> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2-codex-homepage-20260719-015142.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Dash

> Self-hosted data agent that grounds SQL answers in six layers of company context and stores reusable fixes.

**Dash gives data teams a self-hosted agent for answering business questions against company databases.**

Database schemas rarely contain metric definitions, proven query patterns, or the reasons a past query failed. Dash combines that company context with live schema inspection so its SQL and explanations reflect how your organization uses its data.

Agno coordinates an Analyst and Engineer, keeps curated knowledge and learned fixes available across runs, and serves the team through AgentOS. Run Dash against the included SaaS dataset, then ask it for your current MRR or highest-churn plan.

Chat with Dash in Slack, the terminal, or the [AgentOS UI](https://os.agno.com). The code is public at [agno-agi/dash](https://github.com/agno-agi/dash).

## 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

# Generate sample data and load knowledge
docker exec -it dash-api python scripts/generate_data.py
docker exec -it dash-api python scripts/load_knowledge.py
```

Confirm Dash is running at [http://localhost:8000/docs](http://localhost:8000/docs). The [Dash README](https://github.com/agno-agi/dash#quick-start) walks through this step by step.

### Connect to the AgentOS UI

1. Open [os.agno.com](https://os.agno.com) and log in.
2. Click **Connect OS**, choose **Local**, and enter `http://localhost:8000`.
3. Click **Connect**.

<Frame>
  <video autoPlay muted loop controls playsInline style={{ borderRadius: "0.5rem", width: "100%", height: "auto" }}>
    <source src="https://mintcdn.com/agno-v2-codex-homepage-20260719-015142/eh3ruyt1i8EA1Ky0/videos/dash-ui-demo.mp4?fit=max&auto=format&n=eh3ruyt1i8EA1Ky0&q=85&s=ce0991215138a9d22047dafbcecd2fc9" type="video/mp4" data-path="videos/dash-ui-demo.mp4" />
  </video>
</Frame>

<Check>Dash is running locally.</Check>

## How it works

Dash runs as an Agno team in coordinate mode, with a leader that routes each request to two specialists:

| Agent        | Role                                                              |
| ------------ | ----------------------------------------------------------------- |
| **Analyst**  | Reads company data (read-only), generates SQL, interprets results |
| **Engineer** | Builds reusable views and summary tables in the `dash` schema     |
| **Leader**   | Routes queries, coordinates the team, posts to Slack              |

**Schema boundaries:** Company data lives in the `public` schema; agent-created views and summary tables live in the `dash` schema. The Analyst connects with `default_transaction_read_only=on`, so PostgreSQL rejects its writes. The Engineer is instructed to write only to `dash`, and a SQLAlchemy event listener rejects DDL and DML that explicitly targets `public`. PostgreSQL enforces the Analyst boundary. The Engineer boundary depends on the application listener and model instructions.

### 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 `Learning Machine`     |
| **Runtime Context**         | Live schema changes                  | `introspect_schema` tool    |

### Self-learning

The Analyst searches knowledge and learnings before generating SQL. Its workflow tells it to inspect the live schema after an error and call `save_learning` after discovering a reusable fix. Agno's Learning Machine makes those saved fixes available to later runs.

| System        | Stores                                           | How it evolves                                    |
| ------------- | ------------------------------------------------ | ------------------------------------------------- |
| **Knowledge** | Validated queries, table schemas, business rules | Curated by you; Dash can propose reusable queries |
| **Learnings** | Error patterns and discovered fixes              | Saved by the agents through the Learning Machine  |

When a churn query filters on `status` and should use `ended_at IS NULL`, Dash can save that correction for later runs to retrieve. When your team defines MRR as the sum of active subscriptions excluding trials, the rule lives in `knowledge/business/` alongside the queries that use it.

## Deploy to Railway

Railway deployment uses `.env.production` to keep production credentials separate from local dev.

