rivet
The open-source visual AI programming environment and TypeScript library
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Rivet is a desktop application for creating complex AI agents and prompt chaining, and embedding it in your application. Rivet currently has LLM support for OpenAI GPT-3.5 and GPT-4, Anthropic Claude Instant and Claude 2, [Anthropic Claude 3 Haiku, Sonnet, and Opus](https://www.anthropic.com/news/claude-3-family), and AssemblyAI LeMUR framework for voice data. Rivet has embedding/vector database support for OpenAI Embeddings and Pinecone. Rivet also supports these additional integrations: Audio Transcription from AssemblyAI. Rivet core is a TypeScript library for running graphs created in Rivet. It is used by the Rivet application, but can also be used in your own applications, so that Rivet can call into your own application's code, and your application can call into Rivet graphs.
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Rivet, the IDE for creating complex AI agents and prompt chaining, and embedding it in your application.
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https://github.com/Ironclad/rivet/assets/448108/ad1d5e74-fe05-444e-8da7-66a1fc5b6848
Rivet is a desktop application for creating complex AI agents and prompt chaining, and embedding it in your application.
Rivet currently has LLM support for:
- OpenAI GPT-3.5 and GPT-4
- Anthropic Claude Instant and Claude 2
- [Anthropic Claude 3 Haiku, Sonnet, and Opus] (https://www.anthropic.com/news/claude-3-family)
- AssemblyAI LeMUR framework for voice data
Rivet has embedding/vector database support for:
Rivet also supports these additional integrations:
For more information on how to use the application and all of its capabilities, see the documentation!
Rivet core is a TypeScript library for running graphs created in Rivet. It is used by the Rivet application, but can also be used in your own applications, so that Rivet can call into your own application's code, and your application can call into Rivet graphs.
For more information on using Rivet Core, see the Rivet Integration Getting Started page and the related API documentation.
Rivet core is available on NPM as @ironclad/rivet-core. Rivet node is available as @ironclad/rivet-node. Documentation for each is available on the Rivet website.
Check out the releases page for all available releases.
See CONTRIBUTING.md for information on building and running Rivet from source.
All types of contributions are welcome - from code to documentation, bug reports, user experience feedback, and new feature suggestions!
Take a moment to read through the CONTRIBUTING.md file for help with setting up your development environment, and how to get started contributing to Rivet.
We use the All Contributors bot to recognize all our contributors, so every contribution is acknowledged. See the Contributors section below for everyone!
The Rivet project is welcome to all contributors, and as such, we have a Code of Conduct that all contributors must follow.
If you have run into any issues while running the Rivet application, or when integrating it into your code, please check the Issues page for any existing issues, and if you can't find any, please open a new issue!
If you have any other questions on using Rivet, or have any other ideas, feel free to open a discussion!
Thanks goes to these wonderful people (emoji key):
This project follows the all-contributors specification. Contributions of any kind are welcome!
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Rivet is a desktop application for creating complex AI agents and prompt chaining, and embedding it in your application. Rivet currently has LLM support for OpenAI GPT-3.5 and GPT-4, Anthropic Claude Instant and Claude 2, [Anthropic Claude 3 Haiku, Sonnet, and Opus](https://www.anthropic.com/news/claude-3-family), and AssemblyAI LeMUR framework for voice data. Rivet has embedding/vector database support for OpenAI Embeddings and Pinecone. Rivet also supports these additional integrations: Audio Transcription from AssemblyAI. Rivet core is a TypeScript library for running graphs created in Rivet. It is used by the Rivet application, but can also be used in your own applications, so that Rivet can call into your own application's code, and your application can call into Rivet graphs.
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