surrealdb/surrealdb.py

 

The official SurrealDB SDK for Python.

       

     

surrealdb.py

The official SurrealDB SDK for Python.

Documentation

View the SDK documentation here.

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, RecordID, Table

# 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 (sync query() returns a builder - call .execute() to run it)
    db.query("""
    insert into person {
        user: 'me',
        password: 'very_safe',
        tags: ['python', 'documentation']
    };
    """).execute()

    # Read - .first() returns the first statement's result (the rows)
    print(db.query("select * from person").first())
    
    # Update
    print(db.query("""
    update person content {
        user: 'you',
        password: 'more_safe',
        tags: ['awesome']
    };
    """).execute())

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

CRUD builder pattern (v3.0)

create, update, upsert, delete, and insert return an awaitable (or lazy, for sync) builder. The builder exposes chainable clause methods that map directly to SurrealQL clauses.

from surrealdb import AsyncSurreal, RecordID, Table

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

    # Sugar: db.create(record, data) is equivalent to .content(data)
    await db.create(RecordID("person", "tobie"), {"name": "Tobie"})

    # Or use the builder explicitly
    await db.create(RecordID("person", "tobie")).content({"name": "Tobie"})
    await db.update(RecordID("person", "tobie")).replace({"name": "Tobie"})
    await db.update(RecordID("person", "tobie")).merge({"vip": True})
    await db.update(RecordID("person", "tobie")).patch([
        {"op": "replace", "path": "/vip", "value": False},
    ])

    # `insert` accepts a `relation=True` kwarg or a chained `.relation()`
    await db.insert(Table("likes"), {"in": ..., "out": ...}, relation=True)
    await db.insert(Table("likes")).relation().content({"in": ..., "out": ...})

The builder is typed via @overload:

• RecordID target -> dict[str, Value]
• Table target -> list[Value]
• str target -> Value (a record-id string returns a dict; a table-name string returns a list - the type checker can't tell them apart, so falls back to Value)

select() (async and sync) always runs eagerly and unwraps single records:

• select(RecordID(...)) (or a "table:id" string) -> dict[str, Value] | None (None when the record does not exist)
• select(Table(...)) (or a bare table-name string) -> list[Value]

row = await db.select(RecordID("person", "tobie"))  # dict | None
rows = await db.select(Table("person"))             # list

Mapping rows to a model (into=)

Pass the keyword-only into= argument to map each returned record onto a model class - a dataclass, a pydantic BaseModel, or any class whose constructor accepts the record's fields as keyword arguments. The return type is narrowed precisely per overload: a single-record target resolves to Model (or Model | None), a table target to list[Model].

from dataclasses import dataclass

@dataclass
class Person:
    id: RecordID
    name: str

# select: single record -> Person | None, table -> list[Person]
person = await db.select(RecordID("person", "tobie"), into=Person)  # Person | None
people = await db.select(Table("person"), into=Person)              # list[Person]

# create / update / upsert / delete map the written record(s) too
created = await db.create(RecordID("person", "tobie"), {"name": "Tobie"}, into=Person)
updated = await db.update(Table("person"), {"active": True}, into=Person)  # list[Person]

# insert maps the inserted records
inserted = await db.insert(Table("person"), [{"name": "A"}], into=Person)  # list[Person]

# the no-data builder form carries the model through its clause methods
p = await db.create(RecordID("person", "jaime"), into=Person).merge({"name": "Jaime"})

# map each ROW of a single query statement with into(Model, rows=True)
rows = await db.query("SELECT * FROM person").into(Person, rows=True)  # list[Person]

Sync connections take the same into= argument and run eagerly:

person = db.select(RecordID("person", "tobie"), into=Person)  # Person | None
created = db.create(RecordID("person", "tobie"), {"name": "Tobie"}, into=Person)
rows = db.query("SELECT * FROM person").into(Person, rows=True)  # list[Person]

Omitting into= leaves the raw dict / list[Value] results completely unchanged.

