# Vanna

> An MIT-licensed Python framework for text-to-SQL: a user-aware agent that learns from successful queries and streams tables, charts, and summaries back.

Vanna is an open-source Python framework for asking a SQL database questions in English. It retrieves context from a vector store, generates SQL, runs it, and streams back a table, a chart, and a written summary. Version 2.0 replaced the old train-on-DDL workflow with agent memory, and added per-user permissions.

Website: https://vanna.ai

Vanna is a Python framework for turning English questions into SQL against your own database. The mechanism is retrieval, not fine-tuning: context about your schema, your documentation, and queries that have worked before lives in a vector store, and the relevant pieces are pulled in each time a question is asked. That is why it gets better on your data without anyone retraining a model.

Version 2.0 reframed the project around a user-aware agent. Identity flows through the system prompt, tool execution, and SQL filtering, so results can be filtered per user with row-level security and every query is logged per user for audit. It ships a `<vanna-chat>` web component and FastAPI and Flask integrations, so the chat surface is something you embed in your own app rather than a product you log into.

## Highlights

- **RAG-based SQL generation.** Retrieved context, not a memorized schema, so accuracy improves with the examples you accumulate rather than with model size.
- **Agent memory replaced training.** Every successful tool usage is saved to a vector database; on a later question Vanna searches for similar past usage, retrieves examples by semantic similarity, and uses them to choose its tool and its arguments.
- **Streaming output an analyst can read.** A question returns progress updates, a SQL block (shown to admin users by default), an interactive data table, Plotly charts, and a natural-language summary, all streamed to the web component.
- **Pluggable memory backends.** ChromaDB for local persistent storage, DemoAgentMemory for in-memory prototyping, Cloud Agent Memory via Vanna Cloud, or your own implementation, with Pinecone, Weaviate, and Milvus named as examples.
- **Your database, your model.** Documented connectors cover PostgreSQL, MySQL, Snowflake, and BigQuery for production and SQLite for development; the LLM layer works with OpenAI, Anthropic, Ollama, Azure, Google Gemini, AWS Bedrock, Mistral, and others.
- **Permissions as a first-class concern.** A user resolver extracts identity from your existing cookies or JWTs, tools check group membership, and lifecycle hooks handle quotas and rate limiting.

## In an analyst's workflow

The analyst's job with Vanna is curating memory. A generic model plus fifty of your real, correct queries beats a bigger model with none:

```python
from vanna.capabilities.agent_memory import ToolMemory

# Seed the answer your team asks for every Monday
await agent.agent_memory.save_tool_usage(
    question="Weekly active accounts by plan tier",
    tool_name="run_sql",
    args={"sql": "SELECT ..."},
    context=ToolContext(user=your_user),
    success=True,
)

# Seed the definition nobody agrees on
await agent.agent_memory.save_text_memory(
    content="Active means at least one billable event in the trailing 7 days.",
    context=ToolContext(user=your_user),
)
```

Then use in-chat training for everything else: when an answer is right, or you corrected it, it goes into memory automatically. This is the same grounding problem covered in [text-to-SQL with Claude](/guides/analytics/text-to-sql-with-claude), solved with a persistent store instead of a prompt you paste each time.

> [!WARNING]
> The GitHub repository was archived by its owner on March 29, 2026 and is read-only, with the last PyPI release, 2.0.2, dated February 2, 2026. The docs and the commercial offering are still live, but the open-source code is frozen: no upstream fixes, no new connectors. Weigh that before building on it.

## Good to know

The open-source framework is MIT-licensed, copyright Vanna.AI, and installs as the `vanna` package. Vanna Cloud adds managed LLM endpoints with model routing and guardrails, a managed conversation store with encrypted retention and PII scrubbing, hosted vector memory, policy-driven lifecycle hooks for quotas and approvals, session management and permissions, and an evaluation suite. As of September 2026 the published plans are Explorer at $50 per month for small teams of two to three with 20 questions a day, Team at $500 per month with 300 questions a day and setup support, and custom Enterprise pricing with unlimited questions, on-prem deployment support, and SAML SSO; annual subscriptions receive a 20 percent discount.

If your questions are about files and dataframes rather than a warehouse, [PandasAI](/tools/pandasai) is the closer library. If your data already lives in a governed platform, a built-in agent such as [Databricks Genie](/tools/databricks-genie) removes the operational work entirely. [The best text-to-SQL tools in 2026](/guides/comparisons/best-text-to-sql-tools-2026) compares the approaches, and [the best AI tools for data analysts in 2026](/guides/comparisons/best-ai-tools-for-data-analysts-2026) plus [Claude for data analysis](/guides/analytics/claude-for-data-analysis) cover the non-SQL routes to the same answers.

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_Source: https://agentscamp.com/tools/vanna — Tool on AgentsCamp._