chore: merge origin/main into mintlify/af82bda4 and resolve conflicts

2026-09-16 11:46:52 +00:00
parent b5ae772eae
commit 08d9636491
693 changed files with 55668 additions and 22676 deletions
@@ -0,0 +1,49 @@
+# Documentation (`docs/`)
+
+Mintlify site published at https://docs.mem0.ai.
+
+## Commands
+
+```bash
+make docs          # from repo root
+cd docs && mintlify dev
+```
+
+## Structure
+
+| Path | Contents |
+|------|----------|
+| `api-reference/` | Platform REST endpoints |
+| `open-source/` | Self-hosted SDK guides |
+| `platform/` | Hosted platform guides |
+| `integrations/` | One page per integration |
+| `core-concepts/` | Memory model, graph memory, scoping |
+| `cookbooks/` | End-to-end recipes |
+| `contributing/` | Contributor guides |
+| `docs.json` | Navigation tree |
+| `openapi.json` | Platform API spec |
+| `llms.txt` | Scope-tagged index for agents |
+
+## Adding a page
+
+Every new `.mdx` page needs three things, or CI fails:
+
+1. The page itself under the right section.
+2. A navigation entry in `docs.json`.
+3. A line in `llms.txt` with a scope tag (`[Platform]`, `[OSS]`, or `[Both]`) and a description that starts with `Use when ...`.
+
+`docs-llms-txt-check.yml` runs on every PR touching `docs/**/*.mdx` and **blocks the merge** when `llms.txt` is out of sync. To fix:
+
+```bash
+python scripts/check-llms-txt-coverage.py --write
+```
+
+That scaffolds placeholders under `## Unclassified - needs triage`. Then replace each `[TODO: ...]` tag, rewrite the descriptions as `Use when ...`, move entries into the correct section, and delete the triage heading once it is empty.
+
+## Conventions
+
+- Frontmatter needs `title`, `description`, and usually `icon`.
+- Mintlify components (`<Note>`, `<Card>`, `<Tabs>`, `<CodeGroup>`) are available; prefer them over raw HTML.
+- Code samples must be runnable. If a sample calls a public SDK method, it has to match the real signature.
+- Documentation-only PRs are exempt from the `accepted`-issue requirement in the PR gate, but not from the CLA.
+- Any change to a public SDK signature has to update the matching page here in the same PR.
@@ -0,0 +1 @@
+AGENTS.md
@@ -1,5 +1,6 @@
 ---
-title: "Overview"
+title: "API Reference Overview"
+sidebarTitle: "Overview"
 icon: "terminal"
 iconType: "solid"
 description: "Explore the Mem0 REST API for adding, searching, updating, and deleting memories, plus managing users, organizations, projects, and webhooks."
@@ -10,7 +11,7 @@ description: "Explore the Mem0 REST API for adding, searching, updating, and del
 Mem0 provides a comprehensive REST API for integrating advanced memory capabilities into your applications. Create, search, update, and manage memories across users, agents, and custom entities with simple HTTP requests.

 <Info>
-**Quick start:** Get your API key from the <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=api-reference" rel="nofollow">Mem0 Dashboard</a> and make your first memory operation in minutes.
+**Quick start:** Get your API key from the <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=api-reference">Mem0 Dashboard</a> and make your first memory operation in minutes.
 </Info>

