langgraph.md

LangGraph

Pass a prebuilt createReactAgent graph directly — detection handles it via .invoke() + graph shape (.getGraph(), a .nodes Map, or .nodes + .builder). See detection overview for how this compares to the other bridges.

import { createReactAgent } from '@langchain/langgraph/prebuilt';
import { ChatOpenAI } from '@langchain/openai';
import { DynamicStructuredTool } from '@langchain/core/tools';
import { AgentRuntime } from '@io-orkes/conductor-javascript/agents';

const llm = new ChatOpenAI({ model: 'gpt-4o-mini', temperature: 0 });
const graph = createReactAgent({ llm, tools, name: 'math_agent' });

const runtime = new AgentRuntime();
try {
  const result = await runtime.run(graph, 'What is 12 * 9?');
  result.printResult();
} finally {
  await runtime.shutdown();
}

For a complex graph where automatic introspection of the model/tools could fail, import createReactAgent from the SDK wrapper instead. It stamps ._agentspan metadata onto the graph so the serializer skips introspection:

import { createReactAgent } from '@io-orkes/conductor-javascript/agents/langgraph';

You can also pass a model hint at call time when detection can't infer it: runtime.run(graph, prompt, { model: 'anthropic/claude-sonnet-4-6' }).

The @io-orkes/conductor-javascript/agents/langgraph subpath is an optional peer-dependent wrapper — @langchain/langgraph is only required if you use this bridge.

Next steps

Framework agents can be deployed too: runtime.deploy(frameworkAgent). See deploy/serve/run/plan.