chore: update changelog, bump SDK versions to Python 2.0.10 and TypeScript 3.0.12 (#5927)

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>

CONTRIBUTING.md

@@ -1,72 +1,157 @@
-# Contributing to mem0
+# Contributing to Mem0
 
-Let us make contribution easy, collaborative and fun.
+First off, thank you for taking the time to contribute! 🎉 Mem0 is a
+community-driven project and we welcome contributions of all kinds — bug fixes,
+new features, documentation, examples, and integrations.
 
-## Submit your Contribution through PR
+Mem0 is a polyglot monorepo, and this guide covers contributing to both the
+**Python SDK** and the **TypeScript SDK** (and the rest of the repository).
 
-To make a contribution, follow these steps:
+## Before You Start
 
-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
+### 1. Open an Issue First
 
-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).
+**Always open an issue before opening a pull request.** This lets us discuss the
+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.
 
-### 📦 Development Environment
+Every pull request must link to an issue using `Closes #<issue-number>`.
 
-We use `hatch` for managing development environments. To set up:
+### 2. Sign the Contributor License Agreement (CLA)
+
+**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 environment for specific Python version:
-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
+# Activate a dev environment (3.9 / 3.10 / 3.11 / 3.12)
+hatch shell dev_py_3_11
 
-# The environment will automatically install all dev dependencies
-# Run tests within the activated shell:
-make test
-```
-
-### 📌 Pre-commit
-
-To ensure our standards, make sure to install pre-commit before starting to contribute.
-
-```bash
+# Install pre-commit hooks (runs ruff + isort on commit)
 pre-commit install
+
+# 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
 ```
 
-### 🧪 Testing
+- **Linter / formatter:** Ruff (line length **120**)
+- **Import sorting:** isort (`profile = "black"`)
+- **Tests:** pytest (in `tests/`)
 
-We use `pytest` to test our code across multiple Python versions. You can run tests using:
+See the full [Development guide](https://docs.mem0.ai/contributing/development) for
+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
-# Run tests with default Python version
-make test
+cd mem0-ts
+pnpm install
 
-# 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
-
-# When using hatch shells, run tests with:
-make test  # After activating a shell with hatch shell test_XX
+pnpm run build        # tsup (CJS + ESM)
+pnpm run test         # jest (all tests)
+pnpm run test:unit    # unit tests with coverage
 ```
 
-Make sure that all tests pass across all supported Python versions before submitting a pull request.
+- **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()`.
 
-We look forward to your pull requests and can't wait to see your contributions!
+## Good Contribution Practices
 
-### 🚀 Releasing
+- **Keep PRs small and focused.** One logical change per PR is easier to review and
+  merge.
+- **Follow existing patterns.** Match the style, structure, and conventions of the
+  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`.
 
-All packages are published automatically via GitHub Actions when a GitHub Release is created with the correct tag prefix.
+## Pull Request Checklist
 
-#### Tag Prefixes
+Before requesting review, make sure:
+
+- [ ] An issue exists and is linked with `Closes #<number>`
+- [ ] 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
+
+**Do not report security vulnerabilities through public issues or pull requests.**
+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.
+
+### Tag Prefixes
 
 | Package | Registry | Tag Prefix | Example |
 |---------|----------|------------|---------|
@@ -77,15 +162,17 @@ All packages are published automatically via GitHub Actions when a GitHub Releas
 | `@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

@@ -0,0 +1,48 @@
+# 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/add-memories.mdx

@@ -50,6 +50,7 @@ Provide conversation messages for Mem0 to extract memories from. At least one en
 | `app_id` | string | No* | Associates the memory with an app. |
 | `metadata` | object | Optional | Custom key/value metadata (e.g., `{"topic": "preferences"}`). |
 | `infer` | boolean (default `true`) | Optional | Set to `false` to skip inference and store the provided text as-is. |
+| `expiration_date` | string | Optional | Date in `YYYY-MM-DD` format. The memory is visible through this date and hidden by default after it passes. |
 
 > \* At least one entity ID (`user_id`, `agent_id`, `app_id`, or `run_id`) is required.
 
@@ -83,3 +84,11 @@ The request is queued for background processing. The response contains an `event
 <Info>
 Poll the event status via `GET /v1/event/{event_id}/`. Status will be `SUCCEEDED` or `FAILED` once processing completes.
 </Info>
+
+<Info>
+Memories with `expiration_date` remain stored after they expire. Search and get-all hide them by default; pass `show_expired: true` to include them.
+</Info>
+
+<Info>
+Python uses `expiration_date`; TypeScript uses `expirationDate`.
+</Info>

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

@@ -6,6 +6,10 @@ openapi: post /v3/memories/
 
 List memories scoped by filters with paginated results. Entity IDs (`user_id`, `agent_id`, `app_id`, `run_id`) **must** be passed inside the `filters` object — top-level entity IDs are rejected with 400.
 
+Expired memories are hidden by default. Pass `show_expired: true` to include memories whose `expiration_date` has passed.
+
+Python uses `show_expired`; TypeScript uses `showExpired`.
+
 The `filters` object supports complex logical operations (AND, OR, NOT) and comparison operators:
 
 - `in`: Matches any of the values specified
@@ -32,6 +36,7 @@ memories = client.get_all(
             }
         ]
     },
+    show_expired=False,
     page=1,
     page_size=50
 )
@@ -46,12 +51,14 @@ memories = client.get_all(
         {
             "id": "f4cbdb08-7062-4f3e-8eb2-9f5c80dfe64c",
             "memory": "Alex is planning a trip to San Francisco from July 1st to July 10th",
+            "expiration_date": null,
             "created_at": "2024-07-01T12:00:00Z",
             "updated_at": "2024-07-01T12:00:00Z"
         },
         {
             "id": "a2b8c3d4-5e6f-7g8h-9i0j-1k2l3m4n5o6p",
             "memory": "Alex prefers vegetarian restaurants",
+            "expiration_date": null,
             "created_at": "2024-07-05T15:30:00Z",
             "updated_at": "2024-07-05T15:30:00Z"
         }

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

@@ -8,6 +8,10 @@ Relevance-ranked hybrid search across stored memories. V3 uses multi-signal retr
 
 Entity IDs (`user_id`, `agent_id`, `app_id`, `run_id`) **must** be passed inside the `filters` object — top-level entity IDs are rejected with 400. At least one entity ID is required.
 
+Expired memories are hidden by default. Pass `show_expired: true` to include memories whose `expiration_date` has passed.
+
+Python uses `show_expired`; TypeScript uses `showExpired`.
+
 The `filters` object supports complex logical operations (AND, OR, NOT) and comparison operators:
 - `in`: Matches any of the values specified
 - `gte`: Greater than or equal to
@@ -30,6 +34,7 @@ The `filters` object supports complex logical operations (AND, OR, NOT) and comp
 ```python Platform API Example
 related_memories = client.search(
     query="What are Alice's hobbies?",
+    show_expired=False,
     filters={
         "OR": [
             {
@@ -54,6 +59,7 @@ related_memories = client.search(
         "category": "hobbies"
       },
       "score": 0.82,
+      "expiration_date": null,
       "created_at": "2024-07-26T10:29:36.630547-07:00",
       "updated_at": null,
       "categories": ["hobbies"]

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

@@ -1,5 +1,14 @@
 ---
 title: 'Update Memory'
-description: "Update the content or metadata of a single memory by its unique ID using the PUT endpoint."
+description: "Update the content, metadata, timestamp, or expiration date of a single memory by its unique ID using the PUT endpoint."
 openapi: put /v1/memories/{memory_id}/
----
+---
+
+Use this endpoint to update mutable memory fields. To make a memory expire, set `expiration_date` to a `YYYY-MM-DD` date. To make it permanent again, send `expiration_date: null`.
+
+```python
+client.update("mem_123", expiration_date="2030-01-31")
+client.update("mem_123", expiration_date=None)
+```
+
+TypeScript uses `expirationDate`.

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

@@ -0,0 +1,5 @@
+---
+title: "Remove Organization Member"
+description: "Remove a member from an organization to revoke their access to its projects and resources."
+openapi: "delete /api/v1/orgs/organizations/{org_id}/members/"
+---

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

@@ -0,0 +1,5 @@
+---
+title: "Update Organization Member"
+description: "Update an existing member's role within an organization to change their permissions and access level."
+openapi: "put /api/v1/orgs/organizations/{org_id}/members/"
+---

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

@@ -0,0 +1,5 @@
+---
+title: "Remove Project Member"
+description: "Remove a member from a project to revoke their access to its memories, configuration, and resources."
+openapi: "delete /api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/"
+---

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

@@ -0,0 +1,5 @@
+---
+title: "Update Project Member"
+description: "Update an existing member's role within a project to change their permissions and access level."
+openapi: "put /api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/"
+---

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

@@ -0,0 +1,5 @@
+---
+title: "Update Project"
+description: "Update a project's settings, including name, custom instructions, and other configuration options."
+openapi: "patch /api/v1/orgs/organizations/{org_id}/projects/{project_id}/"
+---

docs/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)
+- **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).
 - **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

@@ -7,6 +7,21 @@ 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:**
@@ -121,7 +136,7 @@ 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_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))
+- **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))
 
 **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))
@@ -229,7 +244,7 @@ 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 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.
+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.
 
 </Update>
 
@@ -1070,6 +1085,23 @@ See the [OSS v1 to v2 migration guide](https://docs.mem0.ai/migration/oss-v1-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

@@ -59,5 +59,9 @@ 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/fastembed.mdx

@@ -0,0 +1,50 @@
+---
+title: "FastEmbed"
+description: "Configure FastEmbed as an embedding provider in Mem0 to generate embeddings locally using ONNX-based models without a GPU."
+---
+
+You can use FastEmbed to run embedding models locally in Mem0. FastEmbed is an ONNX-based embedding library that runs efficiently on CPU without requiring a GPU or an external API key.
+
+### Installation
+
+```bash
+pip install fastembed
+```
+
+### Usage
+
+<CodeGroup>
+```python Python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
+
+config = {
+    "embedder": {
+        "provider": "fastembed",
+        "config": {
+            "model": "thenlper/gte-large"
+        }
+    }
+}
+
+m = Memory.from_config(config)
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="john")
+```
+</CodeGroup>
+
+### Config
+
+Here are the parameters available for configuring FastEmbed embedder:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `model` | The name of the FastEmbed model to use | `thenlper/gte-large` |
+| `embedding_dims` | Dimensions of the embedding model (auto-derived from the model if not set) | `None` |

docs/components/embedders/models/google_AI.mdx

@@ -67,14 +67,15 @@ 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     | `1536`                  |
+| `embedding_dims` | Dimensions of the embedding model     | `768`                   |
 | `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             | `1536`                     |
+| `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`                     |
 | `apiKey`          | Google API key                                | `None`                     |
 </Tab>
 </Tabs>

docs/components/embedders/models/lmstudio.mdx

@@ -16,7 +16,7 @@ config = {
     "embedder": {
         "provider": "lmstudio",
         "config": {
-            "model": "nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf"
+            "model": "nomic-ai/nomic-embed-text-v1.5-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-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf` |
+| `model` | The name of the LM Studio model to use | `nomic-ai/nomic-embed-text-v1.5-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>
-  The following embedders are supported in the Python implementation. The TypeScript implementation currently only supports OpenAI.
+  All embedders listed below are supported in the Python implementation. The TypeScript implementation supports: **OpenAI**, **Azure OpenAI**, **Google AI**, **Langchain**, **LM Studio**, and **Ollama**.
 </Note>
 
 <CardGroup cols={4}>
@@ -24,6 +24,7 @@ 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                   | AzureOpenAI       |
+    | `http_client_proxies`| Allow proxy server settings                   | All               |
     | `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)           | Sarvam            |
+    | `reasoning_effort`   | Reasoning level (low, medium, high)           | All               |
     | `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-4-20250514",
+            "model": "claude-sonnet-4-6",
             "temperature": 0.1,
             "max_tokens": 2000,
         }
@@ -45,7 +45,7 @@ const config = {
     provider: 'anthropic',
     config: {
       apiKey: process.env.ANTHROPIC_API_KEY || '',
-      model: 'claude-sonnet-4-20250514',
+      model: 'claude-sonnet-4-6',
       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`, and `AWS_SECRET_ACCESS_KEY` to set environment variables.
+- You will have to export `AWS_REGION`, `AWS_ACCESS_KEY_ID`, 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": "mixtral-8x7b-32768",
+            "model": "llama-3.3-70b-versatile",
             "temperature": 0.1,
             "max_tokens": 2000,
         }
@@ -46,7 +46,7 @@ const config = {
     provider: 'groq',
     config: {
       apiKey: process.env.GROQ_API_KEY || '',
-      model: 'mixtral-8x7b-32768',
+      model: 'llama3-70b-8192',
       temperature: 0.1,
       maxTokens: 1000,
     },

docs/components/llms/models/litellm.mdx

@@ -4,9 +4,12 @@ description: "Use LiteLLM as an LLM provider in Mem0 to access over 100 language
 ---
 [Litellm](https://litellm.vercel.app/docs/) is compatible with over 100 large language models (LLMs), all using a standardized input/output format. You can explore the [available models](https://litellm.vercel.app/docs/providers) to use with Litellm. Ensure you set the `API_KEY` for the model you choose to use.
 
+In the TypeScript SDK, run LiteLLM as a [proxy server](https://docs.litellm.ai/docs/simple_proxy) (an OpenAI-compatible endpoint) and point Mem0 at it via `LITELLM_API_BASE` (defaults to `http://localhost:4000`).
+
 ## Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -33,6 +36,33 @@ messages = [
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+// Point Mem0 at your LiteLLM proxy. apiKey defaults to "sk-anything"
+// (the proxy handles real auth); baseURL defaults to http://localhost:4000.
+const config = {
+  llm: {
+    provider: 'litellm',
+    config: {
+      apiKey: process.env.LITELLM_API_KEY || 'sk-anything',
+      baseURL: process.env.LITELLM_API_BASE || 'http://localhost:4000',
+      model: 'gpt-5-mini',
+    },
+  },
+};
+
+const memory = new Memory(config);
+const messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+];
+await memory.add(messages, { userId: 'alice', metadata: { category: 'movies' } });
+```
+</CodeGroup>
+
 ## Config
 
 All available parameters for the `litellm` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/minimax.mdx

@@ -7,7 +7,8 @@ To use MiniMax LLM models, you have to set the `MINIMAX_API_KEY` environment var
 
 ## Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -36,9 +37,37 @@ messages = [
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+  llm: {
+    provider: 'minimax',
+    config: {
+      apiKey: process.env.MINIMAX_API_KEY || '',
+      model: 'MiniMax-M2.7',
+      temperature: 0.2,
+      maxTokens: 2000,
+      topP: 1.0,
+    },
+  },
+};
+const memory = new Memory(config);
+const messages = [
+  { role: "user", content: "I'm planning to watch a movie tonight. Any recommendations?" },
+  { role: "assistant", content: "How about thriller movies? They can be quite engaging." },
+  { role: "user", content: "I'm not a big fan of thriller movies but I love sci-fi movies." },
+  { role: "assistant", content: "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future." },
+];
+await memory.add(messages, { userId: 'alice', metadata: { category: 'movies' } });
+```
+
+</CodeGroup>
+
 You can also configure the API base URL in the config:
 
-```python
+<CodeGroup>
+```python Python
 config = {
     "llm": {
         "provider": "minimax",
@@ -51,6 +80,20 @@ config = {
 }
 ```
 
