Mozaik

Mozaik is a TypeScript runtime for interoperable AI agents.

Agent Interoperability

Our main goal is to unlock agent interoperability across projects, so Mozaik is built around three core attributes:

• Concurrency - Agents work independently without blocking one another.
• Awareness - Agents understand other participants, events, and their shared environment.
• Adaptivity - Agents change their behavior based on runtime state and emerging needs.

Together, these capabilities free AI agents from specific harnesses, allowing them to work across projects - so every newly created agent adds value to the entire ecosystem.

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Installation

npm

npm install @mozaik-ai/core

yarn

yarn add @mozaik-ai/core

pnpm

pnpm add @mozaik-ai/core

API Key Configuration

Mozaik picks a provider from the model name you pass to runInference, and each provider's SDK reads its credential from the environment. Set the keys for the providers you use:

# .env
OPENAI_API_KEY=your-openai-key-here
ANTHROPIC_API_KEY=your-anthropic-key-here
GEMINI_API_KEY=your-gemini-key-here

DeepSeek models run through the OpenAI-compatible chat-completions endpoint, so they use an OpenAI-style credential and base URL (OPENAI_API_KEY / OPENAI_BASE_URL) pointed at DeepSeek.

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Table of contents

• Runtime
• Runtime state
• Participants
• Concurrent agents
• Semantic events
• Situation handlers
• The agent loop
• Interception
• Examples
• Made with Mozaik
• Contributing

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Runtime

The runtime is the shared space where participants meet, events are published, and agent loops run. Create one with defineRuntime, then call initializeRuntime once with a RuntimeState:

import { defineRuntime, RuntimeState } from "@mozaik-ai/core"

class AppState extends RuntimeState {}

const { initializeRuntime, resolveRuntime, resolveParticipant, join, leave, sendMessage, sendEvent, runLoop } =
	defineRuntime<AppState>()

initializeRuntime({ state: new AppState() })

defineRuntime returns the functions you use for the rest of the session. They are not top-level package exports — keep them in module scope (or re-export them yourself):

Function	Role
initializeRuntime({ state, inferenceRunnerConfig? })	Create the runtime. Throws if called twice.
resolveRuntime()	The initialized runtime (including .state).
resolveParticipant(id)	Look up a joined participant by id.
join(participant) / leave(participant)	Membership.
sendMessage(message, senderId)	Publish a message.sent event.
sendEvent(event, senderId)	Publish any SemanticEvent.
runLoop(agentId, message, inferenceInput, interceptionHandler?)	Start an agent loop.

Calling initializeRuntime a second time throws "Runtime already initialized". Calling any of the other functions before initializeRuntime throws "Runtime not initialized".

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Runtime state

Runtime state is the typed store every participant shares. RuntimeState already holds the participant registry (participants: Map<string, Participant>). Subclass it for anything the team needs to share — an active goal, a work queue, a transcript buffer:

import { defineRuntime, RuntimeState } from "@mozaik-ai/core"

class AppState extends RuntimeState {
	activeGoal?: string
}

const { initializeRuntime, resolveRuntime } = defineRuntime<AppState>()

initializeRuntime({ state: new AppState() })

resolveRuntime().state.activeGoal = "ship the runtime docs"

Participants read and write this object from situation processors (and from an InterceptionHandler). There is no second hidden store.

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Participants

A participant is anyone who can join the runtime. Mozaik ships two factories:

Role	Factory	What it carries
Human	createHuman({ name, capabilities, handlers })	A manifest and situation handlers. Typically calls sendMessage.
Agent	createAgent({ name, capabilities, instruction, tools, handlers })	Manifest, handlers, an instruction, tools, and memory (agent.getMemory().getContext()). Typically starts thinking with runLoop.
Observer	Either factory, handlers only	Never calls sendMessage or runLoop — only reacts.

Every participant has a manifest (id, name, role, capabilities) and a list of situation handlers. Identity is the manifest; behavior is the handlers you register — not method overrides on a base class.

import { createAgent, createHuman } from "@mozaik-ai/core"

const human = createHuman({ name: "User", capabilities: [], handlers: [] })

const agent = createAgent({
	name: "Assistant",
	capabilities: ["inference"],
	instruction: "You are a helpful teammate.",
	tools: [],
	handlers: [],
})

The role is which functions a participant calls and which specifications it registers. A critic that only watches model.answer from others is still just a participant.

