> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agno.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Dash

> Self-learning data agent that grounds answers in 6 layers of context.

**Dash is a self-learning data agent that gets better with every query.**

Most Text-to-SQL agents are stateless. They make mistakes, you fix them, then they make the same mistake again because every session starts fresh. Dash fixes this by grounding its answers in 6 layers of context and learning from every run.

Checkout the [repo](https://github.com/agno-agi/dash) for more details.

## How It Works

Dash retrieves relevant context at query time via hybrid search, generates grounded SQL, executes it, and delivers insights.

### The Six Layers of Context

| Layer                       | Purpose                              | Source                      |
| --------------------------- | ------------------------------------ | --------------------------- |
| **Table Usage**             | Schema, columns, relationships       | `knowledge/tables/*.json`   |
| **Human Annotations**       | Metrics, definitions, business rules | `knowledge/business/*.json` |
| **Query Patterns**          | SQL that is known to work            | `knowledge/queries/*.sql`   |
| **Institutional Knowledge** | Docs, wikis, external references     | MCP (optional)              |
| **Learnings**               | Error patterns and discovered fixes  | Agno `LearningMachine`      |
| **Runtime Context**         | Live schema changes                  | `introspect_schema` tool    |

### Self-Learning

Dash improves without retraining or fine-tuning through two complementary systems:

| System        | Stores                                           | How it evolves                                |
| ------------- | ------------------------------------------------ | --------------------------------------------- |
| **Knowledge** | Validated queries, table schemas, business rules | Curated by your team and refined by Dash      |
| **Learnings** | Error patterns, column quirks, team conventions  | Managed automatically by the Learning Machine |

When a query fails because `position` is TEXT and not INTEGER, Dash saves that. Next time, it knows. When your team is focused on IPO prep, Dash learns that "revenue" means ARR, not bookings, and that the board wants cohort retention broken out by enterprise vs SMB.

### Insights, Not Just Rows

Dash reasons about what makes an answer useful, not just technically correct.

**Question:** Who won the most races in 2019?

| Typical SQL Agent | Dash                                                                                                                                                     |
| ----------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `Hamilton: 11`    | Lewis Hamilton dominated 2019 with **11 wins out of 21 races**, more than double Bottas's 4 wins. This performance secured his sixth world championship. |

## Run Locally

```bash theme={null}
git clone https://github.com/agno-agi/dash.git && cd dash

cp example.env .env
# Edit .env and add your OPENAI_API_KEY

docker compose up -d --build

# Load sample data (F1 races 1950-2020) and knowledge base
docker exec -it dash-api python -m dash.scripts.load_data
docker exec -it dash-api python -m dash.scripts.load_knowledge
```

Confirm Dash is running at `http://localhost:8000/docs`.

### Connect to the control plane

1. Open [os.agno.com](https://os.agno.com) and sign in
2. Click **Add OS** → **Local**
3. Enter `http://localhost:8000`

<Frame>
  <video autoPlay muted loop controls playsInline style={{ borderRadius: "0.5rem", width: "100%", height: "auto" }}>
    <source src="https://mintlify.s3.us-west-1.amazonaws.com/agno-v2/videos/dash-ui-demo.mp4" type="video/mp4" />
  </video>
</Frame>

## Deploy to Railway

```bash theme={null}
railway login
./scripts/railway_up.sh
```

The script provisions PostgreSQL, configures environment variables, and deploys your application.

Then load your data and knowledge in production:

```bash theme={null}
railway run python -m dash.scripts.load_data
railway run python -m dash.scripts.load_knowledge
```

Connect via the control plane:

1. Open [os.agno.com](https://os.agno.com)
2. Click **Add OS** → **Live**
3. Enter your Railway domain

## Example Prompts

Try these on the sample F1 dataset:

* Who won the most F1 World Championships?
* How many races has Lewis Hamilton won?
* Compare Ferrari vs Mercedes points 2015-2020

## Adding Your Own Data

Dash works best when it understands how your organization talks about data. The knowledge base lives in three directories:

**`knowledge/tables/`** for table metadata: schema descriptions, column meanings, data quality notes.

**`knowledge/queries/`** for validated SQL patterns that are known to work.

**`knowledge/business/`** for metric definitions, business rules, and common gotchas.

Load or update knowledge at any time:

```bash theme={null}
python -m dash.scripts.load_knowledge            # upsert changes
python -m dash.scripts.load_knowledge --recreate  # fresh start
```

## Run Evals

Dash ships with an evaluation suite: string matching, LLM grading, and golden SQL comparison.

```bash theme={null}
docker exec -it dash-api python -m dash.evals.run_evals         # string matching
docker exec -it dash-api python -m dash.evals.run_evals -g      # LLM grader
docker exec -it dash-api python -m dash.evals.run_evals -g -r   # both + golden SQL
```

## Source

For architecture details, knowledge format reference, and local development setup, see the [GitHub repo](https://github.com/agno-agi/dash).