+```typescript TypeScript
+const config = {
+  llm: {
+    provider: 'minimax',
+    config: {
+      model: 'MiniMax-M2.7',
+      baseURL: 'https://your-custom-endpoint.com',
+      apiKey: 'your-api-key', // alternatively to using the environment variable
+    },
+  },
+};
+```
+</CodeGroup>
+
 ## Config
 
 All available parameters for the `minimax` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/xAI.mdx

@@ -20,7 +20,7 @@ config = {
     "llm": {
         "provider": "xai",
         "config": {
-            "model": "grok-3-beta",
+            "model": "grok-4.3",
             "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**, and **Groq**.
+  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**.
 </Note>
 
 <CardGroup cols={4}>

docs/components/rerankers/models/llm.mdx

@@ -1,226 +0,0 @@
----
-title: LLM as Reranker
-description: "Use any LLM as a flexible reranker in Mem0 with custom prompts and domain-specific scoring logic."
----
-
-<Warning>
-**This page has been superseded.** Please see [LLM Reranker](/components/rerankers/models/llm_reranker) for the complete and up-to-date documentation on using LLMs for reranking.
-</Warning>
-
-LLM-based reranker provides maximum flexibility by using any Large Language Model to score document relevance. This approach allows for custom prompts and domain-specific scoring logic.
-
-## Supported LLM Providers
-
-Any LLM provider supported by Mem0 can be used for reranking:
-
-- **OpenAI**: GPT-4, GPT-3.5-turbo, etc.
-- **Anthropic**: Claude models
-- **Together**: Open-source models
-- **Groq**: Fast inference
-- **Ollama**: Local models
-- And more...
-
-## Configuration
-
-```python Python
-from mem0 import Memory
-
-config = {
-    "vector_store": {
-        "provider": "chroma",
-        "config": {
-            "collection_name": "my_memories",
-            "path": "./chroma_db"
-        }
-    },
-    "llm": {
-        "provider": "openai",
-        "config": {
-            "model": "gpt-4o-mini"
-        }
-    },
-    "reranker": {
-        "provider": "llm",
-        "config": {
-            "model": "gpt-4o-mini",
-            "provider": "openai",
-            "api_key": "your-openai-api-key",  # or set OPENAI_API_KEY
-            "top_k": 5,
-            "temperature": 0.0
-        }
-    }
-}
-
-memory = Memory.from_config(config)
-```
-
-## Custom Scoring Prompt
-
-You can provide a custom prompt for relevance scoring:
-
-```python Python
-custom_prompt = """You are a relevance scoring assistant. Rate how well this document answers the query.
-
-Query: "{query}"
-Document: "{document}"
-
-Score from 0.0 to 1.0 where:
-- 1.0: Perfect match, directly answers the query
-- 0.8-0.9: Highly relevant, good match  
-- 0.6-0.7: Moderately relevant, partial match
-- 0.4-0.5: Slightly relevant, limited useful information
-- 0.0-0.3: Not relevant or no useful information
-
-Provide only a single numerical score between 0.0 and 1.0."""
-
-config["reranker"]["config"]["scoring_prompt"] = custom_prompt
-```
-
-## Usage Example
-
-```python Python
-import os
-from mem0 import Memory
-
-# Set API key
-os.environ["OPENAI_API_KEY"] = "your-api-key"
-
-# Initialize memory with LLM reranker
-config = {
-    "vector_store": {"provider": "chroma"},
-    "llm": {"provider": "openai", "config": {"model": "gpt-4o-mini"}},
-    "reranker": {
-        "provider": "llm",
-        "config": {
-            "model": "gpt-4o-mini",
-            "provider": "openai",
-            "temperature": 0.0
-        }
-    }
-}
-
-memory = Memory.from_config(config)
-
-# Add memories
-messages = [
-    {"role": "user", "content": "I'm learning Python programming"},
-    {"role": "user", "content": "I find object-oriented programming challenging"}, 
-    {"role": "user", "content": "I love hiking in national parks"}
-]
-
-memory.add(messages, user_id="david")
-
-# Search with LLM reranking
-results = memory.search("What programming topics is the user studying?", filters={"user_id": "david"})
-
-for result in results['results']:
-    print(f"Memory: {result['memory']}")
-    print(f"Vector Score: {result['score']:.3f}")
-    print(f"Rerank Score: {result['rerank_score']:.3f}")
-    print()
-```
-
-```text Output
-Memory: I'm learning Python programming
-Vector Score: 0.856
-Rerank Score: 0.920
-
-Memory: I find object-oriented programming challenging
-Vector Score: 0.782
-Rerank Score: 0.850
-```
-
-## Domain-Specific Scoring
-
-Create specialized scoring for your domain:
-
-```python Python
-medical_prompt = """You are a medical relevance expert. Score how relevant this medical record is to the clinical query.
-
-Clinical Query: "{query}"
-Medical Record: "{document}"
-
-Consider:
-- Clinical relevance and accuracy
-- Patient safety implications
-- Diagnostic value
-- Treatment relevance
-
-Score from 0.0 to 1.0. Provide only the numerical score."""
-
-config = {
-    "reranker": {
-        "provider": "llm",
-        "config": {
-            "model": "gpt-4o-mini",
-            "provider": "openai",
-            "scoring_prompt": medical_prompt,
-            "temperature": 0.0
-        }
-    }
-}
-```
-
-## Multiple LLM Providers
-
-Use different LLM providers for reranking:
-
-```python Python
-# Using Anthropic Claude
-anthropic_config = {
-    "reranker": {
-        "provider": "llm",
-        "config": {
-            "model": "claude-3-haiku-20240307",
-            "provider": "anthropic",
-            "temperature": 0.0
-        }
-    }
-}
-
-# Using local Ollama model
-ollama_config = {
-    "reranker": {
-        "provider": "llm",
-        "config": {
-            "model": "llama2:7b",
-            "provider": "ollama",
-            "temperature": 0.0
-        }
-    }
-}
-```
-
-## Configuration Parameters
-
-| Parameter | Description | Type | Default |
-|-----------|-------------|------|---------|
-| `model` | LLM model to use for scoring | `str` | `"gpt-4o-mini"` |
-| `provider` | LLM provider name | `str` | `"openai"` |
-| `api_key` | API key for the LLM provider | `str` | `None` |
-| `top_k` | Maximum documents to return | `int` | `None` |
-| `temperature` | Temperature for LLM generation | `float` | `0.0` |
-| `max_tokens` | Maximum tokens for LLM response | `int` | `100` |
-| `scoring_prompt` | Custom prompt template | `str` | Default prompt |
-
-## Advantages
-
-- **Maximum Flexibility**: Custom prompts for any use case
-- **Domain Expertise**: Leverage LLM knowledge for specialized domains
-- **Interpretability**: Understand scoring through prompt engineering
-- **Multi-criteria**: Score based on multiple relevance factors
-
-## Considerations
-
-- **Latency**: Higher latency than specialized rerankers
-- **Cost**: LLM API costs per reranking operation
-- **Consistency**: May have slight variations in scoring
-- **Prompt Engineering**: Requires careful prompt design
-
-## Best Practices
-
-1. **Temperature**: Use 0.0 for consistent scoring
-2. **Prompt Design**: Be specific about scoring criteria
-3. **Token Efficiency**: Keep prompts concise to reduce costs
-4. **Caching**: Cache results for repeated queries when possible
-5. **Fallback**: Handle API errors gracefully

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` |
+| `table_name` | Name of the table | `mem0` |
 | `embedding_model_dims` | Dimensions of the embedding model | `1536` |
 | `metric_type` | Distance metric for similarity search | `L2` |
 

docs/components/vectordbs/dbs/elasticsearch.mdx

@@ -56,6 +56,8 @@ 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

@@ -55,6 +55,7 @@ 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";
+import { Memory } from "mem0ai/oss";
 import { OpenAIEmbeddings } from "@langchain/openai";
-import { MemoryVectorStore as LangchainMemoryStore } from "langchain/vectorstores/memory";
+import { MemoryVectorStore } from "langchain/vectorstores/memory";
 
 const embeddings = new OpenAIEmbeddings();
-const vectorStore = new LangchainVectorStore(embeddings);
+const vectorStore = new MemoryVectorStore(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"` |
+| collection_name | Name of the MongoDB collection | `"mem0"` |
 | embedding_model_dims | Dimensions of the embedding vectors | `1536` |
-| mongo_uri | The MongoDB URI connection string | `mongodb://username:password@localhost:27017` |
+| mongo_uri | The MongoDB URI connection string | `mongodb://localhost:27017` |
 
