Merge remote-tracking branch 'origin/main' into docs/stale-migration

# Conflicts: # docs/migration/api-changes.mdx
docs(vectordbs): correct vector store config defaults & imports (#5843 )
2026-06-27 13:10:29 +05:30 · 2026-06-27 12:40:16 +05:30 · 2026-06-26 22:07:53 +05:30 · 2026-06-26 22:05:14 +05:30 · 2026-06-26 22:03:53 +05:30 · 2026-06-26 19:01:58 +05:30
122 changed files with 3687 additions and 1667 deletions
@@ -12,7 +12,7 @@
      "name": "mem0",
      "source": "./integrations/mem0-plugin",
      "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search to Claude workflows.",
-      "version": "0.2.10"
+      "version": "0.2.11"
    }
  ]
 }
@@ -12,7 +12,7 @@
      "name": "mem0",
      "source": "./integrations/mem0-plugin",
      "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search.",
-      "version": "0.2.10"
+      "version": "0.2.11"
    }
  ]
 }
@@ -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!
@@ -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.
@@ -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>
@@ -4,4 +4,4 @@ description: "Submit an export job to create a structured memory export using a
 openapi: post /v1/exports/
 ---

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

-Retrieve the latest structured memory export after submitting an export job. You can filter the export by `user_id`, `run_id`, `session_id`, or `app_id` to get the most recent export matching your filters.
+Retrieve the latest structured memory export after submitting an export job. You can filter the export by `user_id`, `agent_id`, `app_id`, `run_id`, `created_at`, or `updated_at` to get the most recent export matching your filters.
@@ -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
@@ -20,16 +24,17 @@ The `filters` object supports complex logical operations (AND, OR, NOT) and comp

 ### Search parameter defaults

-| Parameter | V1/V2 | V3 |
-| --- | --- | --- |
-| `top_k` | Supported (default 10) | Supported (1-1000, default 10) |
-| `threshold` | No default | Default `0.1` (pass `0.0` to disable) |
-| `rerank` | Default `true` | Default `false` (pass `true` to enable) |
+| Parameter | Default |
+| --- | --- |
+| `top_k` | `10` (range 1–1000) |
+| `threshold` | `0.1` (pass `0.0` to disable) |
+| `rerank` | `false` (pass `true` to enable) |

 <CodeGroup>
 ```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"]
@@ -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`.
@@ -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/"
+---
@@ -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/"
+---
@@ -14,7 +14,7 @@ Organizations and projects are **optional** features. You can use Mem0 without t

 ## Key Capabilities

- **Multi-org/project Support**: Specify organization and project when initializing the Mem0 client to attribute API usage appropriately
+- **Multi-org/project Support**: Organization and project are resolved automatically from your API key via `/v1/ping/` — no org or project params are accepted by `MemoryClient.__init__`. Use a project-specific API key to target a particular project.
 - **Member Management**: Control access to data through organization and project membership
 - **Access Control**: Only members can access memories and data within their organization/project scope
 - **Team Isolation**: Maintain data separation between different teams and projects for secure collaboration
@@ -79,7 +79,7 @@ new_project = client.project.create(

 ### Update Project Settings

-Modify project configuration including custom instructions, categories, and language preferences:
+Modify project configuration including custom instructions, categories, language preferences, retrieval criteria, and memory decay:

 ```python
 # Update project with custom categories
@@ -98,6 +98,17 @@ client.project.update(
 # Use the input language for memory storage and retrieval
 client.project.update(multilingual=True)

+# Set retrieval criteria to control which memories are surfaced in search
+client.project.update(
+    retrieval_criteria=[
+        {"name": "relevance", "description": "How directly relevant this memory is to the current topic or user query", "weight": 3},
+        {"name": "access_frequency", "description": "How often this memory has been accessed or surfaced recently", "weight": 1}
+    ]
+)
+
+# Enable Memory Decay (boosts recently-accessed memories at search time)
+client.project.update(decay=True)
+
 # Update multiple settings at once
 client.project.update(
    custom_instructions="...",
@@ -109,6 +120,34 @@ client.project.update(
 )
 ```

+#### Set Retrieval Criteria
+
+`retrieval_criteria` is a per-project list of dictionaries (`List[Dict]`) that shapes how memories are ranked and filtered during search. Each dictionary has three fields: `name` (identifier), `description` (interpreted by the LLM to score each memory), and `weight` (relative influence on the final score). Use this to focus retrieval on intent-aligned or signal-specific memories:
+
+```python
+client.project.update(
+    retrieval_criteria=[
+        {
+            "name": "joy",
+            "description": "Measure the intensity of positive emotions such as happiness, excitement, or amusement expressed in the memory. A higher score reflects greater joy.",
+            "weight": 3
+        },
+        {
+            "name": "curiosity",
+            "description": "Assess the extent to which the memory reflects inquisitiveness or interest in exploring new information. A higher score reflects stronger curiosity.",
+            "weight": 2
+        },
+        {
+            "name": "access_frequency",
+            "description": "How often this memory has been accessed or surfaced recently.",
+            "weight": 1
+        }
+    ]
+)
+```
+
+Pass an empty list to clear all criteria and restore default retrieval behaviour.
+
 #### Toggle Memory Decay

 `decay` is a per-project boolean that turns on [Memory Decay](/platform/features/memory-decay) — a search-time ranking bias that reinforces recently-accessed memories and gently dampens stale ones. The flag is `false` by default; set it via the same project-update endpoint:
@@ -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/"
+---
@@ -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/"
+---
@@ -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}/"
+---
@@ -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
@@ -7,6 +7,65 @@ mode: "wide"
 <Tabs>
 <Tab title="Python">

+<Update label="2026-06-24" description="v2.0.9">
+
+**Bug Fixes:**
+- **Memory (OSS):** Improve entity extraction precision by avoiding sentence-start common noun noise, preserving useful topic phrases, and exact-deduplicating entity links before semantic matching ([#5829](https://github.com/mem0ai/mem0/pull/5829))
+
+</Update>
+
+<Update label="2026-06-24" description="v2.0.8">
+
+**New Features:**
+- **Embeddings:** Add native `embed_batch` to five embedders — LM Studio, Together, HuggingFace, Vertex AI, and Google GenAI — for batched embedding requests ([#5609](https://github.com/mem0ai/mem0/pull/5609))
+
+**Bug Fixes:**
+- **Core:** Guard against malformed `image_url` entries in `parse_vision_messages` to prevent crashes ([#5631](https://github.com/mem0ai/mem0/pull/5631))
+- **Core:** Return `attributed_to` from `get()`, `get_all()`, and `search()` ([#5629](https://github.com/mem0ai/mem0/pull/5629))
+- **Core:** Fix `reset()` only dropping the history table and leaving stale messages behind ([#5541](https://github.com/mem0ai/mem0/pull/5541))
+- **Core:** Guard against an entity `embed_batch` count mismatch in the v3 add pipeline ([#5604](https://github.com/mem0ai/mem0/pull/5604))
+- **Core:** Fix an async `delete_all` race condition that corrupted the entity store's `linked_memory_ids` ([#5553](https://github.com/mem0ai/mem0/pull/5553))
+- **LLMs:** Skip the JSON `response_format` for Groq compound models that reject it ([#5513](https://github.com/mem0ai/mem0/pull/5513))
+- **LLMs:** Preserve reasoning fields during base-to-provider config conversion ([#5638](https://github.com/mem0ai/mem0/pull/5638))
+- **LLMs:** Pass the configured `anthropic_base_url` to the Anthropic client ([#5626](https://github.com/mem0ai/mem0/pull/5626))
+- **LLMs:** Stop the Azure provider from mutating and corrupting caller messages during content rewrite ([#5731](https://github.com/mem0ai/mem0/pull/5731))
+- **LLMs & Embeddings:** Repair HTTP proxy support for `httpx>=0.28` and preserve `proxies` in `LlmFactory` ([#5447](https://github.com/mem0ai/mem0/pull/5447))
+- **Embeddings:** Forward `embedding_dims` to Titan V2 in the AWS Bedrock embedder ([#5671](https://github.com/mem0ai/mem0/pull/5671))
+- **Rerankers:** Log reranking failures instead of swallowing them silently ([#5717](https://github.com/mem0ai/mem0/pull/5717))
+- **Rerankers:** Clamp out-of-range LLM scores instead of mis-parsing them ([#5635](https://github.com/mem0ai/mem0/pull/5635))
+- **Rerankers:** Export all five rerankers from the package root ([#5636](https://github.com/mem0ai/mem0/pull/5636))
+- **Vector Stores:** Point the FastEmbed-missing warning at `mem0ai[extras]` ([#5622](https://github.com/mem0ai/mem0/pull/5622))
+- **Vector Stores:** Preserve empty Azure AI Search update values ([#5524](https://github.com/mem0ai/mem0/pull/5524))
+- **Vector Stores:** Add an `auto_refresh` option for OpenSearch Serverless compatibility ([#3893](https://github.com/mem0ai/mem0/pull/3893))
+- **Vector Stores:** Wrap a scalar `vector_id` in a list for Chroma `delete()` ([#5703](https://github.com/mem0ai/mem0/pull/5703))
+- **Vector Stores:** Wrap Chroma `update()` ids, embeddings, and metadatas in lists ([#5757](https://github.com/mem0ai/mem0/pull/5757))
+- **Vector Stores:** Wrap a scalar `vector_id` in a list for Milvus `delete()` ([#5704](https://github.com/mem0ai/mem0/pull/5704))
+- **Vector Stores:** Map all comparison operators in the Pinecone `_create_filter()` ([#5707](https://github.com/mem0ai/mem0/pull/5707))
+- **Vector Stores:** Return `None` instead of `{}` from Chroma `_generate_where_clause` for empty filters ([#5713](https://github.com/mem0ai/mem0/pull/5713))
+- **Vector Stores:** Return `[[]]` from the OpenSearch `list()` error path to honor the `list()` contract ([#5727](https://github.com/mem0ai/mem0/pull/5727))
+- **Vector Stores:** Return `[[]]` from the Pinecone `list()` error path instead of a dict ([#5706](https://github.com/mem0ai/mem0/pull/5706))
+- **Vector Stores:** Return `[[]]` for an uninitialized FAISS index to honor the `list()` contract ([#5725](https://github.com/mem0ai/mem0/pull/5725))
+- **Vector Stores:** Wrap the MongoDB `list()` return in an outer list to match the interface contract ([#5729](https://github.com/mem0ai/mem0/pull/5729))
+- **Vector Stores:** Deep-copy Redis `DEFAULT_FIELDS` so instances keep distinct dims ([#5633](https://github.com/mem0ai/mem0/pull/5633))
+- **Vector Stores:** Pass the required `vectors` arg in Vertex AI `list()` and similarity search ([#5627](https://github.com/mem0ai/mem0/pull/5627))
+- **Vector Stores:** Return `None` from Redis `get()` for missing IDs ([#5625](https://github.com/mem0ai/mem0/pull/5625))
+- **Vector Stores:** Drop a stray `print` in Weaviate `list_cols` ([#5637](https://github.com/mem0ai/mem0/pull/5637))
+- **Graph:** Keep distinct entities that share a substring prefix ([#5630](https://github.com/mem0ai/mem0/pull/5630))
+- **Client:** Check the HTTP status before parsing the ping response in `_validate_api_key` ([#5639](https://github.com/mem0ai/mem0/pull/5639))
+- **Server:** Fetch filtered dashboard memories beyond the default page ([#5753](https://github.com/mem0ai/mem0/pull/5753))
+- **Server:** Return 404/400 instead of 502 for not-found and invalid input ([#5634](https://github.com/mem0ai/mem0/pull/5634))
+- **Server:** Return 404 instead of 500 for a malformed API key id on revoke ([#5640](https://github.com/mem0ai/mem0/pull/5640))
+- **Server:** Use `127.0.0.1` in the dashboard healthcheck to avoid IPv6 localhost resolution ([#5612](https://github.com/mem0ai/mem0/pull/5612))
+
+**Improvements:**
+- **Vector Stores:** Batch BM25 sparse encoding in Qdrant insert ([#5592](https://github.com/mem0ai/mem0/pull/5592))
+
+**Security:**
+- **Vector Stores:** Sanitize Milvus and Baidu filter values to prevent expression injection ([#5746](https://github.com/mem0ai/mem0/pull/5746))
+- **Vector Stores:** Reject dict filter values in MongoDB to prevent NoSQL operator injection ([#5748](https://github.com/mem0ai/mem0/pull/5748))
+
+</Update>
+
 <Update label="2026-06-17" description="v2.0.7">

 **New Features:**
@@ -62,7 +121,7 @@ mode: "wide"

 **New Features:**
 - **Memory:** Warn at init time when hybrid/BM25 search silently degrades to semantic-only because the configured vector store does not implement `keyword_search`. Affected stores: Chroma, FAISS, Cassandra, LangChain, Neptune Analytics, S3 Vectors, Supabase, TurboPuffer, Valkey ([#5444](https://github.com/mem0ai/mem0/pull/5444))
- **Memory:** Add opt-in `explain=True` parameter to `Memory.search()` and `AsyncMemory.search()`. When enabled, each result includes a `score_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))
@@ -170,7 +229,7 @@ mode: "wide"
 **Improvements:**
 - **Telemetry:** Sample OSS hot-path events at 10% via PostHog `before_send` hook to reduce event volume ([#4771](https://github.com/mem0ai/mem0/pull/4771))

-See the [OSS 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>

@@ -1011,6 +1070,31 @@ See the [OSS v1 to v2 migration guide](https://docs.mem0.ai/migration/oss-v1-to-

 <Tab title="TypeScript">

+<Update label="2026-06-24" description="v3.0.11">
+
+**Bug Fixes:**
+- **Memory (OSS):** Align entity extraction with Python by reducing generic entity noise, preserving useful topic phrases, and exact-deduplicating entity links before semantic matching ([#5829](https://github.com/mem0ai/mem0/pull/5829))
+
+</Update>
+
+<Update label="2026-06-24" description="v3.0.10">
+
+**Bug Fixes:**
+- **Memory (OSS):** Guard against malformed `image_url` entries in `parseVisionMessages` to prevent crashes ([#5631](https://github.com/mem0ai/mem0/pull/5631))
+- **Memory (OSS):** Return `attributedTo` from `get()`, `search()`, and `getAll()` ([#5675](https://github.com/mem0ai/mem0/pull/5675))
+- **Memory (OSS):** Preserve message roles in the extraction input so assistant facts aren't attributed to the user ([#5643](https://github.com/mem0ai/mem0/pull/5643))
+- **Memory (OSS):** Reject empty or blank messages in `Memory.add()` to prevent hallucinated memories ([#5545](https://github.com/mem0ai/mem0/pull/5545))
+- **Memory (OSS):** Check `message.role` instead of `content` when detecting system messages ([#3921](https://github.com/mem0ai/mem0/pull/3921))
+- **LLMs:** Honor the configured `baseURL` in `AnthropicLLM` ([#5740](https://github.com/mem0ai/mem0/pull/5740))
+- **Client:** Preserve `customCategories` names through key conversion ([#5741](https://github.com/mem0ai/mem0/pull/5741))
+- **Client:** Prevent hallucinated memories on an empty messages payload ([#5613](https://github.com/mem0ai/mem0/pull/5613))
+- **Client:** Preserve user metadata keys across the case-conversion round-trip ([#5515](https://github.com/mem0ai/mem0/pull/5515))
+
+**Security:**
+- **Dependencies:** Upgrade `form-data` to `>=4.0.6` across pnpm workspaces to remediate CVE-2026-12143 ([#5618](https://github.com/mem0ai/mem0/pull/5618))
+
+</Update>
+
 <Update label="2026-06-17" description="v3.0.9">

 **Bug Fixes:**
@@ -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` |
@@ -7,7 +7,8 @@ To use DeepSeek LLM models, you have to set the `DEEPSEEK_API_KEY` environment v

 ## Usage

-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory

@@ -36,6 +37,32 @@ messages = [
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```

+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+  llm: {
+    provider: 'deepseek',
+    config: {
+      apiKey: process.env.DEEPSEEK_API_KEY || '',
+      model: 'deepseek-chat',
+      temperature: 0.2,
+      maxTokens: 2000,
+      top_p: 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
@@ -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).
@@ -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).
@@ -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
@@ -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` |

@@ -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`        |
@@ -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

@@ -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": {
@@ -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`.
@@ -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>

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

@@ -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

@@ -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` |
@@ -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` |
@@ -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}>
@@ -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!
@@ -345,7 +345,7 @@ When evaluating memory systems, keep these considerations in mind:
  <Card title="Research" icon="flask" href="https://mem0.ai/research">
    Published research papers and technical reports
  </Card>
-  <Card title="Blog Post" icon="newspaper" href="https://mem0.ai/blog/new-algorithm">
+  <Card title="Blog Post" icon="newspaper" href="https://mem0.ai/blog/the-token-efficient-memory-algorithm-now-has-temporal-reasoning">
    Detailed writeup of the new algorithm design and results
  </Card>
  <Card title="Platform Migration" icon="arrow-right" href="/migration/platform-v2-to-v3">
@@ -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": "/"
@@ -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
@@ -65,6 +65,7 @@ The plugin uses the same shell scripts as Claude Code, Cursor, and Codex — hoo
 | **User prompt** | `UserPromptSubmit` | Searches relevant memories before each message |
 | **Pre-tool** | `PreToolUse` | Blocks MEMORY.md writes, enforces `user_id`/`app_id` on mem0 tools |
 | **Post-tool** | `PostToolUse` | Tracks stats, scans bash errors for related memories |
+| **Stop** | `Stop` | Stores a session summary when the session ends |

 ## Troubleshooting

@@ -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"
 )

@@ -64,7 +64,7 @@ Add the Mem0 MCP server directly with a single command:
 npx mcp-add \
  --name mem0-mcp \
  --type http \
-  --url "https://mcp.mem0.ai/mcp" \
+  --url "https://mcp.mem0.ai/mcp/" \
  --clients "claude code"
 ```