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Concurrent agents

Several participants can be joined at the same time. Membership is runtime state, and every membership change is an event:

flowchart LR
    joinFn["join(participant)"] --> add["RuntimeState.addParticipant"]
    add --> joined["publish participant.joined"]
    leaveFn["leave(participant)"] --> remove["RuntimeState.removeParticipant"]
    remove --> left["publish participant.left"]
    joined --> all["every joined participant"]
    left --> remaining["remaining participants"]

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• join(participant) adds the participant to RuntimeState and publishes participant.joined. Joining an id that is already present is a no-op.
• leave(participant) removes them and publishes participant.left to whoever is still joined.
• Each runLoop(agentId, ...) creates a new loop with its own loopId and does not await it. Two agents can think, call tools, and answer at the same time.
• Events from any loop fan out to every joined participant. A slow processor is not a barrier: the runtime does not await processor.apply.
• Shared mutable data lives on your RuntimeState subclass. Situation processors (and interception) are how participants adapt to it.

const observer = createHuman({ name: "Observer", capabilities: [], handlers: [] })

join(human)
join(agent)
join(observer)

sendMessage("Hello", human.getId())

leave(observer)

An agent can wait until a collaborator has joined (participant.joined) before calling runLoop, or clean up shared state when someone leaves (participant.left).

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Semantic events

The runtime is an event bus. Everything interesting is a SemanticEvent:

Field	Meaning
type	String name of the event ("message.sent", "inference.completed", …).
producerId	Id of the participant that caused it.
occurredAt	When it was created.
payload	Typed data for that event.

publish delivers every event to every joined participant. There is no built-in “internal vs external” split — a situation specification filters on event.type and on event.producerId versus participant.getId().

Participants never poll. Custom events go through sendEvent(event, senderId) with any type string.

Lifecycle and messaging

Event	Published when	Payload
participant.joined	join(participant)	Participant manifest (id, name, role, capabilities)
participant.left	leave(participant)	Participant manifest
message.sent	sendMessage(message, senderId)	{ message: string }
custom	sendEvent(event, senderId)	Whatever you put on the event

Agent loop

Producer is the agent whose loop is running:

Event	Published when	Payload
message_received.started	The loop begins appending the user message	Received message plus loopId
message_received.completed	Context is ready for inference	InferenceInput plus loopId
inference.started	The model call begins	InferenceInput
inference.stream	Each streaming chunk (only when streaming: true)	The inner provider event
inference.completed	The model call finished	InferenceOutput (items, tokenUsage, rowResponse)
function_call.started	A tool is about to run	{ call, inferenceInput }
function_call.completed	The tool returned	FunctionCallOutputItem
model.answer	The assistant message is committed	{ answer: ModelMessageItem }
interception.started	An InterceptionHandler matched a transition	The pending LoopTransition
interception.finished	The handler returned (possibly rewritten)	The transition that will execute

Streaming

Pass streaming: true on the InferenceInput you give runLoop. The loop takes the inference_streaming path and publishes each provider chunk as inference.stream (the inner event is the payload). Requesting streaming for a model whose specification has supportsStreaming: false fails validation before the API is called.

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Situation handlers

Participants react by registering situation handlers. The runtime publishes events; a handler is a pair of “when” and “then”:

• SituationSpecification — isSatisfiedBy({ event, participant }). Compose with .and(), .or(), and .not().
• SituationProcessor — apply(context) is the reaction: runLoop, sendMessage, mutate runtime state, log, persist, …

flowchart LR
    Human[Participant] -->|"sendMessage(text, senderId)"| Runtime(("Runtime"))
    Agent[Participant] -->|"runLoop"| Runtime
    Observer[Participant] -->|join| Runtime
    Runtime -->|"SemanticEvent"| Human
    Runtime -->|"SemanticEvent"| Agent
    Runtime -->|"SemanticEvent"| Observer

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Fan-out is synchronous and does not await processors, so a slow listener never blocks producers or other listeners. Participants start receiving events as soon as they join().

There are no built-in specifications — write the ones you need. Self versus others is a filter on producerId, not a second handler API.

import {
	defineRuntime,
	RuntimeState,
	createAgent,
	createHuman,
	SituationSpecification,
	Agent,
	type SituationHandler,
	type SituationContext,
} from "@mozaik-ai/core"

class AppState extends RuntimeState {}

const { initializeRuntime, join, sendMessage, runLoop } = defineRuntime<AppState>()

initializeRuntime({ state: new AppState() })

class WhenOthersSendAMessage extends SituationSpecification {
	isSatisfiedBy({ event, participant }: SituationContext): boolean {
		return event.type === "message.sent" && event.producerId !== participant.getId()
	}
}

const thinkOnMessage: SituationHandler = {
	specification: new WhenOthersSendAMessage(),
	processor: {
		apply({ event, participant }) {
			if (!(participant instanceof Agent)) return
			const { message } = event.payload as { message: string }
			runLoop(participant.getId(), message, {
				model: "gpt-5.5",
				context: participant.getMemory().getContext(),
				tools: participant.getTools(),
			})
		},
	},
}

const agent = createAgent({
	name: "Assistant",
	capabilities: ["inference"],
	instruction: "You are a helpful teammate.",
	tools: [],
	handlers: [thinkOnMessage],
})

const human = createHuman({ name: "User", capabilities: [], handlers: [] })

join(human)
join(agent)

sendMessage("Hello", human.getId())

