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.

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)
`az://<container>/<prefix>`	Azure Blob

S3/Azure credentials are read from the ambient environment (AWS_*, AZURE_*).

Quickstart

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

# Append more rows
infino ingest docs --uri file://./data --file more.ndjson --format ndjson

# Search
infino bm25-search docs body "object storage" -k 10 --uri file://./data
infino query "SELECT id, body FROM docs LIMIT 10" --uri file://./data --output json

A table becomes durable on its first commit, so create-table loads initial rows too — from a Parquet file (--from-parquet, which also infers the schema) or a YAML schema plus --file.

Commands

Command	Description
`create-table`	Create a table and load initial rows; declare `--fts` / `--vector` indexes
`ingest`	Append rows from Parquet or NDJSON (file or stdin)
`bm25-search`	Ranked keyword (BM25) search
`vector-search`	Vector similarity (kNN) search
`token-match` / `exact-match`	Unranked token / exact-value match
`query`	Run SQL, including the `bm25_search()` / `vector_search()` table functions
`tables` / `describe`	List tables / show a table’s schema
`update` / `delete`	Change rows matching a `--where` SQL predicate
`optimize`	Compact a table
`skills install`	Install the bundled agent skills for Claude Code / Cursor

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

Give the column a vector index when you create the table: --vector embedding:384:256:cosine (column:dim:centroids:metric).

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

Get started

Guides

Reference

Infino CLI: create tables, ingest, and search

Install

Connect

Quickstart

Commands

Vector search

Agent skills

Learn more

​Install

​Connect

​Quickstart

​Commands

​Vector search

​Agent skills

​Learn more

Install

Connect

Quickstart

Commands

Vector search

Agent skills

Learn more