LangChain vs LangGraph
Detailed side-by-side comparison to help you choose the right tool
LangChain
Orchestration & Chains
Toolkit for composing LLM apps, chains, and agents.
Starting Price
Custom
LangGraph
Agent Frameworks
Graph-based stateful orchestration runtime for agent loops.
Starting Price
Custom
Feature Comparison
| Feature | LangChain | LangGraph |
|---|---|---|
| Category | Orchestration & Chains | Agent Frameworks |
| Pricing Plans | 24 tiers | 19 tiers |
| Starting Price | ||
| Key Features |
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LangChain - Pros & Cons
Pros
- ✓Largest ecosystem with extensive integrations, tools, and community
- ✓Comprehensive documentation with thousands of examples
- ✓Available in both Python and JavaScript/TypeScript
- ✓First-mover advantage with broad industry adoption
- ✓Modular design allows using only what you need
Cons
- ✗Abstraction layers can obscure what's happening under the hood
- ✗Frequent API changes have caused upgrade headaches historically
- ✗Can feel over-engineered for simple use cases
- ✗Performance overhead from abstraction layers
LangGraph - Pros & Cons
Pros
- ✓State-machine approach provides fine-grained control over agent flows
- ✓Tight integration with the broader LangChain ecosystem
- ✓Built-in persistence for durable, long-running workflows
- ✓Cloud deployment option via LangSmith for production scale
- ✓Supports cyclic graphs enabling iterative agent reasoning
Cons
- ✗Tightly coupled to LangChain — harder to use standalone
- ✗Graph-based paradigm has a learning curve for new developers
- ✗Cloud features require a LangSmith subscription
- ✗Verbose configuration for simple linear workflows