-> **Note**: If `mongo_uri` is not provided, it will default to `mongodb://username:password@localhost:27017`.
+> **Note**: If `mongo_uri` is not provided, it will default to `mongodb://localhost:27017`.

docs/components/vectordbs/dbs/neon.mdx

@@ -53,17 +53,14 @@ print(results)
 import "dotenv/config";
 import { Memory } from "mem0ai/oss";
 
-const databaseUrl = new URL(process.env.DATABASE_URL!);
-
 const m = new Memory({
   vectorStore: {
     provider: "pgvector",
     config: {
-      user: decodeURIComponent(databaseUrl.username),
-      password: decodeURIComponent(databaseUrl.password),
-      host: databaseUrl.hostname,
-      port: Number(databaseUrl.port || 5432),
-      dbname: databaseUrl.pathname.slice(1) || "neondb",
+      connectionString: process.env.DATABASE_URL!,
+      ssl: {
+        rejectUnauthorized: false,
+      },
       collectionName: "memories",
       dimension: 1536,
       embeddingModelDims: 1536,
@@ -90,6 +87,7 @@ const results = await m.search("What movies should I recommend?", {
 
 console.log(results);
 ```
+
 </CodeGroup>
 
 ## SQL Migration
@@ -116,20 +114,19 @@ DATABASE_URL=postgresql://user:password@ep-example.us-east-2.aws.neon.tech/neond
 | `sslmode` | PostgreSQL SSL mode. Use `require` for Neon. | Driver default |
 </Tab>
 <Tab title="TypeScript">
-The current Mem0 TypeScript `pgvector` adapter takes individual Postgres fields,
-so parse `DATABASE_URL` before creating `Memory`.
+Use the Neon `DATABASE_URL` directly with `connectionString`. Set `ssl` if your runtime needs an explicit TLS config object.
+
+| Parameter            | Description                                    | Default        |
+| -------------------- | ---------------------------------------------- | -------------- |
+| `connectionString`   | Neon Postgres connection string.               | Required       |
+| `ssl`                | Optional TLS settings passed directly to `pg`. | Driver default |
+| `collectionName`     | Name for the vector collection.                | `memories`     |
+| `dimension`          | Vector dimension for Mem0 config.              | Auto-detected  |
+| `embeddingModelDims` | Embedding model dimensions for table creation. | Required       |
+| `hnsw`               | Enables HNSW indexing.                         | `false`        |
+
+**TLS note:** `ssl: true` is sufficient for most Neon connections since Neon uses valid certificates. Use `ssl: { rejectUnauthorized: false }` only when connecting through Neon's connection pooler on certain edge runtimes (e.g. Cloudflare Workers) that require it, or when your environment does not trust the Neon CA chain.
 
-| Parameter | Description | Default |
-| --- | --- | --- |
-| `user` | Database user. | Required |
-| `password` | Database password. | Required |
-| `host` | Database host. | Required |
-| `port` | Database port. | `5432` |
-| `dbname` | Database name. | `vector_store` |
-| `collectionName` | Name for the vector collection. | `memories` |
-| `dimension` | Vector dimension for Mem0 config. | Auto-detected |
-| `embeddingModelDims` | Embedding model dimensions for table creation. | Required |
-| `hnsw` | Enables HNSW indexing. | `false` |
 </Tab>
 </Tabs>
 

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/pgvector.mdx

@@ -2,6 +2,7 @@
 title: "pgvector"
 description: "Use pgvector as a vector store in Mem0 for PostgreSQL-based vector similarity search with open-source simplicity."
 ---
+
 [pgvector](https://github.com/pgvector/pgvector) is an open-source vector similarity search extension for Postgres. After connecting to Postgres, run `CREATE EXTENSION IF NOT EXISTS vector;` to create the vector extension.
 
 ### Usage
@@ -21,7 +22,7 @@ config = {
             "password": "123",
             "host": "127.0.0.1",
             "port": "5432",
-        }
+        },
     }
 }
 
@@ -30,25 +31,22 @@ messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
     {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
-    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."},
 ]
 m.add(messages, user_id="alice", metadata={"category": "movies"})
+
 ```
 
 ```typescript TypeScript
-import { Memory } from 'mem0ai/oss';
+import { Memory } from "mem0ai/oss";
 
 const config = {
   vectorStore: {
-    provider: 'pgvector',
+    provider: "pgvector",
     config: {
-      collectionName: 'memories',
+      collectionName: "memories",
       embeddingModelDims: 1536,
-      user: 'test',
-      password: '123',
-      host: '127.0.0.1',
-      port: 5432,
-      dbname: 'vector_store', // Optional; TypeScript OSS defaults to `vector_store` when omitted
+      connectionString: "postgresql://test:123@localhost:5432/vector_store",
       diskann: false, // Optional, requires pgvectorscale extension
       hnsw: false, // Optional, for HNSW indexing
     },
@@ -57,37 +55,44 @@ const config = {
 
 const memory = new Memory(config);
 const messages = [
-    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
-    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
-    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
-]
+  { role: "user", content: "I'm planning to watch a movie tonight. Any recommendations?" },
+  { role: "assistant", content: "How about thriller movies? They can be quite engaging." },
+  { role: "user", content: "I'm not a big fan of thriller movies but I love sci-fi movies." },
+  { role: "assistant", content: "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future." },
+];
+
 await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
 ```
+
 </CodeGroup>
 
 ### Config
 
 Here are the parameters available for configuring pgvector:
 
-| Parameter | Description | Default Value |
-| --- | --- | --- |
-| `dbname` | The name of the database | `postgres` |
-| `collection_name` | The name of the collection | `mem0` |
-| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
-| `user` | User name to connect to the database | `None` |
-| `password` | Password to connect to the database | `None` |
-| `host` | The host where the Postgres server is running | `None` |
-| `port` | The port where the Postgres server is running | `None` |
-| `diskann` | Whether to use diskann for vector similarity search (requires pgvectorscale) | `True` |
-| `hnsw` | Whether to use hnsw for vector similarity search | `False` |
-| `sslmode` | SSL mode for PostgreSQL connection (e.g., 'require', 'prefer', 'disable') | `None` |
-| `connection_string` | PostgreSQL connection string (overrides individual connection parameters) | `None` |
-| `connection_pool` | psycopg2 connection pool object (overrides connection string and individual parameters) | `None` |
+| Parameter            | SDK                     | Description                                                                                                                                                    | Default Value                           |
+| -------------------- | ----------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------- |
+| `connectionString`   | TypeScript OSS          | PostgreSQL connection string for direct connections. When set, Mem0 connects to the target database directly and skips the bootstrap `postgres` database flow. | `None`                                  |
+| `ssl`                | TypeScript OSS          | SSL option passed directly to `pg`, either `true` or an SSL config object, for both `connectionString` and split-field connections.                            | `None`                                  |
+| `dbname`             | TypeScript OSS          | Split-field database name. This is only used when `connectionString` is absent.                                                                                | `vector_store`                          |
+| `collectionName`     | TypeScript OSS          | Collection name.                                                                                                                                               | `memories`                              |
+| `embeddingModelDims` | TypeScript OSS          | Dimensions of the embedding model.                                                                                                                             | Required                                |
+| `user`               | TypeScript OSS + Python | Database user for split-field connections.                                                                                                                     | `None`                                  |
+| `password`           | TypeScript OSS + Python | Database password for split-field connections.                                                                                                                 | `None`                                  |
+| `host`               | TypeScript OSS + Python | Database host for split-field connections.                                                                                                                     | `None`                                  |
+| `port`               | TypeScript OSS + Python | Database port for split-field connections.                                                                                                                     | `None`                                  |
+| `diskann`            | TypeScript OSS + Python | Whether to use DiskANN for vector similarity search, requires pgvectorscale.                                                                                   | `False`                                 |
+| `hnsw`               | TypeScript OSS + Python | Whether to use HNSW for vector similarity search.                                                                                                              | TypeScript OSS: `False`, Python: `True` |
+| `connection_string`  | Python only             | PostgreSQL connection string, overrides individual connection parameters.                                                                                      | `None`                                  |
+| `sslmode`            | Python only             | SSL mode for PostgreSQL connections, such as `require`, `prefer`, or `disable`.                                                                                | `None`                                  |
+| `connection_pool`    | Python only             | psycopg connection pool object, overrides connection string and individual connection parameters.                                                              | `None`                                  |
 
-**Note (TypeScript OSS):** If you omit `dbname`, the TypeScript client uses the database name `vector_store`. Python defaults to `postgres` for `dbname`, as in the table above.
+**TypeScript OSS:** Use `connectionString` plus optional `ssl` for managed Postgres setups. If you omit `connectionString`, Mem0 falls back to split fields and uses `dbname`, `user`, `password`, `host`, `port`, and optional `ssl`.
+
+**Python:** The Python SDK uses snake_case keys such as `connection_string`, `sslmode`, `collection_name`, and `embedding_model_dims`.
+
+**Python connection priority**:
 
-**Note**: The connection parameters have the following priority:
 1. `connection_pool` (highest priority)
 2. `connection_string`
-3. Individual connection parameters (`user`, `password`, `host`, `port`, `sslmode`)
+3. Individual connection parameters (`user`, `password`, `host`, `port`, `sslmode`)

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

@@ -18,7 +18,9 @@ os.environ["UPSTASH_VECTOR_REST_TOKEN"] = "..."
 config = {
     "vector_store": {
         "provider": "upstash_vector",
-        "enable_embeddings": True,
+        "config": {
+            "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` |
-| `distance_metric` | Distance metric for vector similarity | `cosine` |
+| `timezone` | Timezone for timestamp handling | `UTC` |
 
 ## 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",                      # Optional: Defaults to GOOGLE_CLOUD_REGION
+            "region": "YOUR_REGION",                      # Required: 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
         }
@@ -45,5 +45,6 @@ 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 | No (defaults to GOOGLE_CLOUD_REGION) |
+| `region` | Google Cloud region | Yes |
 | `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 weaviate-client
+pip install weaviate-client
 ```
 
 ### Usage
@@ -48,4 +48,5 @@ 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 only supports Qdrant, Redis, Valkey, Vectorize and in-memory vector database.
+  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.
 </Note>
 
 <CardGroup cols={3}>

docs/contributing/development.mdx

@@ -1,32 +1,65 @@
 ---
 title: Development
-description: "Guide to contributing code to Mem0, covering the fork and clone workflow, PR submission, and code quality checks."
+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."
 icon: "code"
 ---
 
 # Development Contributions
 
-We strive to make contributions **easy, collaborative, and enjoyable**. Follow the steps below to ensure a smooth contribution process.
+We strive to make contributions **easy, collaborative, and enjoyable**. Mem0 is a
+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.
 
-## Submitting Your Contribution through PR
+<Note>
+  For the complete contributor checklist, see
+  [CONTRIBUTING.md](https://github.com/mem0ai/mem0/blob/main/CONTRIBUTING.md) in
+  the repository root.
+</Note>
 
-To contribute, follow these steps:
+## Before You Start
+
+### 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 for your changes, e.g., `feature/my-new-feature`
-3. **Implement Changes**: If adding a feature or fixing a bug, ensure to:
+2. **Create a Feature Branch**: Use a dedicated branch, e.g., `feature/my-new-feature`
+3. **Implement Changes**: If adding a feature or fixing a bug, be sure 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. **Submit a Pull Request**
+5. **Commit** using [Conventional Commits](https://www.conventionalcommits.org/)
+   (`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).
 
 ---
 
-## Dependency Management
+## Python SDK (`mem0/`)
+
+### Dependency Management
 
 We use `hatch` as our package manager. Install it by following the [official instructions](https://hatch.pypa.io/latest/install/).
 
@@ -44,13 +77,9 @@ 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:
+Ensure `pre-commit` is installed before contributing (hooks run ruff + isort):
 
