💡Vanna
🤖 Chat with your SQL database 📊. Accurate Text-to-SQL Generation via LLMs using Agentic Retrieval 🔄.
⭐ Very popular: 24k stars, gaining about 17 a week
View on GitHub ↗repo profile
momentum
durability
since we covered it
why it's a big deal
- Lets non-technical users chat with SQL databases through Slack, Streamlit, Flask, Chainlit, and more.
- Ships a strong ecosystem of adapters for OpenAI, ChromaDB, Pinecone, and other vector stores.
- Agentic RAG approach produces accurate text-to-SQL that improves as it learns your schema and queries.
under the hood
- A Python RAG pipeline: index documentation, then prompt an LLM to generate SQL.
- Modular, provider-agnostic architecture built around a VannaBase core.
- Deploys across Slack, web apps, and Jupyter notebooks.
our take from PR#13, 2025-07-23
star history
- PR#13 20k 2025-07-23
- now 24k + 4k since first covered
curve is sampled from GitHub's star history, plus our own daily readings since we covered it; the dashed stretch is before we first covered it, the solid line since. figures at coverage are the numbers we printed then (approx.), current count is live.
understory
Better known than its recent output, coasting a little on attention.
- output, commits & releases
- clout, star velocity
output = commits & releases; clout = star velocity, both 0 to 100 monthly indices; the gap where output runs above clout is the understory. The understory →
covered in
-
Natural-Language Interface for SQL Databases
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compare these →- 📊 Wren AI
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17k ACTIVE - 🗜️ headroom
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Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. 20% fewer tokens for coding agents, 60-95% fewer tokens for JSON, same answers. Library, proxy, MCP server.
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16k ACTIVE
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