Agents are the core of AgentKit. Agents are stateless entities with a defined goal and an optional set of Tools that can be used to accomplish a goal.

Agents can be called individually or, more powerfully, composed into a Network with multiple agents that can work together with persisted State.

At the most basic level, an Agent is a wrapper around a specific provider’s model, OpenAI gpt-4 for example, and a set of of tools.

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Creating an Agent

To create a simple Agent, all that you need is a name, system prompt and a model. All configuration options are detailed in the createAgent reference. Here is a simple agent created using the createAgent function:

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

const codeWriterAgent = createAgent({
  name: 'Code writer',
  system:
    'You are an expert TypeScript programmer.  Given a set of asks, you think step-by-step to plan clean, ' +
    'idiomatic TypeScript code, with comments and tests as necessary.' +
    'Do not respond with anything else other than the following XML tags:' +
    '- If you would like to write code, add all code within the following tags (replace $filename and $contents appropriately):' +
    "  <file name='$filename.ts'>$contents</file>",
  model: openai('gpt-4o-mini'),
});

While system prompts can be static strings, they are more powerful when they are dynamic system prompts defined as callbacks that can add additional context at runtime.

Any Agent can be called using run() with a user prompt. This performs an inference call to the model with the system prompt as the first message and the input as the user message.

const { output } = codeWriterAgent.run(
  'Write a typescript function that removes unnecessary whitespace',
);
console.log(output);
// [{ role: 'assistant', content: 'function removeUnecessaryWhitespace(...' }]

When including your Agent in a Network, a description is required. Learn more about using Agents in Networks here.

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Adding tools

Tools are functions that extend the capabilities of an Agent. Along with the prompt (see run()), Tools are included in calls to the language model through features like OpenAI’s “function calling” or Claude’s “tool use.”

Tools are defined using the createTypedTool function and are passed to agents via the tools parameter:

import { createAgent, createTypedTool, openai } from '@inngest/agent-kit';

const listChargesTool = createTypedTool({
  name: 'list_charges',
  description:
    "Returns all of a user's charges. Call this whenever you need to find one or more charges between a date range.",
  parameters: z.array(
    z.object({
      userId: z.string(),
    }),
  ),
  handler: async (output, { network, agent, step }) => {
    // output is strongly typed to match the parameter type.
  },
});

const supportAgent = createAgent({
  name: 'Customer support specialist',
  system: 'You are an customer support specialist...',
  model: openai('gpt-3.5-turbo'),
  tools: [listChargesTool],
});

When run() is called, any step that the model decides to call is immediately executed before returning the output. Read the “How agents work” section for additional information.

Learn more about Tools in this guide.

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How Agents work

Agents themselves are relatively simple. When you call run(), there are several steps that happen:

1

Preparing the prompts

The initial messages are created using the system prompt, the run() user prompt, and Network State, if the agent is part of a Network.

For added control, you can dynamically modify the Agent’s prompts before the next step using the onStart lifecycle hook.

2

Inference call

An inference call is made to the provided model using Inngest’s step.ai. step.ai automatically retries on failure and caches the result for durability.

The result is parsed into an InferenceResult object that contains all messages, tool calls and the raw API response from the model.

To modify the result prior to calling tools, use the optional onResponse lifecycle hook.

3

Tool calling

If the model decides to call one of the available tools, the Tool is automatically called.

After tool calling is complete, the onFinish lifecycle hook is called with the updated InferenceResult. This enables you to modify or inspect the output of the called tools.

4

Complete

The result is returned to the caller.

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Lifecycle hooks

Agent lifecycle hooks can be used to intercept and modify how an Agent works enabling dynamic control over the system. As mentioned in the “How Agents work” section, there are a few lifecycle hooks that can be defined on the Agent’s lifecycle options object.

  • Dynamically alter prompts using Network State or the Network’s history.
  • Parse output of model after an inference call.

Learn more about lifecycle hooks and how to define them in this reference.

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System prompts

An Agent’s system prompt can be defined as a string or an async callback. When Agents are part of a Network, the Network State is passed as an argument to create dynamic prompts, or instructions, based on history or the outputs of other Agents.

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Dynamic system prompts

Dynamic system prompts are very useful in agentic workflows, when multiple models are called in a loop, prompts can be adjusted based on network state from other call outputs.

Dynamic system prompt
const agent = createAgent({
  name: 'Code writer',
  description:
    'An expert TypeScript programmer which can write and debug code.',

  // The system prompt can be dynamically created at runtime using Network state:
  system: async ({ network }) => {
    // A default base prompt to build from:
    const basePrompt =
      'You are an expert TypeScript programmer. ' +
      'Given a set of asks, think step-by-step to plan clean, ' +
      'idiomatic TypeScript code, with comments and tests as necessary.';

    // Inspect the Network state, checking for existing code saved as files:
    const files: Record<string, string> | undefined =
      network?.state.kv.get('files');
    if (!files) {
      return basePrompt;
    }

    // Add the files from Network state as additional context automatically
    let additionalContext = 'The following code already exists:';
    for (const [name, content] of Object.entries(files)) {
      additionalContext += `<file name='${name}'>${content}</file>`;
    }
    return `${basePrompt} ${additionalContext}`;
  },
});

​
Static system prompts

Agents may also just have static system prompts which are more useful for simpler use cases.

const codeWriterAgent = createAgent({
  name: 'Copy editor',
  system:
    `You are an expert copy editor. Given a draft article, you provide ` +
    `actionable improvements for spelling, grammar, punctuation, and formatting.`,
  model: openai('gpt-3.5-turbo'),
});

​
Using Agents in Networks

Agents are the most powerful when combined into Networks. Networks include state and routers to create stateful workflows that can enable Agents to work together to accomplish larger goals.

​
Agent descriptions

Similar to how Tools have a description that enables an LLM to decide when to call it, Agents also have an description parameter. This is required when using Agents within Networks. Here is an example of an Agent with a description:

const codeWriterAgent = createAgent({
  name: 'Code writer',
  description:
    'An expert TypeScript programmer which can write and debug code. Call this when custom code is required to complete a task.',
  system: `...`,
  model: openai('gpt-3.5-turbo'),
});
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