Build with CopilotKit

The Enterprise Agentic Frontend Stack

Connect any agent to any user

Build rich agentic applications that learn from every interaction.
Self-hostable and built to scale with your org.

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Agent prompt

Vue
Angular
React
iOS
Android
Slack
Teams
WhatsApp
Streaming &
Generative UI
User
Authentication
CopilotKit
In-App Actions
& Shared State
Automatic
Learning
OpenAI
Agents SDK
Claude
Agent SDK
LangChain
Google
ADK
AWS
Strands
Mastra

+18 more agents

Fortune 500

Trusted by the majority of the Fortune 500s and Global 50

  • Cisco
  • Apple
  • Walmart
  • Disney
  • Bank of America
  • Deutsche Telekom
  • Tesla
  • Ford
  • Toyota
  • Honda
  • Boeing
  • Salesforce
  • Intel
  • Hewlett Packard
  • Dell
  • AT&T
  • Comcast
  • Wells Fargo
  • Citi
  • Mastercard
  • Costco
  • Nike
  • Shell
  • General Electric
  • Booking.com
  • FedEx
  • UPS
  • Tripadvisor
  • UKG
  • Docusign
  • McDonald's
  • SoundCloud

Use cases & templates

Start from a real agent experience, then make every interaction look and feel like your product.

Advances to the next template every 8 seconds. Rotation pauses while you hover over or interact with this section.

Company knowledge base

Connect the sources your company already trusts. Give every employee one agent that can answer with citations, analyze across systems, and prepare the next action.

Talk to an Engineer

Agent prompt

An internal knowledge-base chat app searches CRM notes and company data with citations.
AG-UI

The protocol behind the agent stacks your teams already use

  • Google
  • Amazon
  • Tako
  • Mastra
  • Microsoft
  • Daytona
  • CrewAI
  • LangChain
  • Anthropic
  • Oracle
  • TanStack
  • Agno
  • Pydantic AI
  • Tavily
  • LlamaIndex
  • AG2

Why teams build with CopilotKit

Your agent acts inside the product, learns from real use, keeps every action auditable, and stays open to your stack.

Acts inside your product

Ask a question and get back a real chart or form, rendered with your components on the same live screen as your users. The agent can take action through your APIs and MCP tools, or directly in your frontend, and whatever it changes stays changed.

Learns from how users work

Every interaction becomes a signal. The agent improves as people use it instead of depreciating like static software.

Every action is auditable

Keep a complete record of every action. Your data stays yours, whether you self-host or use Copilot Cloud.

Open through AG-UI

CopilotKit is not a bet on one framework. Agent frameworks and harnesses across the ecosystem already connect through AG-UI.

Loved by developers

Build rich agentic applications for any surface.

Agent prompt

Choose a user surface
+ more
Choose a capability
and much more
import { createChannel } from "@copilotkit/channels";
import { makeAgent } from "./agent.js";

// Connect Slack in CopilotKit Intelligence
const channel = createChannel({
  name: "support",
  identifyUser: "platform",
  agent: makeAgent,
});

channel.onMessage(async ({ thread }) => {
  await thread.runAgent();
});

