docs: expand short API reference descriptions for SEO

CONTRIBUTING.md

@@ -1,157 +1,72 @@
-# Contributing to Mem0
+# Contributing to mem0
 
-First off, thank you for taking the time to contribute! 🎉 Mem0 is a
+Let us make contribution easy, collaborative and fun.
-community-driven project and we welcome contributions of all kinds — bug fixes,
-new features, documentation, examples, and integrations.
 
-Mem0 is a polyglot monorepo, and this guide covers contributing to both the
+## Submit your Contribution through PR
-**Python SDK** and the **TypeScript SDK** (and the rest of the repository).
 
-## Before You Start
+To make a contribution, follow these steps:
 
-### 1. Open an Issue First
+1. Fork and clone this repository
+2. Do the changes on your fork with dedicated feature branch `feature/f1`
+3. If you modified the code (new feature or bug-fix), please add tests for it
+4. Include proper documentation / docstring and examples to run the feature
+5. Ensure that all tests pass
+6. Submit a pull request
 
-**Always open an issue before opening a pull request.** This lets us discuss the
+For more details about pull requests, please read [GitHub's guides](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request).
-change, avoid duplicate effort, and agree on the approach before you invest time
-in code.
 
-- Search [existing issues](https://github.com/mem0ai/mem0/issues) first to see if
-  your bug or idea already exists.
-- If it doesn't, open a
-  [bug report](https://github.com/mem0ai/mem0/issues/new?template=bug_report.yml) 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
-  before starting significant work.
 
-Every pull request must link to an issue using `Closes #<issue-number>`.
+### 📦 Development Environment
 
-### 2. Sign the Contributor License Agreement (CLA)
+We use `hatch` for managing development environments. To set up:
-
-**We cannot accept or merge any pull request until you have signed our Contributor
-License Agreement (CLA).**
-
-When you open your first PR, the CLA bot will automatically comment with a link to
-sign. Signing takes less than a minute and only needs to be done once. Pull
-requests from contributors who have not signed the CLA will be blocked from
-merging.
-
-## Repository Layout
-
-The two most common contribution targets are the SDKs:
-
-| Package               | Path       | Language     | Package manager |
-| --------------------- | ---------- | ------------ | --------------- |
-| Python SDK (`mem0ai`) | `mem0/`    | Python 3.9+  | `hatch`         |
-| TypeScript SDK (`mem0ai`) | `mem0-ts/` | TypeScript | `pnpm`        |
-
-Other packages include the CLIs (`cli/python/`, `cli/node/`), integrations
-(`integrations/`), the self-hosted `server/`, `openmemory/`, and the docs site
-(`docs/`). See [AGENTS.md](./AGENTS.md) for a full map of the repository.
-
-## Development Workflow
-
-1. **Fork** the repository and **clone** your fork.
-2. Create a **feature branch** from `main` (e.g. `feature/my-new-feature` or
-   `fix/issue-1234`).
-3. Make your changes — add **tests**, **documentation**, and **examples** as
-   appropriate.
-4. Run **linting and tests** for every package you touched (see below).
-5. Commit using [Conventional Commits](https://www.conventionalcommits.org/)
-   (e.g. `feat:`, `fix:`, `docs:`, `refactor:`, `test:`).
-6. Push and open a **pull request** against `main`, linking the issue with
-   `Closes #<number>` and filling out the
-   [PR template](./.github/PULL_REQUEST_TEMPLATE.md).
-
-### Contributing to the Python SDK (`mem0/`)
-
-We use [`hatch`](https://hatch.pypa.io/latest/install/) to manage environments.
-**Do not use `pip` or `conda` for dependency management.**
 
 ```bash
-# Activate a dev environment (3.9 / 3.10 / 3.11 / 3.12)
+# Activate environment for specific Python version:
-hatch shell dev_py_3_11
+hatch shell dev_py_3_9   # Python 3.9
+hatch shell dev_py_3_10  # Python 3.10  
+hatch shell dev_py_3_11  # Python 3.11
+hatch shell dev_py_3_12  # Python 3.12
 
-# Install pre-commit hooks (runs ruff + isort on commit)
+# The environment will automatically install all dev dependencies
-pre-commit install
+# Run tests within the activated shell:
-
-# Lint, format, and sort imports
-make lint
-make format
-make sort
-
-# Run the test suite (run `make install_all` first if deps are missing)
 make test
 ```
 
-- **Linter / formatter:** Ruff (line length **120**)
+### 📌 Pre-commit
-- **Import sorting:** isort (`profile = "black"`)
-- **Tests:** pytest (in `tests/`)
 
-See the full [Development guide](https://docs.mem0.ai/contributing/development) for
+To ensure our standards, make sure to install pre-commit before starting to contribute.
-environment details.
-
-### Contributing to the TypeScript SDK (`mem0-ts/`)
-
-We use [`pnpm`](https://pnpm.io/) (v10+) for all TypeScript packages. **Do not use
-`npm` or `yarn`.**
 
 ```bash
-cd mem0-ts
+pre-commit install
-pnpm install
-
-pnpm run build        # tsup (CJS + ESM)
-pnpm run test         # jest (all tests)
-pnpm run test:unit    # unit tests with coverage
 ```
 
-- **Build:** tsup
+### 🧪 Testing
-- **Formatter:** Prettier
-- **Tests:** jest
-- Always run type checking after changes: `pnpm run typecheck` (or `tsc --noEmit`).
-- Use ES module `import` syntax — never `require()`.
 
-## Good Contribution Practices
+We use `pytest` to test our code across multiple Python versions. You can run tests using:
 
-- **Keep PRs small and focused.** One logical change per PR is easier to review and
+```bash
-  merge.
+# Run tests with default Python version
-- **Follow existing patterns.** Match the style, structure, and conventions of the
+make test
-  code around you. Don't introduce new frameworks or abstractions without
-  discussion.
-- **Write tests** that would fail without your change — regression tests for bugs,
-  coverage for new features.
-- **Update documentation** in `docs/` for any user-facing change. New `.mdx` pages
-  must be added to `docs/llms.txt` (run
-  `python scripts/check-llms-txt-coverage.py --write` to scaffold entries).
-- **Add examples** when introducing new user-facing behavior.
-- **Run linters and tests locally** before pushing — CI re-runs them on every PR
-  via the CI Gate.
-- **Never commit secrets** — no `.env` files, API keys, or credentials.
-- **Don't add core dependencies lightly.** New Python dependencies belong in an
-  optional group in `pyproject.toml`, not the core `dependencies` list.
-- **Be responsive** to review feedback and keep your branch up to date with `main`.
 
-## Pull Request Checklist
+# Test specific Python versions:
+make test-py-3.9   # Python 3.9 environment
+make test-py-3.10  # Python 3.10 environment
+make test-py-3.11  # Python 3.11 environment
+make test-py-3.12  # Python 3.12 environment
 
-Before requesting review, make sure:
+# When using hatch shells, run tests with:
+make test  # After activating a shell with hatch shell test_XX
+```
 
-- [ ] An issue exists and is linked with `Closes #<number>`
+Make sure that all tests pass across all supported Python versions before submitting a pull request.
-- [ ] You have signed the CLA
-- [ ] Your code follows the project's style guidelines (lint passes)
-- [ ] You performed a self-review of your changes
-- [ ] Tests are added/updated and pass locally
-- [ ] Documentation is updated if needed
 
-## Reporting Security Issues
+We look forward to your pull requests and can't wait to see your contributions!
 
-**Do not report security vulnerabilities through public issues or pull requests.**
+### 🚀 Releasing
-Please follow our [Security Policy](./SECURITY.md) to report them privately.
 
-## Releasing
+All packages are published automatically via GitHub Actions when a GitHub Release is created with the correct tag prefix.
 
-All packages are published automatically via GitHub Actions when a GitHub Release
+#### Tag Prefixes
-is created with the correct tag prefix.
-
-### Tag Prefixes
 
 | Package | Registry | Tag Prefix | Example |
 |---------|----------|------------|---------|
@@ -162,17 +77,15 @@ is created with the correct tag prefix.
 | `@mem0/vercel-ai-provider` | npm | `vercel-ai-v*` | `vercel-ai-v2.0.6` |
 | `@mem0/openclaw-mem0` | npm | `openclaw-v*` | `openclaw-v1.0.1` |
 
-### How to Release
+#### How to Release
 
 1. Bump the version in `pyproject.toml` (Python) or `package.json` (Node)
 2. Create a [GitHub Release](https://github.com/mem0ai/mem0/releases/new) with the matching tag prefix
 3. The correct workflow will trigger automatically — verify in the [Actions tab](https://github.com/mem0ai/mem0/actions)
 
-### Publishing Details
+#### Publishing Details
 
 - **PyPI packages** use OIDC trusted publishing via `pypa/gh-action-pypi-publish`
 - **npm packages** use OIDC trusted publishing via npm CLI (>= 11.5.1) — no tokens or secrets required
 - All workflows require `permissions: id-token: write` for OIDC authentication
 - First publish of a new npm package must be done manually; OIDC works for subsequent versions
-
-We look forward to your pull requests and can't wait to see your contributions!

SECURITY.md

@@ -1,48 +0,0 @@
-# Security Policy
-
-We take the security of Mem0 and our community seriously. Thank you for helping
-keep Mem0 and its users safe by disclosing vulnerabilities responsibly.
-
-## Reporting a Vulnerability
-
-Please **do not** report security vulnerabilities through public GitHub issues,
-pull requests, or discussions.
-
-If you believe you have found a security vulnerability in Mem0, please report it
-privately through one of the following channels:
-
-1. **GitHub Private Vulnerability Reporting** — open a
-   [private security advisory](https://github.com/mem0ai/mem0/security/advisories/new)
-   directly on this repository.
-2. **Email** the maintainers at **support@mem0.ai** with the subject line:
-
-   `SECURITY: Mem0 vulnerability report`
-
-To help us triage and resolve the issue quickly, please include as much of the
-following as you can:
-
-- Affected component or package (e.g. Python SDK, TypeScript SDK, server, OpenMemory)
-- Affected version, tag, or commit
-- Clear, step-by-step reproduction instructions
-- The security impact and a proof of concept, if available
-- Any suggested fix or mitigation
-
-## Response Process
-
-- We will acknowledge receipt of your report within **72 hours**.
-- We will work with you privately to confirm the issue and assess its impact.
-- Once a fix or mitigation is ready, we will coordinate a disclosure timeline
-  with you and credit you for the discovery, unless you prefer to remain anonymous.
-
-## Public Disclosure
-
-Please avoid sharing technical details of the vulnerability publicly until the
-maintainers have reviewed the issue and a fix or mitigation has been released. We
-are committed to resolving valid reports promptly and keeping you informed
-throughout the process.
-
-## Supported Versions
-
-We release security fixes against the latest published version of each package.
-Whenever possible, please reproduce the issue on the most recent release before
-reporting.

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

@@ -4,4 +4,4 @@ description: "Submit an export job to create a structured memory export using a
 openapi: post /v1/exports/
 ---
 
-Submit a job to create a structured export of memories using a customizable Pydantic schema. This process may take some time to complete, especially if you're exporting a large number of memories. You can tailor the export by applying various filters (e.g., `user_id`, `agent_id`, `app_id`, or `run_id`) and by modifying the Pydantic schema to ensure the final data matches your exact needs.
+Submit a job to create a structured export of memories using a customizable Pydantic schema. This process may take some time to complete, especially if you're exporting a large number of memories. You can tailor the export by applying various filters (e.g., `user_id`, `agent_id`, `run_id`, or `session_id`) and by modifying the Pydantic schema to ensure the final data matches your exact needs.

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

@@ -4,4 +4,4 @@ description: "Retrieve the latest structured memory export after submitting an e
 openapi: post /v1/exports/get
 ---
 
-Retrieve the latest structured memory export after submitting an export job. You can filter the export by `user_id`, `agent_id`, `app_id`, `run_id`, `created_at`, or `updated_at` to get the most recent export matching your filters.
+Retrieve the latest structured memory export after submitting an export job. You can filter the export by `user_id`, `run_id`, `session_id`, or `app_id` to get the most recent export matching your filters.

