LangGraph vs Traditional Workflow Engines
28 June 2025 • ~10 min read
Workflow orchestration is a solved problem, right? Apache Airflow, n8n, Step Functions – pick your flavour. Then LangChain dropped LangGraph, promising to turn LLM agents into first-class nodes that can branch, loop and remember state. Hype aside, where does LangGraph really fit? We ran side-by-side pilots to find out.
1 · Mental model 🧠
Airflow and friends are DAG-first: tasks are idempotent, state lives in
the database, and failures roll back or retry. LangGraph flips the model: the
agent is stateful; edges represent conversation turns rather than static
dependencies. This matters when your workflow needs to react to the content of
the LLM output (“If the plan includes research then jump to the Web Search
node”).
2 · Experiment 🔬
We rebuilt a product-spec generation flow in three engines:
- Airflow 2.9 – 17 PythonOperator tasks + XCom JSON hand-offs.
- n8n 1.26 – 22 nodes, heavy use of functionItem for loops.
- LangGraph 0.0.35 – 8 nodes, agent memory stored in Redis.
Key metrics over 1,000 runs:
| Engine | MLOC | P95 Latency | Failure Rate |
|---|---|---|---|
| Airflow | 720 | 12.4 s | 0.8 % |
| n8n | –– | 9.1 s | 1.2 % |
| LangGraph | 310 | 6.3 s | 0.9 % |
Lines of code halved and latency improved because we avoided serialising / deserialising large JSON blobs between tasks – the agent just remembered.
3 · When LangGraph wins 🏆
- Dynamic branching – the path depends on LLM output or external feedback loops (human approvals, tool calls).
- Long-running context – the same graph instance serves a user session over hours or days. Traditional schedulers assume tasks finish quickly.
- Streaming UX – token-by-token updates are natively supported; users see “thoughts” in real time.
4 · When the old guard is better 🛠️
Airflow and n8n still shine for deterministic, data-heavy pipelines. Dependency-aware backfills, SLA miss alerts, cron-based scheduling – these are battle-tested features you get for free. Trying to force a 500-table ETL job through LangGraph is a recipe for pain.
5 · Hybrid architecture 🔗
The sweet spot is a two-tier approach:
- Airflow orchestrates nightly data loads, model retraining and safety tests.
- LangGraph sits at the edge, powering user-facing agents that need conversational flexibility.
We emit LangGraph telemetry to OpenTelemetry and let Airflow pick it up for centralised monitoring – same dashboards, no silos.
Take-away: use LangGraph when your workflow needs to think. Otherwise, stick with your existing orchestrator and sleep well.