Lance Store#

Installation#

bash

python3 -m pip install -r requirements.txt

A persistent data store using the Lance columnar format for fast ML data access.

Quick Start#

bash

# List all datasets and their metadata
python3 scripts/command.py list-datasets-info

# Create a dataset
python3 scripts/command.py create-dataset <name> <field1> <field2> ...

# Append data
python3 scripts/command.py append-to-dataset <name> <value1> <value2> ...

# Read all records from a dataset
python3 scripts/command.py read-dataset <name>

Note: list-datasets-info shows dataset metadata (schema, field types, record count) — it does not return the actual data rows. Use read-dataset to retrieve records.

Storage Location#

DataSets are created and stored on the current path '.'

Critical Behavior: Data Type Strictness#

⚠️ Lance is strict about data types — they CANNOT change after the first record

When you append the first record to a dataset, Lance infers the data type for each field. All subsequent records MUST use the same types.

Example — this FAILS:

text

# First record: age as STRING
append-to-dataset users "John" "25" "john@test.com"

# Second record: age as INTEGER (will FAIL!)
append-to-dataset users "Jane" 30 "jane@test.com"
# Error: `age` should have type large_string but type was int64

Correct approach — maintain consistent types:

text

# First record: age as STRING
append-to-dataset users "John" "25" "john@test.com"

# Second record: age as STRING
append-to-dataset users "Jane" "30" "jane@test.com"

Why This Matters#

Unlike traditional databases that may coerce types, Lance rejects type mismatches. If you store numbers as strings initially, you must always pass strings. Plan your schema carefully.

Initialization Workflow#

When starting a session, always initialize by listing existing datasets first:

bash

# This command returns ALL datasets with their structure
python3 scripts/command.py list-datasets-info

Example output:

json

{
    "skill": "lance",
    "operation": "list_datasets_info",
    "status": "success",
    "data": [
        {
            "dataset_name": "users",
            "path": "/data/users",
            "fields": ["name", "age", "email"],
            "field_types": {
                "_id": "large_string",
                "_updated_at": "timestamp[us]",
                "name": "large_string",
                "age": "large_string",
                "email": "large_string"
            },
            "record_count": 2,
            "columns": ["id", "_updated_at", "name", "age", "email"],
            "last_updated": "2026-03-21T17:57:44.595628"
        }
    ],
    "error": null
}

Understanding field_types#

StateMeaning{} (empty)Dataset exists but no records yet — types not yet definedpopulatedTypes are locked — appends must match

Important: If field_types is empty, the first append will define types. Be deliberate about the first record's types.

Commands Reference#

Create Dataset#

bash

python3 scripts/command.py create-dataset <name> <field1> <field2> ...

Creates a metadata entry. Fields have no types until first append.

Append Record#

bash

python3 scripts/command.py append-to-dataset <name> <value1> <value2> ...

Appends one record. Types are inferred from first record.

Batch Append#

bash

python3 scripts/command.py batch-append-to-dataset <name> '<json-array>'

Example: batch-append-to-dataset users '[["Alice", "22", "alice@test.com"], ["Bob", "35", "bob@test.com"]]'

Update Record#

bash

python3 scripts/command.py update-dataset-record <name> <record_id> <value1> <value2> ...

Updates fields for a specific record by ID.

Delete Record#

bash

python3 scripts/command.py delete-dataset-record <name> <record_id>

List All Datasets#

bash

python3 scripts/command.py list-datasets

Get Dataset Info#

bash

python3 scripts/command.py get-dataset-info <name>

Returns schema, field types (if data exists), and record count.

List All Datasets with Full Info#

bash

python3 scripts/command.py list-datasets-info

Recommended for initialization. Returns all datasets with complete metadata.

Get Dataset Path#

bash

python3 scripts/command.py get-dataset-path-info <name>

Backup Dataset#

bash

python3 scripts/command.py backup-dataset <name> <backup_path>

Count Records#

bash

python3 scripts/command.py count-records <name>

Read All Records#

Returns all records from the dataset as a list of objects.

bash

python3 scripts/command.py read-dataset <name>

Drop Dataset#

Requires confirmation if have not created a backup beforehand.#

Delete the entire dataset and its metadata.

bash

python3 scripts/command.py drop-dataset <name>

Internal fields available in every dataset:

FieldTypeDescription_idstringUUID — unique record identifier_updated_attimestampWhen the record was last inserted or updated

List Records (Paginated)#

bash

python3 scripts/command.py list-records <name> --limit 10 --offset 0

Returns records with optional pagination.

Get Single Record#

bash

python3 scripts/command.py get-record <name> <record_id>

Retrieves a specific record by its UUID.

Get Dataset Info#

bash

python3 scripts/command.py get-dataset-info <name>

Returns schema, field types (if data exists), and record count.

Response Format#

All commands return JSON:

json

{
  "skill": "lance",
  "operation": "<operation_name>",
  "status": "success|error",
  "data": <result_data_or_null>,
  "error": <error_message_or_null>
}

Internal Fields#

Every dataset automatically includes:

• _id — UUID for each record
• _updated_at — timestamp of last insert/update

These are managed automatically — when appending, only provide your defined fields.

Data Type Inference#

Lance infers types from the first record:

Python TypeLance Type"string"``large_string``25 (int)int64``25.5 (float)float64``True/ False``bool

CLI caveat: When passing via command line, all values are strings. To ensure integer types, initialize with actual integers in a script rather than CLI.

Tips#

1. Initialize at session start: Run list-datasets-info to understand what data already exists
2. Plan your schema: First record determines types for the entire dataset
3. Use batch append when adding multiple records: More efficient than individual appends

Requirements#

Dependencies are declared in frontmatter ( metadata.openclaw.install) and handled by the OpenClaw install system via uv. The Python packages required are:

• pylance — The Lance columnar format library.

  ⚠️ Naming note: Despite the PyPI package being named pylance, the library is imported as import lance in Python code. This is the official Lance project naming convention — it is NOT the VS Code "pylance" language server. See lance.org for details.

• pandas — Data manipulation