Infino CLI: create tables, ingest, and search

infino is the command-line interface to the retrieval engine: SQL, full-text (BM25), and vector search over a single copy of your data on object storage, run straight from your terminal — or driven by a coding agent. It wraps the same engine as the Python, Node, and Rust SDKs; there’s nothing extra to run.

Install

brew install infino-ai/tap/infino-cli

npm install -g @infino-ai/infino-cli   # or run once with: npx @infino-ai/infino-cli

cargo install infino-cli

curl --proto '=https' --tlsv1.2 -LsSf \
  https://github.com/infino-ai/infino-cli/releases/latest/download/infino-cli-installer.sh | sh

All install the infino binary. Check it landed:

infino --version   # prints e.g. `infino 0.6.0`

On npm 11 and later the install prints a warning that it blocked a postinstall script. That is expected and the install is fine: the platform binary is fetched on first run instead. If you would rather avoid the warning, use Homebrew or the shell installer.

Connect

Every command targets a storage location with --uri (or the INFINO_URI environment variable):

`--uri`	Storage
`memory://`	in-process, ephemeral
`file://<path>`	local disk
`s3://<bucket>/<prefix>`	Amazon S3 (or S3-compatible: MinIO, Ceph, RustFS)
`az://<container>/<prefix>`	Azure Blob
`gs://<bucket>/<prefix>`	Google Cloud Storage
`https://<host>/<database>`	Infino Cloud

Cloud credentials are passed explicitly with --storage-option KEY=VALUE (repeatable), keyed by object_store’s config strings — the same storage_options map the SDKs take. Nothing is read from AWS_* / AZURE_*; omit them to use ambient cloud identity (IAM role / managed identity).

infino table ls --uri s3://my-bucket \
  --storage-option aws_access_key_id=… \
  --storage-option aws_secret_access_key=… \
  --storage-option aws_region=us-east-1 \
  --storage-option aws_endpoint=https://minio.internal:9000   # S3-compatible

Add --validate to any command to fail fast at connect on bad credentials or an unreachable endpoint, instead of on the first query.

Infino Cloud

Point --uri at your https://api.platform.infino.ws/<database> URL and pass an API key with --api-key (or the INFINO_API_KEY environment variable). Every command is identical to local use — only the --uri changes. Provision the database once with database create:

export INFINO_API_KEY=inf_…
infino database create --uri https://api.platform.infino.ws/my-app
infino bm25-search docs body "object storage" -k 10 --uri https://api.platform.infino.ws/my-app

--fields names the columns to return, and it is worth passing on every search: without it a search returns _id and score only, never your text. On Infino Cloud vector-search requires it; everywhere else it is optional but usually what you want. See the hosted Quickstart and Authentication.

Quickstart

A table becomes durable on its first commit, so table create loads initial rows too: either from a Parquet file (--from-parquet, which also infers the schema) or from a YAML schema plus a data file. Taking the second path, write the two input files first.

- { name: id, type: int64 }
- { name: body, type: large_utf8 }

{"id": 1, "body": "Infino stores your data on object storage and searches it in place."}
{"id": 2, "body": "One copy of the data serves SQL, full-text, and vector search."}

{"id": 3, "body": "A table becomes durable on its first commit."}

The schema is a list of {name, type} columns (see column types); a full-text index needs a large_utf8 column. Each line of the .ndjson file is one row, keyed by column name.

# Create the table and load its first rows (body full-text indexed)
infino table create docs --uri file://./data --schema schema.yaml --fts body --file seed.ndjson
# created table `docs` with 2 rows

# Append more rows from another NDJSON file
infino row insert docs --uri file://./data --file more.ndjson --format ndjson

# Search. `--fields` names the columns to return; without it you get
# `_id` and `score` only.
infino bm25-search docs body "object storage" -k 10 --fields _id,body --uri file://./data

+----------------------------------+---------------------------------------------------------------------+
| _id                              | body                                                                |
+----------------------------------+---------------------------------------------------------------------+
| 32964926044218061712633356091392 | Infino stores your data on object storage and searches it in place. |
+----------------------------------+---------------------------------------------------------------------+

# SQL over the same table
infino query "SELECT id, body FROM docs LIMIT 10" --uri file://./data --output json

Column types

Use these in a --schema YAML file. nullable: true is optional per column.

Type	Notes
`int8` `int16` `int32` `int64`	`int` is an alias for `int64`
`uint8` `uint16` `uint32` `uint64`
`float32` `float64`	`float` and `double` are aliases
`utf8`	short strings; `string` and `str` are aliases
`large_utf8`	text; required for a full-text (`--fts`) column
`bool`	`boolean` is an alias
`date32`	days since the Unix epoch
`fixed_size_list<float32,N>`	a vector column of dimension `N`, for `--vector`

Commands

Commands are grouped by what they act on. query and the search commands stay at the top level, since those are what a session mostly runs.

Command	Description
`table create`	Create a table and load initial rows; declare `--fts` / `--vector` indexes
`table ls` / `table describe`	List tables / show a table’s schema
`table rm`	Remove a table and reclaim its storage (`--keep-storage` leaves the bytes)
`table optimize`	Compact a table
`table gc`	Reclaim orphaned storage objects (requires durable storage)
`row insert`	Append rows from Parquet or NDJSON (file or stdin)
`row update` / `row delete`	Change or remove rows matching a `--where` SQL predicate
`database create`	Provision an Infino Cloud database (a no-op for local backends)
`bm25-search`	Ranked keyword (BM25) search
`vector-search`	Vector similarity (kNN) search
`hybrid-search`	Hybrid BM25 + vector search, fused with reciprocal-rank fusion
`token-match` / `exact-match`	Unranked token / exact-value match
`count`	Count rows matching a keyword query, without fetching them
`query`	Run SQL, including the `bm25_search()` / `vector_search()` table functions
`skills install`	Install the bundled agent skills for Claude Code / Cursor

The commands were flat before 0.6.0 (create-table, tables, ingest, and so on). The old names still parse and report their replacement, so an existing script tells you what to change rather than failing with a generic error. They will be removed in a later release.

Run infino <command> --help for the full flags. Every row-returning command takes --output table (default), json, or csv.

Vector search

The CLI does not embed text — embed your query with your own model and pass the vector as a JSON array (or - for stdin):

infino vector-search docs embedding --vector-file query.json -k 10 --uri file://./data

Declare the column as fixed_size_list<float32,384> in the schema and index it when you create the table: --vector embedding:384:cosine (column:dim:metric). Pass --fields here too, or results come back as _id and score alone.

Agent skills

infino skills install writes skill files into ~/.claude/skills so coding agents (Claude Code, Cursor) can drive the CLI in natural language:

infino skills install
infino skills status

This complements the MCP server: skills are for shell-native agents, MCP is for tool-calling agents.

Learn more

Source, issues, and releases: github.com/infino-ai/infino-cli
Packages: crates.io · npm

SDKs & CLI

REST API

Infino CLI: create tables, ingest, and search

Install

Connect

Infino Cloud

Quickstart

Column types

Commands

Vector search

Agent skills

Learn more

​Install

​Connect

​Infino Cloud

​Quickstart

​Column types

​Commands

​Vector search

​Agent skills

​Learn more

Install

Connect

Infino Cloud

Quickstart

Column types

Commands

Vector search

Agent skills

Learn more