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

@@ -24,11 +24,11 @@ The `filters` object supports complex logical operations (AND, OR, NOT) and comp
 
 ### Search parameter defaults
 
-| Parameter | Default |
+| Parameter | V1/V2 | V3 |
-| --- | --- |
+| --- | --- | --- |
-| `top_k` | `10` (range 1–1000) |
+| `top_k` | Supported (default 10) | Supported (1-1000, default 10) |
-| `threshold` | `0.1` (pass `0.0` to disable) |
+| `threshold` | No default | Default `0.1` (pass `0.0` to disable) |
-| `rerank` | `false` (pass `true` to enable) |
+| `rerank` | Default `true` | Default `false` (pass `true` to enable) |
 
 <CodeGroup>
 ```python Platform API Example

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

@@ -1,5 +1,5 @@
 ---
 title: "Remove Organization Member"
-description: "Remove a member from an organization to revoke their access to its projects and resources."
+description: "Remove a member from an organization by email, revoking their access to all projects within that organization."
 openapi: "delete /api/v1/orgs/organizations/{org_id}/members/"
 ---

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

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

docs/api-reference/organizations-projects.mdx

@@ -14,7 +14,7 @@ Organizations and projects are **optional** features. You can use Mem0 without t
 
 ## Key Capabilities
 
-- **Multi-org/project Support**: Organization and project are resolved automatically from your API key via `/v1/ping/` — no org or project params are accepted by `MemoryClient.__init__`. Use a project-specific API key to target a particular project.
+- **Multi-org/project Support**: Specify organization and project when initializing the Mem0 client to attribute API usage appropriately
 - **Member Management**: Control access to data through organization and project membership
 - **Access Control**: Only members can access memories and data within their organization/project scope
 - **Team Isolation**: Maintain data separation between different teams and projects for secure collaboration
@@ -79,7 +79,7 @@ new_project = client.project.create(
 
 ### Update Project Settings
 
-Modify project configuration including custom instructions, categories, language preferences, retrieval criteria, and memory decay:
+Modify project configuration including custom instructions, categories, and language preferences:
 
 ```python
 # Update project with custom categories
@@ -98,17 +98,6 @@ client.project.update(
 # Use the input language for memory storage and retrieval
 client.project.update(multilingual=True)
 