@@ -138,11 +138,13 @@ 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 |
-| **Pre-compact** | `PreCompact` | Stores a session summary before context compaction |
+| **Stop** | `Stop` | Stores a session summary when the session ends |
+| **Pre-compact** | `PreCompact` | Stores a summary before the context is compacted |

 ## Example Workflow

@@ -41,7 +41,17 @@ Install the full plugin including MCP server, lifecycle hooks, and SDK skill.
   codex plugin marketplace add mem0ai/mem0
   ```

-2. Restart Codex, open the Plugin Directory, browse the **Mem0 Plugins** marketplace, and install **Mem0**.
+2. Install the plugin:
+
+   ```bash
+   codex plugin add mem0@mem0-plugins
+   ```
+
+   Or, in the app: restart Codex, open the Plugin Directory, browse the **Mem0 Plugins** marketplace, and install **Mem0**.
+
+<Note>
+  Step 1 is required for the app UI. Mem0 isn't in OpenAI's curated directory yet, so **without `codex plugin marketplace add`, Mem0 won't appear in the Codex app's Plugin Directory** — searching for it returns nothing. Adding the marketplace surfaces it (under **Created by you**) and makes it installable.
+</Note>

 <Info>
  Do not combine with Option B. The plugin manifest auto-registers the `mem0` MCP server, so adding both will create a duplicate registration.
@@ -49,27 +59,30 @@ Install the full plugin including MCP server, lifecycle hooks, and SDK skill.

 ### Option B — Direct MCP

-The fastest way to connect Codex to Mem0 — no plugin, no marketplace. Add to `~/.codex/config.toml`:
+The fastest way to connect Codex to Mem0 — no plugin, no marketplace. Add the MCP server with a single command:
+
+```bash
+codex mcp add mem0 --url https://mcp.mem0.ai/mcp/ --bearer-token-env-var MEM0_API_KEY
+```
+
+Or add it manually to `~/.codex/config.toml`:

 ```toml
 [mcp_servers.mem0]
-url = "https://mcp.mem0.ai/mcp"
+url = "https://mcp.mem0.ai/mcp/"
 bearer_token_env_var = "MEM0_API_KEY"
 ```

 Make sure `MEM0_API_KEY` is exported in the shell you launch Codex from, then restart Codex.

-<Info>
-  Codex's `codex mcp add` CLI only supports stdio MCP servers. Because Mem0's MCP is HTTP/streamable, you configure it by editing `config.toml` directly (or via the **Plugins → Connect to a custom MCP → Streamable HTTP** UI in the Codex app).
-</Info>
-
 This gives you the MCP tools but not the lifecycle hooks or SDK skill.

 ### Managing the Plugin

 ```bash
 codex plugin marketplace upgrade               # pull latest plugin versions
-codex plugin marketplace remove mem0-plugins   # unregister the marketplace
+codex plugin remove mem0@mem0-plugins          # uninstall the plugin (keeps the marketplace)
+codex plugin marketplace remove mem0-plugins   # unregister the marketplace entirely
 ```

 To update, run `codex plugin marketplace upgrade` to pull the latest from the Mem0 repo.
@@ -110,9 +123,10 @@ 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 |
-| **Pre-compact** | `PreCompact` | Stores a session summary before context compaction |
+| **Stop** | `Stop` | Stores a session summary when the session ends |
+| **Pre-compact** | `PreCompact` | Stores a summary before the context is compacted |

 ## Example Workflow

@@ -47,7 +47,7 @@ The fastest way to get started. Click the link below to install the Mem0 MCP ser
 npx mcp-add \
  --name mem0-mcp \
  --type http \
-  --url "https://mcp.mem0.ai/mcp" \
+  --url "https://mcp.mem0.ai/mcp/" \
  --clients "cursor"
 ```

@@ -108,9 +108,10 @@ 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 |
-| **Pre-compact** | `preCompact` | Stores a session summary before context compaction |
+| **Stop** | `stop` | Stores a session summary when the session ends |
+| **Pre-compact** | `preCompact` | Stores a summary before the context is compacted |

 ## Example Workflow

@@ -100,12 +100,12 @@ This section:
 Initialize both the ElevenLabs and Mem0 clients:

 ```python
-    # Initialize ElevenLabs client
-    client = ElevenLabs(api_key=API_KEY)
+# Initialize ElevenLabs client
+client = ElevenLabs(api_key=API_KEY)

-    # Initialize memory client and tools
-    client_tools = ClientTools()
-    mem0_client = AsyncMemoryClient()
+# Initialize memory client and tools
+client_tools = ClientTools()
+mem0_client = AsyncMemoryClient()
 ```

