Which is more popular, LangChain or vLLM?

LangChain has more GitHub stars (147,349 vs 93,014).

Which is more actively developed, LangChain or vLLM?

vLLM had more commits in the last 90 days (3,946 vs 543).

Should I use LangChain or vLLM?

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 vs vLLM

Side-by-side comparison of two AI agent tools

Short answer

LangChain is growing faster: +23,335 GitHub stars in the last 30 days vs +2,951 for vLLM.
Pick LangChain for: the agent engineering platform. Pick vLLM for: a high-throughput and memory-efficient inference and serving engine for LLMs.

From GitHub data refreshed daily.

LangChainopen-source

The agent engineering platform

vLLMopen-source

A high-throughput and memory-efficient inference and serving engine for LLMs

Metrics

	LangChain	vLLM
Stars	147.3k	93.0k
Star velocity /mo	23.3k	3.0k
Commits (90d)	543	3.9k
Releases (6m)	10	10
Overall score	0.9037443835729926	0.9317262077107064

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

+Exceptional serving throughput with PagedAttention memory optimization and continuous batching for production-scale LLM deployment
+Comprehensive hardware support across NVIDIA, AMD, Intel platforms and specialized accelerators with flexible parallelism options
+Seamless Hugging Face integration with OpenAI-compatible API server for easy model deployment and switching

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

-Requires significant GPU memory for optimal performance, limiting accessibility for resource-constrained environments
-Complex setup and configuration for distributed inference across multiple GPUs or nodes
-Primary focus on inference means limited support for training or fine-tuning workflows

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

•Production API serving for applications requiring high-throughput LLM inference with multiple concurrent users
•Research and experimentation with open-source LLMs requiring efficient model switching and testing
•Enterprise deployment of private LLM services with OpenAI-compatible interfaces for existing applications

View LangChain Details View vLLM Details

FAQ

Which is more popular, LangChain or vLLM?: LangChain has more GitHub stars (147,349 vs 93,014).
Which is more actively developed, LangChain or vLLM?: vLLM had more commits in the last 90 days (3,946 vs 543).
Should I use LangChain or vLLM?: 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.