CopilotKit ships OpenDots to give AI agents web, voice, and Slack access. CopilotKit released OpenDots, a self-hostable template for persistent AI coworkers with their own computers, Slack access, and voice calls. · 1 hr ago Read 1 min * CopilotKit released OpenDots, a self-hostable, MIT-licensed template for always-on AI coworkers. * Each Dot gets its own role, permissions, and isolated computer with browser, files, and shell. * Agents move across web chat, voice calls, and Slack while sharing the same conversation context. * Built on AG-UI protocol with CopilotKit React SDK, Threads, Channels SDK, and OpenBot containers. * Human-in-the-loop review cards pause tool calls before any save, with retry-safe deduplication. * Alpha status: single-owner today, no shared editing, Slack and spoken delegation still need live verification. OpenDots packages agent UX for web, voice, and Slack. CopilotKit has released OpenDots, an open-source template for building persistent AI coworkers that operate through a web app, voice calls, and Slack. The MIT-licensed repository packages the interface and orchestration layers developers typically need to build themselves, including editable outputs, isolated agent environments, approval gates, and channel integrations. OpenDots organizes work into Spaces containing editable Pages and specialist agents called Dots. Each Dot has a defined role, scoped permissions, and an isolated computer. Researchers, writers, and Slack responders can complete tasks in separate sandboxes while leaving work on Pages that people can review and revise. Editable pages, scoped agents. OpenDots uses the AG-UI protocol to stream messages, tool calls, and agent state between a backend and a React interface. CopilotKit's React SDK and runtime render the experience, Intelligence preserves durable Threads for ongoing agent context, and the Channels SDK connects Slack. Page content and metadata are stored separately from conversation history, allowing each to persist independently. * Spaces and Pages: A document workspace with a visual editor, slash commands, autosave, and revision checks that stop stale requests from overwriting newer edits. Pro article This story is for Pro members. You've reached the end of the free preview. Upgrade to AlphaSignal Pro to read the full article - and everything else behind the paywall.

OpenMuse puts an agent in a browser and a terminal. OpenMuse is an alpha, self-hosted agent template that browses, runs commands and keeps a task record you can inspect. CopilotKit has published OpenMuse, a personal agent application that runs its own server, task worker and browser worker. The project is on github.com, where it is described as an alpha meant for self-hosting and for building on, under the MIT licence. What OpenMuse actually contains. The core of it is what the repository calls an agent computer: a persistent Chromium profile plus an optional Linux workspace. According to the project description, the agent can read public pages, run commands inside its own container, work with files, and move PDFs between the computer and the app. A person can open the browser or the terminal and continue the work by hand. The repository notes that graphical desktops and autonomous checkout are still future work. The second part is the record around the work. The activity surface is described as holding durable task plans, progress, input requests, pause, resume, cancel and retry, approvals, and saved receipts, with SQL leases used to recover interrupted work. Commands in the container are limited to 30 seconds and leave saved output and exit receipts. The interface is built with CopilotKit React Native for iOS, Android and web, and the repository invites teams to clone the template and customise it. Where this touches office work. Two of the four jobs BARGO works on are visible here. Approval Runner is the closer match: an agent that asks for input, waits for a sign-off and stores a receipt is the shape of a request that has to be routed and recorded rather than answered in a chat window. Document Desk is the second: the PDF path in the repository moves an attachment into a form, produces a filled copy and a reviewed reply, which is the same sequence as an invoice or a contract being read and entered into a business system. The limits matter as much as the features. This is an alpha. Live Google accounts and the richer thread features need their own configuration, and the deployment described in the repository uses a single owner behind a shared access key rather than multi-tenant authentication. The Docker computer and Google mail or calendar are not started by the default deployment blueprint. A team wanting to test the idea could start with one narrow, repetitive request that already ends in a written approval, run it against fictional data first, and check whether the saved receipts give an auditor enough to follow. Whether that holds up on real supplier documents is the question to answer before anything goes near a production system.