 ---
@@ -87,7 +88,7 @@ All API requests require authentication using Token-based authentication. Includ
 Authorization: Token <your-api-key>
 ```

-Get your API key from the <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=api-reference" rel="nofollow">Mem0 Dashboard</a>.
+Get your API key from the <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=api-reference">Mem0 Dashboard</a>.

 <Warning>
 **Keep your API key secure.** Never expose it in client-side code or public repositories. Use environment variables and server-side requests only.
@@ -0,0 +1,5 @@
+---
+title: "Preview Dream Scope"
+description: "A no-write preview of the scope Dream synthesis would analyze for a project."
+openapi: "post /api/v1/orgs/organizations/{org_id}/projects/{project_id}/dream/preview/"
+---
@@ -0,0 +1,5 @@
+---
+title: "Get Dream Activity"
+description: "Supersede/merge activity feed for a project, newest first (keyset-paginated)."
+openapi: "get /api/v1/orgs/organizations/{org_id}/projects/{project_id}/dream/activity/"
+---
@@ -0,0 +1,5 @@
+---
+title: "Get Dream Configuration"
+description: "Retrieve a project's Dream (memory synthesis) configuration and plan entitlements."
+openapi: "get /api/v1/orgs/organizations/{org_id}/projects/{project_id}/dream/config/"
+---
@@ -0,0 +1,5 @@
+---
+title: "Get a Synthesized Memory's Sources"
+description: "The source memories a synthesized (pattern) memory was distilled from."
+openapi: "get /api/v1/orgs/organizations/{org_id}/projects/{project_id}/dream/memory/{memory_id}/sources/"
+---
@@ -0,0 +1,5 @@
+---
+title: "Get Memories in a Dream Run"
+description: "Keyset page of the synthesized memories within a single synthesis run."
+openapi: "get /api/v1/orgs/organizations/{org_id}/projects/{project_id}/dream/runs/{run_id}/memories/"
+---
@@ -0,0 +1,5 @@
+---
+title: "Get Dream Synthesis Runs"
+description: "Synthesis activity grouped per run, newest first (keyset-paginated)."
+openapi: "get /api/v1/orgs/organizations/{org_id}/projects/{project_id}/dream/runs/"
+---
@@ -0,0 +1,5 @@
+---
+title: "Get Dream Stats"
+description: "Lifecycle and synthesis counts for a project, plus reflection freshness."
+openapi: "get /api/v1/orgs/organizations/{org_id}/projects/{project_id}/dream/stats/"
+---
@@ -0,0 +1,5 @@
+---
+title: "Update Dream Configuration"
+description: "Enable or disable Synthesis (reflection) for a project, or change the reflection mode."
+openapi: "patch /api/v1/orgs/organizations/{org_id}/projects/{project_id}/dream/config/"
+---
@@ -1,4 +1,6 @@
 ---
-title: 'Delete Memory'
+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}/
---
+---
@@ -1,5 +1,7 @@
 ---
-title: 'Update Memory'
+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}/
 ---

@@ -1,5 +1,7 @@
 ---
-title: 'Add Member'
+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/
 ---

@@ -1,4 +1,6 @@
 ---
-title: 'Get Members'
+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/
---
+---
@@ -95,6 +95,12 @@ client.project.update(
    custom_instructions="..."
 )

+# Separate extraction instructions for agent-scoped memories
+# (see /platform/features/custom-instructions)
+client.project.update(
+    agent_custom_instructions="..."
+)
+
 # Use the input language for memory storage and retrieval
 client.project.update(multilingual=True)

@@ -1,5 +1,7 @@
 ---
-title: 'Add Member'
+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/
 ---

@@ -1,4 +1,6 @@
 ---
-title: 'Get Members'
+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/
---
+---
@@ -4,6 +4,55 @@ description: "Highlights of major Mem0 product launches, headline features, inte
 mode: "wide"
 ---

+<Update label="2026-08-24" description="DeepSeek Harness plugin">
+
+**DeepSeek Harness: Mem0 as a Native Cordis Plugin**
+
+The DeepSeek Harness agent forgets everything between sessions. [`@mem0/deepseek-plugin`](https://www.npmjs.com/package/@mem0/deepseek-plugin) gives it two Mem0-backed tools, so recall and writes persist across runs against the same memory bank you already use from Claude Code, Codex, and every other connected agent.
+
+- **Two agent-callable tools:** `search_memory` recalls facts relevant to a query, `add_memory` stores a fact for future sessions. Both accept per-call `userId` / `agentId` / `runId` scope overrides.
+- **Native Cordis lifecycle:** The plugin declares `inject = ['tools']` so it waits for the harness tool registry, then registers through `ctx.tools.register()`. Unmounting the plugin removes the tools automatically.
+- **Managed backend, not a memory file:** Server-side extraction, semantic dedup, and conflict resolution, rather than a Markdown file the agent has to maintain itself.
+- **Config:** `userId` is required, `apiKey` defaults to `$MEM0_API_KEY`, and `host` optionally targets a dedicated Mem0 Platform base URL.
+
+See [DeepSeek Harness](/integrations/deepseek-plugin) for setup and [SDK & Tools](/changelog/sdk) for PR links.
+
+<Note>
+  Developer preview. Auto-capture and auto-recall, where memory reaches the context with no explicit tool call, are planned but not yet built.
+</Note>
+
+</Update>
+
+<Update label="2026-08-24" description="Strands Agents integration">
+
+**Strands Agents: Mem0 as a Native MemoryStore**
+
+[`mem0-strands`](https://pypi.org/project/mem0-strands/) plugs Mem0 into AWS's [Strands Agents](https://strandsagents.com/) SDK as a native `MemoryStore`, so recall and writes happen inside the agent loop rather than as tool calls the model has to remember to make.
+
+- **Automatic recall:** The `MemoryManager` drives the store on every turn, searching Mem0 and injecting the results into the prompt with no tool call required.
+- **Server-side extraction:** Because the store implements `add_messages`, enabling extraction routes raw conversation turns straight to Mem0's extraction pipeline, skipping the extra client-side model call needed to distill facts first.
+- **Hosted or self-hosted:** An API key targets the hosted Mem0 Platform; a config dict targets self-hosted Mem0 OSS.
+- **Entity scoping:** Accepts `user_id`, `agent_id`, `run_id`, and `app_id`, with at least one required and invalid combinations rejected at construction rather than on the first write.
+
+See [Strands Agents](/integrations/strands) for setup and [SDK & Tools](/changelog/sdk) for PR links.
+
+</Update>
+
+<Update label="2026-08-13" description="Kimi Code plugin">
+
+**Kimi Code: Mem0 Joins the Editor Plugin Family**
+
+The shared Mem0 editor plugin now covers Kimi Code alongside Claude Code, Cursor, Codex, and Antigravity, running on the same scripts, skills, and memory bank, so context written in one editor is available in the others.
+
+- **Hosted MCP server:** Registers `https://mcp.mem0.ai/mcp/`, authenticated with `MEM0_API_KEY` as a bearer token.
+- **Automatic capture and recall:** SessionStart loads context through the `context-loader` skill, and hooks on prompt submit, file reads, Bash output, stop, and pre-compact capture and inject memory without an explicit tool call.
+- **Guardrails:** Direct `Write` / `Edit` / `MultiEdit` to memory files is blocked, and metadata defaults are enforced on every Mem0 MCP tool call.
+- **Kimi hook adapter:** A shim normalizes Kimi Code's hook contract to the shape the shared scripts already expect, resolving the real project directory and translating the differing prompt, tool-output, and MCP tool-name fields.
+
+See [SDK & Tools](/changelog/sdk) for version details and PR links.
+
+</Update>
+
 <Update label="2026-07-30" description="n8n and Zapier integrations">

 **Workflow Automation: Mem0 Memory in n8n and Zapier**
@@ -7,6 +7,64 @@ mode: "wide"
 <Tabs>
 <Tab title="Python">

+<Update label="2026-09-02" description="v2.0.20">
+
+**Improvements:**
+- **OSS notices:** Notice configuration now comes from a static, cacheable repository file with a bundled disabled fallback and deterministic rollout assignment, instead of calling PostHog's feature-flag evaluation API. This keeps notices fail-safe when the remote config is unavailable and removes the PostHog feature-flag request from notice evaluation ([#7185](https://github.com/mem0ai/mem0/pull/7185))
+- **Vector Stores:** `RedisDBConfig` now uses Pydantic's native `extra="forbid"` handling for unknown fields instead of a custom model validator, preserving strict validation while returning standard Pydantic errors ([#7089](https://github.com/mem0ai/mem0/pull/7089))
+
+</Update>
+
+<Update label="2026-08-24" description="v2.0.19">
+
+**Bug Fixes:**
+- **Embeddings:** `HuggingFaceEmbedding` now falls back to the `HUGGINGFACE_API_KEY` env var, then a placeholder key, when `huggingface_base_url` is set and no `api_key` is configured. The OpenAI-compatible client used to talk to TEI endpoints raises at construction when no key resolves at all, so a TEI deployment that doesn't require a real key previously failed to initialize ([#6947](https://github.com/mem0ai/mem0/pull/6947))
+- **Core:** `remove_code_blocks()` now accepts list-shaped content (a sequence of `{"text": ...}` blocks, as some agent frameworks pass) by joining each block's text before stripping code fences, instead of raising `AttributeError` from calling `.strip()` on a list ([#6947](https://github.com/mem0ai/mem0/pull/6947))
+- **Core:** `create_procedural_memory()` (`Memory` and `AsyncMemory`) now raises a clear `ValueError` when the LLM returns no content for the summary, instead of continuing with empty content that surfaced as a confusing error further down the call ([#6947](https://github.com/mem0ai/mem0/pull/6947))
+- **Proxy:** `mem0.proxy` no longer auto-installs `litellm` via a `pip install` subprocess when the import fails; it now raises `ImportError` with instructions to install it yourself. The auto-install could hang or fail silently in restricted environments and ran an unreviewed install on the caller's behalf ([#6947](https://github.com/mem0ai/mem0/pull/6947))
+- **Client:** `get_all()` (sync and async) now sends `page` and `page_size` as independent query params instead of requiring both to be set before either was sent. Passing only `page_size` without `page` previously had it silently dropped, so results came back at the server's default page size ([#6900](https://github.com/mem0ai/mem0/pull/6900))
+- **LLMs:** Add `provider_override` to `AWSBedrockConfig`, an explicit provider name (for example `"anthropic"`) for when `model` is an application inference profile ARN whose opaque ID has no provider substring for `extract_provider()` to detect. Without it, those ARNs raised `ValueError: Unable to determine provider` ([#6899](https://github.com/mem0ai/mem0/pull/6899))
+- **LLMs:** `VllmConfig` now falls back to the `VLLM_BASE_URL` env var when `vllm_base_url` isn't passed explicitly. The default was filled in before the env var was ever checked, so `VLLM_BASE_URL` was silently ignored ([#6897](https://github.com/mem0ai/mem0/pull/6897))
+
+</Update>
+
+<Update label="2026-08-11" description="v2.0.18">
+
+**Bug Fixes:**
+- **Core:** Percent-escape `%`, `&`, and `=` in `user_id`, `agent_id`, and `run_id` when building the session scope key for the recent-conversation buffer, so the key stays unambiguous for ids containing those characters. Ordinary ids keep their existing key; an id already containing `%`, `&`, or `=` maps to a new key, so its buffer starts empty once and refills on the next `add()`. Stored memories are unaffected ([#6892](https://github.com/mem0ai/mem0/pull/6892))
+- **Vector Stores:** Raise `ValueError` when a PGVector `in`/`nin` filter value is not a list. A string value was previously iterated character by character into the generated `= ANY(...)` array (so `{"user_id": {"in": "alice"}}` matched `a`, `l`, `i`, `c`, `e`), and a non-iterable value raised a bare `TypeError` from deep inside filter building ([#6879](https://github.com/mem0ai/mem0/pull/6879))
+- **Vector Stores:** Reject `index_accuracy=0` in the Oracle AI Vector Search config. The range check sat behind a truthiness test, so `0` skipped validation entirely and was passed through to `WITH TARGET ACCURACY 0` instead of raising ([#6848](https://github.com/mem0ai/mem0/pull/6848))
+- **Vector Stores:** Close the Oracle connection or pool that Mem0 opened when initialization fails. A client version check, a database version check, or a `create_col()` error previously propagated with the connection still open, leaking it for the life of the process. A caller-supplied `client` is left untouched ([#6839](https://github.com/mem0ai/mem0/pull/6839))
+
+</Update>
+
+<Update label="2026-08-05" description="v2.0.17">
+
+**New Features:**
+- **Client:** Add `agent_custom_instructions` to `project.update()`/`update_project()` (sync and async) and to the `ProjectUpdateOptions` and `AddMemoryOptions` typed models. It sets a second extraction instruction set that applies only to agent-scoped memories: an add passing `agent_id` without `user_id` uses it, one passing both splits by attribution, and while it is unset `custom_instructions` continues to apply to every memory ([#6809](https://github.com/mem0ai/mem0/pull/6809))
+
+</Update>
+
+<Update label="2026-08-04" description="v2.0.16">
+
+**New Features:**
+- **Client:** Add `reference_date`, `latest_only`, and `keyword_search` to `SearchMemoryOptions`, and `latest_only` to `GetAllMemoryOptions`, keeping the Python client's typed options in sync with the Platform API and the CLIs ([#6696](https://github.com/mem0ai/mem0/pull/6696))
+
+**Bug Fixes:**
+- **Core:** Stop `add()` metadata from setting a memory's identity scope. `_build_filters_and_metadata()` now strips `user_id`, `agent_id`, `run_id`, and `actor_id` from caller-supplied `metadata` before building the creation template, so metadata can no longer place a memory into a scope that was never passed through the entity params ([#6656](https://github.com/mem0ai/mem0/pull/6656))
+- **Vector Stores:** Validate Upstash filter keys and values in `search()`, `keyword_search()`, and `list()`. Filter keys must match a safe identifier pattern, values must be `str`/`int`/`float`/`bool`, and string values containing a double quote or backslash are now rejected instead of being interpolated unescaped into the generated query string ([#5981](https://github.com/mem0ai/mem0/pull/5981))
+- **Embeddings:** `FastEmbedEmbedding.embed()` now converts its result with `.tolist()` before returning, so callers get a plain `List[float]` instead of a numpy array ([#6770](https://github.com/mem0ai/mem0/pull/6770))
+- **Embeddings:** `HuggingFaceEmbedding` now passes `api_key` to the OpenAI-compatible client when `huggingface_base_url` is set. The configured key was previously dropped, so the client fell back to `OPENAI_API_KEY` from the environment or raised `OpenAIError` at construction when that was unset ([#6770](https://github.com/mem0ai/mem0/pull/6770))
+- **Embeddings:** Replace Ollama's interactive `pip install` prompt on import with a plain `ImportError`. Importing `mem0.embeddings.ollama` without the `ollama` package previously blocked on stdin and then called `sys.exit(1)`, killing the host process instead of raising ([#6770](https://github.com/mem0ai/mem0/pull/6770))
+- **Core:** `remove_code_blocks()` now returns an empty string for `None` input instead of raising `AttributeError` ([#6770](https://github.com/mem0ai/mem0/pull/6770))
+- **Core:** `parse_vision_messages()` now chains the original exception (`raise ... from e`) when an image download fails, so the root cause is preserved in the traceback ([#6770](https://github.com/mem0ai/mem0/pull/6770))
+- **Core:** `process_telemetry_filters(None)` now returns `([], {})`, matching the two-value tuple every caller unpacks, instead of `{}` ([#6770](https://github.com/mem0ai/mem0/pull/6770))
+- **Core:** `LlmFactory.create()` no longer mutates the caller's config dict in place via `.update(kwargs)`; the merge now builds a new dict ([#6770](https://github.com/mem0ai/mem0/pull/6770))
+- **Rerankers:** The Cohere, HuggingFace, SentenceTransformer, and Zero Entropy rerankers' failure-fallback path no longer mutates the caller's document dicts in place when stamping `rerank_score`; it now falls back on copies ([#6770](https://github.com/mem0ai/mem0/pull/6770))
+- **Vector Stores:** Remove `logging.basicConfig()` calls from the MongoDB and Vertex AI Vector Search providers, so selecting either provider no longer reconfigures the host application's root logger as a side effect ([#6770](https://github.com/mem0ai/mem0/pull/6770))
+
+</Update>
+
 <Update label="2026-08-01" description="v2.0.15">

 **Bug Fixes:**
@@ -1169,6 +1227,64 @@ See the [OSS v2 to v3 migration guide](https://docs.mem0.ai/migration/oss-v2-to-

 <Tab title="TypeScript">

+<Update label="2026-09-02" description="v3.1.8">
+
+**Improvements:**
+- **OSS notices:** Notice configuration now comes from a static, cacheable repository file with a bundled disabled fallback and deterministic rollout assignment, instead of calling PostHog's feature-flag evaluation API. This keeps notices fail-safe when the remote config is unavailable and removes the PostHog feature-flag request from notice evaluation ([#7185](https://github.com/mem0ai/mem0/pull/7185))
+
+</Update>
+
+<Update label="2026-08-24" description="v3.1.7">
+
+**Bug Fixes:**
+- **Vector Stores:** Redis and Valkey `search()` / `get()` / `list()` now preserve `agent_id`, `run_id`, and `user_id` as snake_case in the returned payload. The shared payload formatter camelCased every key including those three identity fields, so entity ids came back as `agentId` / `runId` / `userId`, inconsistent with every other vector store ([#6902](https://github.com/mem0ai/mem0/pull/6902))
+- **Memory (OSS):** Embedding-cache lookups now use `Object.prototype.hasOwnProperty.call()` instead of the `in` operator or a falsy `||` check. Memory text matching an inherited `Object.prototype` property name (`constructor`, `toString`, and similar) previously short-circuited the lookup and resolved to that inherited value instead of computing a real embedding, silently corrupting the stored vector ([#6903](https://github.com/mem0ai/mem0/pull/6903))
+- **Client:** `getAll()` now sends `page` and `pageSize` as independent query params instead of requiring both to be set before either was sent. Passing only `pageSize` without `page` previously had it silently dropped, so results came back at the server's default page size ([#6900](https://github.com/mem0ai/mem0/pull/6900))
+- **LLMs:** Add `providerOverride` to the Bedrock `LLMConfig`, an explicit provider name for when `model` is an application inference profile ARN whose opaque ID has no provider substring for `extractProvider()` to detect. Without it, those ARNs threw before the provider-specific settings could be initialized ([#6899](https://github.com/mem0ai/mem0/pull/6899))
+
+**Security:**
+- **Dependencies:** Resolved 17 additional high and critical severity dependency vulnerabilities across 5 pnpm workspaces (`mem0-ts`, `vercel-ai-sdk`, `n8n-nodes-mem0`, `zapier-mem0`, `server/dashboard`) via `pnpm.overrides` and a `tar` patch ([#7032](https://github.com/mem0ai/mem0/pull/7032))
+
+</Update>
+
+<Update label="2026-08-11" description="v3.1.6">
+
+**New Features:**
+- **Vector Stores:** Add an Oracle AI Vector Search vector store (`oracledb`) to the OSS SDK, with pooled connections, `HNSW`/`IVF` indexes, an optional `indexAccuracy` target, JSON payload filtering, and six selectable distance metrics ([#6690](https://github.com/mem0ai/mem0/pull/6690))
+
+**Bug Fixes:**
+- **Core:** Percent-escape `%`, `&`, and `=` in `userId`, `agentId`, and `runId` when building the session scope key for the recent-conversation buffer, so the key stays unambiguous for ids containing those characters. Ordinary ids keep their existing key; an id already containing `%`, `&`, or `=` maps to a new key, so its buffer starts empty once and refills on the next `add()`. Stored memories are unaffected ([#6892](https://github.com/mem0ai/mem0/pull/6892))
+- **Vector Stores:** Close and clear the Oracle pool that Mem0 created when `initialize()` fails, and reset the cached init promise so the next call retries instead of replaying the rejection forever. A caller-supplied `client` is left untouched ([#6839](https://github.com/mem0ai/mem0/pull/6839))
+- **Vector Stores:** Validate Oracle `insert()` batches before touching the database: `ids` and `payloads` must match `vectors` in length, so a short array can no longer write rows with `undefined` ids or silently drop payloads ([#6839](https://github.com/mem0ai/mem0/pull/6839))
+
+**Improvements:**
+- **Vector Stores:** Cut Oracle round trips. `insert()` now sends the whole batch through one `executeMany()` with explicit `bindDefs` instead of one `INSERT` per vector, `list()` reads the total from a `COUNT(*) OVER ()` window in the same statement instead of issuing a second count query, and an unfiltered `search()` adds the `VECTOR_INDEX_TRANSFORM` hint so the vector index is used ([#6835](https://github.com/mem0ai/mem0/pull/6835))
+
+**Security:**
+- **Dependencies:** Patched 8 high and 18 medium severity dependency vulnerabilities across the pnpm workspace via `pnpm.overrides` (`undici`, `brace-expansion`, `ip-address`) ([#6847](https://github.com/mem0ai/mem0/pull/6847))
+
+</Update>
+
+<Update label="2026-08-05" description="v3.1.5">
+
+**New Features:**
+- **Client:** Add `agentCustomInstructions` to `PromptUpdatePayload`, `AddMemoryOptions`, and `ProjectResponse`. It sets a second extraction instruction set that applies only to agent-scoped memories: an add passing `agentId` without `userId` uses it, one passing both splits by attribution, and while it is unset `customInstructions` continues to apply to every memory ([#6809](https://github.com/mem0ai/mem0/pull/6809))
+
+</Update>
+
+<Update label="2026-08-04" description="v3.1.4">
+
+**New Features:**
+- **Client:** Add `referenceDate` and `keywordSearch` to `SearchMemoryOptions`, keeping the TypeScript client's typed search options in sync with the Platform API ([#6696](https://github.com/mem0ai/mem0/pull/6696))
+
+**Bug Fixes:**
+- **Client:** Take the `/v1/ping/` identity/telemetry call off the request critical path. Every method previously did `if (this.telemetryId === "") await this.ping();`, blocking the first call on each client instance on a network round trip; the ping now resolves in the background through a shared, credentials-keyed promise cache (FIFO-capped at 50 entries), so concurrent clients on the same host/API key share one ping instead of issuing one each, and a failed ping is not cached so the process can retry ([#6788](https://github.com/mem0ai/mem0/pull/6788))
+- **Client:** `deleteUsers()` now issues its per-entity DELETE requests through the shared `fetch()`-based helper instead of the axios instance, which defaulted to `keepAlive: false`. Deleting every user, agent, app, and run now reuses pooled connections instead of paying a fresh TCP/TLS handshake per entity ([#6788](https://github.com/mem0ai/mem0/pull/6788))
+- **Memory (OSS):** Stop `add()` metadata from setting or overwriting a memory's identity scope. Metadata now runs through the same `stripIdentityKeys()` helper as `update()`, so `user_id`/`agent_id`/`run_id` (snake_case or camelCase) and `actor_id` passed in metadata can no longer place a memory into a scope the caller didn't request through `userId`/`agentId`/`runId`/`filters` ([#6377](https://github.com/mem0ai/mem0/pull/6377))
+- **Vector Stores:** Fix Redis `search()` and `list()` building an invalid, empty RediSearch filter expression when `filters` was an empty object or contained only null/undefined values. The shared `buildRedisFilterExpr()` helper now falls back to `"*"` (match all) instead of joining zero conditions into `""` ([#6014](https://github.com/mem0ai/mem0/pull/6014))
+
+</Update>
+
 <Update label="2026-08-01" description="v3.1.3">

 **New Features:**
@@ -1427,7 +1543,7 @@ The largest provider release for the TypeScript OSS SDK so far: 17 new vector st
 **Improvements:**
 - **Telemetry:** Sample OSS hot-path events at 10% to reduce PostHog event volume ([#4771](https://github.com/mem0ai/mem0/pull/4771))

-See the [TypeScript SDK migration guide](https://docs.mem0.ai/migration/ts-v2-to-v3) for upgrade instructions.
+See the [OSS v2 to v3 migration guide](https://docs.mem0.ai/migration/oss-v2-to-v3) for upgrade instructions.

 </Update>

@@ -1748,6 +1864,37 @@ See the [TypeScript SDK migration guide](https://docs.mem0.ai/migration/ts-v2-to

 <Tab title="CLI">

+<Update label="2026-08-24" description="Python v0.2.12 / Node v0.2.13">
+
+**New Features:**
+- **`version`:** New `mem0 version` subcommand, alongside the existing `--version` flag, so scripts and agent harnesses can read the CLI version as a regular subcommand instead of a root-level flag (Python and Node [#6907](https://github.com/mem0ai/mem0/pull/6907))
+- **`add`:** New `--agent-custom-instructions` flag, threaded through to `agent_custom_instructions` on the `/v3/memories/add/` payload: a second extraction instruction set that applies only to agent-scoped memories, matching the SDKs' `agentCustomInstructions` / `agent_custom_instructions` support (Python and Node [#6910](https://github.com/mem0ai/mem0/pull/6910))
+
+**Documentation:**
+- **`search --filter`:** The `--filter` help text and `docs/platform/cli.mdx` now spell out the JSON shape (`{"AND": [...]}` / `{"OR": [...]}`) with a concrete example instead of just calling it "an advanced filter expression," and a matching example command was added to both the CLI help text and the docs page (Python and Node [#6907](https://github.com/mem0ai/mem0/pull/6907))
+
+</Update>
+
+<Update label="2026-08-04" description="Python v0.2.11 / Node v0.2.12">
+
+**New Features:**
+- **`add`:** New `--custom-instructions`, `--custom-categories`, `--structured-data-schema`, and `--timestamp` flags, matching the Platform `/v3/memories/add/` payload fields (Python and Node [#6696](https://github.com/mem0ai/mem0/pull/6696))
+- **`search`:** New `--show-expired`, `--reference-date`, and `--latest-only` flags, forwarded to `/v3/memories/search/` as `show_expired`, `reference_date`, and `latest_only` (Python and Node [#6696](https://github.com/mem0ai/mem0/pull/6696))
+- **`list`:** New `--show-expired` and `--latest-only` flags (Python and Node [#6696](https://github.com/mem0ai/mem0/pull/6696))
+- **`update`:** New `--expires` and `--timestamp` flags (Python and Node [#6696](https://github.com/mem0ai/mem0/pull/6696))
+- **`delete`:** New `--delete-linked` flag to also delete memories linked to the target memory (Python and Node [#6696](https://github.com/mem0ai/mem0/pull/6696))
+
+**Bug Fixes:**
+- **`help --json`:** Emit JSON automatically under agent mode (`--agent`), not only when `--json` is passed explicitly. Python checks `is_agent_mode()` and now renders through `console.print_json()` instead of a plain `console.print(json.dumps(...))` call; Node also checks the root `--agent` flag in addition to the subcommand's `--json` flag (Python and Node [#6773](https://github.com/mem0ai/mem0/pull/6773))
+
+**Changes:**
+- **`add`:** `--categories` is no longer forwarded on `add`. The `/v3/memories/add/` payload has no `categories` field (only `custom_categories`), so the CLI was sending a key the endpoint does not accept. Passing the flag now exits 1 with a message pointing to `--custom-categories` (Python and Node [#6696](https://github.com/mem0ai/mem0/pull/6696))
+
+**Security:**
+- **Dependencies (Node):** Tighten the `postcss` pnpm override from `<8.5.10 → >=8.5.10` to `<8.5.18 → >=8.5.18 <9.0.0`, closing a newer CVE range the previous floor didn't cover, as part of a wider dependency patch sweep across the pnpm workspaces ([#6639](https://github.com/mem0ai/mem0/pull/6639))
+
+</Update>
+
 <Update label="2026-07-13" description="Python v0.2.10 / Node v0.2.11">

 **Bug Fixes:**
@@ -1913,6 +2060,58 @@ A full-featured command-line interface for Mem0, available in both Python and No
 <Tabs>
 <Tab title="Mem0 Plugin">

+<Update label="2026-09-08" description="Shared agent plugin runtime">
+
+**Changed:**
+- Consolidated the coding-agent integrations into `integrations/agent-plugin-core/`: one Python runtime, one TypeScript utility library, and six canonical Python-plugin skill templates. Native adapters retain each host's event contracts and capabilities.
+- Python plugins ship generated, self-contained `core/` and `skills/` directories. Builds validate portable schemas and skills, parse native JSON, and reject generated-file drift, missing files, stale generated files, and symlinks. TypeScript packages bundle the shared source into their distributable JavaScript and verify their entry points.
+- Replaced the old `integrations/mem0-plugin/` layout with native host directories and one portable `integrations/mem0-agent-plugin/` package. Updated marketplace paths, installation guides, and integration-skill links. OpenCode now lives in `integrations/opencode-plugin/`.
+- Native Python plugins expose one local, read-only `search_memories` MCP tool and six skills: search, remember, forget, status, pause, and resume. The shared search tool accepts optional `run_id` with every scope (`repo`, `dir`, and `mine`) to recall memories from a specific coding-agent session. Omitting it searches across sessions. This local tool is separate from the hosted Mem0 MCP server's tool set.
+
+**Fixes:**
+- Hooks, controls, MCP servers, and detached workers use the same host-specific data directory. Detached workers retain the host identity and telemetry source; `--plugin-data-dir` reaches the shared resolver.
+- Session-end workers flush the conversation already captured by hooks. Repeated and concurrent response hooks no longer duplicate an answer, while identical answers after separate prompts are preserved.
+- Shared prompts and responses are redacted without the previous 6,000-character cutoff. Python extraction splits oversized messages without dropping text to enforce each request's input budget. Flush event selection and claims share one write transaction, delayed handoffs are replaced atomically, and permanent HTTP polling errors fail promptly.
+- Extraction instructions refer to the current coding agent. Python redaction covers JSON-shaped credentials; both telemetry runtimes recursively remove sensitive keys, including keys inside nested lists.
+- 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.
+- 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)
+
+</Update>
+
+<Update label="2026-08-24" description="mem0-plugin v0.2.15">
+
+**Fixes:**
+- **Search:** A failed search request now prints `[mem0] search request failed: <error>` to stderr before returning no results. `search_memories()` swallowed every exception and returned `[]`, so an expired API key, a network failure, or a 500 from the backend was indistinguishable from a genuine "nothing stored yet" and the agent carried on with no context and no warning. Shared by Claude Code, Cursor, Codex, Antigravity, and Kimi ([#6898](https://github.com/mem0ai/mem0/pull/6898))
+- **Cursor:** `mcpServers` in `.cursor-plugin/plugin.json` now points at `./.cursor-mcp.json` instead of `.cursor-mcp.json`. The un-prefixed path resolved inconsistently depending on Cursor's working directory when it loaded the plugin ([#6948](https://github.com/mem0ai/mem0/pull/6948))
+- **Codex:** `install_codex_hooks.py` now prints all six registered events (`PreToolUse, SessionStart, UserPromptSubmit, PostToolUse, Stop, PreCompact`) after installing, instead of a stale four-event list left over from an earlier version of the installer. The README's hook table is corrected to match, documenting the three `PreToolUse` handlers and two `PostToolUse` handlers that were previously undocumented ([#6948](https://github.com/mem0ai/mem0/pull/6948))
+
+**Documentation:**
+- New [Claude.ai](/integrations/claude-ai) integration page ([#6948](https://github.com/mem0ai/mem0/pull/6948))
+
+<Note>
+  The Claude Code, Cursor, and Codex per-editor manifests (`.claude-plugin/plugin.json`, `.cursor-plugin/plugin.json`, `.codex-plugin/plugin.json`) had drifted to `0.2.13` while the `.claude-plugin/marketplace.json` and `.cursor-plugin/marketplace.json` listings had already moved to `0.2.14`, so installs were pinned one release behind what the marketplace advertised. This release realigns every manifest and marketplace listing to `0.2.15`.
+</Note>
+
+</Update>
+
+<Update label="2026-08-04" description="mem0-plugin v0.2.14">
+
+**Fixes:**
+- **Settings loading no longer crashes on a malformed `settings.json`:** If `~/.mem0/settings.json` parsed as valid JSON but wasn't an object (a list or a bare string, for example), `load_settings()` called `.items()` on it and raised. `resolve_config()` guards only against `ImportError`, so the failure escaped into every hook that resolves identity, not just setup. Settings loading now keeps the defaults instead. Setup also stopped claiming it "Created" the settings file when one already existed; it only prints that when it actually wrote one. Shared across Claude Code, Cursor, Codex, and Antigravity.
+
+**New Features:**
+- **Unknown settings key warning:** Setup now flags keys in `settings.json` that the plugin doesn't recognize, so a typo'd setting no longer fails silently.
+
+**Removed:**
+- Dropped the `mem0_doc_search` "openmemory" section now that OpenMemory has been removed from the docs site, so the skill no longer links to pages that don't exist.
+
+</Update>
+
 <Update label="2026-07-14" description="mem0-plugin v0.2.13">

 **Fixes:**
@@ -2170,8 +2369,124 @@ Initial release of the Mem0 plugin for Claude Code and Cursor, followed by Codex

 </Tab>

+<Tab title="Claude Code">
+
+<Update label="Unreleased" description="Sidekick availability">
+
+Sidekick is now available only in Claude Code, with Sonnet, worktree isolation, and parent memories.
+
+</Update>
+
+<Update label="2026-09-08" description="Claude Code plugin v0.3.1">
+
+**Changed:**
+- Extracted hook orchestration and memory behavior into the shared Python core; Claude transcript parsing remains in its native adapter. The installed package contains the generated runtime rather than importing files outside its plugin directory.
+- Preserves the public `mem0` name, hook declarations, MCP launch configuration, user configuration, and `mem0:sidekick` worktree behavior. The manifest and marketplace version advance from `0.3.0` to `0.3.1` so installations can identify the update.
+
+**Fixes:**
+- Session-end extraction no longer appends a final answer already captured from the transcript while an earlier extraction was running.
+- Existing repository memories remain searchable after the shared-ID change; explicit shared-memory deletion also covers the legacy ID. Background workers and control skills consistently use Claude's data directory.
+- Receives the shared JSON-secret redaction and nested telemetry filtering fixes. The local search tool exposes query, result count, category, scope, and optional `run_id` for session-specific recall across all scopes.
+
+[#7203](https://github.com/mem0ai/mem0/pull/7203)
+
+</Update>
+
+</Tab>
+
+<Tab title="Cursor">
+
+<Update label="Unreleased" description="Sidekick availability">
+
+Removes Sidekick and its start/stop hooks. Memory capture, search, and six skills remain available.
+
+</Update>
+
+<Update label="2026-09-08" description="Cursor plugin v0.3.1">
+
+**Changed:**
+- Moves from the legacy shared editor-plugin directory to a native `integrations/cursor-plugin/` package with generated Python core and skills, Cursor variables, local MCP configuration, and a native Sidekick.
+- Translates Cursor conversation/workspace fields, response and summary fields, tool outcomes, and subagent lifecycle events into the shared runtime. The Sidekick searches memory itself because Cursor's subagent-start response cannot inject parent context.
+
+**Fixes:**
+- Adapter errors are logged and exit successfully so a memory failure does not terminate the host hook. Sidekick telemetry reports zero parent-injected context because this host uses self-search.
+- Repeated response events and Stop/session-end capture do not duplicate the same answer.
+- Receives shared data-directory handling, background-worker identity, redaction, and legacy-memory retrieval fixes.
+
+[#7203](https://github.com/mem0ai/mem0/pull/7203)
+
+</Update>
+
+</Tab>
+
+<Tab title="Codex">
+
+<Update label="Unreleased" description="Sidekick availability">
+
+Renames shared tracking to use subagent terminology. Native subagent memory support remains available.
+
+</Update>
+
+<Update label="2026-09-08" description="Codex plugin v0.3.1">
+
+**Changed:**
+- Moves from the legacy shared editor-plugin directory to a native `integrations/codex-plugin/` package, with generated Python core and six skills, a local search MCP server, and native lifecycle hooks.
+- Resolves MCP repository searches from Codex workspace metadata when supplied. Control skills, hooks, and workers use the same plugin data directory.
+- Native subagent start/stop hooks supply parent-retrieved memory context and record completions for every native subagent. Named custom agents remain project/user configuration; the plugin does not distribute a named Codex Sidekick.
+
+**Fixes:**
+- Receives shared credential redaction, legacy-memory retrieval, background-worker identity, and duplicate-response fixes. Codex tool outcomes use available structured failure indicators; missing outcome information is recorded as unknown.
+
+[#7203](https://github.com/mem0ai/mem0/pull/7203)
+
+</Update>
+
+</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="2026-09-08" description="OpenCode plugin v0.3.0">
+
+**Changed:**
+- Moved the source from `integrations/mem0-plugin/.opencode-plugin/` to `integrations/opencode-plugin/`, retaining the `@mem0/opencode-plugin` package name and native OpenCode hooks.
+- Reuses shared conversation preparation, redaction, scoping, and telemetry. Builds a self-contained Bun/ESM `dist/index.js` and publishes its TypeScript entry declaration.
+- Global memory tool scope requires the user to enable it in plugin settings first; empty and wildcard identities are rejected.
+- Retains the seven commands for context loading, search, remember, forget, scope, status, and tour; bundled skills continue loading through OpenCode's native configuration.
+
+**Removed:**
+- Removed auto-Dream consolidation, its gates and state handling, and the Dream and pin skills/commands. Existing configurations and workflows that use these features must be updated.
+
+**Builds:**
+- Updated build and publish paths for the relocated source directory; the existing release tag prefix and publishing workflow filename are unchanged.
+
+[#7203](https://github.com/mem0ai/mem0/pull/7203)
+
+</Update>
+
 <Update label="2026-07-22" description="OpenCode plugin v0.2.2">

 **Fixes:**
@@ -2252,6 +2567,48 @@ Initial release of the Mem0 plugin for Claude Code and Cursor, followed by Codex

 <Tab title="Antigravity">

+<Update label="Unreleased" description="Sidekick availability">
+
+Removes Sidekick. Memory capture, search, and six skills remain available.
+
+</Update>
+
+<Update label="2026-09-08" description="Antigravity plugin v0.3.1">
+
+**Changed:**
+- Ships a native package with generated Python core and six skills, a local search MCP server, a Sidekick declaration, and an adapter for `PreInvocation`, `PostToolUse`, and `Stop`.
+- Normalizes conversation IDs, transcript paths, workspace paths, tool calls, and errors. The adapter captures all completed user/assistant transcript messages incrementally, including later-turn intent, without replaying earlier messages.
+- Sidekick searches Mem0 itself; this plugin does not provide Claude Code's worktree isolation.
+
+**Fixes and host limitations:**
+- Accepts `MEM0_CWD` as an explicit workspace fallback when the host omits `workspacePaths`. Skips capture when neither is available, rather than writing under an unrelated directory.
+- Documents global MCP registration with `agy mcp add` when the host does not register the plugin-scoped server. Recall on the initial invocation depends on the host providing a prompt or readable transcript.
+- Receives the shared data-directory, redaction, legacy-memory retrieval, and duplicate-response fixes.
+
+[#7203](https://github.com/mem0ai/mem0/pull/7203)
+
+</Update>
+
+<Update label="2026-08-24" description="Antigravity plugin v0.1.7">
+
+**Fixes:**