 Here we:
@@ -118,36 +118,36 @@ Here we:
 Define the two key memory functions that will be registered as tools:

 ```python
-    # Define memory-related functions for the agent
-    async def add_memories(parameters):
-        """Add a message to the memory store"""
-        message = parameters.get("message")
-        await mem0_client.add(
-            messages=message,
-            user_id=USER_ID
-        )
-        return "Memory added successfully"
+# Define memory-related functions for the agent
+async def add_memories(parameters):
+    """Add a message to the memory store"""
+    message = parameters.get("message")
+    await mem0_client.add(
+        messages=message,
+        user_id=USER_ID
+    )
+    return "Memory added successfully"

-    async def retrieve_memories(parameters):
-        """Retrieve relevant memories based on the input message"""
-        message = parameters.get("message")
+async def retrieve_memories(parameters):
+    """Retrieve relevant memories based on the input message"""
+    message = parameters.get("message")

-        # For Platform API, user_id goes in filters
-        filters = {"user_id": USER_ID}
+    # For Platform API, user_id goes in filters
+    filters = {"user_id": USER_ID}

-        # Search for relevant memories using the message as a query
-        results = await mem0_client.search(
-            query=message,
-            filters=filters
-        )
+    # Search for relevant memories using the message as a query
+    results = await mem0_client.search(
+        query=message,
+        filters=filters
+    )

-        # Extract and join the memory texts
-        memories = ' '.join([result["memory"] for result in results.get('results', [])])
-        print("[ Memories ]", memories)
+    # Extract and join the memory texts
+    memories = ' '.join([result["memory"] for result in results.get('results', [])])
+    print("[ Memories ]", memories)

-        if memories:
-            return memories
-        return "No memories found"
+    if memories:
+        return memories
+    return "No memories found"
 ```

 These functions:
@@ -171,9 +171,9 @@ These functions:
 Register the memory functions with the ElevenLabs ClientTools system:

 ```python
-    # Register the memory functions as tools for the agent
-    client_tools.register("addMemories", add_memories, is_async=True)
-    client_tools.register("retrieveMemories", retrieve_memories, is_async=True)
+# Register the memory functions as tools for the agent
+client_tools.register("addMemories", add_memories, is_async=True)
+client_tools.register("retrieveMemories", retrieve_memories, is_async=True)
 ```

 This allows the ElevenLabs agent to:
@@ -186,19 +186,19 @@ This allows the ElevenLabs agent to:
 Configure the conversation with ElevenLabs:

 ```python
-    # Initialize the conversation
-    conversation = Conversation(
-        client,
-        AGENT_ID,
-        # Assume auth is required when API_KEY is set
-        requires_auth=bool(API_KEY),
-        audio_interface=DefaultAudioInterface(),
-        client_tools=client_tools,
-        callback_agent_response=lambda response: print(f"Agent: {response}"),
-        callback_agent_response_correction=lambda original, corrected: print(f"Agent: {original} -> {corrected}"),
-        callback_user_transcript=lambda transcript: print(f"User: {transcript}"),
-        # callback_latency_measurement=lambda latency: print(f"Latency: {latency}ms"),
-    )
+# Initialize the conversation
+conversation = Conversation(
+    client,
+    AGENT_ID,
+    # Assume auth is required when API_KEY is set
+    requires_auth=bool(API_KEY),
+    audio_interface=DefaultAudioInterface(),
+    client_tools=client_tools,
+    callback_agent_response=lambda response: print(f"Agent: {response}"),
+    callback_agent_response_correction=lambda original, corrected: print(f"Agent: {original} -> {corrected}"),
+    callback_user_transcript=lambda transcript: print(f"User: {transcript}"),
+    # callback_latency_measurement=lambda latency: print(f"Latency: {latency}ms"),
+)
 ```

 This sets up the conversation with:
@@ -217,16 +217,16 @@ This sets up the conversation with:
 Start and manage the conversation:

 ```python
-    # Start the conversation
-    print(f"Starting conversation with user_id: {USER_ID}")
-    conversation.start_session()
+# Start the conversation
+print(f"Starting conversation with user_id: {USER_ID}")
+conversation.start_session()

-    # Handle Ctrl+C to gracefully end the session
-    signal.signal(signal.SIGINT, lambda sig, frame: conversation.end_session())
+# Handle Ctrl+C to gracefully end the session
+signal.signal(signal.SIGINT, lambda sig, frame: conversation.end_session())

-    # Wait for the conversation to end and get the conversation ID
-    conversation_id = conversation.wait_for_session_end()
-    print(f"Conversation ID: {conversation_id}")
+# Wait for the conversation to end and get the conversation ID
+conversation_id = conversation.wait_for_session_end()
+print(f"Conversation ID: {conversation_id}")


 if __name__ == '__main__':
@@ -445,4 +445,3 @@ By integrating ElevenLabs Conversational AI with Mem0, you can create voice agen
    Create voice-first AI applications
  </Card>
 </CardGroup>
-
@@ -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>

@@ -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()
 ```

@@ -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."
    )
    
@@ -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

@@ -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.
@@ -1,566 +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
-```python
-# 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>
@@ -120,13 +120,14 @@ client = MemoryClient(api_key="m0-...")

 | Method | Open Source | Platform |
 | ------ | ----------- | -------- |
-| `search()` | `m.search(query, user_id="alex")` | `client.search(query, filters={"user_id": "alex"})` |
-| `get_all()` | `m.get_all(user_id="alex")` | `client.get_all(filters={"user_id": "alex"})` |
+| `search()` | `m.search(query, filters={"user_id": "alex"})` | `client.search(query, filters={"user_id": "alex"})` |
+| `get_all()` | `m.get_all(filters={"user_id": "alex"})` | `client.get_all(filters={"user_id": "alex"})` |
 | `add()` | `m.add(memory, user_id="alex")` | `client.add(memory, user_id="alex")` |
+| `update()` | `m.update(memory_id, data="Updated content")` | `client.update(memory_id, text="Updated content")` |
 | `delete()` | `m.delete(memory_id)` | `client.delete(memory_id)` |
 | `delete_all()` | `m.delete_all(user_id="alex")` | `client.delete_all(user_id="alex")` |

-Note: `add()` and `delete()` methods remain unchanged. The `update()` method is not available in Platform - use delete + add pattern instead.
+Note: `add()` and `delete()` methods remain unchanged. The `update()` method is available in Platform via `client.update(memory_id, text="Updated content")`.

 <AccordionGroup>
  <Accordion title="Search Memories">
@@ -158,18 +159,15 @@ Note: `add()` and `delete()` methods remain unchanged. The `update()` method is
    <CodeGroup>
    ```python Open Source (Old)
    # Get all memories for a user
-    memories = m.get_all(user_id="alex", top_k=10)
-
-    # Get memories with pagination
-    memories = m.get_all(user_id="alex", top_k=5, offset=10)
+    memories = m.get_all(filters={"user_id": "alex"}, top_k=10)
    ```

    ```python Platform (New)
    # Get all memories for a user
    memories = client.get_all(filters={"user_id": "alex"}, top_k=10)

-    # Get memories with pagination
-    memories = client.get_all(filters={"user_id": "alex"}, top_k=5, offset=10)
+    # Get memories with pagination (Platform supports page/page_size)
+    memories = client.get_all(filters={"user_id": "alex"}, page=2, page_size=10)
    ```
    </CodeGroup>
  </Accordion>
@@ -218,16 +216,18 @@ Note: `add()` and `delete()` methods remain unchanged. The `update()` method is
    <CodeGroup>
    ```python Open Source (Old)
    # Update memory content
-    m.update(memory_id="mem_123", new_memory="Updated content")
+    m.update(memory_id="mem_123", data="Updated content")
    ```

    ```python Platform (New)
-    # Update memory (not available in Platform)
-    # Use delete + add pattern instead
-    client.delete(memory_id="mem_123")
-    client.add("Updated content", user_id="alex")
+    # Update memory content
+    client.update(memory_id="mem_123", text="Updated content")
    ```
    </CodeGroup>
+
+    <Note>
+      The parameter name differs between SDKs: OSS `Memory.update()` takes `data=`, while the Platform `MemoryClient.update()` (Python and JS/TS) takes `text=`. When migrating, rename this keyword argument.
+    </Note>
  </Accordion>
 </AccordionGroup>

@@ -392,7 +392,7 @@ The Platform introduces powerful capabilities not available in OSS:
 | **Add Method** | `m.add(memory, user_id="x")` | `client.add(memory, user_id="x")` | No change |
 | **Delete Method** | `m.delete(memory_id)` | `client.delete(memory_id)` | No change |
 | **Delete All** | `m.delete_all(user_id="x")` | `client.delete_all(user_id="x")` | No change |
-| **Update Method** | `m.update(memory_id, new_memory)` | Use delete + add pattern | Replace with delete then add |
+| **Update Method** | `m.update(memory_id, data="Updated content")` | `client.update(memory_id, text="Updated content")` | Rename `data=` kwarg to `text=` |
 | **Config** | Local vector store + LLM config | Managed cloud infrastructure | Remove local config setup |

 ## Rollback plan
@@ -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`

@@ -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).
@@ -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
@@ -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
@@ -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>
@@ -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();

@@ -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"
        }
    },
@@ -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."
 									}
 								}
 							}
@@ -4861,8 +4883,7 @@
 										"items": {
 											"type": "object",
 											"required": [
-												"memory_id",
-												"text"
+												"memory_id"
 											],
 											"properties": {
 												"memory_id": {
@@ -4873,6 +4894,11 @@
 												"text": {
 													"type": "string",
 													"description": "The new text content for the memory"
+												},
+												"metadata": {
+													"type": "object",
+													"additionalProperties": true,
+													"description": "Updated metadata to associate with the memory."
 												}
 											}
 										},
@@ -4948,18 +4974,27 @@
 							"schema": {
 								"type": "object",
 								"properties": {
-									"memory_ids": {
+									"memories": {
 										"type": "array",
 										"items": {
-											"type": "string",
-											"format": "uuid"
+											"type": "object",
+											"properties": {
+												"memory_id": {
+													"type": "string",
+													"format": "uuid",
+													"description": "The unique identifier of the memory to delete."
+												}
+											},
+											"required": [
+												"memory_id"
+											]
 										},
 										"maxItems": 1000,
-										"description": "Array of memory IDs to delete."
+										"description": "Array of memory objects to delete."
 									}
 								},
 								"required": [
-									"memory_ids"
+									"memories"
 								]
 							}
 						}
@@ -6256,4 +6291,4 @@
 		}
 	},
 	"x-original-swagger-version": "2.0"
-}
+}
@@ -117,7 +117,7 @@ results_without_criteria = client.search(
 ### Compare Results

