An autonomous SRE agent that builds your SQL data pipelines from a chat prompt, heals them when they break at runtime — diagnosing root cause against DataHub metadata — and learns from every fix so recurrences repair instantly.
Describe an analysis in plain English. DAGtor grounds it in real DataHub tables, columns and PII tags, then generates a runnable DAG — two loads → join → group → filter — with PII columns pre-masked. Review it on the canvas, then deploy.
When a stage fails at runtime, DAGtor intercepts it, diagnoses the root cause in DataHub's metadata graph, rewrites the SQL (masking PII), and re-runs. Every validated fix is saved — the next identical failure heals instantly from memory.
The metadata layer — schema fields, lineage and PII/governance tags — reached over the Model Context Protocol.
Classifies intent, builds pipelines, and runs the function-calling diagnosis loop over the DataHub tools.
App Router UI with a live DAG canvas; a custom Node server streams real-time state over WebSockets.
One database, chosen by env: SQLite locally, PostgreSQL for remote deploys. Holds the source tables the pipeline runs on plus the Experience Store of learned fixes.
The challenge asks entrants to build on the open-source DataHub platform plus at least one of four agent building blocks. DAGtor points at DataHub Core (OSS) through the official MCP server — a config-only swap, with a protocol-compatible local server for offline dev — and uses three of the four building blocks, so every diagnosis is grounded in real metadata, never guessed.
Speaks the Model Context Protocol to the official mcp-server-datahub against a live DataHub Core (OSS) graph — or a protocol-compatible bundled server for offline dev. The same tool calls either way.
Equips Gemini with grounded DataHub context — schemas, lineage and governance tags — so the agent reasons over the real metadata graph instead of hallucinating.
The official tool registry (npx skills add datahub-project/datahub-skills) gives the agent explicit tools — get_lineage(), list_schema_fields() — it calls while healing.
Not used in this build.
Ask DAGtor for a pipeline and deploy it. When a stage drifts at runtime — a renamed column, a dropped upstream — it diagnoses the root cause against DataHub, repairs the SQL, and remembers the fix so it heals instantly next time.
Launch app