+- **Hooks:** The `mem0-ensure-deps` and `mem0-session-start` hook commands in `hooks.json` no longer redirect stderr to `/dev/null`. Both commands still end in `|| true` so a failure can't block startup, but a broken dependency install or session bootstrap now shows up in the Antigravity hook log instead of failing invisibly ([#6948](https://github.com/mem0ai/mem0/pull/6948))
+
+</Update>
+
+<Update label="2026-08-04" description="Antigravity plugin v0.1.6">
+
+**Fixes:**
+- **Settings loading no longer crashes on a malformed `settings.json`:** If `~/.mem0/settings.json` parsed as valid JSON but wasn't an object, every hook that resolves identity previously raised, not just setup. It now keeps the defaults instead, and setup only reports it "Created" the settings file when it actually wrote one.
+
+**Improvements:**
+- **Unknown settings key warning:** Setup now flags keys in `settings.json` it doesn't recognize instead of ignoring them silently.
+
+**Removed:**
+- Dropped the doc-search skill's "openmemory" section now that OpenMemory has been removed from the docs site.
+
+</Update>
+
 <Update label="2026-07-14" description="Antigravity plugin v0.1.5">

 **Fixes:**
@@ -2306,8 +2663,73 @@ Existing memories written by the previous versions are not rewritten. If your me

 </Tab>

+<Tab title="Kimi">
+
+<Update label="Unreleased" description="Sidekick availability">
+
+Removes Sidekick and its start/stop hooks. Memory capture, recall, and six skills remain available.
+
+</Update>
+
+<Update label="2026-09-08" description="Kimi Code plugin v0.3.1">
+
+**Changed:**
+- Ships a self-contained native package with six skills, nine lifecycle hooks, a local search MCP server, and a native Sidekick declaration. Added a dedicated installation and troubleshooting guide.
+- Translates Kimi's session, prompt, tool, compaction, shutdown, and subagent events into the shared Python runtime. Sidekicks receive parent memory context through Kimi's native lifecycle.
+
+**Fixes:**
+- Recovers completed assistant output from Kimi's indexed v2 wire transcript when Stop events omit the response text. Repeated Sidekick invocations receive distinct run identifiers. Stops without a host ID are left uncorrelated when multiple matching runs are active, preserving their responses without assigning them to the wrong run.
+- Keeps controls, hooks, MCP, and detached workers on the same Kimi data directory and preserves host identity in background workers.
+- Receives the shared redaction, legacy-memory retrieval, and duplicate-response fixes.
+
+**Host compatibility:**
+- Documents `CHOKIDAR_USEPOLLING=1` for the observed macOS watcher issue. Filesystem isolation remains Kimi's responsibility.
+
+[#7203](https://github.com/mem0ai/mem0/pull/7203)
+
+</Update>
+
+<Update label="2026-08-24" description="kimi-plugin v0.1.0">
+
+**Initial release** of the Mem0 plugin for Kimi Code, sharing its scripts, skills, and marketplace listing with the Claude Code / Cursor / Codex / Antigravity plugin family ([#6919](https://github.com/mem0ai/mem0/pull/6919))
+
+**New Features:**
+- **MCP server:** Registers the hosted Mem0 MCP server at `https://mcp.mem0.ai/mcp/`, authenticated via the `MEM0_API_KEY` env var as a bearer token.
+- **Lifecycle hooks:** Wires SessionStart (loads context through the `context-loader` skill), UserPromptSubmit, three PreToolUse hooks (blocks direct `Write`/`Edit`/`MultiEdit` to memory files, enforces metadata defaults on Mem0 MCP tool calls, and injects context on file reads), two PostToolUse hooks (post-tool tracking and Bash-output scanning), Stop, and PreCompact.
+- **Hook adapter:** `kimi_hook_shim.sh` normalizes Kimi Code's hook contract to what the shared hook scripts expect: it resolves the real project directory from the payload's `cwd` (Kimi forces the plugin root as the working directory), converts the array-shaped `prompt` field and the `tool_output` / `tool_input.path` field names to the Claude-style shapes the scripts already handle, and translates the plugin-scoped MCP tool name prefix.
+- **Shared policy skill:** Bundles the `/mem0:policy` skill for managing the `## Instructions` and `## Agent Instructions` sections of `mem0.md`.
+
+</Update>
+
+</Tab>
+
 <Tab title="OpenClaw">

+<Update label="2026-09-08" description="openclaw-mem0 v1.1.0">
+
+**Changed:**
+- Reuses shared conversation preparation, redaction, and telemetry while retaining OpenClaw's native memory backend, tools, CLI, and Platform/OSS modes.
+- Continues to publish a self-contained ESM package under `@mem0/openclaw-mem0`; the plugin manifest and package version now agree.
+
+**Removed:**
+- Removed Dream consolidation: automatic scheduling and locking, `openclaw mem0 dream`, Dream configuration, the memory-dream skill, and Dream-state public artifacts. Triage, recall, and memory/entity artifacts remain available. Update configurations or integrations that use the removed Dream surface.
+
+**Fixes:**
+- `openclaw mem0 status` handles an unconfigured installation without crashing and directs users to setup.
+- Removed OpenClaw's separate 2,000-character extraction cutoff. Selected user and assistant messages retain their full redacted text; recent-message selection, earlier summary selection, and noise filtering still apply.
+- Telemetry removes sensitive properties recursively and uses the shared failure-safe delivery implementation.
+
+[#7203](https://github.com/mem0ai/mem0/pull/7203)
+
+</Update>
+
+<Update label="2026-08-24" description="openclaw-mem0 v1.0.16">
+
+**Security:**
+- **Dependencies:** Tightened the `undici` pnpm override from `<6.27.0 → >=6.27.0 <8.0.0` to `<7.29.0 → >=7.29.0 <8.0.0`, closing a newer CVE range the previous floor didn't cover, as part of a wider dependency patch sweep across the pnpm workspaces ([#6847](https://github.com/mem0ai/mem0/pull/6847))
+
+</Update>
+
 <Update label="2026-08-01" description="openclaw-mem0 v1.0.15">

 **Improvements:**
@@ -2571,6 +2993,31 @@ Existing memories written by the previous versions are not rewritten. If your me

 <Tab title="Pi Agent">

+<Update label="2026-09-08" description="Pi Agent plugin v0.3.0">
+
+**Changed:**
+- Reuses shared conversation preparation, memory formatting, project/session/global scope utilities, and telemetry while preserving Pi's native extension API and `@mem0/pi-agent-plugin` package name.
+- Pi loads the built `dist/entry.js` extension instead of executing source TypeScript from an installed package. Builds also publish the library entry point and declarations.
+
+**Removed:**
+- Removed Dream consolidation and pin commands, skills, configuration, types, and exports. The remaining commands are remember, search, forget, tour, scope, and status. Update integrations that import removed APIs or invoke removed commands.
+
+**Fixes:**
+- Global memory tool scope requires the user to select `/mem0-scope global` or configure a global default first. Empty and wildcard identities are rejected.
+- Memory update and delete accept the `mem0:<uuid>` and `[mem0:<uuid>]` citations displayed in tool results, as well as raw IDs.
+- Shared capture preparation filters conversation roles and redacts content; telemetry removes sensitive keys inside nested structures.
+
+[#7203](https://github.com/mem0ai/mem0/pull/7203)
+
+</Update>
+
+<Update label="2026-08-24" description="Pi Agent plugin v0.1.5">
+
+**Security:**
+- **Dependencies:** Tightened the `undici` pnpm override from `<6.27.0 → >=6.27.0 <8.0.0` / `>=8.0.0 <8.5.0 → >=8.5.0` to `<7.29.0 → >=7.29.0 <8.0.0` / `>=8.0.0 <8.9.0 → >=8.9.0 <9.0.0`, closing a newer CVE range the previous floors didn't cover, as part of a wider dependency patch sweep across the pnpm workspaces ([#6847](https://github.com/mem0ai/mem0/pull/6847))
+
+</Update>
+
 <Update label="2026-08-01" description="Pi Agent plugin v0.1.4">

 **Security:**
@@ -2628,8 +3075,88 @@ Existing memories written by the previous versions are not rewritten. If your me

 </Tab>

+<Tab title="Strands">
+
+<Update label="2026-08-25" description="mem0-strands v0.1.1">
+
+**New Features:**
+- **Usage telemetry:** Anonymous usage events (`strands.store.init`, `strands.store.search`, `strands.store.add`, `strands.store.add_messages`) ride the Mem0 SDK's existing PostHog client, unsampled, with no new dependency. Events carry only counts, durations, booleans, and coarse failure kinds: never queries, memory text, message content, entity ids, metadata, or API keys. Opt out with `MEM0_TELEMETRY=false` ([#7110](https://github.com/mem0ai/mem0/pull/7110))
+
+</Update>
+
+<Update label="2026-08-24" description="mem0-strands v0.1.0">
+
+**Initial release** of [`mem0-strands`](https://pypi.org/project/mem0-strands/), a native `MemoryStore` that plugs Mem0 into the [Strands Agents](https://strandsagents.com/) `MemoryManager` ([#7021](https://github.com/mem0ai/mem0/pull/7021))
+
+**New Features:**
+- **Automatic recall and injection:** `Mem0MemoryStore.search()` runs every turn through the `MemoryManager`, so relevant memories are searched and prepended to the prompt with no explicit tool call required.
+- **Server-side extraction:** `add_messages()` renders raw conversation turns to text and hands them to Mem0's own extraction pipeline (`infer=True`), so enabling extraction skips an extra client-side model call to distill facts first.
+- **Verbatim writes:** `add()` stores a single fact exactly as given (`infer=False`), the sink used by the `add_memory` tool or a client-side extractor.
+- **Entity scoping:** Accepts `user_id`, `agent_id`, `run_id`, and `app_id`; at least one is required, and mixing the platform-only `app_id` with a self-hosted `config` raises at construction instead of failing on the first write.
+- **Hosted or self-hosted:** Defaults to the hosted Mem0 Platform via `api_key` (or `$MEM0_API_KEY`), or pass a `config` dict for a self-hosted Mem0 OSS backend.
+- **Non-blocking construction:** The underlying Mem0 client is built lazily on first use inside `asyncio.to_thread`, so API-key validation and OSS embedder/vector-store setup never block the event loop.
+
+<Note>
+  `Mem0MemoryStore` is the automatic-recall store for the `MemoryManager`. For a model-called tool instead, use the [`mem0_memory`](https://github.com/strands-agents/tools) tool from `strands-agents-tools`; both share the same Mem0 backend and namespace. See [Strands Agents](/integrations/strands) for setup.
+</Note>
+
+</Update>
+
+</Tab>
+
+<Tab title="DeepSeek Harness">
+
+<Update label="2026-09-08" description="deepseek-plugin v0.3.0">
+
+**Added:**
+- Automatic recall during `system-prompt/assemble`, using the latest human prompt and avoiding repeated context injection within a session.
+- Automatic capture from the durable `session/event` stream after a completed turn. Interrupted or incomplete turns are not sent through this automatic capture path. `autoRecall` and `autoCapture` default to `true` and can be disabled.
+
+**Changed:**
+- Reuses shared lifecycle, redaction, identity, and telemetry utilities while retaining the explicit `search_memory` and `add_memory` tools and their per-call agent/session scope. Cross-user `userId` overrides now require operator opt-in with `allowUserOverride: true`.
+- Publishes a self-contained ESM artifact under `@mem0/deepseek-plugin`; native Harness services and the Mem0 SDK remain external dependencies. Plugin cleanup remains tied to the native Cordis lifecycle.
+
+**Host compatibility:**
+- Supports the declared Harness runtime dependencies and documents the macOS watcher workaround. The plugin does not bundle a named Sidekick or provide child filesystem isolation.
+
+[#7203](https://github.com/mem0ai/mem0/pull/7203)
+
+</Update>
+
+<Update label="2026-08-25" description="deepseek-plugin v0.1.1">
+
+**New Features:**
+- **Usage telemetry:** Anonymous usage events (`deepseek.plugin.mounted`, `deepseek.tool.search_memory`, `deepseek.tool.add_memory`) are batched to PostHog over native fetch and flushed in the background. Events carry only tool names, durations, counts, and coarse failure kinds: never queries, memory text, filters, or API keys. Opt out with `MEM0_TELEMETRY=false` ([#7110](https://github.com/mem0ai/mem0/pull/7110))
+
+</Update>
+
+<Update label="2026-08-24" description="deepseek-plugin v0.1.0">
+
+**Initial release** of [`deepseek-plugin`](https://www.npmjs.com/package/@mem0/deepseek-plugin), a native DeepSeek Harness (Cordis) plugin that registers Mem0 as two agent-callable tools ([#7027](https://github.com/mem0ai/mem0/pull/7027))
+
+**New Features:**
+- **`search_memory`:** Recalls facts relevant to a query, with an optional `limit` (default 10) and per-call `userId` / `agentId` / `runId` scope overrides.
+- **`add_memory`:** Stores a fact for future sessions, tagged `source: "DEEPSEEK_HARNESS"` for backend attribution; extraction runs asynchronously server-side, so a stored fact may take a moment to become searchable.
+- **Cordis lifecycle:** `apply(ctx, config)` declares `inject = ['tools']`, so the plugin waits for the harness tool registry to exist, and both tools are registered via `ctx.tools.register()` so they auto-unregister when the plugin unmounts.
+- **Config:** `userId` is required; `apiKey` defaults to `$MEM0_API_KEY`; `host` optionally points at a dedicated Mem0 Platform base URL (not a switch to self-hosted Mem0 OSS).
+
+<Note>
+  Developer preview: auto-capture and auto-recall (memory injected into context automatically, without an explicit tool call) are planned but not yet built. The backend's `KNOWN_EVENT_SOURCES` allowlist also needs `"DEEPSEEK_HARNESS"` added before usage surfaces by name in telemetry rather than bucketing into "OTHERS". See [DeepSeek Harness](/integrations/deepseek-plugin) for setup.
+</Note>
+
+</Update>
+
+</Tab>
+
 <Tab title="Vercel AI SDK">

+<Update label="2026-08-24" description="Vercel AI SDK v3.0.2">
+
+**Security:**
+- **Dependencies:** Tightened the `js-yaml` pnpm overrides from `<3.15.0 → >=3.15.0 <4.0.0` / `>=4.0.0 <4.3.0 → >=4.3.0 <5.0.0` to `<3.15.1 → >=3.15.1 <4.0.0` / `>=4.0.0 <4.3.1 → >=4.3.1 <5.0.0`, closing a newer CVE range the previous floors didn't cover, as part of a wider dependency patch sweep across the pnpm workspaces ([#7032](https://github.com/mem0ai/mem0/pull/7032))
+
+</Update>
+
 <Update label="2026-08-01" description="Vercel AI SDK v3.0.1">

 **Security:**
@@ -2722,6 +3249,28 @@ Existing memories written by the previous versions are not rewritten. If your me

 <Tab title="n8n">

+<Update label="2026-08-24" description="n8n-nodes-mem0 v0.1.4">
+
+**Security:**
+- **Dependencies:** Add `js-yaml` pnpm overrides (`<3.15.1 → >=3.15.1 <4.0.0`, `>=4.0.0 <4.3.1 → >=4.3.1 <5.0.0`) to close a HIGH/CRITICAL severity advisory, as part of a wider dependency patch sweep across the pnpm workspaces ([#7032](https://github.com/mem0ai/mem0/pull/7032))
+
+</Update>
+
+<Update label="2026-08-05" description="n8n-nodes-mem0 v0.1.3">
+
+**Changes:**
+- **License changed to MIT:** The published `@mem0/n8n-nodes-mem0` package is now MIT (was Apache-2.0). n8n's Creator Portal requires verified community nodes to be MIT, and the failing license check was the blocker for verification. The rest of the mem0 repo stays Apache-2.0 ([#6804](https://github.com/mem0ai/mem0/pull/6804))
+- **Themed icons:** The node and credential icons now declare `{ light, dark }` variants instead of a single icon, clearing the remaining `icon-prefer-themed-variants` warnings from the Creator Portal scan. No functional changes ([#6804](https://github.com/mem0ai/mem0/pull/6804))
+
+</Update>
+
+<Update label="2026-08-04" description="n8n-nodes-mem0 v0.1.2">
+
+**Changes:**
+- **Package contact:** `author.email` in the published package is now `integrations@mem0.ai` (was `founders@mem0.ai`), so npm and n8n Creator Portal correspondence reaches the integrations team directly. No functional changes ([#6791](https://github.com/mem0ai/mem0/pull/6791))
+
+</Update>
+
 <Update label="2026-07-30" description="n8n-nodes-mem0 v0.1.1">

 **Changes:**
@@ -2751,6 +3300,29 @@ Existing memories written by the previous versions are not rewritten. If your me

 <Tab title="Zapier">

+<Update label="2026-08-24" description="Zapier app v0.1.2">
+
+**Changes:**
+- **Connection label:** The saved connection now shows the account's email (`{{user_email}}`, read from the `/v1/ping/` test response) in the Zap editor instead of a static "Mem0" label, so a user with more than one Mem0 connection can tell them apart ([#6985](https://github.com/mem0ai/mem0/pull/6985))
+- **Action and search copy:** Reworded labels and descriptions to address Zapier's publishing review: **Get Memories** is now **Find Memories by User**, **Search Memories** is now **Find Memories**, and every description now reads as a third-person statement of what the step does ([#6985](https://github.com/mem0ai/mem0/pull/6985))
+- **Attribution:** Add Memory now tags writes with `source: "ZAPIER"` in the request body, so usage is attributed to this integration server-side ([#6985](https://github.com/mem0ai/mem0/pull/6985))
+
+**Removed:**
+- **Client-side telemetry:** Deleted the embedded PostHog telemetry client (`telemetry.ts`) and its call sites in Add Memory, Get Memories, and Search Memories. Usage attribution now happens server-side via the `source: "ZAPIER"` tag above instead of a separate fire-and-forget analytics call from inside the published app ([#6985](https://github.com/mem0ai/mem0/pull/6985))
+
+**Security:**
+- **Dependencies:** Tightened the `undici` pnpm override to `<7.29.0 → >=7.29.0 <8.0.0` / `>=8.0.0 <8.9.0 → >=8.9.0 <9.0.0` and added a `brace-expansion` override ([#6847](https://github.com/mem0ai/mem0/pull/6847)), then added a `js-yaml` override (`<3.15.1 → >=3.15.1 <4.0.0`, `>=4.0.0 <4.3.1 → >=4.3.1 <5.0.0`) closing a further HIGH/CRITICAL severity advisory ([#7032](https://github.com/mem0ai/mem0/pull/7032))
+
+</Update>
+
+<Update label="2026-08-04" description="Zapier app v0.1.1">
+
+**Bug Fixes:**
+- **Add Memory:** **User ID** is now a required field. Mem0 rejects a write that carries no entity ID, so a Zap left blank failed at the API instead of in the editor. If you scoped memories only by Agent ID or Run ID, set a User ID as well ([#6790](https://github.com/mem0ai/mem0/pull/6790))
+- **Deployment:** Add a root `index.js` that re-exports the compiled app from `dist/`, and point `main` at it. Zapier's Lambda wrapper loads `<root>/index.js` and ignores the `package.json` `main` field, so the deployed app could not resolve its entry point ([#6789](https://github.com/mem0ai/mem0/pull/6789))
+
+</Update>
+
 <Update label="2026-07-29" description="Zapier app v0.1.0">

 **Initial release** of the Mem0 Zapier app, built on the Zapier Platform CLI ([#6518](https://github.com/mem0ai/mem0/pull/6518))
@@ -1,6 +1,7 @@
 ---
-title: Configurations
-description: "Reference for Mem0 embedder configuration in Python and TypeScript, covering provider selection, model names, API keys, and dimension settings."
+title: "Embedder Configuration Reference"
+sidebarTitle: "Configurations"
+description: "Reference for embedder configuration options in Mem0, including provider selection and model settings."
 ---


@@ -97,4 +98,4 @@ Here's a comprehensive list of all parameters that can be used across different

 ## Supported Embedding Models

-For detailed information on configuring specific embedders, please visit the [Embedding Models](./models) section. There you'll find information for each supported embedder with provider-specific usage examples and configuration details.
+For detailed information on configuring specific embedders, please visit the [Embedding Models](./overview) section. There you'll find information for each supported embedder with provider-specific usage examples and configuration details.
@@ -1,6 +1,7 @@
 ---
-title: AWS Bedrock
-description: "Use AWS Bedrock as a Mem0 embedder with Amazon Titan or Cohere models, boto3 or aws-sdk auth, and configurable vector output dimensions."
+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."
 ---

 To use AWS Bedrock embedding models, you need the appropriate AWS credentials and permissions. Python uses `boto3`, and TypeScript uses `@aws-sdk/client-bedrock-runtime`.
@@ -1,6 +1,7 @@
 ---
-title: Azure OpenAI
-description: "Set up Azure OpenAI embeddings in Mem0 with deployment name, endpoint, API version, and optional Azure Identity role-based authentication."
+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."
 ---

 To use Azure OpenAI embedding models, set the `EMBEDDING_AZURE_OPENAI_API_KEY`, `EMBEDDING_AZURE_DEPLOYMENT`, `EMBEDDING_AZURE_ENDPOINT` and `EMBEDDING_AZURE_API_VERSION` environment variables. You can obtain the Azure OpenAI API key from the Azure Portal.
@@ -1,6 +1,7 @@
 ---
-title: Google AI
-description: "Use Google AI Gemini embedding models in Mem0 with the GOOGLE_API_KEY, including gemini-embedding-001 and configurable output dimensionality."
+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."
 ---

 To use Google AI embedding models, set the `GOOGLE_API_KEY` environment variables. You can obtain the Gemini API key from [here](https://aistudio.google.com/app/apikey).
@@ -99,6 +99,7 @@ Here are the parameters available for configuring the Hugging Face embedder:
 | `embedding_dims` | Dimensions of the embedding model | `selected_model_dimensions` |
 | `model_kwargs` | Additional arguments for the model | `None` |
 | `huggingface_base_url` | URL to connect to Text Embeddings Inference (TEI) API | `None` |
+| `api_key` | API key for the endpoint; falls back to the `HUGGINGFACE_API_KEY` env var. Only used on the `huggingface_base_url` path | `"hf"` |
 </Tab>
 <Tab title="TypeScript">
 | Parameter | Description | Default Value |
@@ -1,6 +1,7 @@
 ---
-title: LangChain
-description: "Plug LangChain embedding classes into Mem0 to reuse OpenAI, Cohere, HuggingFace, or Ollama embedding models through a single unified interface."
+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."
 ---

 Mem0 supports LangChain as a provider to access a wide range of embedding models. LangChain is a framework for developing applications powered by language models, making it easy to integrate various embedding providers through a consistent interface.
@@ -1,6 +1,7 @@
 ---
-title: "LM Studio"
-description: "Run LM Studio locally as a Mem0 embedder using GGUF models like nomic-embed-text through the built-in OpenAI-compatible HTTP server."
+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.

@@ -1,6 +1,7 @@
 ---
-title: "Ollama"
-description: "Serve Ollama embedding models like nomic-embed-text or mxbai-embed-large inside Mem0 for private, offline vector generation on your own machine."
+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.

@@ -1,6 +1,7 @@
 ---
-title: OpenAI
-description: "Use OpenAI embedding models such as text-embedding-3-small and text-embedding-3-large inside Mem0 with the OPENAI_API_KEY environment variable."
+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."
 ---

 To use OpenAI embedding models, set the `OPENAI_API_KEY` environment variable. You can obtain the OpenAI API key from the [OpenAI Platform](https://platform.openai.com/account/api-keys).
@@ -1,6 +1,7 @@
 ---
-title: Together
-description: "Use Together AI in Mem0 with the multilingual-e5-large-instruct embedding model at 1024 dimensions for cross-language semantic search."
+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."
 ---

 To use Together embedding models, set the `TOGETHER_API_KEY` environment variable. You can obtain the Together API key from the [Together Platform](https://api.together.ai/settings/projects/~current/api-keys).
@@ -1,6 +1,7 @@
 ---
-title: Overview
-description: "Browse Mem0 embedder providers including OpenAI, Azure, Ollama, Hugging Face, Vertex AI, Bedrock, Together, LM Studio, FastEmbed, and LangChain."
+title: "Embedding Providers Overview"
+sidebarTitle: Overview
+description: "Overview of all supported embedding model providers in Mem0, including OpenAI, Azure, Ollama, and more."
 ---

 Mem0 offers support for various embedding models, allowing users to choose the one that best suits their needs.
@@ -15,15 +16,15 @@ See the list of supported embedders below.

 <CardGroup cols={4}>
  <Card title="OpenAI" icon="/images/provider-icons/openai.svg" href="/components/embedders/models/openai"></Card>
-  <Card title="Azure OpenAI" icon="/images/provider-icons/azure-color.svg" href="/components/embedders/models/azure_openai"></Card>
+  <Card title="Azure OpenAI" icon="/images/provider-icons/azure-color.svg" href="/components/embedders/models/azure-openai"></Card>
  <Card title="Ollama" icon="/images/provider-icons/ollama.svg" href="/components/embedders/models/ollama"></Card>
  <Card title="Hugging Face" icon="/images/provider-icons/huggingface.svg" href="/components/embedders/models/huggingface"></Card>
-  <Card title="Google AI" icon="/images/provider-icons/google-color.svg" href="/components/embedders/models/google_AI"></Card>
+  <Card title="Google AI" icon="/images/provider-icons/google-color.svg" href="/components/embedders/models/google-ai"></Card>
  <Card title="Vertex AI" icon="/images/provider-icons/vertexai.svg" href="/components/embedders/models/vertexai"></Card>
  <Card title="Together" icon="/images/provider-icons/together-color.svg" href="/components/embedders/models/together"></Card>
  <Card title="LM Studio" icon="/images/provider-icons/lmstudio.svg" href="/components/embedders/models/lmstudio"></Card>
  <Card title="Langchain" icon="/images/provider-icons/langchain-color.svg" href="/components/embedders/models/langchain"></Card>
-  <Card title="AWS Bedrock" icon="/images/provider-icons/bedrock-color.svg" href="/components/embedders/models/aws_bedrock"></Card>
+  <Card title="AWS Bedrock" icon="/images/provider-icons/bedrock-color.svg" href="/components/embedders/models/aws-bedrock"></Card>
  <Card title="FastEmbed" icon="/images/provider-icons/qdrant.svg" href="/components/embedders/models/fastembed"></Card>
 </CardGroup>

@@ -1,6 +1,7 @@
 ---
-title: Configurations
-description: "Reference for Mem0 LLM configuration keys in Python and TypeScript, covering provider precedence, environment variables, and per-provider parameters."
+title: "LLM Configuration Reference"
+sidebarTitle: "Configurations"
+description: "Reference for LLM configuration options in Mem0 for Python and TypeScript, including value precedence rules."
 ---

 ## How to define configurations?
@@ -133,4 +134,4 @@ Here's a comprehensive list of all parameters that can be used across different

 ## Supported LLMs

-For detailed information on configuring specific LLMs, please visit the [LLMs](./models) section. There you'll find information for each supported LLM with provider-specific usage examples and configuration details.
+For detailed information on configuring specific LLMs, please visit the [LLMs](./overview) section. There you'll find information for each supported LLM with provider-specific usage examples and configuration details.
@@ -1,6 +1,7 @@
 ---
-title: AWS Bedrock
-description: "Run Claude and other Bedrock LLMs in Mem0 using boto3 or the AWS SDK Converse API, with IAM credentials and cross-region inference profile support."
+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."
 ---

 ### Setup
@@ -78,6 +79,38 @@ await memory.add(messages, { userId: 'alice', metadata: { category: 'movies' } }
  The TypeScript provider calls the Bedrock [Converse API](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html), a single uniform interface across the current Bedrock model families. Streaming and `InvokeModel`-only models are not supported yet.
 </Note>

+### Application inference profiles
+
+Bedrock resolves the model family from the model identifier. An application inference profile ARN ends in an opaque ID, so there is nothing to resolve from. Set `provider_override` (Python) / `providerOverride` (TypeScript) when your model is one:
+
+<CodeGroup>
+```python Python
+config = {
+    "llm": {
+        "provider": "aws_bedrock",
+        "config": {
+            "model": "arn:aws:bedrock:us-east-1:123456789012:application-inference-profile/abc123xyz",
+            "provider_override": "anthropic",
+        }
+    }
+}
+```
+
+```typescript TypeScript
+const config = {
+  llm: {
+    provider: 'aws_bedrock',
+    config: {
+      model: 'arn:aws:bedrock:us-east-1:123456789012:application-inference-profile/abc123xyz',
+      providerOverride: 'anthropic',
+    },
+  },
+};
+```
+</CodeGroup>
+
+Without it, initialization raises `Unknown provider in model` (Python: `ValueError`; TypeScript: `Error`). Plain model IDs and cross-region inference profiles such as `us.anthropic.claude-sonnet-4-20250514-v1:0` still resolve automatically and need no override.
+
 ### Config

 All available parameters for the `aws_bedrock` config are present in [Master List of All Params in Config](../config).
@@ -1,6 +1,7 @@
 ---
-title: Azure OpenAI
-description: "Use Azure OpenAI deployments as a Mem0 LLM with API key or Azure Identity auth, including the azure_openai_structured provider for structured output."
+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."
 ---

 <Note> Mem0 Now Supports Azure OpenAI Models in TypeScript SDK </Note>
@@ -3,7 +3,7 @@ title: DeepSeek
 description: "Use DeepSeek chat models such as deepseek-chat as a Mem0 LLM with the DEEPSEEK_API_KEY and an optional DEEPSEEK_API_BASE custom endpoint URL."
 ---

-To use DeepSeek LLM models, you have to set the `DEEPSEEK_API_KEY` environment variable. You can also optionally set `DEEPSEEK_API_BASE` if you need to use a different API endpoint (defaults to "https://api.deepseek.com").
+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

@@ -1,6 +1,7 @@
 ---
-title: Google AI
-description: "Use Google Gemini models like gemini-2.0-flash-001 as a Mem0 LLM through the new google.genai SDK with the GOOGLE_API_KEY environment variable."
+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."
 ---

 To use the Gemini model, set the `GOOGLE_API_KEY` environment variable. You can obtain the Google/Gemini API key from [Google AI Studio](https://aistudio.google.com/app/apikey).
@@ -1,6 +1,7 @@
 ---
-title: LangChain
-description: "Wire LangChain ChatOpenAI, ChatAnthropic, and other chat model classes into Mem0 as the LLM provider to reuse existing LangChain integrations."
+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."
 ---


@@ -1,6 +1,7 @@
 ---
-title: LM Studio
-description: "Run local GGUF models in Mem0 via LM Studio server on localhost:1234 using its OpenAI-compatible API, with optional lmstudio_response_format schema."
+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."
 ---

 To use LM Studio with Mem0, you'll need to have LM Studio running locally with its server enabled. LM Studio provides a way to run local LLMs with an OpenAI-compatible API.
@@ -76,7 +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
@@ -3,7 +3,7 @@ title: MiniMax
 description: "Use MiniMax chat models like MiniMax-M2.7 as a Mem0 LLM with the MINIMAX_API_KEY and an optional MINIMAX_API_BASE custom endpoint override."
 ---

-To use MiniMax LLM models, you have to set the `MINIMAX_API_KEY` environment variable. You can also optionally set `MINIMAX_API_BASE` if you need to use a different API endpoint (defaults to "https://api.minimax.io/v1").
+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

@@ -1,6 +1,7 @@
 ---
-title: Ollama
-description: "Run local Ollama LLMs such as mixtral:8x7b or llama3.1:8b as a Mem0 provider with tool calling support and a configurable Ollama server URL."
+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."
 ---

 You can use LLMs from Ollama to run Mem0 locally. These [models](https://ollama.com/search?c=tools) support tool calling.
@@ -1,6 +1,7 @@
 ---
-title: OpenAI
-description: "Use OpenAI GPT models like gpt-5-mini as a Mem0 LLM with OPENAI_API_KEY, plus optional Openrouter routing and the openai_structured output mode."
+title: "OpenAI as LLM Provider"
+sidebarTitle: "OpenAI"
+description: "Configure OpenAI as an LLM provider in Mem0 with support for GPT models and Openrouter compatibility."
 ---

 To use OpenAI LLM models, you have to set the `OPENAI_API_KEY` environment variable. You can obtain the OpenAI API key from the [OpenAI Platform](https://platform.openai.com/account/api-keys).
@@ -1,6 +1,7 @@
 ---
-title: Together
-description: "Use Together AI hosted models such as MiniMaxAI/MiniMax-M3 as a Mem0 LLM with the TOGETHER_API_KEY and an optional TOGETHER_API_BASE endpoint."
+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."
 ---

 To use Together LLM models, you have to set the `TOGETHER_API_KEY` environment variable. You can obtain the Together API key from their [Account settings page](https://api.together.ai/settings/projects/~current/api-keys).
@@ -1,6 +1,7 @@
 ---
-title: xAI
-description: "Use xAI Grok models such as grok-4.3 as a Mem0 LLM with the XAI_API_KEY, plus an optional XAI_API_BASE for custom endpoint routing."
+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."
 ---

 [xAI](https://x.ai/) is a new AI company founded by Elon Musk that develops large language models, including Grok. Grok is trained on real-time data from X (formerly Twitter) and aims to provide accurate, up-to-date responses with a touch of wit and humor.
@@ -1,6 +1,7 @@
 ---
-title: Overview
-description: "Compare Mem0 LLM providers including OpenAI, Anthropic, Groq, Ollama, Together, Mistral, Gemini, Bedrock, DeepSeek, xAI, Sarvam, and LangChain."
+title: "LLM Providers Overview"
+sidebarTitle: Overview
+description: "Overview of all supported LLM providers in Mem0, including OpenAI, Anthropic, Groq, Ollama, and more."
 ---

 Mem0 includes built-in support for various popular large language models. Memory can utilize the LLM provided by the user, ensuring efficient use for specific needs.
@@ -22,14 +23,14 @@ See the list of supported LLMs below.
 <CardGroup cols={4}>
  <Card title="OpenAI" icon="/images/provider-icons/openai.svg" href="/components/llms/models/openai" />
  <Card title="Ollama" icon="/images/provider-icons/ollama.svg" href="/components/llms/models/ollama" />
-  <Card title="Azure OpenAI" icon="/images/provider-icons/azure-color.svg" href="/components/llms/models/azure_openai" />
+  <Card title="Azure OpenAI" icon="/images/provider-icons/azure-color.svg" href="/components/llms/models/azure-openai" />
  <Card title="Anthropic" icon="/images/provider-icons/anthropic.svg" href="/components/llms/models/anthropic" />
  <Card title="Together" icon="/images/provider-icons/together-color.svg" href="/components/llms/models/together" />
  <Card title="Groq" icon="/images/provider-icons/groq.svg" href="/components/llms/models/groq" />
  <Card title="Litellm" icon="shuffle" href="/components/llms/models/litellm" />
-  <Card title="Mistral AI" icon="/images/provider-icons/mistral-color.svg" href="/components/llms/models/mistral_AI" />
-  <Card title="Google AI" icon="/images/provider-icons/google-color.svg" href="/components/llms/models/google_AI" />
-  <Card title="AWS bedrock" icon="/images/provider-icons/bedrock-color.svg" href="/components/llms/models/aws_bedrock" />
+  <Card title="Mistral AI" icon="/images/provider-icons/mistral-color.svg" href="/components/llms/models/mistral-ai" />
+  <Card title="Google AI" icon="/images/provider-icons/google-color.svg" href="/components/llms/models/google-ai" />
+  <Card title="AWS bedrock" icon="/images/provider-icons/bedrock-color.svg" href="/components/llms/models/aws-bedrock" />
  <Card title="DeepSeek" icon="/images/provider-icons/deepseek-color.svg" href="/components/llms/models/deepseek" />
  <Card title="MiniMax" icon="/images/provider-icons/minimax-color.svg" href="/components/llms/models/minimax" />
  <Card title="xAI" icon="/images/provider-icons/xai.svg" href="/components/llms/models/xAI" />
@@ -1,6 +1,7 @@
 ---
-title: Overview
-description: 'Compare Mem0 reranker providers including Cohere, Hugging Face, Sentence Transformers, and LLM-based rerankers to boost search relevance for agents.'
+title: "Reranker Providers Overview"
+sidebarTitle: "Overview"
+description: 'Pick the right reranker path to boost Mem0 search relevance.'
 ---

 Mem0 rerankers rescore vector search hits so your agents surface the most relevant memories. Use this hub to decide when reranking helps, configure a provider, and fine-tune performance.
@@ -13,10 +14,10 @@ Reranking trades extra latency for better precision. Start once you have baselin

 <CardGroup cols={3}>
  <Card title="Cohere" icon="/images/provider-icons/cohere.svg" href="/components/rerankers/models/cohere" />
-  <Card title="Sentence Transformers" icon="vector-square" href="/components/rerankers/models/sentence_transformer" />
+  <Card title="Sentence Transformers" icon="vector-square" href="/components/rerankers/models/sentence-transformer" />
  <Card title="Hugging Face" icon="/images/provider-icons/huggingface.svg" href="/components/rerankers/models/huggingface" />
-  <Card title="LLM Reranker" icon="wand-magic-sparkles" href="/components/rerankers/models/llm_reranker" />
-  <Card title="Zero Entropy" icon="/images/provider-icons/zeroentropy.svg" href="/components/rerankers/models/zero_entropy" />
+  <Card title="LLM Reranker" icon="wand-magic-sparkles" href="/components/rerankers/models/llm-reranker" />
+  <Card title="Zero Entropy" icon="/images/provider-icons/zeroentropy.svg" href="/components/rerankers/models/zero-entropy" />
 </CardGroup>

 <Note>
@@ -54,13 +55,13 @@ All five rerankers are available in both the Python and the [TypeScript](/open-s
    title="Zero Entropy Guide"
    description="Adopt the managed neural reranker for production workloads."
    icon="sparkles"
-    href="/components/rerankers/models/zero_entropy"
+    href="/components/rerankers/models/zero-entropy"
  />
  <Card
    title="Sentence Transformers"
    description="Keep reranking on-device with cross-encoder models."
    icon="microchip"
-    href="/components/rerankers/models/sentence_transformer"
+    href="/components/rerankers/models/sentence-transformer"
  />
 </CardGroup>

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

 ## How to define configurations?
@@ -123,10 +124,10 @@ Here's a comprehensive list of all parameters that can be used across different

 Each vector database has its own specific configuration requirements. To customize the config for your chosen vector store:

-1. Identify the vector database you want to use from [supported vector databases](./dbs).
+1. Identify the vector database you want to use from [supported vector databases](./overview).
 2. Refer to the `Config` section in the respective vector database's documentation.
 3. Include only the relevant parameters for your chosen database in the `config` dictionary.

 ## Supported Vector Databases

-For detailed information on configuring specific vector databases, please visit the [Supported Vector Databases](./dbs) section. There you'll find individual pages for each supported vector store with provider-specific usage examples and configuration details.
+For detailed information on configuring specific vector databases, please visit the [Supported Vector Databases](./overview) section. There you'll find individual pages for each supported vector store with provider-specific usage examples and configuration details.
@@ -1,6 +1,7 @@
 ---
-title: LangChain
-description: "Use LangChain as a unified vector store provider in Mem0 to access many vector databases like Chroma, FAISS, and Pinecone through one API."
+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."
 ---

 Mem0 supports LangChain as a provider for vector store integration. LangChain provides a unified interface to various vector databases, making it easy to integrate different vector store providers through a consistent API.
@@ -3,17 +3,25 @@ title: "Oracle AI Vector Search"
 description: "Use Oracle Database AI Vector Search as a vector store in Mem0 with the native VECTOR type to combine semantic memory search and relational queries."
 ---

+{/* Copyright (c) 2026, Oracle and/or its affiliates. */}
+
 [Oracle AI Vector Search](https://www.oracle.com/database/ai-vector-search/) stores embeddings in an Oracle table using the native `VECTOR` data type, so you can combine semantic search over unstructured data with relational queries over business data in a single database.

 ### Requirements

 - Oracle Database 23.4 or later, with a user that can create tables and vector indexes
- The `python-oracledb` driver. In thick mode, Oracle Client 23.4 or later is also required.
+- The `python-oracledb` or `node-oracledb` driver. In thick mode, Oracle Client 23.4 or later is also required.

-```bash
-pip install oracledb
+<CodeGroup>
+```bash Python
+pip install mem0ai
 ```

