langchaingo vs langgraph
Side-by-side comparison of two AI agent tools
langchaingoopen-source
LangChain for Go, the easiest way to write LLM-based programs in Go
langgraphopen-source
Build resilient language agents as graphs.
Metrics
| langchaingo | langgraph | |
|---|---|---|
| Stars | 9.0k | 28.0k |
| Star velocity /mo | 75 | 2.5k |
| Commits (90d) | — | — |
| Releases (6m) | 1 | 10 |
| Overall score | 0.5204162031572881 | 0.8081963872278098 |
Pros
- +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
- +Durable execution ensures agents automatically resume from exactly where they left off after failures or interruptions
- +Comprehensive memory system with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions
- +Seamless human-in-the-loop capabilities allow for inspection and modification of agent state at any point during execution
Cons
- -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
- -Low-level framework requires more technical expertise and setup compared to high-level agent builders
- -Graph-based agent design paradigm may have a steeper learning curve for developers new to agent orchestration
- -Production deployment complexity may be overkill for simple chatbot or single-turn use cases
Use Cases
- •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
- •Long-running autonomous agents that need to persist through system failures and operate over days or weeks
- •Complex multi-step workflows requiring human oversight, approval, or intervention at specific decision points
- •Stateful agents that must maintain context and memory across multiple sessions and interactions