├── README.md ├── main.py ├── output.png ├── output1.png ├── pyproject.toml ├── simple.py └── uv.lock /README.md: -------------------------------------------------------------------------------- 1 | # LangGraph-Tutorial -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | from dotenv import load_dotenv 2 | from typing import Annotated, Literal 3 | from langgraph.graph import StateGraph, START, END 4 | from langgraph.graph.message import add_messages 5 | from langchain.chat_models import init_chat_model 6 | from pydantic import BaseModel, Field 7 | from typing_extensions import TypedDict 8 | 9 | load_dotenv() 10 | 11 | llm = init_chat_model( 12 | "anthropic:claude-3-5-sonnet-latest" 13 | ) 14 | 15 | 16 | class MessageClassifier(BaseModel): 17 | message_type: Literal["emotional", "logical"] = Field( 18 | ..., 19 | description="Classify if the message requires an emotional (therapist) or logical response." 20 | ) 21 | 22 | 23 | class State(TypedDict): 24 | messages: Annotated[list, add_messages] 25 | message_type: str | None 26 | 27 | 28 | def classify_message(state: State): 29 | last_message = state["messages"][-1] 30 | classifier_llm = llm.with_structured_output(MessageClassifier) 31 | 32 | result = classifier_llm.invoke([ 33 | { 34 | "role": "system", 35 | "content": """Classify the user message as either: 36 | - 'emotional': if it asks for emotional support, therapy, deals with feelings, or personal problems 37 | - 'logical': if it asks for facts, information, logical analysis, or practical solutions 38 | """ 39 | }, 40 | {"role": "user", "content": last_message.content} 41 | ]) 42 | return {"message_type": result.message_type} 43 | 44 | 45 | def router(state: State): 46 | message_type = state.get("message_type", "logical") 47 | if message_type == "emotional": 48 | return {"next": "therapist"} 49 | 50 | return {"next": "logical"} 51 | 52 | 53 | def therapist_agent(state: State): 54 | last_message = state["messages"][-1] 55 | 56 | messages = [ 57 | {"role": "system", 58 | "content": """You are a compassionate therapist. Focus on the emotional aspects of the user's message. 59 | Show empathy, validate their feelings, and help them process their emotions. 60 | Ask thoughtful questions to help them explore their feelings more deeply. 61 | Avoid giving logical solutions unless explicitly asked.""" 62 | }, 63 | { 64 | "role": "user", 65 | "content": last_message.content 66 | } 67 | ] 68 | reply = llm.invoke(messages) 69 | return {"messages": [{"role": "assistant", "content": reply.content}]} 70 | 71 | 72 | def logical_agent(state: State): 73 | last_message = state["messages"][-1] 74 | 75 | messages = [ 76 | {"role": "system", 77 | "content": """You are a purely logical assistant. Focus only on facts and information. 78 | Provide clear, concise answers based on logic and evidence. 79 | Do not address emotions or provide emotional support. 80 | Be direct and straightforward in your responses.""" 81 | }, 82 | { 83 | "role": "user", 84 | "content": last_message.content 85 | } 86 | ] 87 | reply = llm.invoke(messages) 88 | return {"messages": [{"role": "assistant", "content": reply.content}]} 89 | 90 | 91 | graph_builder = StateGraph(State) 92 | 93 | graph_builder.add_node("classifier", classify_message) 94 | graph_builder.add_node("router", router) 95 | graph_builder.add_node("therapist", therapist_agent) 96 | graph_builder.add_node("logical", logical_agent) 97 | 98 | graph_builder.add_edge(START, "classifier") 99 | graph_builder.add_edge("classifier", "router") 100 | 101 | graph_builder.add_conditional_edges( 102 | "router", 103 | lambda state: state.get("next"), 104 | {"therapist": "therapist", "logical": "logical"} 105 | ) 106 | 107 | graph_builder.add_edge("therapist", END) 108 | graph_builder.add_edge("logical", END) 109 | 110 | graph = graph_builder.compile() 111 | 112 | 113 | def run_chatbot(): 114 | state = {"messages": [], "message_type": None} 115 | 116 | while True: 117 | user_input = input("Message: ") 118 | if user_input == "exit": 119 | print("Bye") 120 | break 121 | 122 | state["messages"] = state.get("messages", []) + [ 123 | {"role": "user", "content": user_input} 124 | ] 125 | 126 | state = graph.invoke(state) 127 | 128 | if state.get("messages") and len(state["messages"]) > 0: 129 | last_message = state["messages"][-1] 130 | print(f"Assistant: {last_message.content}") 131 | 132 | 133 | if __name__ == "__main__": 134 | run_chatbot() 135 | -------------------------------------------------------------------------------- /output.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/techwithtim/LangGraph-Tutorial/64b1c42d7ab48cbece423b6efd9c5e7b8c8acc57/output.png -------------------------------------------------------------------------------- /output1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/techwithtim/LangGraph-Tutorial/64b1c42d7ab48cbece423b6efd9c5e7b8c8acc57/output1.png -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "langgraph-tutorial" 3 | version = "0.1.0" 4 | description = "Add your description here" 5 | readme = "README.md" 6 | requires-python = ">=3.13" 7 | dependencies = [ 8 | "ipykernel>=6.29.5", 9 | "langchain[anthropic]>=0.3.24", 10 | "langgraph>=0.3.34", 11 | "python-dotenv>=1.1.0", 12 | ] 13 | -------------------------------------------------------------------------------- /simple.py: -------------------------------------------------------------------------------- 1 | from typing import Annotated 2 | from typing_extensions import TypedDict 3 | from langgraph.graph import StateGraph, START, END 4 | from langgraph.graph.message import add_messages 5 | from langchain.chat_models import init_chat_model 6 | from dotenv import load_dotenv 7 | 8 | load_dotenv() 9 | 10 | llm = init_chat_model("anthropic:claude-3-5-sonnet-latest") 11 | 12 | 13 | class State(TypedDict): 14 | # Messages have the type "list". The `add_messages` function 15 | # in the annotation defines how this state key should be updated 16 | # (in this case, it appends messages to the list, rather than overwriting them) 17 | messages: Annotated[list, add_messages] 18 | 19 | 20 | graph_builder = StateGraph(State) 21 | 22 | 23 | def chatbot(state: State): 24 | return {"messages": [llm.invoke(state["messages"])]} 25 | 26 | 27 | # The first argument is the unique node name 28 | # The second argument is the function or object that will be called whenever 29 | # the node is used. 30 | graph_builder.add_node("chatbot", chatbot) 31 | graph_builder.add_edge(START, "chatbot") 32 | graph_builder.add_edge("chatbot", END) 33 | 34 | 35 | graph = graph_builder.compile() 36 | 37 | user_input = input("Enter a message: ") 38 | state = graph.invoke({"messages": [{"role": "user", "content": user_input}]}) 39 | 40 | print(state["messages"][-1].content) 41 | -------------------------------------------------------------------------------- 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