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Federal Reserve Python Packages

Python packages with the GitHub topic federal-reserve. Sorted by monthly downloads, with stars and forks.
nikhilxsunder
fedfred

A feature-rich python package for interacting with the Federal Reserve Bank of St. Louis Economic Database: FRED

36K 9 0
beepboop2025
seiche

Free AGPL funding and market-context research for international workflows. A LiquiLens product, connected to institution evidence and Undertow liquidity via API/MCP.

11K 1 0
RblxDev-ALS
nss-engine

The U.S. Treasury yield curve fitted weekly since 1990: term premium, breakeven inflation and recession odds, tested against the Fed's own numbers.

691 0 1
seokhoonj
fred-client

Read economic data from the Federal Reserve's FRED, ALFRED, and GeoFRED APIs: curated indicators, series, releases, categories, sources, tags, and maps.

592 0 0
beepboop2025
openbb-seiche

Free AGPL funding and market-context research for international workflows. A LiquiLens product, connected to institution evidence and Undertow liquidity via API/MCP.

547 1 0
mcpwright
mcpwright-fred

MCP server for FRED® economic data — 800k+ time series + ALFRED vintage data (the numbers before revisions)

467 0 0
AlexanderRicht
fredbrain

A Python Package for retrieving Federal Reserve Economic Data at Scale and feeding it to OpenAI

433 5 1
hgus107
us-macro-mcp

MCP server exposing 58 US macroeconomic indicators from FRED - inflation, employment, growth, housing, rates, recession signals and the FOMC calendar.

428 0 0
avelkoski
frb

Python Client for Interfacing with the Federal Reserve Bank of St. Louis' Economic Data API (FRED®)

421 181 41
antonio-hickey
fedpy

Python library for all things Federal Reserve related

379 9 0
cfdude
mcp-fred

MCP server for the FRED API for Federal Reserve

245 0 1
aamnv
fred-cli

CLI tool to interface with the FRED (Federal Reserve Economic Data) API.

67 10 1
nikhilxsunder
us-labor

A feature-rich python package for interacting with the Federal Reserve Bank of St. Louis Economic Database: FRED

51 9 0
    • Original data from PyPI, GitHub, and OSV; downloads served through ClickHouse
    • Sources & methodology