docs(changelog): correct score_details fields, graph-store removal note, and dead migration link

CONTRIBUTING.md

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

SECURITY.md

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

docs/changelog/highlights.mdx

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

docs/changelog/sdk.mdx

@@ -121,7 +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))
@@ -229,7 +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>
 

docs/components/vectordbs/dbs/neon.mdx

@@ -21,47 +21,49 @@ from mem0 import Memory
 load_dotenv()
 
 config = {
-"vector_store": {
+    "vector_store": {
-"provider": "pgvector",
+        "provider": "pgvector",
-"config": {
+        "config": {
-"connection_string": os.environ["DATABASE_URL"],
+            "connection_string": os.environ["DATABASE_URL"],
-"collection_name": "memories",
+            "collection_name": "memories",
-"embedding_model_dims": 1536,
+            "embedding_model_dims": 1536,
-"hnsw": True,
+            "hnsw": True,
-},
+        },
-},
+    },
 }
 
 m = Memory.from_config(config)
 messages = [
-{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"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": "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": "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"})
 
 results = m.search(
-"What movies should I recommend?",
+    "What movies should I recommend?",
-filters={"user_id": "alice"},
+    filters={"user_id": "alice"},
 )
 
 print(results)
-
+```
-````
 
 ```typescript TypeScript
 import "dotenv/config";
 import { Memory } from "mem0ai/oss";
 
+const databaseUrl = new URL(process.env.DATABASE_URL!);
+
 const m = new Memory({
   vectorStore: {
     provider: "pgvector",
     config: {
-      connectionString: process.env.DATABASE_URL!,
+      user: decodeURIComponent(databaseUrl.username),
-      ssl: {
+      password: decodeURIComponent(databaseUrl.password),
-        rejectUnauthorized: false,
+      host: databaseUrl.hostname,
-      },
+      port: Number(databaseUrl.port || 5432),
+      dbname: databaseUrl.pathname.slice(1) || "neondb",
       collectionName: "memories",
       dimension: 1536,
       embeddingModelDims: 1536,
@@ -87,8 +89,7 @@ const results = await m.search("What movies should I recommend?", {
 });
 
 console.log(results);
-````
+```
-
 </CodeGroup>
 
 ## SQL Migration
@@ -115,19 +116,20 @@ 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">
-Use the Neon `DATABASE_URL` directly with `connectionString`. Set `ssl` if your runtime needs an explicit TLS config object.
+The current Mem0 TypeScript `pgvector` adapter takes individual Postgres fields,
-
+so parse `DATABASE_URL` before creating `Memory`.
-| Parameter            | Description                                    | Default        |
-| -------------------- | ---------------------------------------------- | -------------- |
-| `connectionString`   | Neon Postgres connection string.               | Required       |
-| `ssl`                | Optional TLS settings passed directly to `pg`. | Driver default |
-| `collectionName`     | Name for the vector collection.                | `memories`     |
-| `dimension`          | Vector dimension for Mem0 config.              | Auto-detected  |
-| `embeddingModelDims` | Embedding model dimensions for table creation. | Required       |
-| `hnsw`               | Enables HNSW indexing.                         | `false`        |
-
-**TLS note:** `ssl: true` is sufficient for most Neon connections since Neon uses valid certificates. Use `ssl: { rejectUnauthorized: false }` only when connecting through Neon's connection pooler on certain edge runtimes (e.g. Cloudflare Workers) that require it, or when your environment does not trust the Neon CA chain.
 
+| Parameter | Description | Default |
+| --- | --- | --- |
+| `user` | Database user. | Required |
+| `password` | Database password. | Required |
+| `host` | Database host. | Required |
+| `port` | Database port. | `5432` |
+| `dbname` | Database name. | `vector_store` |
+| `collectionName` | Name for the vector collection. | `memories` |
+| `dimension` | Vector dimension for Mem0 config. | Auto-detected |
+| `embeddingModelDims` | Embedding model dimensions for table creation. | Required |
+| `hnsw` | Enables HNSW indexing. | `false` |
 </Tab>
 </Tabs>
 

docs/components/vectordbs/dbs/pgvector.mdx

@@ -2,7 +2,6 @@
 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
@@ -15,38 +14,41 @@ from mem0 import Memory
 os.environ["OPENAI_API_KEY"] = "sk-xx"
 
 config = {
-"vector_store": {
+    "vector_store": {
-"provider": "pgvector",
+        "provider": "pgvector",
