LangChain vs LlamaIndex
Updated . Stats refresh daily.
Short answer: Choose based on your primary use case: general LLM apps vs RAG over data.
At a glance
| Attribute | LangChain | LlamaIndex |
|---|---|---|
| Pricing model | Open Source | Open Source |
| Main job | LLM Framework | RAG Framework |
| GitHub stars | 147,111 | 52,326 |
| npm downloads a week | 3.2M | 146k |
| Latest release | langchain-fireworks==1.6.3, 1 day ago | v0.14.25, 5 days ago |
| Founded | 2022 | 2023 |
| Primary Use Case | General LLM apps (chains, agents) | RAG over private data |
| Data Connectors | ~50 loaders | 300+ connectors |
| RAG Optimization | Supported (not specialized) | Core focus (highly optimized) |
| Agents | Full agent support | Limited agent capabilities |
| Chains | Extensive chain types | Query engines (similar concept) |
| Index Types | Vector stores (via connectors) | Multiple index types (optimized) |
| Learning Curve | Steep (many abstractions) | Moderate (focused scope) |
| Breaking Changes | Frequent (rapid development) | Less frequent (more stable) |
| Ecosystem | Massive (de facto standard) | Growing (RAG-focused) |
| Language Support | Python, JavaScript/TypeScript | Python (JS improving) |
Key differences
Primary Focus
LangChain is a general-purpose framework for building LLM applications (chains, agents, chatbots). LlamaIndex is specialized for RAG (Retrieval-Augmented Generation) over private data. LangChain is broad; LlamaIndex is deep.
Data Handling Philosophy
LlamaIndex provides 300+ data connectors and optimized indices for RAG. LangChain offers data loaders but focuses more on orchestration. LlamaIndex is data-first; LangChain is workflow-first.
Complexity & Learning Curve
LangChain has more abstractions and can be over-engineered for simple tasks. LlamaIndex is more focused but has its own complexity. LangChain is harder to learn; LlamaIndex is easier for RAG use cases.
Pricing
LangChain
Open source (MIT). Free to use. LangSmith (observability): Paid SaaS platform for monitoring and debugging LangChain apps.
LlamaIndex
Open source (MIT). Free to use. LlamaCloud (upcoming): Managed hosting and enterprise features (pricing TBD).
Prices change often. Check each vendor's pricing page before you commit.
Strengths and weaknesses
LangChain
Strengths
- De facto standard for LLM applications
- Massive ecosystem and integrations
- Supports all major LLMs and tools
- Powerful for agents and complex workflows
Weaknesses
- Over-engineered for simple tasks
- Steep learning curve
- Frequent breaking changes
- Hard to debug (complex abstractions)
LlamaIndex
Strengths
- Best-in-class for RAG applications
- 300+ data connectors (unmatched)
- Optimized indexing and querying
- Easier to learn for RAG use cases
Weaknesses
- Limited scope (RAG-focused)
- Weaker agent capabilities vs LangChain
- Python-first (JavaScript lagging)
- Smaller community than LangChain
Which should you choose?
Choose LangChain if
- You're building agents or complex LLM workflows.
- You need the broadest ecosystem and integrations.
- You're building chatbots, automation, or AI apps.
- You value being on the industry-standard framework.
Choose LlamaIndex if
- You're building RAG over private/enterprise data.
- Document Q&A or knowledge bases are your focus.
- You need 300+ data connectors.
- You want a more focused, easier-to-learn framework.
Questions
Can I use both LangChain and LlamaIndex together?
Yes! Many developers use LlamaIndex for data ingestion and indexing, then use LangChain for agents and complex workflows. They're complementary, not mutually exclusive.
Which is better for beginners?
LlamaIndex is easier to learn if you're building RAG apps. LangChain has more concepts to grasp. However, LangChain's massive community means more tutorials and Stack Overflow answers.
Is LangChain really over-engineered?
For simple tasks, yes—many developers report it's unnecessarily complex. But for complex agents and workflows, those abstractions become valuable. It's over-engineered for 'Hello World,' right-sized for production apps.
Which has better documentation?
Both have good docs. LangChain's are more extensive due to broader scope. LlamaIndex docs are more focused and often easier to follow for RAG-specific tasks.
What if I don't need RAG?
Use LangChain. LlamaIndex is RAG-specialized. If you're building chatbots, agents, or automation without significant data retrieval, LangChain is the better choice.