{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-copilotkit--copilotkit","slug":"copilotkit--copilotkit","name":"CopilotKit","type":"agent","url":"https://docs.copilotkit.ai","page_url":"https://unfragile.ai/copilotkit--copilotkit","categories":["app-builders"],"tags":["agent","agent-native","agentic-ai","agents","ai","ai-agent","ai-assistant","assistant","assistant-chat-bots","copilot","copilot-chat","generative-ui","js","llm","nextjs","open-source","react","reactjs","ts","typescript"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-copilotkit--copilotkit__cap_0","uri":"capability://tool.use.integration.ag.ui.protocol.compliant.agent.ui.bidirectional.communication","name":"ag-ui protocol-compliant agent-ui bidirectional communication","description":"Implements the AG-UI Protocol (Agent-User Interaction Protocol) as a standardized message format for real-time, bidirectional communication between frontend UI components and backend agents. Uses a schema-based event streaming architecture where agents emit structured events (tool calls, state updates, generative UI renders) that the frontend consumes and renders reactively. The protocol enables human-in-the-loop workflows where UI can interrupt, modify, or approve agent actions before execution.","intents":["I want my agent to communicate with the frontend in a standardized way that multiple providers support","I need to build interactive agents where users can approve or modify actions mid-execution","I want to render dynamic UI components based on agent state without custom serialization"],"best_for":["Teams building agent-native applications requiring cross-provider compatibility","Developers implementing human-in-the-loop AI workflows","Organizations adopting the AG-UI Protocol standard (Google, LangChain, AWS, Microsoft, Mastra, PydanticAI)"],"limitations":["Protocol adoption requires backend agent framework integration (LangGraph, CrewAI, etc.)","Real-time streaming requires WebSocket or Server-Sent Events support","Complex state synchronization can introduce latency in high-frequency updates"],"requires":["Backend runtime (Node.js Express/Next.js/NestJS/Hono or Python FastAPI)","Frontend framework (React 18+ or Angular 15+)","Agent framework with AG-UI Protocol support (LangGraph, CrewAI, or custom implementation)"],"input_types":["agent events (tool calls, state updates, generative UI payloads)","user interactions (approvals, modifications, interrupts)"],"output_types":["structured event streams","rendered UI components","agent execution state"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-copilotkit--copilotkit__cap_1","uri":"capability://tool.use.integration.react.component.library.for.agent.native.ui.rendering","name":"react component library for agent-native ui rendering","description":"Provides pre-built React components (CopilotChat, CopilotTextarea, CopilotSidebar) that integrate with the CopilotKit Provider to render agent conversations, tool outputs, and generative UI. Components use React hooks (useCopilotAction, useCopilotReadable) to bind frontend state to agent context, enabling bidirectional data flow. The library handles streaming message rendering, tool result visualization, and real-time state synchronization without requiring manual WebSocket management.","intents":["I want to add a chat interface to my React app that connects to an agent backend","I need to display agent tool outputs and generative UI components in my app","I want to bind my app's state to the agent context so it can read/modify application data"],"best_for":["React developers building agent-native applications","Teams using Next.js, Remix, or other React frameworks","Developers who want pre-styled, accessible chat and copilot components"],"limitations":["React 18+ required (hooks-based architecture)","Component styling uses Tailwind CSS by default; custom theming requires CSS overrides","No built-in persistence — chat history requires external storage integration","Mobile responsiveness is component-level; full mobile optimization depends on parent layout"],"requires":["React 18+","CopilotKit Provider wrapping the component tree","Backend CopilotRuntime endpoint configured","TypeScript 4.9+ (for type safety with tool definitions)"],"input_types":["agent events from backend","user text input","user interactions (button clicks, approvals)"],"output_types":["rendered chat messages","tool result UI","generative UI components"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-copilotkit--copilotkit__cap_10","uri":"capability://memory.knowledge.codebase.aware.context.injection.for.agent.reasoning","name":"codebase-aware context injection for agent reasoning","description":"Enables agents to access and reason about the application's codebase through useCopilotReadable hook (React) or CopilotReadableService (Angular). Developers can expose code snippets, documentation, or application state as readable context that agents can access during reasoning. The context is sent to the agent's LLM as part of