December 10, 2024 - Learn about Command, a new tool in LangGraph that helps facilitate multi-agent communication.

One or more commands to update the graph's state and send messages to nodes.

"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 StateAnnotation.State) => { console.log("Called B"); return { foo: state.foo + "|b", }; } const nodeC = async (state: typeof StateAnnotation.State) => { console.log("Called C"); return { foo: state.foo + "|c", }; } import { StateGraph } from "@langchain/langgraph"; // NOTE: there are no edges between nodes A, B and C!

langgraph up [OPTIONS] -p, --port INTEGER Port to expose (default: 8123) --wait Wait for services to start --watch Restart on file changes --verbose Show detailed logs -c, --config FILE Config file path -d, --docker-compose Additional services file

December 24, 2024 - Instead of relying on external routing logic, the node itself returns a Command object containing both the updated state and the destination node. For instance, as shown in the example code, the node evaluates whether the metrics are satisfied. If successful, the Command object routes execution to the Output Requirement Analysis node.

March 8, 2026 - The npx @langchain/langgraph-cli dockerfile command translates all the configuration in your langgraph.json file into Dockerfile commands. When using this command, you will have to re-run it whenever you update your langgraph.json file.

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...

3 days ago - Build resilient language agents as graphs. Contribute to langchain-ai/langgraph development by creating an account on GitHub.

September 9, 2025 - We have four: one for getting approval (with interrupt), one for routing (with command), and two terminals for completing or canceling. The get_approval node pauses the graph, prints the task, and waits for input. The router decides the next step dynamically. The others just finalize the status. from langgraph.types import interrupt, Command def get_approval(state: WorkflowState): """ This node uses 'interrupt' to pause the graph.

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.

# Run CLI commands directly uv run langgraph --help # Or use the examples cd examples uv pip install uv run langgraph dev # or other commands

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

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February 24, 2026 - In this case LangGraph’s implementation is neater and more elegant. Command and Send schedule the node for execution — everything else (fan-out, concurrency, and state isolation) is handled by LangGraph behind the scenes. No worker math/guesstimation.