Which is more popular, LangChain Rust or LangChain Go?

LangChain Go has more GitHub stars (9,709 vs 1,348).

Which is more actively developed, LangChain Rust or LangChain Go?

LangChain Rust had more commits in the last 90 days (0 vs 0).

Should I use LangChain Rust or LangChain Go?

Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.

LangChain Rust vs LangChain Go

Side-by-side comparison of two AI agent tools

Short answer

LangChain Go is growing faster: +117 GitHub stars in the last 30 days vs +13 for LangChain Rust.
Pick LangChain Rust for: langChain for Rust, the easiest way to write LLM-based programs in Rust. Pick LangChain Go for: langChain for Go, the easiest way to write LLM-based programs in Go.

From GitHub data refreshed daily.

LangChain Rustopen-source

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

LangChain Goopen-source

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

Metrics

	LangChain Rust	LangChain Go
Stars	1.3k	9.7k
Star velocity /mo	13.105263157894738	117.47368421052632
Commits (90d)	0	0
Releases (6m)	0	0
Overall score	0.20114322752134345	0.270054485196703

Pros

+Supports multiple LLM providers (OpenAI, Claude, Ollama) with consistent API
+Comprehensive vector store integrations including Postgres, Qdrant, and SurrealDB
+Native Rust performance and memory safety for production AI applications

+Native Go implementation with idiomatic patterns and no Python dependencies
+Multi-provider support with consistent API across OpenAI, Gemini, Ollama and other LLM services
+Strong community and documentation including Discord support, comprehensive docs site, and API reference

Cons

-Smaller ecosystem and community compared to Python LangChain
-Requires Rust knowledge which has a steeper learning curve
-Documentation and examples are more limited than the main LangChain project

-Smaller ecosystem compared to the Python LangChain with fewer community plugins and extensions
-Go-specific limitation reduces cross-team collaboration in polyglot environments
-Less mature feature set compared to the original Python implementation

Use Cases

•Building RAG systems with vector databases for semantic document retrieval
•Creating conversational AI applications with persistent memory and context
•Developing high-performance AI pipelines that require Rust's safety and speed

•Go-based web services and APIs that need to integrate ChatGPT-like completion functionality
•Enterprise Go applications requiring LLM capabilities while maintaining existing Go infrastructure
•Building chatbots and conversational interfaces within Go microservices architectures

View LangChain Rust Details View LangChain Go Details

FAQ

Which is more popular, LangChain Rust or LangChain Go?: LangChain Go has more GitHub stars (9,709 vs 1,348).
Which is more actively developed, LangChain Rust or LangChain Go?: LangChain Rust had more commits in the last 90 days (0 vs 0).
Should I use LangChain Rust or LangChain Go?: Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.