+```bash TypeScript
+npm install oracledb
+```
+</CodeGroup>
+
 ### Usage

 <CodeGroup>
@@ -47,11 +55,58 @@ messages = [
 ]
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
+
+```typescript TypeScript
+import { Memory } from "mem0ai/oss";
+
+const config = {
+  vectorStore: {
+    provider: "oracledb",
+    config: {
+      collectionName: "mem0",
+      embeddingModelDims: 1536,
+      connectionParams: {
+        user: "mem0_user",
+        password: "your-password",
+        connectString: "localhost:1521/FREEPDB1",
+      },
+    },
+  },
+};
+
+const memory = new Memory(config);
+
+const messages = [
+  {
+    role: "user",
+    content: "I'm planning to watch a movie tonight. Any recommendations?",
+  },
+  {
+    role: "assistant",
+    content: "How about thriller movies? They can be quite engaging.",
+  },
+  {
+    role: "user",
+    content: "I'm not a big fan of thriller movies but I love sci-fi movies.",
+  },
+  {
+    role: "assistant",
+    content:
+      "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future.",
+  },
+];
+
+await memory.add(messages, {
+  userId: "alice",
+  metadata: { category: "movies" },
+});
+```
 </CodeGroup>

-To reuse a connection or pool you already manage, pass it as `client` instead of `connection_params`:
+To reuse a connection or pool you already manage, pass it as `client` instead of the connection parameters:

-```python
+<CodeGroup>
+```python Python
 import oracledb

 pool = oracledb.create_pool(user="mem0_user", password="your-password", dsn="localhost:1521/FREEPDB1")
@@ -64,33 +119,54 @@ config = {
 }
 ```

+```typescript TypeScript
+import oracledb from "oracledb";
+
+const pool = await oracledb.createPool({
+  user: "mem0_user",
+  password: "your-password",
+  connectString: "localhost:1521/FREEPDB1",
+});
+
+const config = {
+  vectorStore: {
+    provider: "oracledb",
+    config: { client: pool },
+  },
+};
+```
+</CodeGroup>
+
 ### Config

 Here are the parameters available for configuring Oracle AI Vector Search:

-| Parameter | Description | Default Value |
-| --- | --- | --- |
-| `connection_params` | Connection settings passed to `python-oracledb`, such as `user`, `password` and `dsn`. See the [connection handling guide](https://python-oracledb.readthedocs.io/en/latest/user_guide/connection_handling.html). | `None` |
-| `use_connection_pool` | Create a connection pool from `connection_params` instead of a single connection | `True` |
-| `client` | An existing `oracledb.Connection` or `oracledb.ConnectionPool` to use instead of building one from `connection_params` | `None` |
-| `collection_name` | Name of the Oracle table that stores vectors and payloads | `mem0` |
-| `embedding_model_dims` | Dimension of your embedding vectors, must be greater than 0 | `1536` |
-| `distance_metric` | Distance function used for indexing and search: `COSINE`, `EUCLIDEAN`, `EUCLIDEAN_SQUARED`, `DOT`, `HAMMING` or `MANHATTAN` | `COSINE` |
-| `do_create_index` | Whether to create a vector index on the collection | `True` |
-| `index_type` | Vector index type: `HNSW` or `IVF` | `HNSW` |
-| `index_name` | Name of the vector index | `<collection_name>_VEC_IDX` |
-| `index_parameters` | Index tuning parameters. For `HNSW`: `neighbors`, `efconstruction`. For `IVF`: `neighbor partitions`, `samples_per_partition`, `min_vectors_per_partition`. | `None` |
-| `index_accuracy` | Target index accuracy from 1 to 100, applied as `WITH TARGET ACCURACY <n>` | `None` |
+Provide either `connection_params`/`connectionParams` or an existing connection or pool as `client`.
+
+| Python | TypeScript | Description | Default Value |
+| --- | --- | --- | --- |
+| `connection_params` | `connectionParams` | Connection settings passed to the Oracle driver, such as `user`, `password` and `dsn` (`connectString` in TypeScript). Required unless `client` is provided. See the [Python](https://python-oracledb.readthedocs.io/en/latest/user_guide/connection_handling.html) or [Node.js](https://node-oracledb.readthedocs.io/en/latest/user_guide/connection_handling.html) connection handling guide. | `None` |
+| `use_connection_pool` | `useConnectionPool` | Create a connection pool from the connection parameters instead of a single connection | `True` |
+| `client` | `client` | An existing Oracle connection or pool to use instead of building one from the connection parameters. Required unless connection parameters are provided. | `None` |
+| `collection_name` | `collectionName` | Name of the Oracle table that stores vectors and payloads | `mem0` |
+| `embedding_model_dims` | `embeddingModelDims` | Dimension of your embedding vectors, must be greater than 0 | `1536` |
+| `distance_metric` | `distanceMetric` | Distance function used for indexing and search: `COSINE`, `EUCLIDEAN`, `EUCLIDEAN_SQUARED`, `DOT`, `HAMMING` or `MANHATTAN` | `COSINE` |
+| `do_create_index` | `doCreateIndex` | Whether to create a vector index on the collection | `True` |
+| `index_type` | `indexType` | Vector index type: `HNSW` or `IVF` | `HNSW` |
+| `index_name` | `indexName` | Name of the vector index | `<collection_name>_VEC_IDX` |
+| `index_parameters` | `indexParameters` | Index tuning parameters. For `HNSW`: `neighbors`, `efconstruction`. For `IVF`: `neighbor partitions`, `samples_per_partition`, `min_vectors_per_partition`. | `None` |
+| `index_accuracy` | `indexAccuracy` | Target index accuracy from 1 to 100, applied as `WITH TARGET ACCURACY <n>` | `None` |

 <Note>
-  When you pass a pre-built `client`, Mem0 uses it as-is and ignores `connection_params` and `use_connection_pool`. Mem0 does not close a client it did not create.
+  When you pass a pre-built `client`, Mem0 uses it as-is and ignores the connection parameters and pooling options. Mem0 does not close a client it did not create.
 </Note>

 ### Vector indexes

 Set the index type with `index_type` and tune it with `index_parameters`:

-```python
+<CodeGroup>
+```python Python
 config = {
    "vector_store": {
        "provider": "oracledb",
@@ -104,6 +180,25 @@ config = {
 }
 ```

+```typescript TypeScript
+const config = {
+  vectorStore: {
+    provider: "oracledb",
+    config: {
+      connectionParams: {
+        user: "mem0_user",
+        password: "your-password",
+        connectString: "localhost:1521/FREEPDB1",
+      },
+      indexType: "HNSW",
+      indexParameters: { neighbors: 32, efconstruction: 200 },
+      indexAccuracy: 95,
+    },
+  },
+};
+```
+</CodeGroup>
+
 For the full list of supported options, see the Oracle [`CREATE VECTOR INDEX`](https://docs.oracle.com/en/database/oracle/oracle-database/26/sqlrf/create-vector-index.html) reference.

 ### Search scores
@@ -121,14 +216,29 @@ Filters run against the JSON `payload` column and support:
 | Comparison | `{"score": {"gte": 0.5}}`, also `eq`, `ne`, `gt`, `lt`, `lte` |
 | Membership | `{"category": {"in": ["movies", "books"]}}`, also `nin` |
 | String matching | `{"title": {"contains": "sci-fi"}}`, also `icontains` for case-insensitive |
-| Logical groups | `{"AND": [...]}`, `{"OR": [...]}`, `{"NOT": [...]}` |
+| Logical groups | `{"AND": [...]}`, `{"OR": [...]}`, `{"NOT": [...]}`, also `$and`, `$or`, `$not` |

 Multiple fields at the top level are combined with `AND`:

-```python
+<CodeGroup>
+```python Python
 m.search(
    "movie recommendations",
-    user_id="alice",
-    filters={"category": {"in": ["movies", "books"]}, "rating": {"gte": 4}},
+    filters={
+        "user_id": "alice",
+        "category": {"in": ["movies", "books"]},
+        "rating": {"gte": 4},
+    },
 )
 ```
+
+```typescript TypeScript
+await memory.search("movie recommendations", {
+  filters: {
+    user_id: "alice",
+    category: { in: ["movies", "books"] },
+    rating: { gte: 4 },
+  },
+});
+```
+</CodeGroup>
@@ -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>
@@ -1,6 +1,7 @@
 ---
-title: Overview
-description: "Explore Mem0 vector database integrations including Qdrant, Chroma, Pinecone, pgvector, Milvus, Weaviate, Redis, and Oracle for memory storage."
+title: "Vector Store Providers Overview"
+sidebarTitle: "Overview"
+description: "Overview of all supported vector databases in Mem0, including Qdrant, Chroma, PGVector, Pinecone, Oracle, and more."
 ---

 Mem0 includes built-in support for various popular databases. Memory can utilize the database provided by the user, ensuring efficient use for specific needs.
@@ -10,7 +11,7 @@ Mem0 includes built-in support for various popular databases. Memory can utilize
 See the list of supported vector databases below.

 <Note>
-  The following vector databases are supported in the Python implementation. The TypeScript implementation currently supports Qdrant, Redis, PGVector, Supabase, LangChain, Azure AI Search, Vectorize, Amazon S3 Vectors, Milvus, Neptune Analytics, and an in-memory store.
+  The following vector databases are supported in the Python implementation. The TypeScript implementation currently supports Qdrant, Redis, PGVector, Supabase, LangChain, Oracle AI Vector Search, Azure AI Search, Vectorize, Amazon S3 Vectors, Milvus, Neptune Analytics, and an in-memory store.
 </Note>

 <CardGroup cols={3}>
@@ -28,12 +29,12 @@ See the list of supported vector databases below.
  <Card title="Elasticsearch" icon="/images/provider-icons/elasticsearch.svg" href="/components/vectordbs/dbs/elasticsearch"></Card>
  <Card title="OpenSearch" icon="/images/provider-icons/opensearch.svg" href="/components/vectordbs/dbs/opensearch"></Card>
  <Card title="Supabase" icon="/images/provider-icons/supabase.svg" href="/components/vectordbs/dbs/supabase"></Card>
-  <Card title="Vertex AI" icon="/images/provider-icons/vertexai.svg" href="/components/vectordbs/dbs/vertex_ai"></Card>
+  <Card title="Vertex AI" icon="/images/provider-icons/vertexai.svg" href="/components/vectordbs/dbs/vertex-ai"></Card>
  <Card title="Weaviate" icon="circle-nodes" href="/components/vectordbs/dbs/weaviate"></Card>
  <Card title="FAISS" icon="layer-group" href="/components/vectordbs/dbs/faiss"></Card>
  <Card title="LangChain" icon="/images/provider-icons/langchain-color.svg" href="/components/vectordbs/dbs/langchain"></Card>
-  <Card title="Amazon S3 Vectors" icon="/images/provider-icons/aws-color.svg" href="/components/vectordbs/dbs/s3_vectors"></Card>
-  <Card title="Neptune Analytics" icon="/images/provider-icons/aws-color.svg" href="/components/vectordbs/dbs/neptune_analytics"></Card>
+  <Card title="Amazon S3 Vectors" icon="/images/provider-icons/aws-color.svg" href="/components/vectordbs/dbs/s3-vectors"></Card>
+  <Card title="Neptune Analytics" icon="/images/provider-icons/aws-color.svg" href="/components/vectordbs/dbs/neptune-analytics"></Card>
  <Card title="Databricks" icon="/images/provider-icons/databricks.svg" href="/components/vectordbs/dbs/databricks"></Card>
  <Card title="Turbopuffer" icon="/images/provider-icons/turbopuffer.svg" href="/components/vectordbs/dbs/turbopuffer"></Card>
 </CardGroup>
@@ -14,7 +14,8 @@ Follow the steps below for a smooth contribution process.
 <Note>
  For the complete contributor checklist, see
  [CONTRIBUTING.md](https://github.com/mem0ai/mem0/blob/main/CONTRIBUTING.md) in
-  the repository root.
+  the repository root. By participating you agree to our
+  [Code of Conduct](https://github.com/mem0ai/mem0/blob/main/CODE_OF_CONDUCT.md).
 </Note>

 ## Before You Start
@@ -30,7 +31,28 @@ change, avoid duplicate work, and agree on the approach before you write code.
  or [feature request](https://github.com/mem0ai/mem0/issues/new?template=feature_request.yml).
 - For anything beyond a trivial fix, wait for a maintainer to confirm the approach.

-Every pull request must link to an issue using `Closes #<issue-number>`.
+Every pull request must link to an issue using `Closes #<issue-number>`, and that
+issue must carry the `accepted` label. A maintainer applies `accepted` once we
+agree the change is worth making. Pull requests that do not link an accepted
+issue are closed automatically, with instructions to reopen once the label is
+applied. Documentation-only changes are exempt.
+
+<Note>
+  Closed does not mean rejected. Getting the label and reopening takes about a
+  minute, and the check reruns on reopen.
+</Note>
+
+### Show Your Work
+
+Both issue forms ask how you verified the problem: what you ran, the real output
+you saw, and why it is a bug rather than expected behavior. Reports without that
+are hard to act on and usually sit unanswered.
+
+They also ask whether AI was involved. That question is about how the problem was
+found and confirmed, not about how the text was written: drafting the write-up
+with a model is fine. The same applies to pull requests, where the AI disclosure
+covers the code in the diff. We ask because it tells reviewers where to look, not
+because it counts against you.

 ### 2. Sign the Contributor License Agreement (CLA)

@@ -57,6 +79,33 @@ For detailed guidance on pull requests, refer to [GitHub's documentation](https:

 ---

+## Installing from Source
+
+If you just want to run the latest, unreleased SDK code instead of the published `mem0ai` package, for example to try out a fix before it ships, or to depend on a fork, install directly from a local clone rather than setting up the full contributor environment below.
+
+### Python SDK
+
+```bash
+git clone https://github.com/mem0ai/mem0.git
+cd mem0
+pip install -e .
+```
+
+This installs `mem0ai` in editable mode, so edits under `mem0/` take effect immediately without reinstalling. Add an extra if you need one, e.g. `pip install -e ".[vector-stores]"` (see `pyproject.toml` for the full list). If you are contributing to the SDK itself and need every optional dependency for the test suite, use `hatch` instead, see [Dependency Management](#dependency-management).
+
+### TypeScript SDK
+
+```bash
+git clone https://github.com/mem0ai/mem0.git
+cd mem0/mem0-ts
+pnpm install
+pnpm run build
+```
+
+This builds `mem0-ts/dist` (CJS + ESM). To use it from another local project, add it as a `file:` dependency pointing at `mem0-ts`, or run `pnpm link --global` inside `mem0-ts` and `pnpm link --global mem0ai` in the consuming project.
+
+---
+
 ## Python SDK (`mem0/`)

 ### Dependency Management
@@ -38,7 +38,7 @@ Navigate to the `docs/` directory (where `docs.json` is located) and start the d
 mintlify dev
 ```

-The documentation website will be available at: [http://localhost:3000](http://localhost:3000).
+The documentation website will be available at: `http://localhost:3000`.

 ---

@@ -3,6 +3,9 @@ title: Personalized AI Tutor
 description: "Build a Python AI tutor with Mem0 and OpenAI GPT-4 that remembers student progress, learning preferences, and lesson history across every session."
 ---

+<Info icon="server">
+**Works with:** Mem0 OSS (`Memory`)
+</Info>

 You can create a personalized AI Tutor using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.

