Maga222006
MultiagentPersonalAssistant
e6a90e9
from agent.prompts import coder_instructions, coder_system_message
from langchain_core.messages import HumanMessage, SystemMessage
from agent.models import llm_agents, llm_peripheral
from langgraph.prebuilt import create_react_agent
from langgraph.constants import START, END
from agent.states import PlanCodingTask
from langgraph.graph import StateGraph
from agent.tools import coder_tools
agent = create_react_agent(
llm_agents,
tools=coder_tools,
prompt=coder_instructions
)
def planning_node(state: dict):
planer = llm_peripheral.with_structured_output(PlanCodingTask)
plan = planer.invoke(state['messages'][-1].content)
state.update(plan)
return state
def code_agent(state: dict):
system_message = SystemMessage(coder_system_message(state))
state.update(agent.invoke({
'messages': [
system_message,
HumanMessage(state['task_description']),
]
}))
return state
graph = StateGraph(dict)
graph.add_node("planning_node", planning_node)
graph.add_node("code_agent", code_agent)
graph.add_edge(START, "planning_node")
graph.add_edge("planning_node", "code_agent")
graph.add_edge("code_agent", END)
coder_agent = graph.compile(name="coder_agent")