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.

Traditional Text-to-SQL agents start from scratch on every query. They guess column names, repeat the same mistakes, and never learn from corrections. Your team fixes an error, and the agent makes the same error again tomorrow.

Dash solves this with 6 layers of grounded context and a learning loop that captures every fix. The knowledge base functions like manually-editable model weights: update retrieval context, not neural network parameters.

Checkout the repo for more details.

How It Works

Dash is a team of three agents coordinated by a leader:

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 (read-only). Agent-created views and summary tables live in the dash schema. The Analyst’s SQL tools enforce read-only at the PostgreSQL level, not just in prompts.

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

Two paths run in parallel: the online path answers questions using retrieved context, the offline path captures learnings for future queries. Dash improves without retraining or fine-tuning:

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

See Setup guide for detailed instructions.

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.

Connect to the Control Plane

1. Open os.agno.com
2. Click Connect OS → Local
3. Enter http://localhost:8000

Deploy to Railway

See Deploy to Railway for JWT setup and production configuration.

Railway deployment uses .env.production to keep production credentials separate.

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

1. Deploy infrastructure

   railway login
   ./scripts/railway_up.sh
   

   The app will crash-loop until the JWT key is added in the next step. That’s expected.

2. Get your JWT key

   1. Copy your Railway domain from the output (e.g. dash-production-xxxx.up.railway.app)
   2. Open os.agno.com → Connect OS → Live
   3. Paste your Railway URL
   4. Go to Settings and generate a key pair
   5. Add the public key to .env.production:

   JWT_VERIFICATION_KEY='-----BEGIN PUBLIC KEY-----
   MIIBIjANBgkq...
   -----END PUBLIC KEY-----'
   
3. Push environment and redeploy

   ./scripts/railway_env.sh
   ./scripts/railway_redeploy.sh
   

Production Operations

Database scripts must run inside Railway’s network:

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

Connect to Slack

Dash can receive DMs, @mentions, and thread replies, and can post to channels proactively.

1. Run Dash with a public URL (ngrok locally, or your Railway domain)
2. Create the Slack app from the manifest in docs/SLACK_CONNECT.md
3. Set SLACK_TOKEN and SLACK_SIGNING_SECRET in .env
4. Restart Dash

See Slack setup for details.

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?

Adding Your Own Data

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

Directory	Content
knowledge/tables/	Table schemas, column meanings, data quality notes
knowledge/queries/	Validated SQL patterns that are known to work
knowledge/business/	Metric definitions, business rules, common gotchas

Load or update knowledge at any time:

python scripts/load_knowledge.py            # Upsert changes
python scripts/load_knowledge.py --recreate  # Fresh start

Run Evals

Five eval categories using Agno’s eval framework:

Category	What It Tests
accuracy	Correct data and meaningful insights
routing	Team routes to correct agent/tools
security	No credential or secret leaks
governance	Refuses destructive SQL operations
boundaries	Schema access boundaries respected

python -m evals                      # Run all evals
python -m evals --category accuracy  # Run specific category
python -m evals --verbose            # Show response details

Source

For architecture details, data model reference, and security configuration, see the GitHub repo.