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AgentKit is a framework for creating and orchestrating AI Agents, from single model inference calls to multi-agent systems that use tools. Designed with orchestration at it’s core, AgentKit enables developers to build, test, and deploy reliable AI applications at scale.
A networked agent:
// Create a network of agents with separate tasks and instructions
// to solve a specific task.
const network = createNetwork({
agents: [navigator, classifier, summarizer],
defaultModel: openai({ model: "gpt-4o", step }),
});
const input = `Classify then summarize the latest 10 blog posts
on https://www.deeplearning.ai/blog/`;
const result = await network.run(input, ({ network }) => {
// Use an agent which figures out the specific agent to call
// based off of the network's history.
return defaultRoutingAgent;
});A simple agent:
const writer = createAgent({
name: "writer",
system:
"You are an expert writer. You write readable, concise, simple content.",
model: openai({ model: "gpt-4o", step }),
});
const { output } = await writer.run(
"Describe the ideas behind the given input into clear topics, and explain any insight: " +
`<content>${content}</content>`,
);Agent kit requires the Inngest TypeScript SDK as a dependency. You can install both via npm or similar:
npm install @inngest/agent-kit inngestThe full Agent kit documentation is available here. You can also jump to specific guides and references:
See Agent kit in action in fully functioning example projects:
- Test Writing Network - A ready-to-deploy Next.js demo using the Workflow Kit, Supabase, and OpenAI to power some AI content workflows.