Introducing AG-UI 1.0: a stable spec for connecting any agent to any application. By Anmol Baranwal and Eli Berman September 30, 2026 CopilotKit is excited to announce AG-UI 1.0, with a full specification and major new features! AG-UI (Agent-User Interaction Protocol) is the open protocol that standardizes how AI agents connect to user-facing applications. It's adopted by Google, Microsoft, Amazon and Oracle, and supported by most agent frameworks, including LangChain, Mastra and Anthropic's Claude Managed Agents. AG-UI 1.0 introduces a stable specification that lays out clear rules for every event, backed by a JSON Schema that defines the exact fields each event carries. The TypeScript, Python and .NET SDKs are now generated from that schema. CopilotKit also incorporated feedback from the Anthropic, Pydantic AI and TanStack teams into the spec. Beyond the spec, 1.0 brings subagent support, metadata for sending custom data to the frontend, multimodal tool results, human-in-the-loop interrupts, token usage and more. AG-UI 1.0 is backwards compatible, so your existing agents and apps keep working. Let's start with a quick intro to AG-UI, then get into what's new in 1.0, how the spec works, and how to upgrade. npx create-ag-ui-app@latest What is AG-UI. Agents break the traditional request-and-response model. They stream tokens, call tools mid-response and spawn subagents. Since HTTP returns one response per request, the frontend has to parse the stream, track what the agent is doing, and map it all to the UI. Every agent framework does this differently, so you end up maintaining custom boilerplate for each one. AG-UI (Agent-User Interaction Protocol) standardizes how agents communicate with user-facing applications. Instead of a custom streaming layer, it defines a shared bi-directional event stream between the agent backend and the frontend. * The agent endpoint receives the request. A framework bridge turns your agent's native output into AG-UI events, and the endpoint streams them back. * The AG-UI client, usually an SDK in your app, sends the request, checks every event against the spec, and hands each one to your UI. That event stream covers the full agent lifecycle, grouped into event categories. For example, when a user asks an agent to summarize their Notion notes, the agent: * starts the run (RUN_STARTED) * calls the Notion tool (TOOL_CALL_START) * gets the notes back (TOOL_CALL_RESULT) * updates shared state (STATE_DELTA) * writes the summary, token by token (TEXT_MESSAGE_CONTENT) * finishes the run (RUN_FINISHED) AG-UI defines the events, and any client can read them, no matter which framework built the agent. So the same agent can run in any user-facing surface like web app, mobile app, terminal or Slack. AG-UI works with LangChain, Mastra, Google ADK, Claude Managed Agents, OpenAI Agents SDK, Strands Agents, Microsoft Agent Framework, Pydantic AI and more. Try all agentic features across 30 live integrations in the AG-UI Dojo. On the frontend, CopilotKit is its AG-UI client for React, Angular, Vue and React Native, and for chat apps like Slack, Teams, WhatsApp, Telegram and Discord. The ecosystem has evolved a lot. MCP became the standard for connecting agents to external tools, A2A lets agents coordinate with each other, and now AG-UI connects your agents to user-facing apps as shown below. What's New in AG-UI 1.0. * The JSON Schema defines what each event contains. Here's the full schema. * The spec defines how events behave: the order they arrive in, how runs start and end, and how errors are handled. Every rule says who it applies to: the producer that sends events (like your agent) or the consumer that reads them (like a client SDK or UI). If the spec and the schema disagree on a field, the schema is right. If an implementation breaks a rule, the implementation has the bug. No more guessing. CopilotKit published 1.0 as a draft first and discussed it openly on GitHub. The Anthropic, Pydantic AI and TanStack teams shared feedback, and a lot of it made it into the final spec. The 1.0 spec won't change, so what you build on it today keeps working. Future versions will follow the same process, and you can join its AG-UI community to keep up with the updates. Now let's get into the major changes in AG-UI 1.0! Each feature below includes a flow diagram showing your app, the AG-UI event stream and your agent. In each diagram, solid arrows are requests your app sends and dashed arrows are events streaming back. Subagent support. Say you ask a travel agent to plan a trip, and it starts two subagents at once: one searches flights, the other searches hotels. Before 1.0, both streamed into the same chat, and the UI had no idea whose output was whose. Now every event a subagent sends is tagged with its own id, so your UI can show each subagent in its own card while it works: { "type": "SUBAGENT_STARTED", "subagentRunId": "sub-1", "name": "flight-search"} {"type": "TEXT_MESSAGE_CONTENT", "messageId": "msg-1", "subagentRunId": "sub-1", "delta": "Found 3 flights..."