A Ruby library for building LLM-powered applications with a unified interface for multiple providers, RAG systems, and AI assistants.
Langchain.rb is a Ruby library that provides a unified interface for building applications with large language models (LLMs). It simplifies integrating AI capabilities like text generation, embeddings, and conversational assistants into Ruby projects by abstracting differences between multiple LLM providers and offering tools for RAG, prompt management, and output parsing.
Ruby and Rails developers who want to incorporate LLM-powered features—such as chatbots, document Q&A systems, or AI assistants—into their applications without dealing with low-level API complexities.
It offers a consistent, Ruby-native API for over 10 LLM providers, built-in support for popular vector databases, and a modular design that lets developers use only the components they need, reducing boilerplate and accelerating AI integration.
Build LLM-powered applications in Ruby
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Offers a consistent interface for over 10 LLM providers, including OpenAI, Anthropic, and Google Gemini, allowing developers to switch providers with minimal code changes, as shown in the initialization examples.
Built-in support for multiple vector databases like Chroma, Weaviate, and Qdrant simplifies RAG implementation with methods like similarity_search and add_texts, reducing boilerplate for semantic search.
Enables creation of interactive AI assistants with tool integration, conversation management, and streaming support, demonstrated through custom tools like NewsRetriever and vector search wrappers.
Includes structured output parsers with JSON schema validation and automatic error correction via LLM fallback, ensuring reliable data extraction from LLM responses, as detailed in the Output Parsers section.
Requires installing numerous external gems for vector databases and tools, and the README notes installation issues like unicode gem problems, complicating deployment and maintenance.
Only a handful of default tools are provided, and creating custom tools requires manual implementation, which may slow development compared to more mature ecosystems.
The abstraction layer adds complexity that can introduce latency compared to direct API calls, especially for high-throughput applications, and streaming isn't supported for all LLMs.