supabase-py-lite 1.0.0

In-memory Supabase client for testing. Drop-in replacement for supabase-py backed by SQLite.

supabase-py-lite

In-memory Supabase client for testing. Zero infrastructure, drop-in replacement for supabase-py.

Like Qdrant's client(":memory:"), but for Supabase.

from supabase_py_lite import create_client

supabase_client = create_client(":memory:")

supabase_client.table("users").insert({"id": 1, "name": "Alice"}).execute()

result = supabase_client.table("users").select("*").eq("name", "Alice").single().execute()
print(result.data)  # {"id": 1, "name": "Alice"}

Why?

• No Docker — no supabase start, no containers, no waiting
• No network — everything runs in-process with SQLite
• No config — one line to get a working client
• Same API — mirrors supabase-py's query builder interface
• Auto-creates tables — just insert data, tables and columns appear automatically

Install

pip install supabase-py-lite

Usage

from supabase_py_lite import create_client

# In-memory (for testing)
supabase_client = create_client(":memory:")

# File-based (for persistence)
supabase_client = create_client("./my_app.db")

Insert

supabase_client.table("users").insert({"id": 1, "name": "Alice", "age": 30}).execute()

# Batch insert
supabase_client.table("users").insert([
    {"id": 2, "name": "Bob", "age": 25},
    {"id": 3, "name": "Charlie", "age": 35},
]).execute()

Select

# All rows
res = supabase_client.table("users").select("*").execute()

# Specific columns
res = supabase_client.table("users").select("id, name").execute()

# With count
res = supabase_client.table("users").select("*", count="exact").execute()
print(res.count)  # 3

# Single row
res = supabase_client.table("users").select("*").eq("id", 1).single().execute()
print(res.data)  # {"id": 1, "name": "Alice", "age": 30}

# Maybe single (returns None instead of raising)
res = supabase_client.table("users").select("*").eq("id", 999).maybe_single().execute()
print(res.data)  # None

Filters

.eq("col", value)        # col = value
.neq("col", value)       # col != value
.gt("col", value)        # col > value
.gte("col", value)       # col >= value
.lt("col", value)        # col < value
.lte("col", value)       # col <= value
.in_("col", [a, b])      # col IN (a, b)
.like("col", "A%")       # col LIKE 'A%'
.ilike("col", "a%")      # case-insensitive LIKE
.is_("col", None)        # col IS NULL
.contains("col", val)    # JSON array contains val

# Chain them
res = (
    supabase_client.table("users")
    .select("*")
    .gte("age", 25)
    .lte("age", 35)
    .order("age", ascending=False)
    .limit(10)
    .execute()
)

Update

supabase_client.table("users").update({"name": "Alicia"}).eq("id", 1).execute()

Delete

supabase_client.table("users").delete().eq("id", 1).execute()

Upsert

supabase_client.table("users").upsert({"id": 1, "name": "Alice v2"}).execute()

# Custom conflict column
supabase_client.table("users").upsert(
    {"email": "[e-mail]", "name": "A"}, on_conflict="email"
).execute()

JSON columns

supabase_client.table("posts").insert({
    "id": 1,
    "meta": {"tags": ["python", "supabase"], "draft": False}
}).execute()

res = supabase_client.table("posts").select("*").eq("id", 1).execute()
print(res.data[0]["meta"]["tags"])  # ["python", "supabase"]

Foreign Keys

Register FK relationships with define_foreign_key, then use embedded resource syntax in .select() to join related data — exactly like Supabase.

# posts.user_id -> users.id
supabase_client.define_foreign_key("posts", "user_id", "users")

# comments.post_id -> posts.id
supabase_client.define_foreign_key("comments", "post_id", "posts")

Many-to-one (FK on current table → embeds as a single object):

res = supabase_client.table("posts").select("id, title, users(name, email)").execute()
# [{"id": 1, "title": "Hello", "users": {"name": "Alice", "email": "[e-mail]"}}, ...]

One-to-many (FK on related table → embeds as a list):

res = supabase_client.table("posts").select("id, title, comments(body)").execute()
# [{"id": 1, "title": "Hello", "comments": [{"body": "Great post!"}, ...]}, ...]

Wildcard on main table:

res = supabase_client.table("posts").select("*, users(name)").execute()

Alias — rename the embedded key in the result:

res = supabase_client.table("posts").select("id, author:users(name)").execute()
# [{"id": 1, "author": {"name": "Alice"}}, ...]

Hint — explicitly specify which FK column to use (useful when multiple FKs point to the same table):

res = supabase_client.table("posts").select("id, users!user_id(name)").execute()

Multiple embeds in one query:

res = supabase_client.table("posts").select("id, users(name), comments(body)").execute()

The FK join column is added to the query automatically if needed and stripped from the result unless you selected it explicitly.

Use in tests

import pytest
from supabase_py_lite import create_client

@pytest.fixture
def db():
    supabase_client = create_client(":memory:")
    yield supabase_client
    supabase_client.close()

def test_create_user(db):
    db.table("users").insert({"id": 1, "name": "Alice"}).execute()
    res = db.table("users").select("*").eq("id", 1).single().execute()
    assert res.data["name"] == "Alice"

Supported API

Method	Status
.from_() / .table()	✅
.select()	✅
.insert()	✅
.update()	✅
.delete()	✅
.upsert()	✅
.eq/.neq/.gt/.gte/.lt/.lte	✅
.in_/.like/.ilike/.is_	✅
.contains	✅
.order/.limit/.offset/.range	✅
.single/.maybe_single	✅
count="exact"	✅
JSON columns	✅
Auto-create tables	✅
Foreign key joins	✅
.or_()	✅
.not_()	✅

Development

git clone https://github.com/acelikyurek/supabase-py-lite
cd supabase-py-lite
uv sync --group dev
uv run pytest -v

License

MIT