its own as its entity sends more memories.
+</Note>
+
+## When profiles update
+
+You never call an "update profile" endpoint — Mem0 keeps each profile current for you. Two things drive it:
+
+- **Automatically, as memories accumulate.** Mem0 refreshes an entity's profile after roughly every **10 messages** it receives, folding the new memories into the existing profile. There is no schedule to wait for and no extra call to make: the same `add` you already do keeps the profile moving.
+- **On demand.** Call `generate_profile` to build or refresh a profile immediately — useful for a brand-new entity that has not yet crossed the automatic threshold.
+
+Generation is **asynchronous and incremental**. A refresh runs in the background a short while after its trigger, so a read taken immediately after an `add` may still show the previous profile (or `pending`). Branch on `status` rather than assuming the latest memory is already reflected.
+
+<Note>
+  Updates are **incremental**, not a full rebuild each time — Mem0 merges what it newly learns into the stored profile and keeps the fields your schema still defines. After a schema change, existing profiles pick it up as their entities send more memories, or when you call `generate_profile` — see [Apply a new schema to existing entities](#apply-a-new-schema-to-existing-entities).
+</Note>
+
+## Use a profile in a prompt
+
+The point of the structure is that it drops straight into a prompt:
+
+```python
+result = client.get_profile(user_id)
+
+if result["status"] == "succeeded":
+    profile = result["profile"]
+    system_prompt = f"""You are helping {user_id}.
+Communication style: {profile.get("communication_style", "unknown")}
+Areas of expertise: {", ".join(profile.get("expertise_areas", []))}
+Current goals: {", ".join(profile.get("current_goals", []))}
+
+Match their style and do not explain what they already know."""
+else:
+    system_prompt = "You are a helpful assistant."
+```
+
+## Writing a schema that works
+
+- **Describe every field.** The description is the instruction; without it the model guesses.
+- **Prefer durable traits.** "Prefers dark mode" ages well; "is annoyed today" does not.
+- **Keep it small.** Ten focused fields beat forty speculative ones, and cost less to generate.
+- **Say what the field is not.** A description that rules out the near-miss interpretation is worth more than one that only states the obvious.
+- **Sample before you commit.** It is the only way to see what your descriptions actually produce.
+
+<Note>
+  A schema has a size budget of roughly **10,000 tokens** of serialized JSON — the whole schema is sent to the model on every generation, so a handful of verbose fields can cost more than many terse ones. Oversized schemas are rejected on save.
+</Note>
+
+## Availability
+
+The feature is in beta and enabled per organization on request — see the note at the top of this page.
+
+Once it is on, an entity gets a profile when two more things hold:
+
+- profiles are **enabled** with a schema for the project (see [Define the schema](#define-the-schema)), and
+- the memory is scoped to an entity — a `user_id`.
+
+On a project where profiles are turned off, a read returns `status: not_enabled` rather than an error, so you can call it unconditionally and branch on the status.
+
+## Settings reference
+
+| Argument | Type | Description |
+|---|---|---|
+| `enabled` | boolean | Whether profile generation runs for the project |
+| `schema` | object | JSON Schema describing the profile. Every property needs a `description` |
+| `custom_instructions` | string | Extra guidance applied during extraction |
+
+`enabled` is project-wide. `schema` and `custom_instructions` apply to user
+profiles, so the stored settings nest them under `entities`:
+
+```json
+{
+  "enabled": true,
+  "entities": {
+    "user": {
+      "schema": { "type": "object", "properties": { "...": {} } },
+      "custom_instructions": "Prefer durable traits over one-off remarks."
+    }
+  },
+  "capabilities": { "full_rebuild": false }
+}
+```
+
+That is what a read returns and what a write accepts. The SDKs take the fields
+flat and nest them for you, so a schema you write with
+`update_profile_settings` comes back unchanged from `get_profile_settings`.
+
+<Note>
+  Profile settings are per project. An API key is scoped to one project, so profiles never cross a project boundary.
+</Note>
+
+## FAQ
+
+**Do I need to change my `add` or `search` calls to use profiles?**
+No. Profiles are built from the memories you already add. You define a schema once and read the profile when you need it — your ingestion and retrieval code is unchanged.
+
+**Why is `profile` empty even though the entity has memories?**
+Generation is asynchronous and needs enough to work with. Branch on `status`: `pending` means it is still building, and `insufficient_data` means there are not yet enough memories to fill the schema. Read again shortly, or call `generate_profile` to build one now.
+
+**Is sampling free?**
+No. `sample_profiles` runs real generations against real memories and **keeps** the profiles it produces, so it counts toward your usage like any other generation. It exists to check a schema on a few entities before you commit to it — not as a zero-cost dry run.
+
+**Does changing the schema rewrite existing profiles?**
+No. A schema change applies to the next generation. An existing profile keeps its values until its entity is generated again, which happens as that entity sends more memories, or when you call `generate_profile` for it.
+
+**What happens to a field I remove from the schema?**
+It stops being maintained. On an entity's next generation, fields your schema no longer defines are pruned from the stored profile — so keep a field in the schema for as long as you want its value kept.
+
+**How current is a profile?**
+It refreshes automatically as memories accumulate (about every 10 messages for an entity), plus any on-demand `generate_profile` calls. Because refreshes run in the background, expect a short delay after the triggering `add` rather than an instant update.
+
+## Related
+
+<CardGroup cols={2}>
+  <Card title="Entity-Scoped Memory" icon="users" href="/platform/features/entity-scoped-memory">
+    How users, agents, apps and runs partition memories.
+  </Card>
+  <Card title="Custom Instructions" icon="pen" href="/platform/features/custom-instructions">
+    Steer what Mem0 extracts in the first place.
+  </Card>
+</CardGroup>

docs/platform/overview.mdx

@@ -1,7 +1,6 @@
 ---
-title: "Overview"
-seo:
-  title: "Mem0 Platform Overview"
+title: "Platform Overview"
+sidebarTitle: "Overview"
 description: "Managed memory layer for AI agents, production-ready in minutes"
 icon: "cloud"
 ---

docs/templates/api_reference_template.mdx

@@ -4,7 +4,7 @@ description: "Standard layout for documenting Mem0 API endpoints."
 icon: "code"
 ---
 
-# Api Reference Template
+# API Reference Template
 
 API reference pages document a single endpoint contract. Present metadata, request/response examples, and recovery guidance without narrative detours.
 

examples/mem0-demo/package.json

@@ -28,7 +28,7 @@
     "clsx": "^2.1.1",
     "js-cookie": "^3.0.6",
     "lucide-react": "^0.477.0",
-    "next": "15.5.21",
+    "next": "15.5.24",
     "react": "^19.0.0",
     "react-dom": "^19.0.0",
     "react-markdown": "^10.0.1",

examples/misc/movie_recommendation_grok3.py

@@ -45,11 +45,11 @@ grok_client = OpenAI(
 
 def recommend_movie_with_memory(user_id: str, user_query: str):
     # Retrieve prior memory about movies
-    past_memories = memory.search("movie preferences", user_id=user_id)
+    past_memories = memory.search("movie preferences", filters={"user_id": user_id})
 
     prompt = user_query
-    if past_memories:
-        prompt += f"\nPreviously, the user mentioned: {past_memories}"
+    if past_memories["results"]:
+        prompt += f"\nPreviously, the user mentioned: {[m['memory'] for m in past_memories['results']]}"
 
     # Generate movie recommendation using Grok 3
     response = grok_client.chat.completions.create(model="grok-3-beta", messages=[{"role": "user", "content": prompt}])

examples/misc/strands_agent_aws_elasticache_neptune.py

@@ -198,7 +198,7 @@ def search_memory_tool(query: str, user_id: str = "user") -> str:
         Relevant vector memories found or message if none found
     """
     try:
-        results = m.search(query, user_id=user_id)
+        results = m.search(query, filters={"user_id": user_id})
 
         if isinstance(results, dict) and 'results' in results:
             memory_list = results['results']
@@ -245,7 +245,7 @@ def search_graph_memory_tool(query: str, user_id: str = "user") -> str:
     """
     try:
         graph_query = f"relationships connections {query}"
-        results = m.search(graph_query, user_id=user_id)
+        results = m.search(graph_query, filters={"user_id": user_id})
 
         if isinstance(results, dict) and 'results' in results:
             memory_list = results['results']
@@ -290,7 +290,7 @@ def get_all_memories_tool(user_id: str = "user") -> str:
         All memories for the user or message if none found
     """
     try:
-        all_memories = m.get_all(user_id=user_id)
+        all_memories = m.get_all(filters={"user_id": user_id})
 
         if isinstance(all_memories, dict) and 'results' in all_memories:
             memory_list = all_memories['results']

examples/misc/vllm_example.py

@@ -107,16 +107,16 @@ def main():
 
     for query in search_queries:
         print(f"\nQuery: {query}")
-        memories = memory.search(query=query, user_id="user_123")
+        memories = memory.search(query=query, filters={"user_id": "user_123"})
 
-        for memory_item in memories:
+        for memory_item in memories["results"]:
             print(f"  - {memory_item['memory']}")
 
     print("\n--> Getting all memories for user...")
-    all_memories = memory.get_all(user_id="user_123")
-    print(f"Total memories stored: {len(all_memories)}")
+    all_memories = memory.get_all(filters={"user_id": "user_123"})
+    print(f"Total memories stored: {len(all_memories['results'])}")
 
-    for memory_item in all_memories:
+    for memory_item in all_memories["results"]:
         print(f"  - {memory_item['memory']}")
 
     print("\n--> vLLM integration demo completed successfully!")

