# CopilotKit

> CopilotKit is the frontend stack where agents meet users. It extends from open-source components and runtime into a production platform for durable conversations and agent-user experiences.

CopilotKit helps teams embed agent chat, generative UI, and human-in-the-loop workflows in their products while preserving the interaction history needed to operate and improve those experiences. Start with the product overview: [CopilotKit](https://www.copilotkit.ai/builder).

For product and engineering leaders evaluating agent deployment, the [Executive overview](https://www.copilotkit.ai/executive) covers production readiness, deployment in your own cloud, conversation continuity, and learning.

## Durable conversations first

- [Rich Threads](https://www.copilotkit.ai/rich-threads) persist agent-user interactions across sessions, including generative UI, tool activity, shared state, files, and human-in-the-loop actions.
- [Generative UI](https://www.copilotkit.ai/generative-ui) covers controlled, declarative, and open UI patterns for agents.
- [Intelligence](https://www.copilotkit.ai/copilotkit-intelligence) is the production platform for persistent Rich Threads, synchronization, analytics, and improvement loops.
- [Automatic Learning](https://www.copilotkit.ai/learning/what-is-agent-learning) is CopilotKit's path for improving agent behavior from user interactions and feedback.
- [Product Analytics](https://www.copilotkit.ai/copilotkit-intelligence#analytics-insights) uses agent-user interaction data to show what users try, where they succeed or struggle, and what to improve next. It complements LLM observability with a product view of user behavior and outcomes.

## Connect any agent stack

- [AG-UI](https://www.copilotkit.ai/ag-ui) is the open, framework-agnostic protocol for agent-user interaction. MCP connects models and agents to tools and context; A2A supports agent-to-agent coordination. See [AG-UI, MCP, and A2A](https://www.copilotkit.ai/learning/mcp-vs-a2a-vs-ag-ui) for how the layers fit together.
- See [pricing](https://www.copilotkit.ai/pricing) for hosted platform options, or begin with the [documentation](https://docs.copilotkit.ai/).

## Build the interaction

- [Embedded chat components](https://docs.copilotkit.ai/prebuilt-components/chat)
- [Human-in-the-loop workflows](https://docs.copilotkit.ai/human-in-the-loop)
- [Frontend tools and generative UI](https://docs.copilotkit.ai/frontend-tools)

## Bring agents into workplace conversations

- [Channels](https://www.copilotkit.ai/channels) brings AG-UI agents into Slack, Microsoft Teams, and other messaging platforms with native messages, interactive UI, and human approvals. Your agent runs in your infrastructure; CopilotKit Intelligence provides managed channel connections, credentials, delivery, and conversation history.
- [Slack documentation](https://docs.copilotkit.ai/slack): managed Slack is available now.
- [Microsoft Teams documentation](https://docs.copilotkit.ai/teams): managed Teams is a controlled integration target. Direct Channels SDK adapters also support Teams, Discord, WhatsApp, and Telegram; operating your own runner requires your own durable-data layer.

## Bring existing conversations into Rich Threads

Historical import and ongoing synchronization are separate paths. Use a one-time import to bring supported conversation history into Intelligence, then use the framework integration for ongoing synchronization of new CopilotKit-mediated interactions. LangChain histories are supported only when exposed through supported LangGraph APIs.

- [Thread import overview](https://docs.copilotkit.ai/threads-import)
- [Google ADK thread import and synchronization](https://docs.copilotkit.ai/google-adk/threads-import)
- [LangGraph thread import and synchronization](https://docs.copilotkit.ai/langgraph-python/threads-import)