 ### Search Results (with Criteria)
-```python
+```text
 [
    {"memory": "User feels refreshed and ready to take on anything on a beautiful sunny day", "score": 0.666, ...},
    {"memory": "User finally has time to draw something after a long time", "score": 0.616, ...},
@@ -128,7 +128,7 @@ results_without_criteria = client.search(
 ```

 ### Search Results (without Criteria)
-```python
+```text
 [
    {"memory": "User is happy today", "score": 0.607, ...},
    {"memory": "User feels refreshed and ready to take on anything on a beautiful sunny day", "score": 0.512, ...},
@@ -190,7 +190,7 @@ messages = [
 client.add(messages, user_id='alice')
 ```

-```python Memories with categories
+```text Memories with categories
 # Following categories will be created for the memories added
 Sometimes draws and sketches in free time (hobbies)
 Is quite athletic (sports)
@@ -108,10 +108,10 @@ print(response)

 ```javascript JavaScript
 // Basic Export request
-const filters = {"user_id": "alice"};
+const basicFilters = {"user_id": "alice"};
 const response = await client.createMemoryExport({
    schema: json_schema,
-    filters: filters
+    filters: basicFilters
 });

 // Export with custom instructions and additional filters
@@ -124,16 +124,16 @@ const export_instructions = `
 `;

 // For create operation, using only user_id filter as requested
-const filters = {
+const exportFilters = {
    "AND": [
        {"user_id": "alex"},
        {"created_at": {"gte": "2024-01-01"}}
    ]
-}
+};

 const responseWithInstructions = await client.createMemoryExport({
    schema: json_schema,
-    filters: filters,
+    filters: exportFilters,
    exportInstructions: export_instructions
 });

@@ -1,6 +1,6 @@
 {
  "name": "mem0",
-  "version": "0.2.10",
+  "version": "0.2.11",
  "description": "Persistent memory for Claude Code. Remembers decisions, patterns, and preferences across sessions.",
  "author": {
    "name": "Mem0",
@@ -1,6 +1,6 @@
 {
  "name": "mem0",
-  "version": "0.2.10",
+  "version": "0.2.11",
  "description": "Persistent memory for Codex. Remembers decisions, patterns, and preferences across sessions.",
  "author": {
    "name": "Mem0",
@@ -1,6 +1,6 @@
 {
  "name": "mem0",
-  "version": "0.2.10",
+  "version": "0.2.11",
  "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search using the Mem0 Platform MCP server.",
  "author": {
    "name": "Mem0",
@@ -2,6 +2,18 @@

 All notable changes to the Mem0 plugin will be documented in this file.

+## 0.2.11 — Session-summary metadata fix + rerank auto-injected context by default
+
+> Versions: Claude Code / Cursor / Codex `0.2.11`; Antigravity `0.1.3`. All four editors share `scripts/`, so the fix below applies to every editor.
+
+### Fixed
+
+- **`files_touched` was double-JSON-encoded in session summaries (`scripts/capture_session_summary.py`):** the Stop-hook summary set `metadata["files_touched"] = json.dumps(files[:20])` — a pre-serialized JSON string — and then serialized the whole request body again with `json.dumps(body)`. The stored memory therefore carried an escaped string blob (`"[\"mem0/memory/main.py\", \"src/client/index.ts\"]"`) instead of a real array, so file paths surfaced as backslash- and slash-heavy escaped text when those memories were returned by `search_memories`/`get_memories` and shown in Claude Code, Cursor, Codex, and Antigravity. The fix stores the list directly (`metadata["files_touched"] = files[:20]`) so the body is encoded exactly once. New `tests/test_capture_session_summary.py` asserts the posted body contains a JSON array and no escaped-string artifact.
+
+### Changed
+
+- **Auto-injected memory context is now reranked by default (`scripts/_search.py`, `scripts/file_context.py`, `scripts/on_bash_output.sh`, `scripts/on_user_prompt.sh`):** the REST search endpoint does not rerank when `rerank` is omitted, so hook-injected context (file-context, bash-error lookup, session-resume prefetch) was ordered by raw vector similarity and the single most relevant memory could fall outside the injected `top_k` window. A new `should_rerank()` helper turns reranking on for every auto-injection path; the extra ~150–200 ms stays within the hook's curl budget. Opt out with `MEM0_RERANK=0` (also accepts `false`/`no`/`off`). (#5690)
+
 ## 0.2.10 — Accurate per-editor telemetry attribution

 ### Fixed
@@ -1,7 +1,7 @@
 {
  "id": "mem0",
  "name": "mem0",
-  "version": "0.1.2",
+  "version": "0.1.3",
  "description": "Persistent semantic memory for Antigravity agents. Cross-session, user-level recall via the Mem0 Platform MCP server. 16 slash commands, lifecycle hooks for auto-capture and metadata enforcement.",
  "author": { "name": "Mem0", "email": "support@mem0.ai" },
  "publisher": "mem0ai",
@@ -173,7 +173,7 @@ def store_summary(
    if branch:
        metadata["branch"] = branch
    if files:
-        metadata["files_touched"] = json.dumps(files[:20])
+        metadata["files_touched"] = files[:20]

    body = {
        "messages": [{"role": "user", "content": summary_prompt}],
@@ -0,0 +1,79 @@
+"""Regression tests for capture_session_summary.py request body construction.
+
+Guards against the double-JSON-encoding bug where ``files_touched`` was stored
+as a pre-serialized JSON string and then encoded a second time with the rest of
+the request body — surfacing as escaped, slash-heavy blobs in the memories shown
+inside Claude Code / Cursor / Codex / Antigravity (all four editors share this
+script).
+"""
+
+from __future__ import annotations
+
+import json
+
+
+class _FakeResp:
+    status = 200
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, *_):
+        return False
+
+
+def _capture_request_body(monkeypatch):
+    """Patch urlopen so store_summary posts nowhere; capture the request body."""
+    import capture_session_summary as css
+
+    captured: dict = {}
+
+    def fake_urlopen(req, timeout=0):
+        captured["raw"] = req.data.decode("utf-8")
+        captured["body"] = json.loads(captured["raw"])
+        return _FakeResp()
+
+    monkeypatch.setattr(css.urllib.request, "urlopen", fake_urlopen)
+    return captured, css
+
+
+def test_files_touched_is_json_array_not_double_encoded(monkeypatch):
+    """files_touched must be a real JSON array, encoded exactly once."""
+    captured, css = _capture_request_body(monkeypatch)
+    files = ["mem0/memory/main.py", "src/client/index.ts"]
+
+    css.store_summary(
+        api_key="test-key",
+        summary_prompt="did some work",
+        user_id="u1",
+        session_id="s1",
+        project_id="p1",
+        branch="main",
+        files=files,
+    )
+
+    files_touched = captured["body"]["metadata"]["files_touched"]
+    assert isinstance(files_touched, list), (
+        "files_touched must be a JSON array, not a double-encoded string; "
+        f"got {type(files_touched).__name__}: {files_touched!r}"
+    )
+    assert files_touched == files
+    # The file paths must not appear as an escaped JSON string inside the body.
+    assert '\\"' not in captured["raw"]
+
+
+def test_files_touched_omitted_when_no_files(monkeypatch):
+    """No files touched -> no files_touched key (unchanged behaviour)."""
+    captured, css = _capture_request_body(monkeypatch)
+
+    css.store_summary(
+        api_key="test-key",
+        summary_prompt="did some work",
+        user_id="u1",
+        session_id="s1",
+        project_id="p1",
+        branch="main",
+        files=[],
+    )
+
+    assert "files_touched" not in captured["body"]["metadata"]
@@ -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"
    }
  }
 }
@@ -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: {}

@@ -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"
@@ -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();
@@ -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 {
@@ -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"
    }
  }
@@ -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: {}

@@ -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"
@@ -1,6 +1,6 @@
 {
  "name": "mem0ai",
-  "version": "3.0.9",
+  "version": "3.0.11",
  "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"
    }
  }
 }
@@ -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:
@@ -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"
@@ -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]);
@@ -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;
@@ -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",
    );
  });
 });
@@ -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);
+  });
 });
@@ -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";
@@ -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}`);
+    }
+  }
+}
@@ -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}`);
+    }
+  }
+}
@@ -296,6 +296,44 @@ export class Memory {
    return filters;
  }

+  private _normalizeEntityText(value: string): string {
+    return value.trim().toLowerCase().replace(/\s+/g, " ");
+  }
+
+  private async _existingEntitiesByText(
+    entityStore: VectorStore,
+    filters: Record<string, any>,
+  ): Promise<Map<string, { id: string; payload: Record<string, any> }>> {
+    const rowsByText = new Map<
+      string,
+      { id: string; payload: Record<string, any> }
+    >();
+    let rows: Array<{ id: string; payload: Record<string, any> }> = [];
+    try {
+      const listed = await entityStore.list(filters, 10000);
+      rows = (
+        Array.isArray(listed) && Array.isArray(listed[0])
+          ? listed[0]
+          : (listed as any)
+      ) as Array<{ id: string; payload: Record<string, any> }>;
+    } catch (e) {
+      console.debug(
+        `Exact entity lookup failed, falling back to semantic dedup: ${e}`,
+      );
+      return rowsByText;
+    }
+
+    for (const row of rows) {
+      const text = row.payload?.data;
+      if (typeof text !== "string") continue;
+      const key = this._normalizeEntityText(text);
+      if (key && !rowsByText.has(key)) {
+        rowsByText.set(key, row);
+      }
+    }
+    return rowsByText;
+  }
+
  /**
   * Remove `memoryId` from every entity record scoped to `filters`.
   * If an entity's `linkedMemoryIds` becomes empty after removal, the
@@ -393,6 +431,10 @@ export class Memory {
      if (entities.length === 0) return;

      const entityStore = await this.getEntityStore();
+      const exactMatches = await this._existingEntitiesByText(
+        entityStore,
+        filters,
+      );

      for (const entity of entities) {