examples/notebooks/user-profiles.ipynb

@@ -0,0 +1,764 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# User Profiles — live demo\n",
+    "\n",
+    "A **profile** is a structured JSON document about ONE user, filled by an LLM from that\n",
+    "user's memories, shaped by a JSON Schema you supply.\n",
+    "\n",
+    "Search answers *\"what did this user say about X\"*. A profile answers *\"who is this\n",
+    "user\"*, in one read, with no query to write — and it is available on the first turn of a\n",
+    "session, before the user has said anything.\n",
+    "\n",
+    "**What this notebook does:** feed a user 12 conversation turns, watch a profile get\n",
+    "generated from them, add 6 more turns that contradict the first set, and watch the\n",
+    "profile rewrite itself. Then it shows every way the API says no.\n",
+    "\n",
+    "**You need:** an API key, and a project on the **Pro plan or higher**. Never commit one.\n",
+    "\n",
+    "> Set `MEM0_API_KEY`, and `MEM0_API_HOST` if you are pointing at a sandbox rather than\n",
+    "> production. The cells below read both from the environment.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> **Use a disposable project.** This notebook overwrites the project's profile settings\n",
+    "> (enabled, schema, custom instructions). The last cell restores the values saved at the\n",
+    "> start, but only if you reach it: if a cell fails midway, the project keeps the demo\n",
+    "> schema until you run the cleanup cell or reset it yourself. Do not point it at a\n",
+    "> project other people or production traffic depend on.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This notebook drives the SDK from this worktree, not the published mem0ai:\n",
+    "# the profile fixes below are not released yet.\n",
+    "%pip install -q -e ../..\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": "import json\nimport os\nimport time\nimport uuid\n\nimport mem0\nfrom mem0 import MemoryClient\n\nAPI_KEY = os.environ.get(\"MEM0_API_KEY\")\nif not API_KEY:\n    import getpass\n\n    API_KEY = getpass.getpass(\"API key: \")\n\nclient = MemoryClient(api_key=API_KEY, host=os.environ.get(\"MEM0_API_HOST\") or None)\n\n# Fresh id each run, so nothing below is stale from a previous pass.\nUSER_ID = f\"demo_{uuid.uuid4().hex[:8]}\"\n\n# Snapshot the project's profile settings up front. This notebook overwrites the\n# shared project schema/instructions/enabled below; the cleanup cell restores this.\nORIGINAL_SETTINGS = client.get_profile_settings()\n\nprint(\"sdk      :\", mem0.__file__)  # must be this worktree\nprint(\"host     :\", client.host)\nprint(\"demo user:\", USER_ID)"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Define the schema\n",
+    "\n",
+    "The schema is handed to the model as a **tool definition**, and each field's\n",
+    "`description` is the only instruction the model gets about what belongs there. An\n",
+    "undescribed field is a field the model guesses at.\n",
+    "\n",
+    "Rules worth knowing:\n",
+    "\n",
+    "- root `type: object` with a **non-empty** `properties` — an empty one is refused, because\n",
+    "  it would bill you to extract nothing\n",
+    "- the root keys `_profile_config_version` and `entities` are **reserved** and rejected:\n",
+    "  they name the storage envelope, so a schema using them could not be read back\n",
+    "  unambiguously\n",
+    "- keep it small. The whole schema is sent to the model on every generation\n",
+    "\n",
+    "Descriptions are **not** enforced on write in this build — a property without one is\n",
+    "accepted and then quietly underfilled at generation time. Section F1 demonstrates it.\n",
+    "Treat descriptions as your job, not the validator's.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "SCHEMA = {\n",
+    "    \"type\": \"object\",\n",
+    "    \"properties\": {\n",
+    "        \"occupation\": {\n",
+    "            \"type\": \"string\",\n",
+    "            \"description\": \"The person's current job title, in one short phrase.\",\n",
+    "        },\n",
+    "        \"location\": {\n",
+    "            \"type\": \"string\",\n",
+    "            \"description\": \"The city or region the person currently lives in.\",\n",
+    "        },\n",
+    "        \"interests\": {\n",
+    "            \"type\": \"array\",\n",
+    "            \"items\": {\"type\": \"string\"},\n",
+    "            \"description\": \"Hobbies and topics they return to, as short lowercase tags.\",\n",
+    "        },\n",
+    "        \"dietary_restrictions\": {\n",
+    "            \"type\": \"array\",\n",
+    "            \"items\": {\"type\": \"string\"},\n",
+    "            \"description\": \"Foods the person avoids, and why, if they said.\",\n",
+    "        },\n",
+    "        \"communication_style\": {\n",
+    "            \"type\": \"string\",\n",
+    "            \"enum\": [\"concise\", \"detailed\", \"casual\", \"formal\"],\n",
+    "            \"description\": \"How this person prefers to be answered.\",\n",
+    "        },\n",
+    "        \"expertise_level\": {\n",
+    "            \"type\": \"string\",\n",
+    "            \"enum\": [\"beginner\", \"intermediate\", \"advanced\"],\n",
+    "            \"description\": \"Their technical depth, judged from how they discuss their work.\",\n",
+    "        },\n",
+    "    },\n",
+    "}\n",
+    "\n",
+    "settings = client.update_profile_settings(\n",
+    "    enabled=True,\n",
+    "    schema=SCHEMA,\n",
+    "    custom_instructions=(\n",
+    "        \"Prefer facts the person stated outright over anything inferred. \"\n",
+    "        \"Leave a field empty rather than guessing.\"\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "# Sorted, because JSONB storage does not preserve the key order you sent.\n",
+    "# Compare a stored schema by SET, never by string or by key order.\n",
+    "stored = settings[\"entities\"][\"user\"][\"schema\"]\n",
+    "print(\"schema fields:\", sorted(stored[\"properties\"]))\n",
+    "print(\"enabled      :\", settings[\"enabled\"])\n",
+    "print(\"capabilities :\", settings[\"capabilities\"])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`enabled` is project-wide; `schema` and `custom_instructions` apply to user profiles\n",
+    "and are stored under `entities`. The SDK takes them flat and nests them for you, so what\n",
+    "you write comes back unchanged from `get_profile_settings()`.\n",
+    "\n",
+    "Only the arguments you pass are written. To turn the feature off without touching your\n",
+    "schema, send `enabled` alone.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. The turns\n",
+    "\n",
+    "Twelve conversation turns for one user. Nothing about `add()` changes — profiles are a\n",
+    "side effect of the normal pipeline.\n",
+    "\n",
+    "Twelve, not five, because generation fires when an entity crosses a **10-message\n",
+    "boundary**. Below that it waits for a flush window measured in hours, and this notebook\n",
+    "would sit there.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TURNS = [\n",
+    "    (\"user\", \"Hey — I just moved to Berlin for a new job.\"),\n",
+    "    (\"assistant\", \"Congratulations! What's the new role?\"),\n",
+    "    (\"user\", \"Senior data engineer at a logistics company. Mostly Spark and Airflow.\"),\n",
+    "    (\"assistant\", \"Nice stack. How are you finding the pipelines there?\"),\n",
+    "    (\"user\", \"Honestly the DAGs are a mess. I've been rewriting the partitioning to cut shuffle.\"),\n",
+    "    (\"assistant\", \"That usually pays off fast. Anything blocking you?\"),\n",
+    "    (\"user\", \"Just time. Keep it short when you answer me, I skim everything.\"),\n",
+    "    (\"assistant\", \"Understood — short answers from here.\"),\n",
+    "    (\"user\", \"Outside work I climb most weekends, and I'm learning German.\"),\n",
+    "    (\"assistant\", \"Bouldering or ropes?\"),\n",
+    "    (\"user\", \"Bouldering. Also — I'm vegetarian, so skip meat in any recipe suggestions.\"),\n",
+    "    (\"assistant\", \"Noted, vegetarian only.\"),\n",
+    "]\n",
+    "\n",
+    "response = client.add(\n",
+    "    [{\"role\": r, \"content\": c} for r, c in TURNS],\n",
+    "    user_id=USER_ID,\n",
+    ")\n",
+    "print(json.dumps(response, indent=2)[:300])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The add is **async** — it returns an `event_id` and the memories do not exist yet. Poll\n",
+    "`GET /v1/event/{event_id}/` until it is `SUCCEEDED` or `FAILED`; that, not a sleep, is how\n",
+    "you know the add finished. Then let the extracted memories settle.\n",
+    "\n",
+    "Under load this can take a minute or more, so the cell says plainly whether it ran out of\n",
+    "time rather than printing `0 memories` as though that were the answer.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "event_id = response[\"event_id\"]\n",
+    "deadline = time.time() + 300\n",
+    "\n",
+    "# 1. The add itself. Terminal status, not a sleep.\n",
+    "event_status = None\n",
+    "while time.time() < deadline:\n",
+    "    event_status = client.client.get(f\"/v1/event/{event_id}/\").json().get(\"status\")\n",
+    "    if event_status in (\"SUCCEEDED\", \"FAILED\"):\n",
+    "        break\n",
+    "    print(f\"  add {event_status}\")\n",
+    "    time.sleep(5)\n",
+    "print(f\"add finished: {event_status}\")\n",
+    "# Stop here unless the add SUCCEEDED. A failed or unfinished add would otherwise let the\n",
+    "# generation below bill for a profile built without these memories.\n",
+    "if event_status != \"SUCCEEDED\":\n",
+    "    raise RuntimeError(f\"add did not succeed (status={event_status}); not generating a profile\")\n",
+    "\n",
+    "# 2. Extraction lands in batches, so the FIRST non-empty page is not the whole set.\n",
+    "# Wait for the count to stop growing instead of breaking on the first result.\n",
+    "memories, stable = [], 0\n",
+    "while time.time() < deadline:\n",
+    "    page = client.get_all(filters={\"user_id\": USER_ID}, page_size=50)\n",
+    "    found = page.get(\"results\", []) if isinstance(page, dict) else page\n",
+    "    stable = stable + 1 if found and len(found) == len(memories) else 0\n",
+    "    memories = found\n",
+    "    if stable >= 2:  # two identical polls in a row\n",
+    "        break\n",
+    "    print(f\"  ... {len(memories)} so far\")\n",
+    "    time.sleep(5)\n",
+    "\n",
+    "if memories:\n",
+    "    print(f\"\\n{len(memories)} memories extracted:\\n\")\n",
+    "    for m in memories:\n",
+    "        print(\" \\u2022\", m.get(\"memory\"))\n",
+    "else:\n",
+    "    # Say so. Reporting '0 memories' as a result hides a busy or broken environment\n",
+    "    # and makes the profile below look like it came from nothing.\n",
+    "    print(\"\\nNO memories yet — extraction is still catching up, or the ingestion\")\n",
+    "    print(\"worker is down. Everything below will report insufficient_data.\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Read the profile\n",
+    "\n",
+    "Crossing the 10-message boundary should already have queued a generation. Read first —\n",
+    "and note that a known user with no profile yet is a **200 with a status**, not a 404. That\n",
+    "distinction is the whole point of the envelope.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "envelope = client.get_profile(USER_ID)\n",
+    "print(json.dumps(envelope, indent=2))\n",
+    "\n",
+    "print(\"\\nstatus vocabulary:\")\n",
+    "print(\"  succeeded          terminal — a generation ran AND the profile has content\")\n",
+    "print(\"  pending            queued or running\")\n",
+    "print(\"  failed             terminal — the last generation did not complete\")\n",
+    "print(\"  not_enabled        feature off, or plan below Pro\")\n",
+    "print(\"  insufficient_data  no content to show: no row yet, queued, or a\")\n",
+    "print(\"                     generation that legitimately found nothing\")\n",
+    "print()\n",
+    "print(\"`succeeded` is decided by the profile BODY, not by generation_count: an\")\n",
+    "print(\"empty extraction still increments the counter, so counting generations\")\n",
+    "print(\"reports 'done' for a profile with nothing in it.\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Force it, rather than waiting\n",
+    "\n",
+    "`generate_profile()` closes the bootstrapping gap: without it a new user has no profile\n",
+    "until their tenth message. One entity, a few seconds.\n",
+    "\n",
+    "Each call sends a new `Idempotency-Key` unless you pass one, and a new key starts a new job.\n",
+    "To retry a dropped request safely, generate the key yourself and pass the same\n",
+    "`idempotency_key` on every attempt: the server then returns the original job instead of\n",
+    "billing a second one.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TERMINAL = {\"succeeded\", \"failed\", \"not_enabled\"}\n",
+    "\n",
+    "\n",
+    "def wait_for_profile(entity_id, timeout=300, interval=5, since=None):\n",
+    "    \"\"\"Poll until terminal.\n",
+    "\n",
+    "    `since` waits for a generation_count ABOVE that value, which is how you wait\n",
+    "    for an UPDATE rather than accepting the profile you already had.\n",
+    "\n",
+    "    `insufficient_data` is NOT terminal by itself — it also covers 'queued', so\n",
+    "    poll through it and give up on the timeout instead.\n",
+    "    \"\"\"\n",
+    "    deadline = time.time() + timeout\n",
+    "    body = None\n",
+    "    while time.time() < deadline:\n",
+    "        body = client.get_profile(entity_id)\n",
+    "        status = (body.get(\"status\") or \"\").lower()\n",
+    "        count = body.get(\"generation_count\") or 0\n",
+    "        fresh = count > since if since is not None else True\n",
+    "        if status == \"succeeded\" and fresh:\n",
+    "            return body\n",
+    "        if status in (\"failed\", \"not_enabled\"):\n",
+    "            raise RuntimeError(f\"generation stopped: {status}\")\n",
+    "        print(f\"  ... {status} (generation_count={count})\")\n",
+    "        time.sleep(interval)\n",
+    "    raise TimeoutError(f\"not ready in {timeout}s: {body}\")\n",
+    "\n",
+    "\n",
+    "print(json.dumps(client.generate_profile(USER_ID), indent=2))\n",
+    "print(\"\\npolling...\")\n",
+    "\n",
+    "try:\n",
+    "    body = wait_for_profile(USER_ID)\n",
+    "    print(\"\\n=== PROFILE ===\")\n",
+    "    print(json.dumps(body[\"profile\"], indent=2))\n",
+    "    print(f\"\\nstatus={body['status']}  generations={body['generation_count']}  updated={body['updated_at']}\")\n",
+    "except TimeoutError as e:\n",
+    "    # Say so plainly and let the rest of the notebook skip, rather than raising\n",
+    "    # a NameError in every cell below and burying the real cause.\n",
+    "    body = None\n",
+    "    print(f\"\\nNO PROFILE: {e}\")\n",
+    "    print(\"Generation never finished. Usually the ingestion worker is down, or\")\n",
+    "    print(\"this project has no memories for the user yet.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The model must not invent fields outside your schema — the forced tool call is\n",
+    "# what makes that structural rather than a request.\n",
+    "if body is None:\n",
+    "    print(\"skipped — no profile was generated above\")\n",
+    "else:\n",
+    "    extra = set(body[\"profile\"]) - set(SCHEMA[\"properties\"])\n",
+    "    print(\"fields outside the schema:\", extra or \"none\")\n",
+    "\n",
+    "    # A forced JSON-Schema response makes the model emit SOMETHING for every property,\n",
+    "    # so 'I found nothing' arrives as a type default: 0, \"\", [].\n",
+    "    filled = {k: v for k, v in body[\"profile\"].items() if v not in (None, \"\", [], {}, 0)}\n",
+    "    print(f\"genuinely populated: {len(filled)}/{len(SCHEMA['properties'])} -> {list(filled)}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Now watch it update\n",
+    "\n",
