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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. | Create high-quality 8-second videos with VEO 3, AI video generator. Generate cinematic videos with native audio |
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 | Pricing, How to Prompt, Showcase, Sora 2, VEO 3.1 |
Statistics | |
GitHub Stars 9.8K | GitHub Stars - |
GitHub Forks 775 | GitHub Forks - |
Stacks 2 | Stacks 0 |
Followers 4 | Followers 1 |
Votes 0 | Votes 1 |
Integrations | |
| No integrations available | |

Zencoder downloads the video and converts it to as many formats as you need. Every output is encoded concurrently, with virtually no waiting—whether you do one or one hundred. Zencoder then uploads the resulting videos to a server, CDN, an S3 bucket, or wherever you dictate in your API call.

It is a WebRTC media server and a set of client APIs making simple the development of advanced video applications for WWW and smartphone platforms. Media Server features include group communications, transcoding and more.

It is a library for constructing graphs of media-handling components. The applications it supports range from simple Ogg/Vorbis playback, audio/video streaming to complex audio (mixing) and video (non-linear editing) processing.

Cloudflare Stream makes integrating high-quality streaming video into a web or mobile application easy. Using a single, integrated workflow through a robust API or drag and drop UI, application owners can focus on creating the best video experience.

That transforms AI-generated content into natural, undetectable human-like writing. Bypass AI detection systems with intelligent text humanization technology

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.

It is a platform for building production-grade LLM applications. It lets you debug, test, evaluate, and monitor chains and intelligent agents built on any LLM framework and seamlessly integrates with LangChain, the go-to open source framework for building with LLMs.