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What is openarg_backend?

colossus-lab/openarg_backend — explained in plain English

Analysis updated 2026-05-18

135PythonAudience · developerComplexity · 5/5Setup · hard

In one sentence

An AI pipeline that answers plain-language questions about Argentine government data by pulling from nine public data sources.

Mindmap

mindmap
  root((openarg backend))
    What it does
      Answers questions in plain language
      Pulls government data
      Cites sources and charts
    Pipeline
      Planner agent
      Data collector agents
      Analyst agent
      Writer agent
    Data sources
      Economic time series
      Central bank data
      Legislative records
      Public disclosures
    Tech stack
      FastAPI
      PostgreSQL
      Celery workers
      Claude and Gemini
    Audience
      Researchers
      Journalists
      Developers

Code map

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What do people build with it?

USE CASE 1

Ask natural-language questions about Argentine public spending, dollar rates, or central bank data and get a sourced answer.

USE CASE 2

Automatically collect and refresh government datasets from multiple public portals in the background.

USE CASE 3

Generate a database query from a plain-English question instead of writing SQL by hand.

USE CASE 4

Build a research or journalism tool on top of a multi-agent pipeline that cites its sources.

What is it built with?

PythonFastAPIPostgreSQLRedisCeleryLangGraphClaudeGemini

How does it compare?

colossus-lab/openarg_backendvanquishervohonor25/zapret-4.0rss3208/visiomaster
Stars135135134
LanguagePythonPythonPython
Setup difficultyhardmoderatehard
Complexity5/53/53/5
Audiencedevelopergeneralresearcher

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · hard Time to first run · 1h+

Requires Docker, a PostgreSQL and Redis stack, and API keys for AWS Bedrock or Anthropic.

The README does not state a license.

So what is it?

OpenArg Backend is the engine behind openarg.org, a website that answers plain-language questions about Argentine government data, things like public spending, dollar rates, central bank figures, and congressional records. You ask a question in normal words, and the system figures out where to look, pulls the relevant numbers, and writes back an answer with sources and charts. Under the hood, the question passes through a series of AI agents that each handle one job. One agent reads the question and decides which of nine different government data sources to check, ranging from economic time series and central bank indicators to legislative session transcripts and public officials' financial disclosures. Several agents then gather that data at the same time, another agent turns the question into a database query when needed, and a final agent writes up the findings with citations, a confidence level, and any charts the data supports. If the first attempt does not find enough information, the system can try again with a different approach. To avoid repeating expensive work, the system checks whether a very similar question has already been answered recently and reuses that answer instead of starting from scratch. Behind the scenes, background workers continuously scrape, download, and process new government datasets so the cached information stays current. The project is built with FastAPI for the web server, PostgreSQL for storing data, Redis and Celery for background processing, and Claude and Gemini language models to understand questions and write answers. It is organized in layers that separate the web interface, the core logic, and the connections to outside data sources and AI providers, which keeps each part easier to change independently. Getting it running requires Docker along with API keys for the AI providers and cloud services it depends on. The full README is longer than what was shown.

Copy-paste prompts

Prompt 1
Help me set up OpenArg Backend locally with Docker Compose and the required API keys.
Prompt 2
Explain how the Strategist, Researcher, Analyst, and Writer agents work together in this pipeline.
Prompt 3
Walk me through adding a new data connector to the nine existing government data sources.
Prompt 4
Show me how the semantic cache decides whether to reuse a previous answer instead of recomputing it.

Frequently asked questions

What is openarg_backend?

An AI pipeline that answers plain-language questions about Argentine government data by pulling from nine public data sources.

What language is openarg_backend written in?

Mainly Python. The stack also includes Python, FastAPI, PostgreSQL.

What license does openarg_backend use?

The README does not state a license.

How hard is openarg_backend to set up?

Setup difficulty is rated hard, with roughly 1h+ to a first successful run.

Who is openarg_backend for?

Mainly developer.

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