featuremesh 0.2.0

FeatureQL local transpiler and client for managed FeatureMesh infrastructure.

FeatureMesh

FeatureQL transpiler and client. Translate and execute FeatureQL queries locally or via managed FeatureMesh infrastructure.

Quick Start

Works immediately after install -- no server, no account, no config:

pip install featuremesh

from featuremesh import BatchClient

client = BatchClient()

result = client.query("""
    SELECT
        F1 := 1,
        F2 := 2,
        F3 := F1 + F2;
""")

print(result.dataframe)
#    F1  F2  F3
# 0   1   2   3

For common patterns and discovery commands, run featuremesh.help() in a Python shell.

How It Works

FeatureMesh has two independent axes:

Transpilation + Persistence -- where FeatureQL is translated and feature definitions are stored:

• Local (default): bundled engine + SQLite. No network, no account.
• Managed: FeatureMesh infrastructure. Requires an access token.

Execution -- who runs the final SQL:

• BatchClient: your database runs it (DuckDB, Trino, BigQuery, DataFusion). For analytics, ETL, experimentation.
• ServingClient: FeatureMesh's engine runs it (DataFusion). For real-time serving. Managed-only.

Managed Mode

For team collaboration with shared feature definitions and access control:

from featuremesh import BatchClient, Registry, set_default

set_default("registry", Registry.MANAGED)

client = BatchClient(
    access_token="your_access_token",  # from https://console.featuremesh.com
    sql_executor=your_sql_executor,
)

Real-Time Serving

For low-latency feature serving via FeatureMesh's managed engine:

from featuremesh import Registry, ServingClient, set_default

set_default("registry", Registry.MANAGED)
client = ServingClient(access_token="your_access_token")
result = client.query("SELECT ...")

Jupyter Notebooks

%load_ext featuremesh

from featuremesh import BatchClient, set_default
set_default("client", BatchClient())

Then use %%featureql in cells. Options: --show-sql, --debug, --hide-dataframe, --show-slt, --hook VARIABLE.

Result Objects

Method	Returns	Key fields
client.query(fql)	QueryResult	.dataframe, .sql, .success, .errors, .warnings
client.translate(fql)	TranslateResult	.sql, .success, .errors, .warnings

More

• Documentation: featuremesh.com/docs
• Quick reference: featuremesh.help() in Python
• FeatureQL discovery: SHOW FUNCTION SIGNATURES, SHOW DOCS, SHOW FUNCTION TESTS (run as queries)
• Support: [e-mail]

License

FeatureMesh Proprietary License 1.0 Copyright (c) 2026 FeatureMesh