-# Set retrieval criteria to control which memories are surfaced in search
-client.project.update(
-    retrieval_criteria=[
-        {"name": "relevance", "description": "How directly relevant this memory is to the current topic or user query", "weight": 3},
-        {"name": "access_frequency", "description": "How often this memory has been accessed or surfaced recently", "weight": 1}
-    ]
-)
-
-# Enable Memory Decay (boosts recently-accessed memories at search time)
-client.project.update(decay=True)
-
 # Update multiple settings at once
 client.project.update(
     custom_instructions="...",
@@ -120,34 +109,6 @@ client.project.update(
 )
 ```
 
-#### Set Retrieval Criteria
-
-`retrieval_criteria` is a per-project list of dictionaries (`List[Dict]`) that shapes how memories are ranked and filtered during search. Each dictionary has three fields: `name` (identifier), `description` (interpreted by the LLM to score each memory), and `weight` (relative influence on the final score). Use this to focus retrieval on intent-aligned or signal-specific memories:
-
-```python
-client.project.update(
-    retrieval_criteria=[
-        {
-            "name": "joy",
-            "description": "Measure the intensity of positive emotions such as happiness, excitement, or amusement expressed in the memory. A higher score reflects greater joy.",
-            "weight": 3
-        },
-        {
-            "name": "curiosity",
-            "description": "Assess the extent to which the memory reflects inquisitiveness or interest in exploring new information. A higher score reflects stronger curiosity.",
-            "weight": 2
-        },
-        {
-            "name": "access_frequency",
-            "description": "How often this memory has been accessed or surfaced recently.",
-            "weight": 1
-        }
-    ]
-)
-```
-
-Pass an empty list to clear all criteria and restore default retrieval behaviour.
-
 #### Toggle Memory Decay
 
 `decay` is a per-project boolean that turns on [Memory Decay](/platform/features/memory-decay) — a search-time ranking bias that reinforces recently-accessed memories and gently dampens stale ones. The flag is `false` by default; set it via the same project-update endpoint:

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

@@ -1,5 +1,5 @@
 ---
 title: "Remove Project Member"
-description: "Remove a member from a project to revoke their access to its memories, configuration, and resources."
+description: "Remove a member from a project by email, revoking their access to that project's memories and settings."
 openapi: "delete /api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/"
 ---

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

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

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

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

docs/changelog/highlights.mdx

@@ -113,7 +113,7 @@ Launched a unified Mem0 plugin across three major AI development environments
 
 Major expansion of the provider ecosystem:
 
-- **Apache AGE** — New graph store support, bringing the total to 4 graph store backends (Neo4j, Memgraph, Kuzu, Apache AGE). **Note:** All external graph store backends (Neo4j, Memgraph, Kuzu, Apache AGE) were subsequently removed in v2.0.0 (2026-04-14). Graph memory is now built-in entity linking with no external graph store required; see the [v2.0.0 entry above](#mem0-sdk-v2-0-0-v3-0-0).
+- **Apache AGE** — New graph store support, bringing the total to 4 graph store backends (Neo4j, Memgraph, Kuzu, Apache AGE)
 - **Turbopuffer** — New vector database provider for Python SDK
 - **MiniMax** — New LLM provider with dedicated AWS Bedrock support
 - **pgvector for Node.js** — PostgreSQL vector support added to the TypeScript OSS SDK

docs/changelog/sdk.mdx

@@ -7,21 +7,6 @@ mode: "wide"
 <Tabs>
 <Tab title="Python">
 
-<Update label="2026-06-27" description="v2.0.10">
-
-**New Features:**
-- **Client:** Expose `expiration_date` on `MemoryClient.update()` and `AsyncMemoryClient.update()` — callers can now set or clear a memory's expiration date; `None` is preserved and forwarded to the API ([#5874](https://github.com/mem0ai/mem0/pull/5874))
-
-**Bug Fixes:**
-- **Memory (OSS):** Apply `remove_code_blocks()` to the LangChain path in async `_create_procedural_memory` so code fences are stripped consistently ([#5711](https://github.com/mem0ai/mem0/pull/5711))
-- **Rerankers:** Score HuggingFace cross-encoder results with per-document sigmoid instead of set-relative min-max, preventing a single low-score document from collapsing all relevance scores to zero ([#5715](https://github.com/mem0ai/mem0/pull/5715))
-- **Core:** Validate and trim entity IDs (`user_id`, `agent_id`, `run_id`) in `delete_all()` for both sync and async `Memory` ([#5735](https://github.com/mem0ai/mem0/pull/5735))
-- **Vector Stores:** Use `.get()` for `hash` and `created_at` in the Redis `insert()` and `update()` paths so entity payloads that omit those fields no longer raise `KeyError` ([#5709](https://github.com/mem0ai/mem0/pull/5709))
-- **Memory:** Fix scale-threshold notices not firing for Redis and search-engine backends by resolving `col_info()` signature differences and adding `num_docs` to the count-extraction lookup ([#5687](https://github.com/mem0ai/mem0/pull/5687))
-- **Vector Stores:** Escape special characters in Valkey FT.SEARCH tag filter values to prevent wildcard and operator injection through tenant-isolation filters ([#5750](https://github.com/mem0ai/mem0/pull/5750))
-
-</Update>
-
 <Update label="2026-06-24" description="v2.0.9">
 
 **Bug Fixes:**
@@ -136,7 +121,7 @@ mode: "wide"
 
 **New Features:**
 - **Memory:** Warn at init time when hybrid/BM25 search silently degrades to semantic-only because the configured vector store does not implement `keyword_search`. Affected stores: Chroma, FAISS, Cassandra, LangChain, Neptune Analytics, S3 Vectors, Supabase, TurboPuffer, Valkey ([#5444](https://github.com/mem0ai/mem0/pull/5444))
-- **Memory:** Add opt-in `explain=True` parameter to `Memory.search()` and `AsyncMemory.search()`. When enabled, each result includes a `score_details` dict with `semantic_score`, `bm25_score`, `entity_boost`, `raw_score`, `max_possible_score`, `final_score`, and `threshold` so callers can understand and tune retrieval ranking ([#5102](https://github.com/mem0ai/mem0/pull/5102))
+- **Memory:** Add opt-in `explain=True` parameter to `Memory.search()` and `AsyncMemory.search()`. When enabled, each result includes a `score_breakdown` dict with `semantic`, `keyword` (normalized BM25), `entity_boost`, and `temporal_boost` signals so callers can understand and tune retrieval ranking ([#5102](https://github.com/mem0ai/mem0/pull/5102))
 
 **Bug Fixes:**
 - **Vector Stores:** Normalize similarity scores to `[0, 1]` (higher = better) consistently across all backends. 11 adapters previously returned raw distance metrics (lower = better) — FAISS, Chroma, Milvus, Redis, Cassandra, PGVector, S3 Vectors, Supabase, Valkey, Azure MySQL, and Vertex AI Vector Search — causing incorrect ranking in multi-store setups ([#5391](https://github.com/mem0ai/mem0/pull/5391))
@@ -244,7 +229,7 @@ mode: "wide"
 **Improvements:**
 - **Telemetry:** Sample OSS hot-path events at 10% via PostHog `before_send` hook to reduce event volume ([#4771](https://github.com/mem0ai/mem0/pull/4771))
 
-See the [OSS v2 to v3 migration guide](https://docs.mem0.ai/migration/oss-v2-to-v3) and [Platform migration guide](https://docs.mem0.ai/migration/platform-v2-to-v3) for upgrade instructions.
+See the [OSS v1 to v2 migration guide](https://docs.mem0.ai/migration/oss-v1-to-v2) and [Platform migration guide](https://docs.mem0.ai/migration/platform-v2-to-v3) for upgrade instructions.
 
 </Update>
 
@@ -1085,23 +1070,6 @@ See the [OSS v2 to v3 migration guide](https://docs.mem0.ai/migration/oss-v2-to-
 
 <Tab title="TypeScript">
 
-<Update label="2026-06-27" description="v3.0.12">
-
-**New Features:**
-- **Client:** Add `expirationDate` to `AddMemoryOptions`, `update()`, and the `Memory` interface; add `showExpired` to `SearchMemoryOptions` and `GetAllMemoryOptions` ([#5874](https://github.com/mem0ai/mem0/pull/5874))
-- **LLMs:** Add `MiniMaxLLM` provider backed by the OpenAI-compatible MiniMax API (`api.minimax.io/v1`, default model `MiniMax-M2.7`) ([#5858](https://github.com/mem0ai/mem0/pull/5858))
-- **LLMs:** Add `LiteLLM` provider for routing requests through a local or hosted LiteLLM proxy ([#5830](https://github.com/mem0ai/mem0/pull/5830))
-- **Vector Stores:** Add `connectionString` and `ssl` options to the PGVector config, allowing connection via URI instead of individual host/user/password/port fields ([#5789](https://github.com/mem0ai/mem0/pull/5789))
-
-**Bug Fixes:**
-- **Memory (OSS):** Validate and trim entity IDs (`userId`, `agentId`, `runId`) in `deleteAll()` via `validateAndTrimEntityId` ([#5735](https://github.com/mem0ai/mem0/pull/5735))
-- **Vector Stores:** Use nullish coalescing for `hash` and timestamps in the Redis `insert()` and `update()` paths so entity payloads that omit those fields no longer crash ([#5860](https://github.com/mem0ai/mem0/pull/5860))
-
-**Security:**
-- **Dependencies:** Bump `undici` to `>=6.27.0` via pnpm override to remediate CVE-2026-12151 ([#5861](https://github.com/mem0ai/mem0/pull/5861))
-
-</Update>
-
 <Update label="2026-06-24" description="v3.0.11">
 
 **Bug Fixes:**

docs/components/embedders/models/aws_bedrock.mdx

@@ -59,9 +59,5 @@ Here are the parameters available for configuring AWS Bedrock embedder:
 | Parameter | Description | Default Value |
 | --- | --- | --- |
 | `model` | The name of the embedding model to use | `amazon.titan-embed-text-v1` |
-| `aws_region` | AWS region for the Bedrock client | `us-west-2` |
-| `aws_access_key_id` | AWS access key ID for authentication | `None` |
-| `aws_secret_access_key` | AWS secret access key for authentication | `None` |
-| `aws_session_token` | AWS session token for temporary credentials | `None` |
 </Tab>
 </Tabs>

docs/components/embedders/models/google_AI.mdx

@@ -67,15 +67,14 @@ Here are the parameters available for configuring Gemini embedder:
 | Parameter        | Description                          | Default Value           |
 | ---------------- | ------------------------------------ | ----------------------- |
 | `model`          | The name of the embedding model to use| `models/gemini-embedding-001` |
-| `embedding_dims` | Dimensions of the embedding model     | `768`                   |
+| `embedding_dims` | Dimensions of the embedding model     | `1536`                  |
 | `api_key`        | The Google API key                   | `None`                  |
-| `output_dimensionality` | Output dimensionality for the embedding model (Gemini-specific; used when `embedding_dims` is not set) | `None` |
 </Tab>
 <Tab title="TypeScript">
 | Parameter         | Description                                   | Default Value              |
 | ----------------- | --------------------------------------------- | -------------------------- |
 | `model`           | The name of the embedding model to use        | `gemini-embedding-001`     |
-| `embeddingDims`   | Dimensions of the embedding model. When not set, uses the model's native output dimensionality (3072 for `gemini-embedding-001`; MRL truncation to 768, 1536, or 3072 is supported) | `None`                     |
+| `embeddingDims`   | Dimensions of the embedding model             | `1536`                     |
 | `apiKey`          | Google API key                                | `None`                     |
 </Tab>
 </Tabs>

docs/components/embedders/models/lmstudio.mdx

@@ -16,7 +16,7 @@ config = {
     "embedder": {
         "provider": "lmstudio",
         "config": {
-            "model": "nomic-ai/nomic-embed-text-v1.5-GGUF"
+            "model": "nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf"
         }
     }
 }
@@ -37,6 +37,6 @@ Here are the parameters available for configuring LM Studio embedder:
 
 | Parameter | Description | Default Value |
 | --- | --- | --- |
-| `model` | The name of the LM Studio model to use | `nomic-ai/nomic-embed-text-v1.5-GGUF` |
+| `model` | The name of the LM Studio model to use | `nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf` |
 | `embedding_dims` | Dimensions of the embedding model | `1536` |
 | `lmstudio_base_url` | Base URL for LM Studio connection | `http://localhost:1234/v1` |

docs/components/embedders/overview.mdx

@@ -10,7 +10,7 @@ Mem0 offers support for various embedding models, allowing users to choose the o
 See the list of supported embedders below.
 
 <Note>
-  All embedders listed below are supported in the Python implementation. The TypeScript implementation supports: **OpenAI**, **Azure OpenAI**, **Google AI**, **Langchain**, **LM Studio**, and **Ollama**.
+  The following embedders are supported in the Python implementation. The TypeScript implementation currently only supports OpenAI.
 </Note>
 
 <CardGroup cols={4}>
@@ -24,7 +24,6 @@ See the list of supported embedders below.
   <Card title="LM Studio" href="/components/embedders/models/lmstudio"></Card>
   <Card title="Langchain" href="/components/embedders/models/langchain"></Card>
   <Card title="AWS Bedrock" href="/components/embedders/models/aws_bedrock"></Card>
-  <Card title="FastEmbed" href="/components/embedders/models/fastembed"></Card>
 </CardGroup>
 
 ## Usage

docs/components/llms/config.mdx

@@ -98,7 +98,7 @@ Here's a comprehensive list of all parameters that can be used across different
     | `max_tokens`         | Tokens to generate                            | All               |
     | `top_p`              | Probability threshold for nucleus sampling    | All               |
     | `top_k`              | Number of highest probability tokens to keep  | All               |
-    | `http_client_proxies`| Allow proxy server settings                   | All               |
+    | `http_client_proxies`| Allow proxy server settings                   | AzureOpenAI       |
     | `models`             | List of models                                | Openrouter        |
     | `route`              | Routing strategy                              | Openrouter        |
     | `openrouter_base_url`| Base URL for Openrouter API                   | Openrouter        |
@@ -110,7 +110,7 @@ Here's a comprehensive list of all parameters that can be used across different
     | `deepseek_base_url`  | Base URL for DeepSeek API                     | DeepSeek          |
     | `xai_base_url`       | Base URL for XAI API                          | XAI               |
     | `sarvam_base_url`    | Base URL for Sarvam API                       | Sarvam            |
-    | `reasoning_effort`   | Reasoning level (low, medium, high)           | All               |
+    | `reasoning_effort`   | Reasoning level (low, medium, high)           | Sarvam            |
     | `frequency_penalty`  | Penalize frequent tokens (-2.0 to 2.0)        | Sarvam            |
     | `presence_penalty`   | Penalize existing tokens (-2.0 to 2.0)        | Sarvam            |
     | `seed`               | Seed for deterministic sampling               | Sarvam            |

docs/components/llms/models/anthropic.mdx

@@ -20,7 +20,7 @@ config = {
     "llm": {
         "provider": "anthropic",
         "config": {
-            "model": "claude-sonnet-4-6",
+            "model": "claude-sonnet-4-20250514",
             "temperature": 0.1,
             "max_tokens": 2000,
         }
@@ -45,7 +45,7 @@ const config = {
     provider: 'anthropic',
     config: {
       apiKey: process.env.ANTHROPIC_API_KEY || '',
-      model: 'claude-sonnet-4-6',
+      model: 'claude-sonnet-4-20250514',
       temperature: 0.1,
       maxTokens: 2000,
     },

docs/components/llms/models/aws_bedrock.mdx

@@ -6,7 +6,7 @@ description: "Configure AWS Bedrock as an LLM provider in Mem0 with IAM authenti
 ### Setup
 - Before using the AWS Bedrock LLM, make sure you have the appropriate model access from [Bedrock Console](https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/modelaccess).
 - You will also need to authenticate the `boto3` client by using a method in the [AWS documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html#configuring-credentials)
-- You will have to export `AWS_REGION`, `AWS_ACCESS_KEY_ID`, and `AWS_SECRET_ACCESS_KEY` to set environment variables.
+- You will have to export `AWS_REGION`, `AWS_ACCESS_KEY`, and `AWS_SECRET_ACCESS_KEY` to set environment variables.
 
 ### Usage
 

docs/components/llms/models/groq.mdx

@@ -21,7 +21,7 @@ config = {
     "llm": {
         "provider": "groq",
         "config": {
-            "model": "llama-3.3-70b-versatile",
+            "model": "mixtral-8x7b-32768",
             "temperature": 0.1,
             "max_tokens": 2000,
         }
@@ -46,7 +46,7 @@ const config = {
     provider: 'groq',
     config: {
       apiKey: process.env.GROQ_API_KEY || '',
-      model: 'llama3-70b-8192',
+      model: 'mixtral-8x7b-32768',
       temperature: 0.1,
       maxTokens: 1000,
     },

docs/components/llms/models/xAI.mdx

@@ -20,7 +20,7 @@ config = {
     "llm": {
         "provider": "xai",
         "config": {
-            "model": "grok-4.3",
+            "model": "grok-3-beta",
             "temperature": 0.1,
             "max_tokens": 2000,
         }

docs/components/llms/overview.mdx

@@ -16,7 +16,7 @@ For a comprehensive list of available parameters for llm configuration, please r
 See the list of supported LLMs below.
 
 <Note>
-  All LLMs are supported in Python. The following LLMs are also supported in TypeScript: **OpenAI**, **Anthropic**, **Groq**, **Azure OpenAI**, **DeepSeek**, **Google AI**, **Langchain**, **LM Studio**, **Mistral AI**, and **Ollama**.
+  All LLMs are supported in Python. The following LLMs are also supported in TypeScript: **OpenAI**, **Anthropic**, and **Groq**.
 </Note>
 
 <CardGroup cols={4}>

docs/components/vectordbs/dbs/baidu.mdx

@@ -46,7 +46,7 @@ Here are the parameters available for configuring Baidu VectorDB:
 | `account` | Baidu VectorDB account name | `root` |
 | `api_key` | API key for accessing Baidu VectorDB | Required |
 | `database_name` | Name of the database | `mem0` |
-| `table_name` | Name of the table | `mem0` |
+| `table_name` | Name of the table | `mem0_table` |
 | `embedding_model_dims` | Dimensions of the embedding model | `1536` |
 | `metric_type` | Distance metric for similarity search | `L2` |
 

docs/components/vectordbs/dbs/elasticsearch.mdx

@@ -56,8 +56,6 @@ Here are the parameters available for configuring Elasticsearch:
 | `api_key`              | API key for authentication                         | `None`        |
 | `user`                 | Username for basic authentication                  | `None`        |
 | `password`             | Password for basic authentication                  | `None`        |
-| `use_ssl`              | Whether to use SSL for the connection              | `True`        |
-| `ca_certs`             | Path to CA bundle for SSL certificate verification | `None`        |
 | `verify_certs`         | Whether to verify SSL certificates                 | `True`        |
 | `auto_create_index`    | Whether to automatically create the index          | `True`        |
 | `custom_search_query`  | Function returning a custom search query           | `None`        |

docs/components/vectordbs/dbs/faiss.mdx

@@ -55,7 +55,6 @@ Here are the parameters available for configuring FAISS:
 | `path` | Path to store FAISS index and metadata | `/tmp/faiss/<collection_name>` |
 | `distance_strategy` | Distance metric strategy to use (options: 'euclidean', 'inner_product', 'cosine') | `euclidean` |
 | `normalize_L2` | Whether to normalize L2 vectors (only applicable for euclidean distance) | `False` |
-| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
 
 ### Performance Considerations
 

docs/components/vectordbs/dbs/langchain.mdx

@@ -47,12 +47,12 @@ m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
 ```typescript TypeScript
-import { Memory } from "mem0ai/oss";
+import { Memory } from "mem0ai";
 import { OpenAIEmbeddings } from "@langchain/openai";
-import { MemoryVectorStore } from "langchain/vectorstores/memory";
+import { MemoryVectorStore as LangchainMemoryStore } from "langchain/vectorstores/memory";
 
 const embeddings = new OpenAIEmbeddings();
-const vectorStore = new MemoryVectorStore(embeddings);
+const vectorStore = new LangchainVectorStore(embeddings);
 
 const config = {
     "vector_store": {

docs/components/vectordbs/dbs/mongodb.mdx

@@ -42,8 +42,8 @@ Here are the parameters available for configuring MongoDB:
 | Parameter | Description | Default Value |
 | --- | --- | --- |
 | db_name | Name of the MongoDB database | `"mem0_db"` |
-| collection_name | Name of the MongoDB collection | `"mem0"` |
+| collection_name | Name of the MongoDB collection | `"mem0_collection"` |
 | embedding_model_dims | Dimensions of the embedding vectors | `1536` |
-| mongo_uri | The MongoDB URI connection string | `mongodb://localhost:27017` |
+| mongo_uri | The MongoDB URI connection string | `mongodb://username:password@localhost:27017` |
 
-> **Note**: If `mongo_uri` is not provided, it will default to `mongodb://localhost:27017`.
+> **Note**: If `mongo_uri` is not provided, it will default to `mongodb://username:password@localhost:27017`.

docs/components/vectordbs/dbs/neptune_analytics.mdx

@@ -10,7 +10,7 @@ description: "Use AWS Neptune Analytics as a vector store in Mem0, combining gra
 ## Installation
 
 ```bash
-pip install mem0ai[vector-stores]
+pip install mem0ai[vector_stores]
 ```
 
