Agents are defined using the createAgent function. 
import { createAgent, agenticOpenai as openai } from '@inngest/agent-kit';

const agent = 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'),
});

​
Options

​
name
string
required
The name of the agent. Displayed in tracing.
​
description
string
Optional description for the agent, used for LLM-based routing to help the network pick which agent to run next.
​
model
string
required
The provider model to use for inference calls.
​
system
string | function
required
The system prompt, as a string or function. Functions let you change prompts based off of state and memory.
​
tools
array<TypedTool>
Defined tools that an agent can call.Tools are created via createTool.
​
lifecycle
Lifecycle
Lifecycle hooks that can intercept and modify inputs and outputs throughout the stages of execution of run().Learn about each lifecycle hook that can be defined below.

​
lifecycle

​
onStart
function
Called after the initial prompt messages are created and before the inference call request. The onStart hook can be used to:
  • Modify input prompt for the Agent.
  • Prevent the agent from being called by throwing an error.
​
onResponse
function
Called after the inference call request is completed and before tool calling. The onResponse hook can be used to:
  • Inspect the tools that the model decided to call.
  • Modify the response prior to tool calling.
​
onFinish
function
Called after tool calling has completed. The onFinish hook can be used to:
  • Modify the InferenceResult including the outputs prior to the result being added to Network state.
const agent = createAgent({
  name: 'Code writer',
  lifecycles: {
    onStart: ({
      agent,
      network,
      input,
      system, // The system prompt for the agent
      history, // An array of messages
    }) => {
      // Return the system prompt (the first message), and any history added to the
      // model's conversation.
      return { system, history };
    },
  },
});