 ```bash
 pre-commit install
@@ -58,7 +87,7 @@ pre-commit install
 
 ### Linting with `ruff`
 
-Run the linter and fix any reported issues before submitting your PR:
+Run the linter and fix any reported issues before submitting your PR (line length **120**):
 
 ```bash
 make lint
@@ -66,10 +95,11 @@ make lint
 
 ### Code Formatting
 
-To maintain a consistent code style, format your code:
+To maintain a consistent code style, format your code and sort imports (isort, `profile = "black"`):
 
 ```bash
 make format
+make sort
 ```
 
 ### Testing with `pytest`
@@ -84,10 +114,46 @@ make test
 
 ---
 
-## Release Process
+## TypeScript SDK (`mem0-ts/`)
 
-Currently, releases are handled manually. We aim for frequent releases, typically when new features or bug fixes are introduced.
+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()`
 
 ---
 
-Thank you for contributing to Mem0!
+## 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
+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!

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, { userId: userId });
+  const relevantMemories = await memory.search(message, { filters: { user_id: userId } });
   
   const memoriesStr = relevantMemories.results
     .map(entry => `- ${entry.memory}`)

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

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

docs/cookbooks/essentials/exporting-memories.mdx

@@ -58,6 +58,7 @@ 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(
-                user_id=self.student_id,
-                query="learning machine learning"
+                query="learning machine learning",
+                filters={"user_id": self.student_id}
             )
 
             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 = MemoryClient()
+mem0_client = MemoryClient()
 ```
 
 ## Define Memory Tools
@@ -76,7 +76,7 @@ def retrieve_patient_info(query: str) -> dict:
     # Search Mem0
     results = mem0_client.search(
         query,
-        user_id=USER_ID,
+        filters={"user_id": USER_ID},
         top_k=5,
         threshold=0.7  # Higher threshold for more relevant results
     )

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

@@ -53,34 +53,12 @@ async function addUserPreferences() {
 await addUserPreferences();
 ```
 
-```json Output (Memories)
- [
-  {
-    "id": "ff9f3367-9e83-415d-b9c5-dc8befd9a4b4",
-    "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"
-  }
-]
+```json Output
+{
+  "message": "Memory processing has been queued for background execution",
+  "status": "PENDING",
+  "event_id": "9f8c2b1a-4e7d-4c3a-9b21-1a2b3c4d5e6f"
+}
 ```
 </CodeGroup>
 ## Retrieving Memories
@@ -88,7 +66,7 @@ await addUserPreferences();
 Search for relevant memories based on the current user input:
 
 ```javascript
-const relevantMemories = await mem0Client.search(userInput, { userId: USER_ID });
+const relevantMemories = await mem0Client.search(userInput, { filters: { user_id: USER_ID } });
 ```
 
 ## Structured Responses with Zod
@@ -194,7 +172,7 @@ async function main(memory = false) {
   // Search for relevant memories
   let relevantMemories = []
   if (memory) {
-    relevantMemories = await mem0Client.search(input, { userId: USER_ID });
+    relevantMemories = await mem0Client.search(input, { filters: { user_id: USER_ID } });
   }
 
   const response = await openAIClient.responses.create({

docs/cookbooks/integrations/tavily-search.mdx

@@ -4,8 +4,6 @@ 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

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

docs/cookbooks/operations/email-automation.mdx

@@ -314,18 +314,16 @@ 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,
-                user_id=user_id,
-                filters={"memory_category": "email"},
+                filters={"user_id": user_id, "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},
@@ -343,10 +341,9 @@ 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-4",
+            model="gpt-5-mini",
             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 `user_id="alice"` as parameter
+  - **OSS**: Use `filters={"user_id": "alice"}` (passing `user_id` as a top-level kwarg raises `ValueError` in v3)
 - **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) when you have a reranker configured
+- **Enable reranking**: Use `rerank=True` (default is `False`) 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(api_key=os.environ["MEM0_API_KEY"])
+memory = Memory()
 
 # Sticky note: conversation memory
 memory.add(
@@ -72,8 +72,7 @@ memory.add(
 # Later in the session, pull long-term + session context
 results = memory.search(
     "Any hotel preferences?",
-    user_id="alex",
-    run_id="trip-planning-2025",
+    filters={"user_id": "alex", "run_id": "trip-planning-2025"},
 )
 ```
 

docs/docs.json

@@ -123,8 +123,7 @@
                     "icon": "arrow-right",
                     "pages": [
                       "migration/platform-v2-to-v3",
-                      "migration/oss-to-platform",
-                      "migration/api-changes"
+                      "migration/oss-to-platform"
                     ]
                   },
                   {
@@ -273,7 +272,8 @@
                               "components/embedders/models/lmstudio",
                               "components/embedders/models/together",
                               "components/embedders/models/langchain",
-                              "components/embedders/models/aws_bedrock"
+                              "components/embedders/models/aws_bedrock",
+                              "components/embedders/models/fastembed"
                             ]
                           }
                         ]
@@ -533,6 +533,8 @@
                       "api-reference/organization/get-org",
                       "api-reference/organization/get-org-members",
                       "api-reference/organization/add-org-member",
+                      "api-reference/organization/update-org-member",
+                      "api-reference/organization/remove-org-member",
                       "api-reference/organization/delete-org"
                     ]
                   },
@@ -545,6 +547,9 @@
                       "api-reference/project/get-project",
                       "api-reference/project/get-project-members",
                       "api-reference/project/add-project-member",
+                      "api-reference/project/update-project",
+                      "api-reference/project/update-project-member",
+                      "api-reference/project/remove-project-member",
                       "api-reference/project/delete-project"
                     ]
                   },
@@ -624,6 +629,10 @@
     ]
   },
   "redirects": [
+    {
+      "source": "/components/rerankers/models/llm",
+      "destination": "/components/rerankers/models/llm_reranker"
+    },
     {
       "source": "/migration/breaking-changes",
       "destination": "/"
@@ -632,6 +641,10 @@
       "source": "/migration/v0-to-v1",
       "destination": "/"
     },
+    {
+      "source": "/migration/api-changes",
+      "destination": "/migration/oss-v2-to-v3"
+    },
     {
       "source": "/platform/features/expiration-date",
       "destination": "/"

docs/integrations/agno.mdx

@@ -73,7 +73,7 @@ client = MemoryClient()
 # Define the agent
 agent = Agent(
     name="Personal Agent",
-    model=OpenAIChat(id="gpt-4"),
+    model=OpenAIChat(id="gpt-5-mini"),
     description="You are a helpful personal agent that helps me with day to day activities."
                 "You can process both text and images.",
     markdown=True

docs/integrations/autogen.mdx

@@ -43,7 +43,7 @@ OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY')
 memory_client = MemoryClient()
 agent = ConversableAgent(
     "chatbot",
-    llm_config={"config_list": [{"model": "gpt-4", "api_key": OPENAI_API_KEY}]},
+    llm_config={"config_list": [{"model": "gpt-5-mini", "api_key": OPENAI_API_KEY}]},
     code_execution_config=False,
     human_input_mode="NEVER",
 )
@@ -99,7 +99,7 @@ For more complex scenarios, you can create multiple agents:
 manager = ConversableAgent(
     "manager",
     system_message="You are a manager who helps in resolving complex customer issues.",
-    llm_config={"config_list": [{"model": "gpt-4", "api_key": OPENAI_API_KEY}]},
+    llm_config={"config_list": [{"model": "gpt-5-mini", "api_key": OPENAI_API_KEY}]},
     human_input_mode="NEVER"
 )
 

docs/integrations/claude-code.mdx

@@ -138,9 +138,10 @@ When installed via the plugin marketplace, Mem0 hooks into Claude Code's lifecyc
 
 | Hook | Event | What it does |
 |------|-------|-------------|
+| **Setup** | `Setup` | Installs the mem0 SDK and dependencies (runs on init and maintenance) |
 | **Session start** | `SessionStart` | Loads prior memories and displays status banner |
 | **User prompt** | `UserPromptSubmit` | Searches relevant memories before each message; skips short prompts |
-| **Pre-tool** | `PreToolUse` | Blocks MEMORY.md writes, enforces `user_id`/`app_id` on mem0 tool calls |
+| **Pre-tool (3 handlers)** | `PreToolUse` | Blocks MEMORY.md writes; enforces `user_id`/`app_id` on mem0 tool calls; scans files being read for relevant memory context |
 | **Post-tool** | `PostToolUse` | Tracks stats, scans bash errors for related memories |
 | **Stop** | `Stop` | Stores a session summary when the session ends |
 | **Pre-compact** | `PreCompact` | Stores a summary before the context is compacted |

docs/integrations/codex.mdx

@@ -123,7 +123,7 @@ When installed via the plugin marketplace, Mem0 hooks into Codex's lifecycle to
 |------|-------|-------------|
 | **Session start** | `SessionStart` | Loads prior memories and displays status banner |
 | **User prompt** | `UserPromptSubmit` | Searches relevant memories before each message |
-| **Pre-tool** | `PreToolUse` | Blocks MEMORY.md writes, enforces `user_id`/`app_id` on mem0 tool calls |
+| **Pre-tool (3 handlers)** | `PreToolUse` | Blocks MEMORY.md writes; enforces `user_id`/`app_id` on mem0 tool calls; scans files being read for relevant memory context |
 | **Post-tool** | `PostToolUse` | Tracks stats, scans bash errors for related memories |
 | **Stop** | `Stop` | Stores a session summary when the session ends |
 | **Pre-compact** | `PreCompact` | Stores a summary before the context is compacted |

docs/integrations/cursor.mdx

@@ -108,7 +108,7 @@ When installed via the Cursor Marketplace, Mem0 hooks into Cursor's lifecycle:
 |------|-------|-------------|
 | **Session start** | `sessionStart` | Loads prior memories and displays status banner |
 | **User prompt** | `beforeSubmitPrompt` | Searches relevant memories before each message; skips short prompts |
-| **Pre-tool (2 handlers)** | `preToolUse` | Blocks MEMORY.md writes, enforces `user_id`/`app_id` on mem0 tool calls |
+| **Pre-tool (3 handlers)** | `preToolUse` | Blocks MEMORY.md writes; enforces `user_id`/`app_id` on mem0 tool calls; scans files being read for relevant memory context |
 | **Post-tool (2 handlers)** | `postToolUse` | Tracks stats, scans bash errors for related memories |
 | **Stop** | `stop` | Stores a session summary when the session ends |
 | **Pre-compact** | `preCompact` | Stores a summary before the context is compacted |

docs/integrations/langchain-tools.mdx

@@ -98,20 +98,9 @@ add_result = add_tool.invoke(add_input)
 