 ## Usage

docs/components/vectordbs/dbs/upstash-vector.mdx

@@ -18,9 +18,7 @@ os.environ["UPSTASH_VECTOR_REST_TOKEN"] = "..."
 config = {
     "vector_store": {
         "provider": "upstash_vector",
-        "config": {
+        "enable_embeddings": True,
-            "enable_embeddings": True,
-        }
     }
 }
 

docs/components/vectordbs/dbs/valkey.mdx

@@ -9,7 +9,7 @@ description: "Use Valkey as an open-source vector store in Mem0 for high-perform
 ## Installation
 
 ```bash
-pip install mem0ai[vector-stores]
+pip install mem0ai[vector_stores]
 ```
 
 ## Usage
@@ -51,7 +51,7 @@ Here are the parameters available for configuring Valkey:
 | `hnsw_ef_construction` | Size of dynamic candidate list for HNSW | `200` |
 | `hnsw_ef_runtime` | Size of dynamic candidate list for search | `10` |
 | `cluster_mode` | Enable cluster mode for Valkey cluster (CME) deployments | `false` |
-| `timezone` | Timezone for timestamp handling | `UTC` |
+| `distance_metric` | Distance metric for vector similarity | `cosine` |
 
 ## Cluster Mode
 

docs/components/vectordbs/dbs/vertex_ai.mdx

@@ -24,7 +24,7 @@ config = {
             "deployment_index_id": "YOUR_DEPLOYMENT_INDEX_ID",  # Required: Deployment-specific ID
             "project_id": "YOUR_PROJECT_ID",              # Required: Google Cloud project ID
             "project_number": "YOUR_PROJECT_NUMBER",      # Required: Google Cloud project number
-            "region": "YOUR_REGION",                      # Required: Google Cloud region
+            "region": "YOUR_REGION",                      # Optional: Defaults to GOOGLE_CLOUD_REGION
             "credentials_path": "path/to/credentials.json", # Optional: Defaults to GOOGLE_APPLICATION_CREDENTIALS
             "vector_search_api_endpoint": "YOUR_API_ENDPOINT" # Required for get operations
         }
@@ -45,6 +45,5 @@ m.add("Your text here", user_id="user", metadata={"category": "example"})
 | `project_id` | Google Cloud project ID | Yes |
 | `project_number` | Google Cloud project number | Yes |
 | `vector_search_api_endpoint` | Vector search API endpoint | Yes (for get operations) |
-| `region` | Google Cloud region | Yes |
+| `region` | Google Cloud region | No (defaults to GOOGLE_CLOUD_REGION) |
 | `credentials_path` | Path to service account credentials | No (defaults to GOOGLE_APPLICATION_CREDENTIALS) |
-| `service_account_json` | Service account credentials as a dictionary (alternative to `credentials_path`) | `None` |

docs/components/vectordbs/dbs/weaviate.mdx

@@ -7,7 +7,7 @@ description: "Use Weaviate as an open-source vector search engine in Mem0 for st
 
 ### Installation
 ```bash
-pip install weaviate-client
+pip install weaviate weaviate-client
 ```
 
 ### Usage
@@ -48,5 +48,4 @@ Here are the parameters available for configuring Weaviate:
 | `collection_name` | The name of the collection to store the vectors | `mem0` |
 | `embedding_model_dims` | Dimensions of the embedding model | `1536` |
 | `cluster_url` | URL for the Weaviate server | `None` |
-| `auth_client_secret` | API key for Weaviate authentication | `None` |
+| `auth_client_secret` | API key for Weaviate authentication | `None` |
-| `additional_headers` | Additional headers to include in requests (`Dict[str, str]`) | `None` |

docs/components/vectordbs/overview.mdx

@@ -10,7 +10,7 @@ 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, and an in-memory store.
+  The following vector databases are supported in the Python implementation. The TypeScript implementation currently only supports Qdrant, Redis, Valkey, Vectorize and in-memory vector database.
 </Note>
 
 <CardGroup cols={3}>

docs/contributing/development.mdx

@@ -1,65 +1,32 @@
 ---
 title: Development
-description: "Guide to contributing code to Mem0, covering the issue-first workflow, the CLA, environment setup for the Python and TypeScript SDKs, and code quality checks."
+description: "Guide to contributing code to Mem0, covering the fork and clone workflow, PR submission, and code quality checks."
 icon: "code"
 ---
 
 # Development Contributions
 
-We strive to make contributions **easy, collaborative, and enjoyable**. Mem0 is a
+We strive to make contributions **easy, collaborative, and enjoyable**. Follow the steps below to ensure a smooth contribution process.
-polyglot monorepo containing the **Python SDK** (`mem0/`), the **TypeScript SDK**
-(`mem0-ts/`), CLIs, integrations, the self-hosted server, and the docs site.
-Follow the steps below for a smooth contribution process.
 
-<Note>
+## Submitting Your Contribution through PR
-  For the complete contributor checklist, see
-  [CONTRIBUTING.md](https://github.com/mem0ai/mem0/blob/main/CONTRIBUTING.md) in
-  the repository root.
-</Note>
 
-## Before You Start
+To contribute, follow these steps:
-
-### 1. Open an Issue First
-
-**Always open an issue before opening a pull request.** This lets us discuss the
-change, avoid duplicate work, and agree on the approach before you write code.
-
-- Search [existing issues](https://github.com/mem0ai/mem0/issues) first.
-- If none match, open a
-  [bug report](https://github.com/mem0ai/mem0/issues/new?template=bug_report.yml)
-  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>`.
-
-### 2. Sign the Contributor License Agreement (CLA)
-
-**We cannot merge any pull request until you have signed our Contributor License
-Agreement (CLA).** When you open your first PR, the CLA bot will comment with a
-link to sign — it takes less than a minute and only needs to be done once.
-
-## Submitting Your Contribution through a PR
 
 1. **Fork & Clone** the repository: [Mem0 on GitHub](https://github.com/mem0ai/mem0)
-2. **Create a Feature Branch**: Use a dedicated branch, e.g., `feature/my-new-feature`
+2. **Create a Feature Branch**: Use a dedicated branch for your changes, e.g., `feature/my-new-feature`
-3. **Implement Changes**: If adding a feature or fixing a bug, be sure to:
+3. **Implement Changes**: If adding a feature or fixing a bug, ensure to:
    - Write necessary **tests**
    - Add **documentation, docstrings, and runnable examples**
 4. **Code Quality Checks**:
    - Run **linting** to catch style issues
    - Ensure **all tests pass**
-5. **Commit** using [Conventional Commits](https://www.conventionalcommits.org/)
+5. **Submit a Pull Request**
-   (`feat:`, `fix:`, `docs:`, `refactor:`, `test:`)
-6. **Submit a Pull Request** against `main`, linking the issue and filling out the
-   PR template.
 
 For detailed guidance on pull requests, refer to [GitHub's documentation](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request).
 
 ---
 
-## Python SDK (`mem0/`)
+## Dependency Management
-
-### Dependency Management
 
 We use `hatch` as our package manager. Install it by following the [official instructions](https://hatch.pypa.io/latest/install/).
 
@@ -77,9 +44,13 @@ hatch -e dev_py_3_11 shell  # For dev_py_3_11 (differences are mentioned in pypr
 make install_all
 ```
 
+---
+
+## Development Standards
+
 ### Pre-commit Hooks
 
-Ensure `pre-commit` is installed before contributing (hooks run ruff + isort):
+Ensure `pre-commit` is installed before contributing:
 
 ```bash
 pre-commit install
@@ -87,7 +58,7 @@ pre-commit install
 
 ### Linting with `ruff`
 
-Run the linter and fix any reported issues before submitting your PR (line length **120**):
+Run the linter and fix any reported issues before submitting your PR:
 
 ```bash
 make lint
@@ -95,11 +66,10 @@ make lint
 
 ### Code Formatting
 
-To maintain a consistent code style, format your code and sort imports (isort, `profile = "black"`):
+To maintain a consistent code style, format your code:
 
 ```bash
 make format
-make sort
 ```
 
 ### Testing with `pytest`
@@ -114,46 +84,10 @@ make test
 
 ---
 
-## TypeScript SDK (`mem0-ts/`)
-
-We use [`pnpm`](https://pnpm.io/) (v10+) for all TypeScript packages. **Do NOT use
-`npm` or `yarn`.**
-
-```bash
-cd mem0-ts
-pnpm install
-
-pnpm run build        # tsup (CJS + ESM)
-pnpm run test         # jest (all tests)
-pnpm run test:unit    # unit tests with coverage
-```
-
-### Standards
-
-- **Build:** tsup
-- **Formatter:** Prettier
-- **Tests:** jest
-- Always run type checking after changes: `pnpm run typecheck` (or `tsc --noEmit`)
-- Use ES module `import` syntax — never `require()`
-
----
-
-## Reporting Security Issues
-
-**Do not report security vulnerabilities through public issues or pull requests.**
-Please follow our [Security Policy](https://github.com/mem0ai/mem0/blob/main/SECURITY.md)
-to report them privately.
-
----
-
 ## Release Process
 
-Packages are published automatically via GitHub Actions when a GitHub Release is
+Currently, releases are handled manually. We aim for frequent releases, typically when new features or bug fixes are introduced.
-created with the correct tag prefix (e.g. `v*` for the Python SDK, `ts-v*` for the
-TypeScript SDK). See
-[CONTRIBUTING.md](https://github.com/mem0ai/mem0/blob/main/CONTRIBUTING.md#releasing)
-for the full tag-prefix table and publishing details.
 
 ---
 
-Thank you for contributing to Mem0!
+Thank you for contributing to Mem0!

docs/cookbooks/companions/nodejs-companion.mdx

@@ -31,7 +31,7 @@ const openaiClient = new OpenAI();
 const memory = new Memory();
 
 async function chatWithMemories(message, userId = "default_user") {
-  const relevantMemories = await memory.search(message, { filters: { user_id: userId } });
+  const relevantMemories = await memory.search(message, { userId: userId });
   
   const memoriesStr = relevantMemories.results
     .map(entry => `- ${entry.memory}`)

docs/cookbooks/essentials/building-ai-companion.mdx

@@ -289,7 +289,8 @@ print([m["memory"] for m in constraints["results"]])
 ```python
 constraints = memory.search(
     query="injury concerns",
-    filters={"user_id": "max", "memory_bucket": {"in": ["constraints"]}},
+    user_id="max",
+    filters={"memory_bucket": {"in": ["constraints"]}},
     threshold=0.0  # optional: widen recall for short phrases
 )
 print([m["memory"] for m in constraints["results"]])
@@ -735,7 +736,8 @@ mem0_client.add(messages, user_id="max", run_id="nyc-2025")
 # Retrieve only Boston memories
 boston_memories = mem0_client.search(
     "training plan",
-    filters={"user_id": "max", "run_id": "boston-2025"}
+    user_id="max",
+    run_id="boston-2025"
 )
 ```
   </Tab>
@@ -747,7 +749,8 @@ memory.add(messages, user_id="max", run_id="nyc-2025")
 # Retrieve only Boston memories
 boston_memories = memory.search(
     "training plan",
-    filters={"user_id": "max", "run_id": "boston-2025"},
+    user_id="max",
+    run_id="boston-2025",
 )
 ```
   </Tab>
@@ -843,7 +846,8 @@ Prioritize recent training over old data:
 ```python
 recent = mem0_client.search(
     "training progress",
-    filters={"user_id": "max", "created_at": {"gte": "2025-10-01"}}
+    user_id="max",
+    filters={"created_at": {"gte": "2025-10-01"}}
 )
 ```
   </Tab>
@@ -862,7 +866,8 @@ memory.add(
 cutoff = int(datetime(2025, 10, 1).timestamp())
 recent = memory.search(
     "training progress",
-    filters={"user_id": "max", "logged_epoch": {"gte": cutoff}},
+    user_id="max",
+    filters={"logged_epoch": {"gte": cutoff}},
 )
 ```
   </Tab>
@@ -884,7 +889,8 @@ mem0_client.add(
 # Later, find all speed workouts
 speed_sessions = mem0_client.search(
     "speed work",
-    filters={"user_id": "max", "metadata": {"workout_type": "speed"}}
+    user_id="max",
+    filters={"metadata": {"workout_type": "speed"}}
 )
 ```
   </Tab>
@@ -899,7 +905,8 @@ memory.add(
 # Later, find all speed workouts
 speed_sessions = memory.search(
     "speed work",
-    filters={"user_id": "max", "workout_type": "speed"},
+    user_id="max",
+    filters={"workout_type": "speed"},
 )
 ```
   </Tab>

docs/cookbooks/essentials/exporting-memories.mdx

@@ -58,7 +58,6 @@ Use `get_all()` with filters to retrieve everything for a specific user:
 ```python
 dev_memories = client.get_all(
     filters={"user_id": "dev"},
-    page=1,
     page_size=50
 )
 

docs/cookbooks/frameworks/llamaindex-multiagent.mdx

@@ -211,8 +211,8 @@ class MultiAgentLearningSystem:
         try:
             # Search memory for learning patterns
             memories = self.memory.search(
-                query="learning machine learning",
+                user_id=self.student_id,
-                filters={"user_id": self.student_id}
+                query="learning machine learning"
             )
 
             if memories and memories.get('results'):

