SQLite Manager (n8n community node)

Manage local SQLite databases – including vector search with sqlite-vec – directly from your n8n workflows.

This package bundles a single node (sqliteManager) that can:

- discover and manage SQLite database files stored on the n8n host
- create or delete databases with optional bootstrap SQL
- execute arbitrary SQL (including vector KNN queries via sqlite-vec)
- return query results as structured JSON for downstream nodes

> Use case examples
>
> - Maintain lightweight knowledge bases for AI agents hosted in n8n
> - Share local cache tables between workflows without an external DB
> - Run ad‑hoc data migrations or maintenace scripts inside a workflow

---

- Requirements
- Installation
- Configuration
- Node operations
- Usage examples
- Development
- Support
- Version history

Requirements

| Component | Version / notes |
| -------------------- | -------------------------------------------------- |
| n8n | 1.114.4 or newer (tested in Docker & desktop) |
| Node.js | 22.x (matches upstream n8n release) |
| SQLite CLI | ≥ 3.49 with JSON output support (sqlite3 -json) |
| sqlite-vec | Bundled automatically during Docker build |
| File system | Write access to the directory storing databases |

The node does not require external credentials or APIs. All operations happen on the local file system of the n8n host/container.

Installation

$3

1. Open Settings → Community Nodes → Install.
2. Enter the package name: n8n-nodes-rckflr-sqlitemanager.
3. Acknowledge the community-node warning banner and confirm the install.
4. Reload the editor if the new icon does not appear immediately.

$3

``bash
cd ~/.n8n
pnpm install n8n-nodes-rckflr-sqlitemanager
pnpm build
`

Restart the n8n instance afterwards.

$3

Add the package to the N8N_COMMUNITY_NODES_INSTALL environment variable:

`env
N8N_COMMUNITY_NODES_INSTALL=n8n-nodes-rckflr-sqlitemanager
`

The included Dockerfile in this repository demonstrates how we compile sqlite-vec at build time and mount persistent volumes for SQLite data and user-installed Python packages.

Configuration

The node looks up its working directory from the following environment variables:

| Variable | Default | Purpose |
| ----------------------- | ------------------------- | ----------------------------------------- |
| SQLITE_BASE_DIR | /opt/sqlite-db | Root directory where database files live. |
| SQLITE_VEC_LIBRARY | /usr/local/lib/sqlite-extensions/vec0.so | Location of the compiled sqlite-vec shared object. |
| SQLITE_VEC_ENTRYPOINT | sqlite3_vec_init | Entry point name inside the sqlite-vec module. |

Ensure that the directory referenced by SQLITE_BASE_DIR is writable by the user running n8n (inside Docker, usually the node user).

Node operations

| Operation | Parameters | Notes |
| ---------------- | ------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------- |
| List Databases | _None_ | Returns one item per file (name, absolute path, sizeBytes, updatedAt). |
| Create Database | databaseName, overwrite (boolean), initialSql (multiline string) | Creates or truncates a file. initialSql is executed immediately after creation. |
| Delete Database | databaseName, ignoreMissing (boolean) | Deletes the file. When ignoreMissing=true the node emits a result but does not raise an error. |
| Execute Query | databaseName, sql, returnData (boolean), failOnEmpty (boolean) | Runs SQL via the SQLite CLI. When returnData=true, results are parsed from the -json output. |

Vector queries work out of the box. For example:

`sql
SELECT headline, distance
FROM vec_articles
WHERE headline_embedding MATCH lembed('n8n automation')
AND k = 5;
`

The node will automatically preload the sqlite-vec library before executing the statement.

Usage examples

$3

1. Use Create Database with an initialSql that defines a vec0 virtual table.
2. Insert embeddings generated elsewhere (or from an HTTP node).
3. Run Execute Query with a MATCH clause to retrieve similar content.

$3

1. Start the workflow when a new file arrives (e.g. via SFTP or HTTP).
2. Load the data into a temporary SQLite database with staging tables.
3. Run transformation SQL, then export the result to the next node.
4. Clean up the temporary database with Delete Database.

$3

Schedule the node (using Cron plus Execute Query) to vacuum or analyze databases, and funnel results into Slack/email notifications if anomalies are detected.

Development

`bash
pnpm install
pnpm lint
pnpm build
pnpm dev # launches a local n8n instance with hot-reload for the node
`

We rely on the official @n8n/node-cli for builds, linting, and publishing. Make sure you run pnpm build before committing to keep the transpiled artifacts in sync.

$3

Run the end-to-end regression suite (requires sqlite3 and a compiled sqlite-vec library):

`bash
SQLITE_VEC_LIBRARY=/usr/local/lib/sqlite-extensions/vec0 \
SQLITE_VEC_ENTRYPOINT=sqlite3_vec_init \
pnpm test
`

The test script spins up a temporary database, creates a vec0 virtual table, inserts fixture vectors, and asserts that the nearest-neighbour query returns the expected record.

$3

`
nodes/
SqliteManager/
SqliteManager.node.ts # Source TypeScript implementation
SqliteManager.node.json # Metadata consumed by n8n
sqliteManager.svg # Icon (light theme)
sqliteManager.dark.svg # Icon (dark theme)
dist/ # Build output generated by pnpm build
`

$3

- [x] Package name starts with n8n-nodes-.
- [x] README includes installation, usage, compatibility, and support information.
- [x] MIT license file present.
- [x] n8n field in package.json declares provided nodes.
- [x] npm run lint and npm run build` succeed.
- [x] No bundled secrets or credentials.

Support

Please open an issue on GitHub for bug reports or feature requests:

For consultancy or commercial support, reach out to Mauricio Perera at .

Version history

See CHANGELOG.md for a detailed release log.

- 0.1.0 – Initial public release submitted for n8n community verification.

License

Released under the MIT License.