        try {
@@ -409,12 +451,21 @@ export class Memory {
            score?: number;
            payload: Record<string, any>;
          }> = [];
-          try {
-            matches = await entityStore.search(entityVec, 1, filters);
-          } catch {}
+          const exactMatch = exactMatches.get(
+            this._normalizeEntityText(entity.text),
+          );
+          if (!exactMatch) {
+            try {
+              matches = await entityStore.search(entityVec, 1, filters);
+            } catch {}
+          }

-          if (matches.length > 0 && (matches[0].score ?? 0) >= 0.95) {
-            const match = matches[0];
+          const semanticMatch =
+            matches.length > 0 && (matches[0].score ?? 0) >= 0.95
+              ? matches[0]
+              : undefined;
+          const match = exactMatch ?? semanticMatch;
+          if (match) {
            const payload = match.payload || {};
            const linked = new Set<string>(
              Array.isArray(payload.linkedMemoryIds)
@@ -1062,6 +1113,10 @@ export class Memory {

        if (valid.length > 0) {
          const entityStore = await this.getEntityStore();
+          const exactMatches = await this._existingEntitiesByText(
+            entityStore,
+            filters,
+          );

          // 7c: Search for existing entities one by one (no batch search)
          const toInsertVectors: number[][] = [];
@@ -1077,13 +1132,20 @@ export class Memory {
              score?: number;
              payload: Record<string, any>;
            }> = [];
-            try {
-              matches = await entityStore.search(entityVec, 1, filters);
-            } catch {}
+            const exactMatch = exactMatches.get(key);
+            if (!exactMatch) {
+              try {
+                matches = await entityStore.search(entityVec, 1, filters);
+              } catch {}
+            }

-            if (matches.length > 0 && (matches[0].score ?? 0) >= 0.95) {
+            const semanticMatch =
+              matches.length > 0 && (matches[0].score ?? 0) >= 0.95
+                ? matches[0]
+                : undefined;
+            const match = exactMatch ?? semanticMatch;
+            if (match) {
              // Update existing entity
-              const match = matches[0];
              const payload = match.payload || {};
              const linked = new Set<string>(payload.linkedMemoryIds ?? []);
              for (const mid of memoryIds) linked.add(mid);
@@ -1539,7 +1601,9 @@ export class Memory {
      has_agent_id: !!config.agentId,
      has_run_id: !!config.runId,
    });
-    const { userId, agentId, runId } = config;
+    const userId = validateAndTrimEntityId(config.userId, "userId");
+    const agentId = validateAndTrimEntityId(config.agentId, "agentId");
+    const runId = validateAndTrimEntityId(config.runId, "runId");

    // Convert camelCase entity params to snake_case for filters (matches storage and search/getAll)
    const filters: SearchFilters = {};
@@ -4,8 +4,8 @@
 * Extracts four types of entities from text:
 * - PROPER:   Capitalized multi-word sequences (person names, places, brands)
 * - QUOTED:   Text in single or double quotes (titles, specific terms)
- * - COMPOUND: Multi-word noun phrases with specific modifiers (e.g., "machine learning")
- * - NOUN:     Single nouns from circumstantial compound patterns
+ * - TOPIC:    Multi-word noun/topic phrases with specific modifiers
+ * - IDENTIFIER: Dotted technical identifiers such as person.properties.email
 *
 * Uses the `compromise` npm package for NLP-based extraction when available.
 * Falls back to regex-only extraction if `compromise` is not installed.
@@ -196,6 +196,25 @@ const NON_SPECIFIC_ADJ: Set<string> = new Set([
  "final",
  "initial",
  "side",
+  "top",
+]);
+
+/** Leading words that frame a topic but are not part of the topic itself. */
+const TOPIC_PREFIX_WORDS: Set<string> = new Set([
+  "a",
+  "an",
+  "the",
+  "my",
+  "your",
+  "our",
+  "their",
+  "his",
+  "her",
+  "its",
+  "this",
+  "that",
+  "these",
+  "those",
 ]);

 /** Generic tail words to strip from compound entities. */
@@ -267,6 +286,25 @@ const GENERIC_CAPS: Set<string> = new Set([
  "disadvantages",
 ]);

+/** Generic role/title words that should not become single-token entities. */
+const GENERIC_SINGLE_ENTITY_TERMS: Set<string> = new Set([
+  "user",
+  "assistant",
+  "agent",
+  "customer",
+  "client",
+  "person",
+  "people",
+  "human",
+  "memory",
+  "message",
+  "conversation",
+  "chat",
+  "session",
+  "system",
+  "top",
+]);
+
 /** Markdown/formatting markers to skip during extraction. */
 const FORMATTING_MARKERS: Set<string> = new Set([
  "*",
@@ -287,7 +325,7 @@ const FORMATTING_MARKERS: Set<string> = new Set([
 // ---------------------------------------------------------------------------

 export interface ExtractedEntity {
-  type: "PROPER" | "QUOTED" | "COMPOUND" | "NOUN";
+  type: "PROPER" | "QUOTED" | "TOPIC" | "IDENTIFIER";
  text: string;
 }

@@ -338,32 +376,96 @@ function stripGenericEnding(words: string[]): string[] {
  return words;
 }

-/**
- * Determine if a token position is at the start of a sentence.
- * Simple heuristic: index 0, or preceded by sentence-ending punctuation
- * or formatting markers.
- */
-function isSentenceStart(
-  tokens: string[],
-  idx: number,
-  rawText: string,
-): boolean {
-  if (idx === 0) {
-    return true;
+function stripTopicPrefix(words: string[]): string[] {
+  let start = 0;
+  while (
+    start < words.length &&
+    TOPIC_PREFIX_WORDS.has(words[start].toLowerCase())
+  ) {
+    start++;
  }
-  const prev = tokens[idx - 1];
-  if (/[.!?:]$/.test(prev)) {
+  return words.slice(start);
+}
+
+function cleanToken(token: string): string {
+  return token.replace(/^[^\w.]+|[^\w.]+$/g, "");
+}
+
+function tokenize(text: string): string[] {
+  return (
+    text.match(
+      /[A-Za-z_][\w-]*(?:\.[A-Za-z_][\w-]*)*|\d[\d,]*(?:\.\d+)?|[,:;.!?&]/g,
+    ) ?? []
+  );
+}
+
+function isCapitalized(token: string): boolean {
+  return /^[A-Z]/.test(token) && /[A-Za-z]/.test(token);
+}
+
+function hasInternalCapOrDigit(token: string): boolean {
+  return (
+    /\d/.test(token) ||
+    /[A-Z]/.test(token.slice(1)) ||
+    /^[A-Z]{2,}$/.test(token)
+  );
+}
+
+function isBadSingleNameToken(token: string): boolean {
+  const lower = token.toLowerCase();
+  return GENERIC_SINGLE_ENTITY_TERMS.has(lower) || GENERIC_CAPS.has(lower);
+}
+
+function looksLikeMetricCount(token: string): boolean {
+  return /^\d[\d,]*(?:\.\d+)?$/.test(token);
+}
+
+function isMetricListContext(tokens: string[], idx: number): boolean {
+  const prev = idx > 0 ? tokens[idx - 1] : "";
+  const next = idx + 1 < tokens.length ? tokens[idx + 1] : "";
+  return [":", ",", ";"].includes(prev) || [",", ";"].includes(next);
+}
+
+function isSentenceStart(tokens: string[], idx: number): boolean {
+  if (idx === 0) return true;
+  return (
+    [".", "!", "?", ":"].includes(tokens[idx - 1]) ||
+    FORMATTING_MARKERS.has(tokens[idx - 1])
+  );
+}
+
+function isListItemNameToken(tokens: string[], idx: number): boolean {
+  const token = cleanToken(tokens[idx]);
+  if (!isCapitalized(token) || isBadSingleNameToken(token)) return false;
+  const next = idx + 1 < tokens.length ? cleanToken(tokens[idx + 1]) : "";
+  if (!looksLikeMetricCount(next)) return false;
+  return (
+    isMetricListContext(tokens, idx) || isMetricListContext(tokens, idx + 1)
+  );
+}
+
+function isNameToken(tokens: string[], idx: number): boolean {
+  const token = cleanToken(tokens[idx]);
+  if (!token || !isCapitalized(token) || isBadSingleNameToken(token))
+    return false;
+  if (hasInternalCapOrDigit(token) || isListItemNameToken(tokens, idx))
    return true;
-  }
-  if (FORMATTING_MARKERS.has(prev)) {
-    return true;
-  }
-  // Check for newline before this token in the raw text
-  const tokenStart = rawText.indexOf(tokens[idx]);
-  if (tokenStart > 0 && rawText.charAt(tokenStart - 1) === "\n") {
-    return true;
-  }
-  return false;
+  return !isSentenceStart(tokens, idx);
+}
+
+function cleanEntityText(text: string): string {
+  return text
+    .replace(/^\*+\s*|\s*\*+$/g, "")
+    .replace(/\s*:+$/g, "")
+    .replace(/^\d+\s*\.\s*/, "")
+    .replace(/\s+\d[\d,]*(?:\.\d+)?$/g, "")
+    .replace(/[.,;!?]+$/, "")
+    .trim()
+    .replace(/\s+/g, " ");
+}
+
+function isCoordinatedNameTopic(text: string): boolean {
+  return /\b[A-Z][\w-]+\s+and\s+[A-Z][\w-]+\b/.test(text);
 }

 // ---------------------------------------------------------------------------
@@ -397,86 +499,79 @@ function extractQuoted(text: string): ExtractedEntity[] {
 }

 /**
- * Extract proper noun sequences using capitalization heuristics.
- * Finds sequences of capitalized words that are not at sentence starts.
+ * Extract dotted technical identifiers such as person.properties.email.
+ */
+function extractIdentifiers(text: string): ExtractedEntity[] {
+  const entities: ExtractedEntity[] = [];
+  const identifierRe = /\b[A-Za-z_][\w-]*(?:\.[A-Za-z_][\w-]*)+\b/g;
+  let match: RegExpExecArray | null;
+  while ((match = identifierRe.exec(text)) !== null) {
+    entities.push({ type: "IDENTIFIER", text: match[0] });
+  }
+  return entities;
+}
+
+/**
+ * Extract proper names using capitalization and list-context heuristics.
 */
 function extractProper(text: string): ExtractedEntity[] {
  const entities: ExtractedEntity[] = [];
-  // Tokenize on whitespace, preserving order
-  const tokens = text.split(/\s+/).filter(Boolean);
-  const functionWords = new Set([
-    "'s",
-    "of",
-    "the",
-    "in",
-    "and",
-    "for",
-    "at",
-    "is",
-  ]);
+  const tokens = tokenize(text);
+  const innerConnectors = new Set(["of", "the", "in", "for", "at"]);

  let i = 0;
  while (i < tokens.length) {
-    const tok = tokens[i];
-    // Skip formatting markers
-    if (FORMATTING_MARKERS.has(tok)) {
+    const token = cleanToken(tokens[i]);
+    const next = i + 1 < tokens.length ? tokens[i + 1] : "";
+    const afterNext = i + 2 < tokens.length ? cleanToken(tokens[i + 2]) : "";
+    if (
+      token &&
+      next === "&" &&
+      afterNext &&
+      isCapitalized(token) &&
+      isCapitalized(afterNext) &&
+      !isBadSingleNameToken(token) &&
+      !isBadSingleNameToken(afterNext)
+    ) {
+      entities.push({
+        type: "PROPER",
+        text: cleanEntityText(`${token} & ${afterNext}`),
+      });
+      i += 3;
+      continue;
+    }
+
+    if (!isNameToken(tokens, i)) {