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

@@ -50,7 +50,7 @@ load_dotenv()
 USER_ID = "Alex"
 
 # Initialize Mem0 client
-mem0_client = MemoryClient()
+mem0 = MemoryClient()
 ```
 
 ## Define Memory Tools
@@ -76,7 +76,7 @@ def retrieve_patient_info(query: str) -> dict:
     # Search Mem0
     results = mem0_client.search(
         query,
-        filters={"user_id": USER_ID},
+        user_id=USER_ID,
         top_k=5,
         threshold=0.7  # Higher threshold for more relevant results
     )

docs/cookbooks/integrations/openai-tool-calls.mdx

@@ -53,12 +53,34 @@ async function addUserPreferences() {
 await addUserPreferences();
 ```
 
-```json Output
+```json Output (Memories)
-{
+ [
-  "message": "Memory processing has been queued for background execution",
+  {
-  "status": "PENDING",
+    "id": "ff9f3367-9e83-415d-b9c5-dc8befd9a4b4",
-  "event_id": "9f8c2b1a-4e7d-4c3a-9b21-1a2b3c4d5e6f"
+    "data": { "memory": "Loves BMW, Audi, and Porsche" },
-}
+    "event": "ADD"
+  },
+  {
+    "id": "04172ce6-3d7b-45a3-b4a1-ee9798593cb4",
+    "data": { "memory": "Hates Mercedes" },
+    "event": "ADD"
+  },
+  {
+    "id": "db363a5d-d258-4953-9e4c-777c120de34d",
+    "data": { "memory": "Loves red cars and maroon cars" },
+    "event": "ADD"
+  },
+  {
+    "id": "5519aaad-a2ac-4c0d-81d7-0d55c6ecdba8",
+    "data": { "memory": "Has a budget of 120K to 150K USD" },
+    "event": "ADD"
+  },
+  {
+    "id": "523b7693-7344-4563-922f-5db08edc8634",
+    "data": { "memory": "Likes Audi the most" },
+    "event": "ADD"
+  }
+]
 ```
 </CodeGroup>
 ## Retrieving Memories
@@ -66,7 +88,7 @@ await addUserPreferences();
 Search for relevant memories based on the current user input:
 
 ```javascript
-const relevantMemories = await mem0Client.search(userInput, { filters: { user_id: USER_ID } });
+const relevantMemories = await mem0Client.search(userInput, { userId: USER_ID });
 ```
 
 ## Structured Responses with Zod
@@ -172,7 +194,7 @@ async function main(memory = false) {
   // Search for relevant memories
   let relevantMemories = []
   if (memory) {
-    relevantMemories = await mem0Client.search(input, { filters: { user_id: USER_ID } });
+    relevantMemories = await mem0Client.search(input, { userId: USER_ID });
   }
 
   const response = await openAIClient.responses.create({

docs/cookbooks/integrations/tavily-search.mdx

@@ -4,6 +4,8 @@ description: "Blend Tavily's realtime results with personal context stored in Me
 ---
 
 
+<Snippet file="security-compliance.mdx" />
+
 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.

docs/cookbooks/operations/content-writing.mdx

@@ -217,7 +217,8 @@ def apply_writing_style(original_content):
 
     results = memory.search(
         query="What are my writing style preferences?",
-        filters={"user_id": USER_ID, "run_id": RUN_ID},
+        user_id=USER_ID,
+        run_id=RUN_ID,
     )
 
     if not results:

docs/cookbooks/operations/email-automation.mdx

@@ -314,16 +314,18 @@ class EmailProcessor:
             user_id (str): User identifier
             sender (str, optional): Filter by sender email address
         """
+        # In OSS, user_id is an explicit parameter (not inside filters)
         if not sender:
             results = self.memory.search(
                 query=query,
-                filters={"user_id": user_id, "memory_category": "email"},
+                user_id=user_id,
+                filters={"memory_category": "email"},
             )
         else:
             results = self.memory.search(
                 query=query,
+                user_id=user_id,
                 filters={
-                    "user_id": user_id,
                     "AND": [
                         {"memory_category": "email"},
                         {"sender": sender},
@@ -341,9 +343,10 @@ class EmailProcessor:
             subject (str): Email subject to match
             user_id (str): User identifier
         """
+        # In OSS, user_id is an explicit parameter
         thread = self.memory.get_all(
+            user_id=user_id,
             filters={
-                "user_id": user_id,
                 "AND": [
                     {"memory_category": "email"},
                     {"subject": {"icontains": subject}},

docs/cookbooks/operations/support-inbox.mdx

@@ -57,7 +57,7 @@ class CustomerSupportAIAgent:
         """
         # Start a streaming chat completion request to the AI
         stream = self.client.chat.completions.create(
-            model="gpt-5-mini",
+            model="gpt-4",
             stream=True,
             messages=[
                 {"role": "system", "content": "You are a customer support AI agent."},

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

@@ -238,11 +238,11 @@ client.search("preferences", filters={
 - **Use natural language**: Mem0 understands intent, so describe what you're looking for naturally
 - **Scope with user ID**: Always provide `user_id` to scope search to relevant memories
   - **Platform API**: Use `filters={"user_id": "alice"}`
-  - **OSS**: Use `filters={"user_id": "alice"}` (passing `user_id` as a top-level kwarg raises `ValueError` in v3)
+  - **OSS**: Use `user_id="alice"` as parameter
 - **Combine filters**: Use AND/OR logic to create precise queries (Platform)
 - **Consider wildcard filters**: Use wildcard filters (e.g., `run_id: "*"`) for broader matches
 - **Tune parameters**: Adjust `top_k` for result count, `threshold` for relevance cutoff
-- **Enable reranking**: Use `rerank=True` (default is `False`) when you have a reranker configured
+- **Enable reranking**: Use `rerank=True` (default) when you have a reranker configured
 
 <Callout type="tip" icon="plug">
   **MCP Alternative**: With <Link href="/platform/mem0-mcp">Mem0 MCP</Link>, AI agents can search their own memories proactively when needed.

docs/core-concepts/memory-types.mdx

@@ -60,7 +60,7 @@ import os
 
 from mem0 import Memory
 
-memory = Memory()
+memory = Memory(api_key=os.environ["MEM0_API_KEY"])
 
 # Sticky note: conversation memory
 memory.add(
@@ -72,7 +72,8 @@ memory.add(
 # Later in the session, pull long-term + session context
 results = memory.search(
     "Any hotel preferences?",
-    filters={"user_id": "alex", "run_id": "trip-planning-2025"},
+    user_id="alex",
+    run_id="trip-planning-2025",
 )
 ```
 

docs/docs.json

@@ -123,7 +123,8 @@
                     "icon": "arrow-right",
                     "pages": [
                       "migration/platform-v2-to-v3",
-                      "migration/oss-to-platform"
+                      "migration/oss-to-platform",
+                      "migration/api-changes"
                     ]
                   },
                   {
@@ -641,10 +642,6 @@
       "source": "/migration/v0-to-v1",
       "destination": "/"
     },
-    {
-      "source": "/migration/api-changes",
-      "destination": "/migration/oss-v2-to-v3"
-    },
     {
       "source": "/platform/features/expiration-date",
       "destination": "/"

docs/llms.txt

@@ -228,6 +228,7 @@ If the user is on a pre-current major (Python < 2, TS < 3, or Platform `output_f
 - [OSS to Platform Migration](https://docs.mem0.ai/migration/oss-to-platform) [Both]: Use when moving from self-hosted to managed.
 - [OSS v2 to v3 Migration](https://docs.mem0.ai/migration/oss-v2-to-v3) [OSS]: Use when upgrading a self-hosted deployment across major versions.
 - [Platform v2 to v3 Migration](https://docs.mem0.ai/migration/platform-v2-to-v3) [Platform]: Use when upgrading a Platform integration across major versions.
+- [API Changes](https://docs.mem0.ai/migration/api-changes) [Both]: Use when the upgrade involves API surface changes.
 - [Server pgvector Image Upgrade](https://docs.mem0.ai/migration/server-pgvector-upgrade) [OSS]: Use when upgrading the self-hosted server Docker image from ankane/pgvector to pgvector/pgvector.
 - [Changelog](https://docs.mem0.ai/changelog/highlights) [Both]: Use when the user asks what shipped recently.
 

