Up and running in under 2 minutes

python --version   # 3.9 or higher required
pip --version      # 20.0 or higher

# All providers (installs all optional dependencies)
pip install mockmesh

# Install only what you need
pip install mockmesh[aws]              # boto3 + botocore
pip install mockmesh[azure]            # azure-core + azure-* SDKs
pip install mockmesh[gcp]              # google-cloud-* SDKs
pip install mockmesh[aws,azure,gcp]    # multi-cloud
pip install mockmesh[kafka]            # confluent-kafka + kafka-python
pip install mockmesh[rabbitmq]         # pika
pip install mockmesh[sql]              # psycopg2, pymysql, asyncpg, etc.
pip install mockmesh[mongodb]          # pymongo
pip install mockmesh[redis]            # redis-py

# Recommended: virtual environment
python -m venv .venv && source .venv/bin/activate
pip install mockmesh

# Verify version
python -c "import mockmesh; print(mockmesh.__version__)"
# 0.0.1

import mockmesh

# Minimal — auto-detects providers, intercepts everything
engine = mockmesh.initialize()

# With custom config + dedicated workspace
engine = mockmesh.initialize(
    config_path    = "config/custom_overrides.json",
    storage_path   = "/tmp/mockmesh-local",
    fallback_mode  = "error",       # strict mode for tests
)

# With folder of per-service overrides (aws.json, azure.json, gcp.json, …)
engine = mockmesh.initialize(
    responses_path = "config/overrides/",
)

# Context manager — auto-shutdown on exit
with mockmesh.engine(fallback_mode="error") as mm:
    run_tests()

mockmesh.shutdown()   # manual cleanup if not using context manager

import boto3, mockmesh
engine = mockmesh.initialize()

# DynamoDB — stateful (Tier 3: storage-backed)
ddb = boto3.client("dynamodb", region_name="us-east-1")
ddb.put_item(TableName="Users", Item={"id":{"S":"u1"},"name":{"S":"Alice"}})
item = ddb.get_item(TableName="Users", Key={"id":{"S":"u1"}})
print(item["Item"]["name"]["S"])   # → Alice

# S3 — bytes saved to .mockmesh/blob/
s3 = boto3.client("s3", region_name="us-east-1")
s3.put_object(Bucket="assets", Key="logo.png", Body=b"\x89PNG...")
obj = s3.get_object(Bucket="assets", Key="logo.png")
print(obj["Body"].read()[:4])      # → b'\x89PNG'

# MongoDB — pymongo intercepted, in-memory store
import pymongo
db = pymongo.MongoClient().mydb
db.users.insert_one({"name": "Alice", "age": 30})
print(db.users.find_one({"name": "Alice"}))  # → {"name": "Alice", ...}

# GCP — google-cloud SDK intercepted via HTTP dispatch
from google.cloud import storage
client = storage.Client()
bucket = client.bucket("my-gcs-bucket")
blob = bucket.blob("data.json")
blob.upload_from_string('{"key": "value"}')

# conftest.py
import pytest, mockmesh

@pytest.fixture(scope="session", autouse=True)
def mock_cloud():
    engine = mockmesh.initialize(
        config_path    = "tests/fixtures/overrides.json",
        storage_path   = "/tmp/mm-test",
        fallback_mode  = "error",
    )
    yield engine
    mockmesh.shutdown()

# Full isolation per test (context manager):
def test_order_flow(tmp_path):
    with mockmesh.engine(
        config_path  = "tests/fixtures/overrides.json",
        storage_path = tmp_path,
    ) as mm:
        import boto3
        ddb = boto3.client("dynamodb", region_name="us-east-1")
        ddb.put_item(TableName="Orders",
                     Item={"id":{"S":"o1"},"total":{"N":"49.99"}})
        order = ddb.get_item(TableName="Orders",
                             Key={"id":{"S":"o1"}})["Item"]
        assert order["total"]["N"] == "49.99"

        # Pre-seed storage directly
        mm.storage.s3_put("assets", "config.json", b'{"env":"test"}')