      i++;
      continue;
    }

-    const isLabel = i + 1 < tokens.length && tokens[i + 1] === ":";
-    const isCap =
-      tok.length > 0 &&
-      tok.charAt(0) === tok.charAt(0).toUpperCase() &&
-      /[A-Z]/.test(tok.charAt(0));
-
-    if (isCap && !isLabel) {
-      const seq: Array<{ token: string; idx: number }> = [
-        { token: tok, idx: i },
-      ];
-      let j = i + 1;
-      while (j < tokens.length) {
-        const t = tokens[j];
-        const tIsCap =
-          t.length > 0 &&
-          t.charAt(0) === t.charAt(0).toUpperCase() &&
-          /[A-Z]/.test(t.charAt(0));
-        if (tIsCap || functionWords.has(t.toLowerCase())) {
-          seq.push({ token: t, idx: j });
-          j++;
-        } else {
-          break;
-        }
+    const span = [cleanToken(tokens[i])];
+    let j = i + 1;
+    while (j < tokens.length) {
+      const current = cleanToken(tokens[j]);
+      if (isNameToken(tokens, j)) {
+        span.push(current);
+        j++;
+        continue;
      }
-
-      // Strip trailing function words
-      while (
-        seq.length > 0 &&
-        functionWords.has(seq[seq.length - 1].token.toLowerCase())
+      if (
+        innerConnectors.has(current.toLowerCase()) &&
+        j + 1 < tokens.length &&
+        isNameToken(tokens, j + 1)
      ) {
-        seq.pop();
+        span.push(current, cleanToken(tokens[j + 1]));
+        j += 2;
+        continue;
      }
-
-      if (seq.length > 0) {
-        // Check for at least one mid-sentence capitalized word
-        const hasMidCap = seq.some(({ token, idx: tokenIdx }) => {
-          const isCapWord =
-            /[A-Z]/.test(token.charAt(0)) &&
-            !functionWords.has(token.toLowerCase());
-          return isCapWord && !isSentenceStart(tokens, tokenIdx, text);
-        });
-
-        if (hasMidCap) {
-          const phrase = seq.map((s) => s.token).join(" ");
-          if (phrase.length > 2) {
-            entities.push({ type: "PROPER", text: phrase });
-          }
-        }
-      }
-      i = j;
-    } else {
-      i++;
+      break;
    }
+
+    const phrase = cleanEntityText(span.join(" "));
+    if (phrase.length > 2) {
+      entities.push({ type: "PROPER", text: phrase });
+    }
+    i = Math.max(j, i + 1);
  }

  return entities;
@@ -484,7 +579,7 @@ function extractProper(text: string): ExtractedEntity[] {

 /**
 * Extract compound noun phrases using the `compromise` NLP library.
- * Returns COMPOUND and NOUN entities derived from noun chunks.
+ * Returns TOPIC entities derived from noun chunks.
 */
 function extractCompoundsWithNlp(text: string): ExtractedEntity[] {
  if (!nlp) {
@@ -524,12 +619,12 @@ function extractCompoundsWithNlp(text: string): ExtractedEntity[] {
    const filtered = words.filter(
      (w) => !NON_SPECIFIC_ADJ.has(w.toLowerCase()),
    );
-    const cleaned = stripGenericEnding(filtered);
+    const cleaned = stripGenericEnding(stripTopicPrefix(filtered));

    if (cleaned.length >= 2) {
-      const phrase = cleaned.join(" ");
+      const phrase = cleanEntityText(cleaned.join(" "));
      if (phrase.length > 3) {
-        entities.push({ type: "COMPOUND", text: phrase });
+        entities.push({ type: "TOPIC", text: phrase });
      }
    }
  }
@@ -547,7 +642,7 @@ function extractCompoundsRegex(text: string): ExtractedEntity[] {
  // Multi-word sequences with at least one non-trivial word
  // Match sequences like "machine learning", "New York", "data science"
  const compoundRe =
-    /\b([A-Z][a-z]+(?:\s+(?:of|and|the|for|in)\s+)?[A-Z][a-z]+(?:\s+[A-Z][a-z]+)*)\b/g;
+    /\b([A-Z][a-z]+(?:\s+(?:of|the|for|in)\s+)?[A-Z][a-z]+(?:\s+[A-Z][a-z]+)*)\b/g;
  let match: RegExpExecArray | null;
  while ((match = compoundRe.exec(text)) !== null) {
    const phrase = match[1].trim();
@@ -558,9 +653,12 @@ function extractCompoundsRegex(text: string): ExtractedEntity[] {
        const filtered = words.filter(
          (w) => !NON_SPECIFIC_ADJ.has(w.toLowerCase()),
        );
-        const cleaned = stripGenericEnding(filtered);
+        const cleaned = stripGenericEnding(stripTopicPrefix(filtered));
        if (cleaned.length >= 2) {
-          entities.push({ type: "COMPOUND", text: cleaned.join(" ") });
+          entities.push({
+            type: "TOPIC",
+            text: cleanEntityText(cleaned.join(" ")),
+          });
        }
      }
    }
@@ -590,9 +688,12 @@ function extractCompoundsRegex(text: string): ExtractedEntity[] {
          const filtered = words.filter(
            (w) => !NON_SPECIFIC_ADJ.has(w.toLowerCase()),
          );
-          const cleaned = stripGenericEnding(filtered);
+          const cleaned = stripGenericEnding(stripTopicPrefix(filtered));
          if (cleaned.length >= 2) {
-            entities.push({ type: "COMPOUND", text: cleaned.join(" ") });
+            entities.push({
+              type: "TOPIC",
+              text: cleanEntityText(cleaned.join(" ")),
+            });
          }
        }
      }
@@ -614,9 +715,9 @@ function extractCompoundsRegex(text: string): ExtractedEntity[] {
 *
 * Entity types (in priority order for deduplication):
 *   PROPER   - Capitalized multi-word sequences not at sentence start
- *   COMPOUND - Multi-word noun phrases with specific modifiers
+ *   IDENTIFIER - Dotted technical identifiers
 *   QUOTED   - Text in single or double quotes (min 3 chars)
- *   NOUN     - Single nouns from circumstantial patterns
+ *   TOPIC    - Multi-word noun/topic phrases with specific modifiers
 *
 * @param text - Input text to extract entities from.
 * @returns Deduplicated list of extracted entities.
@@ -630,7 +731,10 @@ export function extractEntities(text: string): ExtractedEntity[] {
  // 2. PROPER entities (capitalization heuristics)
  raw.push(...extractProper(text));

-  // 3. COMPOUND entities (NLP or regex fallback)
+  // 3. IDENTIFIER entities
+  raw.push(...extractIdentifiers(text));
+
+  // 4. TOPIC entities (NLP or regex fallback)
  if (nlp) {
    raw.push(...extractCompoundsWithNlp(text));
  } else {
@@ -654,19 +758,17 @@ export function extractEntities(text: string): ExtractedEntity[] {
  const cleaned: ExtractedEntity[] = [];
  for (const entity of deduped) {
    let txt = entity.text.trim();
-    // Strip leading/trailing asterisks
-    txt = txt.replace(/^\*+\s*|\s*\*+$/g, "");
-    // Strip trailing colons
-    txt = txt.replace(/\s*:+$/, "");
-    // Strip leading numbered list markers
-    txt = txt.replace(/^\d+\s*\.\s*/, "");
-    // Strip trailing sentence punctuation (".", ",", ";", "!", "?") — otherwise
-    // "Paris." and "Paris" produce different embeddings and break entity dedup.
-    txt = txt.replace(/[.,;!?]+$/, "").trim();
+    txt = cleanEntityText(txt);

    if (!txt || txt.length <= 2 || hasArtifacts(txt)) {
      continue;
    }
+    if (
+      entity.type === "TOPIC" &&
+      (/^\d/.test(txt) || isCoordinatedNameTopic(txt))
+    ) {
+      continue;
+    }

    // Filter generic single-word PROPER nouns
    if (
@@ -680,12 +782,12 @@ export function extractEntities(text: string): ExtractedEntity[] {
    cleaned.push({ type: entity.type, text: txt });
  }

-  // Keep best type per entity (PROPER > COMPOUND > QUOTED > NOUN)
+  // Keep best type per entity (PROPER > IDENTIFIER > QUOTED > TOPIC)
  const typePriority: Record<string, number> = {
    PROPER: 0,
-    COMPOUND: 1,
+    IDENTIFIER: 1,
    QUOTED: 2,
-    NOUN: 3,
+    TOPIC: 3,
  };
  const best = new Map<string, ExtractedEntity>();
  for (const entity of cleaned) {
@@ -700,14 +802,17 @@ export function extractEntities(text: string): ExtractedEntity[] {
  }
  const bestEntities = Array.from(best.values());

-  // Remove entities that are substrings of longer entities
-  const allLower = bestEntities.map((e) => e.text.toLowerCase());
+  // Remove entities that are token substrings of longer entities.
  return bestEntities.filter(
    (entity) =>
-      !allLower.some(
+      !bestEntities.some(
        (other) =>
-          entity.text.toLowerCase() !== other &&
-          other.includes(entity.text.toLowerCase()),
+          entity.text.toLowerCase() !== other.text.toLowerCase() &&
+          (typePriority[entity.type] ?? 99) >=
+            (typePriority[other.type] ?? 99) &&
+          new RegExp(
+            `(^|\\s)${entity.text.toLowerCase().replace(/[.*+?^${}()|[\]\\]/g, "\\$&")}(\\s|$)`,
+          ).test(other.text.toLowerCase()),
      ),
  );
 }
@@ -22,6 +22,8 @@ import { RedisDB } from "../vector_stores/redis";
 import { OllamaLLM } from "../llms/ollama";
 import { LMStudioLLM } from "../llms/lmstudio";
 import { DeepSeekLLM } from "../llms/deepseek";
+import { LiteLLM } from "../llms/litellm";
+import { MiniMaxLLM } from "../llms/minimax";
 import { SupabaseDB } from "../vector_stores/supabase";
 import { SQLiteManager } from "../storage/SQLiteManager";
 import { MemoryHistoryManager } from "../storage/MemoryHistoryManager";
@@ -85,6 +87,10 @@ export class LLMFactory {
        return new LangchainLLM(config);
      case "deepseek":
        return new DeepSeekLLM(config);
+      case "litellm":
+        return new LiteLLM(config);
+      case "minimax":
+        return new MiniMaxLLM(config);
      default:
        throw new Error(`Unsupported LLM provider: ${provider}`);
    }
@@ -1,4 +1,4 @@
-import type { Client as ClientType } from "pg";
+import type { Client as ClientType, ClientConfig } from "pg";
 import pkg from "pg";
 const { Client, escapeIdentifier } = pkg;
 import { VectorStore } from "./base";
@@ -157,41 +157,90 @@ export function buildFilterConditions(

 interface PGVectorConfig extends VectorStoreConfig {
  dbname?: string;
-  user: string;
-  password: string;
-  host: string;
-  port: number;
+  user?: string;
+  password?: string;
+  host?: string;
+  port?: number;
+  connectionString?: string;
+  ssl?: ClientConfig["ssl"];
  embeddingModelDims: number;
  diskann?: boolean;
  hnsw?: boolean;
 }

+function getConnectionString(config: PGVectorConfig): string | undefined {
+  return config.connectionString?.trim() || undefined;
+}
+
+function validateConnectionConfig(config: PGVectorConfig): void {
+  if (getConnectionString(config)) {
+    return;
+  }
+
+  const missingFields = ["user", "password", "host", "port"].filter((field) => {
+    const v = config[field as keyof PGVectorConfig];
+    return v === undefined || v === null || v === "";
+  });
+
+  if (missingFields.length > 0) {
+    throw new Error(
+      `PGVector requires either connectionString or ${missingFields.join(", ")}`,