+    "Six more turns that contradict and extend what we already know: a promotion, a move, a\n",
+    "dropped hobby. A profile is a living document, not an append-only log — the model gets the\n",
+    "memories and rewrites the whole thing.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if body is None:\n",
+    "    print(\"skipped — no profile was generated above\")\n",
+    "else:\n",
+    "    before = body[\"generation_count\"]\n",
+    "\n",
+    "    MORE_TURNS = [\n",
+    "        (\"user\", \"Update — I got promoted to staff engineer last week.\"),\n",
+    "        (\"assistant\", \"Congratulations. Same team?\"),\n",
+    "        (\"user\", \"Same company, but I'm relocating to Munich for it.\"),\n",
+    "        (\"assistant\", \"Big move. How do you feel about it?\"),\n",
+    "        (\"user\", \"Good. I've stopped climbing though — knee injury. Picked up cycling instead.\"),\n",
+    "        (\"assistant\", \"Sorry about the knee. Cycling's kinder on it.\"),\n",
+    "    ]\n",
+    "\n",
+    "    followup = client.add(\n",
+    "        [{\"role\": r, \"content\": c} for r, c in MORE_TURNS],\n",
+    "        user_id=USER_ID,\n",
+    "    )\n",
+    "\n",
+    "    # Wait for the add to land before triggering: a generation queued before the new\n",
+    "    # memories exist rewrites the profile from the OLD ones and looks like a no-op.\n",
+    "    deadline = time.time() + 300\n",
+    "    status = None\n",
+    "    while time.time() < deadline:\n",
+    "        status = client.client.get(f\"/v1/event/{followup['event_id']}/\").json().get(\"status\")\n",
+    "        if status in (\"SUCCEEDED\", \"FAILED\"):\n",
+    "            break\n",
+    "        time.sleep(5)\n",
+    "    print(\"follow-up add:\", status)\n",
+    "    # Generating after a FAILED or unfinished add bills for a profile built from the OLD\n",
+    "    # memories only, so stop instead.\n",
+    "    if status != \"SUCCEEDED\":\n",
+    "        raise RuntimeError(f\"follow-up add did not succeed (status={status}); not regenerating\")\n",
+    "    time.sleep(15)  # let extraction settle\n",
+    "\n",
+    "    print(json.dumps(client.generate_profile(USER_ID), indent=2))\n",
+    "    print(f\"\\npolling for a NEW generation (count must exceed {before})...\")\n",
+    "    updated = wait_for_profile(USER_ID, since=before)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if body is None:\n",
+    "    print(\"skipped — no profile was generated above\")\n",
+    "else:\n",
+    "    print(f\"{'field':<22} {'before':<34} after\")\n",
+    "    print(\"-\" * 92)\n",
+    "    for field in SCHEMA[\"properties\"]:\n",
+    "        b = json.dumps(body[\"profile\"].get(field))\n",
+    "        a = json.dumps(updated[\"profile\"].get(field))\n",
+    "        mark = \"  \" if a == b else \"->\"\n",
+    "        print(f\"{mark} {field:<20} {b[:32]:<34} {a[:32]}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Use it in a prompt\n",
+    "\n",
+    "The point of the structure is that it drops straight into a prompt — no list of memories\n",
+    "to summarize, no query to write.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def build_system_prompt(entity_id):\n",
+    "    result = client.get_profile(entity_id)\n",
+    "    if result[\"status\"] != \"succeeded\":\n",
+    "        # Branch on status, never on an empty profile: a user whose profile is\n",
+    "        # still building is not a user you know nothing about.\n",
+    "        return \"You are a helpful assistant.\"\n",
+    "\n",
+    "    p = result[\"profile\"]\n",
+    "    return f\"\"\"You are helping {entity_id}.\n",
+    "Occupation: {p.get(\"occupation\", \"unknown\")}\n",
+    "Location: {p.get(\"location\", \"unknown\")}\n",
+    "Interests: {\", \".join(p.get(\"interests\", [])) or \"unknown\"}\n",
+    "Dietary restrictions: {\", \".join(p.get(\"dietary_restrictions\", [])) or \"none stated\"}\n",
+    "Preferred style: {p.get(\"communication_style\", \"unknown\")}\n",
+    "\n",
+    "Match their style and do not explain what they already know.\"\"\"\n",
+    "\n",
+    "\n",
+    "print(build_system_prompt(USER_ID))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Judge a schema before committing to it\n",
+    "\n",
+    "`sample_profiles()` runs your schema against up to 10 **real** users that have memories.\n",
+    "\n",
+    "These are real generations and the results are **kept** — a dry run would cost exactly the\n",
+    "same and leave those users no better off. It is not a free preview.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 202, not 200: the sample generations are queued, not finished.\n",
+    "#\n",
+    "# A 409 `already_running` means a sample from an earlier run is still going.\n",
+    "# That is the cooldown working, not an error — reuse that job rather than\n",
+    "# failing the notebook.\n",
+    "try:\n",
+    "    job = client.sample_profiles(limit=3)\n",
+    "    print(json.dumps(job, indent=2)[:400])\n",
+    "    print(\"\\nsampled\", job.get(\"sampled\"), \"entities:\", job.get(\"entity_ids\"))\n",
+    "except Exception as e:\n",
+    "    detail = str(e)\n",
+    "    print(\"sample refused:\", detail[:200])\n",
+    "    running = json.loads(detail).get(\"error\", {}).get(\"job_id\") if detail.startswith(\"{\") else None\n",
+    "    job = {\"job_id\": running, \"status_url\": f\"/v2/profiles/jobs/{running}/\"} if running else None\n",
+    "    print(\"reusing the running job:\", running)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Poll `status_url` to see how the job went. `total` is `null` until enumeration finishes,\n",
+    "so format it defensively rather than assuming a number.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": "JOB_TERMINAL = {\"SUCCEEDED\", \"PARTIALLY_SUCCEEDED\", \"FAILED\", \"CANCELLED\"}\n\n\ndef wait_for_job(job_response, timeout=300, interval=5):\n    \"\"\"Poll a generation job. Prefer status_url over a bare job id, so a route\n    change needs no client update. Raise on timeout so an unfinished job is never\n    mistaken for a finished one.\"\"\"\n    handle = job_response.get(\"status_url\") or job_response[\"job_id\"]\n    deadline = time.time() + timeout\n    status = None\n    while time.time() < deadline:\n        status = client.get_profile_job(handle)[\"job\"]\n        total = status.get(\"total\")\n        print(\n            f\"  {status['status']} \"\n            f\"completed={status.get('completed', 0)}/{total if total is not None else '?'} \"\n            f\"succeeded={status.get('succeeded', 0)} \"\n            f\"failed={status.get('failed', 0)} \"\n            f\"skipped={status.get('skipped', 0)}\"\n        )\n        if str(status.get(\"status\", \"\")).upper() in JOB_TERMINAL:\n            return status\n        time.sleep(interval)\n    raise TimeoutError(\n        f\"job not terminal in {timeout}s (last status: {status.get('status') if status else 'none'})\"\n    )\n\n\nif job is None:\n    print(\"no sample job to poll\")\nelse:\n    final = wait_for_job(job)\n\n    print(\"\\n--- what the sample produced ---\")\n    for entity_id in job.get(\"entity_ids\", []):\n        got = client.get_profile(entity_id)\n        print(f\"\\n{entity_id} [{got['status']}]\")\n        print(\" \", json.dumps(got[\"profile\"])[:220])"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7. Apply a new schema to existing users\n",
+    "\n",
+    "A new schema shapes the **next** generation. Profiles that already exist keep their values\n",
+    "until their user is generated again — which happens as that user sends more memories, or\n",
+    "when you call `generate_profile()` for them.\n",
+    "\n",
+    "A field you **remove** stops being maintained: on the next generation, fields your schema\n",
+    "no longer defines are pruned. Keep a field for as long as you want its value kept."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "# Failure scenarios\n",
+    "\n",
+    "Everything above is the path that works. These are the ways it says no, and what each one\n",
+    "means. Run this section last: F3 deliberately leaves the project switched off for a moment.\n",
+    "\n",
+    "> **About the `HTTP error occurred:` lines below.** The SDK logs every 4xx at\n",
+    "> ERROR level before raising, so they appear even for the failures these cells\n",
+    "> deliberately catch. Read the line printed *after* each one — that is the cell's\n",
+    "> own verdict. Nothing here is unhandled.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## F1. Schemas that get rejected\n",
+    "\n",
+    "Rejections happen on **write**, where you can see and fix them — not silently at\n",
+    "generation time, where you would only notice as an empty profile weeks later.\n",
+    "\n",
+    "The last case matters for storage: the user schema lives in one JSONB column alongside\n",
+    "the envelope that separates it, so a schema using the envelope's own reserved keys could\n",
+    "not be read back unambiguously. It is refused rather than stored.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "BAD_SCHEMAS = [\n",
+    "    ({\"type\": \"object\", \"properties\": {}}, \"empty — bills you to extract nothing\"),\n",
+    "    ({\"type\": \"array\", \"items\": {\"type\": \"string\"}}, \"root must be an object\"),\n",
+    "    (\n",
+    "        {\n",
+    "            \"type\": \"object\",\n",
+    "            \"properties\": {\"tone\": {\"type\": \"string\", \"description\": \"Preferred tone.\"}},\n",
+    "            # At the schema ROOT, which is where the envelope's own keys live.\n",
+    "            \"_profile_config_version\": 1,\n",
+    "            \"entities\": {\"user\": {}},\n",
+    "        },\n",
+    "        \"reserved settings keys at the schema root\",\n",
+    "    ),\n",
+    "]\n",
+    "\n",
+    "for bad, why in BAD_SCHEMAS:\n",
+    "    try:\n",
+    "        client.update_profile_settings(schema=bad)\n",
+    "        print(f\"ACCEPTED (unexpected): {why}\")\n",
+    "    except Exception as e:\n",
+    "        print(f\"rejected [{why}]:\\n   {str(e)[:160]}\\n\")\n",
+    "\n",
+    "# NOT rejected: a property with no description. The validator allows it and the\n",
+    "# model then has nothing to go on, so the field comes back empty. Descriptions are\n",
+    "# your job, not the validator's.\n",
+    "try:\n",
+    "    client.update_profile_settings(schema={\"type\": \"object\", \"properties\": {\"x\": {\"type\": \"string\"}}})\n",
+    "    print(\"accepted [no description on 'x'] <- the trap: valid to store, useless to generate\")\n",
+    "finally:\n",
+    "    client.update_profile_settings(schema=SCHEMA)  # put the good one back\n",
+    "\n",
+    "restored = client.get_profile_settings()[\"entities\"][\"user\"][\"schema\"]\n",
+    "assert set(restored[\"properties\"]) == set(SCHEMA[\"properties\"])\n",
+    "print(\"\\nschema restored:\", sorted(restored[\"properties\"]))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## F2. A user that does not exist\n",
+    "\n",
+    "404 means only \"no such user\". A known user with no profile yet is a 200 carrying\n",
+    "`insufficient_data`, so an ordinary empty state never looks like an error.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mem0.exceptions import MemoryNotFoundError\n",
+    "\n",
+    "try:\n",
+    "    client.get_profile(\"user_who_never_existed\")\n",
+    "    print(\"ACCEPTED (unexpected)\")\n",
+    "except MemoryNotFoundError as e:\n",
+    "    print(\"404 as intended:\", str(e)[:120])\n",
+    "\n",
+    "# ...versus a real user who simply has no profile row yet.\n",
+    "fresh = f\"demo_never_profiled_{uuid.uuid4().hex[:6]}\"\n",
+    "client.add([{\"role\": \"user\", \"content\": \"One passing remark.\"}], user_id=fresh)\n",
+    "time.sleep(5)\n",
+    "print(\"known but unprofiled:\", client.get_profile(fresh)[\"status\"])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## F3. Profiles turned off\n",
+    "\n",
+    "`enabled` is the one project-wide switch. Every generation path then refuses.\n",
+    "\n",
+    "Nothing is deleted. Your schema and every profile you already built are kept, so turning\n",
+    "it back on resumes rather than restarts.\n",
+    "\n",
+    "Note what a read does **not** do — a profile that already exists keeps reporting\n",
+    "`succeeded` and keeps returning its content. `not_enabled` is only what you get for a user\n",
+    "with no profile yet. Turning the feature off stops new work; it does not hide what has\n",
+    "already been built.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "client.update_profile_settings(enabled=False)\n",
+    "\n",
+    "print(\"read (demo user)     :\", client.get_profile(USER_ID)[\"status\"])\n",
+    "print(\"read (never profiled)  :\", client.get_profile(fresh)[\"status\"])\n",
+    "try:\n",
+    "    client.generate_profile(USER_ID)\n",
+    "    print(\"trigger: ACCEPTED (unexpected)\")\n",
+    "except Exception as e:\n",
+    "    print(\"trigger:\", str(e)[:160])\n",
+    "\n",
+    "back = client.update_profile_settings(enabled=True)  # put it back\n",
+    "print(\"\\nrestored:\", back[\"enabled\"])\n",
+    "print(\"schema survived:\", bool(back[\"entities\"][\"user\"][\"schema\"]))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7. Cleanup\n",
+    "\n",
+    "Removes the demo users. The profile row cascades with the entity.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": "# Restore the project's profile settings to the start-of-run snapshot in `finally`, so a\n# failed delete still leaves a shared project as we found it. Passing the original values\n# (including None) clears anything this notebook set: the SDK treats an explicit None as\n# \"clear\" and an omitted argument as \"unchanged\".\ntry:\n    # `fresh` only exists if the error-handling section ran.\n    for entity_id in (USER_ID, globals().get(\"fresh\")):\n        if entity_id is None:\n            continue\n        r = client.client.delete(f\"/v2/entities/user/{entity_id}/\")\n        print(entity_id, \"->\", r.status_code)\nfinally:\n    _user = ORIGINAL_SETTINGS.get(\"entities\", {}).get(\"user\", {})\n    client.update_profile_settings(\n        enabled=ORIGINAL_SETTINGS.get(\"enabled\", False),\n        schema=_user.get(\"schema\"),\n        custom_instructions=_user.get(\"custom_instructions\"),\n    )\n    print(\"profile settings restored to the pre-notebook snapshot\")"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## Cheat sheet\n",
+    "\n",
+    "| Want | Call | Cost |\n",
+    "| --- | --- | --- |\n",
+    "| configure | `update_profile_settings(...)` | free |\n",
+    "| read | `get_profile(user_id)` | free |\n",
+    "| one user now | `generate_profile(user_id)` | 1 LLM call |\n",
+    "| try a schema | `sample_profiles(limit=n)` | ≤10 real generations, kept |\n",
+    "| poll a job | `get_profile_job(status_url)` | free |\n",
+    "\n",
+    "**Settings apply to user profiles.** The stored shape is:\n",
+    "\n",
+    "```json\n",
+    "{\"enabled\": true,\n",
+    " \"entities\": {\"user\": {\"schema\": {...}, \"custom_instructions\": \"...\"}},\n",
+    " \"capabilities\": {\"full_rebuild\": false}}\n",
+    "```\n",
+    "\n",
+    "The SDK takes these flat and nests them for you. Only the fields you pass are written;\n",
+    "`enabled` is the one project-wide switch.\n",
+    "\n",
+    "**Left alone, generation fires** on a 10-message boundary, or after a flush window\n",
+    "measured in hours. `generate_profile()` is how you skip the wait for one user.\n",
+    "\n",
+    "**Three traps:**\n",
+    "\n",
+    "1. `insufficient_data` is not a terminal verdict — it also covers \"queued\", so poll\n",
+    "   through it and give up on a timeout instead.\n",
+    "2. `succeeded` is decided by the profile **body**, not `generation_count`. An empty\n",
+    "   extraction still increments the counter.\n",
+    "3. A forced JSON-Schema response emits something for every property, so \"nothing found\"\n",
+    "   arrives as a type default — `\"\"`, `[]`, `0` — not as a missing key.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