the system prompt, enabling code-aware suggestions and actions. Supports selective context exposure through metadata filtering.","intents":["I want agents to understand my codebase and make code-aware suggestions","I need agents to access documentation or API specifications","I want to expose application state so agents can make informed decisions"],"best_for":["Developer-focused applications (IDEs, code editors, documentation tools)","Teams building code generation or refactoring agents","Applications where agent reasoning benefits from codebase context"],"limitations":["Large codebase context (>100KB) increases LLM token usage and latency","No automatic code indexing; developers must manually expose relevant code","Context is sent on every agent request; no caching or incremental updates","Sensitive code (API keys, credentials) can be accidentally exposed if not filtered"],"requires":["React 18+ (useCopilotReadable) or Angular 15+ (CopilotReadableService)","CopilotKit Provider/Service configured","Careful context selection to avoid token bloat"],"input_types":["code snippets","documentation strings","application state","metadata for filtering"],"output_types":["context sent to agent LLM","code-aware agent responses"],"categories":["memory-knowledge","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-copilotkit--copilotkit__cap_11","uri":"capability://automation.workflow.cli.scaffolding.and.project.initialization","name":"cli scaffolding and project initialization","description":"Provides a command-line tool (create-copilot-app) that scaffolds new CopilotKit projects with pre-configured frontend (React/Angular) and backend (Express/Next.js/NestJS/Hono/FastAPI) templates. The CLI generates boilerplate code, installs dependencies, and configures the CopilotKit Provider and Runtime. Supports multiple framework combinations and includes example agents to demonstrate patterns.","intents":["I want to quickly start a new CopilotKit project without manual setup","I need a working example with frontend and backend already configured","I want to choose my preferred frontend and backend frameworks during setup"],"best_for":["Developers new to CopilotKit wanting quick onboarding","Teams prototyping agent-native applications","Developers who prefer scaffolding over manual configuration"],"limitations":["Generated code is opinionated; customization requires manual editing","CLI only supports predefined framework combinations; custom stacks require manual setup","Scaffolded projects include example code that must be removed for production","No built-in CI/CD or deployment configuration"],"requires":["Node.js 18+ or Python 3.9+","npm, yarn, or pnpm for Node.js projects","pip for Python projects"],"input_types":["CLI flags for framework selection","project name and directory"],"output_types":["scaffolded project directory","configured package.json/pyproject.toml","example agent and UI code"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-copilotkit--copilotkit__cap_12","uri":"capability://text.generation.language.tool.result.rendering.with.custom.component.support","name":"tool result rendering with custom component support","description":"Automatically renders tool execution results in the chat interface, with support for custom component rendering. When an agent executes a tool, the result is displayed using a registered component renderer. Developers can define custom renderers for specific tool types (e.g., render database query results as a table, render code as syntax-highlighted blocks). The system falls back to JSON rendering for unregistered tool types.","intents":["I want tool results to display nicely in the chat, not as raw JSON","I need custom rendering for specific tool types (tables, charts, code)","I want to control how agent tool outputs appear to users"],"best_for":["Applications with diverse tool types requiring custom visualization","Teams building data-driven copilot experiences","Developers wanting rich tool result presentation"],"limitations":["Custom renderers must be pre-registered; dynamic component loading is unsafe","Renderer selection is based on tool name/type; complex routing requires custom logic","Large tool results (>10MB) may cause performance issues in chat rendering","No built-in pagination or virtualization for large result sets"],"requires":["React 18+ (for custom component renderers)","Tool definitions with consistent naming for renderer matching","Custom renderer components"],"input_types":["tool execution results (JSON, structured data)","tool metadata (name, type)"],"output_types":["rendered React components","formatted tool result display"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-copilotkit--copilotkit__cap_13","uri":"capability://tool.use.integration.multi.provider.llm.support.with.provider.abstraction","name":"multi-provider llm support with provider abstraction","description":"Abstracts LLM provider selection through a provider configuration layer, supporting OpenAI, Anthropic, Google, Azure, and local