Temporal vs LangGraph (2026): Durable Agent Architecture

The real problem

Teams ask if Temporal replaces LangGraph, or if LangGraph removes the need for Temporal. That framing is the source of many late-night incidents.

LangGraph helps you model agent reasoning and tool flow. Temporal keeps multi-step execution durable when workers restart or networks fail. You usually need both once real side effects enter the workflow.

What top ranking posts miss

Source	What it covers	What it misses
AI Workflow Lab (LangGraph + Temporal production architecture)	Strong two-layer architecture explanation with practical examples and operational framing.	Light on concrete cutover thresholds for when teams should accept Temporal operational overhead.
Digital Applied orchestration platforms comparison	Broad 2026 platform landscape and observability emphasis.	Many claims are high level; reproducible benchmark methodology and hard failure tests are absent.
Bitovi: LangChain made durable with Temporal	Clear explanation of Temporal workflow/activity split and deterministic workflow constraints.	Focuses on LangChain integration path, not direct LangGraph durability-mode tradeoffs.

Most ranking posts correctly describe the architecture split. The missing layer is operational threshold guidance: how many external calls, how much wait time, and what failure cost justifies a second runtime.

Side-by-side comparison

Dimension	LangGraph	Temporal
Primary role	Agent graph and tool-routing logic	Durable orchestration runtime
State model	Checkpointer + thread ID + durability mode (`exit`/`async`/`sync`)	Event history + replay + continue-as-new for long chains
Failure recovery	Depends on checkpointer/task design and idempotent node boundaries	Workflow durability + activity retries/timeouts with recommended idempotency
Best fit	Reasoning-heavy agent behavior	Long-running reliable execution

Failure-mode table

Situation	Risk if LangGraph only	Recommended stack
Single-step read-only task under 30s	Low	LangGraph is enough
Workflow with 3+ tool calls or external API retries	Medium to high (partial completion, duplicate effects)	LangGraph + Temporal
Workflow pauses for multi-hour or multi-day approvals/events	High if runtime/process lifecycle is not independently durable	LangGraph + Temporal
Production side effects (deploy, delete, payments, external messaging)	High (unsafe automatic retries)	LangGraph + Temporal + pre-dispatch governance gate

Working code patterns

1) LangGraph baseline

Good starting point for agent logic. Keep node functions small so checkpoint boundaries are explicit.

LangGraph baseline

Python

from langgraph.graph import StateGraph, START, END
from langgraph.checkpoint.memory import InMemorySaver
from typing_extensions import TypedDict

class State(TypedDict):
    prompt: str
    answer: str


def run_agent(state: State):
    # Your LLM/tool logic here
    return {"answer": "ok"}


builder = StateGraph(State)
builder.add_node("run_agent", run_agent)
builder.add_edge(START, "run_agent")
builder.add_edge("run_agent", END)

checkpointer = InMemorySaver()
agent_graph = builder.compile(checkpointer=checkpointer)

config = {
    "configurable": {"thread_id": "incident-42"},
    "durability": "sync",
}
result = agent_graph.invoke({"prompt": "summarize this"}, config)

2) Temporal wrapper for durability

Wrap the LangGraph entrypoint as an activity. Temporal handles retries, timeout boundaries, and restart safety.

Temporal wrapper

Python

from datetime import timedelta
from temporalio import workflow, activity


@activity.defn
async def run_langgraph(prompt: str) -> str:
    # call your LangGraph entrypoint here
    return "ok"


@workflow.defn
class AgentWorkflow:
    @workflow.run
    async def run(self, prompt: str) -> str:
        # Durable retries and timeout policy around the agent step
        return await workflow.execute_activity(
            run_langgraph,
            prompt,
            start_to_close_timeout=timedelta(minutes=2),
            retry_policy={"maximum_attempts": 5},
        )

3) Policy gate before side effects

Durability protects completion. It does not decide if an action should run. Gate dangerous topics before dispatch.

Policy gate

YAML

# Example pre-dispatch gate for side-effect topics
version: v1
rules:
  - id: require-approval-prod-delete
    when:
      topic: infra.delete
      env: production
    decision: require_human

  - id: deny-unreviewed-external-message
    when:
      topic: customer.notify
      channel: external
    decision: deny

Limitations and tradeoffs

- Temporal adds operational components and replay constraints you must respect.
- LangGraph flexibility can hide non-idempotent behavior unless node boundaries are disciplined.
- Layering both systems increases complexity, but usually cuts incident risk for long workflows.
- Governance gates add approval latency for risky actions. That delay is often the point.

Frequently asked questions

What is the difference between Temporal and LangGraph?

They operate at different layers. LangGraph models agent reasoning and tool routing as a graph with checkpointed state, keyed by a thread ID and a durability mode (exit, async, or sync). Temporal is a durable orchestration runtime that backs multi-step execution with an event history, deterministic replay, and activity-level retries and timeouts, so work survives worker restarts and network failures. LangGraph decides what the agent does; Temporal keeps the overall execution durable.

Should I use Temporal or LangGraph for AI agents?

For most production side-effecting agents it is not either/or — the durable pattern is LangGraph for reasoning plus Temporal for orchestration. Use LangGraph alone for single-step, read-only tasks that finish in under ~30 seconds. Add Temporal once a workflow makes 3 or more external/tool calls, pauses for hours or days, or performs actions where duplicate retries would be harmful.

Does LangGraph replace Temporal, or does Temporal replace LangGraph?

Neither replaces the other. LangGraph already supports durable execution via checkpoints, but that durability depends on how you model tasks, side effects, and thread identity. Temporal adds event-history-backed orchestration durability and long-running execution measured in days or years. Asking which one replaces the other is the framing that causes incidents; they solve adjacent problems and compose well.

When should I add Temporal to a LangGraph agent?

Use concrete thresholds rather than intuition: when the execution path exceeds roughly 30 seconds, touches 3 or more external systems, needs to pause for multi-hour or multi-day approvals/events, or performs production side effects (deploys, deletes, payments, external messaging) where unsafe automatic retries would cause damage. Below those thresholds, the operational overhead of a second runtime usually is not justified.

Do Temporal and LangGraph handle governance of risky actions?

No. Durable execution protects completion — it makes sure a workflow finishes despite failures — but it does not decide whether an action should run at all. Temporal will happily, durably, retry a destructive action. For production side effects you add a pre-dispatch policy gate that can require human approval or deny the action before it executes, as a separate control from durability.

Next step

Pick one existing agent workflow and apply this sequence in order:

1. Keep reasoning/tool flow in LangGraph.
2. Wrap execution in Temporal when the path exceeds 30 seconds or touches 3+ external systems.
3. Add pre-dispatch policy checks before any high-risk side effect.

If you are comparing adjacent stacks, read Temporal vs LangChain and LangGraph vs Temporal vs Cordum.