docs/migration/api-changes.mdx

@@ -0,0 +1,574 @@
+---
+title: API Reference Changes
+description: "Comprehensive reference of all API changes between Mem0 v0.x and v1.0.0 Beta, organized by component and method."
+icon: "code"
+iconType: "solid"
+---
+
+## Overview
+
+This page documents all API changes between Mem0 v0.x and v1.0.0 Beta, organized by component and method.
+
+## Memory Class Changes
+
+### Constructor
+
+#### v0.x
+```python
+from mem0 import Memory
+
+# Basic initialization
+m = Memory()
+
+# With configuration
+config = {
+    "version": "v1.0",  # Supported in v0.x
+    "vector_store": {...}
+}
+m = Memory.from_config(config)
+```
+
+#### v1.0.0 
+```python
+from mem0 import Memory
+
+# Basic initialization (same)
+m = Memory()
+
+# With configuration
+config = {
+    "version": "v1.1",  # v1.1+ only
+    "vector_store": {...},
+    # New optional features
+    "reranker": {
+        "provider": "cohere",
+        "config": {...}
+    }
+}
+m = Memory.from_config(config)
+```
+
+### add() Method
+
+#### v0.x Signature
+```python
+def add(
+    self,
+    messages,
+    user_id: str = None,
+    agent_id: str = None,
+    run_id: str = None,
+    metadata: dict = None,
+    filters: dict = None,
+    output_format: str = None,  # ❌ REMOVED
+    version: str = None         # ❌ REMOVED
+) -> Union[List[dict], dict]:
+    ...
+```
+
+#### v1.0.0  Signature
+```python
+def add(
+    self,
+    messages,
+    user_id: str = None,
+    agent_id: str = None,
+    run_id: str = None,
+    metadata: dict = None,
+    filters: dict = None,
+    infer: bool = True          # ✅ NEW: Control memory inference
+) -> dict:  # Always returns dict with "results" key
+    ...
+```
+
+#### Changes Summary
+
+| Parameter | v0.x | v1.0.0  | Change |
+|-----------|------|-----------|---------|
+| `messages` | ✅ | ✅ | Unchanged |
+| `user_id` | ✅ | ✅ | Unchanged |
+| `agent_id` | ✅ | ✅ | Unchanged |
+| `run_id` | ✅ | ✅ | Unchanged |
+| `metadata` | ✅ | ✅ | Unchanged |
+| `filters` | ✅ | ✅ | Unchanged |
+| `output_format` | ✅ | ❌ | **REMOVED** |
+| `version` | ✅ | ❌ | **REMOVED** |
+| `infer` | ❌ | ✅ | **NEW** |
+
+#### Response Format Changes
+
+**v0.x Response (variable format):**
+```python
+# With output_format="v1.0"
+[
+    {
+        "id": "mem_123",
+        "memory": "User loves pizza",
+        "event": "ADD"
+    }
+]
+
+# With output_format="v1.1"
+{
+    "results": [
+        {
+            "id": "mem_123",
+            "memory": "User loves pizza",
+            "event": "ADD"
+        }
+    ]
+}
+```
+
+**v1.0.0  Response (standardized):**
+```python
+# Always returns this format
+{
+    "results": [
+        {
+            "id": "mem_123",
+            "memory": "User loves pizza",
+            "metadata": {...},
+            "event": "ADD"
+        }
+    ]
+}
+```
+
+### search() Method
+
+#### v0.x Signature
+```python
+def search(
+    self,
+    query: str,
+    user_id: str = None,
+    agent_id: str = None,
+    run_id: str = None,
+    limit: int = 100,
+    filters: dict = None,       # Basic key-value only
+    output_format: str = None,  # ❌ REMOVED
+    version: str = None         # ❌ REMOVED
+) -> Union[List[dict], dict]:
+    ...
+```
+
+#### v1.0.0  Signature
+```python
+def search(
+    self,
+    query: str,
+    user_id: str = None,
+    agent_id: str = None,
+    run_id: str = None,
+    limit: int = 100,
+    filters: dict = None,       # ✅ ENHANCED: Advanced operators
+    rerank: bool = True         # ✅ NEW: Reranking support
+) -> dict:  # Always returns dict with "results" key
+    ...
+```
+
+#### Enhanced Filtering
+
+**v0.x Filters (basic):**
+```python
+# Simple key-value filtering only
+filters = {
+    "category": "food",
+    "user_id": "alice"
+}
+```
+
+**v1.0.0  Filters (enhanced):**
+```python
+# Advanced filtering with operators
+filters = {
+    "AND": [
+        {"category": "food"},
+        {"score": {"gte": 0.8}},
+        {
+            "OR": [
+                {"priority": "high"},
+                {"urgent": True}
+            ]
+        }
+    ]
+}
+
+# Comparison operators
+filters = {
+    "score": {"gt": 0.5},      # Greater than
+    "priority": {"gte": 5},     # Greater than or equal
+    "rating": {"lt": 3},        # Less than
+    "confidence": {"lte": 0.9}, # Less than or equal
+    "status": {"eq": "active"}, # Equal
+    "archived": {"ne": True},   # Not equal
+    "tags": {"in": ["work", "personal"]},     # In list
+    "category": {"nin": ["spam", "deleted"]}  # Not in list
+}
+```
+
+### get_all() Method
+
+#### v0.x Signature
+```python
+def get_all(
+    self,
+    user_id: str = None,
+    agent_id: str = None,
+    run_id: str = None,
+    filters: dict = None,
+    output_format: str = None,  # ❌ REMOVED
+    version: str = None         # ❌ REMOVED
+) -> Union[List[dict], dict]:
+    ...
+```
+
+#### v1.0.0  Signature
+```python
+def get_all(
+    self,
+    user_id: str = None,
+    agent_id: str = None,
+    run_id: str = None,
+    filters: dict = None        # ✅ ENHANCED: Advanced operators
+) -> dict:  # Always returns dict with "results" key
+    ...
+```
+
+### update() Method
+
+#### No Breaking Changes
+```python
+# Same signature in both versions
+def update(
+    self,
+    memory_id: str,
+    data: str
+) -> dict:
+    ...
+```
+
+### delete() Method
+
+#### No Breaking Changes
+```python
+# Same signature in both versions
+def delete(
+    self,
+    memory_id: str
+) -> dict:
+    ...
+```
+
+### delete_all() Method
+
+#### Breaking Change — Empty filter no longer silently deletes everything
+
+**Before:** calling `delete_all()` with no filters silently deleted **all memories in the project**.
+
+**After:**
+- No filters → raises a validation error (prevents accidental full-project wipe).
+- Concrete ID (e.g. `user_id="alice"`) → deletes memories for that entity (unchanged).
+- `"*"` for a filter → deletes all memories for that entity type across the project (new).
+- All four filters set to `"*"` → explicit full project wipe (new, requires opt-in on every parameter).
+
+This change replaces the silent full-project delete (triggered by an empty or missing filter) with a validation error, and introduces `"*"` wildcards as the intentional path for bulk deletion.
+
+```python
+# v0.x — no filter silently wiped all project memories
+m.delete_all()                      # DANGER: deleted everything
+m.delete_all(user_id="alice")       # deleted alice's memories
+
+# v1.x — no filter now raises an error; use "*" for intentional bulk deletes
+m.delete_all()                                                       # ERROR: at least one filter required
+m.delete_all(user_id="alice")                                        # unchanged
+m.delete_all(user_id="*")                                            # NEW — delete all users' memories
+m.delete_all(user_id="*", agent_id="*", app_id="*", run_id="*")     # NEW — full project wipe
+```
+
+## Platform Client (MemoryClient) Changes
+
+### async_mode Default Changed
+
+#### v0.x
+```python
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="your-key")
+
+# async_mode had to be explicitly set or had different default
+result = client.add("content", user_id="alice", async_mode=True)
+```
+
+#### v1.0.0
+```python
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="your-key")
+
+# async_mode defaults to True now (better performance)
+result = client.add("content", user_id="alice")  # Uses async_mode=True by default
+
+# Can still override if needed
+result = client.add("content", user_id="alice", async_mode=False)
+```
+
+## Configuration Changes
+
+### Memory Configuration
+
+#### v0.x Config Options
+```python
+config = {
+    "vector_store": {...},
+    "llm": {...},
+    "embedder": {...},
+    "graph_store": {...},
+    "version": "v1.0",              # ❌ v1.0 no longer supported
+    "history_db_path": "...",
+    "custom_instructions": "..."
+}
+```
+
+#### v1.0.0  Config Options
+```python
+config = {
+    "vector_store": {...},
+    "llm": {...},
+    "embedder": {...},
+    "graph_store": {...},
+    "reranker": {                   # ✅ NEW: Reranker support
+        "provider": "cohere",
+        "config": {...}
+    },
+    "version": "v1.1",              # ✅ v1.1+ only
+    "history_db_path": "...",
+    "custom_instructions": "...",
+    "custom_update_memory_prompt": "..."  # ✅ NEW: Custom update prompt
+}
+```
+
+### New Configuration Options
+
+#### Reranker Configuration
+```text
+# Cohere reranker
+"reranker": {
+    "provider": "cohere",
+    "config": {
+        "model": "rerank-english-v3.0",
+        "api_key": "your-api-key",
+        "top_k": 10
+    }
+}
+
+# Sentence Transformer reranker
+"reranker": {
+    "provider": "sentence_transformer",
+    "config": {
+        "model": "cross-encoder/ms-marco-MiniLM-L-6-v2",
+        "device": "cuda"
+    }
+}
+
+# Hugging Face reranker
+"reranker": {
+    "provider": "huggingface",
+    "config": {
+        "model": "BAAI/bge-reranker-base",
+        "device": "cuda"
+    }
+}
+
+# LLM-based reranker
+"reranker": {
+    "provider": "llm_reranker",
+    "config": {
+        "llm": {
+            "provider": "openai",
+            "config": {
+                "model": "gpt-4",
+                "api_key": "your-api-key"
+            }
+        }
+    }
+}
+```
+
+## Error Handling Changes
+
+### New Error Types
+
+#### v0.x Errors
+```python
+# Generic exceptions
+try:
+    result = m.add("content", user_id="alice", version="v1.0")
+except Exception as e:
+    print(f"Error: {e}")
+```
+
+#### v1.0.0  Errors
+```python
+# More specific error handling
+try:
+    result = m.add("content", user_id="alice")
+except ValueError as e:
+    if "v1.0 API format is no longer supported" in str(e):
+        # Handle version compatibility error
+        pass
+    elif "Invalid filter operator" in str(e):
+        # Handle filter syntax error
+        pass
+except TypeError as e:
+    # Handle parameter errors
+    pass
+except Exception as e:
+    # Handle unexpected errors
+    pass
+```
+
+### Validation Changes
+
+#### Stricter Parameter Validation
+
+**v0.x (Lenient):**
+```python
+# Unknown parameters might be ignored
+result = m.add("content", user_id="alice", unknown_param="value")
+```
+
+**v1.0.0  (Strict):**
+```python
+# Unknown parameters raise TypeError
+try:
+    result = m.add("content", user_id="alice", unknown_param="value")
+except TypeError as e:
+    print(f"Invalid parameter: {e}")
+```
+
+## Response Schema Changes
+
+### Memory Object Schema
+
+#### v0.x Schema
+```python
+{
+    "id": "mem_123",
+    "memory": "User loves pizza",
+    "user_id": "alice",
+    "metadata": {...},
+    "created_at": "2024-01-01T00:00:00Z",
+    "updated_at": "2024-01-01T00:00:00Z",
+    "score": 0.95  # In search results
+}
+```
+
+#### v1.0.0  Schema (Enhanced)
+```python
+{
+    "id": "mem_123",
+    "memory": "User loves pizza",
+    "user_id": "alice",
+    "agent_id": "assistant",     # ✅ More context
+    "run_id": "session_001",     # ✅ More context
+    "metadata": {...},
+    "categories": ["food"],      # ✅ NEW: Auto-categorization
+    "immutable": false,          # ✅ NEW: Immutability flag
+    "created_at": "2024-01-01T00:00:00Z",
+    "updated_at": "2024-01-01T00:00:00Z",
+    "score": 0.95,              # In search results
+    "rerank_score": 0.98        # ✅ NEW: If reranking used
+}
+```
+
+## Migration Code Examples
+
+### Simple Migration
+
+#### Before (v0.x)
+```python
+from mem0 import Memory
+
+m = Memory()
+
+# Add with deprecated parameters
+result = m.add(
+    "I love pizza",
+    user_id="alice",
+    output_format="v1.1",
+    version="v1.0"
+)
+
+# Handle variable response format
+if isinstance(result, list):
+    memories = result
+else:
+    memories = result.get("results", [])
+
+for memory in memories:
+    print(memory["memory"])
+```
+
+#### After (v1.0.0 )
+```python
+from mem0 import Memory
+
+m = Memory()
+
+# Add without deprecated parameters
+result = m.add(
+    "I love pizza",
+    user_id="alice"
+)
+
+# Always dict format with "results" key
+for memory in result["results"]:
+    print(memory["memory"])
+```
+
+### Advanced Migration
+
+#### Before (v0.x)
+```python
+# Basic filtering
+results = m.search(
+    "food preferences",
+    user_id="alice",
+    filters={"category": "food"},
+    output_format="v1.1"
+)
+```
+
+#### After (v1.0.0 )
+```python
+# Enhanced filtering with reranking
+results = m.search(
+    "food preferences",
+    user_id="alice",
+    filters={
+        "AND": [
+            {"category": "food"},
+            {"score": {"gte": 0.8}}
+        ]
+    },
+    rerank=True
+)
+```
+
+## Summary
+
+| Component | v0.x | v1.0.0  | Status |
+|-----------|------|-----------|---------|
+| `add()` method | Variable response | Standardized response | ⚠️ Breaking |
+| `search()` method | Basic filtering | Enhanced filtering + reranking | ⚠️ Breaking |
+| `get_all()` method | Variable response | Standardized response | ⚠️ Breaking |
+| Response format | Variable | Always `{"results": [...]}` | ⚠️ Breaking |
+| Reranking | ❌ Not available | ✅ Full support | ✅ New feature |
+| Advanced filtering | ❌ Basic only | ✅ Full operators | ✅ Enhancement |
+| Error handling | Generic | Specific error types | ✅ Improvement |
+
+<Info>
+Use this reference to systematically update your codebase. Test each change thoroughly before deploying to production.
+</Info>

docs/openapi.json

@@ -4883,7 +4883,8 @@
 										"items": {
 											"type": "object",
 											"required": [
-												"memory_id"
+												"memory_id",
+												"text"
 											],
 											"properties": {
 												"memory_id": {
@@ -4894,11 +4895,6 @@
 												"text": {
 													"type": "string",
 													"description": "The new text content for the memory"
-												},
-												"metadata": {
-													"type": "object",
-													"additionalProperties": true,
-													"description": "Updated metadata to associate with the memory."
 												}
 											}
 										},
@@ -4974,27 +4970,18 @@
 							"schema": {
 								"type": "object",
 								"properties": {
-									"memories": {
+									"memory_ids": {
 										"type": "array",
 										"items": {
-											"type": "object",
+											"type": "string",
-											"properties": {
+											"format": "uuid"
-												"memory_id": {
-													"type": "string",
-													"format": "uuid",
-													"description": "The unique identifier of the memory to delete."
-												}
-											},
-											"required": [
-												"memory_id"
-											]
 										},
 										"maxItems": 1000,
-										"description": "Array of memory objects to delete."
+										"description": "Array of memory IDs to delete."
 									}
 								},
 								"required": [
-									"memories"
+									"memory_ids"
 								]
 							}
 						}

docs/platform/advanced-memory-operations.mdx

@@ -9,6 +9,7 @@ description: "Run richer add/search/update/delete flows on the managed platform
   **Prerequisites**
   - Platform workspace with API key
   - Python 3.10+ and Node.js 18+
+  - Async memories enabled in your dashboard (Settings → Memory Options)
 </Info>
 