integrations/AGENTS.md

@@ -49,6 +49,48 @@ Run the type check after every TypeScript change: `pnpm run typecheck` or `tsc -
 - **`zapier-mem0/`** is a Zapier Platform CLI app: add, search, get, delete. It deploys to Zapier, not npm, so it is **not** in the release router. Deploy it with `gh workflow run zapier-mem0-cd.yml --ref main` (needs the `ZAPIER_DEPLOY_KEY` secret).
 - **`mem0-strands/`** is a native Strands `MemoryStore` (Python, published to PyPI as `mem0-strands`). It plugs into the Strands `MemoryManager` for automatic recall and server-side extraction, over the hosted Mem0 platform or self-hosted Mem0 OSS. The package lives under `mem0-strands/python/`.
 
+## Surface attribution
+
+Every integration tells the Mem0 platform which surface it is. Three headers,
+and the rules on them are what keep one layer from erasing another:
+
+| Header | Carries | Rule |
+|--------|---------|------|
+| `X-Mem0-Source` | one canonical source value | **set-once** — write only if absent |
+| `X-Application` | the host app it runs inside | **set-once** — write only if absent |
+| `X-Mem0-Client` | `name/version`, outermost first | **append-only** — add yourself, never replace |
+
+Set-once means check-then-set, never assignment. An integration that wraps the
+SDK is the outermost layer and sets the source; the SDK underneath defers to it.
+Assignment is exactly how every agent plugin came to be indistinguishable from
+every other one at the platform.
+
+How to declare it from an integration, in order of preference:
+
+1. Send the headers yourself, if you make the HTTP call directly.
+2. Pass `source` in the call options, if you go through an SDK.
+3. Set `MEM0_SOURCE` / `MEM0_APPLICATION` / `MEM0_CLIENT_STACK` in the
+   environment before constructing the client. The SDKs read these and defer to
+   anything already present.
+
+Append-only applies where a stack can actually form: an SDK handed a client that
+already carries `X-Mem0-Client` appends itself rather than replacing. An SDK
+constructed with no outer context simply reports itself, which is correct — it
+is the outermost layer in that process.
+
+The backend recognizes a fixed list of source values and buckets everything else
+into `OTHERS`. A new value has to land in the platform's `EventSource` enum, so
+do not invent one without that change going in too.
+
+`X-Application` is allowlisted the same way, and this one has a rule of its own:
+**omit the header when you do not know the host.** A value outside the allowlist
+is discarded server-side, so guessing produces an event that claims an
+attribution we do not actually have. The portable bundle is the case that
+matters. It runs in whatever editor a user drops it into, so its build leaves
+`PLATFORM_APPLICATION` empty and `memory_core` sends no header at all, while the
+native bundles each name the host they were generated for. If you add a build
+target, decide which of those two it is.
+
 ## Adding an integration
 
 1. For a native coding-agent host, add `integrations/<name>-plugin/` with `plugin-build.json`, its manifest, and a thin adapter, then generate its shared runtime. Portable clients use the single `mem0-agent-plugin/` package. Independent TypeScript integrations stay self-contained and import shared lifecycle behavior from `agent-plugin-core/typescript/`.
@@ -59,3 +101,4 @@ Run the type check after every TypeScript change: `pnpm run typecheck` or `tsc -
 5. If it is a Claude Code or editor marketplace plugin, register the generated native bundle path in the applicable marketplace files. Preserve the existing public plugin name.
 6. Document it under `docs/integrations/` and add the page to `docs/docs.json` and `docs/llms.txt`.
 7. Add rows to the table above and to the CI/CD tables in [`../.github/AGENTS.md`](../.github/AGENTS.md).
+8. Send the three headers in [Surface attribution](#surface-attribution), and land the matching `EventSource` value on the platform in the same week. Until it exists, your traffic reports as `OTHERS`.

integrations/agent-plugin-core/README.md

@@ -8,7 +8,7 @@ This directory is the single source of shared memory behavior for Mem0 coding-ag
 integrations/
 ├── agent-plugin-core/       # Shared source; never installed as a plugin
 │   ├── python/              # Claude-derived capture, recall, MCP, scoping, and telemetry
-│   ├── typescript/          # Shared lifecycle, formatting, identity, scoping, and telemetry
+│   ├── typescript/          # Shared lifecycle, search prompts, formatting, identity, scoping, and telemetry
 │   ├── skills/              # The only source for the six generated memory skills
 │   ├── build/               # Bundle builder, schemas, and validation
 │   ├── conformance/         # One offline/live verification entry point
@@ -45,7 +45,7 @@ New Git repository writes use a hashed remote identity for shared `agent_id`. Se
 
 Captured prompts and responses preserve their full text after secret redaction. Python extraction splits oversized input across requests without dropping message text. The session-end worker flushes the conversation already collected by hooks without adding the final answer again. Search queries, retrieved context, and tool evidence have separate limits.
 
-TypeScript hosts reuse redaction and lifecycle utilities but retain their own tools, scopes, and capture events. They do not inherit the Python `repo`/`dir`/`mine` contract or its background batching. OpenCode captures selected user prompts; Pi and DeepSeek capture completed conversation turns; OpenClaw selects recent messages and earlier summaries, then filters noise. Removing message-length truncation does not turn these integrations into complete transcript archives.
+TypeScript hosts reuse redaction, lifecycle utilities, and the search prompts in `typescript/src/prompts.ts`, which a test keeps identical to the Python core. They retain their own tools, scopes, and capture events. They do not inherit the Python `repo`/`dir`/`mine` contract or its background batching. OpenCode captures selected user prompts; Pi and DeepSeek capture completed conversation turns; OpenClaw selects recent messages and earlier summaries, then filters noise. Removing message-length truncation does not turn these integrations into complete transcript archives.
 
 For installation, follow the host guides: [Claude Code](../../docs/integrations/claude-code.mdx), [Cursor](../../docs/integrations/cursor.mdx), [Codex](../../docs/integrations/codex.mdx), [Kimi](../../docs/integrations/kimi.mdx), and [Antigravity](../../docs/integrations/antigravity.mdx).
 

integrations/agent-plugin-core/build/build.py

@@ -81,6 +81,38 @@ def replace_output(staged: Path, output: Path) -> Path:
     return output
 
 
+def _render_harness_id(host: str, *, portable: bool = False) -> str:
+    """Emit core/_harness_id.py for one host.
+
+    Carries both vocabularies from a single definition: the PostHog `source` tag
+    and the platform's X-Mem0-Source / X-Application pair. Keeping them together
+    is what stops the two from drifting into separate vocabularies for the same
+    thing.
+
+    The portable bundle runs in whatever editor a user drops it into, so it does
+    not know its host and must not guess one. HARNESS_ID stays "coding-agent",
+    which is true and useful for grouping in PostHog, but PLATFORM_APPLICATION is
+    left empty: X-Application names a real host app, is checked against an
+    allowlist server-side, and a value that is always discarded is worse than no
+    value -- it reads like an attribution we have and do not.
+    """
+    tag = host.upper().replace("-", "_") + "_PLUGIN"
+    application = "" if portable else host
+    return (
+        '"""Generated by integrations/agent-plugin-core/build/build.py. Do not edit."""\n'
+        "\n"
+        f'HARNESS_ID = "{host}"\n'
+        f'SOURCE_TAG = "{tag}"\n'
+        "\n"
+        "# Platform-side vocabulary (mem0_event.source + X-Application). The whole\n"
+        "# plugin family is one source; which editor it runs in is the application.\n"
+        "# An empty application means the host is unknown, and memory_core omits\n"
+        "# the header entirely rather than sending a placeholder.\n"
+        'PLATFORM_SOURCE = "MEM0_PLUGIN"\n'
+        f'PLATFORM_APPLICATION = "{application}"\n'
+    )
+
+
 def _bundle_python(
     staged: Path,
     host: str,
@@ -96,6 +128,12 @@ def _bundle_python(
             continue
         shutil.copy2(source, core / source.name)
 
+    # Generated per host so identity does not depend on an entrypoint remembering
+    # to call telemetry.init(). mcp_server.py and the detached telemetry.py sender
+    # never did, which is how MCP searches reported harness=generic and every
+    # batch they drained was labelled MEM0_PLUGIN regardless of the real host.
+    (core / "_harness_id.py").write_text(_render_harness_id(host, portable=portable), encoding="utf-8")
+
     values = {
         "PLUGIN_ROOT": plugin_root,
         "PLUGIN_DATA": "${PLUGIN_DATA}",

integrations/agent-plugin-core/python/hook_runner.py

@@ -290,6 +290,11 @@ def run(
     if args.plugin_data_dir:
         os.environ[data_dir_env] = args.plugin_data_dir
 
+    # Snapshot BEFORE anything writes to the data dir: cache_plugin_api_key
+    # writes `api-key` and EvidenceStore creates `evidence.sqlite3`, so asking
+    # after them always saw content and every fresh install reported an upgrade.
+    data_dir_was_empty = telemetry.data_dir_was_empty()
+
     cache_plugin_api_key()
     if args.action == "session-start":
         clear_stale_api_key_cache()
@@ -305,8 +310,19 @@ def run(
             return 0
 
         if args.action == "session-start":
-            if telemetry.is_first_run():
+            # Claims the marker atomically and says which event to record, so a
+            # second session starting alongside this one cannot record it too.
+            first_event = telemetry.claim_install(was_empty=data_dir_was_empty)
+            if first_event == "install":
                 telemetry.record("install")
+            elif first_event == "upgrade":
+                # First run after a build that never wrote the marker; the
+                # predecessor version was never recorded anywhere.
+                telemetry.record("upgrade", from_version="pre-0.3")
+            else:
+                previous = telemetry.claim_version_change()
+                if previous:
+                    telemetry.record("upgrade", from_version=previous)
             recovered = recover_pending_handoffs()
             record_session_start(store, hook_input)
             if recovered:

integrations/agent-plugin-core/python/mcp_server.py

@@ -21,16 +21,9 @@ from memory_core import (
 PROTOCOL_VERSION = "2024-11-05"
 TOOL_NAME = "search_memories"
 TOOL_DESCRIPTION = (
-    "Search memories from earlier work in this repository. ALWAYS call this "
-    "tool before answering anything that could depend on prior context: the "
-    "user's preferences, facts about this codebase, history, people, projects, "
-    "or earlier decisions. Do not rely on the chat window alone. The "
-    "repository's memory is shared by everyone who works in it and includes "
-    "what it took to run, test, or build here, so search before assuming an "
-    "invocation works. The scope argument changes what is searched: 'repo' "
-    "(default) is the whole repository's shared memory plus your own "
-    "preferences, 'dir' narrows the shared part to the directory you are "
-    "working in, and 'mine' is your preferences alone."
+    "Search memories from earlier work in this repository. Use it before "
+    "repeating investigation or when earlier decisions, fixes, commands, or "
+    "results may help."
 )
 TOOL_SCHEMA = {
     "type": "object",

integrations/agent-plugin-core/python/memory_core.py

@@ -29,7 +29,7 @@ from typing import Any, Iterable
 import telemetry
 
 DEFAULT_API_URL = "https://api.mem0.ai"
-PLUGIN_VERSION = "0.3.1"
+PLUGIN_VERSION = "0.3.3"
 
 _harness_name: str = "generic"
 _harness_env_prefix: str = "MEM0_PLUGIN"
@@ -71,15 +71,13 @@ MAX_FLUSH_ATTEMPTS = 5
 FORGET_PAGE_SIZE = 100
 FORGET_MAX_PAGES = 50
 
-PROJECT_MEMORY_INSTRUCTIONS = """Save concise repository facts that will help anyone with future coding work in this repository.
+PROJECT_MEMORY_INSTRUCTIONS = """Save concise repository facts that will help with future coding work.
 
 A completed change should produce one memory explaining the resulting behavior, where it is implemented when useful, and any important constraints or reasoning. Exploration or accepted decisions may produce separate memories only when they are independently useful.
 
-A command that failed and was then made to work should produce one memory naming the failing invocation, the error it returned, and the invocation that succeeded. Do not save one-off errors caused by an edit still in progress, transient network failures, or anything a rerun would fix on its own.
+Use the coding agent's final response for conclusions about current repository behavior. Do not save proposed or recommended changes unless the user accepted them or the coding agent completed them. Treat subagent responses as supporting repository evidence, not as decisions.
 
-Use the current coding agent's final response for conclusions about current repository behavior. Do not save proposed or recommended changes unless the user accepted them or the coding agent completed them. Treat subagent responses as supporting repository evidence, not as decisions.
-
-Write about the repository, not the user, assistant, session, or task. Do not save personal preferences. Do not save a memory that only states which repository, branch, or directory the session worked in. Do not include test results, documentation updates, release notes, or temporary state.
+Write about the repository, not the user, assistant, session, or task. Do not include test results, documentation updates, release notes, or temporary state.
 
 If nothing useful was established, return no memories."""
 
@@ -1800,6 +1798,34 @@ def extraction_message_batches(
     return batches
 
 
+# Platform surface attribution. Read from the generated per-host module so a new
+# entrypoint is correct without remembering to configure anything.
+try:  # pragma: no cover - absent only in the un-built shared source tree
+    from _harness_id import PLATFORM_APPLICATION as _PLATFORM_APPLICATION
+    from _harness_id import PLATFORM_SOURCE as _PLATFORM_SOURCE
+except ImportError:
+    _PLATFORM_SOURCE = "MEM0_PLUGIN"
+    _PLATFORM_APPLICATION = ""
+
+
+def platform_headers(key: str) -> dict[str, str]:
+    """Auth plus the three surface-identity headers.
+
+    X-Mem0-Source and X-Application are set-once by contract: this is the
+    outermost layer, so it sets them, and nothing below may overwrite them.
+    X-Mem0-Client is append-only — anything downstream adds itself to the tail.
+    """
+    headers = {
+        "Authorization": f"Token {key}",
+        "Content-Type": "application/json",
+        "X-Mem0-Source": _PLATFORM_SOURCE,
+        "X-Mem0-Client": f"mem0-plugin/{PLUGIN_VERSION}",
+    }
+    if _PLATFORM_APPLICATION:
+        headers["X-Application"] = _PLATFORM_APPLICATION
+    return headers
+
+
 def _request_json(
     url: str, key: str, payload: dict[str, Any], timeout: float
 ) -> tuple[dict[str, Any] | list[Any], int, int]:
@@ -1807,7 +1833,7 @@ def _request_json(
     request = urllib.request.Request(
         url,
         data=raw,
-        headers={"Authorization": f"Token {key}", "Content-Type": "application/json"},
+        headers=platform_headers(key),
         method="POST",
     )
     with urllib.request.urlopen(request, timeout=timeout) as response:
@@ -1834,7 +1860,7 @@ def _get_json(
 ) -> tuple[dict[str, Any] | list[Any], int]:
     request = urllib.request.Request(
         url,
-        headers={"Authorization": f"Token {key}", "Content-Type": "application/json"},
+        headers=platform_headers(key),
         method="GET",
     )
     with urllib.request.urlopen(request, timeout=timeout) as response:
@@ -1980,6 +2006,13 @@ def flush_session(
         "user_id": write_user,
         "app_id": repo.app_id,
         "run_id": session_id,
+        # Top level, not metadata: the backend reads `source` from the body or
+        # the query string, never from metadata, which is where this used to
+        # sit. The X-Mem0-Source header is also read, but only from the
+        # platform release that ships alongside this change, so the body value
+        # is what makes attribution work on both. The harness tag stays in
+        # metadata as hook provenance.
+        "source": _PLATFORM_SOURCE,
         "metadata": {**metadata, "author": write_user, "dirs": directory_chain(repo)},
         "agent_custom_instructions": PROJECT_MEMORY_INSTRUCTIONS,
         "custom_instructions": PERSONAL_MEMORY_INSTRUCTIONS,
@@ -2523,7 +2556,7 @@ def _collect_memory_ids(
 def _delete_memory(api_url: str, key: str, memory_id: str) -> bool:
     request = urllib.request.Request(
         f"{api_url}/v1/memories/{urllib.parse.quote(memory_id)}/",
-        headers={"Authorization": f"Token {key}", "Content-Type": "application/json"},
+        headers=platform_headers(key),
         method="DELETE",
     )
     try:

integrations/agent-plugin-core/python/telemetry.py

@@ -1,5 +1,9 @@
 #!/usr/bin/env python3
-"""Anonymous usage telemetry for Mem0 agent plugins.
+"""Usage telemetry for Mem0 agent plugins.
+
+Events are linked to your Mem0 account email when an API key is configured, and
+to a random per-machine id otherwise. Not anonymous — the Python SDK and CLI
+attribute the same way.
 
