semlayer

The semantic layer that infers itself — skip the quarter of hand-writing dbt YAML. Fair Source (FSL-1.1-ALv2); the format and its schemas are Apache-2.0.

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 a portable semantic layer in an openly licensed format — 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 lint layer.yaml query.sql  # check any SQL (yours or an agent's) against the layer
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 87% of business questions correctly vs 42% from the raw schema (+107% relative), 89% when it also runs the layer's SQL linter — and the errors it fixes are the silent kind: raw-schema "total revenue" happily sums cancelled orders; a fan-out join quietly triples a total. Full benchmark, methodology changes included, and where we DON'T help →

Related MCP server: Alma Atlas

What gets inferred

Everything lands with confidence (calibrated, with the misses published), 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 (0.780 typing / 0.834 role across 7 fixtures, against 0.809 / 0.838 with the model) — for orgs where LLM access needs procurement.

• --no-sample-egress: no value read from a warehouse cell is sent to the LLM, at no net accuracy cost across our 7 fixtures (0.809 typing / 0.842 role, versus 0.809 / 0.838 with samples). Per warehouse it trades rather than being uniformly free — up to a point better or worse. Re-measured 2026-10-03; see docs/cost-model.md.

• Read-only, minimal-grant: semlayer init generates the grant script; the reader persona is proved unable to write — on PostgreSQL by a test that runs in ordinary CI against a real server (tests/test_postgres.py, no credentials needed), and on Snowflake by a live suite runnable against your own account (tests/test_snowflake_live.py).

• Telemetry: anonymous usage statistics — command, duration, outcome, warehouse type, bucketed table/column counts, version, OS. Never names, SQL, or data. See exactly what is sent with semlayer telemetry --show; opt out with semlayer telemetry --off, or per-shell with SEMLAYER_TELEMETRY=off. (what and why)

Honest scope (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.

• ETL input: dbt (--manifest target/manifest.json) -- reads the model DAG to settle join targets statistics cannot. Catalog comments and --context docs are read as priors on every run.

• Verify it yourself, free: ANTHROPIC_API_KEY= pytest -q replays recorded model responses, so every accuracy floor and benchmark number above is checkable with no API key and no spend — including from a fork, where CI has no secrets (what those recordings are, and what is still untidy about them). The warehouses behind the numbers, with sources, pinned versions and licences, are listed in docs/warehouses.md.

• Scale: tested to 2,000 tables / 30,000 columns; the run says so when a warehouse is past that. Data VOLUME is not the limit -- 4TB and 45TB are the same architecture. How to make it fast.

• Warehouses: Snowflake, BigQuery, PostgreSQL, DuckDB (+ Iceberg on S3 via the DuckDB bridge). PostgreSQL also reaches Redshift and other Postgres-protocol engines, whose catalogs differ in places and are untested. 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.

Beta feedback

Running the beta? File the Beta feedback form (attach the *.report.json the CLI writes next to your output — timings and counts only, never your schema or data), or email hello@semlayer.dev for anything sensitive.

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

Licence

Fair Source, not open source. The engine and CLI are FSL-1.1-ALv2: free for any internal use, including in production and CI, with no fee or key; the one prohibition is offering semlayer to third parties as a competing product or service. Every release converts to Apache-2.0 two years after it is published.

The format specification, its JSON Schemas and the exporters are Apache-2.0 — a semantic layer nobody else can read is worth nothing.

Releases 0.1.0b1 through 0.4.0b5 were published under Apache-2.0 and remain so permanently. LICENSING.md has the paths table, the conversion dates, and policy text for security teams.