semlayer

The open-source semantic layer that infers itself — skip the quarter of hand-writing dbt YAML.

Point it at your warehouse. It profiles every column, discovers the foreign keys nobody declared, decodes the status columns, finds the business rules hiding in your aggregate tables, and writes the whole thing down as an open, portable semantic layer — with confidence, provenance, and lifecycle on every single claim — ready for any AI agent to consume over MCP.

pip install -e ".[warehouses]"
semlayer init snowflake            # generates the minimal-grant setup script
semlayer infer snowflake -o layer.yaml \
  --context ./docs/ --context ./etl-repo/CLAUDE.md   # optional: your wikis/dictionaries as priors
semlayer review layer.yaml         # accept/reject what the engine inferred
semlayer mcp layer.yaml            # serve it to Claude, Cursor, or any MCP client
semlayer drift layer.yaml snowflake  # catch schema changes (cron- and CI-friendly)

Why

AI agents fail on real warehouses: frontier models solved just 21.3% of Spider 2.0's enterprise-warehouse tasks at publication (vs ~91% on the earlier academic Spider 1.0) — and even today's best agentic scaffolds only reach ~30%. The fix is a semantic layer — but every existing tool (dbt, LookML, Cube, Snowflake semantic views) makes humans write it by hand, and it goes stale the day someone runs an ALTER TABLE.

On our messy-warehouse benchmark (cryptic names, zero declared constraints, hidden business rules), an agent using the inferred layer answers 53% of business questions correctly vs 34% from the raw schema (+54% relative) — and the errors it fixes are the silent kind: raw-schema "total revenue" happily sums cancelled orders. Full benchmark, including where we DON'T help →

What gets inferred

Everything lands with confidence, provenance (which signals produced it), and lifecycle (inferred → reviewed → certified, plus deprecated/orphaned), governed by a normative consumer contract that makes silent misuse — summing across a fan-out, joining SCD2 at current-row, filtering on guessed decodes — non-conforming, not merely unwise.

Cost & privacy

• ~$0.70 per 100 tables end-to-end on the cheap model tier, with your own API key (measured cost model). LLM calls are escalation-only; ~80% of columns resolve from statistics alone.
• --no-llm: fully deterministic mode, zero API calls, still useful (0.81 typing accuracy on the messy fixture) — for orgs where LLM access needs procurement.
• --no-sample-egress: cell values never leave for the LLM; measured cost ≈ 1 point of accuracy.
• Read-only, minimal-grant: semlayer init generates the grant script; the nightly test suite proves the reader persona cannot write.
• Telemetry: anonymous command counts spooled locally only — nothing leaves your machine in this release; opt out with SEMLAYER_TELEMETRY=off. (details)

Honest scope (v0.1 beta)

• Best on messy warehouses. On clean, well-named schemas our benchmark shows raw DDL is already sufficient — we publish that negative result rather than hide it. If your warehouse is tidy TPC-DS, you may not need us.
• Warehouses: Snowflake, BigQuery, DuckDB (+ Iceberg on S3 via the DuckDB bridge). Exporter: dbt (losses reported, never silent). LLM: Anthropic API (Bedrock/Vertex routing is next).
• Not included: hosted service, ontology enrichment (it failed our own ablation gate — receipts), LookML/RDF exporters.
• The eval harness ships in this repo — fixtures, gold layers, competency questions, benchmark runner. Run our numbers yourself: python fixtures/build.py && pytest tests/ -q.

Layout

spec/ format schema + consumer contract · src/semlayer/ the engine · fixtures/ 9 eval warehouses + golds + CQ suites · docs/ benchmark, cost model, spike reports · ARCHITECTURE.md how it works

License: Apache-2.0.