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 "semlayer[warehouses]"
semlayer init snowflake            # generates the minimal-grant setup script
# `snowflake` reads the environment; `snowflake:<connection>` reads
# ~/.snowflake/connections.toml; `snowflake://user@account/db?role=..` is a URL
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)

Or start from an agent instead of a terminal: add the MCP server to Claude Desktop, Claude Code or Cursor and ask it what it can see — it discovers the warehouses configured on your machine and says what to do next, with no layer built yet. See docs/mcp.md.

Or have the agent install it. Point any coding agent at this repository and ask it to install the semlayer MCP server: everything it needs is in llms-install.md, written for an agent rather than a person, and it needs nothing else. semlayer is also in the official MCP registry as dev.semlayer/mcp, so a registry-aware client can find and install it without being told where to look.

In VS Code and Cursor, the same layer underlines the fan-out joins and missing required filters as you type — docs/vscode.md.

In CI, the same findings land on the pull request diff:

- uses: semlayer-dev/semlayer@v0.7.0-beta.6
  with:
    mode: lint            # or: drift, to fail when the warehouse moved
    layer: layer.yaml

docs/ci.md.

From a checkout (to develop on semlayer itself): pip install -e ".[warehouses]".

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 82% of business questions correctly vs 42% from the raw schema (+95% relative), with or without 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 →

What gets inferred

... View full README on GitHub