langchain

name	LangChain
description	Workflow patterns and gotchas for LangChain. Directs to RAG for implementation.

LangChain Workflow

When to Choose LangChain

• Need composable chains with many integrations
• Building RAG/retrieval applications
• Want broad LLM provider support
• Need document processing pipelines

Decision Framework

Component Selection

Need	Component	RAG Query
Basic LLM call	ChatModel	"ChatOpenAI initialization"
Prompt templating	PromptTemplate	"prompt template variables"
Composable steps	LCEL chains	"LCEL chain composition"
Tool execution	Tool/StructuredTool	"langchain tool definition"
Document retrieval	Retriever	"retriever vector store"
Conversation memory	Memory classes	"conversation buffer memory"

Query RAG: mcp__agentic-rag__query_sdk("component example", sdk="langchain", mode="build")

Critical Gotchas

These trip up everyone:

1. LCEL is the pattern - Use chain = prompt | llm | parser, not legacy chains
2. RunnablePassthrough for context - Pass data through the chain explicitly
3. Output parsers matter - Without one, you get raw text not structured data
4. Invoke vs stream vs batch - Different methods for different use cases
5. Async needs ainvoke - Sync methods block; use async for concurrency
6. Memory isn't automatic - You must explicitly add and manage it
7. API keys via env vars - Each provider has its own key format

Workflow: Building a LangChain Application

Step 1: Dependencies

RAG Query: mcp__agentic-rag__query_sdk("langchain installation packages", sdk="langchain", mode="explain")

Step 2: LLM Setup

RAG Query: mcp__agentic-rag__query_sdk("ChatOpenAI ChatAnthropic setup", sdk="langchain", mode="build")

Step 3: Prompt Design

RAG Query: mcp__agentic-rag__query_sdk("ChatPromptTemplate messages", sdk="langchain", mode="build")

Step 4: Chain Composition

RAG Query: mcp__agentic-rag__query_sdk("LCEL chain pipe operator", sdk="langchain", mode="build")

Step 5: Tool Integration (if needed)

RAG Query: mcp__agentic-rag__query_sdk("StructuredTool from_function", sdk="langchain", mode="build")

Step 6: Retrieval (if RAG)

RAG Query: mcp__agentic-rag__query_sdk("vector store retriever", sdk="langchain", mode="build")

Common Error Patterns

Symptom	Likely Cause	RAG Query
Chain doesn't work	Wrong pipe order	"LCEL chain debugging"
Output is raw text	Missing parser	"output parser structured"
Context lost	Missing passthrough	"RunnablePassthrough"
Async timeout	Using sync method	"langchain async ainvoke"
Memory not working	Not connected	"memory chain integration"

LangChain vs LangGraph

Use Case	Choose
Linear pipelines	LangChain LCEL
Conditional branching	LangGraph
Cycles/loops	LangGraph
Simple retrieval	LangChain
Complex agent workflows	LangGraph

Advanced Features

Query RAG when you need:

• Streaming: "langchain streaming callback"
• Callbacks/tracing: "langchain callbacks tracing"
• Custom chains: "custom runnable class"
• Caching: "langchain caching responses"
• Fallbacks: "chain fallback retry"