Networks, state, and routing TypeScript only

Networks are a simple class that turns agents into powerful stateful workflows. A network contains three components:

• The agents that the network can access
• The network’s state, including past messages and a key value store (read more below)
• The network’s router, which chooses whether to quit or the next agent to run in the loop (read more below)

It also has an optional default model, which will be used when your Agents have no model provided, and a setting to cap the number of model calls via maxIter.

Here's a simple example:

import { createNetwork, agenticOpenai as openai } from "@inngest/agent-kit";

// Create a network with two agents.
const network = createNetwork({
  agents: [searchAgent, summaryAgent],
  defaultModel: openai({ model: "gpt-4o", step }),  // Optional: used for routing and agents if they have no model
  maxIter: 10,  // Optional: max number of agent calls
});

// Run the network with a user prompt
await network.run("What happened in the 2024 Super Bowl?");

Similar to agents, you call .run on a network with some user input. The network then runs a core loop to call one or more agents to find a suitable answer.

How networks work

Networks are designed to be powerful while being easy to understand:

1. You create a network with a list of available agents. Each agent can use a different model and inference provider.
2. You give the network a user prompt by calling .run
3. The network runs its core loop, and:
   • Calls the router, which picks the first agent to run
   • Runs the agent with your input. This also runs the agent’s lifecycles, and any tools selected.
   • Stores the result in the network's state
   • Re-calls the router with the new state, which either quits or runs another agent

Network state

Network state is the memory of your agent workflow. It stores two things:

1. History Management: It keeps track of all agent interactions, including prompts, responses, and tool calls
2. Key-Value Storage: It provides a simple key-value store for sharing data and facts between different agent calls

Without this state, each agent call would be isolated and unable to build upon previous interactions to solve complex tasks. The history and key value stores are used automatically by the network to give each agent context about what happened before. We’ll dive into both.

History

The history system maintains a chronological record of all agent interactions in your network. Each interaction is stored as an InferenceResult, which includes:

• agent: The agent that created this result
• input: The original input
• system: System instructions
• prompt: The complete prompt
• output: Agent output
• toolCalls: Tool calls and their results
• raw: Raw API responses

Using state in agent prompts

The network state keeps track of every agent interaction, building a chronological list of messages in memory. When the network runs, it calls the onStart lifecycle hook with the given agent, network, user input, system, and history from memory:

const agent = createAgent({
  name: "Code writer",
  lifecycles: {
    onStart: ({
      agent,
      network, // has the entire state
      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 };
    }
  },
});

This lifecycle hook can be used to modify the system prompt and history used within each agent.

The state key-value store

The KV store is a simple way to store information between agent calls. Think of it like a shared memory that all your agents and tools can access. Here's how to use it:

// Store a value
network.state.kv.set("user-name", "Alice");

// Get a value
const name = network.state.kv.get<string>("user-name");

// Delete a value
network.state.kv.delete("user-name");

// Check if a value exists
const exists = network.state.kv.has("user-name");

Common uses for the KV store include:

• Storing intermediate results that other agents might need within lifecycles
• Storing user preferences or context
• Passing data between tools and agents

Here’s a tool described using a Zod schema which stores files created by a coding agent the network’s key-value state:

const writeFiles = createTypedTool({
  name: "write_files",
  description: "Write code with the given filenames",
  parameters: z.object({
    files: z.array(z.object({
      filename: z.string(),
      content: z.string(),
    })),
  }),
  handler: (output, { network }) => {
    // files is the output from the model's response in the format above.
    // Here, we store OpenAI's generated files in the response. 
    const files = network?.state.kv.get("files") || {};
    for (const file of output.files) {
      files[file.filename] = file.content;
    }
    network?.state.kv.set("files", files);
  }
})

Remember: The KV store persists for the entire network run, but is cleared when you create a new network.

This combination of history and key-value storage makes networks powerful for creating complex, stateful agent workflows. Agents can build on each other's work, store and retrieve information, and make decisions based on what's happened before.

Network routing

A router is a function that gets called after each agent runs, which decides whether to:

1. Stop the network (by returning undefined)
2. Call another agent (by returning an Agent)

The routing function gets access to everything it needs to make this decision:

interface RouterArgs {
  network: Network; // The entire network, including the state and history
  stack: Agent[]; // Future agents to be called
  callCount: number; // Number of times we've called agents
  lastResult?: InferenceResult; // The last agent's response
}

Code-based routers (supervised routing)

The simplest way to route is to write code that makes decisions. Here's an example that routes between a classifier and a writer:

const network = createNetwork({
  agents: [classifier, writer],
  router: ({ lastResult, callCount }) => {
    // First call: use the classifier
    if (callCount === 0) {
      return classifier;
    }
    // Second call: if it's a question, use the writer
    if (callCount === 1 && lastResult?.output === "question") {
      return writer;
    }
    // Otherwise, we're done!
    return undefined;
  },
});

Code-based routing is great when you want deterministic, predictable behavior. It's also the fastest option since there's no LLM calls involved.

Agent routers (autonomous routing)

Sometimes you want your network to be more dynamic. Agent-based routing uses an LLM to decide what to do next. The network comes with a built-in routing agent that you can use:

import { Network, agenticOpenai as openai } from "@inngest/agent-kit";

const network = createNetwork({
  agents: [classifier, writer],
  defaultModel: model,
  router: ({ lastResult, callCount }) => {
    return defaultAgenticRouter;
  },
});

The routing agent looks at:

• The original input
• What agents are available
• The conversation history
• Each agent's description

It then decides whether to call another agent or stop. This is great for building autonomous workflows where you're not sure what steps are needed up front. Note that the default agentic router is a starting point. In production apps it’s likely that you define your own agentic router props specifically for the network’s use case.

Hybrid code and agent routers (semi-supervised routing)

And, of course, you can mix code and agent-based routing. Here's an example that uses code for the first step, then lets an agent take over:

const network = createNetwork({
  agents: [classifier, writer],
  router: ({ lastResult, callCount }) => {
    // Always start with the classifier
    if (callCount === 0) {
      return classifier;
    }
    // Then let the routing agent take over
    return defaultAgenticRouter;
  },
});

This gives you the best of both worlds:

• Predictable first steps when you know what needs to happen
• Flexibility when the path forward isn't clear

Using state in routing

The router is the brain of your network - it decides which agent to call next. You can use state to make smart routing decisions:

const router = ({ network, lastResult }): Agent | undefined => {
  // Check if we've solved the problem
  const solution = network.state.kv.get("solution");
  if (solution) {
    // We're done - return undefined to stop the network
    return undefined;
  }

  // Check the last result to decide what to do next
  if (lastResult?.output[0].content.includes("need more context")) {
    return contextAgent;
  }

  return mathAgent;
};

Tips for routing

• Start simple with code-based routing
• Use agent-based routing when you need flexibility
• Remember that routers can access the network's state
• You can return agents that weren't in the original network
• The router runs after each agent call

That's it! Routing is what makes networks powerful - it lets you build workflows that can be as simple or complex as you need.