tinybirdco/llm-benchmark

Evaluate the accuracy of LLM generated outputs

[ICLR 2025 Oral] Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows

An open-source execution engine for AI-SQL and LLM-powered dataflow

🤖 Chat with your SQL database 📊. Accurate Text-to-SQL Generation via LLMs using Agentic Retrieval 🔄.

Data-engineering benchmark for coding agents (DuckDB + Snowflake dbt tasks).

Open-source text-to-SQL engine you tune and measure on your own database

XiYanSQL models for Text-to-SQL.

Agentic text-to-SQL SDK: hand the LLM one execute_sql tool and let it explore the schema, test queries, and self-correct — no RAG, no semantic layer. 20/20 on an 80-table Spider run. Built-in tracing and scenario support.

GenBI (Generative BI) for AI agents, an open-source, governed text-to-SQL through an open context layer that turns natural-language questions into trusted dashboards, charts, and SQL across 20+ data sources, such as BigQuery, Snowflake, PostgreSQL, ClickHouse, Amazon Redshift, Databricks and more.