-"config": {
+        "config": {
-"user": "test",
+            "user": "test",
-"password": "123",
+            "password": "123",
-"host": "127.0.0.1",
+            "host": "127.0.0.1",
-"port": "5432",
+            "port": "5432",
-},
+        }
-}
+    }
 }
 
 m = Memory.from_config(config)
 messages = [
-{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"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": "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": "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,
-      connectionString: "postgresql://test:123@localhost:5432/vector_store",
+      user: 'test',
+      password: '123',
+      host: '127.0.0.1',
+      port: 5432,
+      dbname: 'vector_store', // Optional; TypeScript OSS defaults to `vector_store` when omitted
       diskann: false, // Optional, requires pgvectorscale extension
       hnsw: false, // Optional, for HNSW indexing
     },
@@ -55,44 +57,37 @@ const config = {
 
 const memory = new Memory(config);
 const messages = [
-  { role: "user", content: "I'm planning to watch a movie tonight. Any recommendations?" },
+    {"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": "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": "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."}
-];
+]
-
 await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
-````
+```
-
 </CodeGroup>
 
 ### Config
 
 Here are the parameters available for configuring pgvector:
 
-| Parameter            | SDK                     | Description                                                                                                                                                    | Default Value                           |
+| Parameter | 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`                                  |
+| `dbname` | The name of the database | `postgres` |
-| `ssl`                | TypeScript OSS          | SSL option passed directly to `pg`, either `true` or an SSL config object, for both `connectionString` and split-field connections.                            | `None`                                  |
+| `collection_name` | The name of the collection | `mem0` |
-| `dbname`             | TypeScript OSS          | Split-field database name. This is only used when `connectionString` is absent.                                                                                | `vector_store`                          |
+| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
-| `collectionName`     | TypeScript OSS          | Collection name.                                                                                                                                               | `memories`                              |
+| `user` | User name to connect to the database | `None` |
-| `embeddingModelDims` | TypeScript OSS          | Dimensions of the embedding model.                                                                                                                             | Required                                |
+| `password` | Password to connect to the database | `None` |
-| `user`               | TypeScript OSS + Python | Database user for split-field connections.                                                                                                                     | `None`                                  |
+| `host` | The host where the Postgres server is running | `None` |
-| `password`           | TypeScript OSS + Python | Database password for split-field connections.                                                                                                                 | `None`                                  |
+| `port` | The port where the Postgres server is running | `None` |
-| `host`               | TypeScript OSS + Python | Database host for split-field connections.                                                                                                                     | `None`                                  |
+| `diskann` | Whether to use diskann for vector similarity search (requires pgvectorscale) | `True` |
-| `port`               | TypeScript OSS + Python | Database port for split-field connections.                                                                                                                     | `None`                                  |
+| `hnsw` | Whether to use hnsw for vector similarity search | `False` |
-| `diskann`            | TypeScript OSS + Python | Whether to use DiskANN for vector similarity search, requires pgvectorscale.                                                                                   | `False`                                 |