 Hooks run on a 3-6 second budget and fire on every tool call, so recording never
 touches the network: `record` appends one JSON line to a local spool and returns.
@@ -9,7 +13,8 @@ started once per session and again from the flush worker that is already detache
 Pure stdlib, matching the rest of the plugin. Opt out with MEM0_TELEMETRY=false.
 
 Never sends prompts, memory text, queries, file paths, repository names, or API
-keys: only event names, durations, counts, coarse outcomes, and salted hashes.
+keys: only event names, durations, counts, coarse outcomes, and repo/session
+identifiers hashed with a random per-install salt.
 """
 
 from __future__ import annotations
@@ -29,8 +34,24 @@ from typing import Any
 
 import memory_core
 
-_harness: str = "generic"
-_source_tag: str = "MEM0_PLUGIN"
+# Seeded from the per-host module the build generates into core/. Two processes
+# in this pipeline never call init() — mcp_server.py, and the detached
+# `python3 telemetry.py` sender that spawn_flush() starts — so a module default
+# was what every one of their events got labelled with.
+try:  # pragma: no cover - absent only in the un-built shared source tree
+    from _harness_id import HARNESS_ID as _DEFAULT_HARNESS
+    from _harness_id import PLATFORM_APPLICATION as _PLATFORM_APPLICATION
+    from _harness_id import PLATFORM_SOURCE as _PLATFORM_SOURCE
+    from _harness_id import SOURCE_TAG as _DEFAULT_SOURCE_TAG
+except ImportError:
+    _DEFAULT_HARNESS = "generic"
+    _DEFAULT_SOURCE_TAG = "MEM0_PLUGIN"
+    _PLATFORM_SOURCE = "MEM0_PLUGIN"
+    _PLATFORM_APPLICATION = ""
+
+_salt_cache: str = ""
+_harness: str = _DEFAULT_HARNESS
+_source_tag: str = _DEFAULT_SOURCE_TAG
 _PRIVATE_KEYS = {
     "apikey",
     "authorization",
@@ -56,10 +77,19 @@ _PRIVATE_KEYS = {
 }
 
 
-def init(harness: str = "generic", source_tag: str = "") -> None:
+def init(harness: str = "", source_tag: str = "") -> None:
+    """Override the generated identity. Optional — core/_harness_id.py is the default.
+
+    The fallback shape matches memory_core.configure_harness's (``<HOST>_PLUGIN``).
+    It used to be ``MEM0_<HOST>_PLUGIN`` here and ``<host>_plugin`` there, which
+    meant one plugin could emit three different source values depending on which
+    process happened to send the batch.
+    """
     global _harness, _source_tag
-    _harness = harness
-    _source_tag = source_tag or f"MEM0_{harness.upper().replace('-', '_')}_PLUGIN"
+    _harness = harness or _DEFAULT_HARNESS
+    _source_tag = source_tag or (
+        f"{_harness.upper().replace('-', '_')}_PLUGIN" if harness else _DEFAULT_SOURCE_TAG
+    )
 
 POSTHOG_API_KEY = "phc_hgJkUVJFYtmaJqrvf6CYN67TIQ8yhXAkWzUn9AMU4yX"
 POSTHOG_CAPTURE_URL = "https://us.i.posthog.com/i/v0/e/"
@@ -70,6 +100,16 @@ BATCH_SIZE = 100
 SEND_TIMEOUT = 5
 CLAIM_STALE_SECONDS = 120
 CLAIM_EXPIRY_SECONDS = 7 * 24 * 60 * 60
+# A batch is only discarded once it has genuinely been retried this many times.
+MAX_CLAIM_ATTEMPTS = 3
+# Parked claims drained per run, after the live spool. Bounded so a long backlog
+# cannot turn one flush into an unbounded send loop.
+MAX_PARKED_PER_RUN = 3
+# Added to the wait before a released claim becomes reclaimable, per attempt
+# already spent. Releasing straight to "reclaimable now" let two senders burn the
+# whole budget within seconds of one another on a single momentary failure, and
+# discard a batch a retry a minute later would have delivered.
+RETRY_COOLDOWN_SECONDS = 60
 
 
 def is_enabled() -> bool:
@@ -83,9 +123,126 @@ def is_enabled() -> bool:
 
 
 def _digest(value: str, length: int = 16) -> str:
+    """Unsalted digest. Only for values that are already secrets (API keys)."""
     return hashlib.sha256(value.encode("utf-8")).hexdigest()[:length]
 
 
+def _salt_path() -> Path:
+    return memory_core.data_dir() / "telemetry-salt"
+
+
+def _install_salt() -> str:
+    """Random per-install salt, created once and memoized for the process.
+
+    Deliberately its own file, claimed with O_CREAT|O_EXCL, rather than a key in
+    the identity file. Three reasons, all of which produced wrong data when this
+    lived in the identity dict:
+
+    - Hooks are short-lived separate processes firing on every tool call, and
+      people run more than one agent window. A read-modify-write would let each
+      process mint its own salt, so one repository would hash several ways in the
+      window before a writer won.
+    - resolve_distinct_id holds a copy of the identity dict across a network call
+      to /v1/ping/, so whichever write landed second erased the other's key —
+      losing either the salt (repo_hash changes mid-stream) or the email (a
+      second $identify, splitting the person).
+    - Touching the identity file from record() would create it, and is_first_run
+      keys off that file, so recording an event would silently suppress the
+      install event.
+
+    Published atomically, and there is deliberately no derived fallback. Creating
+    the file with O_CREAT|O_EXCL and then writing into it leaves a window where
+    the file exists and is empty, and a concurrent hook that reads it in that
+    window gets nothing. Falling back to a digest of the path would hand that
+    process a salt an attacker can compute, memoized for its whole run, which is
+    the privacy control this function exists to provide silently turning itself
+    off under load. The salt is written to a private temp file first and linked
+    into place, so the name either does not exist or already has the full value.
+
+    Returns "" when it genuinely cannot persist. Callers omit the hash entirely
+    rather than emit an unsalted one.
+    """
+    global _salt_cache
+    if _salt_cache:
+        return _salt_cache
+
+    path = _salt_path()
+    # Read before writing. Hooks are separate processes firing on every tool
+    # call, so all but the first find the salt already published; going straight
+    # to create-fsync-link-unlink meant every one of them paid an fsync to
+    # discover that, on a path whose whole promise is appending a line and
+    # returning.
+    try:
+        _salt_cache = path.read_text(encoding="utf-8").strip()
+        if _salt_cache:
+            return _salt_cache
+    except OSError:
+        pass
+
+    temporary = path.with_name(f"{path.name}.{os.getpid()}.tmp")
+    try:
+        path.parent.mkdir(parents=True, exist_ok=True)
+        handle = os.open(temporary, os.O_CREAT | os.O_EXCL | os.O_WRONLY, 0o600)
+        with os.fdopen(handle, "w", encoding="utf-8") as stream:
+            stream.write(uuid.uuid4().hex)
+            stream.flush()
+            os.fsync(stream.fileno())
+        try:
+            # Atomic claim: fails if another process already published one.
+            # os.link rather than replace, which would clobber theirs.
+            os.link(temporary, path)
+        except FileExistsError:
+            pass
+        except OSError:
+            # No hardlinks here (some network mounts, some container volumes).
+            # Claim the name directly instead. That reopens the empty-file
+            # window, but the window is now benign: a reader that lands in it
+            # gets "" and omits the hash for that process rather than caching a
+            # guessable one. Losing the hashes on every run of an entire
+            # filesystem is the worse failure.
+            try:
+                fallback = os.open(path, os.O_CREAT | os.O_EXCL | os.O_WRONLY, 0o600)
+                with os.fdopen(fallback, "w", encoding="utf-8") as stream:
+                    stream.write(temporary.read_text(encoding="utf-8"))
+            except OSError:
+                pass
+    except OSError:
+        pass
+    finally:
+        try:
+            temporary.unlink()
+        except OSError:
+            pass
+
+    try:
+        _salt_cache = path.read_text(encoding="utf-8").strip()
+    except OSError:
+        _salt_cache = ""
+    return _salt_cache
+
+
+def _scoped_digest(value: str, length: int = 16) -> str:
+    """Salted digest for values drawn from a guessable space.
+
+    repo.identity is a git remote URL, or ``local:<absolute path>`` when there is
+    no remote — which normally contains the account username. Sixteen unsalted
+    hex characters over that input space is enumerable, so this is not a
+    privacy control without the salt. Salting per install keeps every
+    within-account join the analytics actually use and gives up only
+    cross-machine joins on the same repository, which nothing computes.
+
+    Returns "" when there is no salt, so record() omits the property. An
+    unsalted digest over this input space is close to plaintext, and emitting one
+    under a name that implies it is hashed is worse than sending nothing.
+    """
+    if not value:
+        return ""
+    salt = _install_salt()
+    if not salt:
+        return ""
+    return hashlib.sha256(f"{salt}:{value}".encode("utf-8")).hexdigest()[:length]
+
+
 def _safe_value(value: Any) -> Any:
     if isinstance(value, str):
         return memory_core.redact(value)
@@ -145,9 +302,176 @@ def anonymous_id(identity: dict[str, str] | None = None) -> str:
     return created
 
 
+def _rotate_anonymous_id(identity: dict[str, str]) -> str:
+    """Mint a fresh anonymous id because the account context is gone.
+
+    The previous id may already have been merged into a person profile by an
+    $identify, and that merge is permanent. Reusing it after a logout or a key
+    change attributes everything that follows to the account that just went
+    away, which is the same misattribution the key fingerprint exists to stop,
+    only arriving through the anonymous path instead.
+
+    `aliased` is cleared with it: the new id has never been merged, so it is
+    eligible to be aliased into whatever account comes next.
+    """
+    created = f"code-anon-{uuid.uuid4().hex}"
+    identity["anonymous_id"] = created
+    identity.pop("aliased", None)
+    _write_identity(identity)
+    return created
+
+
+def _install_state_path() -> Path:
+    return memory_core.data_dir() / "install-state.json"
+
+
 def is_first_run() -> bool:
-    """Whether this machine has never recorded a plugin event before."""
-    return not _identity_path().exists()
+    """Whether install has never been recorded on this machine.
+
+    Deliberately NOT the identity file. That file is only written by a
+    successful flush, so an offline or firewalled user recorded code.install on
+    every single session, forever — and every 0.2.x user recorded one on their
+    first 0.3.x session because 0.2.x never wrote it at all.
+    """
+    return not _install_state_path().exists()
+
+
+def data_dir_was_empty() -> bool:
+    """Whether the data directory is untouched. Call BEFORE anything writes to it.
+
+    hook_runner reaches claim_install() only after cache_plugin_api_key() has
+    written `api-key` and EvidenceStore() has created `evidence.sqlite3`, so
+    asking at claim time always saw content and every fresh install reported an
+    upgrade. The caller snapshots this at the top of the run instead.
+    """
+    return not _data_dir_has_content()
+
+
+def claim_install(was_empty: bool | None = None) -> str | None:
+    """Claim the one install/upgrade record for this machine, atomically.
+
+    Returns the event to record ("install" or "upgrade"), or None if another
+    session already claimed it. O_CREAT|O_EXCL so two sessions starting together
+    cannot both win.
+
+    `was_empty` must come from data_dir_was_empty() called before this process
+    wrote anything. Omitting it falls back to checking now, which is only
+    correct for a caller that has touched nothing.
+    """
+    if not is_enabled():
+        # Never consume the one-shot claim while the user is opted out, or they
+        # would silently lose their install event if they later opt in.
+        return None
+
+    path = _install_state_path()
+    upgrading = not (data_dir_was_empty() if was_empty is None else was_empty)
+    try:
+        path.parent.mkdir(parents=True, exist_ok=True)
+        handle = os.open(path, os.O_CREAT | os.O_EXCL | os.O_WRONLY, 0o600)
+    except FileExistsError:
+        return None
+    except OSError:
+        return None
+    try:
+        with os.fdopen(handle, "w", encoding="utf-8") as stream:
+            json.dump(
+                {
+                    "plugin_version": memory_core.PLUGIN_VERSION,
+                    "installed_at": memory_core.utc_now(),
+                    "upgraded": upgrading,
+                },
+                stream,
+            )
+            # Durable before this returns. The O_EXCL open is what makes the
+            # claim exclusive, so it cannot be replaced by a temp-and-rename
+            # without losing that, which leaves the content as the thing to make
+            # safe. A kill between the open and this fsync used to leave a marker
+            # that exists but parses to nothing: is_first_run reads it as claimed
+            # and claim_version_change cannot read a version out of it.
+            stream.flush()
+            os.fsync(stream.fileno())
+    except OSError:
+        pass
+    return "upgrade" if upgrading else "install"
+
+
+def _data_dir_has_content() -> bool:
+    """Whether anything predates this session in the plugin data directory."""
+    try:
+        for entry in memory_core.data_dir().iterdir():
+            if entry.name != "install-state.json":
+                return True
+    except OSError:
+        pass
+    return False
+
+
+def _repair_install_state(path: Path) -> None:
+    """Rewrite an unparseable marker so version tracking can resume."""
+    try:
+        temporary = path.with_suffix(f".{os.getpid()}.tmp")
+        temporary.write_text(
+            json.dumps({"plugin_version": memory_core.PLUGIN_VERSION, "repaired_at": memory_core.utc_now()}),
+            encoding="utf-8",
+        )
+        temporary.replace(path)
+    except OSError:
+        pass
+
+
+def claim_version_change() -> str | None:
+    """Return the previously recorded version if it differs, updating the marker.
+
+    Only meaningful once the marker exists — the first transition into 0.3.x has
+    no recorded predecessor and reports "pre-0.3" instead. Claiming by rewriting
+    the marker means the next session sees no change and records nothing.
+    """
+    path = _install_state_path()
+    try:
+        state = json.loads(path.read_text(encoding="utf-8"))
+    except OSError:
+        return None
+    except json.JSONDecodeError:
+        # A crash between O_EXCL and the write leaves an empty marker. Left
+        # alone it disables every future upgrade event on this machine, because
+        # claim_install sees the file and this function cannot parse it.
+        state = None
+    if not isinstance(state, dict):
+        _repair_install_state(path)
+        return None
+    previous = str(state.get("plugin_version") or "")
+    if not previous or previous == memory_core.PLUGIN_VERSION:
+        return None
+    # Claim the transition with an exclusive sentinel before rewriting the
+    # marker. A plain read-modify-write let every concurrently starting session
+    # observe the old version and each record its own upgrade — and the first
+    # session after a version bump is exactly when several agent windows restart
+    # together.
+    sentinel = path.with_name(f"upgraded-{memory_core.PLUGIN_VERSION}")
+    try:
+        os.close(os.open(sentinel, os.O_CREAT | os.O_EXCL | os.O_WRONLY, 0o600))
+    except FileExistsError:
+        return None
+    except OSError:
+        return None
+
+    state["plugin_version"] = memory_core.PLUGIN_VERSION
+    state["upgraded_at"] = memory_core.utc_now()
+    temporary = path.with_suffix(f".{os.getpid()}.tmp")
+    try:
+        temporary.write_text(json.dumps(state), encoding="utf-8")
+        temporary.replace(path)
+    except OSError:
+        # Release the claim. The marker still records the old version, so
+        # without this the sentinel makes claim_version_change return early on
+        # every later run and this version's upgrade is never recorded again.
+        for leftover in (sentinel, temporary):
+            try:
+                leftover.unlink()
+            except OSError:
+                pass
+        return None
+    return previous
 