An observer is the same pattern with processors that only take side actions:

class WhenModelAnswers extends SituationSpecification {
	isSatisfiedBy({ event }: SituationContext): boolean {
		return event.type === "model.answer"
	}
}

const transcript: SituationHandler = {
	specification: new WhenModelAnswers(),
	processor: {
		apply({ event }) {
			console.log("[model.answer]", event.producerId, event.payload)
		},
	},
}

const observer = createHuman({ name: "Transcript", capabilities: [], handlers: [transcript] })
join(observer)

Three things to note:

1. “Act on my own outputs” versus “observe others” is a specification filter on event.producerId versus participant.getId() — not a separate onExternal* API.
2. Do not await runLoop or sendMessage inside a processor. They return void and keep running in the background while the runtime keeps delivering events.
3. Behaviors compose by reaction, not orchestration. Add a second agent whose spec matches model.answer and you get a critique loop. Add a transcript observer and you get a UI stream. Neither change touches the existing participants.

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The agent loop

runLoop(agentId, message, inferenceInput, interceptionHandler?) drives one agent turn as a state machine. It starts at message_received and runs until idle. Tool execution and the follow-up inference live inside the loop — you do not call a separate function-call runner.

Each step: optionally intercept the pending transition, execute the state, then resolve the next transition.

flowchart TD
    Start([runLoop]) --> CU[message_received]
    CU --> INF[inference]
    INF -->|function_call item| FC[function_call]
    INF -->|assistant message| MM[model_message]
    FC --> INF
    MM --> Idle[idle]

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State	What it does
message_received	Appends the user message to the agent's context.
inference	Calls the model and waits for the full response.
inference_streaming	Same call, streaming; each chunk is published as inference.stream.
function_call	Runs the matching tool, writes the call and output back into context, then returns to inference.
model_message	Publishes model.answer, then the loop goes idle.

runLoop is fire-and-forget. Each invocation gets a unique loopId, so concurrent agents (or two loops on the same agent) do not share a cursor.

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Interception

An InterceptionHandler inspects or rewrites a loop transition before that state runs. Pass it as the optional fourth argument to runLoop:

interface InterceptionHandler {
	isSatisfiedBy(transition: ExecutableTransition): boolean
	handle(transition: ExecutableTransition): Promise<ExecutableTransition>
}

When isSatisfiedBy is true, the loop publishes interception.started, awaits handle (which may change nextStateId and input), publishes interception.finished, then executes the returned transition.

import { type InterceptionHandler } from "@mozaik-ai/core"

const inspectFunctionCalls: InterceptionHandler = {
	isSatisfiedBy(transition) {
		return transition.nextStateId === "function_call"
	},
	async handle(transition) {
		if (transition.nextStateId === "function_call") {
			console.log("about to call", transition.input.call.name)
		}
		return transition
	},
}

runLoop(agent.getId(), message, inferenceInput, inspectFunctionCalls)

Use it to steer loop execution by rewriting the next state — for example, swapping a tool call’s input — without putting that logic inside the agent’s situation handlers. That is how you keep a human in the loop, or another agent, to control what runs next.

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Examples

Working examples are available here: mozaik-examples.

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Made with Mozaik

• baro — a Claude agent orchestrator where ten specialized participants (planner, executors, reviewer, fixer, librarian, auditor, and more) work fully concurrently on the same goal, like a team collaborating in real time instead of a single agent doing everything alone.

flowchart LR
    Conductor[Conductor] -->|"RunStart / LevelCompute / StorySpawn"| Bus(("Mozaik Runtime"))
    Factory[StoryFactory] -->|"spawn StoryAgent"| Bus
    Story[StoryAgent] -->|"StoryResult / retries"| Bus
    Librarian[Librarian] -->|"index exploration outputs"| Bus
    Sentry[Sentry] -->|"flag file conflicts"| Bus
    Critic[Critic] -->|"per-turn verdict"| Bus
    Surgeon[Surgeon] -->|"emit ReplanItem"| Bus
    Operator[Operator] -->|"bridge TUI commands"| Bus
    Auditor[Auditor] -->|"write JSONL log"| Bus
    Cartographer[Cartographer] -->|"emit UI frames"| Bus
    Bus -->|"StorySpawnRequest"| Factory
    Bus -->|"StoryResult / LevelCompleted"| Conductor
    Bus -->|"tool calls"| Librarian
    Bus -->|"Edit/Write calls"| Sentry
    Bus -->|"agent turns"| Critic
    Bus -->|"terminal failure"| Surgeon
    Bus -->|"user input"| Operator
    Bus -->|"all events"| Auditor
    Bus -->|"all events"| Cartographer

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Contributing

Contributions are welcome. Please read the Contributing Guidelines before opening an issue or pull request.

Author & License

Created by the JigJoy team.
Licensed under the MIT License.