+| `sslmode` | SSL mode for PostgreSQL connection (e.g., 'require', 'prefer', 'disable') | `None` |
-| `hnsw`               | TypeScript OSS + Python | Whether to use HNSW for vector similarity search.                                                                                                              | TypeScript OSS: `False`, Python: `True` |
+| `connection_string` | PostgreSQL connection string (overrides individual connection parameters) | `None` |
-| `connection_string`  | Python only             | PostgreSQL connection string, overrides individual connection parameters.                                                                                      | `None`                                  |
+| `connection_pool` | psycopg2 connection pool object (overrides connection string and individual 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`                                  |
 
-**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`.
+**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.
-
-**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`)

docs/contributing/development.mdx

@@ -1,65 +1,32 @@
 ---
 title: Development
-description: "Guide to contributing code to Mem0, covering the issue-first workflow, the CLA, environment setup for the Python and TypeScript SDKs, and code quality checks."
+description: "Guide to contributing code to Mem0, covering the fork and clone workflow, PR submission, and code quality checks."
 icon: "code"
 ---
 
 # Development Contributions
 
-We strive to make contributions **easy, collaborative, and enjoyable**. Mem0 is a
+We strive to make contributions **easy, collaborative, and enjoyable**. Follow the steps below to ensure a smooth contribution process.
-polyglot monorepo containing the **Python SDK** (`mem0/`), the **TypeScript SDK**
-(`mem0-ts/`), CLIs, integrations, the self-hosted server, and the docs site.
-Follow the steps below for a smooth contribution process.
 
-<Note>
+## Submitting Your Contribution through PR
-  For the complete contributor checklist, see
-  [CONTRIBUTING.md](https://github.com/mem0ai/mem0/blob/main/CONTRIBUTING.md) in
-  the repository root.
-</Note>
 
-## Before You Start
+To contribute, follow these steps:
-
-### 1. Open an Issue First
-
-**Always open an issue before opening a pull request.** This lets us discuss the
-change, avoid duplicate work, and agree on the approach before you write code.
-
-- Search [existing issues](https://github.com/mem0ai/mem0/issues) first.
-- If none match, open a
-  [bug report](https://github.com/mem0ai/mem0/issues/new?template=bug_report.yml)
-  or [feature request](https://github.com/mem0ai/mem0/issues/new?template=feature_request.yml).
-- For anything beyond a trivial fix, wait for a maintainer to confirm the approach.
-
-Every pull request must link to an issue using `Closes #<issue-number>`.
-
-### 2. Sign the Contributor License Agreement (CLA)
-
-**We cannot merge any pull request until you have signed our Contributor License
-Agreement (CLA).** When you open your first PR, the CLA bot will comment with a
-link to sign — it takes less than a minute and only needs to be done once.
-
-## Submitting Your Contribution through a PR
 
 1. **Fork & Clone** the repository: [Mem0 on GitHub](https://github.com/mem0ai/mem0)
-2. **Create a Feature Branch**: Use a dedicated branch, e.g., `feature/my-new-feature`
+2. **Create a Feature Branch**: Use a dedicated branch for your changes, e.g., `feature/my-new-feature`
-3. **Implement Changes**: If adding a feature or fixing a bug, be sure to:
+3. **Implement Changes**: If adding a feature or fixing a bug, ensure to:
    - Write necessary **tests**
    - Add **documentation, docstrings, and runnable examples**
 4. **Code Quality Checks**:
    - Run **linting** to catch style issues
    - Ensure **all tests pass**
-5. **Commit** using [Conventional Commits](https://www.conventionalcommits.org/)
+5. **Submit a Pull Request**
-   (`feat:`, `fix:`, `docs:`, `refactor:`, `test:`)
-6. **Submit a Pull Request** against `main`, linking the issue and filling out the
-   PR template.
 
 For detailed guidance on pull requests, refer to [GitHub's documentation](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request).
 
 ---
 
-## Python SDK (`mem0/`)
+## Dependency Management
-
-### Dependency Management
 
 We use `hatch` as our package manager. Install it by following the [official instructions](https://hatch.pypa.io/latest/install/).
 
@@ -77,9 +44,13 @@ hatch -e dev_py_3_11 shell  # For dev_py_3_11 (differences are mentioned in pypr
 make install_all
 ```
 