models (Ollama). Agents can be configured to use any provider without code changes. The abstraction handles provider-specific API differences (function calling schemas, streaming formats, token limits) transparently. Supports provider fallback and cost-aware provider selection.","intents":["I want to switch LLM providers without changing my agent code","I need to use multiple LLM providers (OpenAI for reasoning, Anthropic for safety)","I want to use local models (Ollama) for privacy-sensitive applications"],"best_for":["Teams evaluating multiple LLM providers","Applications requiring provider flexibility for cost optimization","Organizations with privacy requirements (local models)"],"limitations":["Provider abstraction adds ~50-100ms latency per request due to translation layer","Not all providers support all features (e.g., vision, function calling); fallback behavior varies","Provider-specific optimizations (e.g., OpenAI's parallel function calling) may not be available through abstraction","Cost tracking requires manual provider-specific billing integration"],"requires":["API keys for selected providers (OpenAI, Anthropic, Google, Azure)","Or local Ollama instance for local models","Provider configuration in CopilotRuntime"],"input_types":["provider configuration (API keys, model names, parameters)","agent prompts and tool definitions"],"output_types":["LLM responses","function calls","streaming tokens"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-copilotkit--copilotkit__cap_14","uri":"capability://planning.reasoning.agent.registry.and.multi.agent.orchestration","name":"agent registry and multi-agent orchestration","description":"Provides AgentRegistry for registering multiple agents and routing requests to the appropriate agent based on user input or configuration. Agents are registered by name and can be selected at runtime. The registry handles agent lifecycle, tool execution context, and state isolation between agents. Supports agent composition where one agent can delegate to another.","intents":["I want to run multiple specialized agents in the same application","I need to route user requests to different agents based on intent","I want agents to delegate tasks to other agents"],"best_for":["Applications with multiple agent personas (customer service, technical support, sales)","Teams building agent orchestration systems","Complex workflows requiring agent composition"],"limitations":["Agent selection logic must be implemented manually; no built-in intent routing","State isolation between agents requires careful design; shared state can cause conflicts","Agent-to-agent delegation adds latency (network round-trip per delegation)","No built-in load balancing across agent instances"],"requires":["CopilotRuntime with AgentRegistry configured","Multiple agent implementations (LangGraph, CrewAI, or custom)"],"input_types":["agent name/ID for selection","user input and context"],"output_types":["agent response","delegation requests to other agents"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-copilotkit--copilotkit__cap_2","uri":"capability://tool.use.integration.angular.service.based.agent.integration.with.dependency.injection","name":"angular service-based agent integration with dependency injection","description":"Provides Angular services (CopilotService, CopilotChatService) and directives that integrate with Angular's dependency injection system to connect agent backends. Services expose RxJS Observables for agent state, messages, and tool outputs, enabling reactive data binding in Angular templates. Handles WebSocket lifecycle management and automatic reconnection within Angular's service lifecycle hooks.","intents":["I want to integrate an agent copilot into my Angular application","I need to bind agent state to Angular form controls and reactive forms","I want to use Angular's dependency injection to manage agent service configuration"],"best_for":["Angular developers (15+) building agent-native applications","Enterprise teams using Angular with existing DI patterns","Developers who prefer RxJS Observables over React hooks"],"limitations":["Angular 15+ required","RxJS learning curve for developers unfamiliar with reactive programming","No pre-built UI components like React; requires custom template implementation","Smaller ecosystem of third-party integrations compared to React version"],"requires":["Angular 15+","RxJS 7+","CopilotKit backend runtime","TypeScript 4.9+"],"input_types":["agent events from backend","user input via Angular forms","template-driven interactions"],"output_types":["RxJS Observables emitting agent state","rendered template bindings","tool execution results"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-copilotkit--copilotkit__cap_3","uri":"capability://tool.use.integration.frontend.action.and.tool.definition.with.type.safe.schema.binding","name":"frontend action and tool definition with type-safe schema binding","description":"Enables developers to define frontend actions (useFrontendAction hook