 ```json Output
 {
-  "results": [
-    {
-      "memory": "Name is Alex",
-      "event": "ADD"
-    },
-    {
-      "memory": "Is a vegetarian", 
-      "event": "ADD"
-    },
-    {
-      "memory": "Is allergic to nuts",
-      "event": "ADD"
-    }
-  ]
+  "message": "Memory processing has been queued for background execution",
+  "status": "PENDING",
+  "event_id": "3a1b2c3d-4e5f-6789-abcd-ef0123456789"
 }
 ```
 </CodeGroup>
@@ -173,23 +162,25 @@ result = search_tool.invoke(search_input)
 ```
 
 ```json Output
-[
-  {
-    "id": "1a75e827-7eca-45ea-8c5c-cfd43299f061",
-    "memory": "Name is Alex",
-    "user_id": "alex", 
-    "hash": "d0fccc8fa47f7a149ee95750c37bb0ca",
-    "metadata": {
-      "food": "vegan"
-    },
-    "categories": [
-      "personal_details"
-    ],
-    "created_at": "2024-11-27T16:53:43.276872-08:00",
-    "updated_at": "2024-11-27T16:53:43.276885-08:00",
-    "score": 0.3810526501504994
-  }
-]
+{
+  "results": [
+    {
+      "id": "1a75e827-7eca-45ea-8c5c-cfd43299f061",
+      "memory": "Name is Alex",
+      "user_id": "alex",
+      "hash": "d0fccc8fa47f7a149ee95750c37bb0ca",
+      "metadata": {
+        "food": "vegan"
+      },
+      "categories": [
+        "personal_details"
+      ],
+      "created_at": "2024-11-27T16:53:43.276872-08:00",
+      "updated_at": "2024-11-27T16:53:43.276885-08:00",
+      "score": 0.3810526501504994
+    }
+  ]
+}
 ```
 </CodeGroup>
 

docs/integrations/langgraph.mdx

@@ -41,7 +41,7 @@ load_dotenv()
 # MEM0_API_KEY = 'your-mem0-key'  # Replace with your actual Mem0 API key
 
 # Initialize LangChain and Mem0
-llm = ChatOpenAI(model="gpt-4")
+llm = ChatOpenAI(model="gpt-5-mini")
 mem0 = MemoryClient()
 ```
 

docs/integrations/pipecat.mdx

@@ -121,7 +121,7 @@ async def websocket_endpoint(websocket: WebSocket):
     # LLM for response generation
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        model="gpt-3.5-turbo",
+        model="gpt-5-mini",
         system_prompt="You are a helpful assistant that remembers past conversations."
     )
     

docs/integrations/vercel-ai-sdk.mdx

@@ -26,12 +26,12 @@ Install the SDK provider and AI SDK:
 npm install @mem0/vercel-ai-provider ai@^6
 ```
 
-### Peer Dependencies
+### Dependencies
 
-`@mem0/vercel-ai-provider` v3.0.0 requires:
-- `ai` v6+ (`^6.0.199`)
-- `@ai-sdk/provider` v3+ (`^3.0.10`)
-- Provider packages at v3+: `@ai-sdk/openai@^3`, `@ai-sdk/anthropic@^3`, `@ai-sdk/google@^3`, `@ai-sdk/groq@^3`, `@ai-sdk/cohere@^3`
+`@mem0/vercel-ai-provider` bundles `ai`, all `@ai-sdk/*` provider packages, and `@ai-sdk/provider` as regular dependencies — you do **not** need to install them separately. The install command above (`npm install @mem0/vercel-ai-provider ai@^6`) is sufficient.
+
+The only true peer dependency is `zod` (optional):
+- `zod` v3+ (`^3.0.0`) — required only if you use Zod schemas in tool definitions
 
 ## Getting Started
 
@@ -305,6 +305,8 @@ These options can be passed per-request when creating a model instance:
 | `rerank` | `boolean` | Enable reranking of results |
 | `page` | `number` | Page number for pagination |
 | `page_size` | `number` | Results per page |
+| `mem0ApiKey` | `string` | Mem0 API key; overrides the `MEM0_API_KEY` env var |
+| `host` | `string` | Custom Mem0 API base URL for self-hosted deployments |
 
 ## Key Features
 
@@ -312,6 +314,7 @@ These options can be passed per-request when creating a model instance:
 - `retrieveMemories()`: Retrieves memory context for prompts as a formatted system prompt string.
 - `getMemories()`: Get memories from your profile in array format.
 - `addMemories()`: Adds user memories to enhance contextual responses.
+- `searchMemories()`: Searches memories and returns the raw results array (semantic search rather than the full retrieval pipeline).
 
 ## Migrating from v2.x
 

docs/llms.txt

@@ -228,7 +228,6 @@ 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.
 
@@ -367,6 +366,8 @@ All API Reference docs describe Mem0 Platform REST endpoints (requires API key).
 - [Get Organization](https://docs.mem0.ai/api-reference/organization/get-org) [Platform]: Use when fetching one org.
 - [Get Organization Members](https://docs.mem0.ai/api-reference/organization/get-org-members) [Platform]: Use when listing org members.
 - [Add Organization Member](https://docs.mem0.ai/api-reference/organization/add-org-member) [Platform]: Use when inviting a member to an org.
+- [Update Organization Member](https://docs.mem0.ai/api-reference/organization/update-org-member) [Platform]: Use when updating an org member's role.
+- [Remove Organization Member](https://docs.mem0.ai/api-reference/organization/remove-org-member) [Platform]: Use when removing a member from an organization.
 - [Delete Organization](https://docs.mem0.ai/api-reference/organization/delete-org) [Platform]: Use when removing an org.
 
 ### Projects
@@ -375,6 +376,9 @@ All API Reference docs describe Mem0 Platform REST endpoints (requires API key).
 - [Get Project](https://docs.mem0.ai/api-reference/project/get-project) [Platform]: Use when fetching one project.
 - [Get Project Members](https://docs.mem0.ai/api-reference/project/get-project-members) [Platform]: Use when listing project members.
 - [Add Project Member](https://docs.mem0.ai/api-reference/project/add-project-member) [Platform]: Use when inviting a member to a project.
+- [Update Project](https://docs.mem0.ai/api-reference/project/update-project) [Platform]: Use when updating project settings.
+- [Update Project Member](https://docs.mem0.ai/api-reference/project/update-project-member) [Platform]: Use when updating a project member's role.
+- [Remove Project Member](https://docs.mem0.ai/api-reference/project/remove-project-member) [Platform]: Use when removing a member from a project.
 - [Delete Project](https://docs.mem0.ai/api-reference/project/delete-project) [Platform]: Use when removing a project.
 
 ### Webhooks
@@ -460,6 +464,7 @@ Everything below is OSS-only provider configuration. Skip this entire section wh
 - [LM Studio Embeddings](https://docs.mem0.ai/components/embedders/models/lmstudio) [OSS]: Use when embeddings run through LM Studio.
 - [Together Embeddings](https://docs.mem0.ai/components/embedders/models/together) [OSS]: Use when embeddings run on Together.
 - [LangChain Embeddings](https://docs.mem0.ai/components/embedders/models/langchain) [OSS]: Use when embeddings are wrapped behind a LangChain adapter.
+- [FastEmbed](https://docs.mem0.ai/components/embedders/models/fastembed) [OSS]: Use when embeddings run locally via FastEmbed (ONNX).
 
 ### Vector Databases [OSS]
 - [Vector Database Overview](https://docs.mem0.ai/components/vectordbs/overview) [OSS]: Use when choosing a vector store.
@@ -498,7 +503,5 @@ Everything below is OSS-only provider configuration. Skip this entire section wh
 - [Custom Reranker Prompts](https://docs.mem0.ai/components/rerankers/custom-prompts) [OSS]: Use when rewriting reranker prompts.
 - [Cohere Reranker](https://docs.mem0.ai/components/rerankers/models/cohere) [OSS]: Use for Cohere Rerank.
 - [Sentence Transformer Reranker](https://docs.mem0.ai/components/rerankers/models/sentence_transformer) [OSS]: Use for local cross-encoder rerankers.
-- [Hugging Face Reranker](https://docs.mem0.ai/components/rerankers/models/huggingface) [OSS]: Use for HF-hosted reranker models.
-- [LLM Reranker (prompt)](https://docs.mem0.ai/components/rerankers/models/llm) [OSS]: Use when the reranker is a prompted LLM (config guide).
-- [LLM Reranker](https://docs.mem0.ai/components/rerankers/models/llm_reranker) [OSS]: Use when the reranker is a prompted LLM (implementation reference).
+- [Hugging Face Reranker](https://docs.mem0.ai/components/rerankers/models/huggingface) [OSS]: Use for HF-hosted reranker models.- [LLM Reranker](https://docs.mem0.ai/components/rerankers/models/llm_reranker) [OSS]: Use when the reranker is a prompted LLM (implementation reference).
 - [Zero Entropy Reranker](https://docs.mem0.ai/components/rerankers/models/zero_entropy) [OSS]: Use for the Zero Entropy reranker.

docs/migration/api-changes.mdx

@@ -1,574 +0,0 @@
----
-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/migration/oss-v2-to-v3.mdx

@@ -62,7 +62,7 @@ These changes produce a **+20 point improvement on LoCoMo** (71.4 → 91.6) and
 |---|---|---|---|
 | Constructor | `MemoryClient(api_key, org_id, project_id)` | `MemoryClient(api_key)` | Remove `org_id`, `project_id` from constructor |
 | Method options | `client.add(messages, **kwargs)` | `client.add(messages, options=AddMemoryOptions(...))` | Use typed option classes (or `**kwargs` still works) |
-| Removed params | `api_version`, `output_format`, `async_mode`, `filter_memories`, `expiration_date`, `keyword_search`, `force_add_only`, `batch_size`, `immutable`, `includes`, `excludes`, `enable_graph`, `org_name`, `project_name` | — | Remove from all calls |
+| Removed params | `api_version`, `output_format`, `async_mode`, `filter_memories`, `keyword_search`, `force_add_only`, `batch_size`, `immutable`, `includes`, `excludes`, `enable_graph`, `org_name`, `project_name` | — | Remove from all calls |
 
 ### TypeScript Client SDK
 
@@ -70,7 +70,7 @@ These changes produce a **+20 point improvement on LoCoMo** (71.4 → 91.6) and
 |---|---|---|---|
 | Constructor | `new MemoryClient({ apiKey, organizationId, projectId })` | `new MemoryClient({ apiKey })` | Remove `organizationId`, `projectId`, `organizationName`, `projectName` |
 | All params | snake_case: `user_id`, `agent_id`, `top_k` | camelCase: `userId`, `agentId`, `topK` | Rename all params to camelCase |
-| Removed params | `api_version`, `output_format`, `async_mode`, `enable_graph`, `org_id`, `project_id`, `org_name`, `project_name`, `filter_memories`, `batch_size`, `force_add_only`, `immutable`, `expiration_date`, `includes`, `excludes`, `keyword_search` | — | Remove from all calls |
+| Removed params | `api_version`, `output_format`, `async_mode`, `enable_graph`, `org_id`, `project_id`, `org_name`, `project_name`, `filter_memories`, `batch_size`, `force_add_only`, `immutable`, `includes`, `excludes`, `keyword_search` | — | Remove from all calls |
 | Output format enum | `OutputFormat.V1`, `OutputFormat.V1_1` | Removed | v1.1 is now always used |
 | API version enum | `API_VERSION.V1`, `API_VERSION.V2` | Removed | Handled internally |
 
@@ -423,7 +423,7 @@ These parameters have been removed across all SDKs. Remove them from your code:
 
 **All methods:** `api_version`, `output_format`, `async_mode`, `org_name`, `project_name`, `org_id`, `project_id`
 
-**add():** `enable_graph`, `immutable`, `expiration_date`, `filter_memories`, `batch_size`, `force_add_only`, `includes`, `excludes`, `keyword_search`
+**add():** `enable_graph`, `immutable`, `filter_memories`, `batch_size`, `force_add_only`, `includes`, `excludes`, `keyword_search`
 
 **search():** `enable_graph`
 
@@ -437,7 +437,7 @@ These parameters have been removed across all SDKs. Remove them from your code:
 
