# getnao/nao

👾 nao is an open source analytics agent. (1) Create context with nao-core cli, (2) deploy nao chat interface for everyone

Repository: https://github.com/getnao/nao
Canonical: https://ross.abutalabs.com/products/nao
Homepage: https://docs.getnao.io
Language: TypeScript
License: NOASSERTION
License Family: other
Topics: agentic-analytics, analytics, analytics-engineering, bigquery, business-intelligence, chat-with-your-data, context-engineering, data, data-analysis, data-analyst, data-engineering, databricks, postgresql, self-hosted, snowflake, sql, text-to-sql, slackbot, slack-bot
Last push: 2026-08-26T14:43:44+00:00

## Health v2 (maintenance only)
Score: 82/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 98, longevity 17
- inputs: {"age_days": 246, "days_push": 7, "days_rel": 13, "gap_med": 2, "n_releases_24m": 54}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1583, forks 243 (observed 2026-08-28T04:05:07.302595+00:00)

## What it is
nao is an open-source framework for building and deploying analytics agents that answer natural-language questions about data. It combines a Python CLI (nao-core) for building agent context (data, metadata, rules, docs, MCPs) with a deployable chat interface for business users to run analytics and visualize results.

## Use cases
- chat with your data warehouse in natural language
- build a text-to-sql analytics agent for my team
- self-host a business intelligence chatbot over BigQuery or Snowflake
- create a Slack bot that answers data questions
- version and unit test an analytics agent before deploying
- let non-technical users query Postgres without writing SQL
- add data visualization to a chat-based analytics interface

## When to choose
- you want an open-source, self-hosted alternative to hosted analytics copilots
- your data team needs to control agent context, rules, and reliability testing
- you need natural-language analytics across multiple warehouses (BigQuery, Snowflake, Databricks, Postgres)
- you want to expose data insights to business users via chat UI or Slack

## When to avoid
- you need a traditional BI dashboard tool with drag-and-drop report builders
- you cannot use LLMs or lack API keys for language models
- you need fully deterministic, non-agentic SQL reporting
- your project requires a permissively licensed dependency (license is custom/unspecified)

## Facets
- artifact type: framework
- maturity: active
- function: agent-framework, chatbot, data-visualization, cli, rag, llm-inference, analytics
- domain: analytics, data-science, artificial-intelligence, large-language-models, self-hosted, developer-tools
- platform: python, self-hosted, cli
- tags: text-to-sql, analytics-agent, context-engineering, business-intelligence, chat-with-your-data, slack-bot, data-warehouse, bigquery, snowflake, databricks, postgresql, ai-agents, typescript, web-server, docker

## Member repositories
- getnao/nao (main) score 82

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:07.302595+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 2026-08-30T03:56:00.365788+00:00, confidence not recorded.
  - readme: https://github.com/getnao/nao (fetched 2026-08-28T04:05:07.302595+00:00, sha 9111b516e37d)
  - homepage: https://docs.getnao.io (fetched 2026-08-29T11:26:38.044249+00:00, sha b48153e3f16c)
  - site_page: https://docs.getnao.io/nao-agent/quickstart (fetched 2026-08-29T11:26:38.047132+00:00, sha 37dd80890014)
- Data as of 2026-08-30T08:39:29.467469+00:00.