// Register with CopilotRuntime({ channels: [channel] })
Slack
Acme Inc
# support · thread
DK
Dana K.2:13 PM
Customer in the ticket queue is asking about a refund on order #4821, can someone take a look?
Refund AgentAPP2:14 PM
Customer asked about order #4821. Policy allows a full refund, @dana needs to approve amounts over $50.
Refund approved: $86.40 back on your Visa, 3 - 5 days.
Item$61.20
Shipping$16.80
Tax$8.40
Human_Approval
Refund #4821 is above your $50 auto-limit.
Approve full $86.40
Approve $69.60, keep shipping
Or reply with a note…
What card did they pay with?
Full card numbers aren’t in your permissions. I can share the last 4: ·· 4242.
How many people are in this channel, and how many are in PT?
23 members in #support. 9 of them are in Pacific Time.
channel.members read from Slack context · TOOL_CALL
Dana approved this from her phone. The thread updated the moment she did.
STATE_DELTA · synced across devices
✓ 2+1 3
3 replies · today at 2:16 PM
DK
Dana K.2:16 PM
Approved. Refund is on its way to the customer.
DK
Dana K.2:13 PM
Analyze our Q3 vendor spend
Vendor AgentAPP2:14 PM
Parsed 47 vendors. Top spend:
AWS$52.4k
Salesforce$31.2k
Datadog$12.8k
Figma$8.9k
Other$78.9k
DK
Dana K.2:15 PM
Use the updated spreadsheet insteadq3-vendors.xlsx2.1 MB
Reparsed with the new sheet.
Reply in thread…

Agents that improve with every interaction

Turn real usage into a controlled learning loop that makes agent quality and speed rise while cost falls.

User interactions over time
  • Quality ↑
  • Speed ↑
  • Cost ↓

Illustrative trends: quality and speed rise while cost falls as user interactions accumulate. These curves are conceptual, not measured performance data.

How automatic learning works

Follow each production interaction from captured signal to a safer, more capable agent.

Every interaction is captured.
In-chat & in-app.

In-app interactions. What users do in your app: clicks, edits, and navigation outside the chat.

Agent-user interactions. The back-and-forth with the agent: ask, feedback signals, approvals, edits, retries.

Define learning containers for users, groups of users, or entire organizations.

Self-learning lets you define learning containers for users, groups of users, or entire organizations.

Per user
Per group of users
Per organization

Your agent automatically improves with every interaction.

Self-improving skills – automatically adopted by your agents.

analytics-skill.mdimproved
search-skill.mdcreated

Export datasets to fine-tune custom models.

CompanyIssue DateValueStatus
JSON

Product analytics for agentic products

See what users try, where they succeed, where they struggle, and what to improve next.

Illustrative values, not live account data.

Agent activity

4,832↑ 12%
Runs
4,832
Tool calls
18,672
Avg. response time
2.4s

Agents performance

Customer Support
Research
Sales Assistant
Onboarding
View all agents

Outcomes

68%completed
Completed
68%
Failed
12%
Handed off
11%
Abandoned
9%

Performance over time

Response timeRuns completed

Understand user behavior

See which jobs users attempt, the paths they take, and where they abandon or ask for help.

Measure product outcomes

Connect agent interactions to task completion, adoption, satisfaction, and key product outcomes.

Find what works

Identify where users succeed, struggle, retry, or hand off so you know what to improve next.

Works alongside your LLM observability

CopilotKit adds the product view: what users try, where they succeed, and where they get stuck.

Enterprise Ready. Future-proof your organization.

Your agent acts inside the product, learns from real use, keeps every action auditable, and stays open to your stack.

  • Own your data and security

    Self-host both the open-source SDK and the enterprise runtime. Nothing leaves your infrastructure. Or use our managed Cloud for added convenience.

  • Future-proof: any agent stack, any frontend, any model

    AG-UI works with the stacks your teams use today, and whatever they adopt next.

  • Bring your own database

    No new data store to trust. Everything persists in the database you already run.

  • Continuous improvement

    Use observed outcomes and human feedback to improve prompts, routing, workflows, and UI behavior while you own the learning loop.

Built with teams shipping agentic products

Docusign
The CopilotKit SDK has accelerated our development processes and enabled us to help our users accomplish their tasks more quickly and with more confidence.

Mark Peterson, VP of Software Engineering

Docusign

Evergreen Wealth
The AG-UI architecture lets us cleanly decouple our agent framework from the frontend, so we can evolve each independently. The pre-built component library saved us from rolling our own.

Bryan Godwin, CTO

Evergreen Wealth

Start building

Enhance your applications
with powerful AI capabilities