simonw/llm
llm
Access large language models from the command-line
Usage guide
llm is an open-source project around llms, openai with 12,115 GitHub stars. This guide focuses on when to use it, how to install it, how to run the first example, and what to verify before adopting it.
Key features
- Implemented mainly in Python, useful for judging integration effort in a similar stack.
- GitHub detected the Apache-2.0 repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
- The project has a homepage, so cross-check docs, examples, and release information beyond GitHub.
Best for
- Evaluating llm for Python AI workflows.
- Comparing a GitHub project with 12,115 stars and current repository activity.
Pros
- llm has visible GitHub traction with 12,115 stars. Topics: ai, llms, openai.
- The project provides an external homepage for deeper evaluation.
Cons
- Production fit still depends on documentation depth, issue activity, and release cadence.
- License review should confirm the Apache-2.0 terms fit your use case.
Production readiness
llm should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
Apache-2.0 is reported by GitHub; review the repository license before redistribution or commercial use.
llm architecture preview
llm's main path starts at the entry surface, runs through llm core runtime, combines OpenAI, Files / repository context, Shell commands, and returns User-facing result.
Entry
Web / product entry
Users start from a web UI, hosted product surface, or browser-based workflow.
https://llm.datasette.io
Runtime
llm core runtime
The core coordinates project logic, configuration, and AI-related execution in Python.
Python
Model
OpenAI
Model calls are likely routed through OpenAI based on README and topic signals.
OpenAI
Context
Files / repository context
Context comes from Files / repository context, which constrains what the model or runtime can use.
Files / repository context
Tools
Shell commands
Tool adapters let the runtime act outside the model through Shell commands.
Shell commands
Output
User-facing result
The final output is returned to the user, workflow, API caller, or downstream system.
output
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Install tutorial
Before you install
- Python runtime and an isolated virtual environment
- A clean working directory for the first test run
Check the runtime environment
llm depends on a Python-style environment. Use venv, conda, or a container to keep dependencies isolated.
Get the project files
Start from the official repository or package so the first run matches the documented behavior.
$ git clone https://github.com/simonw/llm.gitInstall or build dependencies
Run the next setup command detected from the project documentation.
$ pip install llmAdoption guidance and sources
Practical use cases
Access large language models from the command-line
This is one of the documented reasons to evaluate llm before choosing a stack.
Focus area: ai
This is one of the documented reasons to evaluate llm before choosing a stack.
All project comparison
Compare llm with similar projects before committing to a stack.
Before adopting
- Complete one clean-environment verification using the official llm setup path.
- Review repository license, model weights, external services, and dependency terms for your use case.
- Check recent commits, release cadence, issue response, and documentation depth.
- Evaluate output quality, latency, resource usage, and recovery behavior with a small dataset.
Configuration notes
- Review README configuration notes before using production data.
Sources checked
These links are used to verify repository, documentation, or tutorial details. Review the source pages before adopting the project.
Troubleshooting
- If installation fails, first confirm the command is being run from the README-specified directory.
- If dependencies conflict, retry in a fresh virtual environment, container, or working directory.
- If output looks wrong, return to the smallest documented llm example before adding complex data.
- For keys, model files, or external services, verify environment variables, local paths, and permissions one by one.
- Before production use, review recent updates, open issues, license terms, and safety boundaries.
What is llm?
llm is an open-source all project. Access large language models from the command-line
How do I install llm?
Start with the official README. The first detected setup step is: git clone https://github.com/simonw/llm.git.
Is llm beginner-friendly?
If you already know the Python ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can llm be used commercially?
GitHub detected the Apache-2.0 repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
Does llm need a GPU?
GPU requirements depend on the workload, model, and dataset size. Start with the smallest README example before scaling up.
How should I decide whether to adopt llm?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.