@@ -3,6 +3,9 @@ title: Self-Hosted AI Companion
 description: "Run a fully local Mem0 companion with Ollama for both the LLM and embedder, keeping user data on your machine with no cloud API calls required."
 ---

+<Info icon="server">
+**Works with:** Mem0 OSS (`Memory`)
+</Info>

 Mem0 can be utilized entirely locally by leveraging Ollama for both the embedding model and the language model (LLM). This guide will walk you through the necessary steps and provide the complete code to get you started.

@@ -3,6 +3,9 @@ title: Build a Node.js Companion
 description: "Ship a Node.js fitness coach powered by Mem0 and OpenAI that remembers user goals, workout history, and preferences run after run in JavaScript."
 ---

+<Info icon="server">
+**Works with:** Mem0 OSS (`Memory`)
+</Info>

 You can create a personalized AI Companion using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.

@@ -3,6 +3,9 @@ title: Interactive Memory Demo
 description: "Clone the Mem0 showcase companion app to see memory writes, retrievals, and personalized chat responses in an interactive Next.js demo."
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform
+</Info>

 You can create a personalized AI Companion using Mem0. This guide will walk you through the necessary steps and provide the complete setup instructions to get you started.

@@ -45,7 +48,7 @@ Before you begin, follow these steps to set up the demo application:
   OPENAI_API_KEY=your_openai_api_key
   MEM0_API_KEY=your_mem0_api_key
   ```
-   You can obtain your `MEM0_API_KEY` by signing up at <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cookbook-companions-quickstart" rel="nofollow">Mem0 API Dashboard</a>.
+   You can obtain your `MEM0_API_KEY` by signing up at <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cookbook-companions-quickstart">Mem0 API Dashboard</a>.

 5. Start the development server:
   ```bash
@@ -3,6 +3,9 @@ title: Smart Travel Assistant
 description: "Design a Python travel assistant with Mem0 and GPT-4 that recalls destinations, budgets, and traveler preferences to plan smarter itineraries."
 ---

+<Info icon="server">
+**Works with:** Mem0 OSS (`Memory`)
+</Info>

 Create a personalized AI Travel Assistant using Mem0. This guide provides step-by-step instructions and the complete code to get you started.

@@ -3,6 +3,9 @@ title: Voice-First AI Companion
 description: "Combine the OpenAI Agents SDK voice pipeline with Mem0 to build a Python voice assistant that remembers user preferences across spoken sessions."
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform (`MemoryClient`)
+</Info>

 This guide demonstrates how to combine OpenAI's Agents SDK for voice applications with Mem0's memory capabilities to create a voice assistant that remembers user preferences and past interactions.

@@ -3,6 +3,9 @@ title: Research Assistant for YouTube
 description: "Install the Mem0 YouTube Chrome extension to chat about any video with personal context, layering AI answers over the native YouTube player."
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform
+</Info>

 Enhance your YouTube experience with Mem0's YouTube Assistant, a Chrome extension that brings AI-powered chat directly to your YouTube videos. Get instant, personalized answers about video content while leveraging your own knowledge and memories, all without leaving the page.

@@ -3,6 +3,9 @@ title: Build a Companion with Mem0
 description: "Learn the full Mem0 memory lifecycle by building a stateful fitness companion that stores goals, adapts tone, and personalizes every chat reply."
 ---

+<Info icon="layer-group">
+**Works with:** Mem0 OSS (`Memory`) and Mem0 Platform (`MemoryClient`)
+</Info>

 Essentially, creating a companion out of LLMs is as simple as a loop. But these loops work great for one type of character without personalization and fall short as soon as you restart the chat.

@@ -1,516 +0,0 @@
---
-title: Control Memory Ingestion
-description: "Use Mem0 filters, format rules, and confidence gates to keep speculation and low-quality data out of your memory store in a medical assistant demo."
---
-
-
-AI assistants plugged with memory systems face a problem - they often store everything. Not every conversation needs to be remembered, and not every detail should go to the memory store. Without proper controls, memory systems accumulate unreliable data.
-
-Mem0 lets you control your memory ingestion pipeline. In this cookbook, we'll demonstrate these controls using a medical assistant example - showing how to filter unwanted data, enforce data formats, and implement confidence-based storage.
-
---
-
-## Overview
-
-Without controls, everything gets stored - speculation, low-confidence data, and information that shouldn't persist. This uncontrolled ingestion leads to cluttered memory and retrieval failures.
-
-Mem0 provides **three tools to control** what gets stored:
-
-1. **Custom instructions** define what to remember and what to ignore.
-2. **Confidence thresholds** ensure only verified facts persist.
-3. **Memory updates** let you change information without creating duplicates.
-
-In this tutorial, we will:
-
- Filter speculative statements with custom instructions
- Configure confidence thresholds for fact verification
- Update stored information without duplication
- Build a complete ingestion pipeline
-
---
-
-## Setup
-
-```python
-from mem0 import MemoryClient
-
-client = MemoryClient(api_key="your-api-key")
-```
-
-<Note>
-Replace `your-api-key` with your actual Mem0 API key from the <a href="https://app.mem0.ai?utm_source=oss&utm_medium=cookbook-memory-ingestion" rel="nofollow">dashboard</a>. Without proper API authentication, memory operations will fail.
-</Note>
-
---
-
-## The Problem
-
-Uncontrolled ingestion stores everything, including speculation:
-
-```python
-# Patient mentions speculation
-messages = [{"role": "user", "content": "I think I might be allergic to penicillin"}]
-client.add(messages, user_id="patient_123")
-
-# Check what got stored
-results = client.search("patient allergies", filters={"user_id": "patient_123"})
-print(results['results'][0]['memory'])
-
-```
-
-**Output:**
-
-```
-Patient is allergic to penicillin
-```
-
-<Warning>
-Without custom instructions, AI assistants treat speculation as confirmed facts. "I think I might be allergic" becomes "Patient is allergic": a dangerous transformation in sensitive domains like healthcare, legal, or financial services.
-</Warning>
-
-The speculation became a confirmed fact. Let's add controls.
-
---
-
-## Custom Instructions
-
-Custom instructions tell Mem0 what to store and what to ignore.
-
-```python
-instructions = """
-Only store CONFIRMED medical facts.
-
-Store:
- Confirmed diagnoses from doctors
- Known allergies with documented reactions
- Current medications being taken
-
-Ignore:
- Speculation (words like "might", "maybe", "I think")
- Unverified symptoms
- Casual mentions without confirmation
-"""
-
-client.project.update(custom_instructions=instructions)
-
-# Same speculative statement
-messages = [{"role": "user", "content": "I think I might be allergic to penicillin"}]
-client.add(messages, user_id="patient_123")
-
-# Check what got stored
-results = client.get_all(filters={"user_id": "patient_123"})
-print(f"Memories stored: {len(results['results'])}")
-
-```
-
-**Output:**
-
-```
-Memories stored: 0
-```
-
-<Info>
-**Expected output:** Zero memories stored. The speculative statement "I think I might be allergic" was filtered out before reaching storage. Custom instructions are actively blocking unreliable data.
-</Info>
-
-The speculation was filtered out.
-
---
-
-## Designing Custom Instructions
-
-When designing instructions, consider the trade-off between precision and recall:
-
-**Too restrictive:** You'll miss important information (false negatives)
-
-```python
-# Too strict - filters out useful context
-"""
-Only store information if explicitly stated by a doctor with full name,
-date, time, and medical license number.
-"""
-
-```
-
-**Too permissive:** You'll store unreliable data (false positives)
-
-```python
-# Too loose - stores speculation as fact
-"""
-Store any health-related information mentioned.
-"""
-
-```
-
-**Balanced approach:**
-
-```python
-# Clear categories with examples
-"""
-Store CONFIRMED facts:
- Diagnoses: "Dr. Smith diagnosed hypertension on March 15th"
- Allergies: "Patient had hives reaction to penicillin"
- Medications: "Taking Lisinopril 10mg daily"
-
-Ignore SPECULATION:
- "I think I might have..."
- "Maybe it's..."
- "Could be related to..."
-"""
-
-```
-
-<Tip>
-Start with strict instructions (only store confirmed facts), then relax them based on your use case. It's easier to allow more data than to clean up polluted memory. Test with sample conversations before deploying to production.
-</Tip>
-
-Start with clear categories and iterate based on retrieval quality.
-
---
-
-## Confidence Thresholds
-
-Mem0 assigns confidence scores to extracted memories. Use these to filter low-quality data.
-
-### Setting Thresholds
-
-Setting the right confidence threshold depends on your application:
-
- **High-stakes domains** (medical, legal): Require 0.8+ confidence
- **General assistants**: 0.6+ confidence is often sufficient
- **Exploratory systems**: Lower thresholds (0.4+) capture more data
-
-Test your pipeline with multiple input examples and threshold combinations to find what works for your use case.
-
-```python
-# Configure stricter instructions
-client.project.update(
-    custom_instructions="""
-Only extract memories with HIGH confidence.
-Require specific details (dates, dosages, doctor names) for medical facts.
-Skip vague or uncertain statements.
-"""
-)
-
-# Test with uncertain statement
-messages = [{"role": "user", "content": "The doctor mentioned something about my blood pressure"}]
-result1 = client.add(messages, user_id="patient_123")
-
-# Test with confirmed fact
-messages = [{"role": "user", "content": "Dr. Smith diagnosed me with hypertension on March 15th"}]
-result2 = client.add(messages, user_id="patient_123")
-
-print("Vague statement stored:", len(result1['results']) > 0)
-print("Confirmed fact stored:", len(result2['results']) > 0)
-
-```
-
-**Output:**
-
-```
-Vague statement stored: False
-Confirmed fact stored: True
-```
-
-<Info icon="check">
-**Expected behavior:** Low-confidence extractions are now filtered out automatically. Only verified facts with specific details (names, dates, dosages) persist in memory. The confidence threshold is working.
-</Info>
-
-The vague statement was filtered for low confidence. The confirmed fact with specific details was stored.
-
---
-
-## Filtering Sensitive Information
-
-Custom instructions can prevent storing personal identifiers:
-
-```python
-client.project.update(
-    custom_instructions="""
-Medical memory rules:
-
-STORE:
- Confirmed diagnoses
- Verified allergies
- Current medications
-
-NEVER STORE:
- Social Security Numbers
- Insurance policy numbers
- Credit card information
- Full addresses
- Phone numbers
-
-Replace identifiers with generic references if mentioned.
-"""
-)
-
-# Test with PII
-messages = [
-    {"role": "user", "content": "My SSN is 123-45-6789 and I'm allergic to penicillin"}
-]
-client.add(messages, user_id="patient_123")
-
-# Check what was stored
-results = client.get_all(filters={"user_id": "patient_123"})
-for result in results['results']:
-    print(result['memory'])
-
-```
-
-**Output:**
-
-```
-Patient is allergic to penicillin
-```
-
-The SSN was filtered out, but the allergy was stored.
-
---
-
-## Updating Memories
-
-When information changes, update existing memories instead of creating duplicates.
-
-```python
-# Initial allergy stored
-result = client.add(
-    [{"role": "user", "content": "Patient confirmed allergy to penicillin with documented hives reaction"}],
-    user_id="patient_123"
-)
-
-memory_id = result['results'][0]['id']
-print(f"Stored memory: {memory_id}")
-
-# Later, patient gets retested - allergy was false positive
-client.update(
-    memory_id=memory_id,
-    text="Patient tested negative for penicillin allergy on April 2nd, 2025. Previous allergy was false positive.",
-    metadata={"verified": True, "updated_date": "2025-04-02"}
-)
-
-# Retrieve the updated memory
-updated = client.get(memory_id)
-print(f"\\nUpdated memory: {updated['memory']}")
-print(f"Metadata: {updated['metadata']}")
-
-```
-
-**Output:**
-
-```
-Stored memory: mem_abc123
-
-Updated memory: Patient tested negative for penicillin allergy on April 2nd, 2025. Previous allergy was false positive.
-Metadata: {'verified': True, 'updated_date': '2025-04-02'}
-
-```
-
-### Benefits of Updating
-
-**Preserves history:**
-
- `created_at` shows when the memory was first stored
- `updated_at` shows when it was modified
- Audit trail for compliance
-
-**Avoids conflicts:**
-
- No duplicate or contradicting memories
- Single source of truth for each fact
-
-<Warning>
-That “no duplicates” promise comes from the inference pipeline. Keep `infer=True` when you rely on automatic updates. Raw imports (`infer=False`) skip conflict checks, so mixing the two modes for the same fact will create duplicates.
-</Warning>
-
-### Pick the right inference mode
-
-| Mode | What it does | Best for | Watch out for |
-| --- | --- | --- | --- |
-| `infer=True` *(default)* | Runs the LLM pipeline so Mem0 extracts structured facts and resolves conflicts automatically. | Daily conversations, preference tracking, anything you want deduped. | Slightly slower because inference runs on every write. |
-| `infer=False` | Stores your payload exactly as-is: no inference, no dedupe. | Bulk imports, compliance snapshots, curated facts you already trust. | Later `infer=True` calls for the same fact will create duplicates you must clean manually. |
-
-<Tip>
-Stay consistent per data source. If you need both behaviors, keep them in separate scopes (e.g., different `app_id` or `run_id`) so you always know which memories are inferred vs direct imports.
-</Tip>
-
---
-
-## Update vs Delete
-
-When should you update vs delete?
-
-### Update when:
-
- Information changes but remains relevant
- You need audit history
- The memory has relationships to other data
-
-```python
-# Medication dosage changed
-client.update(
-    memory_id=med_id,
-    text="Taking Lisinopril 20mg daily (increased from 10mg on March 1st)"
-)
-
-```
-
-### Delete when:
-
- Information was completely wrong
- Memory is no longer relevant
- Duplicate entry
-
-```python
-# Duplicate entry
-client.delete(memory_id)
-
-```
-
---
-
-## Putting It Together
-
-Here's a complete ingestion pipeline with all controls:
-
-```python
-from mem0 import MemoryClient
-import os
-
-# Initialize client
-client = MemoryClient(api_key=os.getenv("MEM0_API_KEY"))
-
-# Configure custom instructions
-client.project.update(
-    custom_instructions="""
-Medical memory assistant rules:
-
-STORE:
- Confirmed diagnoses (with doctor name and date)
- Verified allergies (with reaction details)
- Current medications (with dosage)
-
-IGNORE:
- Speculation (might, maybe, possibly)
- Unverified symptoms
- Personal identifiers (SSN, insurance numbers)
-
-CONFIDENCE:
-Require high confidence. Reject vague or uncertain statements.
-Require specific details: names, dates, dosages.
-"""
-)
-
-# Helper function for safe ingestion
-def add_medical_memory(content, user_id, metadata=None):
-    """Add memory with automatic filtering."""
-    result = client.add(
-        [{"role": "user", "content": content}],
-        user_id=user_id,
-        metadata=metadata or {}
-    )
-
-    if result['results']:
-        print(f"✓ Stored: {result['results'][0]['memory']}")
-    else:
-        print(f"✗ Filtered: {content}")
-
-    return result
-
-# Test cases
-print("Testing ingestion pipeline:\\n")
-
-test_cases = [
-    "I think I might be allergic to penicillin",
-    "Dr. Johnson confirmed penicillin allergy on Jan 15th with hives reaction",
-    "Patient SSN is 123-45-6789",
-    "Currently taking Lisinopril 10mg daily for hypertension",
-    "Feeling tired lately",
-    "Dr. Martinez diagnosed Type 2 diabetes on February 3rd, 2025"
-]
-
-for content in test_cases:
-    add_medical_memory(content, user_id="patient_123")
-    print()
-
-```
-
-**Output:**
-
-```
-Testing ingestion pipeline:
-
-✗ Filtered: I think I might be allergic to penicillin
-
-✓ Stored: Patient has confirmed penicillin allergy diagnosed by Dr. Johnson on January 15th with hives reaction
-
-✗ Filtered: Patient SSN is 123-45-6789
-
-✓ Stored: Patient is currently taking Lisinopril 10mg daily for hypertension
-
-✗ Filtered: Feeling tired lately
-
-✓ Stored: Patient diagnosed with Type 2 diabetes by Dr. Martinez on February 3rd, 2025
-
-```
-
---
-
-## Per-Call Instructions
-
-You can override project-level instructions for specific conversations:
-
-First define custom instructions
-
-```python
-custom_instructions="""Emergency intake mode:Store ALL symptoms and observations immediately.
-Flag for later review and verification."""
- 
-```
-
-```python
-# Emergency intake - store everything temporarily
-emergency_messages = [
-    {"role": "user", "content": "Patient arrived with chest pain and shortness of breath"}
-]
-
-client.add(
-    emergency_messages,
-    user_id="patient_456",
-    custom_instructions=custom_instructions,
-    metadata={"type": "emergency", "review_required": True}
-)
-
-```
-
-This is useful for:
-
- Different conversation types (emergency vs routine)
- Channel-specific rules (phone vs in-person)
- Temporary data collection that needs review
-
---
-
-## What You Built
-
-You now have a medical assistant with production-grade memory controls:
-
- **Custom instructions** - Filter speculation and enforce confirmed facts only
- **Confidence thresholds** - Gate extractions below 0.7 confidence score
- **Memory updates** - Modify stored information without creating duplicates
- **Per-call instructions** - Apply temporary rules for specific conversations
- **PII filtering** - Block sensitive data (SSNs, insurance numbers) automatically
-
-These controls prevent retrieval failures and ensure your AI assistant works with reliable, verified information.
-
---
-
-## Summary
-
-Start with conservative filters (only store confirmed facts) and iterate based on your application's needs. Combine custom instructions with confidence thresholds for the most reliable memory ingestion pipeline.
-
-<Card title="Build a Mem0 Companion" icon="users" href="/cookbooks/essentials/building-ai-companion">
-  Learn core memory patterns including temporary vs permanent data handling.
-</Card>
-
-<Snippet file="star-on-github.mdx" />
@@ -3,6 +3,10 @@ title: Partition Memories by Entity
 description: Scope Mem0 memories per user, agent, app, or session so a travel service never leaks one traveler's allergies into another customer's booking.
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform (`MemoryClient`)
+</Info>
+
 Nora runs a travel service. When she stored all memories in one bucket, a recruiter's nut allergy accidentally appeared in a traveler's dinner reservation. Let's fix this by properly separating memories for different users, agents, and applications.

 <Info icon="clock">
@@ -17,7 +21,7 @@ from mem0 import MemoryClient
 client = MemoryClient(api_key="m0-...")
 ```

-Grab an API key from the <a href="https://app.mem0.ai/?utm_source=oss&utm_medium=cookbook-entity-partitioning" rel="nofollow">Mem0 dashboard</a> to get started.
+Grab an API key from the <a href="https://app.mem0.ai/?utm_source=oss&utm_medium=cookbook-entity-partitioning">Mem0 dashboard</a> to get started.

 ## Store and Retrieve Scoped Memories

@@ -328,10 +332,10 @@ You learned how to:
    href="/platform/features/v2-memory-filters"
  />
  <Card
-    title="Control Memory Ingestion"
-    description="Pair scoped storage with rules that block low-quality facts."
+    title="Custom Instructions"
+    description="Pair scoped storage with instructions that steer what Mem0 extracts and stores."
    icon="shield-check"
-    href="/cookbooks/essentials/controlling-memory-ingestion"
+    href="/platform/features/custom-instructions"
  />
 </CardGroup>

@@ -3,6 +3,9 @@ title: Export Stored Memories
 description: "Export Mem0 user memories as structured data with the Python client for review, compliance audits, migrations, and downstream analytics workflows."
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform (`MemoryClient`)
+</Info>

 Mem0 is a dynamic memory store that gives you full control over your data. Along with storing memories, it gives you the ability to retrieve, export, and migrate your data whenever you need.

@@ -20,7 +23,7 @@ client = MemoryClient(api_key="your-api-key")
 ```

 <Note>
-Your API key needs export permissions to download memory data. Check your project settings on the <a href="https://app.mem0.ai?utm_source=oss&utm_medium=cookbook-exporting-memories" rel="nofollow">dashboard</a> if export operations fail with authentication errors.
+Your API key needs export permissions to download memory data. Check your project settings on the <a href="https://app.mem0.ai?utm_source=oss&utm_medium=cookbook-exporting-memories">dashboard</a> if export operations fail with authentication errors.
 </Note>

 Let's add some sample memories to work with:
@@ -169,20 +172,20 @@ export_job = client.create_memory_export(
 )

 print(f"Export ID: {export_job['id']}")
-print(f"Status: {export_job['status']}")
+print(f"Message: {export_job['message']}")

 ```

 **Output:**

 ```
-Export ID: exp_abc123
-Status: processing
+Export ID: 550e8400-e29b-41d4-a716-446655440000
+Message: Memory export request received. The export will be ready in a few seconds.

 ```

 <Info>
-**Export initiated:** Status is "processing". Large exports may take a few seconds. Poll with `get_memory_export()` until status changes to "completed" before downloading data.
+**Export initiated:** The export runs asynchronously and is usually ready within a few seconds. Retry `get_memory_export()` with the returned ID until it stops returning a "no export found" error.
 </Info>

 ### Step 3: Download the export
@@ -193,7 +196,7 @@ export_data = client.get_memory_export(
    memory_export_id=export_job['id']
 )

-print(export_data['data'])
+print(export_data)

 ```

@@ -216,7 +219,7 @@ export_by_filters = client.get_memory_export(
    filters={"user_id": "dev"}
 )

-print(export_by_filters['data'])
+print(export_by_filters)

 ```

@@ -240,7 +243,7 @@ export_with_instructions = client.create_memory_export(
 ```

 <Tip>
-Always check export status before downloading. Call `get_memory_export()` in a loop with a short delay until `status == "completed"`. Attempting to download while still processing returns incomplete data.
+If the export is still processing, `get_memory_export()` returns a 404 with `{"error": "No memory export request found"}`. Retry after a short delay until the call succeeds instead of polling a status field.
 </Tip>

 ---
@@ -284,8 +287,8 @@ Use **`get_all()`** for bulk retrieval, **`search()`** for specific questions, a
  <Card title="Build a Mem0 Companion" icon="users" href="/cookbooks/essentials/building-ai-companion">
    Learn core memory patterns including temporary vs permanent data handling.
  </Card>
-  <Card title="Control Memory Ingestion" icon="filter" href="/cookbooks/essentials/controlling-memory-ingestion">
-    Ensure only verified insights make it into your export pipeline.
+  <Card title="Custom Instructions" icon="filter" href="/platform/features/custom-instructions">
+    Steer what Mem0 extracts so only verified insights make it into your export pipeline.
  </Card>
 </CardGroup>

@@ -3,6 +3,9 @@ title: Tag and Organize Memories
 description: "Auto-categorize Mem0 memories with custom project categories so support teams retrieve tickets, resolutions, and account facts in one Python call."
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform (`MemoryClient`)
+</Info>

 When you have large volumes of memory data, sorting it during post-processing becomes difficult. What if your memory store understood the importance of creating tags and buckets without a lot of effort?

@@ -246,8 +249,8 @@ Categories make retrieval faster and compliance easier. Define 3-5 clear categor
 Instead of searching through everything, agents jump directly to the information type they need: billing issues, account details, or support tickets.

 <CardGroup cols={2}>
-  <Card title="Control Memory Ingestion" icon="filter" href="/cookbooks/essentials/controlling-memory-ingestion">
-    Keep categories meaningful by filtering noise before it lands in storage.
+  <Card title="Custom Instructions" icon="filter" href="/platform/features/custom-instructions">
+    Keep categories meaningful by steering what Mem0 extracts before it lands in storage.
  </Card>
  <Card title="Export Tagged Memories" icon="download" href="/cookbooks/essentials/exporting-memories">
    Use categories to drive audits, migrations, and compliance reports.
@@ -3,6 +3,9 @@ title: Persistent Eliza Characters
 description: "Give ElizaOS characters long-term memory with Mem0 so your agent keeps personality, backstory, and user context across every conversation and restart."
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform
+</Info>

 You can create a personalized Eliza OS Character using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.

@@ -3,6 +3,10 @@ title: "Gemini 3 with Mem0 MCP"
 description: "Connect Google Gemini 3 to the Mem0 cloud MCP server to add memory tools like add_memory and search_memories to any MCP-compatible AI client."
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform (MCP server)
+</Info>
+
 Gemini 3, when paired with Mem0's cloud MCP server, works in synergy to create snappy, smart, memory-aware agents.