 **All methods:** `OutputFormat` enum, `API_VERSION` enum
 
-**add():** `enable_graph` / `enableGraph`, `async_mode` / `asyncMode`, `output_format` / `outputFormat`, `immutable`, `expiration_date` / `expirationDate`, `filter_memories` / `filterMemories`, `batch_size` / `batchSize`, `force_add_only` / `forceAddOnly`, `includes`, `excludes`, `keyword_search` / `keywordSearch`
+**add():** `enable_graph` / `enableGraph`, `async_mode` / `asyncMode`, `output_format` / `outputFormat`, `immutable`, `filter_memories` / `filterMemories`, `batch_size` / `batchSize`, `force_add_only` / `forceAddOnly`, `includes`, `excludes`, `keyword_search` / `keywordSearch`
 
 **search():** `enable_graph` / `enableGraph`
 

docs/migration/platform-v2-to-v3.mdx

@@ -215,7 +215,7 @@ client.add(messages, user_id="alice")
 # async_mode and output_format removed (async by default, v1.1 always)
 ```
 
-**Removed parameters:** `org_id`, `project_id`, `api_version`, `output_format`, `async_mode`, `enable_graph`, `immutable`, `expiration_date`, `filter_memories`, `batch_size`, `force_add_only`, `includes`, `excludes`, `keyword_search`, `org_name`, `project_name`
+**Removed parameters:** `org_id`, `project_id`, `api_version`, `output_format`, `async_mode`, `enable_graph`, `immutable`, `filter_memories`, `batch_size`, `force_add_only`, `includes`, `excludes`, `keyword_search`, `org_name`, `project_name`
 
 ### TypeScript Client SDK
 
@@ -242,7 +242,7 @@ await client.search("query", {
 });
 ```
 
-**Removed:** `OutputFormat` enum, `API_VERSION` enum, `organizationId`, `projectId`, `organizationName`, `projectName`, `enableGraph`, `asyncMode`, `outputFormat`, `immutable`, `expirationDate`, `filterMemories`, `batchSize`, `forceAddOnly`, `includes`, `excludes`, `keywordSearch`
+**Removed:** `OutputFormat` enum, `API_VERSION` enum, `organizationId`, `projectId`, `organizationName`, `projectName`, `enableGraph`, `asyncMode`, `outputFormat`, `immutable`, `filterMemories`, `batchSize`, `forceAddOnly`, `includes`, `excludes`, `keywordSearch`
 
 <Info>
 For the full list of parameter changes across all SDKs, see the [OSS migration guide](/migration/oss-v2-to-v3#removed-parameters-reference).

docs/open-source/configuration.mdx

@@ -130,7 +130,7 @@ memory = Memory.from_config_file("config.yaml")
 </Tabs>
 
 <Info icon="check">
-  Run `memory.add(["Remember my favorite cafe in Tokyo."], user_id="alex")` and then `memory.search("favorite cafe", filters={"user_id": "alex"})`. You should see the Qdrant collection populate and the reranker mark the memory as a top hit.
+  Run `memory.add("Remember my favorite cafe in Tokyo.", user_id="alex")` and then `memory.search("favorite cafe", filters={"user_id": "alex"})`. You should see the Qdrant collection populate and the reranker mark the memory as a top hit.
 </Info>
 
 ## Tune component settings

docs/open-source/features/async-memory.mdx

@@ -18,7 +18,7 @@ icon: "bolt"
 </Warning>
 
 <Note>
-  Working in TypeScript? The Node SDK still uses synchronous calls—use `Memory` there and rely on Python’s `AsyncMemory` when you need awaited operations.
+  Working in TypeScript? The OSS `Memory` class in the Node SDK (`mem0ai/oss`) is also fully async — every method returns a `Promise` and must be `await`ed. Python’s `AsyncMemory` serves the same purpose within Python async frameworks like FastAPI. Both runtimes support awaited memory operations; choose the SDK that matches your language.
 </Note>
 
 ## Feature anatomy

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

@@ -165,8 +165,7 @@ await memory.add("Yesterday, I ordered a laptop, the order id is 12345", { userI
     {"memory": "Ordered a laptop", "event": "ADD"},
     {"memory": "Order ID: 12345", "event": "ADD"},
     {"memory": "Order placed yesterday", "event": "ADD"}
-  ],
-  "relations": []
+  ]
 }
 ```
 </CodeGroup>
@@ -188,8 +187,7 @@ await memory.add("I like going to hikes", { userId: "user123" });
 
 ```json Output
 {
-  "results": [],
-  "relations": []
+  "results": []
 }
 ```
 </CodeGroup>

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

@@ -41,6 +41,14 @@ Multimodal support lets Mem0 extract facts from images alongside regular text. A
 
 ## Configure it
 
+<Warning>
+  You must set `enable_vision: True` in your LLM config for image content to be processed. Without it, image turns are silently dropped and no vision memories are created. Example:
+  ```python
+  config = {"llm": {"provider": "openai", "config": {"enable_vision": True, "vision_details": "auto"}}}
+  client = Memory.from_config(config)
+  ```
+</Warning>
+
 ### Add image messages from URLs
 
 <CodeGroup>
@@ -66,7 +74,7 @@ client.add(messages, user_id="alice")
 ```
 
 ```ts TypeScript
-import { Memory } from "mem0ai";
+import { Memory } from "mem0ai/oss";
 
 const client = new Memory();
 
@@ -123,7 +131,7 @@ client.add(messages, user_id="alice")
 
 ```ts TypeScript
 import fs from "fs";
-import { Memory } from "mem0ai";
+import { Memory } from "mem0ai/oss";
 
 function encodeImage(imagePath: string) {
   const buffer = fs.readFileSync(imagePath);
@@ -226,7 +234,7 @@ client.add(messages, user_id="user123")
 <CodeGroup>
 ```python Python
 from mem0 import Memory
-from mem0.exceptions import InvalidImageError, FileSizeError
+from mem0.exceptions import ValidationError
 
 client = Memory()
 
@@ -242,16 +250,14 @@ try:
     client.add(messages, user_id="user123")
     print("Image processed successfully")
 
-except InvalidImageError:
-    print("Invalid image format or corrupted file")
-except FileSizeError:
-    print("Image file too large")
+except ValidationError as exc:
+    print(f"Image validation error: {exc}")
 except Exception as exc:
     print(f"Unexpected error: {exc}")
 ```
 
 ```ts TypeScript
-import { Memory } from "mem0ai";
+import { Memory } from "mem0ai/oss";
 
 const client = new Memory();
 

docs/open-source/features/reranker-search.mdx

@@ -124,7 +124,7 @@ config = {
         "provider": "llm_reranker",
         "config": {
             "provider": "openai",
-            "model": "gpt-4o-mini",
+            "model": "gpt-5-mini",
             "api_key": "your-openai-api-key",
             "top_k": 5
         }
@@ -150,7 +150,7 @@ config = {
     "llm": {
         "provider": "openai",
         "config": {
-            "model": "gpt-4",
+            "model": "gpt-5-mini",
             "api_key": "your-openai-api-key"
         }
     },

docs/openapi.json

@@ -1779,6 +1779,11 @@
 										"type": "object",
 										"description": "Entity and metadata filters. Must include at least one entity ID (`user_id`, `agent_id`, `app_id`, or `run_id`).",
 										"additionalProperties": true
+									},
+									"show_expired": {
+										"type": "boolean",
+										"default": false,
+										"description": "When true, include memories whose `expiration_date` has passed. Expired memories are hidden by default."
 									}
 								}
 							},
@@ -1977,6 +1982,12 @@
 										"additionalProperties": true,
 										"description": "User-supplied metadata to attach to each extracted memory."
 									},
+									"expiration_date": {
+										"type": "string",
+										"format": "date",
+										"nullable": true,
+										"description": "Optional expiration date in YYYY-MM-DD format. After this date, memories are hidden from search and get-all unless `show_expired` is true."
+									},
 									"custom_instructions": {
 										"type": "string",
 										"description": "Project-level instructions that guide extraction for this call."
@@ -2094,6 +2105,11 @@
 										"description": "Entity and metadata filters. Must include at least one entity ID (`user_id`, `agent_id`, `app_id`, or `run_id`). Supports `AND`, `OR`, `NOT`, and comparison operators (`in`, `gte`, `lte`, `gt`, `lt`, `contains`, `icontains`, `ne`).",
 										"additionalProperties": true
 									},
+									"show_expired": {
+										"type": "boolean",
+										"default": false,
+										"description": "When true, include memories whose `expiration_date` has passed. Expired memories are hidden by default."
+									},
 									"top_k": {
 										"type": "integer",
 										"minimum": 1,
@@ -2432,6 +2448,12 @@
 									"metadata": {
 										"type": "object",
 										"description": "Additional metadata associated with the memory"
+									},
+									"expiration_date": {
+										"type": "string",
+										"format": "date",
+										"nullable": true,
+										"description": "Expiration date in YYYY-MM-DD format, or null to clear the expiration date."
 									}
 								}
 							}

docs/platform/advanced-memory-operations.mdx

@@ -9,7 +9,6 @@ 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>
@@ -21,9 +20,9 @@ description: "Run richer add/search/update/delete flows on the managed platform
 <Tabs>
   <Tab title="Python">
 <Steps>
-<Step title="Install the SDK with async extras">
+<Step title="Install the SDK">
 ```bash
-pip install "mem0ai[async]"
+pip install mem0ai
 ```
 </Step>
 <Step title="Export your API key">
@@ -55,9 +54,9 @@ export MEM0_API_KEY="sk-platform-..."
 </Step>
 <Step title="Instantiate the client">
 ```typescript
-import { Memory } from "mem0ai";
+import MemoryClient from 'mem0ai';
 
-const memory = new Memory({ apiKey: process.env.MEM0_API_KEY!, async: true });
+const memory = new MemoryClient({ apiKey: process.env.MEM0_API_KEY! });
 ```
 </Step>
 </Steps>
@@ -121,10 +120,8 @@ const result = await memory.add(conversation, {
 ```python
 matches = await memory.search(
     "Any food alerts?",
-    user_id="traveler-42",
-    filters={"metadata.trip": "japan-2025"},
+    filters={"user_id": "traveler-42", "metadata.trip": "japan-2025"},
     rerank=True,
-    include_vectors=False,
 )
 ```
 </Step>
@@ -132,7 +129,7 @@ matches = await memory.search(
 ```python
 await memory.update(
     memory_id=matches["results"][0]["id"],
-    content="Morgan avoids shellfish and prefers boutique hotels in central Tokyo.",
+    data="Morgan avoids shellfish and prefers boutique hotels in central Tokyo.",
 )
 ```
 </Step>
@@ -143,17 +140,15 @@ await memory.update(
 <Step title="Search with metadata filters">
 ```typescript
 const matches = await memory.search("Any food alerts?", {
-  userId: "traveler-42",
-  filters: { "metadata.trip": "japan-2025" },
+  filters: { user_id: "traveler-42", "metadata.trip": "japan-2025" },
   rerank: true,
-  includeVectors: false,
 });
 ```
 </Step>
 <Step title="Apply an update">
 ```typescript
 await memory.update(matches.results[0].id, {
-  content: "Morgan avoids shellfish and prefers boutique hotels in central Tokyo.",
+  text: "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,
-    user_id="user123"
+    filters={"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
-    user_id="user123"
+    filters={"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",
-    user_id="customer456"
+    filters={"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
-    user_id="patient789"
+    filters={"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
-    user_id="student123"
+    filters={"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,
-        user_id=user_id
+        filters={"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,
-        user_id=user_id
+        filters={"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,
-        user_id=user_id
+        filters={"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, {
-        user_id: userId
+        filters: { user_id: userId },
     });
 }
 
 // Reranked search - good for most applications
 function standardSearch(query, userId) {
     return client.search(query, {
-        user_id: userId,
-        rerank: true
+        filters: { user_id: userId },
+        rerank: true,
     });
 }
 
