This is for those who are interested in advanced use cases or contributing. If you just want to get started, please go to Getting Started. We recommend you come back to this once you have a working chat and are building custom AI workflows. We’ll go through an overview of the core modules, and then how we built the internal sendMessage() function that composes all the slices into end to end flows.

​
The Zustand Store Structure

Cedar-OS uses Zustand as its core state management solution, organized into specialized “slices” that each handle different aspects of the system. This allows us to modularise and encapsulate functionality internally to modules.

​
Core Slices Overview

// The complete Cedar store combines all slices
interface CedarStore =
  // core modules
  & AgentConnectionSlice      // LLM communication & response processing
  & StateSlice               // Allows the agent to read & write to the state
  & AgentInputContextSlice    // Allows us to control the context we send to the agent, including state ^
  & MessagesSlice            // Chat message storage & rendering
  // additional modules
  & SpellSlice              // Interactive UI components ("spells")
  & StylingSlice            // Theme & appearance
  & VoiceSlice              // Voice interaction
  & DebuggerSlice           // Development tools
AgentConnectionSlice - Handles all LLM communication and response processing StateSlice - Allows agents to read and write to local react states AgentInputContextSlice - Gathers and formats context (such as state) for AI consumption MessagesSlice - Stores and renders chat messages

​
Breaking down an end-to-end AI workflow with Cedar-OS

Let’s say you want to build this an AI-native email flow where you can say “Write a response” and have an AI agent compose a draft to respond to the currently opened email.

​
Preparing the input

Cedar-OS Diagram 1. The first you’d do is pass in the user ask: “Write a response” to an LLM. This part is easy. We get the user request and pass it in as prompt. 2. Now we should add the user input as a message We call addMessage from messagesSlice to persist and add the user message to chat history. 3. Let agent read state through stateSlice But now, how does the agent know what it’s supposed to write a response to? Our next step is to allow the agent to read from local states. To do this, we have stateSlice which provides functions like registerState. This lets us register a state into a central place no matter where and how the local states are distributed. 4. agentInputContext agentInputContext handles the context that we pass into the agent. It stringifies the context & editor through stringifyInputContext and stringifyAdditionalContext and helps generally manage the context we’re giving the agent. For example, it allows users to use @notation to mention a specific email or user through useStateBasedMentionProvider(). 5. callLLM() Now that we’ve pulled all the necessary context for the agent to execute its task effectively, we can pass it all in through the agentConnectionSlice which calls the LLM.

​
Handling the LLM response

Cedar-OS Diagram Once we get a response from the LLM, Cedar-OS processes it through a structured pipeline that handles both text content and structured actions. 1. Response routing through handleLLMResponse When the LLM returns a response, it gets passed to handleLLMResponse() which processes an array of items that can be either strings (text content) or structured objects. 2. Type-based processing with response processors The system uses a switch-like mechanism through processStructuredResponse() that looks at the type field of structured responses and routes them to registered response processors:
  • message type - Handled by messageResponseProcessor, converts backend message format to chat messages
  • action type - Handled by actionResponseProcessor, executes state mutations through executeCustomSetter()
  • progress_update type - Shows loading states and progress indicators
3. Adding to chat through messagesSlice Text content and processed messages get added to the chat history via addMessage() in the messagesSlice. This handles the display and persistence of conversation history. 4. State mutations through registered setters For action type responses, the system calls executeCustomSetter() which uses the parameters from registerState() to execute the appropriate custom setter function, allowing the AI to modify application state based on the registered schema and available setters.

​
Response Processors Deep Dive

Response processors handle different types of structured AI responses:

​
Built-in Processors

  1. ActionResponseProcessor - Executes state mutations
  2. MessageResponseProcessor - Adds text messages to chat
  3. ProgressUpdateResponseProcessor - Handles loading states

​
Custom Response Processors

import { createResponseProcessor } from 'cedar-os';

const emailResponseProcessor = createResponseProcessor({
	type: 'email_action',
	namespace: 'email',
	execute: async (obj, store) => {
		if (obj.action === 'send') {
			await sendEmail(obj.draftId);
			store.addMessage({
				role: 'assistant',
				type: 'text',
				content: `Email sent successfully!`,
			});
		}
	},
	validate: (obj) => obj.type === 'email_action',
});

// Register the processor
store.registerResponseProcessor(emailResponseProcessor);