+    );
+  }
+}
+
+function buildClientConfig(
+  config: PGVectorConfig,
+  database?: string,
+): ClientConfig {
+  const connectionString = getConnectionString(config);
+  if (connectionString) {
+    return {
+      connectionString,
+      ...(config.ssl !== undefined ? { ssl: config.ssl } : {}),
+    };
+  }
+
+  return {
+    database,
+    user: config.user,
+    password: config.password,
+    host: config.host,
+    port: config.port,
+    ...(config.ssl !== undefined ? { ssl: config.ssl } : {}),
+  };
+}
+
 export class PGVector implements VectorStore {
  private client: ClientType;
  private collectionName: string;
  private useDiskann: boolean;
  private useHnsw: boolean;
  private readonly dbName: string;
+  private readonly useDirectConnection: boolean;
  private config: PGVectorConfig;
  private _initPromise?: Promise<void>;

  constructor(config: PGVectorConfig) {
+    validateConnectionConfig(config);
    this.collectionName = validateIdentifier(
      config.collectionName || "memories",
      "collectionName",
    );
    this.useDiskann = config.diskann || false;
    this.useHnsw = config.hnsw || false;
-    this.dbName = validateIdentifier(config.dbname || "vector_store", "dbname");
+    this.useDirectConnection = !!getConnectionString(config);
+    this.dbName = this.useDirectConnection
+      ? ""
+      : validateIdentifier(config.dbname || "vector_store", "dbname");
    this.config = config;

-    this.client = new Client({
-      database: "postgres", // Initially connect to default postgres database
-      user: config.user,
-      password: config.password,
-      host: config.host,
-      port: config.port,
-    });
+    this.client = new Client(
+      buildClientConfig(
+        config,
+        this.useDirectConnection ? undefined : "postgres",
+      ),
+    );
    this.initialize().catch(console.error);
  }

@@ -210,29 +259,20 @@ export class PGVector implements VectorStore {
    try {
      await this.client.connect();

-      // Check if database exists
-      const dbExists = await this.checkDatabaseExists(this.dbName);
-      if (!dbExists) {
-        await this.createDatabase(this.dbName);
+      if (!this.useDirectConnection) {
+        const dbExists = await this.checkDatabaseExists(this.dbName);
+        if (!dbExists) {
+          await this.createDatabase(this.dbName);
+        }
+
+        await this.client.end();
+
+        this.client = new Client(buildClientConfig(this.config, this.dbName));
+        await this.client.connect();
      }

-      // Disconnect from postgres database
-      await this.client.end();
-
-      // Connect to the target database
-      this.client = new Client({
-        database: this.dbName,
-        user: this.config.user,
-        password: this.config.password,
-        host: this.config.host,
-        port: this.config.port,
-      });
-      await this.client.connect();
-
-      // Create vector extension
      await this.client.query("CREATE EXTENSION IF NOT EXISTS vector");

-      // Create memory_migrations table
      await this.client.query(`
        CREATE TABLE IF NOT EXISTS memory_migrations (
          id SERIAL PRIMARY KEY,
@@ -240,7 +280,6 @@ export class PGVector implements VectorStore {
        )
      `);

-      // Check if the collection exists
      const collections = await this.listCols();
      if (!collections.includes(this.collectionName)) {
        await this.createCol(this.config.embeddingModelDims);
@@ -337,11 +337,14 @@ export class RedisDB implements VectorStore {
      const id = ids[idx];

      // Create entry with required fields
+      const createdAt = payload.created_at
+        ? new Date(payload.created_at).getTime()
+        : 0;
      const entry: Record<string, any> = {
        memory_id: id,
-        hash: payload.hash,
-        memory: payload.data,
-        created_at: new Date(payload.created_at).getTime(),
+        hash: payload.hash ?? "",
+        memory: payload.data ?? "",
+        created_at: createdAt,
        embedding: new Float32Array(vector).buffer,
      };

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

@@ -0,0 +1,45 @@
+import { extractEntities } from "../src/utils/entity_extraction";
+
+describe("extractEntities", () => {
+  it("handles product lists, coordinated names, and identifiers", () => {
+    const text =
+      "User reported top inbound integration pages: OpenClaw 25,443, " +
+      "Claude Code 8,916, Codex 2,573, Dify 656. " +
+      "User compared Cartesia and Deepgram. " +
+      "The email field for Mem0 lives at person.properties.email. " +
+      "The qwen endpoint uses person.properties.email. " +
+      "Johnson & Johnson was mentioned. " +
+      "Glasses around my window. " +
+      "On 2026-05-27 there were 90 days of stats.";
+
+    const entityTexts = new Set(
+      extractEntities(text).map((entity) => entity.text),
+    );
+    const normalized = new Set(
+      [...entityTexts].map((entityText) => entityText.toLowerCase()),
+    );
+
+    for (const expected of [
+      "OpenClaw",
+      "Claude Code",
+      "Codex",
+      "Dify",
+      "Cartesia",
+      "Deepgram",
+      "Mem0",
+    ]) {
+      expect(entityTexts.has(expected)).toBe(true);
+    }
+    expect(entityTexts.has("person.properties.email")).toBe(true);
+    expect(entityTexts.has("qwen endpoint")).toBe(true);
+    expect(entityTexts.has("Johnson & Johnson")).toBe(true);
+    expect(entityTexts.has("Johnson")).toBe(false);
+    expect(normalized.has("top")).toBe(false);
+    expect(normalized.has("glasses")).toBe(false);
+    expect(entityTexts.has("Cartesia and Deepgram")).toBe(false);
+    expect(entityTexts.has("Claude Code 8,916")).toBe(false);
+    for (const rejected of ["8,916", "2,573", "656", "2026-05-27", "90"]) {
+      expect(entityTexts.has(rejected)).toBe(false);
+    }
+  });
+});
@@ -92,6 +92,16 @@ jest.mock("../src/llms/deepseek", () => ({
    .fn()
    .mockImplementation((config) => ({ type: "deepseek-llm", config })),
 }));
+jest.mock("../src/llms/litellm", () => ({
+  LiteLLM: jest
+    .fn()
+    .mockImplementation((config) => ({ type: "litellm-llm", config })),
+}));
+jest.mock("../src/llms/minimax", () => ({
+  MiniMaxLLM: jest
+    .fn()
+    .mockImplementation((config) => ({ type: "minimax-llm", config })),
+}));

 jest.mock("../src/vector_stores/qdrant", () => ({
  Qdrant: jest
@@ -206,6 +216,8 @@ describe("LLMFactory", () => {
    ["langchain"],
    ["lmstudio"],
    ["deepseek"],
+    ["litellm"],
+    ["minimax"],
  ])("creates LLM for provider '%s'", (provider) => {
    expect(() => LLMFactory.create(provider, dummyLLMConfig)).not.toThrow();
  });
@@ -0,0 +1,131 @@
+/// <reference types="jest" />
+/**
+ * LiteLLM — unit tests (mocked OpenAI).
+ */
+
+import { LiteLLM } from "../src/llms/litellm";
+
+const mockCreate = jest.fn();
+
+jest.mock("openai", () => {
+  return jest.fn().mockImplementation(() => ({
+    chat: { completions: { create: mockCreate } },
+  }));
+});
+
+describe("LiteLLM (unit)", () => {
+  beforeEach(() => mockCreate.mockClear());
+
+  it("uses default baseURL when none is provided", () => {
+    const llm = new LiteLLM({});
+    expect(llm).toBeDefined();
+  });
+
+  it("generateResponse() returns a text response", async () => {
+    mockCreate.mockResolvedValueOnce({
+      choices: [
+        {
+          message: {
+            content: "Hello, world!",
+            role: "assistant",
+            tool_calls: null,
+          },
+        },
+      ],
+    });
+
+    const llm = new LiteLLM({ baseURL: "http://localhost:4000" });
+    const result = await llm.generateResponse([
+      { role: "user", content: "Hi" },
+    ]);
+
+    expect(mockCreate).toHaveBeenCalledTimes(1);
+    expect(result).toBe("Hello, world!");
+  });
+
+  it("generateResponse() handles tool calls", async () => {
+    mockCreate.mockResolvedValueOnce({
+      choices: [
+        {
+          message: {
+            content: "",
+            role: "assistant",
+            tool_calls: [
+              {
+                function: {
+                  name: "get_weather",
+                  arguments: '{"city": "London"}',
+                },
+              },
+            ],
+          },
+        },
+      ],
+    });
+
+    const llm = new LiteLLM({});
+    const result = await llm.generateResponse(
+      [{ role: "user", content: "What is the weather?" }],
+      undefined,
+      [{ type: "function", function: { name: "get_weather" } }],
+    );
+
+    expect(result).toEqual({
+      content: "",
+      role: "assistant",
+      toolCalls: [{ name: "get_weather", arguments: '{"city": "London"}' }],
+    });
+  });
+
+  it("generateResponse() wraps API errors with a clear message", async () => {
+    mockCreate.mockRejectedValueOnce(new Error("Connection refused"));
+
+    const llm = new LiteLLM({});
+
+    await expect(
+      llm.generateResponse([{ role: "user", content: "Hi" }]),
+    ).rejects.toThrow("LiteLLM failed: Connection refused");
+  });
+
+  it("generateChat() returns LLMResponse shape", async () => {
+    mockCreate.mockResolvedValueOnce({
+      choices: [
+        {
+          message: { content: "I can help with that.", role: "assistant" },
+        },
+      ],
+    });
+
+    const llm = new LiteLLM({});
+    const result = await llm.generateChat([
+      { role: "user", content: "Help me" },
+    ]);
+
+    expect(result).toEqual({
+      content: "I can help with that.",
+      role: "assistant",
+    });
+  });
+
+  it("generateChat() wraps API errors with a clear message", async () => {
+    mockCreate.mockRejectedValueOnce(new Error("Timeout"));
+
+    const llm = new LiteLLM({});
+
+    await expect(
+      llm.generateChat([{ role: "user", content: "Hi" }]),
+    ).rejects.toThrow("LiteLLM failed: Timeout");
+  });
+
+  it("respects LITELLM_API_BASE env var", () => {
+    const original = process.env.LITELLM_API_BASE;
+    process.env.LITELLM_API_BASE = "http://custom-proxy:8080";
+    try {
+      const llm = new LiteLLM({});
+      expect(llm).toBeDefined();
+    } finally {
+      if (original !== undefined) process.env.LITELLM_API_BASE = original;
+      else delete process.env.LITELLM_API_BASE;
+    }
+  });
+});
@@ -314,4 +314,27 @@ describe("Memory Input Validation", () => {
      expect(result.results).toBeDefined();
    });
  });
+
+  describe("deleteAll() entity ID validation", () => {
+    it("should throw error when userId is whitespace-only", async () => {
