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Documentation Index

Fetch the complete documentation index at: https://laminar.sh/docs/llms.txt

Use this file to discover all available pages before exploring further.

The lmnr-cli is a command-line tool for querying Laminar data, managing datasets, and running the interactive agent debugger from your terminal. It is the fastest way to pull traces into a shell pipeline, script dataset maintenance, or connect an AI coding agent that can shell out, all without leaving the command line.
The CLI is a standalone npm package (lmnr-cli) and ships independently from the @lmnr-ai/lmnr SDK. You do not need the SDK installed to query data or manage datasets.

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Install

# Run directly with npx (no install)
npx lmnr-cli@latest <command>

# Or install globally
npm install -g lmnr-cli

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Authenticate

Every command talks to your Laminar project with a project API key. Grab one from Settings → Project API Keys in the dashboard, then set it once: 
export LMNR_PROJECT_API_KEY=<your-key>
Alternatively, pass --project-api-key <key> on any command. For self-hosted deployments, override the API URL and port: 
lmnr-cli --base-url http://localhost --port 8000 sql schema

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Query data with SQL

Run ClickHouse SQL against your project’s spans, traces, signal events, and more: 
lmnr-cli sql query "SELECT name, duration FROM spans WHERE start_time > now() - INTERVAL 1 HOUR LIMIT 20"
Add --json to emit structured JSON on stdout (messages and errors go to stderr), which makes it trivial to pipe into jq, a local script, or an AI coding agent: 
lmnr-cli sql query "SELECT trace_id, total_cost FROM spans WHERE span_type = 'LLM' LIMIT 10" --json \
  | jq '.[] | select(.total_cost > 0.01)'
List the available tables and columns: 
lmnr-cli sql schema
For the full schema and query guide, see SQL Editor.
Only SELECT queries are allowed. The query runs scoped to your project automatically; no tenant filter is needed in your WHERE clause.

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Manage datasets

List, push, pull, and create datasets from files on disk. Supported formats: .jsonl, .json, .csv. 
# List all datasets in the project
lmnr-cli dataset list --json

# Push new datapoints into an existing dataset
lmnr-cli dataset push data.jsonl -n my-dataset

# Pull a dataset down to a local file
lmnr-cli dataset pull output.jsonl -n my-dataset

# Create a new dataset from a file, writing a local copy with dataset IDs
lmnr-cli dataset create my-dataset data.jsonl -o my-dataset.jsonl
See Datasets CLI for the full dataset workflow including versioning semantics.

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Run the agent debugger

lmnr-cli dev spins up an interactive debugging session for a function in your codebase and connects it to the Laminar debugger UI so you can rerun, inspect, and edit inputs live. 
# TypeScript
lmnr-cli dev agent.ts

# Python (script mode)
lmnr-cli dev agent.py

# Python (module mode)
lmnr-cli dev -m src.agent

# Pick a specific entrypoint when multiple are present
lmnr-cli dev agent.ts --function myAgent
See Debugger for the full workflow.

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Piping and agent-friendly output

The CLI is designed to be scriptable and to plug cleanly into AI coding agents:
  • Structured output on stdout, logs on stderr. Every command that supports it takes --json and prints machine-readable output to stdout, while human-friendly progress messages are written to stderr. This keeps pipes clean.
  • Non-zero exit codes on failure. Scripts and agents can branch on success/failure without parsing output.
  • Stable noun-verb command surface (sql query, dataset push, dataset pull, dataset list, dataset create, dev), so commands are easy for an agent to compose.
Combined with sql query --json, this makes the CLI a drop-in SQL layer for any agent that can run shell commands, with no SDK install required.

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Help

Every command accepts -h / --help: 
lmnr-cli --help
lmnr-cli sql --help
lmnr-cli sql query --help
lmnr-cli dataset --help
lmnr-cli dev --help

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What’s next

SQL Editor

The UI counterpart for ad-hoc SQL queries, with schema reference and examples.

MCP Server

Connect Claude Code, Cursor, or any MCP client to Laminar directly.

Datasets CLI workflow

End-to-end example of pulling, editing, and pushing a dataset.

Debugger

Rerun long-running agents from a checkpoint without leaving the page.