 // Reranked search - good for critical applications
 function preciseSearch(query, userId) {
     return client.search(query, {
-        user_id: userId,
-        rerank: true
+        filters: { user_id: userId },
+        rerank: true,
     });
 }
 ```
@@ -178,7 +178,7 @@ start_time = time.time()
 results = client.search(
     query="user preferences",
     rerank=True,         # +150ms
-    user_id="user123"
+    filters={"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", version: "v2" });
+await client.add(messages, { userId: "user123" });
 ```
 </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", version: "v2" });
+await client.add(messages1, { userId: "sarah" });
 
 // 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", version: "v2" });
+await client.add(messages2, { userId: "sarah" });
 // 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", version: "v2" });
+await client.add(messages, { userId: "user123" });
 // 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", version: "v2" });
+await client.add(messages1, { userId: "user123", runId: "tokyo-trip-2024" });
 
 // 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", version: "v2" });
+await client.add(messages2, { userId: "user123", runId: "tokyo-trip-2024" });
 
 // 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", version: "v2" });
+await client.add(workMessages, { userId: "user123", runId: "q4-marketing" });
 ```
 </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.
+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.
 </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, APIError
+from mem0.exceptions import MemoryNotFoundError, NetworkError
 
 client = MemoryClient(api_key="your_api_key")
 
@@ -163,8 +163,8 @@ try:
     print("Feedback submitted successfully")
 except MemoryNotFoundError:
     print("Memory not found")
-except APIError as e:
-    print(f"API error: {e}")
+except NetworkError as e:
+    print(f"Network error: {e}")
 except Exception as e:
     print(f"Unexpected error: {e}")
 ```

docs/platform/features/group-chat.mdx

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

docs/platform/features/memory-export.mdx

@@ -251,7 +251,6 @@ 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 14+ (for npx)
+  - Node.js 18+ (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 14+, or cURL
+- Python 3.10+, Node.js 18+, or cURL
 
 ## Installation
 

integrations/openclaw/package.json

@@ -71,7 +71,8 @@
       "picomatch@<2.3.2": "^2.3.2",
       "@qdrant/js-client-rest": "^1.18.0",
       "uuid@<11.1.1": ">=11.1.1",
-      "esbuild": ">=0.28.1"
+      "esbuild": ">=0.28.1",
+      "undici@<6.27.0": ">=6.27.0 <8.0.0"
     }
   }
 }

integrations/openclaw/pnpm-lock.yaml

@@ -13,6 +13,7 @@ overrides:
   '@qdrant/js-client-rest': ^1.18.0
   uuid@<11.1.1: '>=11.1.1'
   esbuild: '>=0.28.1'
+  undici@<6.27.0: '>=6.27.0 <8.0.0'
 
 importers:
 
@@ -1996,9 +1997,9 @@ packages:
   undici-types@6.21.0:
     resolution: {integrity: sha512-iwDZqg0QAGrg9Rav5H4n0M64c3mkR59cJ6wQp+7C4nI0gsmExaedaYLNO44eT4AtBBwjbTiGPMlt2Md0T9H9JQ==}
 
-  undici@6.26.0:
-    resolution: {integrity: sha512-4yqz8a3n5HmGTlsbADNtr/dJlhkh/55Rq798G6ibiULcXbDtaLpTl1pvdqcbFfeoj3iSi52lePFM7h9H21cw/A==}
-    engines: {node: '>=18.17'}
+  undici@7.28.0:
+    resolution: {integrity: sha512-cRZYrTDwWznlnRiPjggAGxZXanty6M8RV1ff8Wm4LWXBp7/IG8v5DnOm74DtUBp9OONpK75YlPnIjQqX0dBDtA==}
+    engines: {node: '>=20.18.1'}
 
   util-deprecate@1.0.2:
     resolution: {integrity: sha512-EPD5q1uXyFxJpCrLnCc1nHnq3gOa6DZBocAIiI2TaSCA7VCJ1UJDMagCzIkXNsUYfD1daK//LTEQ8xiIbrHtcw==}
@@ -2509,7 +2510,7 @@ snapshots:
     dependencies:
       '@qdrant/openapi-typescript-fetch': 1.2.6
       typescript: 5.9.3
-      undici: 6.26.0
+      undici: 7.28.0
 
   '@qdrant/openapi-typescript-fetch@1.2.6': {}
 
@@ -4063,7 +4064,7 @@ snapshots:
 
   undici-types@6.21.0: {}
 
-  undici@6.26.0: {}
+  undici@7.28.0: {}
 
   util-deprecate@1.0.2: {}
 

integrations/openclaw/pnpm-workspace.yaml

@@ -21,3 +21,4 @@ overrides:
   "@qdrant/js-client-rest": "^1.18.0"
   "uuid@<11.1.1": ">=11.1.1"
   "esbuild": ">=0.28.1"
+  "undici@<6.27.0": ">=6.27.0 <8.0.0"

integrations/openclaw/skill-loader.test.ts

@@ -4,10 +4,12 @@
 import { describe, it, expect } from "vitest";
 import {
   safePath,
+  normalizeModuleUrlToPath,
   loadSkill,
   loadTriagePrompt,
   loadCompactTriagePrompt,
 } from "./skill-loader.ts";
+import { fileURLToPath, pathToFileURL } from "node:url";
 
 // ---------------------------------------------------------------------------
 // safePath — path containment
@@ -74,6 +76,31 @@ describe("loadSkill path traversal", () => {
   });
 });
 
+describe("normalizeModuleUrlToPath", () => {
+  it("normalizes raw Windows paths before fileURLToPath conversion", () => {
+    const rawWindowsMetaUrl = "C:\\Users\\example\\openclaw\\index.ts";
+    const result = normalizeModuleUrlToPath(rawWindowsMetaUrl);
+    // Assert the decoded property directly rather than reconstructing via the function body
+    expect(typeof result).toBe("string");
+    expect(result).not.toContain("%5C");
+  });
+
+  it("leaves already-correct file URLs unchanged", () => {
+    const fileMetaUrl = "file:///C:/Users/example/openclaw/index.ts";
+    const expected = fileURLToPath(fileMetaUrl);
+
+    expect(normalizeModuleUrlToPath(fileMetaUrl)).toBe(expected);
+  });
+
+  it.skipIf(process.platform === "win32")(
+    "passes POSIX absolute paths through unchanged",
+    () => {
+      const posixPath = "/home/user/openclaw/index.ts";
+      expect(normalizeModuleUrlToPath(posixPath)).toBe(posixPath);
+    },
+  );
+});
+
 describe("loadCompactTriagePrompt", () => {
   it("keeps the core triage instructions without inlining the full skill body", () => {
     const prompt = loadCompactTriagePrompt();

integrations/openclaw/skill-loader.ts

@@ -4,7 +4,7 @@
  */
 
 import * as path from "node:path";
-import { fileURLToPath } from "node:url";
+import { fileURLToPath, pathToFileURL } from "node:url";
 import type { SkillsConfig, CategoryConfig } from "./types.ts";
 import { readText, exists } from "./fs-safe.ts";
 
@@ -84,6 +84,14 @@ function parseSkillFile(content: string): ParsedSkill {
   };
 }
 
+/** @internal — exported for testing only */
+export function normalizeModuleUrlToPath(moduleUrl: string): string {
+  const normalizedUrl = moduleUrl.startsWith("file:")
+    ? moduleUrl
+    : pathToFileURL(moduleUrl).toString();
+  return fileURLToPath(normalizedUrl);
+}
+
 // ============================================================================
 // Skill Loader
 // ============================================================================
@@ -96,7 +104,7 @@ function resolveSkillsDir(): string {
 
   // Strategy 1: import.meta.url (works in native ESM)
   try {
-    const metaDir = path.dirname(fileURLToPath(import.meta.url));
+    const metaDir = path.dirname(normalizeModuleUrlToPath(import.meta.url));
     candidates.push(path.join(metaDir, "skills"));
     candidates.push(path.join(metaDir, "..", "skills"));
   } catch {

integrations/pi-agent-plugin/package.json

@@ -74,6 +74,7 @@
       "form-data@<4.0.6": ">=4.0.6",
       "uuid@<11.1.1": ">=11.1.1",
       "esbuild": ">=0.28.1",
+      "undici@<6.27.0": ">=6.27.0 <8.0.0",
       "undici@>=8.0.0 <8.5.0": ">=8.5.0"
     }
   }

integrations/pi-agent-plugin/pnpm-lock.yaml

@@ -8,6 +8,7 @@ overrides:
   form-data@<4.0.6: '>=4.0.6'
   uuid@<11.1.1: '>=11.1.1'
   esbuild: '>=0.28.1'
+  undici@<6.27.0: '>=6.27.0 <8.0.0'
   undici@>=8.0.0 <8.5.0: '>=8.5.0'
 
 importers:
@@ -2351,9 +2352,9 @@ packages:
   undici-types@7.24.6:
     resolution: {integrity: sha512-WRNW+sJgj5OBN4/0JpHFqtqzhpbnV0GuB+OozA9gCL7a993SmU+1JBZCzLNxYsbMfIeDL+lTsphD5jN5N+n0zg==}
 
-  undici@6.26.0:
-    resolution: {integrity: sha512-4yqz8a3n5HmGTlsbADNtr/dJlhkh/55Rq798G6ibiULcXbDtaLpTl1pvdqcbFfeoj3iSi52lePFM7h9H21cw/A==}
-    engines: {node: '>=18.17'}
+  undici@7.28.0:
+    resolution: {integrity: sha512-cRZYrTDwWznlnRiPjggAGxZXanty6M8RV1ff8Wm4LWXBp7/IG8v5DnOm74DtUBp9OONpK75YlPnIjQqX0dBDtA==}
+    engines: {node: '>=20.18.1'}
 
   undici@8.5.0:
     resolution: {integrity: sha512-xamtWoB1EshgjpmlXd7GGm2VfdDtw1+rD8uhry8pSNW3If6S8E0m2T2+orSKeZXEn/aPJMviCpDBA65WJt8zhg==}
@@ -3202,7 +3203,7 @@ snapshots:
     dependencies:
       '@qdrant/openapi-typescript-fetch': 1.2.6
       typescript: 6.0.3
-      undici: 6.26.0
+      undici: 7.28.0
 
   '@qdrant/openapi-typescript-fetch@1.2.6': {}
 
@@ -4894,7 +4895,7 @@ snapshots:
 
   undici-types@7.24.6: {}
 
-  undici@6.26.0: {}
+  undici@7.28.0: {}
 
   undici@8.5.0: {}
 

integrations/pi-agent-plugin/pnpm-workspace.yaml

@@ -5,4 +5,5 @@ overrides:
   "form-data@<4.0.6": ">=4.0.6"
   "uuid@<11.1.1": ">=11.1.1"
   "esbuild": ">=0.28.1"
+  "undici@<6.27.0": ">=6.27.0 <8.0.0"
   "undici@>=8.0.0 <8.5.0": ">=8.5.0"

mem0-ts/package.json

@@ -1,6 +1,6 @@
 {
   "name": "mem0ai",
-  "version": "3.0.11",
+  "version": "3.0.12",
   "description": "The Memory Layer For Your AI Apps",
   "main": "./dist/index.js",
   "module": "./dist/index.mjs",
@@ -156,7 +156,8 @@
       "tar-fs@>=2.0.0 <2.1.4": "^2.1.4",
       "glob@>=10.2.0 <10.5.0": "^10.5.0",
       "@modelcontextprotocol/sdk": "^1.25.4",
-      "esbuild": ">=0.28.1"
+      "esbuild": ">=0.28.1",
+      "undici@<6.27.0": ">=6.27.0 <8.0.0"
     }
   }
 }

mem0-ts/pnpm-lock.yaml

@@ -23,6 +23,7 @@ overrides:
   glob@>=10.2.0 <10.5.0: ^10.5.0
   '@modelcontextprotocol/sdk': ^1.25.4
   esbuild: '>=0.28.1'
+  undici@<6.27.0: '>=6.27.0 <8.0.0'
 
 importers:
 
