The core LLM that handles reasoning, intent understanding, and decision-making.
def tool_node(state): # Execute the tool the LLM requested return execute_tools(state['tool_calls']) building agentic workflows pdf free download
The agent needs instructions on how to behave. The core LLM that handles reasoning, intent understanding,
An agent needs a memory system that persists throughout the workflow. The core LLM that handles reasoning
# 4. Add the Logic (The Loop) workflow.add_conditional_edges( "agent", # If tool call requested -> go to tools, else -> END should_continue, {"tools": "tools", "end": END} ) workflow.add_edge("tools", "agent") # Loop back