```bash theme={null}
cp example.env .env.production
# Edit .env.production and set OPENAI_API_KEY
```

<Steps>
  <Step title="Deploy infrastructure">
    ```bash theme={null}
    railway login
    ./scripts/railway_up.sh
    ```

    This creates the Railway project, database, and app service. The app will crash-loop until the JWT key is added in the next step. That's expected.
  </Step>

  <Step title="Get your JWT key">
    Production requires a `JWT_VERIFICATION_KEY` from AgentOS. You need the Railway domain from step 1 to set this up.

    1. Copy your Railway domain from the output of step 1 (e.g. `dash-production-xxxx.up.railway.app`).
    2. Open [os.agno.com](https://os.agno.com) and log in.
    3. Click **Connect OS**, choose **Live**, and paste your Railway URL.
    4. Go to **Settings** → **OS & Security** and turn on **Token-Based Authorization (JWT)**. The UI generates a key pair and shows you the public key.
    5. Add the public key to `.env.production`, wrapped in single quotes:

    ```bash theme={null}
    JWT_VERIFICATION_KEY='-----BEGIN PUBLIC KEY-----
    MIIBIjANBgkq...
    -----END PUBLIC KEY-----'
    ```
  </Step>

  <Step title="Push environment and redeploy">
    ```bash theme={null}
    ./scripts/railway_env.sh
    ./scripts/railway_redeploy.sh
    ```

    `railway_env.sh` reads `.env.production` and sets each variable on the Railway service. It handles multiline values like PEM keys and is safe to run repeatedly.
  </Step>
</Steps>

<Check>Dash is live on Railway.</Check>

The [Dash README](https://github.com/agno-agi/dash#deploy-to-railway) covers this flow in more detail.

### Production operations

Database scripts must run inside Railway's network. The internal hostname `pgvector.railway.internal` is unreachable from your local machine, so SSH into the running container:

```bash theme={null}
railway ssh --service dash
# Inside the container:
python scripts/generate_data.py
python scripts/load_knowledge.py
```

Other operations run locally:

```bash theme={null}
railway logs --service dash
railway open
```

## Connect to Slack

Dash can receive DMs, @mentions, and thread replies, and can post to channels proactively. Each Slack thread maps to one Dash session.

1. Run Dash with a public URL (ngrok locally, or your Railway domain).
2. Create and install the Slack app from the manifest in `docs/SLACK_CONNECT.md`.
3. Set `SLACK_TOKEN` and `SLACK_SIGNING_SECRET`, then restart Dash.
4. In Slack, confirm Event Subscriptions shows verified, then send a DM or @mention to test.

See the [Slack setup guide](/agent-os/interfaces/slack/setup) for the manifest, ngrok commands, permissions, and troubleshooting.

## Example prompts

Try these on the sample SaaS metrics dataset:

* What's our current MRR?
* Which plan has the highest churn rate?
* Show me revenue trends by plan over the last 6 months
* Which customers are at risk of churning?

## Add your own data

Dash works best when it understands how your organization talks about data:

| Directory             | Content                                            |
| --------------------- | -------------------------------------------------- |
| `knowledge/tables/`   | Table meaning, column notes, data quality caveats  |
| `knowledge/queries/`  | Proven SQL patterns                                |
| `knowledge/business/` | Metric definitions, business rules, common gotchas |

Load or update knowledge at any time:

```bash theme={null}
python scripts/load_knowledge.py             # Upsert changes
python scripts/load_knowledge.py --recreate  # Fresh start
```

The [Dash README](https://github.com/agno-agi/dash#load-knowledge) covers loading your own data and scheduled proactive tasks.

## Run evals

Five eval categories using Agno's eval framework:

| Category       | Eval type                   | What it tests                              |
| -------------- | --------------------------- | ------------------------------------------ |
| **accuracy**   | `AccuracyEval` (1-10)       | Correct data and meaningful insights       |
| **routing**    | `ReliabilityEval`           | Team routes to the correct agent and tools |
| **security**   | `AgentAsJudgeEval` (binary) | No credential or secret leaks              |
| **governance** | `AgentAsJudgeEval` (binary) | Refuses destructive SQL operations         |
| **boundaries** | `AgentAsJudgeEval` (binary) | Schema access boundaries respected         |

```bash theme={null}
python -m evals                      # Run all evals
python -m evals --category accuracy  # Run specific category
python -m evals --verbose            # Show response details
```

## Source

Dash is public at [agno-agi/dash](https://github.com/agno-agi/dash). The README covers the full architecture, the data model, and the security setup.