| Workflow Orchestration | ZenML defines ML/AI workflows as pipelines (DAGs) of steps and executes them on configurable stacks, with artifacts and metadata tracked by default. | LangGraph natively orchestrates agent workflows as executable graphs with branching and cycles, optimized for stateful LLM/agent control flow. |
| Integration Flexibility | ZenML's stack architecture lets teams swap orchestrators, artifact stores, experiment trackers, and deployers without rewriting pipeline logic. | LangGraph integrates tightly with the LangChain ecosystem, but doesn't provide an MLOps-style plug-in stack for infrastructure components. |
| Vendor Lock-In | ZenML is cloud-agnostic by design: pipelines run on stacks you control, and you can move between infrastructures by swapping stack components. | LangGraph's core library is open-source (MIT) and runs anywhere Python runs; vendor coupling mainly appears when adopting LangSmith for managed operations. |
| Setup Complexity | ZenML can start locally and scale via stacks, but production setups require configuring orchestrators, artifact stores, and other components. | LangGraph's getting-started path is lightweight (pip install + define a graph), and the CLI can bootstrap local dev servers and Docker-based runs. |
| Learning Curve | ZenML maps closely to familiar ML concepts (steps, pipelines, artifacts), and its abstractions align with production ML workflow structure. | LangGraph's explicit state/graph model is powerful, but teams face a learning curve around state design, reducers, interrupts, and debugging cyclical flows. |
| Scalability | ZenML scales by delegating execution to orchestrators (e.g., Kubernetes-native options) and by externalizing artifacts and metadata into stack components. | LangGraph scales to production workloads when deployed with an agent server architecture (Postgres + Redis) or via LangSmith Deployment. |
| Cost Model | ZenML is free in open source, with paid plans pricing around pipeline-run volume and team governance features. | LangGraph OSS is free; LangSmith adds transparent per-seat pricing plus usage-based charges for deployments and traces. |
| Collaboration | ZenML Pro adds projects/workspaces, RBAC, and UI control planes for models and artifacts to enable team collaboration on production workflows. | LangGraph collaboration is strongest when paired with LangSmith (workspaces, team features, deployment management); the OSS library alone is single-app code. |
| ML Frameworks | ZenML is designed to wrap ML training/evaluation/inference across frameworks via steps, artifacts, and stack integrations. | LangGraph is framework-agnostic at the code level but optimized for LLM/agent workflows rather than deep integration with ML training frameworks. |
| Monitoring | ZenML tracks pipeline/step metadata and artifacts to support operational debugging, governance, and integration with monitoring tooling. | LangGraph pairs with LangSmith for deep tracing and debugging of agent execution, with visual trace inspection and replay capabilities. |
| Governance | ZenML Pro plans include RBAC/SSO and enterprise features (custom roles, audit logs) aligned with governance requirements. | Governance controls (SSO/RBAC, enterprise support) are delivered through LangSmith Enterprise rather than the LangGraph OSS library. |
| Experiment Tracking | ZenML treats pipeline runs as experiments and supports experiment tracker components to log metrics, parameters, and model metadata. | LangGraph captures execution traces and state trajectories, but is not an experiment tracking system for ML training runs and hyperparameter sweeps. |
| Reproducibility | ZenML automatically tracks artifact lineage (inputs/outputs, producing steps, dependencies) and uses that to enable reproducibility and caching. | LangGraph supports checkpointing and replay for agent state, but doesn't natively version datasets/models/environments the way an MLOps platform does. |
| Auto-Retraining | ZenML is built for scheduled and trigger-based pipelines that can retrain models, validate data, and promote artifacts through environments. | LangGraph is not designed as an auto-retraining or ML CI/CD system; it focuses on orchestrating agent behaviors and stateful execution. |
from zenml import pipeline, step
@step
def load_data():
# Load and preprocess your data
...
return train_data, test_data
@step
def train_model(train_data):
# Train using ANY ML framework
...
return model
@step
def evaluate(model, test_data):
# Evaluate and log metrics
...
return metrics
@pipeline
def ml_pipeline():
train, test = load_data()
model = train_model(train)
evaluate(model, test)
from typing import Annotated
from typing_extensions import TypedDict
from langchain.chat_models import init_chat_model
from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
class State(TypedDict):
messages: Annotated[list, add_messages]
llm = init_chat_model("anthropic:claude-3-5-sonnet-latest")
def chatbot(state: State) -> dict:
return {"messages": [llm.invoke(state["messages"])]}
builder = StateGraph(State)
builder.add_node("chatbot", chatbot)
builder.add_edge(START, "chatbot")
builder.add_edge("chatbot", END)
graph = builder.compile(checkpointer=MemorySaver())
out = graph.invoke(
{"messages": [{"role": "user", "content": "Hello!"}]},
config={"configurable": {"thread_id": "demo-thread"}},
)
print(out["messages"][-1].content)
.png)