 
 def record(
@@ -168,19 +492,32 @@ def record(
         except OSError:
             pass
         properties = _safe_value(properties)
+        # Stamped in the RECORDING process, beside harness. `source` used to be
+        # read in the sending process from a module global, so whichever process
+        # drained the spool named every event in it. flush() spreads per-event
+        # properties last, so this now wins over any sender's default.
         properties.update(
             harness=_harness,
+            source=_source_tag,
             plugin_version=memory_core.PLUGIN_VERSION,
             os=sys.platform,
             python_version=platform.python_version(),
         )
+        # Assigned only when the digest is real. _scoped_digest returns "" when
+        # the salt could not be persisted, and an empty property is worse than an
+        # absent one: it survives the None filter below and reads as a value.
         if repo is not None:
-            properties["repo_hash"] = _digest(getattr(repo, "identity", ""))
+            repo_hash = _scoped_digest(getattr(repo, "identity", ""))
+            if repo_hash:
+                properties["repo_hash"] = repo_hash
         if session_id:
-            properties["session_hash"] = _digest(session_id)
+            session_hash = _scoped_digest(session_id)
+            if session_hash:
+                properties["session_hash"] = session_hash
         line = json.dumps(
             {
                 "event": f"{EVENT_PREFIX}.{event}",
+                "uuid": str(uuid.uuid4()),
                 "timestamp": memory_core.utc_now(),
                 "properties": {
                     key: value for key, value in properties.items() if value is not None
@@ -239,38 +576,201 @@ def spawn_flush() -> bool:
         return False
 
 
+def _claim_name(attempt: int = 0) -> str:
+    """Claim filename. The attempt count rides in the name so the 7-day expiry
+    only ever discards a batch that was actually retried and failed."""
+    return f"telemetry-{os.getpid()}-{uuid.uuid4().hex[:8]}-a{attempt}.sending"
+
+
+def _claim_attempt(claim: Path) -> int:
+    """Attempts recorded in a claim filename; 0 for the pre-attempt-count shape.
+
+    Anchored on field position, not on a leading "a": the legacy shape is
+    ``telemetry-<pid>-<hex>.sending`` and a hex id such as ``a1234567`` would
+    otherwise parse as attempt 1234567 and be discarded unsent on the first
+    flush after an upgrade.
+    """
+    stem = claim.name[: -len(".sending")] if claim.name.endswith(".sending") else claim.name
+    parts = stem.split("-")
+    if len(parts) != 4:
+        return 0
+    tail = parts[3]
+    if tail.startswith("a") and tail[1:].isdigit():
+        return int(tail[1:])
+    return 0
+
+
+def _touch(path: Path) -> None:
+    """Refresh mtime so a claim's age measures time since it was claimed.
+
+    ``Path.replace`` is ``os.rename``, which preserves mtime — so a claim created
+    after a quiet minute inherited the spool's last-write time and looked
+    abandoned the instant it was made. A second sender would then take it over
+    while the first was still posting, and both would deliver the batch.
+    """
+    try:
+        os.utime(path, None)
+    except OSError:
+        pass
+
+
 def _claim_spool() -> Path | None:
     """Rename the spool aside so exactly one sender owns each batch."""
     directory = memory_core.data_dir()
-    claim = directory / f"telemetry-{os.getpid()}-{uuid.uuid4().hex[:8]}.sending"
+    claim = directory / _claim_name()
     spool = _spool_path()
     try:
         spool.replace(claim)
+        _touch(claim)
         return claim
     except OSError:
         pass
+    return _claim_parked(directory)
+
+
+def _sweep_debris(directory: Path) -> None:
+    """Remove files nothing else will ever pick up again.
+
+    *.partial is a temp file orphaned by a crash between write and rename.
+    *.corrupt is a batch quarantined for undecodable content. No glob in this
+    module matches either, so without this they accumulate on disk for the life
+    of the install.
+
+    Quarantined batches are kept far longer than debris: they are the only
+    evidence left of events that could not be delivered, and someone diagnosing
+    a report of missing telemetry has to be able to find one.
+    """
     now = time.time()
-    for orphan in sorted(directory.glob("telemetry-*.sending")):
+    for debris in directory.glob("telemetry-*.partial"):
+        try:
+            if now - debris.stat().st_mtime > CLAIM_STALE_SECONDS:
+                debris.unlink()
+        except OSError:
+            continue
+    for quarantined in directory.glob("telemetry-*.corrupt"):
+        try:
+            if now - quarantined.stat().st_mtime > CLAIM_EXPIRY_SECONDS:
+                quarantined.unlink()
+        except OSError:
+            continue
+    # The same reasoning covers *.tmp. _write_identity and _install_salt both
+    # create one and unlink it in a finally, which a SIGKILL skips, and no glob
+    # in this module matches the leftovers either.
+    for temporary in directory.glob("telemetry-*.tmp"):
+        try:
+            if now - temporary.stat().st_mtime > CLAIM_STALE_SECONDS:
+                temporary.unlink()
+        except OSError:
+            continue
+
+
+def _claim_parked(directory: Path) -> Path | None:
+    """Take the oldest abandoned claim, if any lease has actually expired.
+
+    Kept separate from the live spool so flush() can drain both in one run.
+    Previously parked batches were only reachable when no spool existed at all,
+    and because sessions keep recording there usually was one — so a batch
+    parked by a failed send waited until the 7-day expiry deleted it unsent,
+    even though its own presence is what started the sender.
+    """
+    now = time.time()
+    for orphan in sorted(directory.glob("telemetry-*.sending"), key=_safe_mtime):
         try:
             age = now - orphan.stat().st_mtime
         except OSError:
             continue
-        if age > CLAIM_EXPIRY_SECONDS:
+        if age < CLAIM_STALE_SECONDS:
+            # Someone else holds a live lease on it. This check has to come
+            # first. Claiming a file bumps its attempt count and refreshes its
+            # mtime, so a sender that has just taken the final attempt looks
+            # exhausted to everyone else while it is actively draining. Judging
+            # exhaustion before liveness let a second sender unlink a batch out
+            # from under its owner, losing every event in it.
+            continue
+        # Attempts, not age. Every re-claim touches the mtime and every release
+        # backdates it by a fixed amount, so age is pinned near the stale
+        # threshold and never reaches the expiry. Age stays only as a backstop
+        # for files that never carried an attempt marker.
+        if _claim_attempt(orphan) >= MAX_CLAIM_ATTEMPTS or age > CLAIM_EXPIRY_SECONDS:
             try:
                 orphan.unlink()
             except OSError:
                 pass
             continue
-        if age < CLAIM_STALE_SECONDS:
-            continue
+        claim = orphan.parent / _claim_name(_claim_attempt(orphan) + 1)
         try:
             orphan.replace(claim)
+            _touch(claim)
             return claim
         except OSError:
             continue
     return None
 
 
+def _safe_mtime(path: Path) -> float:
+    try:
+        return path.stat().st_mtime
+    except OSError:
+        return 0.0
+
+
+def _rewrite_claim(claim: Path, remaining: list[dict[str, Any]]) -> bool:
+    """Persist the unsent remainder, atomically, and refresh the lease.
+
+    Called after every successful batch. Two jobs: a retry resumes where the
+    send stopped instead of re-posting from the top, and the rewrite doubles as
+    the lease heartbeat, so a slow sender does not have its claim stolen
+    mid-flight. Interval is one batch, well inside CLAIM_STALE_SECONDS.
+    """
+    if not remaining:
+        try:
+            claim.unlink()
+        except OSError:
+            pass
+        return True
+    temporary = claim.with_suffix(f".{os.getpid()}.partial")
+    try:
+        payload = "".join(json.dumps(event, separators=(",", ":"), default=str) + "\n" for event in remaining)
+        # fsync before the rename: without it the rename can land while the
+        # bytes have not, and the claim comes back empty or truncated after a
+        # crash. _drain then reads zero events and unlinks it.
+        with open(temporary, "w", encoding="utf-8") as handle:
+            handle.write(payload)
+            handle.flush()
+            os.fsync(handle.fileno())
+        temporary.replace(claim)
+        _touch(claim)
+        return True
+    except OSError:
+        try:
+            temporary.unlink()
+        except OSError:
+            pass
+        return False
+
+
+def _release_claim(claim: Path, remaining: list[dict[str, Any]]) -> None:
+    """Persist the remainder and drop the lease, because this sender has given up.
+
+    Distinct from the per-batch heartbeat: heartbeating on the way out would
+    make an abandoned batch look actively owned for a further
+    CLAIM_STALE_SECONDS, delaying the retry for no reason. Ageing it past the
+    threshold lets the next flush pick it up immediately, while the attempt
+    count in the filename still bounds how many times that can happen.
+    """
+    if not _rewrite_claim(claim, remaining):
+        return
+    try:
+        # Backdate past the stale threshold so the next flush can pick it up,
+        # minus a cooldown that grows with the attempts already spent. Clamped so
+        # the mtime never lands in the future, which would read as a live lease.
+        cooldown = min(_claim_attempt(claim) * RETRY_COOLDOWN_SECONDS, CLAIM_STALE_SECONDS)
+        released = time.time() - CLAIM_STALE_SECONDS - 1 + cooldown
+        os.utime(claim, (released, released))
+    except OSError:
+        pass
+
+
 def _resolve_email(key: str) -> str:
     """Trade the API key for the account email so events join other Mem0 surfaces."""
     url = os.environ.get("MEM0_API_URL", memory_core.DEFAULT_API_URL).rstrip("/") + "/v1/ping/"
@@ -300,34 +800,130 @@ def _post(payload: dict[str, Any], url: str) -> bool:
 
 
 def resolve_distinct_id() -> tuple[str, str]:
-    """Return the PostHog distinct id and the anonymous id it replaced, if any."""
+    """Return the PostHog distinct id and the anonymous id it replaced, if any.
+
+    The second value becomes a PostHog $identify alias. It is ONLY ever an
+    anonymous id: aliasing one account email to another merges two real person
+    profiles and cannot be undone, so a key that now belongs to a different
+    account re-resolves with no alias.
+    """
     identity = _read_identity()
-    email = identity.get("email", "")
-    if email:
-        return email, ""
     key = memory_core.api_key()
+    fingerprint = _digest(key) if key else ""
+    email = identity.get("email", "")
+
+    if email and fingerprint:
+        recorded = identity.get("key_fingerprint", "")
+        if recorded == fingerprint:
+            return email, ""
+        if not recorded:
+            # Rows written before fingerprints existed. Verify rather than
+            # adopt: a key changed before the upgrade would otherwise bind the
+            # new key to the previous account's email, permanently, and the
+            # fingerprint would then agree with itself forever after.
+            verified = _resolve_email(key)
+            if not verified:
+                # Offline, firewalled, or the API is down. Keep the previous
+                # behaviour and retry on the next flush rather than dropping a
+                # real account attribution. Safe because the same network that
+                # failed /v1/ping/ is about to fail the PostHog POST, so nothing
+                # is delivered under the unverified identity in the meantime.
+                return email, ""
+            identity["email"] = verified
+            identity["key_fingerprint"] = fingerprint
+            _write_identity(identity)
+            return verified, ""
+
     if not key:
+        # No key to verify the account with; do not keep attributing to it.
+        if email:
+            identity.pop("email", None)
+            identity.pop("key_fingerprint", None)
+            return _rotate_anonymous_id(identity), ""
         return anonymous_id(identity), ""
-    email = _resolve_email(key)
-    if not email:
+
+    resolved = _resolve_email(key)
+    if not resolved:
+        # The key changed and will not resolve (revoked, offline, API down).
+        # Reaching here with an email means the recorded fingerprint disagreed,
+        # so the key really did change. Drop the account and rotate: the stored
+        # anonymous id may already be merged into that account's person, and
+        # reusing it would keep the events on the profile we are trying to
+        # leave.
+        if email:
+            identity.pop("email", None)
+            identity.pop("key_fingerprint", None)
+            return _rotate_anonymous_id(identity), ""
         return anonymous_id(identity), ""
-    previous = identity.get("anonymous_id", "")
-    identity["email"] = email
+
+    # Alias only when going anonymous -> email for the first time. Once an anon
+    # id has been merged into an account it must never be offered again: an
+    # alias naming an already-identified id is what could link two real people.
+    previous = "" if (email or identity.get("aliased")) else identity.get("anonymous_id", "")
+    if previous:
+        identity["aliased"] = True
+    identity["email"] = resolved
+    identity["key_fingerprint"] = fingerprint
     _write_identity(identity)
-    return email, previous
+    return resolved, previous
 