+---
+
+## Development Standards
+
 ### Pre-commit Hooks
 
-Ensure `pre-commit` is installed before contributing (hooks run ruff + isort):
+Ensure `pre-commit` is installed before contributing:
 
 ```bash
 pre-commit install
@@ -87,7 +58,7 @@ pre-commit install
 
 ### Linting with `ruff`
 
-Run the linter and fix any reported issues before submitting your PR (line length **120**):
+Run the linter and fix any reported issues before submitting your PR:
 
 ```bash
 make lint
@@ -95,11 +66,10 @@ make lint
 
 ### Code Formatting
 
-To maintain a consistent code style, format your code and sort imports (isort, `profile = "black"`):
+To maintain a consistent code style, format your code:
 
 ```bash
 make format
-make sort
 ```
 
 ### Testing with `pytest`
@@ -114,45 +84,9 @@ make test
 
 ---
 
-## TypeScript SDK (`mem0-ts/`)
-
-We use [`pnpm`](https://pnpm.io/) (v10+) for all TypeScript packages. **Do NOT use
-`npm` or `yarn`.**
-
-```bash
-cd mem0-ts
-pnpm install
-
-pnpm run build        # tsup (CJS + ESM)
-pnpm run test         # jest (all tests)
-pnpm run test:unit    # unit tests with coverage
-```
-
-### Standards
-
-- **Build:** tsup
-- **Formatter:** Prettier
-- **Tests:** jest
-- Always run type checking after changes: `pnpm run typecheck` (or `tsc --noEmit`)
-- Use ES module `import` syntax — never `require()`
-
----
-
-## Reporting Security Issues
-
-**Do not report security vulnerabilities through public issues or pull requests.**
-Please follow our [Security Policy](https://github.com/mem0ai/mem0/blob/main/SECURITY.md)
-to report them privately.
-
----
-
 ## Release Process
 
-Packages are published automatically via GitHub Actions when a GitHub Release is
+Currently, releases are handled manually. We aim for frequent releases, typically when new features or bug fixes are introduced.
-created with the correct tag prefix (e.g. `v*` for the Python SDK, `ts-v*` for the
-TypeScript SDK). See
-[CONTRIBUTING.md](https://github.com/mem0ai/mem0/blob/main/CONTRIBUTING.md#releasing)
-for the full tag-prefix table and publishing details.
 
 ---
 

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

@@ -1,4 +1,4 @@
-import type { Client as ClientType, ClientConfig } from "pg";
+import type { Client as ClientType } from "pg";
 import pkg from "pg";
 const { Client, escapeIdentifier } = pkg;
 import { VectorStore } from "./base";
@@ -157,90 +157,41 @@ export function buildFilterConditions(
 
 interface PGVectorConfig extends VectorStoreConfig {
   dbname?: string;
-  user?: string;
+  user: string;
-  password?: string;
+  password: string;
-  host?: string;
+  host: string;
-  port?: number;
+  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.useDirectConnection = !!getConnectionString(config);
+    this.dbName = validateIdentifier(config.dbname || "vector_store", "dbname");
-    this.dbName = this.useDirectConnection
-      ? ""
-      : validateIdentifier(config.dbname || "vector_store", "dbname");
     this.config = config;
 
-    this.client = new Client(
+    this.client = new Client({
-      buildClientConfig(
+      database: "postgres", // Initially connect to default postgres database
-        config,
+      user: config.user,
-        this.useDirectConnection ? undefined : "postgres",
+      password: config.password,
-      ),
+      host: config.host,
-    );
+      port: config.port,
+    });
     this.initialize().catch(console.error);
   }
 
@@ -259,20 +210,29 @@ export class PGVector implements VectorStore {
     try {
       await this.client.connect();
 
-      if (!this.useDirectConnection) {
+      // Check if database exists
-        const dbExists = await this.checkDatabaseExists(this.dbName);
+      const dbExists = await this.checkDatabaseExists(this.dbName);
-        if (!dbExists) {
+      if (!dbExists) {
-          await this.createDatabase(this.dbName);
+        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,
@@ -280,6 +240,7 @@ 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);

mem0-ts/src/oss/tests/pgvector.filters.test.ts

@@ -1,3 +1,5 @@
+/// <reference types="jest" />
+
 jest.mock("pg", () => {
   const Client = jest.fn().mockImplementation(() => ({
     connect: jest.fn().mockResolvedValue(undefined),

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

@@ -1,3 +1,5 @@
+/// <reference types="jest" />
+
 const searchRows = [
   {
     id: "a",
@@ -21,13 +23,9 @@ const searchRows = [
   },
 ];
 
-const mockState = {
-  databaseExists: true,
-};
-
 function mockPgQuery(sql: string) {
   if (sql.includes("SELECT 1 FROM pg_database")) {
-    return { rows: mockState.databaseExists ? [{ "?column?": 1 }] : [] };
+    return { rows: [{ "?column?": 1 }] };
   }
 
   if (sql.includes("FROM information_schema.tables")) {
@@ -71,129 +69,13 @@ jest.mock("pg", () => {
 
 import { PGVector } from "../src/vector_stores/pgvector";
 