 <Tip>
@@ -20,9 +21,9 @@ description: "Run richer add/search/update/delete flows on the managed platform
 <Tabs>
   <Tab title="Python">
 <Steps>
-<Step title="Install the SDK">
+<Step title="Install the SDK with async extras">
 ```bash
-pip install mem0ai
+pip install "mem0ai[async]"
 ```
 </Step>
 <Step title="Export your API key">
@@ -54,9 +55,9 @@ export MEM0_API_KEY="sk-platform-..."
 </Step>
 <Step title="Instantiate the client">
 ```typescript
-import MemoryClient from 'mem0ai';
+import { Memory } from "mem0ai";
 
-const memory = new MemoryClient({ apiKey: process.env.MEM0_API_KEY! });
+const memory = new Memory({ apiKey: process.env.MEM0_API_KEY!, async: true });
 ```
 </Step>
 </Steps>
@@ -120,8 +121,10 @@ const result = await memory.add(conversation, {
 ```python
 matches = await memory.search(
     "Any food alerts?",
-    filters={"user_id": "traveler-42", "metadata.trip": "japan-2025"},
+    user_id="traveler-42",
+    filters={"metadata.trip": "japan-2025"},
     rerank=True,
+    include_vectors=False,
 )
 ```
 </Step>
@@ -129,7 +132,7 @@ matches = await memory.search(
 ```python
 await memory.update(
     memory_id=matches["results"][0]["id"],
-    data="Morgan avoids shellfish and prefers boutique hotels in central Tokyo.",
+    content="Morgan avoids shellfish and prefers boutique hotels in central Tokyo.",
 )
 ```
 </Step>
@@ -140,15 +143,17 @@ await memory.update(
 <Step title="Search with metadata filters">
 ```typescript
 const matches = await memory.search("Any food alerts?", {
-  filters: { user_id: "traveler-42", "metadata.trip": "japan-2025" },
+  userId: "traveler-42",
+  filters: { "metadata.trip": "japan-2025" },
   rerank: true,
+  includeVectors: false,
 });
 ```
 </Step>
 <Step title="Apply an update">
 ```typescript
 await memory.update(matches.results[0].id, {
-  text: "Morgan avoids shellfish and prefers boutique hotels in central Tokyo.",
+  content: "Morgan avoids shellfish and prefers boutique hotels in central Tokyo.",
 });
 ```
 </Step>

docs/platform/features/advanced-retrieval.mdx

@@ -26,7 +26,7 @@ Reorders results using deep semantic understanding to put the most relevant memo
 results = client.search(
     query="What are my upcoming travel plans?",
     rerank=True,
-    filters={"user_id": "user123"},
+    user_id="user123"
 )
 
 # Before reranking:        After reranking:
@@ -52,7 +52,7 @@ results = client.search(
 results = client.search(
     query="How do I like my bedroom temperature?",
     rerank=True,           # Get most recent preferences first
-    filters={"user_id": "user123"},
+    user_id="user123"
 )
 
 # Finds: "Keep bedroom at 68°F", "Too cold last night at 65°F", etc.
@@ -63,7 +63,7 @@ results = client.search(
 # Find specific product issues with high precision
 results = client.search(
     query="Problems with premium subscription billing",
-    filters={"user_id": "customer456"},
+    user_id="customer456"
 )
 
 # Returns only relevant billing problems, not general questions
@@ -75,7 +75,7 @@ results = client.search(
 results = client.search(
     query="Patient allergies and contraindications",
     rerank=True,            # Most important info first
-    filters={"user_id": "patient789"},
+    user_id="patient789"
 )
 
 # Ensures critical allergy info appears first
@@ -87,7 +87,7 @@ results = client.search(
 results = client.search(
     query="Python programming progress and difficulties",
     rerank=True,           # Recent progress first
-    filters={"user_id": "student123"},
+    user_id="student123"
 )
 
 # Gets comprehensive view of Python learning journey
@@ -105,7 +105,7 @@ results = client.search(
 def quick_search(query, user_id):
     return client.search(
         query=query,
-        filters={"user_id": user_id},
+        user_id=user_id
     )
 
 # Reranked search - good for most applications
@@ -113,7 +113,7 @@ def standard_search(query, user_id):
     return client.search(
         query=query,
         rerank=True,
-        filters={"user_id": user_id},
+        user_id=user_id
     )
 
 # Reranked search - good for critical applications
@@ -121,7 +121,7 @@ def precise_search(query, user_id):
     return client.search(
         query=query,
         rerank=True,
-        filters={"user_id": user_id},
+        user_id=user_id
     )
 ```
 
@@ -129,23 +129,23 @@ def precise_search(query, user_id):
 // Basic search - good for exploration
 function quickSearch(query, userId) {
     return client.search(query, {
-        filters: { user_id: userId },
+        user_id: userId
     });
 }
 
 // Reranked search - good for most applications
 function standardSearch(query, userId) {
     return client.search(query, {
-        filters: { user_id: userId },
+        user_id: userId,
-        rerank: true,
+        rerank: true
     });
 }
 
 // Reranked search - good for critical applications
 function preciseSearch(query, userId) {
     return client.search(query, {
-        filters: { user_id: userId },
+        user_id: userId,
-        rerank: true,
+        rerank: true
     });
 }
 ```
@@ -178,7 +178,7 @@ start_time = time.time()
 results = client.search(
     query="user preferences",
     rerank=True,         # +150ms
-    filters={"user_id": "user123"},
+    user_id="user123"
 )
 latency = time.time() - start_time
 print(f"Search completed in {latency:.2f}s")

docs/platform/features/contextual-add.mdx

@@ -25,7 +25,7 @@ const messages = [
     {"role": "assistant", "content": "Great! I'll remember your preference for Italian cuisine."}
 ];
 
-await client.add(messages, { userId: "user123" });
+await client.add(messages, { userId: "user123", version: "v2" });
 ```
 </CodeGroup>
 
@@ -65,14 +65,14 @@ const messages1 = [
     {"role": "user", "content": "Hi, I'm Sarah from New York"},
     {"role": "assistant", "content": "Hello Sarah! Nice to meet you."}
 ];
-await client.add(messages1, { userId: "sarah" });
+await client.add(messages1, { userId: "sarah", version: "v2" });
 
 // Later interaction - just send new messages
 const messages2 = [
     {"role": "user", "content": "I'm planning a trip to Italy next month"},
     {"role": "assistant", "content": "How exciting! Italy is beautiful this time of year."}
 ];
-await client.add(messages2, { userId: "sarah" });
+await client.add(messages2, { userId: "sarah", version: "v2" });
 // Mem0 automatically knows Sarah is from New York and can use this context
 ```
 </CodeGroup>
@@ -104,7 +104,7 @@ const messages = [
     {"role": "assistant", "content": "I've noted your allergies for future reference."}
 ];
 
-await client.add(messages, { userId: "user123" });
+await client.add(messages, { userId: "user123", version: "v2" });
 // This allergy info will be available in ALL future interactions
 ```
 </CodeGroup>
@@ -143,21 +143,21 @@ const messages1 = [
     {"role": "user", "content": "I want to plan a 5-day trip to Tokyo"},
     {"role": "assistant", "content": "Perfect! Let's plan your Tokyo adventure."}
 ];
-await client.add(messages1, { userId: "user123", runId: "tokyo-trip-2024" });
+await client.add(messages1, { userId: "user123", runId: "tokyo-trip-2024", version: "v2" });
 
 // Later in the same trip planning session
 const messages2 = [
     {"role": "user", "content": "I prefer staying near Shibuya"},
     {"role": "assistant", "content": "Great choice! Shibuya is very convenient."}
 ];
-await client.add(messages2, { userId: "user123", runId: "tokyo-trip-2024" });
+await client.add(messages2, { userId: "user123", runId: "tokyo-trip-2024", version: "v2" });
 
 // Different session for work project (separate context)
 const workMessages = [
     {"role": "user", "content": "Let's discuss the Q4 marketing strategy"},
     {"role": "assistant", "content": "Sure! What are your main goals for Q4?"}
 ];
-await client.add(workMessages, { userId: "user123", runId: "q4-marketing" });
+await client.add(workMessages, { userId: "user123", runId: "q4-marketing", version: "v2" });
 ```
 </CodeGroup>
 

docs/platform/features/criteria-retrieval.mdx

@@ -182,7 +182,7 @@ If no criteria are defined for a project, search behaves normally based on seman
 This lets you prioritize memories that align with your agent's goals and not just those that look similar to the query.
 
 <Note>
-Criteria retrieval is automatically enabled when criteria are defined in your project. Use `use_criteria=False` in search to temporarily disable it for a specific query. `use_criteria` is a server-side parameter passed through to the Platform API — it is not a typed option in the SDK's `SearchMemoryOptions` interface, but the server accepts and processes it when included in the request body.
+Criteria retrieval is automatically enabled when criteria are defined in your project. Use `use_criteria=False` in search to temporarily disable it for a specific query.
 </Note>
 
