SQLsaber
SQLsaber is an open-source agentic SQL assistant. Think Claude Code but for SQL.
Ask questions about databases, SQLite/DuckDB files, and CSVs in plain English from your terminal or Python code. SQLsaber reads your schema, writes SQL, executes read-only queries by default, and explains the results.
Featured in research: SQLsaber appears in an ACM Conference on AI and Agentic Systems '26 paper. Read the paper.
Quickstart
# Recommended
uv tool install sqlsaber
Try SQLsaber with the sample SQLite database:
curl -L -o legislators.db https://github.com/SarthakJariwala/sqlsaber/raw/refs/heads/main/legislators.db
saber -d ./legislators.db "How many VPs became president by election in the 20th century?"
Or connect your own database:
saber db add analytics
saber "Show me revenue by month"
On first launch, SQLsaber walks you through connecting a database and setting up authentication.
Use it with your data
# Interactive mode
saber
# Single question
saber "show me users who signed up this week"
# Pipe from stdin
echo "top 10 customers by revenue" | saber
# Use a saved database connection
saber -d analytics "count active subscriptions"
# Use a connection string directly
saber -d "postgresql://user:pass@localhost:5432/mydb" "count users"
# Query local files
saber -d ./customers.csv "How many customers are from each state?"
saber -d ./warehouse.duckdb "Show me the latest partition"
# Connect multiple databases in one session
saber -d sales -d analytics "Compare last month's revenue to web sessions"
Why SQLsaber?
- No context switching — Stay in your terminal, ask questions, get answers.
- Schema-aware — Automatically discovers tables, columns, indexes, comments, and relationships.
- Safe by default — Runs read-only queries unless you explicitly enable dangerous mode.
- Works with your stack — PostgreSQL, MySQL, SQLite, DuckDB, and CSV files.
- Remembers your work — Resume previous analysis with conversation threads.
- Learns your business context — Store KPI definitions, SQL patterns, and domain notes in a searchable knowledge base.
- Flexible model support — Use Anthropic, OpenAI, Google, Groq, Mistral, Cohere, Hugging Face, and other supported providers.
Common workflows
| Workflow | Command |
|---|---|
| Explore data interactively | saber |
| Ask a one-off question | saber "monthly active users" |
| Analyze a CSV | saber -d ./customers.csv "customers by state" |
| Compare multiple databases | saber -d sales -d analytics "compare revenue to traffic" |
| Save a KPI definition | saber knowledge add "Revenue KPI" "Recognized revenue from shipped orders only" |
| Resume previous analysis | saber threads list then saber threads resume <id> |
| Use deeper reasoning | saber --thinking "analyze retention by cohort" |
Knowledge base
Save reusable business context so SQLsaber can answer consistently:
saber knowledge add \
"Revenue KPI" \
"Recognized revenue from shipped orders only" \
--sql "SELECT SUM(amount) FROM orders WHERE status = 'shipped'" \
--source "finance-wiki"
saber knowledge search "revenue shipped orders"
Knowledge entries are scoped per database and are discovered automatically when relevant.
Optional plugins
Install official plugins alongside SQLsaber:
# Render charts in your terminal
uv tool install --with sqlsaber-viz sqlsaber
# Delegate multi-step analysis to a sandboxed notebook agent (recommended)
uv tool install --with sqlsaber-notebook sqlsaber
# Run one-off Python snippets in a remote sandbox
uv tool install --with sqlsaber-sandbox sqlsaber
# Install all official analysis plugins
uv tool install --with sqlsaber-viz,sqlsaber-notebook,sqlsaber-sandbox sqlsaber
Python SDK
Use the same SQLsaber agent from Python scripts, notebooks, web apps, or pipelines:
import asyncio
from sqlsaber import SQLSaber, SQLSaberOptions
async def main() -> None:
async with SQLSaber(options=SQLSaberOptions(database="sqlite:///my.db")) as saber:
result = await saber.query("Top 5 customers by revenue")
print(result)
print(result.usage)
asyncio.run(main())
Or compose SQLsaber's tools into an agent you own:
from pydantic_ai import Agent
from sqlsaber import SqlTools
sql = SqlTools(database="sqlite:///my.db")
agent = Agent(
"anthropic:claude-sonnet-4-6",
instructions="You are my analytics copilot.",
capabilities=[sql],
)
async with agent: # opens and closes connections owned by SqlTools
result = await agent.run("Top 5 customers by revenue")
See the Capabilities guide for multi-database use, knowledge search, custom capabilities, and lifecycle details.
How it works
- Discovery — Lists tables and identifies relevant ones based on your question.
- Schema analysis — Introspects only the tables needed.
- Knowledge retrieval — Searches saved KPI definitions and SQL patterns when useful.
- Query generation — Writes SQL tailored to your database dialect.
- Execution — Runs the query with safety checks.
- Results — Formats the output with an explanation.
Documentation
Full docs at sqlsaber.com:
- Installation
- Getting Started
- Database Setup
- Running Queries
- Multiple Databases
- Knowledge Base
- Python SDK
- Command Reference
Contributing
Contributions welcome! Please open an issue first to discuss changes.
If you find SQLsaber useful, a ⭐ on GitHub helps others discover it.
License
Apache-2.0 — see LICENSE
1 maintainer
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