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LlamaIndex Agents vs LangGraph
Side-by-side comparison of LlamaIndex Agents and LangGraph — features, pricing, performance scores, and which to choose for your AI agents.
View All Framework ComparisonsQuick Verdict
| Dimension | LlamaIndex Agents | LangGraph | Winner |
|---|---|---|---|
Ease of Use | 4/5 | 3/5 | LlamaIndex Agents |
Scalability | 4/5 | 5/5 | LangGraph |
Documentation | 5/5 | 4/5 | LlamaIndex Agents |
Community | 5/5 | 4/5 | LlamaIndex Agents |
Performance | 4/5 | 5/5 | LangGraph |
Overall: LlamaIndex Agents wins 3 categories, LangGraph wins 2, 0 tied
Feature Comparison
| Feature | LlamaIndex Agents | LangGraph |
|---|---|---|
| Primary Language | Python | Python |
| License | MIT | MIT |
| Pricing | Open Source + Cloud | Open Source + Commercial |
| GitHub Stars | 44,600 | 19,900 |
| Difficulty | Intermediate | Advanced |
| Enterprise Ready | ||
| Community Size | Very Large | Large |
| Category | RAG & Knowledge | Multi-Agent Systems |
Pros & Cons
LlamaIndex Agents
Advantages
Best-in-class for RAG applications
Excellent data connectors and loaders
Strong documentation and examples
Active community and development
MIT license
LlamaCloud for managed services
Works well with LangChain
Limitations
Primarily focused on RAG not general agents
Agent features less mature than core RAG
Can be complex for simple use cases
LlamaCloud requires subscription
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
LlamaIndex Agents
Document Q&A systems
Knowledge base retrieval
Semantic search applications
Chat over documents
Agent-based data retrieval
Research assistants
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
LlamaIndex Agents
Installation
pip install llama-index
LangGraph
Installation
pip install langgraph
Learn More
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