Dash

Dash is a self-learning data agent built with systems engineering principles. It grounds answers in six layers of context and improves with every query. 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 six layers of grounded context, a self-learning loop that captures every fix, and a focus on delivering insights you can act on. 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:

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 any write it attempts. A SQLAlchemy event listener blocks Engineer writes that target public. These guardrails live in the infrastructure, so they hold regardless of what the model generates.

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

Every query retrieves knowledge and learnings before generating SQL. When a query fails, Dash diagnoses the error, fixes it, and saves the fix as a learning. Two complementary systems make this work:

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	Managed automatically by the Learning Machine

When a churn query returns wrong numbers because it filtered on the status column instead of ended_at IS NULL, Dash saves the fix and doesn’t make that mistake again. When your team defines MRR as the sum of active subscriptions excluding trials, that rule lives in knowledge/business/ and every future query respects it.

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

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

Dash is running locally.

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

Deploy infrastructure

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.

Get your JWT key

Production requires a JWT_VERIFICATION_KEY from AgentOS. You need the Railway domain from step 1 to set this up.

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

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

Push environment and redeploy

./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.

Dash is live on Railway.

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.

Run Dash with a public URL (ngrok locally, or your Railway domain).
Create and install the Slack app from the manifest in docs/SLACK_CONNECT.md.
Set SLACK_TOKEN and SLACK_SIGNING_SECRET, then restart Dash.
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:

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:

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

The Dash README 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

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. The README covers the full architecture, the data model, and the security setup.

Get Started

Starters

Apps

Agno CLI

How it works

Six layers of context

Self-learning

Insights you can act on

Run locally

Connect to the AgentOS UI

Deploy to Railway

Production operations

Connect to Slack

Example prompts

Add your own data

Run evals

Source

​How it works

​Six layers of context

​Self-learning

​Insights you can act on

​Run locally

​Connect to the AgentOS UI

​Deploy to Railway

​Production operations

​Connect to Slack

​Example prompts

​Add your own data

​Run evals

​Source

How it works

Six layers of context

Self-learning

Insights you can act on

Run locally

Connect to the AgentOS UI

Deploy to Railway

Production operations

Connect to Slack

Example prompts

Add your own data

Run evals

Source