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LangChain vs LlamaIndex Agents
Side-by-side comparison of LangChain and LlamaIndex Agents — features, pricing, performance scores, and which to choose for your AI agents.
View All Framework ComparisonsQuick Verdict
| Dimension | LangChain | LlamaIndex Agents | Winner |
|---|---|---|---|
Ease of Use | 4/5 | 4/5 | Tie |
Scalability | 5/5 | 4/5 | LangChain |
Documentation | 5/5 | 5/5 | Tie |
Community | 5/5 | 5/5 | Tie |
Performance | 4/5 | 4/5 | Tie |
Overall: LangChain wins 1 categories, LlamaIndex Agents wins 0, 4 tied
Feature Comparison
| Feature | LangChain | LlamaIndex Agents |
|---|---|---|
| Primary Language | Python | Python |
| License | MIT | MIT |
| Pricing | Open Source | Open Source + Cloud |
| GitHub Stars | 117,000 | 44,600 |
| Difficulty | Intermediate | Intermediate |
| Enterprise Ready | ||
| Community Size | Very Large | Very Large |
| Category | RAG & Knowledge | RAG & Knowledge |
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
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
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
LlamaIndex Agents
Document Q&A systems
Knowledge base retrieval
Semantic search applications
Chat over documents
Agent-based data retrieval
Research assistants
Getting Started
LangChain
Installation
pip install langchain
LlamaIndex Agents
Installation
pip install llama-index
Learn More
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