hsql

hsql is your agent's favorite SQL client. It's the headless CLI for Harlequin, and shares the same config and query engine, with an interface optimized for agents, scripts, and automations.

If you already use Harlequin and know about adapters and config files, jump ahead to Running hsql.

Installing hsql

hsql is packaged with Harlequin, so if you already use Harlequin, hsql is already installed.

Otherwise, you can install hsql directly. hsql is a Python program, and there are many ways to install and run it. We strongly recommend using uv:

1. Install uv. From a POSIX shell, run:

   curl -LsSf https://astral.sh/uv/install.sh | sh
   

   Or using Windows Powershell:

   powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
   

2. Install hsql as a tool using uv:

   uv tool install hsql
   

   This command will install hsql into an isolated environment and add it to your PATH so you can easily run the executable.

Installing Database Adapters

hsql can connect to dozens of databases using adapter plug-ins. Adapters are distributed as their own Python packages that need to be installed into the same environment as hsql and harlequin.

For a list of known adapters provided either by the Harlequin maintainers or the broader community, see the adapters page.

The adapter docs also include installation instructions (installing an adapter with Harlequin also installs it for hsql). Some adapters can be installed as Harlequin extras, like postgres. If you used uv to install hsql directly, you can add adapter packages using --with:

uv tool install hsql --with harlequin-postgres

You can install multiple extras:

uv tool install hsql --with harlequin-postgres --with harlequin-mysql

Running hsql

Once hsql is installed, you run it from the command line. If you have used psql or the duckdb CLI, hsql will feel familiar, but hsql has the major advantage that is works with most databases and provides the same interface and produces the same output, regardless of the connected database. This means you (and your agent) can learn one tool, instead of several. In your shell, all hsql commands take the same form:

hsql [OPTIONS] [CONN_STR]

where [OPTIONS] is 0 or more pairs of the form --[option-name] [option-value], and [CONN_STR] is 0 or more connection strings. [OPTIONS] are composed of both hsql options and adapter options. For a full list of options, run hsql with the --help option:

hsql --help

Using hsql with DuckDB

hsql defaults to using its DuckDB database adapter, which ships with hsql and includes the full DuckDB in-process database.

Run a query against an in-memory DuckDB session, run hsql and pass in a query with the -c option:

$ hsql -c "select 1"
 1
---
 1
(1 row)

To query one or more DuckDB database files, pass in relative or absolute paths as connection strings (hsql will create DuckDB databases if they do not exist):

$ hsql "path/to/duck.db" -tAc "select count(*) from orders"
42

Using hsql with SQLite and Other Adapters

hsql also ships with a SQLite3 adapter. To use that adapter, you specify the --adapter sqlite option. Like DuckDB, you can open an in-memory SQLite database by omitting the connection string:

$ hsql --adapter sqlite -c "select 'Ted' as author"
 author
--------
 Ted
(1 row)

You can query one or more SQLite database files by passing in their paths as connection strings; note that the --adapter option has a short alias, -a:

$ hsql -a sqlite "path/to/sqlite.db" -c "select * from users"
 id | name
----+---------
 1  | Ted
 2  | Patrick
(2 rows)

Other adapters work the same way; for example, Postgres:

$ hsql -a postgres "postgresql://example.com/postgres:5432" -c "select * from invoices"

Configuring hsql and Using Profiles

hsql supports a number of options for setting the query limit, configuring output formats, and defining connection parameters. Options can be passed as command-line flags, or read from config files. Config files store configurations under separate profiles, so you can easily switch between databases by reading from different profiles with the -P option:

$ hsql -P prod -c "select count(*) from orders" --csv
$ hsql -P dev -c "select * from users" --vertical --limit 5
$ hsql -P warehouse -c "..." --format parquet -o invoices.pq

hsql reads the same config files as Harlequin, and merges them one profile at a time, nearest file first; pass --config-path PATH to read a single file instead, or -P None to skip them entirely.

Keeping Credentials Out of Config Files

A profile's string values can name environment variables, so a config file your team shares — and commits — holds no credentials:

[profiles.prod]
adapter = "postgres"
host = "${MYHOST:-localhost}"  # ${VAR:-default} supplies a default
password = "${MYPASSWORD}"     # ${VAR} is required; hsql exits 2 if it is unset

Write $${ for a literal ${, if a value really does start with one.

Values an adapter declares as secrets are masked wherever hsql prints them — --config show, --info, and error messages — as is the password inside a connection string.

