Autonomous Multi-Agent Workflows in Python

Design agentic networks using LangGraph state graphs, tool execution loops, SQL persistent memory checkpointers, and human approval gates.

Python 3.10+ (LangGraph) AI Engineering Intermediate ⏱️ 22 min lab • 6 Steps End-to-End

Environment & Multi-Agent Framework Setup

Install langgraph, langchain-core, and pydantic to build stateful multi-agent workflows in Python.

Terminal - Install LangGraph & Core Tools

pip install langgraph langchain-core pydantic requests

PYTHON • test_agent_env.py

from langgraph.graph import StateGraph, END
print("LangGraph module successfully loaded.")

Defining Pydantic Schemas & State Graph Memory Structures

Define shared agent state transitions using Python TypedDict and Pydantic validation schemas.

PYTHON • agent_state.py

from typing import TypedDict, List, Annotated
import operator
from pydantic import BaseModel

class TaskArtifact(BaseModel):
    title: str
    code_content: str
    verified: bool = False

class AgentState(TypedDict):
    task_description: str
    messages: Annotated[List[str], operator.add]
    current_agent: str
    code_artifact: TaskArtifact
    next_step: str

Implementing Specialist Agent Nodes (Planner, Coder, Tester)

Create distinct Python agent functions that inspect state, perform specialized subtasks, and update memory.

PYTHON • agent_nodes.py

def planner_agent_node(state: dict) -> dict:
    print("\n[🤖 Planner Agent] Breaking down task instructions...")
    return {
        "messages": [f"Plan created for: {state['task_description']}"],
        "current_agent": "coder",
        "next_step": "code_generation"
    }

def coder_agent_node(state: dict) -> dict:
    print("\n[💻 Coder Agent] Writing production Python implementation...")
    generated_code = "def execute_task():\n    return 'Task Accomplished'"
    return {
        "messages": ["Code generation complete."],
        "current_agent": "tester",
        "next_step": "test_verification"
    }

Stateful Tool Binding & Conditional Routing Logic

Assemble agent nodes into a directed StateGraph and add conditional branching logic in Python.

PYTHON • agent_graph.py

from langgraph.graph import StateGraph, END

def build_workflow_graph():
    workflow = StateGraph(dict)
    workflow.add_node("planner", planner_agent_node)
    workflow.add_node("coder", coder_agent_node)
    workflow.set_entry_point("planner")
    workflow.add_edge("planner", "coder")
    return workflow.compile()

Persistent Memory Checkpointing & Session Management

Use MemorySaver / Postgres checkpointer to record historical state transitions for long-running workflows.

PYTHON • memory_checkpointer.py

from langgraph.checkpoint.memory import MemorySaver

def get_checkpointer():
    memory = MemorySaver()
    print("Initialized In-Memory State Checkpointer.")
    return memory

Running End-to-End Multi-Agent Tasks Execution Script

Run this complete Python script to execute the multi-agent graph from start to finish.

PYTHON • run_agent_workflow.py

from agent_graph import build_workflow_graph

def run_agents():
    print("=== STARTING AUTONOMOUS MULTI-AGENT WORKFLOW ===")
    app = build_workflow_graph()
    initial_state = {
        "task_description": "Build automated threat log parser in Python",
        "messages": [],
        "current_agent": "planner"
    }
    final_state = app.invoke(initial_state)
    print("=== MULTI-AGENT EXECUTION COMPLETE ===")

if __name__ == "__main__":
    run_agents()

Expected Execution Output

=== STARTING AUTONOMOUS MULTI-AGENT WORKFLOW ===
[🤖 Planner Agent] Breaking down task instructions...
[💻 Coder Agent] Writing production Python implementation...
=== MULTI-AGENT EXECUTION COMPLETE ===