The official SurrealDB SDK for Python.

surrealdb.py

The official SurrealDB SDK for Python.

Documentation

• Official Documentation
• Examples

How to install

# Using pip
pip install surrealdb

# Using uv
uv add surrealdb

Quick start

In this short guide, you will learn how to install, import, and initialize the SDK, as well as perform the basic data manipulation queries.

This guide uses the Surreal class, but this example would also work with AsyncSurreal class, with the addition of await in front of the class methods.

Running SurrealDB

You can run SurrealDB locally or start with a free SurrealDB cloud account.

For local, two options:

1. Install SurrealDB and run SurrealDB. Run in-memory with:

surreal start -u root -p root

2. Run with Docker.

docker run --rm --pull always -p 8000:8000 surrealdb/surrealdb:latest start

Learn the basics

# Import the Surreal class
from surrealdb import Surreal

# Using a context manger to automatically connect and disconnect
with Surreal("ws://localhost:8000/rpc") as db:
    db.signin({"username": 'root', "password": 'root'})
    db.use("namepace_test", "database_test")

    # Create a record in the person table
    db.create(
        "person",
        {
            "user": "me",
            "password": "safe",
            "marketing": True,
            "tags": ["python", "documentation"],
        },
    )

    # Read all the records in the table
    print(db.select("person"))

    # Update all records in the table
    print(db.update("person", {
        "user":"you",
        "password":"very_safe",
        "marketing": False,
        "tags": ["Awesome"]
    }))

    # Delete all records in the table
    print(db.delete("person"))

    # You can also use the query method 
    # doing all of the above and more in SurrealQl
    
    # In SurrealQL you can do a direct insert 
    # and the table will be created if it doesn't exist
    
    # Create
    db.query("""
    insert into person {
        user: 'me',
        password: 'very_safe',
        tags: ['python', 'documentation']
    };
    """)

    # Read
    print(db.query("select * from person"))
    
    # Update
    print(db.query("""
    update person content {
        user: 'you',
        password: 'more_safe',
        tags: ['awesome']
    };
    """))

    # Delete
    print(db.query("delete person"))

Embedded Database

SurrealDB can also run embedded directly within your Python application natively. This provides a fully-featured database without needing a separate server process.

Installation

The embedded database is included when you install surrealdb.

For source builds, you'll need Rust toolchain and maturin:

uv run maturin develop --release

In-Memory Database

Perfect for embedded applications, development, testing, caching, or temporary data.

import asyncio
from surrealdb import AsyncSurreal

async def main():
    # Create an in-memory database (can use "mem://" or "memory")
    async with AsyncSurreal("memory") as db:
        await db.use("test", "test")
        await db.signin({"username": "root", "password": "root"})
        
        # Use like any other SurrealDB connection
        person = await db.create("person", {
            "name": "John Doe",
            "age": 30
        })
        print(person)
        
        people = await db.select("person")
        print(people)

asyncio.run(main())

File-Based Persistent Database

For persistent local storage:

import asyncio
from surrealdb import AsyncSurreal

async def main():
    # Create a file-based database (can use "file://", "surrealkv://", or "surrealkv+versioned://")
    async with AsyncSurreal("file://mydb") as db:
        await db.use("test", "test")
        await db.signin({"username": "root", "password": "root"})
        
        # Data persists across connections
        await db.create("company", {
            "name": "Acme Corp",
            "employees": 100
        })
        
        companies = await db.select("company")
        print(companies)

asyncio.run(main())

Blocking (Sync) API

The embedded database also supports the blocking API:

from surrealdb import Surreal

# In-memory (can use "mem://" or "memory")
with Surreal("memory") as db:
    db.use("test", "test")
    db.signin({"username": "root", "password": "root"})
    
    person = db.create("person", {"name": "Jane"})
    print(person)

# File-based
with Surreal("file://mydb") as db:
    db.use("test", "test")
    db.signin({"username": "root", "password": "root"})
    
    company = db.create("company", {"name": "TechStart"})
    print(company)

When to Use Embedded vs Remote

Use Embedded (memory, mem://, file://, surrealkv://, or surrealkv+versioned://) when:

• Building desktop applications
• Running tests (in-memory is very fast)
• Local development without server setup
• Embedded systems or edge computing
• Single-application data storage

Use Remote (ws:// or http://) when:

• Multiple applications share data
• Distributed systems
• Cloud deployments
• Need horizontal scaling
• Centralized data management

For more examples, see the examples/embedded/ directory.

Sessions and transactions

Multi-session and client-side transactions are supported only for WebSocket connections (ws:// or wss://). They are not available for HTTP or embedded connections.

• Sessions: Call attach() on a WS connection to create a new session (returns a UUID). Use new_session() to get an AsyncSurrealSession or BlockingSurrealSession that scopes all operations to that session. Call close_session() on the session (or detach(session_id) on the connection) to drop it.
• Transactions: On a session (or the default connection), call begin_transaction() to start a transaction (returns AsyncSurrealTransaction or BlockingSurrealTransaction). Run queries on the transaction object; then call commit() or cancel() to finish.

Example (async WS):

from surrealdb import AsyncSurreal

async with AsyncSurreal("ws://localhost:8000/rpc") as db:
    await db.signin({"username": "root", "password": "root"})
    await db.use("test", "test")

    session = await db.new_session()
    await session.use("test", "test")
    result = await session.query("SELECT 1")

    txn = await session.begin_transaction()
    await txn.query("CREATE person SET name = 'x'")
    await txn.commit()

    await session.close_session()

On HTTP or embedded connections, attach(), detach(), begin(), commit(), cancel(), and new_session() raise NotImplementedError with a message that sessions/transactions are only supported for WebSocket connections.

Observability with Logfire

Pydantic Logfire provides automatic instrumentation for SurrealDB operations, giving you instant observability into your database interactions. Logfire exports standard OpenTelemetry spans, making it compatible with any observability platform.

Quick Start

Install Logfire using pip:

pip install logfire

Or install using uv:

uv add logfire

Enable instrumentation:

import logfire
from surrealdb import AsyncSurreal

# Configure Logfire
logfire.configure()

# Instrument all SurrealDB operations
logfire.instrument_surrealdb()

# All database operations are now automatically traced
async with AsyncSurreal("ws://localhost:8000") as db:
    await db.signin({"username": "root", "password": "root"})
    await db.use("test", "test")
    
    # These operations will appear as spans in your traces
    await db.create("person", {"name": "Alice"})
    await db.query("SELECT * FROM person")

Features

• Automatic tracing: All database methods are instrumented automatically
• Smart parameter logging: Sensitive data (tokens, passwords) are automatically scrubbed
• OpenTelemetry compatible: Works with Jaeger, DataDog, Honeycomb, and other OTel platforms
• Minimal overhead: Efficient instrumentation with negligible performance impact
• Works with all connection types: HTTP, WebSocket, and embedded databases

Learn More

For a complete example with configuration options and best practices, see examples/logfire/.

Contributing

Contributions to this library are welcome! If you encounter issues, have feature requests, or want to make improvements, feel free to open issues or submit pull requests.

If you want to contribute to the Github repo please read the general contributing guidelines on concepts such as how to create a pull requests here.

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.