+      await expect(memory.deleteAll({ userId: "   " })).rejects.toThrow(
+        "Invalid userId",
+      );
+    });
+
+    it("should throw error when userId contains internal whitespace", async () => {
+      await expect(memory.deleteAll({ userId: "user 123" })).rejects.toThrow(
+        "Invalid userId: cannot contain whitespace",
+      );
+    });
+
+    it("should trim userId before listing memories", async () => {
+      const listSpy = jest.spyOn(memory["vectorStore"], "list");
+      listSpy.mockResolvedValue([[], null]);
+
+      await memory.deleteAll({ userId: "  alice  " });
+
+      expect(listSpy).toHaveBeenCalledWith({ user_id: "alice" });
+    });
+  });
 });
@@ -0,0 +1,166 @@
+/// <reference types="jest" />
+/**
+ * MiniMax LLM - unit tests (mocked OpenAI).
+ */
+
+let capturedConstructorArgs: any;
+const mockCreate = jest.fn();
+
+jest.mock("openai", () => {
+  return jest.fn().mockImplementation((args: any) => {
+    capturedConstructorArgs = args;
+    return {
+      chat: { completions: { create: mockCreate } },
+    };
+  });
+});
+
+import { MiniMaxLLM } from "../src/llms/minimax";
+
+describe("MiniMaxLLM (unit)", () => {
+  beforeEach(() => {
+    capturedConstructorArgs = undefined;
+    mockCreate.mockClear();
+    delete process.env.MINIMAX_API_KEY;
+    delete process.env.MINIMAX_API_BASE;
+  });
+
+  it("throws when no API key is provided", () => {
+    expect(() => new MiniMaxLLM({})).toThrow("MiniMax API key is required");
+  });
+
+  it("uses MiniMax defaults with an explicit API key", () => {
+    new MiniMaxLLM({ apiKey: "test-key" });
+
+    expect(capturedConstructorArgs).toMatchObject({
+      apiKey: "test-key",
+      baseURL: "https://api.minimax.io/v1",
+    });
+  });
+
+  it("uses environment variables when config does not provide credentials", () => {
+    process.env.MINIMAX_API_KEY = "env-key";
+    process.env.MINIMAX_API_BASE = "https://example.minimax.test/v1";
+
+    new MiniMaxLLM({});
+
+    expect(capturedConstructorArgs).toMatchObject({
+      apiKey: "env-key",
+      baseURL: "https://example.minimax.test/v1",
+    });
+  });
+
+  it("config values take precedence over environment variables", () => {
+    process.env.MINIMAX_API_KEY = "env-key";
+    process.env.MINIMAX_API_BASE = "https://env.minimax.test/v1";
+
+    new MiniMaxLLM({
+      apiKey: "config-key",
+      baseURL: "https://config.minimax.test/v1",
+      model: "MiniMax-M1",
+    });
+
+    expect(capturedConstructorArgs).toMatchObject({
+      apiKey: "config-key",
+      baseURL: "https://config.minimax.test/v1",
+    });
+  });
+
+  it("generateResponse() returns a text response", async () => {
+    mockCreate.mockResolvedValueOnce({
+      choices: [
+        {
+          message: {
+            content: "Hello from MiniMax",
+            role: "assistant",
+            tool_calls: null,
+          },
+        },
+      ],
+    });
+
+    const llm = new MiniMaxLLM({ apiKey: "test-key" });
+    const result = await llm.generateResponse([
+      { role: "user", content: "Hi" },
+    ]);
+
+    expect(mockCreate).toHaveBeenCalledWith(
+      expect.objectContaining({ model: "MiniMax-M2.7" }),
+    );
+    expect(result).toBe("Hello from MiniMax");
+  });
+
+  it("generateResponse() handles tool calls", async () => {
+    mockCreate.mockResolvedValueOnce({
+      choices: [
+        {
+          message: {
+            content: "",
+            role: "assistant",
+            tool_calls: [
+              {
+                function: {
+                  name: "search_memory",
+                  arguments: '{"query": "alice"}',
+                },
+              },
+            ],
+          },
+        },
+      ],
+    });
+
+    const llm = new MiniMaxLLM({ apiKey: "test-key" });
+    const result = await llm.generateResponse(
+      [{ role: "user", content: "Find Alice" }],
+      undefined,
+      [{ type: "function", function: { name: "search_memory" } }],
+    );
+
+    expect(result).toEqual({
+      content: "",
+      role: "assistant",
+      toolCalls: [{ name: "search_memory", arguments: '{"query": "alice"}' }],
+    });
+  });
+
+  it("generateResponse() wraps API errors with a clear message", async () => {
+    mockCreate.mockRejectedValueOnce(new Error("Connection refused"));
+
+    const llm = new MiniMaxLLM({ apiKey: "test-key" });
+
+    await expect(
+      llm.generateResponse([{ role: "user", content: "Hi" }]),
+    ).rejects.toThrow("MiniMax LLM failed: Connection refused");
+  });
+
+  it("generateChat() returns LLMResponse shape", async () => {
+    mockCreate.mockResolvedValueOnce({
+      choices: [
+        {
+          message: { content: "I can help with that.", role: "assistant" },
+        },
+      ],
+    });
+
+    const llm = new MiniMaxLLM({ apiKey: "test-key" });
+    const result = await llm.generateChat([
+      { role: "user", content: "Help me" },
+    ]);
+
+    expect(result).toEqual({
+      content: "I can help with that.",
+      role: "assistant",
+    });
+  });
+
+  it("generateChat() wraps API errors with a clear message", async () => {
+    mockCreate.mockRejectedValueOnce(new Error("Timeout"));
+
+    const llm = new MiniMaxLLM({ apiKey: "test-key" });
+
+    await expect(
+      llm.generateChat([{ role: "user", content: "Hi" }]),
+    ).rejects.toThrow("MiniMax LLM failed: Timeout");
+  });
+});
@@ -1,5 +1,3 @@
-/// <reference types="jest" />
-
 jest.mock("pg", () => {
  const Client = jest.fn().mockImplementation(() => ({
    connect: jest.fn().mockResolvedValue(undefined),
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
kartik-mem0	d5b04e70fa	Merge remote-tracking branch 'origin/main' into docs/stale-migration # Conflicts: # docs/migration/api-changes.mdx	2026-06-27 13:10:29 +05:30
Kartik	7e7682a06d	docs(vectordbs): correct vector store config defaults & imports (#5843 )	2026-06-27 12:40:16 +05:30
Kartik	f38608fb50	docs(api-reference): align API docs & openapi.json with v3 spec (#5848 )	2026-06-26 22:07:53 +05:30
Kartik	fb11cdffbb	docs(changelog): fix score_details fields, graph-store note & dead link (#5845 )	2026-06-26 22:05:14 +05:30
Kartik	ee600705c2	docs: remove inaccurate api-changes migration page (#5857 )	2026-06-26 22:03:53 +05:30
Hrushikesh Yadav	f4ccef5157	fix(ts/redis): use nullish coalescing for hash/timestamps in insert/update (#5860 ) Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>	2026-06-26 19:01:58 +05:30
mintlify[bot]	08da741a31	SEO & metadata audit: expand short API reference descriptions (#5897 ) Co-authored-by: mintlify[bot] <109931778+mintlify[bot]@users.noreply.github.com>	2026-06-26 16:34:52 +05:30
kartik-mem0	dc857e43ca	docs(migration): clarify data= vs text= parameter divergence in update() Add a Note inside the Update Memory accordion explaining that OSS Memory.update() takes data= while Platform MemoryClient.update() (Python and JS/TS) takes text=, so migrating users know to rename the keyword argument. Addresses reviewer comment on oss-to-platform.mdx:224/391.	2026-06-26 16:06:00 +05:30
Kartik	e4efdd2e29	docs: add SECURITY.md and improve contribution guidelines (#5855 )	2026-06-26 16:02:53 +05:30
Kartik	a87c9ce367	docs: fix mangled Python formatting in pgvector & Neon vector store guides (#5853 )	2026-06-26 16:00:58 +05:30
Kartik	d258b638ef	docs: document FastEmbed embedder + missing org/project API endpoints (#5852 )	2026-06-26 16:00:12 +05:30
Harsh Vardhan Gupta	bbbfcfea07	fix(deps): bump undici to >=6.27.0 (CVE-2026-12151) (#5861 )	2026-06-26 15:32:09 +05:30
Kartik	fbef369b91	docs(open-source): fix OSS feature docs for v3 SDK (#5847 )	2026-06-26 14:14:33 +05:30
Kartik	a7ed68e697	docs(integrations): fix retired model IDs, v3 response shapes & vercel deps (#5842 )	2026-06-26 13:59:33 +05:30
Kartik	8a92cf0306	docs(integrations): sync agent-plugin hook tables with actual hooks.json (#5839 )	2026-06-26 13:58:28 +05:30
soumil-rathi	0fbbb2f525	feat(memory): expose expiration controls in client docs (#5874 ) Co-authored-by: Soumil Rathi <soumilrathi@gmail.com>	2026-06-25 17:14:33 -07:00
Barry Collins	818c2981b7	feat(ts-sdk): add MiniMax LLM provider (#5858 ) Co-authored-by: kartik-mem0 <kartik.labhshetwar@mem0.ai>	2026-06-25 17:01:50 +05:30
Rod Boev	1f66aadfa3	fix(openclaw): normalize Windows skill-loader URLs before fileURLToPath (#5679 )	2026-06-25 16:36:11 +05:30
Hrushikesh Yadav	b91c745fbc	fix: apply remove_code_blocks() to LangChain path in async _create_procedural_memory (#5711 )	2026-06-25 16:34:28 +05:30
Muhammad Furqan	af70668308	fix(reranker): score HuggingFace reranker with sigmoid, not min-max (#5715 )	2026-06-25 16:18:02 +05:30
rafid001	890473f891	fix(core): validate and trim entity IDs in delete_all() (#5735 ) Co-authored-by: Cursor <cursoragent@cursor.com> Co-authored-by: Kartik <kartik.labhshetwar@mem0.ai>	2026-06-25 16:14:30 +05:30
Hrushikesh Yadav	d2ff83cf72	fix(redis): use .get() for hash/created_at in insert() to handle entity payloads (#5709 )	2026-06-25 16:13:44 +05:30
Rod Boev	0e02effaf7	fix(notices): derive scale counts for Redis and search backends (#5687 )	2026-06-25 16:04:44 +05:30
Rod Boev	9269a0ad6e	feat(ts-sdk): add LiteLLM as LLM provider (#5830 ) Co-authored-by: kartik-mem0 <kartik.labhshetwar@mem0.ai>	2026-06-25 15:52:46 +05:30
Hrushikesh Yadav	3d06006f36	fix(valkey): escape special chars in FT.SEARCH tag filter values (#5750 )	2026-06-25 15:20:13 +05:30
kartik-mem0	7e056281e5	docs(migration): drop api-changes redirect (page stays in nav with corrected content)	2026-06-25 12:30:25 +05:30
kartik-mem0	b5789d4afe	docs(migration): correct migration guides to match v3 (update available, no async_mode/graph_store/offset)	2026-06-25 12:00:49 +05:30
Rod Boev	6bb1d328ad	fix(mem0-ts): support pgvector connection strings and ssl (#5789 )	2026-06-25 11:57:37 +05:30
Taranjeet Singh	ac296f7534	docs: make example code fences copy-safe (#5833 )	2026-06-24 22:19:40 -07:00
Kartik	ac8f862ff7	fix(mem0-plugin): store files_touched as a list to stop double JSON-encoding (#5806 )	2026-06-25 09:06:11 +05:30
soumil-rathi	b33fa5427c	fix(memory): align entity extraction precision (#5829 ) Making the entity extraction function cleaner Co-authored-by: Soumil Rathi <soumilrathi@gmail.com>	2026-06-24 15:48:37 -07:00
Ashutosh Kasudhan	5d573dd2ae	docs: added typescript support to deepseek provider (#5723 )	2026-06-24 20:55:14 +05:30
Kartik	98dbf90864	chore: update changelog, bump SDK versions to Python 2.0.8 and TypeScript 3.0.10 (#5825 )	2026-06-24 20:21:06 +05:30