 
 def flush() -> int:
-    """Drain claimed spools to PostHog and return the number of events sent."""
+    """Drain the live spool, then any parked claims, and return events sent."""
     if not is_enabled():
         return 0
-    claim = _claim_spool()
+    sent, delivered = _drain(_claim_spool())
+    if not delivered:
+        # The network is failing. Retrying other batches now would only burn
+        # their attempt budget against the same broken connection.
+        return sent
+
+    # Parked batches used to starve behind the live spool indefinitely. Bounded
+    # per run so a long backlog cannot turn one flush into an unbounded loop.
+    directory = memory_core.data_dir()
+    _sweep_debris(directory)
+    for _ in range(MAX_PARKED_PER_RUN):
+        parked = _claim_parked(directory)
+        if parked is None:
+            break
+        count, delivered = _drain(parked)
+        sent += count
+        if not delivered:
+            break
+    return sent
+
+
+def _drain(claim: Path | None) -> tuple[int, bool]:
+    """Post one claimed batch file, recording progress after every batch.
+
+    Returns (events sent, whether everything was delivered).
+    """
     if claim is None:
-        return 0
+        return 0, True
     try:
         lines = claim.read_text(encoding="utf-8").splitlines()
+    except ValueError:
+        # UnicodeDecodeError from a torn write: the content is unrecoverable, so
+        # quarantine rather than retry. flush() runs from a bare `finally:` in
+        # flush_worker, so raising here also skips the handoff cleanup, and an
+        # undecodable file would otherwise be re-read on every flush forever.
+        # Reported as delivered because there is nothing left to deliver and the
+        # rest of the run should continue.
+        try:
+            claim.replace(claim.with_suffix(".corrupt"))
+        except OSError:
+            try:
+                claim.unlink()
+            except OSError:
+                pass
+        return 0, True
     except OSError:
-        return 0
+        # Could not read it, which is not the same as having nothing to send.
+        # The file is left exactly where it is: a vanished or briefly unreadable
+        # claim is retryable, and quarantining it here would discard events over
+        # a transient filesystem error. Reported as undelivered so the run stops
+        # instead of counting a batch nothing was posted from as delivered.
+        return 0, False
     events = []
     for line in lines:
         try:
@@ -337,11 +933,18 @@ def flush() -> int:
         if isinstance(value, dict) and value.get("event"):
             events.append(value)
     if not events:
+        # Only delete when the file really is empty. A non-empty file that
+        # parses to nothing is a torn write, and its contents are the unsent
+        # remainder — deleting it is the data loss this PR exists to prevent.
         try:
-            claim.unlink()
+            empty = claim.stat().st_size == 0
+        except OSError:
+            empty = True
+        try:
+            claim.replace(claim.with_suffix(".corrupt")) if not empty else claim.unlink()
         except OSError:
             pass
-        return 0
+        return 0, True
 
     distinct_id, aliased_anonymous_id = resolve_distinct_id()
     if aliased_anonymous_id:
@@ -360,12 +963,17 @@ def flush() -> int:
 
     sent = 0
     for start in range(0, len(events), BATCH_SIZE):
+        chunk = events[start : start + BATCH_SIZE]
         batch = [
             {
                 "event": event["event"],
                 "distinct_id": distinct_id,
+                # Carried through from record() so a resend can be collapsed.
+                "uuid": event.get("uuid"),
                 "timestamp": event.get("timestamp"),
                 "properties": {
+                    # Fallback only: events recorded by a build before source
+                    # moved into record() have none of their own.
                     "source": _source_tag,
                     "language": "python",
                     "$process_person_profile": False,
@@ -373,16 +981,24 @@ def flush() -> int:
                     **(event.get("properties") or {}),
                 },
             }
-            for event in events[start : start + BATCH_SIZE]
+            for event in chunk
         ]
         if not _post({"api_key": POSTHOG_API_KEY, "batch": batch}, POSTHOG_BATCH_URL):
-            return sent
-        sent += len(batch)
-    try:
-        claim.unlink()
-    except OSError:
-        pass
-    return sent
+            # Keep only what has not been delivered, and release the lease.
+            # Previously the whole file was kept and the retry re-posted every
+            # batch, including the ones that had already arrived.
+            _release_claim(claim, events[start:])
+            return sent, False
+        sent += len(chunk)
+        # Record progress and refresh the lease after each successful batch, so
+        # a crash repeats at most one batch instead of the entire file. If the
+        # rewrite fails the claim still holds delivered events, so stop rather
+        # than carry on as though progress were recorded — continuing is how the
+        # duplicate delivery this PR fixes would come back.
+        if not _rewrite_claim(claim, events[start + len(chunk) :]):
+            _release_claim(claim, events[start + len(chunk) :])
+            return sent, False
+    return sent, True
 
 
 def main() -> int:

