Databao Agent

Talk to your data in plain English.
Ask questions → Get answers (Text, SQL, and interactive visual insights).

Website • Quickstart • Local models • Contributing • Discord

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🏆 Ranked #1 in the DBT track of the Spider 2.0 Text2SQL benchmark

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What is Databao Agent?

Databao Agent is an open-source AI agent that lets you query your data sources using natural language.

Simply ask:

• "Show me all German shows"
• "Plot revenue by month"
• "Which customers churned last quarter?"

Get back tables, charts, and explanations — no SQL or code needed.

Why choose Databao Agent?

Feature	What it means for you
Interactive outputs	Tables you can sort/filter and charts you can zoom/hover (Vega-Lite)
Simple, Pythonic API	thread.ask("question").df()just works
Python-native	Fits perfectly into existing data science and exploratory workflows
Natural language	Ask questions about your data just like asking a colleague
Broad DB support	PostgreSQL, MySQL, SQLite, DuckDB... anything SQLAlchemy supports
Auto-generated charts	Get Vega-Lite visualizations without writing plotting code
Local first	Use Ollama or LM Studio — your data never leaves your machine
Cloud LLM ready	Built-in support for OpenAI, Anthropic, and OpenAI-compatible APIs
Conversational	Maintains context for follow-up questions and iterative analysis

Installation

pip install databao

Supported data sources

• Pandas DataFrame
• PostgreSQL
• MySQL
• SQLite
• DuckDB

For PostgreSQL, MySQL, and SQLite, pass a SQLAlchemy Engine to add_db(). For DuckDB, pass DuckDBPyConnection.

Quickstart

1. Create a database connection (SQLAlchemy)

import os
from sqlalchemy import create_engine

user = os.environ.get("DATABASE_USER")
password = os.environ.get("DATABASE_PASSWORD")
host = os.environ.get("DATABASE_HOST")
database = os.environ.get("DATABASE_NAME")

engine = create_engine(
   f"postgresql://{user}:{password}@{host}/{database}"
)

2. Create a Databao agent and register sources

import databao
from databao import LLMConfig

# Option A - Local: install and run any compatible local LLM
# For list of compatible models, see "Local Models" below
# llm_config = LLMConfig(name="ollama:gpt-oss:20b", temperature=0)

# Option B - Cloud (requires an API key, e.g. OPENAI_API_KEY)
llm_config = LLMConfig(name="gpt-4o-mini", temperature=0)
agent = databao.new_agent(name="demo", llm_config=llm_config)

# Add your database to the agent
agent.add_db(engine)

3. Ask questions and materialize results

# Start a conversational thread
thread = agent.thread()

# Ask a question and get a DataFrame
df = thread.ask("list all german shows").df()
print(df.head())

# Get a textual answer
print(thread.text())

# Generate a visualization (Vega-Lite under the hood)
plot = thread.plot("bar chart of shows by country")
print(plot.code)  # access generated plot code if needed

Environment variables

Specify your API keys in the environment variables:

Variable	Description
OPENAI_API_KEY	Required for OpenAI models or OpenAI-compatible APIs
ANTHROPIC_API_KEY	Required for Anthropic models

Optional for local/OpenAI-compatible servers:

Variable	Description
OPENAI_BASE_URL	Custom endpoint (aka api_base_url in code)
OLLAMA_HOST	Ollama server address (e.g., 127.0.0.1:11434)

Local Models

Databao agent works great with local LLMs — your data never leaves your machine.

Ollama

1. Install Ollama for your OS and make sure it’s running

2. Use an LLMConfig with name of the form "ollama:<model_name>":

   llm_config = LLMConfig(name="ollama:gpt-oss:20b", temperature=0)

   The model will be downloaded automatically if it doesn't exist. Or run ollama pull <model_name> to download manually.

OpenAI-compatible servers

You can use any OpenAI-compatible server by setting api_base_url in the LLMConfig.

For an example, see examples/configs/qwen3-8b-oai.yaml.

Compatible servers:

• LM Studio: macOS-friendly, supports OpenAI Responses API
• Ollama: OLLAMA_HOST=127.0.0.1:8080 ollama serve
• llama.cpp: llama-server
• vLLM

Alternatives

How does Databao agent compare to other agentic data tools?

Tool	Open source	Local LLMs	SQL + DataFrames	Multiple sources	Interactive output
Databao	✅	✅ Native Ollama	✅ Both	✅ Multiple sources	✅ Tables + charts
PandasAI	✅	✅ Ollama/LM Studio	✅ Both	❌ One source	❌ Static
Chat2DB	✅	✅ Custom LLM, SQL only	❌ One DB	✅ Dashboards
Vanna	✅	✅ Ollama	SQL only	❌ One DB	✅ Plotly

Development

Installation (using uv)

Clone this repo and run:

# Install dependencies
uv sync

# Optionally include example extras (notebooks, dotenv)
uv sync --extra examples

We recommend using the same version of uv as GitHub Actions:

uv self update 0.9.5

Makefile targets

# Lint and static checks (pre-commit on all files)
make check

# Run tests (loads .env if present)
make test

Direct commands

uv run pytest -v
uv run pre-commit run --all-files

Tests

The test suite uses pytest. Some tests require API keys and are marked with @pytest.mark.apikey.

# Run all tests
uv run pytest -v

# Run only tests that do NOT require API keys
uv run pytest -v -m "not apikey"

Contributing

We love contributions! Here’s how you can help:

• ⭐ Star this repo — it helps others find us!
• 🐛 Found a bug? Open an issue
• 💡 Have an idea? We’re all ears — create a feature request
• 👍 Upvote issues you care about — helps us prioritize
• 🔧 Submit a PR
• 📝 Improve docs — typos, examples, tutorials — everything helps!

New to open source? No worries! We’re friendly and happy to help you get started.

License

Apache 2.0 — use it however you want. See the LICENSE file for details.

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Website • Discord