langgraph

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

LangGraph Workflow

When to Choose LangGraph

• Need stateful, cyclic agent workflows
• Building complex multi-step reasoning
• Want conditional branching and loops
• Require human-in-the-loop checkpoints

Decision Framework

Graph Pattern Selection

Need	Pattern	RAG Query
Sequential steps	Linear graph	"simple sequential graph"
Decision branching	Conditional edges	"conditional edge routing"
Iteration/retry	Cycles	"graph cycle loop"
Parallel execution	Parallel branches	"parallel graph execution"
Human approval	Checkpoints	"human in loop checkpoint"
Agent with tools	ReAct pattern	"react agent langgraph"

Query RAG: mcp__agentic-rag__query_sdk("pattern example", sdk="langgraph", mode="build")

Critical Gotchas

These cause debugging nightmares:

1. State must be TypedDict - Not a regular dict; needs type annotations
2. Node returns partial state - Return only keys you're updating, not full state
3. END is special - Import it: from langgraph.graph import END
4. Edges define flow - Forgetting an edge = node never reached
5. Conditional edges return node names - Return the string name, not the function
6. Compile before run - graph.compile() is required before invoke
7. Checkpointer for memory - Without it, state resets each run
8. State channels merge - Multiple updates to same key need reducer

Workflow: Building a LangGraph Agent

Step 1: State Definition

RAG Query: mcp__agentic-rag__query_sdk("TypedDict state definition", sdk="langgraph", mode="build")

Define your state schema with TypedDict and Annotated for reducers.

Step 2: Node Functions

RAG Query: mcp__agentic-rag__query_sdk("graph node function", sdk="langgraph", mode="build")

Each node takes state, returns partial state update.

Step 3: Graph Construction

RAG Query: mcp__agentic-rag__query_sdk("StateGraph add_node add_edge", sdk="langgraph", mode="build")

Step 4: Edge Definition

RAG Query: mcp__agentic-rag__query_sdk("conditional_edges routing", sdk="langgraph", mode="build")

Step 5: Compilation

RAG Query: mcp__agentic-rag__query_sdk("graph compile checkpointer", sdk="langgraph", mode="build")

Step 6: Execution

RAG Query: mcp__agentic-rag__query_sdk("compiled graph invoke stream", sdk="langgraph", mode="build")

Common Error Patterns

Symptom	Likely Cause	RAG Query
Node never runs	Missing edge	"graph edge definition"
State not updating	Returning wrong keys	"node state return"
Infinite loop	No END condition	"conditional edge END"
Type error	State not TypedDict	"TypedDict state"
Memory lost	No checkpointer	"MemorySaver persistence"
Merge conflict	Missing reducer	"Annotated reducer operator"

Graph Patterns

Decision Tree

Nodes for each decision point, conditional edges for branching. RAG Query: mcp__agentic-rag__query_sdk("decision tree graph", sdk="langgraph", mode="build")

ReAct Agent

Reason-Act-Observe loop with tool calling. RAG Query: mcp__agentic-rag__query_sdk("react agent pattern", sdk="langgraph", mode="build")

Plan-and-Execute

Planning node, execution loop, verification. RAG Query: mcp__agentic-rag__query_sdk("plan execute pattern", sdk="langgraph", mode="build")

Advanced Features

Query RAG when you need:

• Streaming: "langgraph streaming events"
• Subgraphs: "nested subgraph composition"
• Human-in-loop: "interrupt checkpoint approval"
• Time travel: "state history replay"
• Parallel branches: "parallel node execution"