in React, FrontendActionService in Angular) that agents can invoke with type-safe arguments. Actions are registered with JSON Schema definitions that the agent receives, allowing it to understand available frontend capabilities. The system validates arguments against schemas before execution and provides TypeScript type inference for action handlers, ensuring compile-time safety.","intents":["I want agents to be able to call functions in my frontend application","I need type-safe communication between agent tool calls and frontend handlers","I want to expose my app's capabilities (navigate, update state, trigger animations) to agents"],"best_for":["Developers building agent-native UIs where agents modify frontend state","Teams requiring type safety between agent and frontend boundaries","Applications with complex frontend logic that agents need to orchestrate"],"limitations":["Schema validation adds ~50-100ms per tool call overhead","Complex nested schemas may require manual JSON Schema authoring","No automatic schema generation from TypeScript types (requires manual definition)","Frontend actions are synchronous; async operations require wrapping in state management"],"requires":["React 18+ (useFrontendAction) or Angular 15+ (FrontendActionService)","JSON Schema knowledge for action parameter definition","CopilotKit Provider/Service configured"],"input_types":["JSON Schema action definitions","agent tool call requests with typed arguments"],"output_types":["action execution results","error responses with validation details"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-copilotkit--copilotkit__cap_4","uri":"capability://text.generation.language.generative.ui.rendering.with.shared.state.synchronization","name":"generative ui rendering with shared state synchronization","description":"Implements the Generative UI system where agents can emit UI component definitions (as JSON/JSX) that the frontend renders dynamically. Uses a shared state management layer (useCopilotReadable hook) where frontend state is synchronized bidirectionally with agent context. Agents can read current UI state, generate new components based on that state, and trigger re-renders when state changes, enabling truly interactive generative interfaces.","intents":["I want agents to generate and render custom UI components dynamically","I need agents to read my app's current state and generate contextual UI","I want to build interfaces where the UI itself is generated by the agent"],"best_for":["Developers building highly interactive, agent-driven UIs","Applications requiring dynamic form generation or content rendering","Teams building copilot experiences where UI adapts to agent reasoning"],"limitations":["Generative UI components must be pre-registered; arbitrary component generation is unsafe","State synchronization latency increases with large state objects (>1MB)","No built-in conflict resolution for concurrent state updates from agent and user","Requires careful component design to avoid infinite re-render loops"],"requires":["React 18+ with useCopilotReadable hook","Pre-defined component registry for safe generative rendering","Backend agent with generative UI support"],"input_types":["agent-generated UI component definitions","frontend state updates","user interactions on generated components"],"output_types":["rendered React components","updated shared state","agent context updates"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-copilotkit--copilotkit__cap_5","uri":"capability://automation.workflow.copilotruntime.backend.orchestration.with.multi.framework.support","name":"copilotruntime backend orchestration with multi-framework support","description":"Provides a backend runtime abstraction (CopilotRuntime class) that works across Node.js frameworks (Express, Next.js, NestJS, Hono) and Python (FastAPI). The runtime handles agent lifecycle management, tool execution, event streaming, and state persistence. It abstracts framework-specific HTTP/WebSocket details, allowing developers to define agents once and deploy to any supported framework. Includes built-in agent support (default agent) and custom agent registration via AgentRegistry.","intents":["I want to set up a backend agent endpoint that works with multiple frameworks","I need to manage agent lifecycle, tool execution, and state persistence","I want to integrate existing agents (LangGraph, CrewAI) without rewriting infrastructure"],"best_for":["Backend developers building agent services","Teams with polyglot stacks (Node.js + Python)","Organizations migrating between frameworks without rewriting agent logic"],"limitations":["Framework-specific adapters required for each runtime (Express, Next.js, NestJS, Hono, FastAPI)","State persistence requires external storage (no built-in database); default uses in-memory store","Tool execution is sequential; parallel tool execution requires custom agent implementation","Event streaming latency depends on framework's WebSocket implementation"],"requires":["Node.js 18+ (for