pkg.sopackage field notes

brew / rank 1176

Install llm with Homebrew

Access large language models from the command-line. Version 0.31.1 via Homebrew; verified 2026-07-10.

install

Additional install commands

macOS

Homebrewverified ยท 100%
brew install llm

provider-native install command

overview

Package summary

Access large language models from the command-line

Commands and aliases

  • llm

history

Project history and usage

LLM is Simon Willison's command-line tool and Python library for working with large language models. It started as an OpenAI-focused CLI in 2023 and evolved into a plugin-based interface for remote APIs, local models, embeddings, prompt templates, logging, attachments, schemas, and tool use.

Project history

The changelog records version 0.1 on April 1, 2023 as the initial prototype release. Version 0.5 on July 12, 2023 added a plugin mechanism for additional language models, a key change that moved LLM beyond a single-provider OpenAI wrapper.

During 2023 and 2024, LLM added chat, embeddings, templates, SQLite logging and search, model aliases, attachments, async models, and broader provider support. In May 2025, version 0.26 added tool support, letting models execute Python functions through the CLI and Python API.

Adoption history

LLM became part of the Datasette-adjacent command-line culture around small composable tools, SQLite-backed logs, and plugin systems. Homebrew, Debian, and Nix packaging made it easy to install as a normal developer utility rather than only as a Python package.

Its adoption expanded with the growth of provider-specific plugins and local-model integrations, giving users one command-line interface across OpenAI, Anthropic, Gemini, Ollama-backed models, and many community plugins.

How it is used

Common uses include running one-off prompts from the shell, piping files into a model, starting interactive chats, storing API keys, saving prompt templates, logging responses to SQLite, generating embeddings, and calling models from Python code.

The documented configuration paths and keys.json locations matter to package users because Homebrew installs the executable, while user-specific model keys, templates, logs, and extra model definitions live outside the package prefix.

Why package nerds care

LLM is package-nerd significant because it turns rapidly changing AI APIs into a stable Unix-style command with plugins. The packaging story is unusually important: users want a single binary-ish command in PATH, but the real extension surface is Python plugins and user configuration.

It is also a useful example of modern CLI state management: credentials, templates, provider models, logs, and tool definitions are intentionally external to the package, so upgrades can move the application forward without overwriting user data.

Timeline

  • 2023-04-01: LLM 0.1 initial prototype release.
  • 2023-07-12: LLM 0.5 added the plugin mechanism for additional language models.
  • 2024-10-29: LLM 0.17 added attachment support for multimodal models.
  • 2025-05-27: LLM 0.26 added tool support.
  • 2026-06-09: LLM 0.32a3 appeared in the changelog with tool-call and human-in-the-loop improvements.

Related projects

  • Datasette is related through the broader Simon Willison/Datasette tooling ecosystem and the io.datasette.llm application data namespace.
  • LLM plugins such as provider adapters and local-model integrations are central to the project's architecture.
  • SQLite is related through LLM's prompt and response logging features.

security posture

No protected-tool coverage found yet

No matching local secret-handling manifest was found for llm. Nucleus package metadata is still published here so future coverage has a stable package URL.

Install behavior

  • No Homebrew bottle metadata was recorded.

Recommended review

Before unattended agent use, check whether the tool reads plaintext credentials, writes remote state, publishes artifacts, or shells out to plugins.

local files

Configuration and credential file locations

These source-backed paths show where this package keeps local settings or durable credentials. Automic Vault can use them as review targets for secret scanning, migration, and command approval.

Configuration files

Config paths the tool may read or write during local use.

Linux
~/.config/io.datasette.llm/~/.config/io.datasette.llm/templates/*.yaml~/.config/io.datasette.llm/extra-openai-models.yaml
macOS
~/Library/Application Support/io.datasette.llm/~/Library/Application Support/io.datasette.llm/templates/*.yaml~/Library/Application Support/io.datasette.llm/extra-openai-models.yaml

Credential files

Credential-bearing paths to review before unattended agent runs.

Linux
~/.config/io.datasette.llm/keys.json
macOS
~/Library/Application Support/io.datasette.llm/keys.json

executables

Installed executables

CommandKindExposureNote
llmexecutableindexed executableDiscovered from the local executable index.

freshness

Version and freshness

These signals separate page generation age, package-manager activity, and upstream release comparison. Version lag is warned only when an evidence URL and comparable versions are present.

page generated2026-08-03
manager version0.31.1
manager updated2026-07-10
local dataunknown
upstreamnot available
latest detectednot detected
  • okNo freshness warnings were generated.

install metadata

Package metadata

Package keybrew:llm
Version0.31.1
Package managerHomebrew
Homepagehttps://llm.datasette.io/
Last updated2026-07-10T19:29:48Z
Pulseupdated
Bottlenot recorded
Servicenone declared

source trail

Generated from repository data

This page is generated by av-web from the private package SQLite artifact built by scripts/generate-pkg-sqlite.py.

Used sources

  • Geiger risk classifier
  • Nucleus package database
  • curated configuration and credential file locations
  • curated package history
  • pkgdb category and tag curation