integrations/agent-plugin-core/skills/pause/SKILL.md.tmpl

@@ -7,8 +7,8 @@ disable-model-invocation: true
 # Pause memory capture
 
 To pause (hooks stop capturing and sending session content; a minimal
-anonymous telemetry ping still fires at session start unless
-`MEM0_TELEMETRY=false`):
+telemetry ping still fires at session start, under your Mem0 account email,
+unless `MEM0_TELEMETRY=false`):
 
 ```bash
 python3 "{{PLUGIN_ROOT}}/core/memory_cli.py" --harness "{{HARNESS_ID}}" {{PLUGIN_DATA_ARG}} pause

integrations/agent-plugin-core/skills/search/SKILL.md.tmpl

@@ -12,11 +12,9 @@ Call `search_memories` with the user's question. Treat `--top-k`, `--category`,
 query.
 
 Omit `top_k` to use Mem0's configured default. Omit `category` to search every
-category; a category is a best-effort label Mem0 assigned when it saved the
-memory, so if a category search misses, repeat it without the category. Omit
-`scope` to use the configured default, normally `repo`: this repository's
-shared memory, which everyone who works in it contributes to, plus your own
-preferences.
+category. Omit `scope` to use the configured default, normally `repo`: this
+repository's shared memory, which everyone who works in it contributes to,
+plus your own preferences.
 
 Pass `scope` when the question needs something else: `dir` to narrow the
 shared memory to the directory you are working in (a package inside a

integrations/agent-plugin-core/tests/test_build.py

@@ -70,6 +70,38 @@ def test_portable_bundle_is_conformant_and_self_contained(tmp_path: Path) -> Non
     assert not any(path.is_symlink() for path in root.rglob("*"))
 
 
+def _harness_identity(root: Path) -> dict[str, str]:
+    """Read the generated core/_harness_id.py without importing it."""
+    values: dict[str, str] = {}
+    for line in (root / "core" / "_harness_id.py").read_text(encoding="utf-8").splitlines():
+        if "=" in line and not line.lstrip().startswith("#"):
+            name, _, raw = line.partition("=")
+            values[name.strip()] = raw.strip().strip('"')
+    return values
+
+
+def test_the_portable_bundle_declares_no_host_application(tmp_path: Path) -> None:
+    """It runs in whatever editor a user drops it into, so it cannot know the host.
+
+    X-Application is allowlisted server-side. A guessed value is silently dropped
+    there, which is the worst outcome: the wire says we know the host and the
+    stored event says we do not.
+    """
+    identity = _harness_identity(build("mem0-agent-plugin", "portable", tmp_path / "portable"))
+
+    assert identity["PLATFORM_APPLICATION"] == ""
+    # The PostHog-side label is still useful for grouping and stays populated.
+    assert identity["HARNESS_ID"] == "coding-agent"
+    assert identity["PLATFORM_SOURCE"] == "MEM0_PLUGIN"
+
+
+@pytest.mark.parametrize("host", ["claude-code", "cursor", "codex", "kimi", "antigravity"])
+def test_a_native_bundle_names_the_host_it_was_built_for(host: str, tmp_path: Path) -> None:
+    identity = _harness_identity(build(host, "native", tmp_path / host))
+
+    assert identity["PLATFORM_APPLICATION"] == host
+
+
 @pytest.mark.parametrize("host", ["claude-code", "cursor", "codex", "kimi", "antigravity"])
 def test_native_bundle_is_self_contained(host: str, tmp_path: Path) -> None:
     root = build(host, "native", tmp_path / host)

integrations/agent-plugin-core/tests/test_spool_delivery.py

@@ -0,0 +1,446 @@
+"""Delivery semantics of the telemetry spool: no duplicates, no starvation.
+
+These run against a built host's core in-process (not a subprocess) because they
+need to inject failures into ``_post``. The identity tests next door cover the
+uninitialised-process case that needs a real interpreter.
+"""
+
+from __future__ import annotations
+
+import importlib
+import json
+import os
+import sys
+import time
+from pathlib import Path
+
+import pytest
+
+CORE_ROOT = Path(__file__).resolve().parents[1]
+REPOSITORY_ROOT = CORE_ROOT.parents[1]
+HOST_CORE = REPOSITORY_ROOT / "integrations" / "claude-code-plugin" / "core"
+
+pytestmark = pytest.mark.skipif(not HOST_CORE.exists(), reason="claude-code-plugin is not built")
+
+
+@pytest.fixture()
+def telemetry(tmp_path, monkeypatch):
+    # CI runs this directory and claude-code-plugin/tests in ONE pytest process,
+    # and that suite's conftest sets MEM0_TELEMETRY=false at import, process-wide.
+    # Without this the whole file silently no-ops: record() returns early and
+    # every assertion sees an empty spool. Do not rely on ambient env.
+    monkeypatch.setenv("MEM0_TELEMETRY", "true")
+    monkeypatch.setenv("MEM0_CODE_DATA_DIR", str(tmp_path / "data"))
+    monkeypatch.syspath_prepend(str(HOST_CORE))
+
+    # Save and RESTORE rather than delete. claude-code-plugin/tests/conftest.py
+    # imports memory_core once at collection and calls configure_harness() on it;
+    # dropping the module left a later re-import with default harness config, so
+    # tests in that suite failed depending on collection order.
+    names = ("telemetry", "memory_core", "_harness_id")
+    saved = {name: sys.modules.get(name) for name in names}
+    for name in names:
+        sys.modules.pop(name, None)
+
+    module = importlib.import_module("telemetry")
+    monkeypatch.setattr(module, "resolve_distinct_id", lambda: ("tester@example.com", ""))
+    try:
+        yield module
+    finally:
+        for name in names:
+            sys.modules.pop(name, None)
+            if saved[name] is not None:
+                sys.modules[name] = saved[name]
+
+
+def _delivered(payloads):
+    return [event for payload in payloads if "batch" in payload for event in payload["batch"]]
+
+
+def test_a_partial_failure_does_not_redeliver_what_already_arrived(telemetry):
+    """Defect 2a: flush kept the whole claim on failure and retried from the top.
+
+    150 events across two batches, the second failing, previously delivered 250.
+    """
+    for index in range(150):
+        telemetry.record("search", index=index)
+
+    sent: list[dict] = []
+    calls = {"n": 0}
+
+    def flaky(payload, url):
+        calls["n"] += 1
+        if calls["n"] == 2:  # second batch fails
+            return False
+        sent.append(payload)
+        return True
+
+    telemetry._post = flaky
+    telemetry.flush()
+
+    telemetry._post = lambda payload, url: sent.append(payload) or True
+    telemetry.flush()
+
+    events = _delivered(sent)
+    assert len(events) == 150
+    assert len({event["uuid"] for event in events}) == 150
+
+
+def test_a_fresh_claim_is_not_immediately_stealable(telemetry):
+    """Defect 2b: rename preserves mtime, so a claim inherited the spool's age.
+
+    With the last write older than the stale threshold, a claim made now looked
+    abandoned the instant it existed and a second sender took it over.
+    """
+    telemetry.record("search")
+    spool = telemetry._spool_path()
+    old = time.time() - (telemetry.CLAIM_STALE_SECONDS + 60)
+    os.utime(spool, (old, old))
+
+    first = telemetry._claim_spool()
+    assert first is not None
+
+    # A second sender starting right now must find nothing to take.
+    assert telemetry._claim_parked(first.parent) is None
+
+
+def test_a_live_final_attempt_is_not_deleted_by_another_sender(telemetry):
+    """Review finding: exhaustion was judged before liveness, so owners lost batches.
+
+    Claiming a parked file bumps its attempt count and refreshes its mtime. Once
+    the count reaches the budget, the owner draining it looked exhausted to every
+    other sender, which unlinked the file out from under it. Everything in that
+    batch was gone, which is precisely the loss this PR exists to stop.
+    """
+    telemetry.record("search", reason="owned-by-the-first-sender")
+    spool = telemetry._spool_path()
+    stale = time.time() - (telemetry.CLAIM_STALE_SECONDS + 60)
+    os.utime(spool, (stale, stale))
+
+    claim = telemetry._claim_spool()
+    assert claim is not None
+
+    # Walk it to the final attempt, ageing it each round so it can be re-claimed.
+    # _claim_spool hands back a0 and _release_claim keeps the name, so it takes
+    # one full round per attempt to reach the budget.
+    for _ in range(telemetry.MAX_CLAIM_ATTEMPTS):
+        # Carry the marker through each rewrite so the final assertion proves the
+        # events survived, not merely that some file with the right name did.
+        telemetry._release_claim(claim, [{"event": "code.search", "uuid": "owned-by-the-first-sender"}])
+        parked = sorted(claim.parent.glob("telemetry-*.sending"))
+        assert parked, "the batch was dropped while still inside its budget"
+        os.utime(parked[0], (stale, stale))
+        claim = telemetry._claim_parked(claim.parent)
+        assert claim is not None
+
+    assert telemetry._claim_attempt(claim) >= telemetry.MAX_CLAIM_ATTEMPTS
+    assert claim.exists()
+
+    # The owner is draining it right now: fresh mtime, live lease.
+    second_sender = telemetry._claim_parked(claim.parent)
+
+    assert second_sender is None, "a second sender took a batch under a live lease"
+    assert claim.exists(), "a second sender deleted a batch its owner was draining"
+    assert "owned-by-the-first-sender" in claim.read_text(encoding="utf-8")
+
+
+def test_an_exhausted_batch_is_still_discarded_once_its_lease_lapses(telemetry):
+    """The liveness check must defer the cleanup, not cancel it.
+
+    Guards the obvious over-correction: skipping live claims is only safe if an
+    abandoned one at the same attempt count is still reaped on a later run.
+    """
+    telemetry.record("search")
+    spool = telemetry._spool_path()
+    stale = time.time() - (telemetry.CLAIM_STALE_SECONDS + 60)
+    os.utime(spool, (stale, stale))
+
+    claim = telemetry._claim_spool()
+    assert claim is not None
+    exhausted = claim.parent / telemetry._claim_name(telemetry.MAX_CLAIM_ATTEMPTS)
+    claim.replace(exhausted)
+    os.utime(exhausted, (stale, stale))
+
+    assert telemetry._claim_parked(exhausted.parent) is None
+    assert not exhausted.exists(), "an abandoned exhausted batch was left behind forever"
+
+
+def test_a_parked_batch_is_drained_behind_the_live_spool(telemetry):
+    """Defect 6: parked claims were only reachable when no spool existed.
+
+    Because sessions keep recording there usually was one, so a batch parked by
+    a failed send waited until the 7-day expiry deleted it unsent — even though
+    its own presence is what starts the sender.
+    """
+    telemetry.record("parked")
+    telemetry._post = lambda payload, url: False
+    telemetry.flush()
+
+    parked = list(telemetry.memory_core.data_dir().glob("telemetry-*.sending"))
+    assert len(parked) == 1
+    old = time.time() - (telemetry.CLAIM_STALE_SECONDS + 60)
+    os.utime(parked[0], (old, old))
+
+    telemetry.record("fresh")
+    sent: list[dict] = []
+    telemetry._post = lambda payload, url: sent.append(payload) or True
+    telemetry.flush()
+
+    names = {event["event"] for event in _delivered(sent)}
+    assert names == {"code.parked", "code.fresh"}
+
+
+def test_a_batch_is_retried_until_the_budget_is_spent_not_discarded(telemetry):
+    """Expiry discards what failed repeatedly, not what merely sat for a while.
+
+    The budget is the attempt count, because age cannot be one: every re-claim
+    touches the mtime and every release backdates it, so age never accumulates.
+    """
+    telemetry.record("parked")
+    telemetry._post = lambda payload, url: False
+    telemetry.flush()
+
+    parked = list(telemetry.memory_core.data_dir().glob("telemetry-*.sending"))
+    assert len(parked) == 1
+    assert telemetry._claim_attempt(parked[0]) < telemetry.MAX_CLAIM_ATTEMPTS
+
+    sent: list[dict] = []
+    telemetry._post = lambda payload, url: sent.append(payload) or True
+    telemetry.flush()
+
+    assert [event["event"] for event in _delivered(sent)] == ["code.parked"]
+
+
+def test_progress_is_recorded_after_every_batch(telemetry):
+    """A crash repeats at most one batch, not the whole file."""
+    for index in range(250):
+        telemetry.record("search", index=index)
+
+    calls = {"n": 0}
+
+    def die_after_two(payload, url):
+        calls["n"] += 1
+        if calls["n"] > 2:
+            return False
+        return True
+
+    telemetry._post = die_after_two
+    telemetry.flush()
+
+    parked = list(telemetry.memory_core.data_dir().glob("telemetry-*.sending"))
+    assert len(parked) == 1
+    remaining = parked[0].read_text(encoding="utf-8").strip().splitlines()
+    # Two batches of 100 landed; only the last 50 should still be pending.
+    assert len(remaining) == 50
+    assert json.loads(remaining[0])["properties"]["index"] == 200
+
+
+def test_the_heartbeat_actually_refreshes_the_lease(telemetry):
+    """The claim rewrite doubles as the lease heartbeat.
+
+    Previously asserted `SEND_TIMEOUT * 4 < CLAIM_STALE_SECONDS`, which compares
+    two constants and executes none of the code under test. Drive the real
+    rewrite and watch the mtime move instead.
+    """
+    for index in range(150):
+        telemetry.record("search", index=index)
+    claim = telemetry._claim_spool()
+    assert claim is not None
+
+    stale = time.time() - (telemetry.CLAIM_STALE_SECONDS + 60)
+    os.utime(claim, (stale, stale))
+    assert time.time() - claim.stat().st_mtime > telemetry.CLAIM_STALE_SECONDS
+
+    telemetry._rewrite_claim(claim, [{"event": "code.x", "properties": {}}])
+    assert time.time() - claim.stat().st_mtime < telemetry.CLAIM_STALE_SECONDS
+
+
+def test_an_undeliverable_batch_is_eventually_given_up_on(telemetry):
+    """Expiry has to be reachable from a state the state machine can produce.
+
+    It was not: every re-claim touched the mtime and every release backdated it
+    by a fixed amount, so age hovered near the stale threshold and the 7-day
+    expiry never fired. An undeliverable batch lived on disk forever, and
+    spawn_flush saw it and started a sender on every hook.
+    """
+    telemetry.record("doomed")
+    telemetry._post = lambda payload, url: False
+
+    directory = telemetry.memory_core.data_dir()
+    for _ in range(telemetry.MAX_CLAIM_ATTEMPTS + 3):
+        telemetry.flush()
+        # Attempts now carry a cooldown, so a released claim is not instantly
+        # reclaimable. Age it to stand in for the wall time a real retry waits;
+        # without this the loop spins inside one cooldown and proves nothing.
+        for parked in directory.glob("telemetry-*.sending"):
+            stale = time.time() - (telemetry.CLAIM_STALE_SECONDS + 60)
+            os.utime(parked, (stale, stale))
+
+    leftover = list(directory.glob("telemetry-*.sending"))
+    assert leftover == [], f"batch never given up on: {[p.name for p in leftover]}"
+
+
+def test_a_batch_that_cannot_be_read_is_not_counted_as_delivered(telemetry):
+    """Review finding: a read failure reported 'everything delivered'.
+
+    Nothing was posted, so calling it delivered lets flush() carry on to other
+    claims as though this batch had arrived, and hides the failure from the one
+    signal that says the run went badly. It also must not quarantine: a briefly
+    unreadable file is retryable, and moving it to .corrupt discards the events
+    over a transient filesystem error, because nothing ever re-globs .corrupt.
+    """
+    telemetry.record("search")
+    spool = telemetry._spool_path()
+    stale = time.time() - (telemetry.CLAIM_STALE_SECONDS + 60)
+    os.utime(spool, (stale, stale))
+    claim = telemetry._claim_spool()
+    assert claim is not None
+
+    original = Path.read_text
+
+    def unreadable(self, *args, **kwargs):
+        if self == claim:
+            raise OSError(5, "I/O error")
+        return original(self, *args, **kwargs)
+
+    Path.read_text = unreadable
+    try:
+        sent, delivered = telemetry._drain(claim)
+    finally:
+        Path.read_text = original
+
+    assert sent == 0
+    assert delivered is False, "an unread batch was reported as delivered"
+    assert claim.exists(), "a transient read error discarded the batch"
+    assert not list(claim.parent.glob("*.corrupt")), "quarantined over a transient error"
+
+
+def test_undecodable_content_is_still_quarantined_and_the_run_continues(telemetry):
+    """The other half: genuinely unrecoverable content must not block the run.
+
+    Guards the over-correction. If every read problem returned undelivered, one
+    torn file would stop every later claim on every flush, forever.
+    """
+    telemetry.record("search")
+    spool = telemetry._spool_path()
+    stale = time.time() - (telemetry.CLAIM_STALE_SECONDS + 60)
+    os.utime(spool, (stale, stale))
+    claim = telemetry._claim_spool()
+    assert claim is not None
+    claim.write_bytes(b"\xff\xfe torn \x00 write")
+
+    sent, delivered = telemetry._drain(claim)
+
+    assert (sent, delivered) == (0, True)
+    assert not claim.exists()
+    assert list(claim.parent.glob("*.corrupt")), "unrecoverable content was not quarantined"
+
+
+def test_retries_are_spread_over_real_time_not_burned_at_once(telemetry):
+    """Review finding: releasing straight to reclaimable spent the budget instantly.
+
+    Two senders hitting one momentary failure could walk a batch from attempt 0
+    to the limit within seconds and discard it, when a retry a minute later would
+    have delivered. Each release now has to age past a cooldown that grows with
+    the attempts already spent.
+    """
+    telemetry.record("doomed")
+    telemetry._post = lambda payload, url: False
+
+    directory = telemetry.memory_core.data_dir()
+    telemetry.flush()
+
+    parked = list(directory.glob("telemetry-*.sending"))
+    assert parked, "the batch was discarded on its first failure"
+    assert telemetry._claim_attempt(parked[0]) == 0
+
+    # Second sender, immediately: the cooldown has not elapsed, so it must not
+    # be able to spend another attempt.
+    telemetry.flush()
+    still = list(directory.glob("telemetry-*.sending"))
+    assert len(still) == 1
+    assert telemetry._claim_attempt(still[0]) <= 1, "burned attempts without waiting"
+
+
+def test_a_legacy_claim_filename_is_not_mistaken_for_a_huge_attempt_count(telemetry):
+    """The old shape is telemetry-<pid>-<hex>.sending, and hex can start with 'a'."""
+    assert telemetry._claim_attempt(Path("telemetry-999-deadbeef.sending")) == 0
+    assert telemetry._claim_attempt(Path("telemetry-999-a1234567.sending")) == 0
+    assert telemetry._claim_attempt(Path("telemetry-999-deadbeef-a2.sending")) == 2
+
+
+def test_a_torn_claim_is_quarantined_not_deleted(telemetry):
+    """A non-empty file that parses to nothing is the remainder, not garbage."""
+    telemetry.record("search")
+    claim = telemetry._claim_spool()
+    claim.write_bytes(b"\xff\xfe not utf-8 at all")
+    stale = time.time() - (telemetry.CLAIM_STALE_SECONDS + 60)
+    os.utime(claim, (stale, stale))
+
+    sent = telemetry.flush()
+
+    assert sent == 0
+    assert not claim.exists()
+    quarantined = list(telemetry.memory_core.data_dir().glob("*.corrupt"))
+    assert len(quarantined) == 1, "torn claim was destroyed instead of kept"
+
+
+def test_a_failed_rewrite_stops_instead_of_redelivering(telemetry):
+    """Ignoring the rewrite result reintroduced the duplicates this PR fixes."""
+    for index in range(250):
+        telemetry.record("search", index=index)
+
+    telemetry._rewrite_claim = lambda claim, remaining: False
+    delivered = []
+    telemetry._post = lambda payload, url: delivered.extend(payload.get("batch", [])) or True
+
+    telemetry.flush()
+    assert len(delivered) == 100, f"kept going after a failed rewrite: {len(delivered)}"
+
+
+def test_partial_files_are_swept(telemetry):
+    """Nothing else globs *.partial, so a crash mid-rename orphans one forever."""
+    data_dir = telemetry.memory_core.data_dir()
+    data_dir.mkdir(parents=True, exist_ok=True)
+    debris = data_dir / "telemetry-1-abc-a0.1.partial"
+    debris.write_text("x", encoding="utf-8")
+    old = time.time() - (telemetry.CLAIM_STALE_SECONDS + 60)
+    os.utime(debris, (old, old))
+
+    telemetry.flush()
+    assert not debris.exists()
+
+
+def test_quarantined_batches_are_eventually_collected(telemetry):
+    """Nothing re-globs .corrupt, so without a sweep they live on disk forever.
+
+    Kept much longer than .partial debris on purpose: a quarantined batch is the
+    only remaining evidence of events that could not be delivered.
+    """
+    directory = telemetry.memory_core.data_dir()
+    directory.mkdir(parents=True, exist_ok=True)
+    fresh = directory / "telemetry-1-aaaaaaaa-a0.corrupt"
+    old = directory / "telemetry-2-bbbbbbbb-a0.corrupt"
+    for path in (fresh, old):
+        path.write_text("torn", encoding="utf-8")
+    expired = time.time() - (telemetry.CLAIM_EXPIRY_SECONDS + 60)
+    os.utime(old, (expired, expired))
+
+    telemetry._sweep_debris(directory)
+
+    assert fresh.exists(), "a recent quarantine was discarded before anyone could look at it"
+    assert not old.exists(), "an expired quarantine was left on disk forever"
+
+
+def test_temp_files_orphaned_by_a_kill_are_collected(telemetry):
+    """_write_identity and _install_salt unlink in a finally, which SIGKILL skips."""
+    directory = telemetry.memory_core.data_dir()
+    directory.mkdir(parents=True, exist_ok=True)
+    orphan = directory / "telemetry-salt.999.tmp"
+    orphan.write_text("abandoned", encoding="utf-8")
+    stale = time.time() - (telemetry.CLAIM_STALE_SECONDS + 60)
+    os.utime(orphan, (stale, stale))
+
+    telemetry._sweep_debris(directory)
+
+    assert not orphan.exists(), "a killed process left a temp file on disk forever"