LangGraph vs LlamaIndex
Detailed side-by-side comparison to help you choose the right tool
LangGraph
Agent Frameworks
Graph-based stateful orchestration runtime for agent loops.
Starting Price
Custom
LlamaIndex
Orchestration & Chains
Data framework for RAG pipelines, indexing, and agent retrieval.
Starting Price
Custom
Feature Comparison
| Feature | LangGraph | LlamaIndex |
|---|---|---|
| Category | Agent Frameworks | Orchestration & Chains |
| Pricing Plans | 19 tiers | 19 tiers |
| Starting Price | ||
| Key Features |
|
|
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
LlamaIndex - Pros & Cons
Pros
- ✓Best-in-class framework for RAG and data-augmented LLM applications
- ✓Extensive data connector library (LlamaHub) for 100+ sources
- ✓Sophisticated indexing strategies for different retrieval needs
- ✓Open-source with optional managed cloud service
- ✓Strong focus on production-grade retrieval quality
Cons
- ✗Primarily retrieval-focused — less suited for general agent orchestration
- ✗Index creation can be slow and resource-intensive for large datasets
- ✗Learning curve for choosing the right index type and retrieval strategy
- ✗Cloud service pricing can add up for high-volume applications