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Langchain-rust

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LangChain for Rust ![GitHub Repo stars](https://img.shields.io/github/stars/Abraxas-365/langchain-rust?style=social)

FreeFree tier
Inputs: textOutputs: text
Type
Open Source

About Langchain-rust

LangChain-rust is a Rust implementation of the LangChain framework, designed to simplify building applications with large language models (LLMs) through composability. It provides a modular and Rust-native way to chain LLM calls, integrate with vector stores, use agents, and load documents. The library supports multiple LLM providers (OpenAI, Azure OpenAI, Ollama, Anthropic Claude), embeddings (OpenAI, Azure, Ollama, Local FastEmbed, MistralAI), vector stores (OpenSearch, Postgres, Qdrant, Sqlite, SurrealDB), various chain types (LLMChain, ConversationalChain, RetrievalQA, SQLChain, etc.), agents with tools (web search, math, command line, text-to-speech), semantic routing, and document loaders (PDF, Pandoc, HTML, CSV, Git). The project is open source and leverages Rust's performance and safety for production-grade LLM applications.

Key Features

LLM integrations: OpenAI, Azure OpenAI, Ollama, Anthropic Claude
Embeddings: OpenAI, Azure, Ollama, Local FastEmbed, MistralAI
Vector stores: OpenSearch, Postgres, Qdrant, Sqlite, SurrealDB
Chains: LLMChain, ConversationalChain, RetrievalQA, SQLChain, SequentialChain
Agents: Chat Agent with Tools, OpenAI compatible tools
Agent tools: Serpapi/Google, DuckDuckGo, Wolfram/Math, Command line, Text2Speech
Semantic routing: Static and dynamic routing
Document loaders: PDF, Pandoc, HTML, HTML-to-Markdown, CSV, Git commits
Composable architecture built with Rust for safety and performance

Pros & Cons

Pros
  • Open source with MIT license (free to use)
  • Leverages Rust's performance, memory safety, and concurrency
  • Modular and composable design inspired by LangChain
  • Supports multiple major LLM providers and embedding models
  • Wide range of vector store backends
  • Built-in document loaders for common formats (PDF, HTML, CSV, etc.)
  • Active development with 1.3k+ stars and 176 forks
Cons
  • Smaller community and ecosystem compared to Python LangChain
  • Fewer integrations and third-party contributions than the Python version
  • Documentation may be less comprehensive than established Python framework
  • May lack some advanced features available in the Python LangChain
  • Relatively new project, potential for breaking changes

Best For

Building conversational AI chatbots in RustCreating question-answering systems over documentsDeveloping LLM-powered SQL query generatorsBuilding multi-step reasoning chains with agentsIntegrating LLMs with vector databases for semantic searchAutomating document processing and analysis pipelines