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Tyler Slaton 2596e8d932 chore: release monorepo v1.56.3 (#4138)
## Release monorepo v1.56.3

**Scope:** `monorepo` | **Bump:** `patch`

---

### How this release process works

1. **This PR was created automatically** by the "release / create-pr"
workflow.
   It bumped the `monorepo` packages to `1.56.3`
   and generated AI-enhanced release notes.

2. **CI runs on this PR** — the full test suite (unit tests, lint, type
checks, build)
   must pass before merging. This is the review gate.

3. **Review the release notes** in `release-notes.md` in this PR.
If a Notion draft was created, you can edit the release notes there
before merging.

4. **When this PR is merged**, the `release / publish` workflow
automatically:
   - Builds all packages
   - Publishes the `monorepo` packages to npm at version `1.56.3`
   - Creates git tag `monorepo/v1.56.3`
   - Creates a GitHub Release with the final release notes

### Before merging

- [ ] CI is green (tests, lint, types, build)
- [ ] Version bumps look correct
- [ ] Release notes are accurate (edit in Notion if a draft was created)

---

> **Do not merge until CI is fully green.** The full test suite runs
automatically on this PR.
2026-04-22 13:59:09 -07:00
..
2026-04-10 23:38:59 +00:00
2026-04-21 23:48:16 +00:00

CopilotKit - React UI

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Why CopilotKit?

  • Minutes to integrate - Get started quickly with our CLI
  • Framework agnostic - Works with React, Next.js, AGUI and more
  • Production-ready UI - Use customizable components or build with headless UI
  • Built-in security - Prompt injection protection
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class-support-ecosystem

🧑‍💻 Real life use cases

Deploy deeply-integrated AI assistants & agents that work alongside your users inside your applications.

headless-ui

🖥️ Code Samples

Drop in these building blocks and tailor them to your needs.

Build with Headless APIs and Pre-Built Components

// Headless UI with full control
const { visibleMessages, appendMessage, setMessages, ... } = useCopilotChat();

// Pre-built components with deep customization options (CSS + pass custom sub-components)
<CopilotPopup
  instructions={"You are assisting the user as best as you can. Answer in the best way possible given the data you have."}
  labels={{ title: "Popup Assistant", initial: "Need any help?" }}
/>
// Frontend actions + generative UI, with full streaming support
useCopilotAction({
  name: "appendToSpreadsheet",
  description: "Append rows to the current spreadsheet",
  parameters: [
    { name: "rows", type: "object[]", attributes: [{ name: "cells", type: "object[]", attributes: [{ name: "value", type: "string" }] }] }
  ],
  render: ({ status, args }) => <Spreadsheet data={canonicalSpreadsheetData(args.rows)} />,
  handler: ({ rows }) => setSpreadsheet({ ...spreadsheet, rows: [...spreadsheet.rows, ...canonicalSpreadsheetData(rows)] }),
});

Integrate In-App CoAgents with LangGraph

// Share state between app and agent
const { agentState } = useCoAgent({
  name: "basic_agent",
  initialState: { input: "NYC" }
});

// agentic generative UI
useCoAgentStateRender({
  name: "basic_agent",
  render: ({ state }) => <WeatherDisplay {...state.final_response} />,
});

// Human in the Loop (Approval)
useCopilotAction({
  name: "email_tool",
  parameters: [
    {
      name: "email_draft",
      type: "string",
      description: "The email content",
      required: true,
    },
  ],
  renderAndWaitForResponse: ({ args, status, respond }) => {
    return (
      <EmailConfirmation
        emailContent={args.email_draft || ""}
        isExecuting={status === "executing"}
        onCancel={() => respond?.({ approved: false })}
        onSend={() =>
          respond?.({
            approved: true,
            metadata: { sentAt: new Date().toISOString() },
          })
        }
      />
    );
  },
});
// intermediate agent state streaming (supports both LangGraph.js + LangGraph python)
const modifiedConfig = copilotKitCustomizeConfig(config, {
  emitIntermediateState: [
    {
      stateKey: "outline",
      tool: "set_outline",
      toolArgument: "outline",
    },
  ],
});
const response = await ChatOpenAI({ model: "gpt-4o" }).invoke(
  messages,
  modifiedConfig,
);

Documentation

To get started with CopilotKit, please check out the documentation.