Sync usage is eager - there is no await to defer to, so the connection methods run single-shot operations immediately and return the plain result. A builder is only handed back for the deferred no-data form so you can pick a clause; there are no magic methods, so a builder never auto-executes on bool(), ==, indexing, iteration, or attribute access.

from surrealdb import Surreal

with Surreal("ws://localhost:8000/rpc") as db:
    db.signin({"username": "root", "password": "root"})
    db.use("ns", "db")

    # Passing data runs immediately and returns the created record dict.
    tobie = db.create(RecordID("person", "tobie"), {"name": "Tobie"})

    # No-data form returns a builder; a terminal clause method runs it.
    out = db.create(RecordID("person", "alice")).merge({"name": "Alice"})

    # Clause-less run: call .execute() explicitly.
    empty = db.create(RecordID("person", "bob")).execute()

    # select() and delete() always run eagerly and return the result.
    row = db.select(RecordID("person", "tobie"))  # dict | None
    db.delete(RecordID("person", "bob"))

    # query() returns a builder; call .execute()/.first()/.into().
    db.query("DELETE temp_data;").execute()

Thread safety

The no-data sync builder guards its cache with a per-builder lock so calling .execute() from multiple threads issues exactly one RPC. It is not safe for concurrent reconfiguration though — calling .merge() on one thread while another calls .execute() is a race on the builder's clause/data state that the lock does not cover. Treat builders as single-shot, single-owner values; pass the realised result between threads, not the builder itself.

The underlying BlockingWsSurrealConnection is itself thread-safe (it serialises send/recv with an internal lock), so sharing a connection across threads and issuing per-thread operations against it is fine.

Async cancellation and server truth

If you cancel() an async task that's awaiting an in-flight builder, the SDK does the right thing on the client side: the cache is reset so fresh callers retry, and concurrent peer awaiters see a SurrealError rather than a phantom CancelledError they didn't request.

What it cannot do is roll back the server. Once an RPC has reached SurrealDB, the operation may still complete server-side even after the client cancels. For mutations this means cancellation is not an abort — re-read the affected records before assuming "nothing happened", or wrap mutations in a BEGIN ... COMMIT block via query() if you need atomic rollback semantics.

Multi-statement queries and transactions (issue #232 fix)

query() always returns a list[Value] - one entry per statement, even for a single statement - so multi-statement queries and BEGIN ... COMMIT blocks never silently drop results. Use .first() for the first statement's result (or None when there are no statements).

rows = await db.query("SELECT * FROM person")       # [people_list]
first = await db.query("SELECT * FROM person").first()  # people_list
many = await db.query(
    "SELECT * FROM person; SELECT count() FROM person GROUP ALL"
)
# many is [people_list, count_list]

# Sync: query() returns a builder - run it explicitly.
rows = db.query("SELECT * FROM person").execute()   # [people_list]
first = db.query("SELECT * FROM person").first()    # people_list

You can also map the N statement results onto a dataclass via .into():

from dataclasses import dataclass

@dataclass
class Stats:
    created: dict
    all_people: list
    count: int

result = await db.query(
    "CREATE person:tobie SET name = 'Tobie';"
    "SELECT * FROM person;"
    "SELECT count() FROM person GROUP ALL"
).into(Stats)

Or map each row of a single statement's result onto a model with .into(Model, rows=True), which returns list[Model]:

people = await db.query("SELECT * FROM person").into(Person, rows=True)  # list[Person]

For the raw server response (status, time, error per statement), keep using query_raw().

Client-side transactions and sessions

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

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

    # Create a session
    session = await db.new_session()
    await session.use("ns", "db")

    # Start a transaction on the session
    txn = await session.begin_transaction()
    await txn.create(RecordID("account", "alice"), {"balance": 100})
    await txn.update(RecordID("account", "bob")).merge({"balance": 50})

    # Commit (or call `await txn.cancel()` to roll back)
    await txn.commit()

    await session.close_session()

The same CRUD builder, query, and run() API is available on both AsyncSurrealSession / BlockingSurrealSession and AsyncSurrealTransaction / BlockingSurrealTransaction.

run() - calling SurrealDB functions

result = await db.run("fn::increment", [1])
greeting = await db.run("fn::greet", ["world"])

Live queries

Live queries let you subscribe to changes on a table and receive a notification whenever a record is created, updated, or deleted. They are a WebSocket-only feature (ws:// or wss://).

