SLayer
<p align="center">
<img src="https://raw.githubusercontent.com/MotleyAI/slayer/main/docs/images/slayer-hero.png" alt="SLayer — AI agent operating a semantic layer" width="600">
</p>
[](https://pypi.org/project/motley-slayer/)
[](https://pypi.org/project/motley-slayer/)
[](https://docs.motley.ai/slayer/)
[](LICENSE)
[](https://github.com/MotleyAI/slayer/stargazers)
[](https://discord.gg/egWxMctHCA)
# SLayer
An expressive, embeddable semantic layer for AI agents and humans.
SLayer enables AI-powered data analytics on top of your warehouse. Agents get a governed, shared surface through which they access your data and metrics and give you reliable answers.
SLayer handles database connectivity (read-only), SQL translation, common data transformations, and row-level security, so LLMs and humans don't have to. Adapt it to your workflows, not the other way around. Manage definitions easily with an agent or by yourself.
SLayer can be used as a standalone tool or imported as a Python library, easily embeddable into any Python app. Use it for powering analytical MCP servers or APIs or simply to query databases semantically.
SLayer is at the core of [Motley](https://motley.ai) and is maintained with ♡ by the same team.
## How SLayer is different
Traditionally, semantic layers were a part of the BI stack, where every metric and its aggregation
had to be predefined. Agents need more flexibility because users ask questions that involve metric combinations (like ratios), transforms (like time shifts), or different aggregations
of the same metric (like average instead of sum).
SLayer allows to define a column `revenue` once and query it using [expressions](https://docs.motley.ai/slayer/concepts/formulas/) like `revenue:sum`, `revenue:avg`, `revenue:sum / *:count`, `time_shift(revenue:sum, -1, 'year')` etc.; [multi-stage queries](https://docs.motley.ai/slayer/examples/06_multistage_queries/multistage_queries/) are also supported.
SLayer is focused on the common agentic **search → inspect → query** flow. It has a [search](https://docs.motley.ai/slayer/concepts/search/) tool for efficient discovery and a [memory store](https://docs.motley.ai/slayer/concepts/memories/) for linking the relevant business context.
Agents, apps and humans can talk to SLayer via [MCP](https://docs.motley.ai/slayer/reference/mcp/), [REST API](https://docs.motley.ai/slayer/reference/rest-api/), [CLI](https://docs.motley.ai/slayer/reference/cli/), [Python](https://docs.motley.ai/slayer/reference/python-client/), [Flight SQL](https://docs.motley.ai/slayer/interfaces/flight-sql/), or Postgres-based [SQL API](https://docs.motley.ai/slayer/interfaces/pg-facade/). SLayer [supports](https://docs.motley.ai/slayer/configuration/datasources/#supported-database-types) most popular databases.
SLayer fits next to your existing data stack. It also provides importers for [dbt](https://docs.motley.ai/slayer/dbt/dbt_import/), [Cube](https://docs.motley.ai/slayer/cube/cube_import/), and [Ossie](https://docs.motley.ai/slayer/osi/osi_import/) configs.
**See [docs](https://docs.motley.ai/slayer/) for more.**
### Example
Question (run on the built-in demo Jaffle Shop database): **"show monthly revenue by store, with month-over-month % change"**
Side by side, here's LLM-generated SQL and the equivalent SLayer query.
## Quickstart
We recommend using [uv](https://docs.astral.sh/uv/), especially if you don't work in a Python project.
```bash
uv tool install 'motley-slayer[all]'
```
If `slayer` isn't found on PATH afterwards, run `uv tool update-shell` and reopen your terminal.
### Using demo dataset
```bash
# With the Jaffle Shop demo preloaded (zero-config quickstart)
claude mcp add slayer_demo -- slayer mcp --demo
```
### Using your own data
Set up your datasource, substituting the correct database, username, hostname, and db_name.
```bash
slayer datasources create 'postgresql://user:${DB_PASSWORD}@hostname/db_name'
```
The password will be read by SLayer at init time, not saved to disk nor exposed to Claude.
Then add SLayer to Claude Code:
```bash
claude mcp add slayer -- slayer mcp --ingest-on-startup
```
Now SLayer MCP will be visible in Claude Code next time you start it. Make sure to launch Claude Code from a shell where `DB_PASSWORD` is exported — the MCP subprocess inherits its environment from the launching process.
Read more on how to get started with [MCP](https://docs.motley.ai/slayer/getting-started/mcp/), [CLI](https://docs.motley.ai/slayer/getting-started/cli/), [REST API](https://docs.motley.ai/slayer/getting-started/rest-api/), [Python](https://docs.motley.ai/slayer/getting-started/python/) in the docs.
## License
MIT
TDQS
Scored across 20 tools
Most tools have clearly distinct purposes (datasource lifecycle, model CRUD, querying, search/memory). However, inspect_model and models_summary are explicitly superseded by the broader inspect tool, creating redundancy that could cause misselection. Descriptions clearly mark these overlaps, but the presence of deprecated tools adds mild ambiguity.
The predominant pattern is verb_noun (create_model, delete_datasource, set_datasource_priority, etc.). Deviations include bare verbs like 'search' and 'inspect', and noun-led 'models_summary'. The deprecated 'inspect_model' also creates an inconsistency with 'inspect'. Overall, the convention is mostly consistent with a few outliers.
At 20 tools, this is on the heavy side but reasonable for a server covering datasource management, model CRUD, querying, and memory/search. It sits at the borderline between well-scoped (3-15) and slightly over (16-25), and the breadth of functionality justifies the count, though a few redundant tools could be trimmed.
The surface covers datasource lifecycle (create/describe/edit/delete/priority), model lifecycle (create/edit/delete/inspect/validate/ingest), querying, and search/memory. Minor gaps include lack of datasource credential updates and model renaming, but these are workaroundable. Deprecated tools add redundancy but don't create functional dead ends.