Multi-steps tools

In this guide, we’ll learn how to create a multi-steps tool that can be used in your AgentKit Tools to reliably perform complex operations.

By combining your AgentKit network with Inngest, each step of your tool will be retried automatically and you’ll be able to configure concurrency and throttling.

Creating a multi-steps tool

Creating a multi-steps tool is done by creating an Inngest Function that will be used as a tool in your AgentKit network.

To create an Inngest Function, you’ll need to create an Inngest Client:

import { Inngest } from 'inngest';

const inngest = new Inngest({
  id: 'my-agentkit-network',
});

Then, we will implement our AgentKit Tool as an Inngest Function with multiple steps. For example, we’ll create a tool that searches for perform a research by crawling the web:

import { inngest } from '../client';

export const researchWebTool = inngest.createFunction({
  id: 'research-web-tool',
}, {
  event: "research-web-tool/run"
}, async ({ event, step }) => {
    const { input } = event.data;

    const searchQueries = await step.ai.infer('generate-search-queries', {
      model: step.ai.models.openai({ model: "gpt-4o" }),
      // body is the model request, which is strongly typed depending on the model
      body: {
        messages: [{
          role: "user",
          content: `From the given input, generate a list of search queries to perform. \n ${input}`,
        }],
      },
    });

    const searchResults = await Promise.all(
        searchQueries.map(query => step.run('crawl-web', async (query) => {
        // perform crawling...
        })
    ));

    const summary = await step.ai.infer('summarize-search-results', {
      model: step.ai.models.openai({ model: "gpt-4o" }),
      body: {
        messages: [{
          role: "user",
          content: `Summarize the following search results: \n ${searchResults.join('\n')}`,
        }],
      },
    });

    return summary.choices[0].message.content;
});

Our researchWebTool Inngest defines 3 main steps.

The step.ai.infer() call will offload the LLM requests to the Inngest infrastructe which will also handle retries.
The step.run() call will run the crawl-web step in parallel.

All the above steps will be retried automatically in case of failure, resuming the AgentKit network upon completion of the tool.

Using the multi-steps tool in your AgentKit network

We can now add our researchWebTool to our AgentKit network:

import { createAgent, createNetwork, openai } from '@inngest/agent-kit';
import { createServer } from '@inngest/agent-kit/server';

import { researchWebTool } from './inngest/tools/research-web';


const deepResearchAgent = createAgent({
  name: 'Deep Research Agent',
  tools: [researchWebTool],
});

const network = createNetwork({
  name: 'My Network',
  defaultModel: openai({ model: "gpt-4o" }),
  agents: [deepResearchAgent],
});

const server = createServer({
  networks: [network],
  functions: [researchWebTool],
});

server.listen(3010, () => console.log("Agent kit running!"));

We first import our researchWebTool function and pass it to the deepResearchAgent tools array.

Finally, we also need to pass the researchWebTool function to the createServer()’s functions array.

Going further

Configuring Multitenancy Learn how to configure user-based capacity for your AgentKit network.

Customizing the retries Learn how to customize the retries of your multi-steps tools.