## LLM CLI: llm [`llm`](https://pypi.org/project/llm) is a command-line utility for interacting with large language models—simplifying prompts, managing models and plugins, logging every conversation, and extracting structured data for pipelines. [](https://youtu.be/QUXQNi6jQ30?t=100) ### Basic Usage [Install llm](https://github.com/simonw/llm#installation). Then set up your [`OPENAI_API_KEY`](https://platform.openai.com/api-keys) environment variable. See [Getting started](https://github.com/simonw/llm?tab=readme-ov-file#getting-started). **TDS Students**: See [Large Language Models](large-language-models.md) for instructions on how to get and use `OPENAI_API_KEY`. ```bash # Run a simple prompt llm 'five great names for a pet pelican' # Continue a conversation llm -c 'now do walruses' # Start a memory-aware chat session llm chat # Specify a model llm -m gpt-4.1-nano 'Summarize tomorrow’s meeting agenda' # Extract JSON output llm 'List the top 5 Python viz libraries with descriptions' \ --schema-multi 'name,description' ``` Or use llm without installation using [`uvx`](uv.md): ```bash # Run llm via uvx without any prior installation uvx llm 'Translate "Hello, world" into Japanese' # Specify a model uvx llm -m gpt-4.1-nano 'Draft a 200-word blog post on data ethics' # Use structured JSON output uvx llm 'List the top 5 programming languages in 2025 with their release years' \ --schema-multi 'rank,language,release_year' ``` ### Key Features - **Interactive prompts**: `llm '…'` — Fast shell access to any LLM. - **Conversational flow**: `-c '…'` — Continue context across prompts. - **Model switching**: `-m MODEL` — Use OpenAI, Anthropic, local models, and more. - **Structured output**: `llm json` — Produce JSON for automation. - **Logging & history**: `llm logs path` — Persist every prompt/response in SQLite. - **Web UI**: `datasette "$(llm logs path)"` — Browse your entire history with Datasette. - **Persistent chat**: `llm chat` — Keep the model in memory across multiple interactions. - **Plugin ecosystem**: `llm install PLUGIN` — Add support for new models, data sources, or workflows. ([Language models on the command-line - Simon Willison's Weblog](https://simonwillison.net/2024/Jun/17/cli-language-models/?utm_source=chatgpt.com)) ### Practical Uses - **Automated coding**. Generate code scaffolding, review helpers, or utilities on demand. For example, after running`llm install llm-cmd`, run `llm cmd 'Undo the last git commit'`. Inspired by [Simon’s post on using LLMs for rapid tool building](https://simonwillison.net/2025/Mar/11/using-llms-for-code/). - **Transcript processing**. Summarize YouTube or podcast transcripts using Gemini. See [Putting Gemini 2.5 Pro through its paces](https://www.macstories.net/mac/llm-youtube-transcripts-with-claude-and-gemini-in-shortcuts/). - **Commit messages**. Turn diffs into descriptive commit messages, e.g. `git diff | llm 'Write a concise git commit message explaining these changes'`. - **Data extraction**. Convert free-text into structured JSON for automation. [Structured data extraction from unstructured content using LLM schemas](https://simonwillison.net/2025/Feb/28/llm-schemas/).