Dash

This guide follows the reviewed application revision, which pins Agno 2.7.0. Use its checked-in requirements to reproduce the application.

Dash is a self-learning data agent for teams that need grounded answers from company data, business rules, and proven query patterns.

Data and analytics teams need answers that respect metric definitions, schema quirks, business rules, and queries that are known to work. Dash brings those inputs together as six layers of context and retains useful corrections through a learning loop.

Ask a question in English and Dash queries read-only company data, interprets the result, and explains it using your business context.

Chat with Dash in Slack, the terminal, or the AgentOS UI. The code is public at 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:

Schema boundaries: Company data lives in the public schema; agent-created views and summary tables live in the dash schema. The Analyst’s engine defaults to read-only transactions. The Engineer’s SQLAlchemy event listener blocks explicitly public-qualified writes, but its regex does not cover unqualified names resolved through search_path. Use PostgreSQL roles and grants to enforce the required production write boundary.

Six layers of context

Self-learning

Dash is instructed to retrieve relevant knowledge before generating SQL and can use stored learnings. When a query fails, it can diagnose the error and save a useful correction. Two complementary systems make this work:

For example, Dash can save a correction when a churn query filters on status instead of ended_at IS NULL. Your MRR definition can live in knowledge/business/ for later retrieval. Saved context helps the team reuse corrections; validate its answers and queries against your data.

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

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. The Dash README walks through this step by step.

Connect to the AgentOS UI

1. Open os.agno.com and log in.
2. Click Connect OS, choose Local, and enter http://localhost:8000.
3. Click Connect.

Deploy to Railway

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

cp example.env .env.production
# Edit .env.production and set OPENAI_API_KEY

railway login
./scripts/railway_up.sh

JWT_VERIFICATION_KEY='-----BEGIN PUBLIC KEY-----
MIIBIjANBgkq...
-----END PUBLIC KEY-----'

set -a
source .env.production
set +a
railway variables --set "JWT_VERIFICATION_KEY=$JWT_VERIFICATION_KEY" --service dash
./scripts/railway_redeploy.sh

The Dash README 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:

railway ssh --service dash
# Inside the container:
python scripts/generate_data.py
python scripts/load_knowledge.py

Other operations run locally:

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 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:

Load or update knowledge at any time:

docker exec -it dash-api python scripts/load_knowledge.py
docker exec -it dash-api python scripts/load_knowledge.py --recreate  # Rebuild vectors

The Dash README covers loading your own data and scheduled proactive tasks.

Run evals

Five eval categories using Agno's eval framework:

docker exec -it dash-api python -m evals
docker exec -it dash-api python -m evals --category accuracy
docker exec -it dash-api python -m evals --verbose

Source

Dash is public at agno-agi/dash. The README covers the full architecture, the data model, and the security setup.