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.
| Tool | GitHub stars | Stars / 30d | Last commit |
|---|---|---|---|
| LangChain Dart(original) | 688 | +3 | 2026-08-27 |
| LangChain Go | 9.7k | +118 | 2026-01-11 |
| LangChain | 18.2k | +143 | 2026-09-29 |
| Langchainrb | 2.0k | +4 | 2026-09-09 |
| LangChain Rust | 1.3k | +13 | 2025-04-30 |
| Eino | 13.2k | +468 | 2026-09-29 |
| llm-chain | 1.6k | +1 | 2024-10-31 |
| Agency | 515 | +1 | 2024-12-30 |
| Flappy | 304 | +-0 | 2024-04-11 |
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. 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. 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. 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. 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. 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. 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. 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