├── README.md ├── langgraph.json ├── main.py ├── odsc.py ├── poetry.lock ├── pyproject.toml ├── react ├── __init__.py ├── chains │ ├── __init__.py │ └── reasoning_chain.py ├── consts.py ├── graph.py ├── nodes │ ├── __init__.py │ └── nodes.py └── state.py └── static └── graph.png /README.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | # ReAct Implementation with LangGraph🦜🕸 4 | 5 | Implementation a ReAct Flow with LangGraph 6 | 7 | 8 | ![Logo](https://github.com/emarco177/react-langgraph/blob/main/static/graph.png) 9 | [![udemy](https://img.shields.io/badge/LangGraph%20Udemy%20Course-Coupon%20%2412.99-brightgreen)](https://www.udemy.com/course/langgraph/?referralCode=FEA50E8CBA24ECD48212) 10 | 11 | 12 | ## Environment Variables 13 | 14 | To run this project, you will need to add the following environment variables to your .env file 15 | 16 | `PYTHONPATH=/{YOUR_PATH_TO_PROJECT}/react-langgraph` 17 | 18 | `MISTRALAI_API_KEY` 19 | 20 | `LANGCHAIN_API_KEY` 21 | 22 | `LANGCHAIN_TRACING_V2=true` 23 | 24 | `LANGCHAIN_PROJECT` 25 | 26 | ## Run Locally 27 | 28 | Clone the project 29 | 30 | ```bash 31 | git clone https://github.com/emarco177/react-langgraph.git 32 | ``` 33 | 34 | Go to the project directory 35 | 36 | ```bash 37 | cd react-langgraph 38 | ``` 39 | 40 | Install dependencies 41 | 42 | ```bash 43 | poetry install 44 | ``` 45 | 46 | Run 47 | 48 | ```bash 49 | poetry run main.py 50 | ``` 51 | 52 | 53 | ## Running Tests 54 | 55 | To run tests, run the following command 56 | 57 | ```bash 58 | poetry run pytest . -s -v 59 | ``` 60 | 61 | ## 🔗 Links 62 | [![portfolio](https://img.shields.io/badge/my_portfolio-000?style=for-the-badge&logo=ko-fi&logoColor=white)](https://www.udemy.com/course/langgraph/?referralCode=FEA50E8CBA24ECD48212) 63 | [![linkedin](https://img.shields.io/badge/linkedin-0A66C2?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/eden-marco/) 64 | [![twitter](https://img.shields.io/badge/twitter-1DA1F2?style=for-the-badge&logo=twitter&logoColor=white)](https://www.udemy.com/user/eden-marco/) -------------------------------------------------------------------------------- /langgraph.json: -------------------------------------------------------------------------------- 1 | { 2 | "graphs": { 3 | "agent": "./odsc.py:graphh" 4 | }, 5 | "env": ".env", 6 | "dependencies": ["."] 7 | } -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | from react.graph import app 2 | 3 | if __name__ == "__main__": 4 | print("Hello ReAct with LangGraph") 5 | config = {"configurable": {"thread_id": "89"}} 6 | 7 | for event in app.stream( 8 | {"input": "current weather in New York? In Celsius-. Then triple it"}, 9 | config, 10 | ): 11 | for node, values in event.items(): 12 | print(f"Receiving update from node: '{node}'") 13 | print(values) 14 | print("\n\n") 15 | -------------------------------------------------------------------------------- /odsc.py: -------------------------------------------------------------------------------- 1 | from typing import Literal 2 | 3 | from langchain_community.tools.tavily_search import TavilySearchResults 4 | from langchain_mistralai import ChatMistralAI 5 | from langgraph.graph import END, MessagesState, StateGraph 6 | from langgraph.prebuilt import ToolNode 7 | 8 | tools = [TavilySearchResults()] 9 | tool_node = ToolNode(tools) 10 | model = ChatMistralAI(model="mistral-large-latest", temperature=0).bind_tools(tools) 11 | 12 | 13 | def should_continue(state: MessagesState) -> Literal["tools", END]: 14 | messages = state["messages"] 15 | last_message = messages[-1] 16 | if last_message.tool_calls: 17 | return "tools" 18 | return END 19 | 20 | 21 | def call_model(state: MessagesState): 22 | messages = state["messages"] 23 | response = model.invoke(messages) 24 | return {"messages": [response]} 25 | 26 | 27 | workflow = StateGraph(MessagesState) 28 | workflow.add_node("agent", call_model) 29 | workflow.add_node("tools", tool_node) 30 | workflow.set_entry_point("agent") 31 | workflow.add_conditional_edges("agent", should_continue) 32 | workflow.add_edge("tools", "agent") 33 | 34 | graphh = workflow.compile() 35 | 36 | # graph.get_graph().draw_mermaid_png(output_file_path="graph.png") 37 | 38 | 39 | # user_input = "what is the current weather in London? 