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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
| Date | Stars |
|---|---|
| 2026-07-31 | 611 |
| 2026-08-04 | 612 |
| 2026-08-06 | 612 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
35.0
growth rate 0.00%/day
<!-- README.md is generated from README.Rmd. Please edit that file -->
# ellmer <a href="https://ellmer.tidyverse.org"><img src="man/figures/logo.png" align="right" height="138" alt="ellmer website" /></a>
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[](https://lifecycle.r-lib.org/articles/stages.html#experimental)
[](https://github.com/tidyverse/ellmer/actions/workflows/R-CMD-check.yaml)
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ellmer makes it easy to use large language models (LLM) from R. It
supports a wide variety of LLM providers and implements a rich set of
features including streaming outputs, tool/function calling, structured
data extraction, and more.
ellmer is one of a number of LLM-related packages created by Posit:
- Looking for something similar in python? Check out
[chatlas](https://github.com/posit-dev/chatlas)!
- Want to evaluate your LLMs? Try
[vitals](https://vitals.tidyverse.org).
- Need RAG? Take a look at [ragnar](https://ragnar.tidyverse.org).
- Want to make a beautiful LLM powered chatbot? Consider
[shinychat](https://posit-dev.github.io/shinychat/).
- Working with MCP? Check out
[mcptools](https://posit-dev.github.io/mcptools/).
## Installation
You can install ellmer from CRAN with:
``` r
install.packages("ellmer")
```
## Providers
ellmer supports a wide variety of model providers. Official providers
are actively maintained, with priority support for bug fixes and new
features. Community providers are contributed and maintained by the
community; contributions to improve them are especially welcome.
### Official providers
- Anthropic’s Claude: `chat_anthropic()`.
- AWS Bedrock: `chat_aws_bedrock()`.
- Azure OpenAI: `chat_azure_openai()`.
- Databricks: `chat_databricks()`.
- DeepSeek: `chat_deepseek()`.
- Google Gemini/Vertex AI: `chat_google_gemini()`,
`chat_google_vertex()`.
- Ollama: `chat_ollama()`.
- OpenAI: `chat_openai()`.
- Posit AI: `chat_posit()`.
- Snowflake Cortex: `chat_snowflake()` and `chat_cortex_analyst()`.
### Community providers
- Cloudflare: `chat_cloudflare()`.
- Groq: `chat_groq()`.
- Hugging Face: `chat_huggingface()`.
- LM Studio: `chat_lmstudio()`.
- Mistral: `chat_mistral()`.
- OpenRouter: `chat_openrouter()`.
- perplexity.ai: `chat_perplexity()`.
- Portkey: `chat_portkey()`.
- VLLM: `chat_vllm()`.
### Provider/model choice
If you’re using ellmer inside an organisation, you may have internal
policies that limit you to models from big cloud providers,
e.g. `chat_azure_openai()`, `chat_aws_bedrock()`, `chat_databricks()`,
or `chat_snowflake()`.
If you’re using ellmer for your own exploration, you’ll have a lot more
freedom, so we have a few recommendations to help you get started:
- `chat_openai()` or `chat_anthropic()` are good places to start.
`chat_openai()` defaults to **GPT-5.4**, but you can use
`model = "gpt-5.4-nano"` for a cheaper, faster model.
`chat_anthropic()` defaults to **Claude Sonnet 4.6**, which we have
found to be particularly good at writing R code.
- `chat_google_gemini()` is a strong model with a free tier (with the
downside that [your data is
used](https://ai.google.dev/gemini-api/terms#unpaid-services) to
improve the model), making it a great place to start if you don’t want
to spend any money.
- `chat_ollama()`, which uses [Ollama](https://ollama.com), allows you
to run models on your own computer. While the biggest models you can
run locally aren’t as good as the state of the art hosted models, they
don’t share your data and are effectively free.
### Authentication
Authentication works a little differently depending on the provider. A
few popular ones (including OpenAI and Anthropic) require you to obtain
an API key. We recommend you save it in an environment variable rather
than using it directly in your code, and if you deploy an app or report
that uses ellmExcerpt of 9,130 characters
Read on GitHub
Hadley Wickham · @posit-pbc · United States
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Garrick Aden-Buie · @posit-dev · United States
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Charlie Gao · @posit-pbc · United Kingdom
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Brandon Greenwell · 84.51° and University of Cincinnati
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f60c2e027b6c0663, llm:Repository description: 'Call LLM APIs from R' (tidyverse/ellmer).
matched fp:f60c2e027b6c0663, llm:Repository description: 'Call LLM APIs from R' (tidyverse/ellmer).