Inspecting and Writing Config Files

Five --config modes work on your config files instead of running SQL, and none of them connects to a database:

• list-profiles — the names you can pass to -P, with each one's adapter, and which is the default.
• show — the merged config as TOML, with the file each value came from beside it; --json for JSON.
• validate — every problem in every discovered file, exiting 2 if it finds any.
• schema — a JSON Schema for a config file, covering every adapter you have installed; point your editor at it for completion and validation as you type.
• init — a profile written from the options you pass, prompting for nothing: hsql --config init -P prod -a sqlite ./my.db --limit -1 writes [profiles.prod] into the nearest config file.

$ hsql --config show
default_profile = "dev" # from /home/user/.config/harlequin/harlequin.toml

[profiles.dev] # from /home/user/proj/harlequin.toml, overriding /home/user/.config/harlequin/harlequin.toml
adapter = "postgres"
host = "${MYHOST:-localhost}"
password = "********"
limit = 100

list-profiles and validate are result sets, so --csv, -t, -A and -o work on them as they do on a query.

Data Layouts and File Formats

hsql supports all of the following formats for displaying and writing data:

• table
• markdown (alias: md)
• vertical
• csv
• tsv
• json
• jsonl (alias: ndjson)
• parquet
• orc
• feather (alias: arrow)
• none (suppresses output)

You can select a format with the --format <name> or using the shorthand --<name>, so these are equivalent: --format csv, --csv.

Some layouts can present the results from multiple queries. Others will raise an error and exit with code 2 if multiple queries are executed.

Additionally, for any layout, pass --stats to print summary info as JSON to stderr:

$ hsql -c "select 1" --format none  --stats
{"status":"ok","statements":1,"rows":1,"truncated":false,"limit":500,"elapsed_ms":1,"columns":[{"name":"1","type":"#"}]}

Exploring the Catalog

Before you can write a query, you have to know what is in the database. --catalog lists the objects one level below --path, and exits without running SQL:

$ hsql "path/to/duck.db" --catalog
 path | name | query_name | type     | type_label
------+------+------------+----------+------------
 duck | duck | "duck"     | database | db
(1 row)

Every row's path is what lists that object's own children, so you walk down one level at a time:

$ hsql "path/to/duck.db" --catalog --path duck
 path           | name      | query_name         | type   | type_label
----------------+-----------+--------------------+--------+------------
 duck.analytics | analytics | "duck"."analytics" | schema | sch
 duck.main      | main      | "duck"."main"      | schema | sch
(2 rows)

$ hsql "path/to/duck.db" --catalog --path duck.analytics
 path                        | name         | query_name                 | type       | type_label
-----------------------------+--------------+----------------------------+------------+------------
 duck.analytics.customers    | customers    | "analytics"."customers"    | BASE TABLE | t
 duck.analytics.order_totals | order_totals | "analytics"."order_totals" | VIEW       | v
 duck.analytics.orders       | orders       | "analytics"."orders"       | BASE TABLE | t
(3 rows)

$ hsql "path/to/duck.db" --catalog --path duck.analytics.orders
 path                              | name        | query_name    | type          | type_label
-----------------------------------+-------------+---------------+---------------+------------
 duck.analytics.orders.customer_id | customer_id | "customer_id" | BIGINT        | ##
 duck.analytics.orders.id          | id          | "id"          | BIGINT        | ##
 duck.analytics.orders.placed_at   | placed_at   | "placed_at"   | TIMESTAMP     | ts
 duck.analytics.orders.total       | total       | "total"       | DECIMAL(18,2) | #.#
(4 rows)

A listing is rows, so every format and output option applies to it, exactly as they do to a query:

$ hsql "path/to/duck.db" --catalog --path duck.analytics -tA --csv
duck.analytics.customers,customers,"""analytics"".""customers""",BASE TABLE,t
duck.analytics.order_totals,order_totals,"""analytics"".""order_totals""",VIEW,v
duck.analytics.orders,orders,"""analytics"".""orders""",BASE TABLE,t

Searching the Catalog

Walking is the wrong tool for where does orders live and which tables have a customer_id. --catalog-search TERM searches every level of the catalog at once, for objects whose name contains TERM:

$ hsql "path/to/duck.db" --catalog-search customer_id
 path                                    | name        | query_name    | type   | type_label
-----------------------------------------+-------------+---------------+--------+------------
 duck.analytics.order_totals.customer_id | customer_id | "customer_id" | BIGINT | ##
 duck.analytics.orders.customer_id       | customer_id | "customer_id" | BIGINT | ##
 duck.main.staging_events.customer_id    | customer_id | "customer_id" | BIGINT | ##
(3 rows)

--path narrows the search to one subtree, and the output can be laid out like any query:

$ hsql "path/to/duck.db" --catalog-search order --path duck.analytics -tA
duck.analytics.order_totals|order_totals|"analytics"."order_totals"|VIEW|v
duck.analytics.orders|orders|"analytics"."orders"|BASE TABLE|t

Note: not all adapters support catalog search at this time.