} {"type": "SUBAGENT_FINISHED", "subagentRunId": "sub-1"} Metadata. With metadata, you can send any custom data from your agent to the frontend. For example, you can show which model answered under each message, or attach a trace id that links to your logs. Add it on the backend: TextMessageEndEvent( message_id="msg-1", metadata={"acme.model": "gpt-5.5", "acme.trace_id": "tr_8f2a"},) And it shows up on the message in the browser: agent.messages.at(-1)?.metadata; // {"acme.model": "gpt-5.5", "acme.trace_id": "tr_8f2a"} Multimodal tool results. Tool results can now include images, audio, video and documents, instead of just text. For example, an invoice tool can now return the PDF itself: { "type": "TOOL_CALL_RESULT", "messageId": "msg-2", "toolCallId": "call-1", "content": [{ "type": "text", "text": "Here is your invoice."}, {"type": "document", "source": { "type": "url", "value": "https://example.com/invoice.pdf", "mimeType": "application/pdf"}}]} Human-in-the-loop with interrupts. AG-UI 1.0 adds interrupts, so a run can pause when the agent needs something from the user. Say your agent wants approval before sending an email. The run pauses, your UI shows an approve button, and the next run picks up right where it left off with the user's answer. Runs can also end as cancelled, so a run the user stopped no longer looks like one that finished or failed. Token usage. Runs can now report token usage when they finish, with clear rules for input, output, cached and reasoning tokens. So you can show how much each run cost right in your UI. Tokens used by subagents also count toward the run that started them: { "type": "RUN_FINISHED", "threadId": "t1", "runId": "r1", "usage": [{ "model": "gpt-5.5", "inputTokens": 1200, "outputTokens": 340, "cachedInputTokens": 800}]} Those are the highlights! For the full list of changes in AG-UI 1.0, check out the changelog. How the AG-UI 1.0 spec works. The JSON Schema is a single file: schema.json. It describes every event, the request your app sends (RunAgentInput) and every type they use. * Any JSON Schema validator, in any language, can check an event against it. * It's strict. A field the schema doesn't define fails validation, except in spots meant for your own data, like metadata. The spec covers the rules a schema can't express, like event order and how runs start and end. For example, while the schema rejects fields it doesn't know, a 1.0 client talking to a newer agent will run into fields and events it has never seen. The spec tells the client to skip them with a warning instead of failing, so older clients keep working as the protocol grows. Here's how it all fits together, from the user's request to the events streaming back into your UI: Upgrading to AG-UI 1.0. AG-UI 1.0 is backwards compatible. A 0.x agent works with a 1.0 client, and a 1.0 agent works with a 0.x client, so nothing breaks when one side upgrades first. When you update to the 1.0 SDKs, there are a few small changes to make. In TypeScript: * Validators moved to @ag-ui/core/schemas. * Custom fields you read off events are gone. Use metadata instead. * SubAgentInfo is now SubagentInfo, and subAgents is now subagents. * ToolMessage.content can now be a string or a list of content parts. In Python, the models are now generated from the schema, and JSON Patch entries are typed objects, so patch["path"] becomes patch.path. The migration guide walks through every SDK, including .NET. Get started. Spin up your first AG-UI app with a single command. npx create-ag-ui-app@latest Once it's running, explore more: If you have ideas for the next version, jump into GitHub Discussions and tell CopilotKit what you want to see. If you're building an agent framework or SDK and want to add AG-UI support, CopilotKit'd love to work with you. Just reach out in the CopilotKit or AG-UI communities. Want to bring CopilotKit into your stack? Talk to its engineers and CopilotKit'll help you set it up. Follow CopilotKit on Twitter for updates.