 <Callout type="info" icon="sparkles" color="#8B5CF6">
@@ -55,7 +59,7 @@ GEMINI_API_KEY=your-gemini-api-key-here
 ```

 <Note>
-Ensure you have your Mem0 API key from the <a href="https://app.mem0.ai?utm_source=oss&utm_medium=cookbook-gemini-3" rel="nofollow">Mem0 Dashboard</a> and your Gemini API key from the [Google AI Studio](https://ai.studio/app/api-keys).
+Ensure you have your Mem0 API key from the <a href="https://app.mem0.ai?utm_source=oss&utm_medium=cookbook-gemini-3">Mem0 Dashboard</a> and your Gemini API key from the [Google AI Studio](https://ai.studio/app/api-keys).
 </Note>

 ## Gemini Memory Agent
@@ -3,6 +3,9 @@ title: Multi-Agent Collaboration
 description: "Coordinate LlamaIndex AgentWorkflow tutors and reviewers over one shared Mem0 memory layer so specialized agents collaborate with the same context."
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform (`Mem0Memory.from_client`)
+</Info>

 <Snippet file="blank-notif.mdx" />

@@ -41,7 +44,7 @@ Set up your environment variables:
 - `MEM0_API_KEY`: Your Mem0 Platform API key
 - `OPENAI_API_KEY`: Your OpenAI API key

-You can obtain your Mem0 Platform API key from the <a href="https://app.mem0.ai?utm_source=oss&utm_medium=cookbook-llamaindex-multiagent" rel="nofollow">Mem0 Platform</a>.
+You can obtain your Mem0 Platform API key from the <a href="https://app.mem0.ai?utm_source=oss&utm_medium=cookbook-llamaindex-multiagent">Mem0 Platform</a>.

 ## Complete Implementation

@@ -357,7 +360,7 @@ Based on our previous session, I remember we covered Vision Language Models and
 ## Help & Resources

 - [LlamaIndex Agent Workflows](https://docs.llamaindex.ai/en/stable/use_cases/agents/)
- <a href="https://app.mem0.ai/?utm_source=oss&utm_medium=cookbook-llamaindex-multiagent" rel="nofollow">Mem0 Platform</a>
+- <a href="https://app.mem0.ai/?utm_source=oss&utm_medium=cookbook-llamaindex-multiagent">Mem0 Platform</a>

 ---

@@ -3,6 +3,9 @@ title: ReAct Agents with Memory
 description: "Add persistent Mem0 memory to a LlamaIndex ReAct agent so it can reason, call tools, and recall prior interactions across every conversation turn."
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform (`Mem0Memory.from_client`)
+</Info>

 Create a ReAct Agent with LlamaIndex which uses Mem0 as the memory store.

@@ -25,7 +28,7 @@ os.environ["OPENAI_API_KEY"] = "<your-openai-api-key>"
 llm = OpenAI(model="gpt-5-mini")
 ```

-Initialize the Mem0 client. You can find your API key <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cookbook-llamaindex-react" rel="nofollow">here</a>. Read about Mem0 [Open Source](https://docs.mem0.ai/open-source/overview).
+Initialize the Mem0 client. You can find your API key <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cookbook-llamaindex-react">here</a>. Read about Mem0 [Open Source](https://docs.mem0.ai/open-source/overview).
 ```python
 os.environ["MEM0_API_KEY"] = "<your-mem0-api-key>"

@@ -78,7 +81,7 @@ from llama_index.core.agent import FunctionCallingAgent
 agent = FunctionCallingAgent.from_tools(
    [call_tool, email_tool, order_food_tool],
    llm=llm,
-    memory=memory_from_client,  # or memory_from_config
+    memory=memory_from_client,
    verbose=True,
 )
 ```
@@ -161,7 +164,7 @@ agent = FunctionCallingAgent.from_tools(
    [call_tool, email_tool, order_food_tool],
    llm=llm,
    # memory is provided
-    memory=memory_from_client,  # or memory_from_config
+    memory=memory_from_client,
    verbose=True,
 )
 response = agent.chat("I am feeling hungry, order me something and send me the bill")
@@ -3,6 +3,9 @@ title: Visual Memory Retrieval
 description: "Store, link, and recall images alongside text with Mem0 multimodal memory so agents keep visual context across cross-session conversations."
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform
+</Info>

 Enhance your AI interactions with Mem0's multimodal capabilities. Mem0 now supports image understanding, allowing for richer context and more natural interactions across supported AI platforms.

@@ -3,6 +3,9 @@ title: Memory-Powered Agent SDK
 description: "Expose Mem0 memory as callable tools inside the OpenAI Agents SDK so Python agents can persist facts and recall them across future conversations."
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform (`MemoryClient`)
+</Info>

 Integrate Mem0's memory capabilities with OpenAI's Agents SDK to create AI agents with persistent memory. You can create agents that remember past conversations and use that context to provide better responses.

@@ -223,7 +226,7 @@ context = Mem0Context(user_id="user123")
 ## Resources

 - [Mem0 Documentation](https://docs.mem0.ai/introduction)
- <a href="https://app.mem0.ai/dashboard?utm_source=oss&utm_medium=cookbook-agents-sdk-tool" rel="nofollow">Mem0 Dashboard</a>
+- <a href="https://app.mem0.ai/dashboard?utm_source=oss&utm_medium=cookbook-agents-sdk-tool">Mem0 Dashboard</a>
 - [API Reference](https://docs.mem0.ai/api-reference)

 ---
@@ -3,6 +3,9 @@ title: Bedrock with Persistent Memory
 description: "Configure the mem0ai Python SDK with AWS Bedrock LLMs and OpenSearch Serverless to run a fully managed, persistent agent memory stack on AWS."
 ---

+<Info icon="server">
+**Works with:** Mem0 OSS (`Memory`)
+</Info>

 This example demonstrates how to configure and use the `mem0ai` SDK with **AWS Bedrock** and **OpenSearch Service (AOSS)** for persistent memory capabilities in Python.

@@ -35,8 +38,8 @@ print(os.environ['AWS_SECRET_ACCESS_KEY'])
 ## Configuration and Usage

 This sets up Mem0 with:
- [AWS Bedrock for LLM](https://docs.mem0.ai/components/llms/models/aws_bedrock)
- [AWS Bedrock for embeddings](https://docs.mem0.ai/components/embedders/models/aws_bedrock#aws-bedrock)
+- [AWS Bedrock for LLM](https://docs.mem0.ai/components/llms/models/aws-bedrock)
+- [AWS Bedrock for embeddings](https://docs.mem0.ai/components/embedders/models/aws-bedrock#aws-bedrock)
 - [OpenSearch as the vector store](https://docs.mem0.ai/components/vectordbs/dbs/opensearch)

 ```python
@@ -3,6 +3,9 @@ title: Healthcare Coach with ADK
 description: "Build a Google ADK healthcare coach with Mem0 that remembers symptoms, medical history, and reminders across every patient chat and follow-up session."
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform (`MemoryClient`)
+</Info>

 This example demonstrates how to build a healthcare assistant that remembers patient information across conversations using Google ADK and Mem0.

@@ -123,7 +126,7 @@ Now we'll create our main agent with all the tools:
 # Create the agent
 healthcare_agent = Agent(
    name="healthcare_assistant",
-    model="gemini-1.5-flash",  # Using Gemini for healthcare assistant
+    model="gemini-2.0-flash",  # Using Gemini for healthcare assistant
    description="Healthcare assistant that helps patients with health information and appointment scheduling.",
    instruction="""You are a helpful Healthcare Assistant with memory capabilities.

@@ -3,10 +3,13 @@ title: Persistent Mastra Agents
 description: "Add long-term Mem0 memory to Mastra agents through tool-use so your TypeScript agents can save and retrieve user facts across every workflow run."
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform (`@mastra/mem0`)
+</Info>

 In this example you'll learn how to use Mem0 to add long-term memory capabilities to [Mastra's agent](https://mastra.ai/) via tool-use. This memory integration can work alongside Mastra's [agent memory features](https://mastra.ai/docs/agents/01-agent-memory).

-You can find the complete example code in the [Mastra repository](https://github.com/mastra-ai/mastra/tree/main/examples/memory-with-mem0).
+The complete example code, from installing the integration to wiring it into a Mastra agent, is shown below. Mem0's integration is published on npm as [`@mastra/mem0`](https://www.npmjs.com/package/@mastra/mem0).

 ## Overview

@@ -3,6 +3,9 @@ title: Memory as OpenAI Tool
 description: "Wire Mem0 into OpenAI function-calling so JavaScript agents automatically save and search memories through the native OpenAI tools API using Zod schemas."
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform (`MemoryClient`)
+</Info>

 Integrate Mem0’s memory capabilities with OpenAI’s Inbuilt Tools to create AI agents with persistent memory.

@@ -23,7 +26,7 @@ MEM0_API_KEY=your_mem0_api_key
 OPENAI_API_KEY=your_openai_api_key
 ```

-Get your Mem0 API key from the <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cookbook-openai-tool-calls" rel="nofollow">Mem0 Dashboard</a>.
+Get your Mem0 API key from the <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cookbook-openai-tool-calls">Mem0 Dashboard</a>.

 ### Configuration

@@ -280,7 +283,7 @@ run().catch(console.error);
 ## Resources

 - [Mem0 Documentation](https://docs.mem0.ai/introduction)
- <a href="https://app.mem0.ai/dashboard?utm_source=oss&utm_medium=cookbook-openai-tool-calls" rel="nofollow">Mem0 Dashboard</a>
+- <a href="https://app.mem0.ai/dashboard?utm_source=oss&utm_medium=cookbook-openai-tool-calls">Mem0 Dashboard</a>
 - [API Reference](https://docs.mem0.ai/api-reference)
 - [OpenAI Documentation](https://platform.openai.com/docs)

@@ -290,8 +293,8 @@ run().catch(console.error);
  <Card title="Agents SDK Tool with Mem0" icon="robot" href="/cookbooks/integrations/agents-sdk-tool">
    Extend the OpenAI Agents SDK with Mem0 integration capabilities.
  </Card>
-  <Card title="Control Memory Ingestion" icon="filter" href="/cookbooks/essentials/controlling-memory-ingestion">
-    Fine-tune what memories get stored during tool calls.
+  <Card title="Custom Instructions" icon="filter" href="/platform/features/custom-instructions">
+    Steer what memories get stored during tool calls.
  </Card>
 </CardGroup>

@@ -3,12 +3,14 @@ title: Search with Personal Context
 description: "Blend Tavily realtime web search with Mem0 stored preferences to build a Python search assistant that personalizes results based on past chats."
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform (`MemoryClient`)
+</Info>

 Imagine asking a search assistant for "coffee shops nearby" and instead of generic results, it shows remote-work-friendly cafes with great WiFi in your city because it remembers you mentioned working remotely before. Or when you search for "lunchbox ideas for kids" it knows you have a 7-year-old daughter and recommends peanut-free options that align with her allergy.

 That's what we are going to build today, a Personalized Search Assistant powered by Mem0 for memory and [Tavily](https://tavily.com) for real-time search.

-
 ## Why Personalized Search

 Most assistants treat every query like they've never seen you before. That means repeating yourself about your location, diet, or preferences, and getting results that feel generic.
@@ -3,6 +3,9 @@ title: Content Creation Workflow
 description: "Store tone rules and style guides once in Mem0, then have OpenAI apply them across drafts so every article matches your writing voice consistently."
 ---

+<Info icon="layer-group">
+**Works with:** Mem0 OSS (`Memory`) and Mem0 Platform (`MemoryClient`)
+</Info>

 This guide demonstrates how to leverage **Mem0** to streamline content writing by applying your unique writing style and preferences using persistent memory.

@@ -357,8 +360,8 @@ Mem0 enables a seamless, intelligent content-writing workflow, perfect for conte
 ---

 <CardGroup cols={2}>
-  <Card title="Control Memory Ingestion" icon="filter" href="/cookbooks/essentials/controlling-memory-ingestion">
-    Filter and curate content examples to maintain consistent writing style.
+  <Card title="Custom Instructions" icon="filter" href="/platform/features/custom-instructions">
+    Steer what Mem0 extracts and stores to maintain a consistent writing style.
  </Card>
  <Card title="Email Automation with Mem0" icon="envelope" href="/cookbooks/operations/email-automation">
    Automate email drafting with memory-powered context and tone matching.
@@ -3,11 +3,12 @@ title: Multi-Session Research Agent
 description: "Build a personalized deep research agent that reuses Mem0 memories to keep findings, sources, and preferences aligned across multi-session investigations."
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform
+</Info>

 Deep Research is an intelligent agent that synthesizes large amounts of online data and completes complex research tasks, customized to your unique preferences and insights. Built on Mem0's technology, it enhances AI-driven online exploration with personalized memories.

-You can check out the GitHub repository here: [Personalized Deep Research](https://github.com/mem0ai/personalized-deep-research/tree/mem0)
-
 ## Overview

 Deep Research leverages Mem0's memory capabilities to:
@@ -61,12 +62,6 @@ Watch Deep Research in action:
 - **Technical Research**: Technology evaluation, solution comparison
 - **Business Research**: Strategic planning, opportunity analysis

-## Try It Out
-
-> To try it yourself, clone the repository and follow the instructions in the README to run it locally or deploy it.
-
- [Personalized Deep Research GitHub](https://github.com/mem0ai/personalized-deep-research/tree/mem0)
-
 ---

 <CardGroup cols={2}>
@@ -3,6 +3,9 @@ title: Automated Email Intelligence
 description: "Turn your inbox into searchable Mem0 memories that categorize threads, surface past conversations, and generate summaries with an OpenAI Python agent."
 ---

+<Info icon="layer-group">
+**Works with:** Mem0 OSS (`Memory`) and Mem0 Platform (`MemoryClient`)
+</Info>

 This guide demonstrates how to build an intelligent email processing system using Mem0's memory capabilities. You'll learn how to store, categorize, retrieve, and analyze emails to create a smart email management solution.

@@ -3,6 +3,9 @@ title: Memory-Powered Support Agent
 description: "Build a Python customer support agent with Mem0 and OpenAI that recalls past tickets, resolutions, and user context to deliver faster ticket replies."
 ---

+<Info icon="server">
+**Works with:** Mem0 OSS (`Memory`)
+</Info>

 You can create a personalized Customer Support AI Agent using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.

@@ -3,6 +3,9 @@ title: Collaborative Task Assistant
 description: "Coordinate a multi-user task assistant with Mem0 shared project memories, tracking authored messages, roles, and updates across teammates in Python."
 ---

+<Info icon="server">
+**Works with:** Mem0 OSS (`Memory`)
+</Info>

 ## Overview

@@ -1,6 +1,7 @@
 ---
-title: Overview
-description: "Browse Mem0 cookbook tutorials for building stateful AI companions, RAG chatbots, and memory-powered agents with OpenAI, LlamaIndex, and more frameworks."
+title: "Cookbooks and Tutorials"
+sidebarTitle: "Overview"
+description: "Browse cookbook examples and tutorials for building AI applications with Mem0, from companion chatbots to AI agents."
 ---

 With Mem0, you can create stateful LLM-based applications such as chatbots, virtual assistants, or AI agents. Mem0 enhances your applications by providing a memory layer that makes responses:
@@ -13,6 +14,55 @@ With Mem0, you can create stateful LLM-based applications such as chatbots, virt

 Here are some examples of how Mem0 can be integrated into various applications:

+## Pick by compatibility
+
+Every cookbook opens with a **Works with** badge naming the SDK surface it uses. Pick your setup below to see only the cookbooks that run on it. Three cookbooks work on both and appear under either tab.
+
+<Tabs>
+  <Tab title="Self-hosted OSS">
+    Ten cookbooks run on the open-source `Memory` class, with no Mem0 Platform account.
+
+    | Cookbook | Category | Works with |
+    | --- | --- | --- |
+    | [Personalized AI Tutor](/cookbooks/companions/ai-tutor) | Companions | OSS only |
+    | [Build a Node.js Companion](/cookbooks/companions/nodejs-companion) | Companions | OSS only |
+    | [Self-Hosted AI Companion](/cookbooks/companions/local-companion-ollama) | Companions | OSS only |
+    | [Smart Travel Assistant](/cookbooks/companions/travel-assistant) | Companions | OSS only |
+    | [Build a Companion with Mem0](/cookbooks/essentials/building-ai-companion) | Essentials | OSS and Platform |
+    | [Bedrock with Persistent Memory](/cookbooks/integrations/aws-bedrock) | Integrations | OSS only |
+    | [Automated Email Intelligence](/cookbooks/operations/email-automation) | Operations | OSS and Platform |
+    | [Collaborative Task Assistant](/cookbooks/operations/team-task-agent) | Operations | OSS only |
+    | [Content Creation Workflow](/cookbooks/operations/content-writing) | Operations | OSS and Platform |
+    | [Memory-Powered Support Agent](/cookbooks/operations/support-inbox) | Operations | OSS only |
+  </Tab>
+  <Tab title="Hosted Platform">
+    Twenty cookbooks run on the hosted Platform, using `MemoryClient` or the Mem0 MCP server with a `MEM0_API_KEY`.
+
+    | Cookbook | Category | Works with |
+    | --- | --- | --- |
+    | [Interactive Memory Demo](/cookbooks/companions/quickstart-demo) | Companions | Platform only |
+    | [Research Assistant for YouTube](/cookbooks/companions/youtube-research) | Companions | Platform only |
+    | [Voice-First AI Companion](/cookbooks/companions/voice-companion-openai) | Companions | Platform only |
+    | [Build a Companion with Mem0](/cookbooks/essentials/building-ai-companion) | Essentials | OSS and Platform |
+    | [Export Stored Memories](/cookbooks/essentials/exporting-memories) | Essentials | Platform only |
+    | [Partition Memories by Entity](/cookbooks/essentials/entity-partitioning-playbook) | Essentials | Platform only |
+    | [Tag and Organize Memories](/cookbooks/essentials/tagging-and-organizing-memories) | Essentials | Platform only |
+    | [Gemini 3 with Mem0 MCP](/cookbooks/frameworks/gemini-3-with-mem0-mcp) | Frameworks | Platform only |
+    | [Multi-Agent Collaboration](/cookbooks/frameworks/llamaindex-multiagent) | Frameworks | Platform only |
+    | [Persistent Eliza Characters](/cookbooks/frameworks/eliza-os-character) | Frameworks | Platform only |
+    | [ReAct Agents with Memory](/cookbooks/frameworks/llamaindex-react) | Frameworks | Platform only |
+    | [Visual Memory Retrieval](/cookbooks/frameworks/multimodal-retrieval) | Frameworks | Platform only |
+    | [Healthcare Coach with ADK](/cookbooks/integrations/healthcare-google-adk) | Integrations | Platform only |
+    | [Memory as OpenAI Tool](/cookbooks/integrations/openai-tool-calls) | Integrations | Platform only |
+    | [Memory-Powered Agent SDK](/cookbooks/integrations/agents-sdk-tool) | Integrations | Platform only |
+    | [Persistent Mastra Agents](/cookbooks/integrations/mastra-agent) | Integrations | Platform only |
+    | [Search with Personal Context](/cookbooks/integrations/tavily-search) | Integrations | Platform only |
+    | [Automated Email Intelligence](/cookbooks/operations/email-automation) | Operations | OSS and Platform |
+    | [Content Creation Workflow](/cookbooks/operations/content-writing) | Operations | OSS and Platform |
+    | [Multi-Session Research Agent](/cookbooks/operations/deep-research) | Operations | Platform only |
+  </Tab>
+</Tabs>
+
 ## Start here

 The most popular cookbooks to get going fast:
@@ -47,11 +97,18 @@ The most popular cookbooks to get going fast:
    Balance personalization with consistent behavior across users, agents, and apps.
  </Card>
  <Card
-    title="Control Memory Ingestion"
-    icon="filter"
-    href="/cookbooks/essentials/controlling-memory-ingestion"
+    title="Tag and Organize Memories"
+    icon="tags"
+    href="/cookbooks/essentials/tagging-and-organizing-memories"
  >
-    Filter speculation and low-confidence data.
+    Use categories to keep retrieval fast and audits simple.
+  </Card>
+  <Card
+    title="Export Memories"
+    icon="download"
+    href="/cookbooks/essentials/exporting-memories"
+  >
+    Back up, migrate, and audit stored memory data.
  </Card>
 </CardGroup>

@@ -46,7 +46,7 @@ When new messages arrive, Mem0 extracts durable facts and stores them with the i
 1. **Context lookup.** Mem0 checks related existing memories so it can avoid storing the same fact again.
 2. **Fact extraction.** An LLM extracts preferences, decisions, plans, and other details your agent can reuse.
 3. **Deduplication and embedding.** Redundant facts are removed, then each memory is embedded for semantic search.
-4. **Entity linking.** When configured, Mem0 links people, places, organizations, and concepts across memories.
+4. **Entity extraction.** Mem0 pulls out the people, places, organizations, and concepts each memory mentions and stores them for entity matching at search time. On Platform these entities also become the nodes of [Graph Memory](/platform/features/graph-memory).

 The automatic extraction path is additive. If a user says, "I moved from Austin to Seattle," Mem0 can store the new fact without silently rewriting the old one. Use explicit `update` or `delete` operations when your application needs to correct or remove a memory.

@@ -61,7 +61,7 @@ When you call `search`, Mem0 ranks stored memories against your query and filter
 | **Entity** | Boosts memories linked to entities in the query | Questions about a person, project, or account |
 | **Temporal** | Scores candidates on time metadata extracted at write time against the query's temporal intent | Temporal questions ("when did...", current state, recency) |

-Platform retrieval fuses these signals in the managed service. OSS retrieval depends on your configured vector store, optional reranker, and graph store.
+Platform retrieval fuses these signals in the managed service, where the entity signal is powered by built-in [Graph Memory](/platform/features/graph-memory). OSS retrieval depends on your configured vector store and optional reranker, and boosts on entity overlap alone: it has no graph memory.

 <Note>
  Always scope searches with filters such as `user_id`, `agent_id`, or `run_id`. This keeps memories from different users, agents, or sessions from mixing.
@@ -75,7 +75,7 @@ Mem0 stores different parts of a memory in stores built for different lookup pat
 |---|---|---|
 | **SQL database** | Facts and metadata | The source of truth for each memory |
 | **Vector database** | Embeddings | Semantic similarity search |
-| **Entity or graph store** | Entities and relationships | Relationship-aware retrieval when graph memory is enabled |
+| **Entity store** | Entities extracted from memory text | Boosts memories sharing entities with the query. On Platform it also backs [Graph Memory](/platform/features/graph-memory) |

 On Mem0 Platform, these stores are managed for you. In OSS, you choose and operate the backing stores through your configuration.

@@ -66,7 +66,7 @@ The combined score outperformed every individual signal across every category te

 ### LoCoMo

-[LoCoMo](https://github.com/snap-stanford/locomo) tests single-hop, multi-hop, open-domain, and temporal memory recall across conversational sessions.
+[LoCoMo](https://github.com/snap-research/locomo) tests single-hop, multi-hop, open-domain, and temporal memory recall across conversational sessions.

 | Category | Score |
 |---|---|
@@ -1,6 +1,7 @@
 ---
-title: Delete Memory
-description: Delete Mem0 memories one at a time, in bulk by ID, or with filters to honor GDPR erasure requests and keep your knowledge base clean.
+title: "Delete Memory Operation"
+sidebarTitle: "Delete Memory"
+description: Remove memories from Mem0 either individually, in bulk, or via filters.
 icon: "trash"
 iconType: "solid"
 ---
@@ -1,6 +1,7 @@
 ---
-title: Update Memory
-description: Update an existing Mem0 memory by memory_id to fix content, refresh preferences, or enrich metadata without deleting and re-adding the record.
+title: "Update Memory Operation"
+sidebarTitle: "Update Memory"
+description: Modify an existing memory by updating its content or metadata.
 icon: "pen-to-square"
 iconType: "solid"
 ---
@@ -1,68 +1,69 @@
 ---
 title: Memory Types
-description: "Learn how Mem0 layers conversation, session, and user memory types so your agents surface the right context at the right point in a chat."
+description: "What memory_type actually does in Mem0: procedural memory is implemented, semantic and episodic are not."
 icon: "tag"
 iconType: "solid"
 ---

-# How Mem0 Organizes Memory
+# Memory Types

-Mem0 separates memory into layers so agents remember the right detail at the right time. Think of it like a notebook: a sticky note for the current task, a daily journal for the session, and an archive for everything a user has shared.
+Mem0's Python SDK exposes a `memory_type` parameter on `add()`. The underlying `MemoryType` enum defines three values, but only one of them is wired up. This page states plainly which is which so you don't build against a type that doesn't exist yet.

