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LangChain vs LangGraph
Side-by-side comparison of LangChain and LangGraph — features, pricing, performance scores, and which to choose for your AI agents.
View All Framework ComparisonsQuick Verdict
| Dimension | LangChain | LangGraph | Winner |
|---|---|---|---|
Ease of Use | 4/5 | 3/5 | LangChain |
Scalability | 5/5 | 5/5 | Tie |
Documentation | 5/5 | 4/5 | LangChain |
Community | 5/5 | 4/5 | LangChain |
Performance | 4/5 | 5/5 | LangGraph |
Overall: LangChain wins 3 categories, LangGraph wins 1, 1 tied
Feature Comparison
| Feature | LangChain | LangGraph |
|---|---|---|
| Primary Language | Python | Python |
| License | MIT | MIT |
| Pricing | Open Source | Open Source + Commercial |
| GitHub Stars | 117,000 | 19,900 |
| Difficulty | Intermediate | Advanced |
| Enterprise Ready | ||
| Community Size | Very Large | Large |
| Category | RAG & Knowledge | Multi-Agent Systems |
Pros & Cons
LangChain
Advantages
Largest ecosystem of integrations (700+) in LLM space
Well-established with strong community support (2000+ contributors)
Excellent documentation and learning resources
MIT license allows commercial use
Strong backing and funding from Sequoia and Benchmark
Production-ready with LangSmith observability
Easy to get started with high-level API
Model agnostic - swap providers easily
Limitations
Linear chain-based architecture may be limiting for complex workflows
Can be overkill for simple applications
Learning curve for understanding the full ecosystem
Some features require understanding of LangGraph for advanced use
Abstractions may add overhead
Rapid evolution means documentation can lag behind releases
LangGraph
Advantages
Full control over agent behavior with low-level primitives
Excellent for complex non-linear workflows
Built-in state persistence and memory management
Production-proven by major companies (Klarna Uber LinkedIn)
Strong streaming and observability features
Human-in-the-loop support is first-class
Can be used standalone or with LangChain
MIT licensed with commercial platform option
Limitations
Steeper learning curve than LangChain
Requires understanding of graph theory concepts
May be overkill for simple linear workflows
Smaller community than LangChain (but growing)
Some advanced features require LangGraph Platform
Documentation still maturing compared to LangChain
More complex setup for basic use cases
Best Use Cases
LangChain
Chatbots and conversational AI
Question-answering systems over documents
Retrieval-Augmented Generation (RAG) applications
Document analysis and summarization
Code generation and analysis
Internal knowledge bases and support bots
LangGraph
Complex customer support workflows with escalation
Multi-agent research and analysis systems
Task management and orchestration
Long-running business process automation
Interactive assistants with memory
Decision support systems with conditional logic
Getting Started
LangChain
Installation
pip install langchain
LangGraph
Installation
pip install langgraph
Learn More
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