-function getClientQueries(client: { query: jest.Mock }) {
+describe("PGVector - search()", () => {
-  return client.query.mock.calls.map(([sql]) => sql as string);
-}
-
-describe("PGVector", () => {
   beforeEach(() => {
     const pg = require("pg");
-    mockState.databaseExists = true;
     pg.__mock.Client.mockClear();
     pg.__mock.clients.length = 0;
   });
 
-  test("uses one direct client for connectionString mode and skips bootstrap database creation", async () => {
-    mockState.databaseExists = false;
-
-    const ssl = { rejectUnauthorized: false };
-    const store = new PGVector({
-      collectionName: "memories",
-      connectionString:
-        "postgresql://postgres:postgres@db.example.com:5432/neondb",
-      ssl,
-      embeddingModelDims: 3,
-      dimension: 3,
-    } as any);
-
-    await store.initialize();
-
-    const pg = require("pg");
-    expect(pg.__mock.Client).toHaveBeenCalledTimes(1);
-    expect(pg.__mock.Client).toHaveBeenCalledWith({
-      connectionString:
-        "postgresql://postgres:postgres@db.example.com:5432/neondb",
-      ssl,
-    });
-
-    const directClient = pg.__mock.clients[0];
-    const queries = getClientQueries(directClient);
-
-    expect(queries).not.toEqual(
-      expect.arrayContaining([
-        expect.stringContaining("SELECT 1 FROM pg_database"),
-      ]),
-    );
-    expect(queries).not.toEqual(
-      expect.arrayContaining([expect.stringContaining("CREATE DATABASE")]),
-    );
-    expect(queries).toEqual(
-      expect.arrayContaining([
-        "CREATE EXTENSION IF NOT EXISTS vector",
-        expect.stringContaining("FROM information_schema.tables"),
-      ]),
-    );
-  });
-
-  test("keeps the split-field bootstrap flow when connectionString is absent", async () => {
-    mockState.databaseExists = false;
-    const ssl = { rejectUnauthorized: false };
-
-    const store = new PGVector({
-      collectionName: "memories",
-      user: "postgres",
-      password: "postgres",
-      host: "localhost",
-      port: 5432,
-      dbname: "vector_store",
-      ssl,
-      embeddingModelDims: 3,
-      dimension: 3,
-    } as any);
-
-    await store.initialize();
-
-    const pg = require("pg");
-    expect(pg.__mock.Client).toHaveBeenCalledTimes(2);
-    expect(pg.__mock.Client).toHaveBeenNthCalledWith(1, {
-      database: "postgres",
-      user: "postgres",
-      password: "postgres",
-      host: "localhost",
-      port: 5432,
-      ssl,
-    });
-    expect(pg.__mock.Client).toHaveBeenNthCalledWith(2, {
-      database: "vector_store",
-      user: "postgres",
-      password: "postgres",
-      host: "localhost",
-      port: 5432,
-      ssl,
-    });
-
-    const bootstrapClient = pg.__mock.clients[0];
-    const activeClient = pg.__mock.clients[1];
-    const bootstrapQueries = getClientQueries(bootstrapClient);
-
-    expect(bootstrapQueries).toEqual(
-      expect.arrayContaining([
-        "SELECT 1 FROM pg_database WHERE datname = $1",
-        'CREATE DATABASE "vector_store"',
-      ]),
-    );
-    expect(bootstrapClient.end).toHaveBeenCalledTimes(1);
-    expect(getClientQueries(activeClient)).toEqual(
-      expect.arrayContaining([
-        "CREATE EXTENSION IF NOT EXISTS vector",
-        expect.stringContaining("FROM information_schema.tables"),
-      ]),
-    );
-  });
-
-  test("throws when connectionString is absent and split-field params are missing", () => {
-    expect(
-      () =>
-        new PGVector({
-          collectionName: "memories",
-          embeddingModelDims: 3,
-          dimension: 3,
-        } as any),
-    ).toThrow(
-      "PGVector requires either connectionString or user, password, host, port",
-    );
-  });
-
   test("returns similarity score (1 - distance) clamped to [0, 1]", async () => {
     const store = new PGVector({
       collectionName: "memories",