-## Key terms
+## Status

- **Conversation memory**: In-flight messages inside a single turn (what was just said).
- **Session memory**: Short-lived facts that apply for the current task or channel.
- **User memory**: Long-lived knowledge tied to a person, account, or workspace.
- **Organizational memory**: Shared context available to multiple agents or teams.
+| Type | Enum value | Status | Notes |
+| --- | --- | --- | --- |
+| Procedural memory | `procedural_memory` | **Implemented** | Python OSS only (`Memory`/`AsyncMemory`). Pass `memory_type="procedural_memory"` and `agent_id` to `add()`. Not available on the Platform `MemoryClient`, and not available in the TypeScript SDK (OSS or Platform). |
+| Semantic memory | `semantic_memory` | **Not implemented** | Defined in the `MemoryType` enum but never read anywhere else in the codebase. Passing it to `add()` raises a validation error. There is no evidence in this repo of a roadmap date for this. |
+| Episodic memory | `episodic_memory` | **Not implemented** | Same as above: defined, never wired into the extraction pipeline, rejected by validation, no documented roadmap. |

-```mermaid
-graph LR
-  A[Conversation turn] --> B[Session memory]
-  B --> C[User memory]
-  C --> D[Org memory]
-  C --> E[Mem0 retrieval layer]
-```
+<Warning>
+  Only `procedural_memory` is a real, working value. Calling `memory.add(messages, memory_type="semantic_memory")` (or `episodic_memory`) is rejected and tells you to pass `procedural_memory` instead. Sync `Memory.add()` raises `Mem0ValidationError`; `AsyncMemory.add()` raises a plain `ValueError`.
+</Warning>

-## Short-term vs long-term memory
+## Procedural memory

-Short-term memory keeps the current conversation coherent. It includes:
-
- **Conversation history**: recent turns in order so the agent remembers what was just said.
- **Working memory**: temporary state such as tool outputs or intermediate calculations.
- **Attention context**: the immediate focus of the assistant, similar to what a person holds in mind mid-sentence.
-
-Long-term memory preserves knowledge across sessions. It captures:
-
- **Factual memory**: user preferences, account details, and domain facts.
- **Episodic memory**: summaries of past interactions or completed tasks.
- **Semantic memory**: relationships between concepts so agents can reason about them later.
-
-Mem0 maps these classic categories onto its layered storage so you can decide what should fade quickly versus what should last for months.
-
-## How does it work?
-
-Mem0 stores each layer separately and merges them when you query:
-
-1. **Capture**: Messages enter the conversation layer while the turn is active.
-2. **Promote**: Relevant details persist to session or user memory based on your `user_id`, `run_id`, and metadata.
-3. **Retrieve**: The search pipeline pulls from all layers, ranking user memories first, then session notes, then raw history.
+Procedural memory stores step-by-step task knowledge (how an agent performs a workflow) rather than facts about a user. It requires `agent_id`:

 ```python
-import os
-
 from mem0 import Memory

 memory = Memory()

-# Sticky note: conversation memory
 memory.add(
-    ["I'm Alex and I prefer boutique hotels."],
+    [
+        {"role": "user", "content": "Book a flight from SFO to NYC"},
+        {"role": "assistant", "content": "1. Search flights. 2. Filter by price. 3. Confirm booking."},
+    ],
+    agent_id="travel-agent",
+    memory_type="procedural_memory",
+)
+```
+
+Omit `memory_type` entirely and Mem0 stores the messages as an ordinary memory: there is no semantic/episodic pathway for it to fall into. Any other explicit value is rejected by validation rather than quietly falling back to an ordinary memory.
+
+## How every other memory is scoped
+
+Outside of the `procedural_memory` special case, Mem0 does not sort memories into named types. Every memory is scoped by the identifiers you pass in, and the same identifiers are used to retrieve it later:
+
+- **`user_id`**: ties a memory to a specific person or account.
+- **`agent_id`**: ties a memory to a specific agent or assistant persona.
+- **`run_id`**: ties a memory to a specific session, task, or conversation thread.
+- **`app_id`** (Platform only): ties a memory to a specific application or tenant, in addition to the three above. See <Link href="/platform/features/entity-scoped-memory">Entity-Scoped Memory</Link>.
+
+At least one identifier is required on `add()`. Passing more than one narrows the scope further (for example, `user_id` + `run_id` together).
+
+```python
+from mem0 import Memory
+
+memory = Memory()
+
+memory.add(
+    "I'm Alex and I prefer boutique hotels.",
    user_id="alex",
    run_id="trip-planning-2025",
 )

-# Later in the session, pull long-term + session context
 results = memory.search(
    "Any hotel preferences?",
    filters={"user_id": "alex", "run_id": "trip-planning-2025"},
@@ -70,52 +71,48 @@ results = memory.search(
 ```

 <Tip>
-  Use `run_id` when you want short-term context to expire automatically; rely on `user_id` for lasting personalization.
+  Use `run_id` when you want a set of memories to stay tied to one session or task; use `user_id` alone for anything that should persist across every session for that person.
 </Tip>

-## When should you use each layer?
+## How memories are extracted and updated

- **Conversation memory**: Tool calls or chain-of-thought that only matter within the current turn.
- **Session memory**: Multi-step tasks (onboarding flows, debugging sessions) that should reset once complete.
- **User memory**: Personal preferences, account state, or compliance details that must persist across interactions.
- **Organizational memory**: Shared FAQs, product catalogs, or policies that every agent should recall.
+When `infer=True` (the default) on `add()`, Mem0 runs a single pipeline rather than routing through separate type-specific paths:

-## How it compares
+1. **Context gathering**: pulls the most recent messages already stored for the same `user_id`/`agent_id`/`run_id` scope.
+2. **Existing memory retrieval**: embeds the new messages and runs a vector search against memories already in that same scope, to find candidates that might need to change.
+3. **Extraction**: a single LLM call compares the new messages against the retrieved candidates and decides, per fact, whether to `ADD`, `UPDATE`, `DELETE`, or leave a memory alone.

-| Layer | Lifetime | Short or long term | Best for | Trade-offs |
-| --- | --- | --- | --- | --- |
-| Conversation | Single response | Short-term | Tool execution detail | Lost after the turn finishes |
-| Session | Minutes to hours | Short-term | Multi-step flows | Clear it manually when done |
-| User | Weeks to forever | Long-term | Personalization | Requires consent/governance |
-| Org | Configured globally | Long-term | Shared knowledge | Needs owner to keep current |
+Alongside this, both OSS and Platform extract named entities (people, places, organizations) from memory text and use shared entities between memories to boost related results at search time. On Platform, that entity graph is also queryable directly; see <Link href="/platform/features/graph-memory">Graph Memory</Link>. In OSS, entities only affect ranking, there is no separate graph to query.

 <Warning>
-  Avoid storing secrets or unredacted PII in user or org memories: Mem0 is retrievable by design. Encrypt or hash sensitive values first.
+  Avoid storing secrets or unredacted PII in memories: they are retrievable by design. Encrypt or hash sensitive values before calling `add()`.
 </Warning>

