LangChain vs Windmill
Side-by-side comparison of two AI agent tools
LangChainopen-source
The agent engineering platform
Windmillopen-source
Open-source developer platform to power your entire infra and turn scripts into webhooks, workflows and UIs. Fastest workflow engine (13x vs Airflow). Open-source alternative to Retool and Temporal.
Metrics
| LangChain | Windmill | |
|---|---|---|
| Stars | 147.3k | 18.1k |
| Star velocity /mo | 23.5k | 317.80748663101605 |
| Commits (90d) | 511 | 1.2k |
| Releases (6m) | 10 | 10 |
| Overall score | 0.9379447030691768 | 0.8726503315063926 |
Pros
- +Extensive ecosystem with seamless integration between LangGraph, LangSmith, and hundreds of third-party components
- +Future-proof architecture that adapts to evolving LLM technologies without requiring application rewrites
- +Strong community support with 131k+ GitHub stars and comprehensive documentation for both Python and JavaScript
- +Multi-language support with automatic UI generation from scripts in Python, TypeScript, Go, Bash, SQL, and more
- +High performance workflow engine claiming 13x faster execution than Airflow
- +Self-hostable open-source solution with AGPLv3 license providing full control and customization
Cons
- -Significant learning curve due to the framework's extensive feature set and multiple abstraction layers
- -Potential over-engineering for simple use cases that might be better served by direct API calls
- -Heavy dependency on the LangChain ecosystem which can create vendor lock-in concerns
- -AGPLv3 license may restrict some commercial use cases and require careful compliance consideration
- -Being a comprehensive platform may introduce complexity for simple automation tasks
- -Self-hosting requires infrastructure management and maintenance overhead
Use Cases
- •Building complex multi-agent systems that require planning, tool use, and coordination between different AI components
- •Creating production LLM applications with observability, debugging, and deployment infrastructure via LangSmith
- •Developing chatbots and conversational AI with memory, context management, and integration with external data sources
- •Building internal APIs and webhooks from existing scripts without additional infrastructure
- •Creating automated workflows for background jobs and data processing pipelines
- •Developing low-code internal applications with custom UIs for non-technical team members