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+# LangGraph Notes
+
+## What is LangGraph?
+
+LangGraph is a library for building stateful, multi-actor applications with LLMs. It extends LangChain's capabilities by providing:
+
+- State management for complex workflows
+- Support for cycles in agent workflows
+- Built-in persistence for long-running applications
+- Integration with LangChain tools and models
+
+## Key Concepts
+
+### State
+State in LangGraph is represented as a dictionary that flows through the graph nodes. Each node can modify the state.
+
+### Nodes
+Nodes are individual processing units that can be:
+- LLM calls
+- Tool calls
+- Custom functions
+
+### Edges
+Edges connect nodes and can be:
+- Direct connections
+- Conditional routing based on state
+
+## Example Usage
+
+```python
+from langgraph.graph import StateGraph, END
+
+# Define state
+class AgentState:
+    messages: List
+    next_action: str
+
+# Build graph
+graph = StateGraph(AgentState)
+graph.add_node("agent", call_llm)
+graph.add_node("tool", call_tool)
+graph.add_edge("agent", "tool")
+graph.add_edge("tool", "agent")
+graph.add_edge("agent", END)
+```
+
+## Benefits
+
+- More control over agent behavior
+- Better for complex, multi-step reasoning
+- Native support for human-in-the-loop workflows
+- Easy to add memory and state persistence