TypeScript runtime) or Python 3.9+ (for Python SDK)","Express 4.18+, Next.js 13+, NestJS 9+, Hono 3+, or FastAPI 0.95+","Agent framework (LangGraph, CrewAI) or custom agent implementation"],"input_types":["HTTP POST requests with agent input","WebSocket connections for streaming","tool execution requests"],"output_types":["event streams (tool calls, state updates, messages)","agent execution results","error responses"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-copilotkit--copilotkit__cap_6","uri":"capability://tool.use.integration.langgraph.and.crewai.agent.framework.integration","name":"langgraph and crewai agent framework integration","description":"Provides native integration with LangGraph (LangChain's agentic framework) and CrewAI through the Python SDK. Developers can define agents in LangGraph or CrewAI, then wrap them with CopilotKitRemoteEndpoint to expose them via FastAPI. The integration handles graph execution, tool calling, and event streaming, translating LangGraph/CrewAI events into AG-UI Protocol messages. Supports custom agents via Python SDK's Agent base class.","intents":["I want to use my existing LangGraph agent with CopilotKit frontend","I need to expose a CrewAI agent through a web API","I want to build agents in Python and connect them to React/Angular frontends"],"best_for":["Python developers familiar with LangGraph or CrewAI","Teams with existing LangGraph/CrewAI agents wanting to add UI","Organizations preferring Python for agent development"],"limitations":["Python SDK requires Python 3.9+; no Python 3.8 support","LangGraph integration tested with LangGraph 0.1+; older versions may have compatibility issues","CrewAI integration is newer; fewer examples and community patterns than LangGraph","Event streaming from Python to frontend adds ~100-200ms latency compared to Node.js"],"requires":["Python 3.9+","LangGraph 0.1+ or CrewAI 0.1+","FastAPI 0.95+","CopilotKit Python SDK"],"input_types":["LangGraph graph definitions","CrewAI agent configurations","custom Python Agent implementations"],"output_types":["AG-UI Protocol event streams","tool execution results","agent state updates"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-copilotkit--copilotkit__cap_7","uri":"capability://automation.workflow.real.time.event.streaming.with.websocket.and.server.sent.events","name":"real-time event streaming with websocket and server-sent events","description":"Implements real-time communication between backend agents and frontend using WebSocket (preferred) or Server-Sent Events (SSE) fallback. The runtime automatically handles connection lifecycle, reconnection logic, and message buffering. Events are streamed as they occur (tool calls, state updates, messages) without batching, enabling low-latency agent-UI interaction. Supports event filtering and metadata attachment for selective frontend updates.","intents":["I want real-time updates from my agent to appear instantly in the UI","I need reliable streaming that reconnects automatically on network failures","I want to filter events so only relevant updates reach the frontend"],"best_for":["Applications requiring low-latency agent-UI interaction","Teams building interactive copilot experiences","Developers needing reliable streaming over unreliable networks"],"limitations":["WebSocket requires server support; some hosting platforms (Vercel, Netlify) have limitations","SSE fallback is unidirectional (server-to-client only); requires separate HTTP POST for client-to-server","Event buffering adds memory overhead for long-running agents; no built-in size limits","Network latency varies; typical latency is 50-200ms depending on geography and connection"],"requires":["WebSocket support on backend framework or SSE support","Frontend WebSocket client (built into CopilotKit)","Network connectivity with WebSocket/HTTP support"],"input_types":["agent events (tool calls, state updates, messages)","event metadata (priority, type, filtering criteria)"],"output_types":["real-time event stream to frontend","connection status updates","error notifications"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-copilotkit--copilotkit__cap_8","uri":"capability://planning.reasoning.human.in.the.loop.workflow.execution.with.approval.gates","name":"human-in-the-loop workflow execution with approval gates","description":"Enables agents to pause execution and request human approval before executing sensitive actions. Agents emit approval requests with action details; the frontend displays these to users who can approve, reject, or modify the action. The agent receives the user's decision and continues or aborts execution accordingly. Supports conditional approval (e.g., approve if cost < $100) and action modification before execution.","intents":["I want agents to ask for approval before executing sensitive operations","I need users to review and modify agent actions before they take effect","I want to implement cost controls where agents ask for approval for expensive operations"],"best_for":["Applications with sensitive operations (financial transactions, data deletion, API calls)","Teams requiring audit trails and user oversight of agent actions","Compliance-heavy industries (finance, healthcare) requiring human approval"],"limitations":["Approval workflow adds latency (user response time + network round-trip); typical 1-10 seconds","No built-in timeout for approval requests; agents wait indefinitely unless custom timeout implemented","Approval state is not persisted; if frontend disconnects, approval request is lost","Complex approval logic (conditional approval, escalation) requires custom implementation"],"requires":["Backend agent with approval request support","Frontend UI to display and handle approval requests","User interaction capability (not suitable for headless/API-only agents)"],"input_types":["agent approval requests with action details","user approval/rejection decisions","modified action parameters"],"output_types":["approval decision sent to agent","modified action parameters","execution continuation or abort signal"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-copilotkit--copilotkit__cap_9","uri":"capability://planning.reasoning.built.in.agent.with.persistence.and.state.management","name":"built-in agent with persistence and state management","description":"Provides a default agent implementation (DefaultAgent) that handles basic conversational AI and tool calling without requiring external agent frameworks. The agent maintains conversation history, manages tool execution state, and supports persistence to external storage (database, file system). Developers can extend DefaultAgent with custom logic or replace it entirely with LangGraph/CrewAI agents. State is stored in a configurable StateStore interface, enabling pluggable persistence backends.","intents":["I want a simple agent without learning LangGraph or CrewAI","I need agent state to persist across sessions","I want to extend the default agent with custom logic"],"best_for":["Developers building simple copilot experiences without complex reasoning","Teams wanting to start with a default agent and upgrade to LangGraph later","Applications requiring conversation history and state persistence"],"limitations":["DefaultAgent lacks advanced reasoning capabilities (no chain-of-thought, no multi-step planning)","No built-in persistence backend; requires custom StateStore implementation","Conversation history is not automatically summarized; long conversations consume memory","Tool execution is sequential; no parallel tool calling support"],"requires":["CopilotRuntime configured","Optional: StateStore implementation for persistence","LLM API key (OpenAI, Anthropic, etc.)"],"input_types":["user messages","tool execution results"],"output_types":["agent responses","tool calls","state updates"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":50,"verified":false,"data_access_risk":"high","permissions":["Backend runtime (Node.js Express/Next.js/NestJS/Hono or Python FastAPI)","Frontend framework (React 18+ or Angular 15+)","Agent framework with AG-UI Protocol support (LangGraph, CrewAI, or custom implementation)","React 18+","CopilotKit Provider wrapping the component tree","Backend CopilotRuntime endpoint configured","TypeScript 4.9+ (for type safety with tool definitions)","React 18+ (useCopilotReadable) or Angular 15+ (CopilotReadableService)","CopilotKit Provider/Service configured","Careful context selection to avoid token bloat"],"failure_modes":["Protocol adoption requires backend agent framework integration (LangGraph, CrewAI, etc.)","Real-time streaming requires WebSocket or Server-Sent Events support","Complex state synchronization can introduce latency in high-frequency updates","React 18+ required (hooks-based architecture)","Component styling uses Tailwind CSS by default; custom theming requires CSS overrides","No built-in persistence — chat history requires external storage integration","Mobile responsiveness is component-level; full mobile optimization depends on parent layout","Large codebase context (>100KB) increases LLM token usage and latency","No automatic code indexing; developers must manually expose relevant code","Context is sent on every agent request; no caching or incremental updates","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7846166186086917,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:57:04.027Z","last_commit":"2026-05-02T07:03:24Z"},"community":{"stars":30591,"forks":3951,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=copilotkit--copilotkit","compare_url":"https://unfragile.ai/compare?artifact=copilotkit--copilotkit"}},"signature":"W8u4IXJuY++j6J2y2ybZ3L02vo65EtAAMDbjoein/aZHn7G6NU1PsKL+7Fd4izyo8yGMh6LocRIo6zDx7DB7CA==","signedAt":"2026-06-22T02:20:08.771Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/copilotkit--copilotkit","artifact":"https://unfragile.ai/copilotkit--copilotkit","verify":"https://unfragile.ai/api/v1/verify?slug=copilotkit--copilotkit","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}