December 24, 2024 - We know the workflow would hit an interrupt.abs # Hence by reading the state we can surface the interrupt reason. current_state = rc_agent.get_state() # Surface the reason for interrupt print(f"\n---Interrupt---\nGraph is interrupted with following message:\n{current_state.tasks[0].interrupts[0].value['evaluate_decision']}") decision = input("Decision [proceed/reflect_back]: ") rationale = input("Rationale for the decision: ") assessment_output = {'decision':decision,'rationale':rationale} # Continue graph execution with human response # Instead of passing state into the invoke, we are passing Command with human input # for resume key rc_agent.invoke(Command(resume=assessment_output)) Apart from these two examples, Command object is also useful to update state of the graph, especially from a Tool during the execution of the graph.

"nodeB" : "nodeC"; // note how Command allows you to BOTH update the graph state AND route to the next node return new Command({ // this is the state update update: { foo: "a", }, // this is a replacement for an edge goto, }); }; // Nodes B and C are unchanged const nodeB = async (state: typeof ...

December 10, 2024 - With this in mind, we are excited to release Command in LangGraph. This is a special type that when returned from a node specifies not only the update to the state (as usual) but also which node to go to next.

September 9, 2025 - Commands, on the other hand, provide expressive communication between nodes. They support dynamic routing without predefined edges, handoffs in multi-agent setups, and enhanced control flow by updating state and dictating paths. Our example workflow presents a task for approval, pauses for a user decision, and then routes to either completion or cancellation.

LangGraph Swarm · Language · ... the graph's state and send messages to nodes. Copy · Command( self, *, graph: str | None = None, update: Any | None = None ·...

3 days ago - If you're looking to quickly build agents with LangChain's create_agent (built on LangGraph), check out the LangChain Agents documentation.

Use Command instead of conditional edges if you want to combine state updates and routing in a single function. The entry point is the first node(s) that are run when the graph starts. You can use the add_edge method from the virtual START node to the first node to execute to specify where to enter the graph. from langgraph.graph import START graph.add_edge(START, "node_a")

2 weeks ago - LangGraph makes it easy to build structured, stateful applications like chatbots. In this example we’ll learn how to create a basic chatbot that can classify user input as either a greet, search query and respond accordingly.

December 10, 2024 - We've released Command , a new tool in LangGraph that lets you manage dynamic, edgeless agent flows. Now, nodes can dynamically decide which node to execute...

July 20, 2025 - Modern LLM applications need dynamic control flows, not rigid pipelines. LangGraph’s Command object is a powerful abstraction that lets your nodes decide the next step at runtime, turning static DAGs into adaptive multi-agent systems.

Design agents that reliably handle complex tasks with LangGraph, an agent runtime and low-level orchestration framework.

November 5, 2025 - This example shows a basic agent graph. The AI receives a request, reasons about it, decides to use the tool, and completes the task. You can imagine extending this to more complex graphs where the AI can retry, call APIs, or fetch new information.

langgraph build -t IMAGE_TAG [OPTIONS] --platform TEXT Target platforms (e.g., linux/amd64,linux/arm64) --pull / --no-pull Use latest/local base image -c, --config FILE Config file path