8 Best LangChain Dart Alternatives in 2026 (Open Source)

LangChain Dart — Build LLM-powered Dart/Flutter applications.. The only LangChain implementation for Dart/Flutter — enables the massive Flutter developer community to build LLM apps with familiar patterns, including LCEL composability that no other Dart AI library offers

These 8 open-source tools do the same job. They are ordered by how closely they match LangChain Dart, with live GitHub data so you can see which projects are actively maintained.

ToolGitHub starsStars / 30dLast commit
LangChain Dart(original)688+32026-08-27
LangChain Go9.7k+1182026-01-11
LangChain18.2k+1432026-09-29
Langchainrb2.0k+42026-09-09
LangChain Rust1.3k+132025-04-30
Eino13.2k+4682026-09-29
llm-chain1.6k+12024-10-31
Agency515+12024-12-30
Flappy304+-02024-04-11
  1. 1. LangChain Go

    LangChain for Go, the easiest way to write LLM-based programs in Go

    What sets it apart: vs Python LangChain: native Go implementation with Go idioms, type safety, and goroutine-friendly concurrency for Go backend services

    Best for: Go teams building LLM-powered applications; Backend services needing LLM integration in Go

  2. 2. LangChain

    The agent engineering platform

    What sets it apart: vs LlamaIndex.TS: broader agent/chain abstractions and larger integration ecosystem; vs AI SDK: more opinionated with built-in chain patterns and LangSmith observability

    Best for: Building LLM-powered apps in TypeScript/JavaScript; Rapid prototyping with multiple LLM providers; RAG applications with diverse data sources

  3. 3. Langchainrb

    Build LLM-powered applications in Ruby

    What sets it apart: vs Python LangChain: native Ruby implementation with deep Rails integration, unified 11+ LLM provider interface, and built-in RAGAS evaluation — the only serious LangChain for Ruby

    Best for: Ruby/Rails teams building LLM-powered applications; Adding RAG and AI assistant features to existing Rails apps

  4. 4. LangChain Rust

    🦜️🔗LangChain for Rust, the easiest way to write LLM-based programs in Rust

    What sets it apart: vs Python LangChain: native Rust with compile-time type safety, zero-cost abstractions, and memory safety for performance-critical LLM applications

    Best for: Rust teams building LLM-powered applications with type safety; Performance-critical LLM services in Rust backend systems

  5. 5. Eino

    The ultimate LLM/AI application development framework in Go.

    What sets it apart: The most mature LLM application framework for Go — vs LangChain/LlamaIndex which are Python/JS only

    Best for: Go-based AI agent and RAG application development; Teams already in the CloudWeGo/ByteDance ecosystem

  6. 6. llm-chain

    `llm-chain` is a powerful rust crate for building chains in large language models allowing you to summarise text and complete complex tasks

    What sets it apart: vs LangChain / LlamaIndex (Python): native Rust LLM framework with macro-based ergonomic API — the most comprehensive Rust crate ecosystem for LLM chains, prompt templates, and vector stores

    Best for: Rust developers wanting native LLM application building; Performance-critical LLM applications requiring Rust's speed and safety; Teams wanting cloud + local LLM support in a single Rust framework

  7. 7. Agency

    🕵️‍♂️ Library designed for developers eager to explore the potential of Large Language Models (LLMs) and other generative AI through a clean, effective, and Go-idiomatic approach.

    What sets it apart: vs LangChainGo: Go-native design from scratch (not a Python port) — composable operations, interceptors, and multimodal support with clean Go-idiomatic architecture

    Best for: Go developers wanting an idiomatic AI framework (not a Python port); Building multimodal AI applications in Go (text, image, speech); Teams preferring clean architecture with composable operations

  8. 8. Flappy

    Production-Ready LLM Agent SDK for Every Developer

    What sets it apart: vs Python-centric frameworks (LangChain, etc.): language-agnostic agent framework supporting Node.js, Java/Kotlin, C# — production-ready with sandbox security and cost-efficiency balancing

    Best for: Multi-language AI agent development beyond Python; Production applications needing sandboxed code execution; ETL data processing and external API orchestration