The API is three methods:

• live(table, diff=False) - start a live query on a table and return its UUID. Pass diff=True to receive JSON Patch diffs instead of full records.
• subscribe_live(query_uuid) - return a generator (async generator for the async client) that yields notification dicts. Each notification has an "action" ("CREATE", "UPDATE", or "DELETE") and a "result" (the affected record).
• kill(query_uuid) - stop a running live query.

You can also start a live query through query("LIVE SELECT * FROM ..."), which returns the same UUID you can pass to subscribe_live().

Async

import asyncio
from surrealdb import AsyncSurreal

async def main():
    # Connection that owns the subscription.
    async with AsyncSurreal("ws://localhost:8000/rpc") as db:
        await db.signin({"username": "root", "password": "root"})
        await db.use("ns", "db")

        live_id = await db.live("person")           # -> UUID
        subscription = await db.subscribe_live(live_id)

        # Drive the mutation on a SEPARATE connection (see caveats below).
        async with AsyncSurreal("ws://localhost:8000/rpc") as writer:
            await writer.signin({"username": "root", "password": "root"})
            await writer.use("ns", "db")
            await writer.create("person", {"name": "Jaime"})

        # Wait for the notification (guard with a timeout in real code).
        notification = await asyncio.wait_for(subscription.__anext__(), timeout=10)
        print(notification["action"])   # "CREATE"
        print(notification["result"])   # the created record

        await db.kill(live_id)

asyncio.run(main())

Blocking

from surrealdb import Surreal

with Surreal("ws://localhost:8000/rpc") as db:
    db.signin({"username": "root", "password": "root"})
    db.use("ns", "db")

    live_id = db.live("person")            # -> UUID
    subscription = db.subscribe_live(live_id)

    # Mutate on a SEPARATE connection so the notification can arrive.
    with Surreal("ws://localhost:8000/rpc") as writer:
        writer.signin({"username": "root", "password": "root"})
        writer.use("ns", "db")
        writer.create("person", {"name": "Jaime"})

    for notification in subscription:
        print(notification["action"], notification["result"])
        break                              # generator blocks for the next one

    db.kill(live_id)

Caveats

• Mutate on a separate connection. The connection that owns a subscription is busy receiving live notifications, so running CREATE/UPDATE/DELETE on that same connection races the query responses against the incoming notifications. Perform the mutations that should trigger notifications on a second connection (this is exactly what the test suite does).
• Blocking client: one subscriber per connection. The blocking subscribe_live() reads notifications straight off the socket, so a single blocking connection supports only one concurrent subscriber. Use a separate connection per live subscription (or the async client, which fans notifications out to per-subscriber queues).

Migrating from 2.x

v3.0 is a breaking change. Highlights:

Bare-string resource targets are now strictly validated against the safe-identifier pattern ([A-Za-z_][A-Za-z0-9_]*) so user-supplied strings can never be concatenated into the generated SurrealQL. Names with hyphens, spaces, or other special characters must be wrapped in Table(...) or RecordID(...), both of which are parameter-bound.

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.

Install the SDK using pip:

pip install surrealdb

Or install using uv:

uv add 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():
    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://, or surrealkv://) 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 in detail

• 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 - though typical practice is to start on a session), call begin_transaction() to obtain a Transaction whose builder calls all participate in the same transaction. Call commit() to apply, or cancel() to roll back.

On HTTP or embedded connections, attach(), detach(), begin(), commit(), cancel(), and new_session() raise UnsupportedFeatureError 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

Usage:

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/.

Spectron

Spectron is a memory service, and its client is bundled with surrealdb. It is no longer re-exported at the top level - import it from its own submodule:

from surrealdb.spectron import Spectron, AsyncSpectron

with Spectron(
    context="acme-prod",
    endpoint="https://api.spectron.example",
    api_key="sk-spec-...",
) as memory:
    memory.remember("I work at Acme as CTO")
    hits = memory.recall("what do I do at Acme")
    print(hits.hits)

Spectron is synchronous (backed by requests); AsyncSpectron is the await-able equivalent (backed by aiohttp). See src/surrealdb/spectron/README.md for the full client documentation.

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.