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Elixir implementation of a LangChain style framework that lets Elixir projects integrate with and leverage LLMs.
| Date | Stars |
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| 2026-07-31 | 1188 |
| 2026-08-01 | 1188 |
| 2026-08-02 | 1188 |
| 2026-08-03 | 1188 |
| 2026-08-04 | 1189 |
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| 2026-08-06 | 1189 |
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[](https://github.com/brainlid/langchain/actions/workflows/elixir.yml) [](https://hex.pm/packages/langchain) [](https://hexdocs.pm/langchain) #  Elixir LangChain Elixir LangChain enables Elixir applications to integrate AI services and self-hosted models into an application. **Supported chat models:** - **Anthropic Claude** - Claude models including extended thinking support and AWS Bedrock - **AWS Bedrock Mantle** - OpenAI-compatible gateway for third-party models hosted on Bedrock (Moonshot Kimi K2 family, OpenAI gpt-oss, and many others) - **OpenAI ChatGPT** - GPT models via the Chat Completions API - **OpenAI Responses API** - OpenAI's newer Responses API with WebSocket transport support - **Cloudflare Workers AI** - OpenAI-compatible gateway via `ChatOpenAI` (e.g. Moonshot Kimi K2.6 and other Workers AI models) - **xAI Grok** - Grok-4, Grok-3-mini, Grok-4 Heavy (multi-agent), and more - **Google Gemini** - Gemini AI models - **Google Vertex AI** - Google's enterprise AI offering - **DeepSeek** - DeepSeek models with prompt caching support - **Ollama** - Locally hosted open-source models - **Mistral** - Mistral AI models - **Perplexity** - Perplexity AI models - **orq.ai** - orq.ai Deployments API - **Bumblebee** - Self-hosted models via Nx (Llama, Mistral, Zephyr) - **ReqLLM** - Multi-provider adapter via the `req_llm` library (Anthropic, OpenAI, Gemini, Groq, Ollama, AWS Bedrock, etc.) **LangChain** is short for Language Chain. An LLM, or Large Language Model, is the "Language" part. This library makes it easier for Elixir applications to "chain" or connect different processes, integrations, libraries, services, or functionality together with an LLM. **LangChain** is a framework for developing applications powered by language models. It enables applications that are: - **Data-aware:** connect a language model to other sources of data - **Agentic:** allow a language model to interact with its environment The main value props of LangChain are: 1. **Components:** abstractions for working with language models, along with a collection of implementations for each abstraction. Components are modular and easy-to-use, whether you are using the rest of the LangChain framework or not 1. **Off-the-shelf chains:** a structured assembly of components for accomplishing specific higher-level tasks Off-the-shelf chains make it easy to get started. For more complex applications and nuanced use-cases, components make it easy to customize existing chains or build new ones. ## What is this? Large Language Models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. But using these LLMs in isolation is often not enough to create a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge. This library is aimed at assisting in the development of those types of applications. ## Documentation The online documentation can be [found here](https://hexdocs.pm/langchain). ## Demo Check out the [demo project](https://github.com/brainlid/langchain_demo) that you can download and review. ## Relationship with JavaScript and Python LangChain This library is written in [Elixir](https://elixir-lang.org/) and intended to be used with Elixir applications. The original libraries are [LangChain JS/TS](https://js.langchain.com/) and [LangChain Python](https://python.langchain.com/). The JavaScript and Python projects aim to integrate with each other as seamlessly as possible. The intended integration is so strong that that all objects (prompts, LLMs, chains, etc) are designe
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:e00a29c024f3953f, topic:llm
matched fp:e00a29c024f3953f, topic:chatgpt