The data analysis problem Byaan tries to solve isn't that teams lack tools—it's that AI analysts often start from zero every time someone asks a question, forget context, and guess wrong about which tables matter. Byaan positions itself as an open-source "AI data agent" that learns your database schema, metric definitions, and business rules over time, then applies that accumulated knowledge to answer questions in plain English. It runs locally or self-hosted, connects to multiple database types, and includes read-only safeguards to prevent accidental writes. The project describes this as a system where data knowledge compounds rather than being rebuilt in each conversation.

Key capabilities

Byaan supports PostgreSQL, MongoDB, MySQL, SQLite, MSSQL, ODBC, and file uploads including CSV, Excel, Parquet, and JSON. It translates natural language into SQL, Mongo queries, or other database-specific languages, then surfaces results as interactive dashboards with dynamic filters. Users can bring their own model providers—Claude, OpenAI, Azure OpenAI, AWS Bedrock, Groq, OpenRouter, or xAI—rather than being locked into a hosted service. The read-only guardrails explicitly validate queries to block known destructive operations across SQL and NoSQL databases. A Model Context Protocol (MCP) server integration lets coding assistants query databases through the same interface. Teams can annotate schemas, save queries, define metrics, and export dashboards as standalone HTML or PDF.

How it works

At its core, Byaan is a Python project that combines a FastAPI backend with a React frontend, containerized via Docker for deployment. The community version uses SQLite for lightweight, single-user setups, while the team version bundles PostgreSQL for multi-user collaboration. It connects directly to your chosen model provider and database, meaning query traffic doesn't route through external infrastructure in local deployments. The MCP integration allows Claude Code, Cursor, and similar tools to interact with connected databases through Byaan's learned context. Development is streamlined with make commands for building, running, and managing services.

Who it fits (and who it doesn't)

Byaan works best for technical teams that want to keep their data private while giving multiple users access to an evolving, shared understanding of their databases. The team version adds authentication, RBAC, Slack integration, and automatic HTTPS—making it suitable for production use. Individual contributors or small teams comfortable with Docker will find the community version adequate. It's less compelling if you're already locked into a commercial platform like Metabase or dbt, or if you prefer GUI-only query builders without the complexity of managing an additional service. The 63-star GitHub repository suggests active development but a relatively small adoption footprint so far.

Setup, briefly

Installation varies by deployment: the Mac app downloads as a standard .dmg, the community version requires cloning the repository and running docker compose up -d, and the team version uses a single install script. All approaches require basic familiarity with terminal commands and Docker. Details for each deployment are documented in the project's self-hosted docs.

Context matters

In the self-hosted analytics space, Byaan sits alongside tools like Metabase, Redash, and Superset—most of which focus on query building and visualization rather than conversational analysis with memory. Its emphasis on compounding business context and MCP integration positions it as closer to a programmable data assistant than a traditional dashboard platform. The project's read-only design and local-first philosophy address security concerns that often derail AI experiments in regulated environments. The source is on GitHub.