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15 | python-dotenv = "^1.0.1" 16 | langchain-community = "^0.2.4" 17 | langgraph = "^0.1.1" 18 | langchain-mistralai = "^0.1.9" 19 | 20 | 21 | [build-system] 22 | requires = ["poetry-core"] 23 | build-backend = "poetry.core.masonry.api" 24 | -------------------------------------------------------------------------------- /react/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/emarco177/react-langgraph/554fbda84d10a3c576da2b5554a0a68e6f2edc7c/react/__init__.py -------------------------------------------------------------------------------- /react/chains/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/emarco177/react-langgraph/554fbda84d10a3c576da2b5554a0a68e6f2edc7c/react/chains/__init__.py -------------------------------------------------------------------------------- /react/chains/reasoning_chain.py: -------------------------------------------------------------------------------- 1 | from dotenv import load_dotenv 2 | 3 | load_dotenv() 4 | 5 | from langchain import hub 6 | from langchain.agents import create_react_agent 7 | from langchain_community.tools.tavily_search import TavilySearchResults 8 | from langchain_core.prompts import PromptTemplate 9 | from langchain_core.tools import tool 10 | from langchain_openai.chat_models import ChatOpenAI 11 | 12 | react_prompt: PromptTemplate = hub.pull("hwchase17/react") 13 | 14 | 15 | @tool 16 | def fahrenheit_to_celsius(num: float) -> float: 17 | """ 18 | :param num: a number to convert from Fahrenheit to Celsius 19 | :return: the number converted to Celsius 20 | """ 21 | num = float(num) 22 | celsius = (num - 32) * 5 / 9 23 | return celsius 24 | 25 | 26 | @tool 27 | def triple(num: float) -> float: 28 | """ 29 | :param num: a number to triple 30 | :return: the number multiplied by 3 (tripled( 31 | """ 32 | 33 | return num * 3 34 | 35 | 36 | tools = [TavilySearchResults(max_results=3), fahrenheit_to_celsius, triple] 37 | 38 | llm = ChatOpenAI(model="gpt-4o") 39 | 40 | react_reasoning_runnable = create_react_agent(llm, tools, react_prompt) 41 | -------------------------------------------------------------------------------- /react/consts.py: -------------------------------------------------------------------------------- 1 | AGENT_REASON = "agent_reason" 2 | ACT = "act" 3 | -------------------------------------------------------------------------------- /react/graph.py: -------------------------------------------------------------------------------- 1 | from dotenv import load_dotenv 2 | 3 | from react.nodes.nodes import execute_tools, run_agent_reasoning_engine 4 | 5 | load_dotenv() 6 | 7 | from langchain_core.agents import AgentFinish 8 | from langgraph.graph import END, StateGraph 9 | 10 | from react.consts import ACT, AGENT_REASON 11 | from react.state import AgentState 12 | 13 | 14 | def should_continue(state: AgentState) -> str: 15 | if isinstance(state["agent_outcome"], AgentFinish): 16 | return END 17 | else: 18 | return ACT 19 | 20 | 21 | flow = StateGraph(AgentState) 22 | 23 | flow.add_node(AGENT_REASON, run_agent_reasoning_engine) 24 | flow.set_entry_point(AGENT_REASON) 25 | flow.add_node(ACT, execute_tools) 26 | 27 | 28 | flow.add_conditional_edges( 29 | AGENT_REASON, 30 | should_continue, 31 | ) 32 | flow.add_edge(ACT, AGENT_REASON) 33 | from langgraph.checkpoint.sqlite import SqliteSaver 34 | 35 | memory = SqliteSaver.from_conn_string(":checkpoints.sqlite:") 36 | 37 | app = flow.compile(checkpointer=memory) 38 | # app.get_graph().draw_mermaid_png(output_file_path="../static/graph.png") 39 | -------------------------------------------------------------------------------- /react/nodes/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/emarco177/react-langgraph/554fbda84d10a3c576da2b5554a0a68e6f2edc7c/react/nodes/__init__.py -------------------------------------------------------------------------------- /react/nodes/nodes.py: -------------------------------------------------------------------------------- 1 | from dotenv import load_dotenv 2 | 3 | load_dotenv() 4 | 5 | from langgraph.prebuilt.tool_executor import ToolExecutor 6 | 7 | from react.chains.reasoning_chain import react_reasoning_runnable, tools 8 | from react.state import AgentState 9 | 10 | 11 | def run_agent_reasoning_engine(state: AgentState): 12 | agent_outcome = react_reasoning_runnable.invoke(state) 13 | return {"agent_outcome": agent_outcome} 14 | 15 | 16 | tool_executor = ToolExecutor(tools) 17 | 18 | 19 | def execute_tools(state: AgentState): 20 | agent_action = state["agent_outcome"] 21 | output = tool_executor.invoke(agent_action) 22 | return {"intermediate_steps": [(agent_action, str(output))]} 23 | -------------------------------------------------------------------------------- /react/state.py: -------------------------------------------------------------------------------- 1 | import operator 2 | from typing import Annotated, TypedDict, Union 3 | 4 | from langchain_core.agents import AgentAction, AgentFinish 5 | 6 | 7 | class AgentState(TypedDict): 8 | input: str 9 | agent_outcome: Union[AgentAction, AgentFinish, None] 10 | intermediate_steps: Annotated[list[tuple[AgentAction, str]], operator.add] 11 | -------------------------------------------------------------------------------- /static/graph.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/emarco177/react-langgraph/554fbda84d10a3c576da2b5554a0a68e6f2edc7c/static/graph.png --------------------------------------------------------------------------------