Scripting with hsql

hsql can write data to files, either with the -o option or by piping output (hsql only writes data to stdout; other messages go to stderr):

$ hsql -P prod --limit -1 -c "select * from users" --format parquet -o "users.pq"
$ hsql -P prod --limit -1 -c "select * from users" --csv > users.csv

hsql can execute multiple statements in one invocation, and supports several methods for doing so:

• Pass -c multiple times
• Include multiple queries, separated by ;, in one -c option
• Pass one or more .sql files with -f, with multiple statements in each
• Use --result to define which queries output data to stdout
• Use --on-error to either stop or continue if one or more queries produces an error.

In other words, this works:

$ hsql -P prod --limit -1 --format md --result all --on-error stop \
    -f ./setup.sql \
    -c "select count(*) from raw_table" \
    -f ./build-models.sql \
    -c "select count(*) from modeled_table" 

hsql's exit codes are meaningful and stable:

• 0: Success
• 1: Query error
• 2: Usage/config error
• 3: Connection error
• 4: Timeout
• 130: Interrupted

You can also use --stats and jq together to error on a truncated query:

hsql --limit -1 -c "select 1" --csv -o data.csv --stats 2>&1 | jq -e '.truncated | not' > /dev/null

Running Safely

Two options bound what an invocation can do, and hsql refuses to run at all if the adapter cannot enforce them.

--read-only (or -r) connects in a mode the database itself refuses writes in:

$ hsql -r "path/to/duck.db" -c "insert into orders values (1)"
hsql: error: Invalid Input Error: Cannot execute statement of type "INSERT" on database "duck" which is attached in read-only mode!

--timeout SECONDS limits the duration of executing and fetching a query, and exits 4 when it runs out:

$ hsql --timeout 0.5 -c "select count(*) from range(100000000000) t(i)"
hsql: error: timed out after 0.5s

Both are also profile keys:

[profiles.agent]
adapter = "postgres"
read_only = true
timeout = 30

hsql refuses to open a connection with an adapter that cannot enforce --read-only or --timeout.

hsql --info reports implements_read_only and implements_cancel for every installed adapter.

Describing hsql to an Agent

hsql --help is written for a person; two flags answer the same questions as JSON, so an agent can find its way around an installation it has never seen. --spec is a machine-readable --help: every option on the command, plus the connection options every installed adapter declares, each with its type, choices, default and help text. --info describes the installation instead — versions, platform, the config files hsql found, the profile that would be active, and what each installed adapter declares it supports — so a script can check that an adapter is there, and what it can do, before depending on it.

$ hsql --info -a sqlite | jq '.adapters.sqlite'
{
  "distribution": "harlequin",
  "version": "2.10.0",
  "capabilities": {
    "implements_cancel": true,
    "implements_catalog_search": true,
    "implements_read_only": true,
    "implements_validate_sql": false
  },
  "error": null
}

Neither connects to a database, both write JSON to stdout, and -a NAME narrows either one to a single adapter, which is much faster than importing them all.

Installing the Agent Skill

hsql ships an Agent Skill that teaches an agent how to drive it: ask before you assume, keep credentials off the command line, read the catalog before writing SQL, consider a warm session, watch the 500-row limit, branch on the exit code, and hand off to harlequin when a human should drive. hsql --skill installs it, with no network and matched to the version you have:

$ hsql --skill -o ~/.claude/skills/hsql/          # for you, in every project
$ hsql --skill -o .claude/skills/hsql/            # for this repo, committed with it
note: wrote 6 files to .claude/skills/hsql: SKILL.md, references/config.md, references/queries.md, references/scripting.md, references/sessions.md, references/troubleshooting.md

With no -o, hsql --skill writes SKILL.md to stdout, so you can read it before you install it.

In Claude Code, the same skill is a plugin in Harlequin's repository:

/plugin marketplace add tconbeer/harlequin
/plugin install hsql@harlequin

Keep Reading at harlequin.sh

Visit harlequin.sh for an overview of features and full documentation.

Getting Help

To view all command-line options for Harlequin and all installed adapters, after installation, simply type:

hsql --help

GitHub Discussions are a good place to ask questions, request features, and say hello.

GitHub Issues are the best place to report bugs.

Sponsoring Harlequin and hsql

Please consider sponsoring Harlequin's author, so he can continue to dedicate time to hsql.

Contributing

Thanks for your interest in Harlequin! Harlequin and hsql are primarily maintained by Ted Conbeer, but he welcomes all contributions!

Please see CONTRIBUTING.md for more information.

Differences from psql

-c, -f, -t, -A and -x mean what they mean in psql, so hsql -tAc "select count(*) from orders" prints a bare number, exactly as it would there. These do not: