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AI Frameworks · CopilotKit

CopilotKit

CopilotKit is an open-source SDK for building AI agent applications with generative UI across web (React, Angular, Vue), mobile (React Native), and chat platforms (Slack, Teams). It provides a unified agent backend that can power multiple frontends, with features like shared state, human-in-the-loop workflows, and tool rendering.

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Source: GitHub — github.com/CopilotKit/CopilotKit
35.8k
GitHub stars
4.4k
Forks
TypeScript
Primary language
MIT
License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

FieldValue
RepositoryCopilotKit/CopilotKit
OwnerCopilotKit
Primary languageTypeScript
LicenseMIT — OSI-approved
Stars35.8k
Forks4.4k
Open issues642
Latest releasev1.62.2 (2026-07-02)
Last updated2026-07-08
Sourcehttps://github.com/CopilotKit/CopilotKit

What CopilotKit is

TypeScript/JavaScript framework implementing the AG-UI Protocol for agent-native UIs. Supports LLM connections (OpenAI, Anthropic, Gemini), backend tool calls returning UI components, stateful agent-UI synchronization, and deployment to React/Next.js, Angular, Vue, React Native, and chat integrations (Slack/Teams beta).

Quickstart

Get the CopilotKit source

Clone the repository and explore it locally.

terminalbash
git clone https://github.com/CopilotKit/CopilotKit.gitcd CopilotKit# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Multi-platform AI Assistant Deployment

Build a single agent backend that powers chat in your web app, mobile app, and Slack/Teams simultaneously without rewriting agent logic per platform.

Generative UI Applications

Applications where agents dynamically render UI components at runtime based on user intent and agent state, enabling interactive agent-driven workflows.

Human-in-the-Loop Agent Workflows

Enterprise workflows requiring agents to pause and request user confirmation, input, or edits before proceeding—e.g., approvals, form validation, decision gates.

Implementation considerations

  • Requires LLM API keys (OpenAI, Anthropic, Gemini, etc.). Cost and latency are directly dependent on LLM provider and usage patterns.
  • AG-UI Protocol adoption by major vendors (Google, AWS, Microsoft, LangChain) suggests future compatibility, but the protocol and ecosystem are still in active evolution.
  • Early access features (Self-Learning agents, Slack/Teams integrations) are beta; production deployments should verify feature stability and SLA commitments.
  • State synchronization between agent and UI relies on protocol correctness; validate state consistency in complex multi-step workflows before deploying.
  • Documentation shows quickstarts for React/Next.js are GA; Angular, Vue, React Native have source code but docs are 'coming soon'—implementation effort varies by framework.

When to avoid it — and what to weigh

  • Backend-only or non-UI agent applications — CopilotKit is frontend-heavy. If you need pure backend agents without UI rendering, lighter frameworks (LangChain, Anthropic SDK) may be more suitable.
  • Strict non-JavaScript tech stacks — Primary language is TypeScript/JavaScript. Python, Go, or JVM-only shops would face integration friction despite the backend being framework-agnostic in theory.
  • Offline-first or low-latency constraints — Architecture assumes real-time agent-UI communication and LLM availability. Not suitable for disconnected or ultra-low-latency scenarios.
  • Production-grade self-hosted scaling at high volume — Early ecosystem maturity (v1.62.2 released 2026-07-02). Self-hosting intelligence features requires your own infrastructure; adopting an unproven pattern at scale carries risk.

License & commercial use

MIT License. Permits commercial use, modification, and distribution with attribution. No patent or trademark grants implied; review for your organization's compliance requirements.

MIT is OSI-approved and permissive for commercial use. However, review CopilotKit's Terms of Service, any associated cloud services (CopilotKit Cloud, Intelligence Platform), and third-party LLM provider agreements separately. No warranty or liability protection is implied by the license.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

SignalAssessment
MaintenanceActive
DocumentationStrong
License clarityClear
Deployment complexityLow
DEV.co fitStrong
Assessment confidenceHigh
Security considerations

LLM API keys and user data in agent-UI communication must be protected via HTTPS and proper secret management. Backend tool calls should validate inputs rigorously to prevent prompt injection or unintended tool execution. Early-access features (Self-Learning, cloud services) should be audited for data handling and compliance before production use. No security audit or penetration test results are disclosed in the README.

Alternatives to consider

LangChain / LangServe

Mature Python-first agent framework with multi-platform UI support via Streamlit/web. Steeper learning curve but more backend flexibility; less opinionated UI layer.

Anthropic's Claude with Tools API + custom UI

Lightweight, pure-function approach. No framework lock-in; full control over UI and agent loop. Requires more boilerplate but offers maximum flexibility for non-standard UIs.

Microsoft Bot Framework / Azure AI Agent Service

Enterprise-grade with Teams/Slack/web integration out-of-the-box. Heavier, more expensive, but includes compliance and support for large organizations.

Software development agency

Build on CopilotKit with DEV.co software developers

Start with CopilotKit's React quickstart (npx create), then scale to mobile and Slack with the same agent backend. Try it free—no credit card needed.

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Related open-source tools

Surfaced by semantic similarity across the DEV.co open-source index.

CopilotForXcodeComfyUI-Copilotopen-multi-agent

Related on DEV.co

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CopilotKit FAQ

Can I use CopilotKit with my own LLM (e.g., self-hosted Llama)?
Partially. CopilotKit is designed to connect to LLM APIs. Self-hosted models are possible if you expose them via an API-compatible endpoint (e.g., OpenAI-compatible server), but this requires custom integration.
Is CopilotKit suitable for enterprise production?
For React/Next.js, yes—it is GA. For other frameworks (Angular, Vue, React Native) and early-access features (Self-Learning, Slack/Teams), maturity is lower. Evaluate SLA, support, and feature stability with the team before committing.
How is data handled in CopilotKit Cloud vs. self-hosted?
Data handling for cloud services is not detailed in the README. Self-hosted deployments give you full control. Review the privacy policy and data processing agreement for CopilotKit Cloud before using production data.
What is the AG-UI Protocol and why should I care?
It's a standardized wire protocol for agent-UI communication, adopted by Google, AWS, Microsoft, and others. Adopting it future-proofs your agent code against tool/framework changes and enables multi-platform deployment.

Custom software development, end to end

Standing up CopilotKit properly takes more than a deploy. As a software development agency, DEV.co handles the ai frameworks architecture, the web development on top, and the operational work underneath.

Build Multi-Platform AI Agents in Minutes

Start with CopilotKit's React quickstart (npx create), then scale to mobile and Slack with the same agent backend. Try it free—no credit card needed.

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