From 9ed9b45afb54fc0e4cdf86cd028d3a0f1ea8a340 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=D0=AD=D0=BC=D0=B8=D0=BB=D1=8C=20=D0=90=D0=BC=D0=B8=D1=80?=
 =?UTF-8?q?=D0=BE=D0=B2?= <emillamirov555777@mail.ru>
Date: Thu, 28 May 2026 16:47:33 +0000
Subject: [PATCH] add documents/langgraph_notes.txt

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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
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