LangGraph vs Temporal for AI workflow orchestration
I’ve shipped agent workflows on both LangGraph and Temporal, and I keep seeing them framed as competitors. They’re not. They solve different problems, and the mistake I made early — picking LangGraph for a job that needed durability — cost me a weekend of debugging a half-finished run that died on a transient API error.
Here’s how I actually decide between them now.
The problem I hit
My first “agent” was a multi-step research pipeline: fetch sources, summarize each, synthesize, then generate a report. In LangGraph it was elegant — a graph of nodes, clean state passing, easy to reason about locally.
Then it ran for real. Step 3 hit a rate limit, the process crashed, and the four expensive LLM calls from steps 1–2 were just… gone. I re-ran the whole thing from scratch.
That’s the line in the sand: LangGraph orchestrates within a process; Temporal makes execution survive the process dying.
How I think about each
LangGraph is a library for building stateful agent graphs — branches, loops, shared state — that run in your process. It’s the right tool for the shape of agent reasoning: “if the critic rejects, loop back to the writer.”
Temporal is a durable execution engine. Your workflow code is replayed deterministically from an event history, so a crash, deploy, or rate-limit retry resumes exactly where it left off. Each LLM call is an Activity with its own retry policy.
from temporalio import workflow, activity
from datetime import timedelta
@activity.defn
async def summarize(doc: str) -> str:
return await call_llm(doc) # your provider call
@workflow.defn
class ResearchWorkflow:
@workflow.run
async def run(self, docs: list[str]) -> list[str]:
# If the worker dies after doc 3, replay resumes at doc 4 — 1, 2, 3 are not redone.
return [
await workflow.execute_activity(
summarize, d, start_to_close_timeout=timedelta(minutes=5)
)
for d in docs
]
The same loop in LangGraph is lovely to write but offers no crash recovery on its own — if the process dies mid-run, the in-memory state dies with it (unless you wire up a checkpointer and your own resume logic).
flowchart TD
task["Multi-step AI task"] --> q["Needs crash recovery?"]
q --> dur["Yes: long, expensive, scheduled"]
q --> loop["No: short, interactive"]
dur --> temporal["Temporal durable workflow"]
loop --> langgraph["LangGraph in-process graph"]
temporal --> both["Call LangGraph inside an Activity"]
The combination I actually ship
The bottom node above is the real answer. I run LangGraph inside a Temporal Activity: Temporal owns durability, retries, and scheduling; LangGraph owns the agent’s reasoning shape within a single durable step. The reasoning loop is in-process (where it belongs), and the overall run survives anything.
When I pick which
- Short, interactive, single-request agents (a chat tool, a one-shot RAG answer): LangGraph alone. Durability is overkill.
- Long-running, expensive, or scheduled (overnight research, a publishing pipeline, anything with many paid API calls): Temporal, with the agent logic as Activities.
- Both: complex reasoning that must also be crash-safe → LangGraph nodes wrapped in Temporal Activities.
Results
After moving my research pipeline to Temporal, a mid-run failure stopped meaning “pay for everything again.” Re-run cost on a transient error dropped from four wasted LLM calls to near zero, because completed Activities are never re-executed.
summarize[0..2]: cache hit (not re-executed)
summarize[3]: executed
run complete — 1 of 4 LLM calls repeated
Lessons
- They’re not competitors. LangGraph is the shape of the reasoning; Temporal is the guarantee that it finishes.
- If your workflow makes more than a couple of paid LLM calls, ask “what happens if this crashes at step N?” before picking a tool. A lost overnight run was my answer the hard way.
- Keep Activities deterministic at the boundary and let the engine handle retries — don’t hand-roll your own retry/resume logic on top of an in-process graph.