AI Workflow Tools Comparison Complete Guide 2026: LangGraph, CrewAI, n8n, Temporal

from langgraph.graph import StateGraph, END
from typing import TypedDict, List

class AgentState(TypedDict):
    input: str
    steps: List[str]
    output: str

def step1(state: AgentState) -> AgentState:
    state["steps"].append("Analyzing input")
    state["output"] = llm.invoke(f"Analyze: {state['input']}")
    return state

def step2(state: AgentState) -> AgentState:
    state["steps"].append("Generating response")
    state["output"] = llm.invoke(f"Refine: {state['output']}")
    return state

graph = StateGraph(AgentState)
graph.add_node("analyze", step1)
graph.add_node("generate", step2)
graph.set_entry_point("analyze")
graph.add_edge("analyze", "generate")
graph.add_edge("generate", END)
app = graph.compile()

result = app.invoke({"input": "Explain graph-based workflows", "steps": [], "output": ""})
print(result["output"])

from crewai import Agent, Task, Crew, Process

researcher = Agent(
    role="Research Analyst",
    goal="Find and synthesize information",
    backstory="Expert at research and data gathering",
    verbose=True
)

writer = Agent(
    role="Technical Writer",
    goal="Create clear documentation from research",
    backstory="Skilled at explaining complex topics",
    verbose=True
)

research = Task(
    description="Research: {topic}",
    expected_output="Research brief with key findings",
    agent=researcher
)

write = Task(
    description="Write article from research",
    expected_output="Complete article draft",
    agent=writer
)

crew = Crew(
    agents=[researcher, writer],
    tasks=[research, write],
    process=Process.sequential
)

result = crew.kickoff(inputs={"topic": "AI workflow orchestration"})

# Example n8n workflow (visual, not code):
#
# 1. Webhook Trigger  →  Incoming request
# 2. AI Agent Node    →  Analyzes intent with Claude
# 3. Switch Node      →  Routes by intent (support/sales/info)
# 4. Slack Node       →  Posts to appropriate channel
# 5. Database Node    →  Logs interaction

# Zapier Agent workflow:
#
# 1. Trigger: New email in Gmail
# 2. AI Step: Classify email urgency (urgent/routine/spam)
# 3. If urgent → Send Slack DM + create Asana task
# 4. If routine → Draft reply with AI, send after approval
# 5. If spam → Move to spam folder, no notification

from temporalio import workflow

@workflow.defn
class ResearchAgent:
    @workflow.run
    async def run(self, topic: str) -> str:
        # Step 1: Research (can take minutes)
        results = await workflow.execute_activity(
            research_task, topic,
            start_to_close_timeout=timedelta(hours=1)
        )

        # Step 2: Human review (can take days)
        approval = await workflow.execute_activity(
            request_human_approval, results,
            start_to_close_timeout=timedelta(days=7)
        )

        if not approval:
            return "Research rejected by reviewer"

        # Step 3: Finalize
        final = await workflow.execute_activity(
            generate_report, results,
            start_to_close_timeout=timedelta(hours=2)
        )
        return final