nao
Open source analytics agent builder for context engineering and data exploration.
Pricing
About nao
nao is an open source analytics agent builder designed for data teams to create, evaluate, and deploy reliable analytics agents with their own data stack. It functions as an AI code editor directly connected to data warehouses, powered by an AI copilot with built-in understanding of data schemas and data-specific tools. The platform enables users to engineer agent context like a file system, incorporating databases, repositories, external sources, and documentation. A distinctive feature is its context engineering approach—optimizing agent performance by structuring context for reliability and efficiency. The platform also includes context reliability testing to measure performance through unit tests of questions converted to SQL. One limitation is that it requires setup and configuration of database connections and LLM keys for deployment.
At a glance
- Company
- Nao Labs (est. 2025)
- Platforms
- Web, Slack, Teams, WhatsApp, Telegram, Claude, Cursor, API
- API
- Available
- Integrations
- Slack, Teams, WhatsApp, Telegram, Claude, Cursor, Notion, dbt
- Last verified
- June 2026
Who It's For
- •Data teams and data engineers
- •Business users needing self-service analytics
- •Organizations with complex data warehouses
- •Companies prioritizing data security and self-hosting
- •Teams using dbt for data transformation
How It Works
- 1Initialize a project with nao init to create a file system-based context structure
- 2Synchronize existing context from databases, repositories, and external sources like Notion using nao sync
- 3Configure database connections and LLM settings in nao_config.yaml
- 4Test context reliability with nao test to measure performance metrics on questions converted to SQL
- 5Deploy the agent using nao chat UI for users to query data in plain English
- 6The AI copilot understands your data warehouse schema and uses configured tools to answer questions
How to Use nao
- 1Run nao init to set up your project and database connections
- 2Organize your context like a file system—add data, metadata, rules, documentation, and tools
- 3Use nao sync to automatically pull context from databases, dbt repositories, and external sources
- 4Create unit tests with nao test to measure context reliability and efficiency
- 5Deploy your agent with nao chat to enable users to ask questions in plain English
- 6Alternatively, self-host your agent using your own LLM keys for maximum security
Key Features
- •Context engineering framework for structuring agent knowledge
- •File system-based context organization
- •Automatic context synchronization from databases, repositories, and external sources
- •Context reliability testing and monitoring
- •Multi-platform deployment (Slack, Teams, WhatsApp, Telegram, Claude, Cursor)
- •Self-hosting capability with custom LLM keys
- •Plain English natural language querying
- •nao IDE for local editing
- •Built-in data warehouse schema understanding
- •Unit testing for questions-to-SQL conversion
Use Cases
- •Data teams building reliable analytics agents for business users without SQL knowledge
- •Organizations deploying AI-powered data exploration across Slack, Teams, WhatsApp, or Telegram
- •Companies optimizing agent context for better performance and faster query responses
- •Teams integrating analytics agents into existing tools using nao MCP (Model Context Protocol)
- •Enterprises requiring self-hosted solutions with full data control and security
Pros & Cons
Advantages
- •100% open source with transparent, community-driven development
- •Context engineering approach optimizes both reliability and efficiency of analytics agents
- •Multi-platform deployment flexibility including Slack, Teams, and messaging apps
- •Self-hosting option ensures data stays within your infrastructure under your control
Disadvantages
- •Requires technical setup and configuration of database connections and LLM keys
- •Performance depends on quality of engineered context structure, requiring iterative optimization
- •Limited out-of-the-box setup compared to managed BI platforms
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Frequently Asked Questions
What is nao?
nao is an open source analytics agent builder designed for data teams to create, evaluate, and deploy reliable analytics agents with their own data stack. It functions as an AI code editor directly connected to data warehouses, powered by an AI copilot with built-in understanding of data schemas and data-specific tools.
How much does nao cost?
nao is free to use. A free trial is available.
Is nao free?
Yes, nao offers a free plan you can start with.
What are the best nao alternatives?
Popular nao alternatives include AirOps, AI2sql, SQLAI.ai.
What is nao used for?
nao is commonly used for Data teams building reliable analytics agents for business users without SQL knowledge, Organizations deploying AI-powered data exploration across Slack, Teams, WhatsApp, or Telegram, Companies optimizing agent context for better performance and faster query responses.
Does nao have an API?
Yes, nao offers an API for developers.
What platforms does nao support?
nao is available on Web, Slack, Teams, WhatsApp, Telegram, Claude, Cursor, API.
Information Accuracy
Please note: While we regularly update all tool information including descriptions, features, pricing, and other details, this information may change over time as tools evolve and update their offerings. For the most current and accurate information, we recommend visiting the official website directly. Our goal is to provide you with comprehensive and up-to-date information to help you make informed decisions.