Most multi-agent tutorials stop at “here’s how to wire two agents together.” Production systems need more: structured message passing, durable state across restarts, and an audit trail you can debug when something goes wrong at 2am. This guide builds a Planner/Executor/Validator architecture with LangGraph that’s actually ready for production.

Architecture Overview

The system uses three specialized agents:

  • Planner — Receives a task, decomposes it into steps, publishes to the message bus
  • Executor — Consumes steps from the bus, executes them, publishes results
  • Validator — Checks Executor outputs against criteria, flags failures, loops back to Planner if needed

These agents communicate via a structured ACP-style message bus (Pydantic schemas), checkpoint state to SQLite via langgraph-checkpoint-sqlite, and log every message to JSONL for auditability.

Prerequisites

pip install langgraph langgraph-checkpoint-sqlite pydantic langchain-anthropic

Step 1: Define the Message Bus Schema

Use Pydantic to define typed message schemas. Typed messages prevent the #1 cause of multi-agent failures: agents silently passing malformed data to each other.

from pydantic import BaseModel, Field
from typing import Literal, Optional
from datetime import datetime
import uuid

class AgentMessage(BaseModel):
    message_id: str = Field(default_factory=lambda: str(uuid.uuid4()))
    timestamp: str = Field(default_factory=lambda: datetime.utcnow().isoformat())
    sender: Literal["planner", "executor", "validator"]
    recipient: Literal["planner", "executor", "validator", "bus"]
    message_type: Literal["task", "step", "result", "validation", "error"]
    payload: dict
    
class PlannerOutput(BaseModel):
    steps: list[str]
    task_id: str
    priority: Literal["high", "medium", "low"] = "medium"

class ExecutorResult(BaseModel):
    step: str
    output: str
    success: bool
    error: Optional[str] = None

class ValidatorVerdict(BaseModel):
    task_id: str
    passed: bool
    feedback: Optional[str] = None
    retry_step: Optional[str] = None

Step 2: Build the LangGraph State

from typing import TypedDict, Annotated
from langgraph.graph import StateGraph, END
import operator

class PipelineState(TypedDict):
    task: str
    task_id: str
    messages: Annotated[list[AgentMessage], operator.add]
    steps: list[str]
    results: list[ExecutorResult]
    validation: Optional[ValidatorVerdict]
    status: Literal["planning", "executing", "validating", "complete", "failed"]
    retry_count: int

Step 3: Implement the Agent Nodes

from langchain_anthropic import ChatAnthropic
import json

llm = ChatAnthropic(model="claude-sonnet-4-6")

def planner_node(state: PipelineState) -> dict:
    """Decomposes task into executable steps."""
    prompt = f"""You are a Planner agent. Decompose this task into 3-5 concrete steps.
    
Task: {state['task']}

Return JSON: {{"steps": ["step1", "step2", ...], "task_id": "{state['task_id']}"}}"""
    
    response = llm.invoke(prompt)
    data = json.loads(response.content)
    output = PlannerOutput(**data)
    
    message = AgentMessage(
        sender="planner",
        recipient="executor",
        message_type="step",
        payload=output.model_dump()
    )
    
    return {
        "steps": output.steps,
        "messages": [message],
        "status": "executing"
    }

def executor_node(state: PipelineState) -> dict:
    """Executes each planned step."""
    results = []
    
    for step in state["steps"]:
        prompt = f"""You are an Executor agent. Execute this step and report results.
        
Step: {step}

Return JSON: {{"step": "{step}", "output": "...", "success": true/false}}"""
        
        response = llm.invoke(prompt)
        data = json.loads(response.content)
        result = ExecutorResult(**data)
        results.append(result)
        
        message = AgentMessage(
            sender="executor",
            recipient="validator",
            message_type="result",
            payload=result.model_dump()
        )
    
    return {
        "results": results,
        "messages": [message],
        "status": "validating"
    }

def validator_node(state: PipelineState) -> dict:
    """Validates executor outputs and decides pass/retry."""
    results_summary = "\n".join([
        f"- {r.step}: {'✓' if r.success else '✗'} {r.output[:100]}"
        for r in state["results"]
    ])
    
    prompt = f"""You are a Validator agent. Review these execution results.

Task: {state['task']}
Results:
{results_summary}

Return JSON: {{"task_id": "{state['task_id']}", "passed": true/false, "feedback": "..."}}"""
    
    response = llm.invoke(prompt)
    data = json.loads(response.content)
    verdict = ValidatorVerdict(**data)
    
    message = AgentMessage(
        sender="validator",
        recipient="bus",
        message_type="validation",
        payload=verdict.model_dump()
    )
    
    new_status = "complete" if verdict.passed else (
        "planning" if state["retry_count"] < 2 else "failed"
    )
    
    return {
        "validation": verdict,
        "messages": [message],
        "status": new_status,
        "retry_count": state["retry_count"] + (0 if verdict.passed else 1)
    }

Step 4: Wire the Graph with SQLite Checkpointing

from langgraph.checkpoint.sqlite import SqliteSaver
import sqlite3

# SQLite-backed persistence — survives restarts
conn = sqlite3.connect("pipeline_state.db", check_same_thread=False)
checkpointer = SqliteSaver(conn)

def route_after_validation(state: PipelineState):
    if state["status"] == "complete":
        return END
    elif state["status"] == "planning":
        return "planner"
    else:
        return END  # failed

graph = StateGraph(PipelineState)
graph.add_node("planner", planner_node)
graph.add_node("executor", executor_node)
graph.add_node("validator", validator_node)

graph.set_entry_point("planner")
graph.add_edge("planner", "executor")
graph.add_edge("executor", "validator")
graph.add_conditional_edges("validator", route_after_validation)

pipeline = graph.compile(checkpointer=checkpointer)

Step 5: Add JSONL Audit Logging

import jsonlines
from datetime import datetime

def log_messages(messages: list[AgentMessage], run_id: str):
    with jsonlines.open(f"audit_{run_id}.jsonl", mode="a") as writer:
        for msg in messages:
            writer.write({
                **msg.model_dump(),
                "run_id": run_id,
                "logged_at": datetime.utcnow().isoformat()
            })

Step 6: Run the Pipeline

import uuid

run_id = str(uuid.uuid4())[:8]
config = {"configurable": {"thread_id": run_id}}

initial_state = {
    "task": "Analyze Q1 2026 sales data and generate executive summary",
    "task_id": run_id,
    "messages": [],
    "steps": [],
    "results": [],
    "validation": None,
    "status": "planning",
    "retry_count": 0
}

result = pipeline.invoke(initial_state, config=config)
log_messages(result["messages"], run_id)

print(f"Status: {result['status']}")
print(f"Messages logged: {len(result['messages'])}")

What Makes This Production-Ready

Pydantic schemas — Every inter-agent message is validated at the boundary. Malformed payloads fail loudly, not silently.

SQLite checkpointing — If the process crashes mid-run, you can resume from the last checkpoint with the same thread_id. No lost progress.

JSONL audit trail — Every agent message is logged with sender, recipient, timestamp, and payload. When something breaks, you have a complete record of what happened.

Retry loop with cap — The Validator can send work back to the Planner, but the retry_count cap prevents infinite loops.

This architecture scales: swap SQLite for Postgres with langgraph-checkpoint-postgres for multi-process deployments, or replace the LLM calls with specialized tools as your agents mature.

Sources

  1. MarkTechPost — LangGraph ACP Production Tutorial
  2. LangGraph Documentation
  3. langgraph-checkpoint-sqlite on PyPI

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260301-2000

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