LLM Application Frameworks
LangChain
Overview Most popular framework for building LLM applications. Provides abstractions for chains, agents, memory, and tools.
Key Components
| Component | Purpose |
|---|---|
| Chains | Sequential LLM calls |
| Agents | Dynamic tool selection |
| Memory | Conversation history |
| Retrievers | RAG integration |
| Tools | External capabilities |
Example: ReAct Agent
from langchain.agents import create_react_agent
from langchain_openai import ChatOpenAI
from langchain.tools import WikipediaTool
llm = ChatOpenAI(model="gpt-4o")
tools = [WikipediaTool()]
agent = create_react_agent(llm, tools, prompt)
result = agent.invoke({"input": "What is the capital of France?"})
LlamaIndex
Overview Specialized for data-intensive LLM applications, particularly RAG. Excellent for indexing and querying documents.
Key Components
| Component | Purpose |
|---|---|
| Documents | Data containers |
| Nodes | Chunked text units |
| Indices | Search structures |
| Query Engines | RAG pipelines |
| Response Synthesizers | Answer generation |
Example: RAG
from llama_index import VectorStoreIndex, SimpleDirectoryReader
# Load and index documents
documents = SimpleDirectoryReader("data/").load_data()
index = VectorStoreIndex.from_documents(documents)
# Query
query_engine = index.as_query_engine()
response = query_engine.query("What is the main topic?")
Comparison
| Feature | LangChain | LlamaIndex |
|---|---|---|
| Primary focus | General LLM apps | Data/RAG |
| Agent support | Excellent | Good |
| RAG capabilities | Good | Excellent |
| Community size | Largest | Large |
| Complexity | Higher | Lower |
Other Frameworks
| Framework | Highlights |
|---|---|
| Haystack | Production RAG |
| Semantic Kernel | Microsoft, enterprise |
| DSPy | Prompt optimization |
| CrewAI | Multi-agent |
When to Use
- LangChain: Complex agents, diverse tools, general LLM apps
- LlamaIndex: Document QA, knowledge bases, RAG-heavy apps
- Both together: LangChain agents + LlamaIndex for data
langchainllamaindexframework
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