 ## Put it into practice

- Use the <Link href="/core-concepts/memory-operations/add">Add Memory</Link> guide to persist user preferences.
- Follow <Link href="/platform/advanced-memory-operations">Advanced Memory Operations</Link> to tune metadata and retrieval.
-
-## See it live
-
- <Link href="/cookbooks/companions/ai-tutor">AI Tutor with Mem0</Link> shows session vs user memories in action.
- <Link href="/cookbooks/operations/support-inbox">Support Inbox with Mem0</Link> demonstrates shared org memory.
-
-{/* DEBUG: verify CTA targets */}
-
 <CardGroup cols={2}>
  <Card
    title="Explore Memory Operations"
-    description="Dive into the add/search/update/delete concepts next."
+    description="Dive into the add/search/update/delete operations next."
    icon="circle-check"
    href="/core-concepts/memory-operations/add"
  />
  <Card
-    title="See a Cookbook"
-    description="Apply layered memories inside a customer support agent."
+    title="Advanced Memory Operations"
+    description="Tune metadata, filters, and retrieval on Platform."
+    icon="sliders"
+    href="/platform/advanced-memory-operations"
+  />
+  <Card
+    title="AI Tutor Cookbook"
+    description="See user_id-scoped memory used in a real tutoring agent."
    icon="rocket"
+    href="/cookbooks/companions/ai-tutor"
+  />
+  <Card
+    title="Support Inbox Cookbook"
+    description="See user_id-scoped memory used in a support workflow."
+    icon="inbox"
    href="/cookbooks/operations/support-inbox"
  />
 </CardGroup>
@@ -152,7 +152,7 @@
                      "open-source/features/multimodal-support",
                      "open-source/features/custom-instructions",
                      "open-source/features/rest-api",
-                      "open-source/features/openai_compatibility",
+                      "open-source/features/openai-compatibility",
                      "platform/features/memory-expiration"
                    ]
                  },
@@ -173,14 +173,14 @@
                            "pages": [
                              "components/llms/models/openai",
                              "components/llms/models/anthropic",
-                              "components/llms/models/azure_openai",
+                              "components/llms/models/azure-openai",
                              "components/llms/models/ollama",
                              "components/llms/models/together",
                              "components/llms/models/groq",
                              "components/llms/models/litellm",
-                              "components/llms/models/mistral_AI",
-                              "components/llms/models/google_AI",
-                              "components/llms/models/aws_bedrock",
+                              "components/llms/models/mistral-ai",
+                              "components/llms/models/google-ai",
+                              "components/llms/models/aws-bedrock",
                              "components/llms/models/deepseek",
                              "components/llms/models/minimax",
                              "components/llms/models/xAI",
@@ -210,7 +210,7 @@
                              "components/vectordbs/dbs/mongodb",
                              "components/vectordbs/dbs/oracledb",
                              "components/vectordbs/dbs/azure",
-                              "components/vectordbs/dbs/azure_mysql",
+                              "components/vectordbs/dbs/azure-mysql",
                              "components/vectordbs/dbs/redis",
                              "components/vectordbs/dbs/valkey",
                              "components/vectordbs/dbs/elasticsearch",
@@ -218,16 +218,16 @@
                              "components/vectordbs/dbs/supabase",
                              "components/vectordbs/dbs/upstash-vector",
                              "components/vectordbs/dbs/vectorize",
-                              "components/vectordbs/dbs/vertex_ai",
+                              "components/vectordbs/dbs/vertex-ai",
                              "components/vectordbs/dbs/weaviate",
                              "components/vectordbs/dbs/faiss",
                              "components/vectordbs/dbs/langchain",
                              "components/vectordbs/dbs/baidu",
                              "components/vectordbs/dbs/cassandra",
-                              "components/vectordbs/dbs/s3_vectors",
+                              "components/vectordbs/dbs/s3-vectors",
                              "components/vectordbs/dbs/databricks",
                              "components/vectordbs/dbs/neon",
-                              "components/vectordbs/dbs/neptune_analytics",
+                              "components/vectordbs/dbs/neptune-analytics",
                              "components/vectordbs/dbs/turbopuffer"
                            ]
                          }
@@ -244,15 +244,15 @@
                            "icon": "list",
                            "pages": [
                              "components/embedders/models/openai",
-                              "components/embedders/models/azure_openai",
+                              "components/embedders/models/azure-openai",
                              "components/embedders/models/ollama",
                              "components/embedders/models/huggingface",
                              "components/embedders/models/vertexai",
-                              "components/embedders/models/google_AI",
+                              "components/embedders/models/google-ai",
                              "components/embedders/models/lmstudio",
                              "components/embedders/models/together",
                              "components/embedders/models/langchain",
-                              "components/embedders/models/aws_bedrock",
+                              "components/embedders/models/aws-bedrock",
                              "components/embedders/models/fastembed"
                            ]
                          }
@@ -271,10 +271,10 @@
                            "icon": "list",
                            "pages": [
                              "components/rerankers/models/cohere",
-                              "components/rerankers/models/sentence_transformer",
+                              "components/rerankers/models/sentence-transformer",
                              "components/rerankers/models/huggingface",
-                              "components/rerankers/models/llm_reranker",
-                              "components/rerankers/models/zero_entropy"
+                              "components/rerankers/models/llm-reranker",
+                              "components/rerankers/models/zero-entropy"
                            ]
                          }
                        ]
@@ -325,7 +325,9 @@
                      "integrations/google-ai-adk",
                      "integrations/mastra",
                      "integrations/vercel-ai-sdk",
-                      "integrations/chatdev"
+                      "integrations/vercel",
+                      "integrations/chatdev",
+                      "integrations/strands"
                    ]
                  },
                  {
@@ -368,8 +370,10 @@
                    "icon": "terminal",
                    "pages": [
                      "integrations/claude-code",
+                      "integrations/claude-ai",
                      "integrations/cursor",
                      "integrations/codex",
+                      "integrations/kimi",
                      "integrations/opencode",
                      "integrations/antigravity"
                    ]
@@ -380,7 +384,8 @@
                    "pages": [
                      "integrations/openclaw",
                      "integrations/hermes",
-                      "integrations/pi-agent"
+                      "integrations/pi-agent",
+                      "integrations/deepseek-plugin"
                    ]
                  }
                ]
@@ -401,7 +406,6 @@
                    "pages": [
                      "cookbooks/essentials/building-ai-companion",
                      "cookbooks/essentials/entity-partitioning-playbook",
-                      "cookbooks/essentials/controlling-memory-ingestion",
                      "cookbooks/essentials/tagging-and-organizing-memories",
                      "cookbooks/essentials/exporting-memories"
                    ]
@@ -536,6 +540,20 @@
                      "api-reference/project/delete-project"
                    ]
                  },
+                  {
+                    "group": "Dream",
+                    "icon": "sparkles",
+                    "pages": [
+                      "api-reference/dream/get-dream-config",
+                      "api-reference/dream/update-dream-config",
+                      "api-reference/dream/get-dream-stats",
+                      "api-reference/dream/get-dream-activity",
+                      "api-reference/dream/get-dream-runs",
+                      "api-reference/dream/get-dream-run-memories",
+                      "api-reference/dream/get-dream-memory-sources",
+                      "api-reference/dream/dream-preview"
+                    ]
+                  },
                  {
                    "group": "Webhooks",
                    "icon": "webhook",
@@ -611,6 +629,10 @@
    ]
  },
  "redirects": [
+    {
+      "source": "/open-source/features/reranking",
+      "destination": "/open-source/features/reranker-search"
+    },
    {
      "source": "/platform/features/contextual-add",
      "destination": "/core-concepts/memory-operations/add"
@@ -621,7 +643,7 @@
    },
    {
      "source": "/components/rerankers/models/llm",
-      "destination": "/components/rerankers/models/llm_reranker"
+      "destination": "/components/rerankers/models/llm-reranker"
    },
    {
      "source": "/migration/breaking-changes",
@@ -797,7 +819,7 @@
    },
    {
      "source": "/components/llms/models/azure_openai_structured",
-      "destination": "/components/llms/models/azure_openai"
+      "destination": "/components/llms/models/azure-openai"
    },
    {
      "source": "/components/llms/models/openai_structured",
@@ -813,7 +835,7 @@
    },
    {
      "source": "/components/vectordbs/dbs/vertex_ai_vector_search",
-      "destination": "/components/vectordbs/dbs/vertex_ai"
+      "destination": "/components/vectordbs/dbs/vertex-ai"
    },
    {
      "source": "/platform/features/selective-memory",
@@ -948,24 +970,20 @@
      "destination": "/platform/features/memory-export"
    },
    {
-      "source": "/v0x/components/:a/:b/:c",
-      "destination": "/components/:a/:b/:c"
+      "source": "/v0x/components/:slug*",
+      "destination": "/components/:slug*"
    },
    {
-      "source": "/v0x/components/:a/:b",
-      "destination": "/components/:a/:b"
+      "source": "/v0x/core-concepts/:slug*",
+      "destination": "/core-concepts/:slug*"
    },
    {
-      "source": "/v0x/core-concepts/:a/:b",
-      "destination": "/core-concepts/:a/:b"
+      "source": "/v0x/integrations/:slug*",
+      "destination": "/integrations/:slug*"
    },
    {
-      "source": "/v0x/integrations/:slug",
-      "destination": "/integrations/:slug"
-    },
-    {
-      "source": "/v0x/open-source/:slug",
-      "destination": "/open-source/:slug"
+      "source": "/v0x/open-source/:slug*",
+      "destination": "/open-source/:slug*"
    },
    {
      "source": "/v0x/introduction",
@@ -1036,8 +1054,12 @@
      "destination": "/platform/features/graph-memory"
    },
    {
-      "source": "/features/:slug",
-      "destination": "/platform/features/:slug"
+      "source": "/open-source/features/graph-memory",
+      "destination": "/platform/features/graph-memory"
+    },
+    {
+      "source": "/features/:slug*",
+      "destination": "/platform/features/:slug*"
    },
    {
      "source": "/platform/features/online-memory",
@@ -1061,7 +1083,7 @@
    },
    {
      "source": "/components/embedders/models/google_ai",
-      "destination": "/components/embedders/models/google_AI"
+      "destination": "/components/embedders/models/google-ai"
    },
    {
      "source": "/components/embedders/models/lm_studio",
@@ -1073,24 +1095,16 @@
    },
    {
      "source": "/components/llms/models/google_ai",
-      "destination": "/components/llms/models/google_AI"
+      "destination": "/components/llms/models/google-ai"
    },
    {
      "source": "/components/llms/models/mistral_ai",
-      "destination": "/components/llms/models/mistral_AI"
+      "destination": "/components/llms/models/mistral-ai"
    },
    {
      "source": "/components/llms/models/lm_studio",
      "destination": "/components/llms/models/lmstudio"
    },
-    {
-      "source": "/components/vectordbs/dbs/neptune-analytics",
-      "destination": "/components/vectordbs/dbs/neptune_analytics"
-    },
-    {
-      "source": "/components/vectordbs/dbs/s3-vectors",
-      "destination": "/components/vectordbs/dbs/s3_vectors"
-    },
    {
      "source": "/open-source/python_quickstart",
      "destination": "/open-source/python-quickstart"
@@ -1164,7 +1178,7 @@
      "destination": "/open-source/overview"
    },
    {
-      "source": "/self-hosting/:slug",
+      "source": "/self-hosting/:slug*",
      "destination": "/open-source/overview"
    },
    {
@@ -1172,7 +1186,7 @@
      "destination": "/open-source/overview"
    },
    {
-      "source": "/self-hosted/:slug",
+      "source": "/self-hosted/:slug*",
      "destination": "/open-source/overview"
    },
    {
@@ -1180,7 +1194,7 @@
      "destination": "/platform/quickstart"
    },
    {
-      "source": "/getting-started/:slug",
+      "source": "/getting-started/:slug*",
      "destination": "/platform/quickstart"
    },
    {
@@ -1188,7 +1202,7 @@
      "destination": "/open-source/setup"
    },
    {
-      "source": "/deployment/:slug",
+      "source": "/deployment/:slug*",
      "destination": "/open-source/setup"
    },
    {
@@ -1200,12 +1214,12 @@
      "destination": "/open-source/overview"
    },
    {
-      "source": "/oss/:slug",
+      "source": "/oss/:slug*",
      "destination": "/open-source/overview"
    },
    {
-      "source": "/concepts/:slug",
-      "destination": "/core-concepts/:slug"
+      "source": "/concepts/:slug*",
+      "destination": "/core-concepts/:slug*"
    },
    {
      "source": "/pricing",
@@ -1242,6 +1256,314 @@
    {
      "source": "/integrations/keywords",
      "destination": "/integrations/respan"
+    },
+    {
+      "source": "/cookbooks/essentials/controlling-memory-ingestion",
+      "destination": "/platform/features/custom-instructions"
+    },
+    {
+      "source": "/open-source/quickstart",
+      "destination": "/open-source/overview"
+    },
+    {
+      "source": "/open-source/graph-memory/overview",
+      "destination": "/open-source/overview"
+    },
+    {
+      "source": "/what-is-mem0",
+      "destination": "https://mem0.ai/"
+    },
+    {
+      "source": "/platform-vs-oss",
+      "destination": "/platform/platform-vs-oss"
+    },
+    {
+      "source": "/components/overview",
+      "destination": "/components/llms/overview"
+    },
+    {
+      "source": "/v0x/core-concepts/memory-operations/add",
+      "destination": "/core-concepts/memory-operations/add"
+    },
+    {
+      "source": "/v0x/integrations/llama-index",
+      "destination": "/integrations/llama-index"
+    },
+    {
+      "source": "/api-reference/event/get-event",
+      "destination": "/api-reference/events/get-event"
+    },
+    {
+      "source": "/api-reference/webhook/get-webhooks",
+      "destination": "/api-reference/webhook/get-webhook"
+    },
+    {
+      "source": "/api-reference/organization/update-organization-members",
+      "destination": "/api-reference/organization/update-org-member"
+    },
+    {
+      "source": "/api-reference/organization/delete-project",
+      "destination": "/api-reference/project/delete-project"
+    },
+    {
+      "source": "/api-reference/entities/get-entities",
+      "destination": "/api-reference/entities/get-users"
+    },
+    {
+      "source": "/api-reference/event/get-events",
+      "destination": "/api-reference/events/get-events"
+    },
+    {
+      "source": "/api-reference/entities/delete-users",
+      "destination": "/api-reference/entities/delete-user"
+    },
+    {
+      "source": "/api-reference/organization/update-organization-member",
+      "destination": "/api-reference/organization/update-org-member"
+    },
+    {
+      "source": "/api-reference/organization/get-organizations",
+      "destination": "/api-reference/organization/get-orgs"
+    },
+    {
+      "source": "/api-reference/entities/get-user",
+      "destination": "/api-reference/entities/get-users"
+    },
+    {
+      "source": "/api-reference/export/create-memory-export",
+      "destination": "/api-reference/memory/create-memory-export"
+    },
+    {
+      "source": "/integrations/mcp",
+      "destination": "/platform/mem0-mcp"
+    },
+    {
+      "source": "/platform/api-reference",
+      "destination": "/api-reference"
+    },
+    {
+      "source": "/api-reference/memory/add-memory",
+      "destination": "/api-reference/memory/add-memories"
+    },
+    {
+      "source": "/api-reference/overview",
+      "destination": "/api-reference"
+    },
+    {
+      "source": "/platform/reference/python-sdk",
+      "destination": "/open-source/python-quickstart"
+    },
+    {
+      "source": "/platform/python",
+      "destination": "/open-source/python-quickstart"
+    },
+    {
+      "source": "/platform/features/memory",
+      "destination": "/platform/features/memory-decay"
+    },
+    {
+      "source": "/platform/api-reference/python",
+      "destination": "/open-source/python-quickstart"
+    },
+    {
+      "source": "/api-reference/webhooks",
+      "destination": "/api-reference/webhook/create-webhook"
+    },
+    {
+      "source": "/platform/python-quickstart",
+      "destination": "/open-source/python-quickstart"
+    },
+    {
+      "source": "/api-reference/organization/delete-org-project",
+      "destination": "/api-reference/organization/delete-org"
+    },
+    {
+      "source": "/platform/features/scoping",
+      "destination": "/platform/features/entity-scoped-memory"
+    },
+    {
+      "source": "/platform/quickstart/python",
+      "destination": "/open-source/python-quickstart"
+    },
+    {
+      "source": "/api-reference/memory/history",
+      "destination": "/api-reference/memory/history-memory"
+    },
+    {
+      "source": "/api-reference/entities/get-user-memories",
+      "destination": "/api-reference/entities/get-users"
+    },
+    {
+      "source": "/platform/api-reference/webhooks/create",
+      "destination": "/api-reference/webhook/create-webhook"
+    },
+    {
+      "source": "/platform/python/quickstart",
+      "destination": "/open-source/python-quickstart"
+    },
+    {
+      "source": "/api-reference/memory/batch-update-memory",
+      "destination": "/api-reference/memory/batch-update"
+    },
+    {
+      "source": "/api-reference/memories/add",
+      "destination": "/api-reference/memory/add-memories"
+    },
+    {
+      "source": "/python-quickstart",
+      "destination": "/open-source/python-quickstart"
+    },
+    {
+      "source": "/api-reference/webhook/list-webhooks",
+      "destination": "/api-reference/webhook/get-webhook"
+    },
+    {
+      "source": "/platform/reference/memory/delete-memory",
+      "destination": "/api-reference/memory/delete-memory"
+    },
+    {
+      "source": "/platform/webhooks",
+      "destination": "/platform/features/webhooks"
+    },
+    {
+      "source": "/platform/api-reference/memories/add",
+      "destination": "/api-reference/memory/add-memories"
+    },
+    {
+      "source": "/api-reference/organization/organizations",
+      "destination": "/api-reference/organization/create-org"
+    },
+    {
+      "source": "/sdk/python/quickstart",
+      "destination": "/open-source/python-quickstart"
+    },
+    {
+      "source": "/platform/export",
+      "destination": "/platform/features/memory-export"
+    },
+    {
+      "source": "/open-source/python-memory",
+      "destination": "/open-source/python-quickstart"
+    },
+    {
+      "source": "/platform/reference",
+      "destination": "/api-reference"
+    },
+    {
+      "source": "/api-reference/memory/get-all-memories",
+      "destination": "/api-reference/memory/get-memories"
+    },
+    {
+      "source": "/api-reference/user/delete-users",
+      "destination": "/api-reference/entities/delete-user"
+    },
+    {
+      "source": "/api-reference/memories/search-memories",
+      "destination": "/api-reference/memory/search-memories"
+    },
+    {
+      "source": "/api-reference/organization/update-members",
+      "destination": "/api-reference/project/update-project-member"
+    },
+    {
+      "source": "/platform/api-reference/memory/batch-update",
+      "destination": "/api-reference/memory/batch-update"
+    },
+    {
+      "source": "/api-reference/memory/search",
+      "destination": "/api-reference/memory/search-memories"
+    },
+    {
+      "source": "/api-reference/batch/update-memories",
+      "destination": "/api-reference/memory/batch-update"
+    },
+    {
+      "source": "/api-reference/organization/update-organization-member-role",
+      "destination": "/api-reference/project/update-project-member"
+    },
+    {
+      "source": "/api-reference/webhooks/delete",
+      "destination": "/api-reference/webhook/delete-webhook"
+    },
+    {
+      "source": "/api-reference/memory/delete-all-memories",
+      "destination": "/api-reference/memory/delete-memories"
+    },
+    {
+      "source": "/platform/reference/overview",
+      "destination": "/api-reference"
+    },
+    {
+      "source": "/api-reference/mem0/add-memories",
+      "destination": "/api-reference/memory/add-memories"
+    },
+    {
+      "source": "/platform/mcp/overview",
+      "destination": "/platform/mem0-mcp"
+    },
+    {
+      "source": "/openmemory/mcp/introduction",
+      "destination": "https://mem0.ai/blog/introducing-openmemory-mcp"
+    },
+    {
+      "source": "/components/embedders/models/aws_bedrock",
+      "destination": "/components/embedders/models/aws-bedrock"
+    },
+    {
+      "source": "/components/embedders/models/azure_openai",
+      "destination": "/components/embedders/models/azure-openai"
+    },
+    {
+      "source": "/components/embedders/models/google_AI",
+      "destination": "/components/embedders/models/google-ai"
+    },
+    {
+      "source": "/components/llms/models/aws_bedrock",
+      "destination": "/components/llms/models/aws-bedrock"
+    },
+    {
+      "source": "/components/llms/models/azure_openai",
+      "destination": "/components/llms/models/azure-openai"
+    },
+    {
+      "source": "/components/llms/models/google_AI",
+      "destination": "/components/llms/models/google-ai"
+    },
+    {
+      "source": "/components/llms/models/mistral_AI",
+      "destination": "/components/llms/models/mistral-ai"
+    },
+    {
+      "source": "/components/rerankers/models/llm_reranker",
+      "destination": "/components/rerankers/models/llm-reranker"
+    },
+    {
+      "source": "/components/rerankers/models/sentence_transformer",
+      "destination": "/components/rerankers/models/sentence-transformer"
+    },
+    {
+      "source": "/components/rerankers/models/zero_entropy",
+      "destination": "/components/rerankers/models/zero-entropy"
+    },
+    {
+      "source": "/components/vectordbs/dbs/azure_mysql",
+      "destination": "/components/vectordbs/dbs/azure-mysql"
+    },
+    {
+      "source": "/components/vectordbs/dbs/neptune_analytics",
+      "destination": "/components/vectordbs/dbs/neptune-analytics"
+    },
+    {
+      "source": "/components/vectordbs/dbs/s3_vectors",
+      "destination": "/components/vectordbs/dbs/s3-vectors"
+    },
+    {
+      "source": "/components/vectordbs/dbs/vertex_ai",
+      "destination": "/components/vectordbs/dbs/vertex-ai"
+    },
+    {
+      "source": "/open-source/features/openai_compatibility",
+      "destination": "/open-source/features/openai-compatibility"
    }
  ]
 }
@@ -0,0 +1,88 @@
+<svg viewBox="0 0 600 420" role="img" aria-labelledby="venn-title venn-desc" xmlns="http://www.w3.org/2000/svg">
+  <title id="venn-title">Mem0 Memory Scoping</title>
+  <desc id="venn-desc">Venn diagram showing how a single add call produces shared project memory and personal memory, with search returning the union of both.</desc>
+
+  <rect width="600" height="420" fill="#f5f5f5"/>
+
+  <text x="300" y="36" fill="#2d3142" font-size="20" font-weight="400"
+        font-family="Georgia, serif" text-anchor="middle">Memory Scoping</text>
+  <text x="300" y="56" fill="#7a8399" font-size="9"
+        font-family="monospace" text-anchor="middle" letter-spacing="0.08em">ONE ADD CALL · TWO MEMORY BUCKETS</text>
+
+  <circle cx="228" cy="212" r="132" fill="rgba(45,49,66,0.05)" stroke="#4f5d75" stroke-width="1"/>
+  <circle cx="372" cy="212" r="132" fill="rgba(79,93,117,0.05)" stroke="#7a8399" stroke-width="1"/>
+
+  <defs>
+    <clipPath id="clip-left">
+      <circle cx="228" cy="212" r="132"/>
+    </clipPath>
+  </defs>
+  <circle cx="372" cy="212" r="132" fill="rgba(235,108,54,0.10)" clip-path="url(#clip-left)"/>
+
+  <text x="124" y="84" fill="#2d3142" font-size="13" font-weight="600"
+        font-family="sans-serif" text-anchor="middle">Shared Project</text>
+  <text x="124" y="100" fill="#4f5d75" font-size="9"
+        font-family="monospace" text-anchor="middle">agent_id = repo slug</text>
+
+  <text x="476" y="84" fill="#2d3142" font-size="13" font-weight="600"
+        font-family="sans-serif" text-anchor="middle">Personal</text>
+  <text x="476" y="100" fill="#4f5d75" font-size="9"
+        font-family="monospace" text-anchor="middle">user_id = you</text>
+
+  <text x="168" y="176" fill="#2d3142" font-size="11" font-weight="500"
+        font-family="sans-serif" text-anchor="middle">Conventions</text>
+  <text x="168" y="196" fill="#2d3142" font-size="11" font-weight="500"
+        font-family="sans-serif" text-anchor="middle">Commands</text>
+  <text x="168" y="216" fill="#2d3142" font-size="11" font-weight="500"
+        font-family="sans-serif" text-anchor="middle">Decisions</text>
+  <text x="168" y="236" fill="#2d3142" font-size="11" font-weight="500"
+        font-family="sans-serif" text-anchor="middle">Fixes</text>
+
+  <text x="168" y="264" fill="#7a8399" font-size="9"
+        font-family="monospace" text-anchor="middle">no user_id</text>
+  <text x="168" y="276" fill="#7a8399" font-size="9"
+        font-family="monospace" text-anchor="middle">visible to team</text>
+
+  <text x="432" y="192" fill="#2d3142" font-size="11" font-weight="500"
+        font-family="sans-serif" text-anchor="middle">Preferences</text>
+  <text x="432" y="212" fill="#2d3142" font-size="11" font-weight="500"
+        font-family="sans-serif" text-anchor="middle">Habits</text>
+  <text x="432" y="232" fill="#2d3142" font-size="11" font-weight="500"
+        font-family="sans-serif" text-anchor="middle">Style</text>
+
+  <text x="432" y="260" fill="#7a8399" font-size="9"
+        font-family="monospace" text-anchor="middle">no agent_id</text>
+  <text x="432" y="272" fill="#7a8399" font-size="9"
+        font-family="monospace" text-anchor="middle">private to you</text>
+
+  <text x="300" y="196" fill="#eb6c36" font-size="12" font-weight="600"
+        font-family="sans-serif" text-anchor="middle">Search</text>
+  <text x="300" y="212" fill="#eb6c36" font-size="12" font-weight="600"
+        font-family="sans-serif" text-anchor="middle">Result</text>
+  <text x="300" y="232" fill="#7a8399" font-size="9"
+        font-family="monospace" text-anchor="middle">OR [agent_id+app_id,</text>
+  <text x="300" y="244" fill="#7a8399" font-size="9"
+        font-family="monospace" text-anchor="middle">user_id+app_id]</text>
+
+  <line x1="40" y1="360" x2="560" y2="360" stroke="rgba(45,49,66,0.12)" stroke-width="0.8"/>
+
+  <text x="80" y="380" fill="#4f5d75" font-size="9" font-weight="600"
+        font-family="monospace" text-anchor="middle" letter-spacing="0.06em">agent_id</text>
+  <text x="80" y="396" fill="#7a8399" font-size="8"
+        font-family="monospace" text-anchor="middle">repo slug</text>
+
+  <text x="220" y="380" fill="#4f5d75" font-size="9" font-weight="600"
+        font-family="monospace" text-anchor="middle" letter-spacing="0.06em">app_id</text>
+  <text x="220" y="396" fill="#7a8399" font-size="8"
+        font-family="monospace" text-anchor="middle">repo</text>
+
+  <text x="380" y="380" fill="#4f5d75" font-size="9" font-weight="600"
+        font-family="monospace" text-anchor="middle" letter-spacing="0.06em">user_id</text>
+  <text x="380" y="396" fill="#7a8399" font-size="8"
+        font-family="monospace" text-anchor="middle">you</text>
+
+  <text x="520" y="380" fill="#4f5d75" font-size="9" font-weight="600"
+        font-family="monospace" text-anchor="middle" letter-spacing="0.06em">run_id</text>
+  <text x="520" y="396" fill="#7a8399" font-size="8"
+        font-family="monospace" text-anchor="middle">session</text>
+</svg>
@@ -0,0 +1,133 @@
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 860 784" width="860" role="img" aria-labelledby="seq-title seq-desc">
+  <title id="seq-title">Mem0 Plugin Architecture</title>
+  <desc id="seq-desc">Sequence diagram showing the full Claude Code plugin: lifecycle hooks, first-prompt memory recall, capture events, background extraction via detached worker, and on-demand skills and search tool.</desc>
+
+  <defs>
+    <marker id="arrow" markerWidth="8" markerHeight="6" refX="7" refY="3" orient="auto">
+      <polygon points="0 0, 8 3, 0 6" fill="#4f5d75"/>
+    </marker>
+    <marker id="arrow-accent" markerWidth="8" markerHeight="6" refX="7" refY="3" orient="auto">
+      <polygon points="0 0, 8 3, 0 6" fill="#eb6c36"/>
+    </marker>
+    <marker id="arrow-link" markerWidth="8" markerHeight="6" refX="7" refY="3" orient="auto">
+      <polygon points="0 0, 8 3, 0 6" fill="#2e5aa8"/>
+    </marker>
+    <marker id="arrow-open" markerWidth="8" markerHeight="6" refX="7" refY="3" orient="auto">
+      <polyline points="0 0, 8 3, 0 6" fill="none" stroke="#4f5d75" stroke-width="1.2"/>
+    </marker>
+    <style>
+      text { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif; }
+    </style>
+  </defs>
+
+  <rect width="100%" height="100%" fill="#f5f5f5"/>
+
+  <!-- LIFELINES -->
+  <line x1="108" y1="76" x2="108" y2="720" stroke="rgba(45,49,66,0.12)" stroke-width="1" stroke-dasharray="3,3"/>
+  <line x1="320" y1="76" x2="320" y2="720" stroke="rgba(45,49,66,0.12)" stroke-width="1" stroke-dasharray="3,3"/>
+  <line x1="540" y1="76" x2="540" y2="720" stroke="rgba(45,49,66,0.12)" stroke-width="1" stroke-dasharray="3,3"/>
+  <line x1="740" y1="76" x2="740" y2="720" stroke="rgba(45,49,66,0.12)" stroke-width="1" stroke-dasharray="3,3"/>
+
+  <!-- PHASE 1: SESSION INIT -->
+  <text x="32" y="100" fill="#7a8399" font-size="9" font-style="italic">Session init</text>
+
+  <rect x="164" y="104" width="84" height="12" rx="2" fill="#f5f5f5"/>
+  <text x="206" y="114" fill="#7a8399" font-size="8" font-family="monospace" text-anchor="middle" letter-spacing="0.06em">SESSION-START</text>
+  <line x1="112" y1="128" x2="316" y2="128" stroke="#4f5d75" stroke-width="1" marker-end="url(#arrow)"/>
+
+  <!-- PHASE 2: CAPTURE & RECALL -->
+  <text x="32" y="160" fill="#7a8399" font-size="9" font-style="italic">Capture and recall</text>
+
+  <rect x="162" y="168" width="88" height="12" rx="2" fill="#f5f5f5"/>
+  <text x="206" y="178" fill="#7a8399" font-size="8" font-family="monospace" text-anchor="middle" letter-spacing="0.06em">USER-PROMPT</text>
+  <line x1="112" y1="192" x2="316" y2="192" stroke="#4f5d75" stroke-width="1" marker-end="url(#arrow)"/>
+
+  <!-- OPT fragment -->
+  <rect x="260" y="212" width="520" height="148" rx="4" fill="rgba(235,108,54,0.08)" stroke="#eb6c36" stroke-width="1" stroke-opacity="0.4"/>
+  <rect x="260" y="212" width="36" height="16" rx="2" fill="#f5f5f5" stroke="#eb6c36" stroke-width="1" stroke-opacity="0.4"/>
+  <text x="278" y="224" fill="#eb6c36" font-size="8" font-family="monospace" text-anchor="middle" letter-spacing="0.12em">OPT</text>
+  <text x="308" y="236" fill="#7a8399" font-size="8" font-family="monospace" letter-spacing="0.04em">[first prompt]</text>
+
+  <rect x="472" y="248" width="108" height="12" rx="2" fill="#f5f5f5"/>
+  <text x="526" y="258" fill="#2e5aa8" font-size="8" font-family="monospace" text-anchor="middle" letter-spacing="0.06em">SEARCH MEMORIES</text>
+  <line x1="324" y1="272" x2="736" y2="272" stroke="#2e5aa8" stroke-width="1" marker-end="url(#arrow-link)"/>
+
+  <rect x="480" y="280" width="80" height="12" rx="2" fill="#f5f5f5"/>
+  <text x="520" y="290" fill="#7a8399" font-size="8" font-family="monospace" text-anchor="middle" letter-spacing="0.06em">≤5 MEMORIES</text>
+  <line x1="736" y1="304" x2="324" y2="304" stroke="#4f5d75" stroke-width="1" stroke-dasharray="5,4" marker-end="url(#arrow)"/>
+
+  <rect x="148" y="316" width="120" height="12" rx="2" fill="#f5f5f5"/>
+  <text x="208" y="326" fill="#eb6c36" font-size="8" font-family="monospace" text-anchor="middle" letter-spacing="0.06em">CONTEXT INJECTED</text>
+  <line x1="316" y1="348" x2="112" y2="348" stroke="#eb6c36" stroke-width="1" stroke-dasharray="5,4" marker-end="url(#arrow-accent)"/>
+
+  <rect x="176" y="380" width="60" height="12" rx="2" fill="#f5f5f5"/>
+  <text x="206" y="390" fill="#7a8399" font-size="8" font-family="monospace" text-anchor="middle" letter-spacing="0.06em">POST-TOOL</text>
+  <line x1="112" y1="404" x2="316" y2="404" stroke="#4f5d75" stroke-width="1" marker-end="url(#arrow)"/>
+
+  <rect x="156" y="416" width="100" height="12" rx="2" fill="#f5f5f5"/>
+  <text x="206" y="426" fill="#7a8399" font-size="8" font-family="monospace" text-anchor="middle" letter-spacing="0.06em">SIDEKICK-START</text>
+  <line x1="112" y1="440" x2="316" y2="440" stroke="#4f5d75" stroke-width="1" marker-end="url(#arrow)"/>
+
+  <rect x="184" y="452" width="48" height="12" rx="2" fill="#f5f5f5"/>
+  <text x="208" y="462" fill="#7a8399" font-size="8" font-family="monospace" text-anchor="middle" letter-spacing="0.06em">STOP</text>
+  <line x1="112" y1="476" x2="316" y2="476" stroke="#4f5d75" stroke-width="1" marker-end="url(#arrow)"/>
+
+  <text x="332" y="466" fill="#7a8399" font-size="8" font-style="italic">repeats each exchange</text>
+
+  <!-- PHASE 3: BACKGROUND EXTRACTION -->
+  <text x="32" y="508" fill="#7a8399" font-size="9" font-style="italic">Background extraction</text>
+
+  <rect x="380" y="516" width="96" height="12" rx="2" fill="#f5f5f5"/>
+  <text x="428" y="526" fill="#7a8399" font-size="8" font-family="monospace" text-anchor="middle" letter-spacing="0.06em">HAND_OFF_FLUSH</text>
+  <line x1="324" y1="540" x2="536" y2="540" stroke="#4f5d75" stroke-width="1" stroke-dasharray="5,4" marker-end="url(#arrow-open)"/>
+  <text x="332" y="556" fill="#7a8399" font-size="8" font-style="italic">periodic, idle (5 min), or session-end</text>
+
+  <rect x="620" y="568" width="28" height="12" rx="2" fill="#f5f5f5"/>
+  <text x="634" y="578" fill="#2e5aa8" font-size="8" font-family="monospace" text-anchor="middle" letter-spacing="0.06em">ADD</text>
+  <line x1="544" y1="592" x2="736" y2="592" stroke="#2e5aa8" stroke-width="1" marker-end="url(#arrow-link)"/>
+  <rect x="564" y="596" width="160" height="12" rx="2" fill="#f5f5f5"/>
+  <text x="644" y="606" fill="#7a8399" font-size="8" font-family="monospace" text-anchor="middle">agent_id + user_id + app_id + run_id</text>
+
+  <rect x="616" y="616" width="48" height="12" rx="2" fill="#f5f5f5"/>
+  <text x="640" y="626" fill="#7a8399" font-size="8" font-family="monospace" text-anchor="middle" letter-spacing="0.06em">STORED</text>
+  <line x1="736" y1="640" x2="544" y2="640" stroke="#4f5d75" stroke-width="1" stroke-dasharray="5,4" marker-end="url(#arrow)"/>
+
+  <!-- PHASE 4: ON-DEMAND -->
+  <text x="32" y="672" fill="#7a8399" font-size="9" font-style="italic">On-demand (skills + tool)</text>
+
+  <rect x="136" y="680" width="144" height="12" rx="2" fill="#f5f5f5"/>
+  <text x="208" y="690" fill="#7a8399" font-size="8" font-family="monospace" text-anchor="middle" letter-spacing="0.04em">/MEM0:SEARCH, FORGET</text>
+  <line x1="112" y1="704" x2="316" y2="704" stroke="#4f5d75" stroke-width="1" marker-end="url(#arrow)"/>
+  <rect x="468" y="680" width="108" height="12" rx="2" fill="#f5f5f5"/>
+  <text x="522" y="690" fill="#2e5aa8" font-size="8" font-family="monospace" text-anchor="middle" letter-spacing="0.04em">SEARCH / DELETE</text>
+  <line x1="324" y1="704" x2="736" y2="704" stroke="#2e5aa8" stroke-width="1" marker-end="url(#arrow-link)"/>
+
+  <!-- ACTIVATION BARS -->
+  <rect x="316" y="124" width="8" height="232" fill="rgba(45,49,66,0.06)" stroke="#4f5d75" stroke-width="0.8"/>
+  <rect x="316" y="400" width="8" height="84" fill="rgba(45,49,66,0.06)" stroke="#4f5d75" stroke-width="0.8"/>
+  <rect x="316" y="536" width="8" height="12" fill="rgba(45,49,66,0.06)" stroke="#4f5d75" stroke-width="0.8"/>
+  <rect x="316" y="700" width="8" height="12" fill="rgba(45,49,66,0.06)" stroke="#4f5d75" stroke-width="0.8"/>
+  <rect x="736" y="268" width="8" height="40" fill="rgba(45,49,66,0.06)" stroke="#4f5d75" stroke-width="0.8"/>
+  <rect x="536" y="536" width="8" height="108" fill="rgba(45,49,66,0.06)" stroke="#4f5d75" stroke-width="0.8"/>
+  <rect x="736" y="588" width="8" height="56" fill="rgba(45,49,66,0.06)" stroke="#4f5d75" stroke-width="0.8"/>
+  <rect x="736" y="700" width="8" height="12" fill="rgba(45,49,66,0.06)" stroke="#4f5d75" stroke-width="0.8"/>
+
+  <!-- ACTOR BOXES -->
+  <rect x="48" y="36" width="120" height="40" rx="6" fill="#ececec" stroke="#2d3142" stroke-width="1"/>
+  <text x="108" y="60" fill="#2d3142" font-size="12" font-weight="600" text-anchor="middle">Claude Code</text>
+
+  <rect x="268" y="36" width="104" height="40" rx="6" fill="rgba(235,108,54,0.08)" stroke="#eb6c36" stroke-width="1"/>
+  <rect x="276" y="42" width="44" height="12" rx="2" fill="transparent" stroke="#eb6c36" stroke-width="0.8" stroke-opacity="0.5"/>
+  <text x="298" y="51" fill="#eb6c36" font-size="7" font-family="monospace" text-anchor="middle" letter-spacing="0.08em">PLUGIN</text>
+  <text x="320" y="64" fill="#2d3142" font-size="12" font-weight="600" text-anchor="middle">Mem0</text>
+
+  <rect x="476" y="36" width="128" height="40" rx="6" fill="rgba(45,49,66,0.05)" stroke="#4f5d75" stroke-width="1"/>
+  <rect x="484" y="42" width="52" height="12" rx="2" fill="transparent" stroke="#4f5d75" stroke-width="0.8" stroke-opacity="0.5"/>
+  <text x="510" y="51" fill="#7a8399" font-size="7" font-family="monospace" text-anchor="middle" letter-spacing="0.08em">DETACHED</text>
+  <text x="540" y="64" fill="#2d3142" font-size="12" font-weight="600" text-anchor="middle">flush_worker</text>
+
+  <rect x="680" y="36" width="120" height="40" rx="6" fill="rgba(45,49,66,0.03)" stroke="rgba(45,49,66,0.3)" stroke-width="1"/>
+  <rect x="688" y="42" width="28" height="12" rx="2" fill="transparent" stroke="rgba(45,49,66,0.3)" stroke-width="0.8"/>
+  <text x="702" y="51" fill="#7a8399" font-size="7" font-family="monospace" text-anchor="middle" letter-spacing="0.08em">API</text>
+  <text x="740" y="64" fill="#2d3142" font-size="12" font-weight="600" text-anchor="middle">Mem0 Platform</text>
+</svg>
@@ -1,6 +1,7 @@
 ---
-title: Overview
-description: "Browse Mem0 integrations with LangChain, CrewAI, Vercel AI SDK, LangGraph, and other agent frameworks and tools for persistent memory."
+title: "Integrations Overview"
+sidebarTitle: "Overview"
+description: "Overview of Mem0 integrations with popular AI frameworks and tools for persistent memory and context management."
 ---

 Mem0 seamlessly integrates with popular AI frameworks and tools to enhance your LLM-based applications with persistent memory capabilities. By integrating Mem0, your applications benefit from:
@@ -15,6 +16,8 @@ Mem0 seamlessly integrates with popular AI frameworks and tools to enhance your
  **Universal Integration**: Use <Link href="/platform/mem0-mcp">Mem0 MCP</Link> for a standardized protocol that works with ANY AI client.
 </Callout>

+Sidekick is available only in the [Claude Code plugin](/integrations/claude-code#sidekick-agent). Other plugins, including the portable package, provide memory without Sidekick.
+
 Here are the available integrations for Mem0:

 ## Integrations
@@ -22,9 +22,9 @@ pip install mem0ai agentops python-dotenv
 ```

 2. Valid API keys:
-   - [AgentOps API Key](https://app.agentops.ai/dashboard/api-keys)
+   - [AgentOps API Key](https://app.agentops.ai/projects)
   - OpenAI API Key (for LLM operations)
-   - <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-agentops" rel="nofollow">Mem0 API Key</a> (optional, for cloud operations)
+   - <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-agentops">Mem0 API Key</a> (optional, for cloud operations)

 ## Basic Integration Example

@@ -23,7 +23,7 @@ pip install agno mem0ai python-dotenv
 ```

 2. Valid API keys:
-   - <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-agno" rel="nofollow">Mem0 API Key</a>
+   - <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-agno">Mem0 API Key</a>
   - OpenAI API Key (for the agent model)

 ## Quick Integration (Using `Mem0Tools`)
--- a/Show More
+++ b/Show More