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It is a Rust ecosystem of libraries for running inference on large language models, inspired by llama.cpp. On top of llm, there is a CLI application, llm-cli, which provides a convenient interface for running inference on supported models. | It is the interface between your app and hosted LLMs. It streamlines API requests to OpenAI, Anthropic, Mistral, LLama2, Anyscale, Google Gemini, and more with a unified API. |
Rust ecosystem of libraries for running inference on LLMs;
llm-cli provides a convenient interface for running inference on supported models | Blazing fast (9.9x faster) with a tiny footprint (~45kb installed);
Load balance across multiple models, providers, and keys;
Fallbacks make sure your app stays resilient;
Automatic Retries with exponential fallbacks come by default;
Plug-in middleware as needed;
Battle-tested over 100B tokens |
Statistics | |
GitHub Stars 6.1K | GitHub Stars 9.8K |
GitHub Forks 372 | GitHub Forks 775 |
Stacks 45 | Stacks 2 |
Followers 39 | Followers 4 |
Votes 0 | Votes 0 |
Integrations | |

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It is a framework built around LLMs. It can be used for chatbots, generative question-answering, summarization, and much more. The core idea of the library is that we can “chain” together different components to create more advanced use cases around LLMs.

It allows you to run open-source large language models, such as Llama 2, locally.

It is a project that provides a central interface to connect your LLMs with external data. It offers you a comprehensive toolset trading off cost and performance.

It is a library for building stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain. It extends the LangChain Expression Language with the ability to coordinate multiple chains (or actors) across multiple steps of computation in a cyclic manner.