@@ -2925,9 +2926,9 @@ packages:
   undici-types@6.21.0:
     resolution: {integrity: sha512-iwDZqg0QAGrg9Rav5H4n0M64c3mkR59cJ6wQp+7C4nI0gsmExaedaYLNO44eT4AtBBwjbTiGPMlt2Md0T9H9JQ==}
 
-  undici@6.26.0:
-    resolution: {integrity: sha512-4yqz8a3n5HmGTlsbADNtr/dJlhkh/55Rq798G6ibiULcXbDtaLpTl1pvdqcbFfeoj3iSi52lePFM7h9H21cw/A==}
-    engines: {node: '>=18.17'}
+  undici@7.28.0:
+    resolution: {integrity: sha512-cRZYrTDwWznlnRiPjggAGxZXanty6M8RV1ff8Wm4LWXBp7/IG8v5DnOm74DtUBp9OONpK75YlPnIjQqX0dBDtA==}
+    engines: {node: '>=20.18.1'}
 
   update-browserslist-db@1.2.3:
     resolution: {integrity: sha512-Js0m9cx+qOgDxo0eMiFGEueWztz+d4+M3rGlmKPT+T4IS/jP4ylw3Nwpu6cpTTP8R1MAC1kF4VbdLt3ARf209w==}
@@ -3747,7 +3748,7 @@ snapshots:
     dependencies:
       '@qdrant/openapi-typescript-fetch': 1.2.6
       typescript: 5.5.4
-      undici: 6.26.0
+      undici: 7.28.0
 
   '@qdrant/openapi-typescript-fetch@1.2.6': {}
 
@@ -6113,7 +6114,7 @@ snapshots:
 
   undici-types@6.21.0: {}
 
-  undici@6.26.0: {}
+  undici@7.28.0: {}
 
   update-browserslist-db@1.2.3(browserslist@4.28.2):
     dependencies:

mem0-ts/pnpm-workspace.yaml

@@ -24,3 +24,4 @@ overrides:
   "glob@>=10.2.0 <10.5.0": "^10.5.0"
   "@modelcontextprotocol/sdk": "^1.25.4"
   "esbuild": ">=0.28.1"
+  "undici@<6.27.0": ">=6.27.0 <8.0.0"

mem0-ts/src/client/mem0.ts

@@ -281,19 +281,22 @@ export default class MemoryClient {
       text,
       metadata,
       timestamp,
+      expirationDate,
     }: {
       text?: string;
       metadata?: Record<string, any>;
       timestamp?: number | string;
+      expirationDate?: string | null;
     },
   ): Promise<Array<Memory>> {
     if (
       text === undefined &&
       metadata === undefined &&
-      timestamp === undefined
+      timestamp === undefined &&
+      expirationDate === undefined
     ) {
       throw new Error(
-        "At least one of text, metadata, or timestamp must be provided for update.",
+        "At least one of text, metadata, timestamp, or expirationDate must be provided for update.",
       );
     }
 
@@ -302,6 +305,7 @@ export default class MemoryClient {
     if (text !== undefined) payload.text = text;
     if (metadata !== undefined) payload.metadata = metadata;
     if (timestamp !== undefined) payload.timestamp = timestamp;
+    if (expirationDate !== undefined) payload.expiration_date = expirationDate;
 
     const payloadKeys = Object.keys(payload);
     this._captureEvent("update", [payloadKeys]);

mem0-ts/src/client/mem0.types.ts

@@ -13,6 +13,7 @@ export interface AddMemoryOptions extends EntityOptions {
   customCategories?: custom_categories[];
   customInstructions?: string;
   timestamp?: number;
+  expirationDate?: string;
   structuredDataSchema?: Record<string, any>;
 }
 
@@ -25,6 +26,7 @@ export interface SearchMemoryOptions {
   latestOnly?: boolean;
   fields?: string[];
   categories?: string[];
+  showExpired?: boolean;
 }
 
 export interface GetAllMemoryOptions {
@@ -35,6 +37,7 @@ export interface GetAllMemoryOptions {
   endDate?: string;
   latestOnly?: boolean;
   categories?: string[];
+  showExpired?: boolean;
 }
 
 export interface DeleteAllMemoryOptions extends EntityOptions {}
@@ -119,6 +122,7 @@ export interface Memory {
   memoryType?: string;
   score?: number;
   metadata?: any | null;
+  expirationDate?: string | null;
   owner?: string | null;
   agentId?: string | null;
   appId?: string | null;

mem0-ts/src/client/tests/memoryClient.crud.test.ts

@@ -59,6 +59,21 @@ describe("MemoryClient - add()", () => {
     expect(getFetchBody(call!).user_id).toBe("user_1");
   });
 
+  test("serializes expirationDate as expiration_date", async () => {
+    const extra = new Map<string, { status: number; body: unknown }>();
+    extra.set("/v3/memories/add/", { status: 200, body: [createMockMemory()] });
+    const mock = setupMockFetch(extra);
+
+    const client = new MemoryClient({ apiKey: TEST_API_KEY });
+    await client.add([{ role: "user", content: "test" }], {
+      userId: "u1",
+      expirationDate: "2030-01-31",
+    });
+
+    const call = findFetchCall(mock, "/v3/memories/add/", "POST");
+    expect(getFetchBody(call!).expiration_date).toBe("2030-01-31");
+  });
+
   test("throws an error when given an empty messages array", async () => {
     setupMockFetch();
 
@@ -176,11 +191,26 @@ describe("MemoryClient - update()", () => {
     expect(body.timestamp).toBe(1710600000);
   });
 
+  test("sends expirationDate as expiration_date, including null", async () => {
+    const extra = new Map<string, { status: number; body: unknown }>();
+    extra.set("/v1/memories/mem_123/", {
+      status: 200,
+      body: createMockMemory(),
+    });
+    const mock = setupMockFetch(extra);
+
+    const client = new MemoryClient({ apiKey: TEST_API_KEY });
+    await client.update("mem_123", { expirationDate: null });
+
+    const call = findFetchCall(mock, "/v1/memories/mem_123/", "PUT");
+    expect(getFetchBody(call!).expiration_date).toBeNull();
+  });
+
   test("throws when no fields provided", async () => {
     setupMockFetch();
     const client = new MemoryClient({ apiKey: TEST_API_KEY });
     await expect(client.update("mem_123", {})).rejects.toThrow(
-      "At least one of text, metadata, or timestamp must be provided",
+      "At least one of text, metadata, timestamp, or expirationDate must be provided",
     );
   });
 });

mem0-ts/src/client/tests/memoryClient.search.test.ts

@@ -81,6 +81,24 @@ describe("MemoryClient - search()", () => {
     expect(getFetchBody(call!).latest_only).toBe(true);
   });
 
+  test("serializes showExpired as show_expired", async () => {
+    const extra = new Map<string, { status: number; body: unknown }>();
+    extra.set("/v3/memories/search/", {
+      status: 200,
+      body: { results: [] },
+    });
+    const mock = setupMockFetch(extra);
+
+    const client = new MemoryClient({ apiKey: TEST_API_KEY });
+    await client.search("test", {
+      filters: { user_id: "u1" },
+      showExpired: true,
+    });
+
+    const call = findFetchCall(mock, "/v3/memories/search/", "POST");
+    expect(getFetchBody(call!).show_expired).toBe(true);
+  });
+
   test("passes complex OR filters through to the API body", async () => {
     const extra = new Map<string, { status: number; body: unknown }>();
     extra.set("/v3/memories/search/", {
@@ -300,4 +318,22 @@ describe("MemoryClient - getAll() entity param rejection", () => {
     const call = findFetchCall(mock, "/v3/memories/", "POST");
     expect(getFetchBody(call!).latest_only).toBe(true);
   });
+
+  test("serializes showExpired as show_expired", async () => {
+    const extra = new Map<string, { status: number; body: unknown }>();
+    extra.set("/v3/memories/", {
+      status: 200,
+      body: { results: [] },
+    });
+    const mock = setupMockFetch(extra);
+
+    const client = new MemoryClient({ apiKey: TEST_API_KEY });
+    await client.getAll({
+      filters: { user_id: "u1" },
+      showExpired: true,
+    });
+
+    const call = findFetchCall(mock, "/v3/memories/", "POST");
+    expect(getFetchBody(call!).show_expired).toBe(true);
+  });
 });

mem0-ts/src/oss/src/index.ts

@@ -18,6 +18,7 @@ export * from "./llms/ollama";
 export * from "./llms/lmstudio";
 export * from "./llms/mistral";
 export * from "./llms/langchain";
+export * from "./llms/litellm";
 export * from "./vector_stores/base";
 export * from "./vector_stores/memory";
 export * from "./vector_stores/qdrant";

mem0-ts/src/oss/src/llms/litellm.ts

@@ -0,0 +1,39 @@
+import { OpenAILLM } from "./openai";
+import { LLMConfig, Message } from "../types";
+import { LLMResponse } from "./base";
+
+export class LiteLLM extends OpenAILLM {
+  constructor(config: LLMConfig) {
+    super({
+      ...config,
+      apiKey: config.apiKey || process.env.LITELLM_API_KEY || "sk-anything",
+      baseURL:
+        config.baseURL ||
+        process.env.LITELLM_API_BASE ||
+        "http://localhost:4000",
+      model: config.model || "gpt-5-mini",
+    });
+  }
+
+  async generateResponse(
+    messages: Message[],
+    responseFormat?: { type: string },
+    tools?: any[],
+  ): Promise<string | LLMResponse> {
+    try {
+      return await super.generateResponse(messages, responseFormat, tools);
+    } catch (err) {
+      const message = err instanceof Error ? err.message : String(err);
+      throw new Error(`LiteLLM failed: ${message}`);
+    }
+  }
+
+  async generateChat(messages: Message[]): Promise<LLMResponse> {
+    try {
+      return await super.generateChat(messages);
+    } catch (err) {
+      const message = err instanceof Error ? err.message : String(err);
+      throw new Error(`LiteLLM failed: ${message}`);
+    }
+  }
+}

mem0-ts/src/oss/src/llms/minimax.ts

@@ -0,0 +1,43 @@
+import { OpenAILLM } from "./openai";
+import { LLMConfig, Message } from "../types";
+import { LLMResponse } from "./base";
+
+export class MiniMaxLLM extends OpenAILLM {
+  constructor(config: LLMConfig) {
+    const apiKey = config.apiKey || process.env.MINIMAX_API_KEY;
+    if (!apiKey) {
+      throw new Error("MiniMax API key is required");
+    }
+    super({
+      ...config,
+      apiKey,
+      baseURL:
+        config.baseURL ||
+        process.env.MINIMAX_API_BASE ||
+        "https://api.minimax.io/v1",
+      model: config.model || "MiniMax-M2.7",
+    });
+  }
+
+  async generateResponse(
+    messages: Message[],
+    responseFormat?: { type: string },
+    tools?: any[],
+  ): Promise<string | LLMResponse> {
+    try {
+      return await super.generateResponse(messages, responseFormat, tools);
+    } catch (err) {
+      const message = err instanceof Error ? err.message : String(err);
+      throw new Error(`MiniMax LLM failed: ${message}`);
+    }
+  }
+
+  async generateChat(messages: Message[]): Promise<LLMResponse> {
+    try {
+      return await super.generateChat(messages);
+    } catch (err) {
+      const message = err instanceof Error ? err.message : String(err);
+      throw new Error(`MiniMax LLM failed: ${message}`);
+    }
+  }
+}