 

docs/platform/features/feedback-mechanism.mdx

@@ -150,7 +150,7 @@ Handle potential errors when submitting feedback:
 
 ```python Python
 from mem0 import MemoryClient
-from mem0.exceptions import MemoryNotFoundError, NetworkError
+from mem0.exceptions import MemoryNotFoundError, APIError
 
 client = MemoryClient(api_key="your_api_key")
 
@@ -163,8 +163,8 @@ try:
     print("Feedback submitted successfully")
 except MemoryNotFoundError:
     print("Memory not found")
-except NetworkError as e:
+except APIError as e:
-    print(f"Network error: {e}")
+    print(f"API error: {e}")
 except Exception as e:
     print(f"Unexpected error: {e}")
 ```

docs/platform/features/group-chat.mdx

@@ -111,37 +111,32 @@ print(all_memories)
 ```
 
 ```json Output
-{
+[
-    "count": 3,
+    {
-    "next": null,
+        "id": "147559a8-c5f7-44d0-9418-91f53f7a89a4",
-    "previous": null,
+        "memory": "suggests considering Angular because it has great enterprise support",
-    "results": [
+        "user_id": "charlie",
-        {
+        "run_id": "group_chat_1",
-            "id": "147559a8-c5f7-44d0-9418-91f53f7a89a4",
+        "created_at": "2025-06-21T05:51:11.007223-07:00",
-            "memory": "suggests considering Angular because it has great enterprise support",
+        "updated_at": "2025-06-21T05:51:11.626562-07:00"
-            "user_id": "charlie",
+    },
-            "run_id": "group_chat_1",
+    {
-            "created_at": "2025-06-21T05:51:11.007223-07:00",
+        "id": "1d8b8f39-7b17-4d18-8632-ab1c64fa35b9",
-            "updated_at": "2025-06-21T05:51:11.626562-07:00"
+        "memory": "prefers Vue.js for our use case",
-        },
+        "user_id": "bob",
-        {
+        "run_id": "group_chat_1",
-            "id": "1d8b8f39-7b17-4d18-8632-ab1c64fa35b9",
+        "created_at": "2025-06-21T05:51:08.675301-07:00",
-            "memory": "prefers Vue.js for our use case",
+        "updated_at": "2025-06-21T05:51:09.319269-07:00",
-            "user_id": "bob",
+    },
-            "run_id": "group_chat_1",
+    {
-            "created_at": "2025-06-21T05:51:08.675301-07:00",
+        "id": "4d82478a-8d50-47e6-9324-1f65efff5829",
-            "updated_at": "2025-06-21T05:51:09.319269-07:00"
+        "memory": "prefers using React for the frontend",
-        },
+        "user_id": "alice",
-        {
+        "run_id": "group_chat_1",
-            "id": "4d82478a-8d50-47e6-9324-1f65efff5829",
+        "created_at": "2025-06-21T05:51:05.943223-07:00",
-            "memory": "prefers using React for the frontend",
+        "updated_at": "2025-06-21T05:51:06.982539-07:00",
-            "user_id": "alice",
+    }
-            "run_id": "group_chat_1",
+]
-            "created_at": "2025-06-21T05:51:05.943223-07:00",
-            "updated_at": "2025-06-21T05:51:06.982539-07:00"
-        }
-    ]
-}
 ```
 
 </CodeGroup>
@@ -166,21 +161,17 @@ print(charlie_memories)
 ```
 
 ```json Output
-{
+[
-    "count": 1,
+    {
-    "next": null,
+        "id": "147559a8-c5f7-44d0-9418-91f53f7a89a4",
-    "previous": null,
+        "memory": "suggests considering Angular because it has great enterprise support",
-    "results": [
+        "user_id": "charlie",
-        {
+        "run_id": "group_chat_1",
-            "id": "147559a8-c5f7-44d0-9418-91f53f7a89a4",
+        "created_at": "2025-06-21T05:51:11.007223-07:00",
-            "memory": "suggests considering Angular because it has great enterprise support",
+        "updated_at": "2025-06-21T05:51:11.626562-07:00",
-            "user_id": "charlie",
+
-            "run_id": "group_chat_1",
+    }
-            "created_at": "2025-06-21T05:51:11.007223-07:00",
+]
-            "updated_at": "2025-06-21T05:51:11.626562-07:00"
-        }
-    ]
-}
 ```
 
 </CodeGroup>
@@ -208,18 +199,16 @@ print(search_response)
 ```
 
 ```json Output
-{
+[
-    "results": [
+    {
-        {
+        "id": "147559a8-c5f7-44d0-9418-91f53f7a89a4",
-            "id": "147559a8-c5f7-44d0-9418-91f53f7a89a4",
+        "memory": "suggests considering Angular because it has great enterprise support",
-            "memory": "suggests considering Angular because it has great enterprise support",
+        "user_id": "charlie",
-            "user_id": "charlie",
+        "run_id": "group_chat_1",
-            "run_id": "group_chat_1",
+        "created_at": "2025-06-21T05:51:11.007223-07:00",
-            "created_at": "2025-06-21T05:51:11.007223-07:00",
+        "updated_at": "2025-06-21T05:51:11.626562-07:00",
-            "updated_at": "2025-06-21T05:51:11.626562-07:00"
+    }
-        }
+]
-    ]
-}
 ```
 
 </CodeGroup>

docs/platform/features/memory-export.mdx

@@ -251,6 +251,7 @@ You can apply various filters to customize which memories are included in the ex
 - `user_id`: Filter memories by specific user
 - `agent_id`: Filter memories by specific agent
 - `run_id`: Filter memories by specific run
+- `session_id`: Filter memories by specific session
 - `created_at`: Filter memories by date
 
 <Note>

docs/platform/mem0-mcp.mdx

@@ -9,7 +9,7 @@ estimatedTime: "~2 minutes"
   **Prerequisites**
   - Mem0 Platform account (<a href="https://app.mem0.ai?utm_source=oss&utm_medium=platform-mem0-mcp" rel="nofollow">Sign up here</a>)
   - API key (<a href="https://app.mem0.ai/settings/api-keys?utm_source=oss&utm_medium=platform-mem0-mcp" rel="nofollow">Get one from dashboard</a>)
-  - Node.js 18+ (for npx)
+  - Node.js 14+ (for npx)
   - An MCP-compatible client (Claude, Claude Code, Codex, Cursor, Windsurf, VS Code, OpenCode)
 </Info>
 

docs/platform/quickstart.mdx

@@ -15,7 +15,7 @@ Get started with Mem0 Platform's hosted API in under 5 minutes. This guide shows
 
 - Mem0 Platform account (<a href="https://app.mem0.ai?utm_source=oss&utm_medium=platform-quickstart" rel="nofollow">Sign up here</a>)
 - API key (<a href="https://app.mem0.ai/dashboard/settings?tab=api-keys&subtab=configuration" rel="nofollow">Get one from dashboard</a>)
-- Python 3.10+, Node.js 18+, or cURL
+- Python 3.10+, Node.js 14+, or cURL
 
 ## Installation
 

mem0-ts/package.json

@@ -1,6 +1,6 @@
 {
   "name": "mem0ai",
-  "version": "3.0.12",
+  "version": "3.0.11",
   "description": "The Memory Layer For Your AI Apps",
   "main": "./dist/index.js",
   "module": "./dist/index.mjs",

mem0-ts/src/oss/src/vector_stores/redis.ts

@@ -337,14 +337,11 @@ export class RedisDB implements VectorStore {
       const id = ids[idx];
 
       // Create entry with required fields
-      const createdAt = payload.created_at
-        ? new Date(payload.created_at).getTime()
-        : 0;
       const entry: Record<string, any> = {
         memory_id: id,
-        hash: payload.hash ?? "",
+        hash: payload.hash,
-        memory: payload.data ?? "",
+        memory: payload.data,
-        created_at: createdAt,
+        created_at: new Date(payload.created_at).getTime(),
         embedding: new Float32Array(vector).buffer,
       };
 
@@ -564,18 +561,12 @@ export class RedisDB implements VectorStore {
     payload: Record<string, any>,
   ): Promise<void> {
     const snakePayload = toSnakeCase(payload);
-    const createdAt = snakePayload.created_at
-      ? new Date(snakePayload.created_at).getTime()
-      : 0;
-    const updatedAt = snakePayload.updated_at
-      ? new Date(snakePayload.updated_at).getTime()
-      : 0;
     const entry: Record<string, any> = {
       memory_id: vectorId,
-      hash: snakePayload.hash ?? "",
+      hash: snakePayload.hash,
-      memory: snakePayload.data ?? "",
+      memory: snakePayload.data,
-      created_at: createdAt,
+      created_at: new Date(snakePayload.created_at).getTime(),
-      updated_at: updatedAt,
+      updated_at: new Date(snakePayload.updated_at).getTime(),
       embedding: Buffer.from(new Float32Array(vector).buffer),
     };
 

mem0-ts/src/oss/tests/redis.unit.test.ts

@@ -1,128 +0,0 @@
-import { RedisDB } from "../src/vector_stores/redis";
-
-jest.mock("redis", () => ({
-  createClient: jest.fn(() => ({
-    connect: jest.fn(),
-    on: jest.fn(),
-    moduleList: jest.fn().mockResolvedValue([{ name: "search", ver: 20800 }]),
-    ft: {
-      create: jest.fn(),
-      search: jest.fn(),
-      info: jest.fn().mockRejectedValue(new Error("Unknown index")),
-      _list: jest.fn().mockResolvedValue([]),
-    },
-    hSet: jest.fn(),
-    hGetAll: jest.fn(),
-    del: jest.fn(),
-    exists: jest.fn(),
-    quit: jest.fn(),
-  })),
-}));
-
-function createStore(): RedisDB {
-  return new RedisDB({
-    redisUrl: "redis://localhost:6379",
-    collectionName: "test",
-    embeddingModelDims: 4,
-  });
-}
-
-describe("RedisDB – entity payload handling", () => {
-  let store: RedisDB;
-  let mockClient: any;
-
-  beforeAll(async () => {
-    store = createStore();
-    await store.initialize();
-    mockClient = (store as any).client;
-  });
-
-  beforeEach(() => {
-    jest.clearAllMocks();
-  });
-
-  test("insert with entity payload (no hash/created_at) does not produce NaN", async () => {
-    const entityPayload = {
-      data: "OpenAI",
-      entityType: "organization",
-      linkedMemoryIds: ["mem-1"],
-      userId: "test_user",
-    };
-
-    await store.insert([[0.1, 0.2, 0.3, 0.4]], ["entity-1"], [entityPayload]);
-
-    expect(mockClient.hSet).toHaveBeenCalledTimes(1);
-    const call = mockClient.hSet.mock.calls[0];
-    const entry = call[1];
-
-    expect(entry.memory_id).toBe("entity-1");
-    expect(entry.memory).toBe("OpenAI");
-    expect(entry.hash).toBe("");
-    expect(entry.created_at).toBe(0);
-    expect(Number.isNaN(entry.created_at)).toBe(false);
-  });
-
-  test("update with entity payload (no hash/created_at/updated_at) does not produce NaN", async () => {
-    const entityPayload = {
-      data: "OpenAI",
-      entityType: "organization",
-      linkedMemoryIds: ["mem-1"],
-      userId: "test_user",
-    };
-
-    await store.update("entity-1", [0.1, 0.2, 0.3, 0.4], entityPayload);
-
-    expect(mockClient.hSet).toHaveBeenCalledTimes(1);
-    const call = mockClient.hSet.mock.calls[0];
-    const entry = call[1];
-
-    expect(entry.memory_id).toBe("entity-1");
-    expect(entry.memory).toBe("OpenAI");
-    expect(entry.hash).toBe("");
-    expect(entry.created_at).toBe(0);
-    expect(entry.updated_at).toBe(0);
-    expect(Number.isNaN(entry.created_at)).toBe(false);
-    expect(Number.isNaN(entry.updated_at)).toBe(false);
-  });
-
-  test("update with normal payload preserves timestamps", async () => {
-    const normalPayload = {
-      data: "likes coffee",
-      hash: "abc123",
-      createdAt: "2026-06-25T10:00:00.000Z",
-      updatedAt: "2026-06-25T12:00:00.000Z",
-      userId: "test_user",
-    };
-
-    await store.update("mem-1", [0.1, 0.2, 0.3, 0.4], normalPayload);
-
-    const call = mockClient.hSet.mock.calls[0];
-    const entry = call[1];
-
-    expect(entry.hash).toBe("abc123");
-    expect(entry.memory).toBe("likes coffee");
-    expect(entry.created_at).toBeGreaterThan(0);
-    expect(entry.updated_at).toBeGreaterThan(0);
-    expect(Number.isNaN(entry.created_at)).toBe(false);
-    expect(Number.isNaN(entry.updated_at)).toBe(false);
-  });
-
-  test("insert with normal payload preserves timestamp", async () => {
-    const normalPayload = {
-      data: "likes coffee",
-      hash: "abc123",
-      createdAt: "2026-06-25T10:00:00.000Z",
-      userId: "test_user",
-    };
-
-    await store.insert([[0.1, 0.2, 0.3, 0.4]], ["mem-1"], [normalPayload]);
-
-    const call = mockClient.hSet.mock.calls[0];
-    const entry = call[1];
-
-    expect(entry.hash).toBe("abc123");
-    expect(entry.memory).toBe("likes coffee");
-    expect(entry.created_at).toBeGreaterThan(0);
-    expect(Number.isNaN(entry.created_at)).toBe(false);
-  });
-});

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "mem0ai"
-version = "2.0.10"
+version = "2.0.9"
 description = "Long-term memory for AI Agents"
 authors = [
     { name = "Mem0", email = "support@mem0.ai" }