# hitsz-ids/airda

airda(Air Data Agent)是面向数据分析的多智能体，能够理解数据开发和数据分析需求、理解数据、生成面向数据查询、数据可视化、机器学习等任务的SQL和Python代码

Repository: https://github.com/hitsz-ids/airda
Canonical: https://ross.abutalabs.com/products/airda
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2025-01-07T03:43:46+00:00

## Health v2 (maintenance only)
Score: 17/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 69
- inputs: {"age_days": 966, "days_push": 603, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1785, forks 274 (observed 2026-08-28T04:05:36.003558+00:00)

## What it is
airda (Air Data Agent) is a Python-based multi-agent system for data analysis that understands natural language data requirements and generates SQL and Python code for querying, visualization, and machine learning tasks. It coordinates specialized agents (data lookup, SQL generation, code generation, visualization) through multi-turn dialogue with self-debugging capabilities.

## Use cases
- generate SQL from natural language questions
- find the right tables across hundreds of database tables
- analyze business metrics with code generation
- visualize query results as charts and dashboards
- chat with a MySQL database in plain language
- auto-debug generated data analysis code
- build data APIs and metric dashboards from analysis results

## When to choose
- you need natural-language text-to-SQL over MySQL data sources
- you want a multi-agent pipeline that plans and executes data analysis tasks
- your team needs business-metric-aware data retrieval across many tables
- you want a pip-installable CLI agent backed by MongoDB for knowledge storage

## When to avoid
- you only support non-MySQL databases, since datasource support is currently MySQL-only
- you need production-grade chart generation or task planning, which are still unfinished roadmap items
- you want a fully self-contained tool without external dependencies like MongoDB and an OpenAI-compatible LLM endpoint
- you need a no-code GUI product rather than a developer-oriented library and CLI

## Facets
- artifact type: library
- maturity: active
- function: agent-framework, rag, llm-inference, data-visualization, nlp, search-engine, cli
- domain: data-science, analytics, large-language-models, databases, data-visualization
- platform: python, cli, cross-platform
- tags: text-to-sql, multi-agent, data-analysis, chatbot, mongodb, mysql, embedding-model, self-debugging, ai-agents, docker

## Member repositories
- hitsz-ids/airda (main) score 17

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:36.003558+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:24:12.554325+00:00, confidence not recorded.
  - readme: https://github.com/hitsz-ids/airda (fetched 2026-08-28T04:05:36.003558+00:00, sha 04170e08a674)
- Data as of 2026-08-30T08:39:29.467469+00:00.