Files
Alem Tuzlak 5ecdee36b8 feat(bot): pluggable StateStore persistence + cross-platform transcripts
Adds a durable persistence layer for @copilotkit/bot, replacing the
in-memory-only ActionStore with a pluggable StateStore.

- StateStore interface (kv/list/lock/dedup/queue) with a shared
  conformance suite; MemoryStore default plus @copilotkit/bot-store-redis
  and @copilotkit/bot-store-postgres backends.
- createBot({ store }): typed per-thread state via Standard Schema,
  action snapshots persisted through the store, per-conversation turn
  lock (onLockConflict drop|force), and inbound-event dedup keyed on a
  stable eventId. ActionStore is kept as a deprecated alias.
- Cross-platform transcripts (bot.transcripts + identity resolver) with
  age-bounded retention (prune on append + filter on read), and
  runAgent({ transcript: true }) to auto-inject history and capture the
  reply.
- createBot({ components }) re-registers components so durable actions
  re-fire after a restart; restart-durability demo in examples/slack.
- Dedup is marked seen only after the turn lock is acquired, so a turn
  dropped on lock-conflict does not burn its eventId (no lost retries).
- Release lockstep: bot-store-redis/postgres version with bot + bot-ui.
2026-06-23 18:33:38 +02:00
..
2026-04-10 23:38:59 +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
  • Open source - Full transparency and community-driven
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🧑‍💻 Real life use cases

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

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🖥️ 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.