> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2-codex-docs-audit-20260719-0149.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Dash

> Data agent that grounds SQL analysis in five context layers and can save fixes as learnings.

**Dash is a data agent that grounds SQL analysis in five context layers and can save fixes as learnings.**

Ask a question in English and get a correct, meaningful answer. That's the goal. But raw LLMs writing SQL hit a wall fast: schemas lack meaning, types are misleading, tribal knowledge is missing, there's no way to learn from mistakes, and results lack interpretation. The root cause is missing context and missing memory. Dash solves this with five layers of grounded context, a learning loop that can save fixes for later queries, and a focus on delivering insights you can act on.

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).

## 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**  | Queries company data; use a SELECT-only role for enforced read-only access |
| **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 connection uses `default_transaction_read_only=on`, so each transaction starts read-only. A writable role can switch a transaction to read-write, and the bundled `ai` role is writable. Use a separate SELECT-only role to enforce the Analyst boundary. The Engineer uses the application's database role, and a regex-based SQLAlchemy listener blocks common writes to `public`. That listener is a best-effort guard, not a database privilege boundary. Use a role without `public` write privileges when the boundary must hold for every SQL form.

### Five 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`   |
| **Learnings**         | Error patterns and discovered fixes  | Agno `Learning Machine`     |
| **Runtime Context**   | Live schema changes                  | `introspect_schema` tool    |

### Self-learning

The Analyst is instructed to retrieve knowledge and learnings before generating SQL. It can diagnose a failed query and save the fix as a learning. These tools are model-callable, so retrieval and saving depend on the model's tool choices.

| System        | Stores                                           | How it evolves                        |
| ------------- | ------------------------------------------------ | ------------------------------------- |
| **Knowledge** | Validated queries, table schemas, business rules | Curated by you and refined by Dash    |
| **Learnings** | Error patterns and discovered fixes              | Added when Dash calls `save_learning` |

When a churn query uses the wrong filter, Dash can save the corrected `ended_at IS NULL` pattern for later retrieval. When your team defines MRR as the sum of active subscriptions excluding trials, that rule can live in `knowledge/business/` for later queries.

### Insights you can act on

Dash reasons about what makes an answer useful. Ask "Which plan has the highest churn rate?" and you get the number, the comparison across plans, the trend behind it, and any caveats from your business rules.

## 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-docs-audit-20260719-0149/pW8PmtN5WDvNK9a4/videos/dash-ui-demo.mp4?fit=max&auto=format&n=pW8PmtN5WDvNK9a4&q=85&s=33077e736272104b53b540e98d92b449" type="video/mp4" data-path="videos/dash-ui-demo.mp4" />
  </video>
</Frame>

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

## 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. Leave `JWT_VERIFICATION_KEY` unset in `.env.production`. Set it directly on the `dash` service, preserving the PEM line breaks:

    ```bash theme={null}
    railway variables --set "JWT_VERIFICATION_KEY=-----BEGIN PUBLIC KEY-----
    MIIBIjANBgkq...
    -----END PUBLIC KEY-----" --service dash
    ```

    <Warning>
      The current Dash scripts cannot safely share a multiline PEM through `.env.production`. `railway_up.sh` sources the file and requires shell quoting, while `railway_env.sh` leaves a trailing quote in a quoted multiline value.
    </Warning>
  </Step>

  <Step title="Push environment and redeploy">
    <Warning>
      The current `railway_env.sh` exits after its first variable because `set -e` treats the initial `((count++))` result as a failure. Replace `((count++))` with `((++count))` in `scripts/railway_env.sh` before running it.
    </Warning>

    ```bash theme={null}
    ./scripts/railway_env.sh
    ./scripts/railway_redeploy.sh
    ```

    After this change, `railway_env.sh` reads the remaining values from `.env.production` and sets them on the Railway service. It 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.