{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-shinkai","slug":"shinkai","name":"Shinkai","type":"mcp","url":"http://github.com/dcSpark/shinkai-apps/","page_url":"https://unfragile.ai/shinkai","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-shinkai__cap_0","uri":"capability://automation.workflow.low.code.agent.creation.via.form.based.ui","name":"low-code agent creation via form-based ui","description":"Enables rapid AI agent scaffolding through a React-based form interface (agent-form.tsx) that abstracts agent configuration complexity into visual controls. The system captures agent metadata, model selection, system prompts, and tool bindings, then serializes this configuration into a structured format that the Shinkai Node backend consumes. This eliminates the need to write YAML or JSON manually, reducing agent creation from hours to minutes.","intents":["I want to create a new AI agent without writing configuration files","I need to quickly prototype an agent with specific tools and model preferences","I want a visual way to bind tools to agents and set system prompts"],"best_for":["non-technical founders prototyping AI workflows","teams building internal AI tools without DevOps expertise","rapid iteration cycles where configuration speed matters"],"limitations":["Form-based UI may not expose all advanced Shinkai Node configuration options","No version control or diff-based agent configuration comparison","Agent updates require re-submission through the form; no direct config editing"],"requires":["Shinkai Desktop application running","Active connection to local or remote Shinkai Node","At least one LLM provider configured (OpenAI, Anthropic, etc.)"],"input_types":["form fields (text, dropdowns, toggles)","tool selection from available tool registry"],"output_types":["agent configuration object","serialized agent definition sent to Shinkai Node"],"categories":["automation-workflow","ui-builder"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-shinkai__cap_1","uri":"capability://tool.use.integration.tool.creation.and.playground.with.live.testing","name":"tool creation and playground with live testing","description":"Provides an interactive tool development environment (tool-details-card.tsx, tool-card.tsx) where developers can define tool schemas, test execution with sample inputs, and validate outputs before binding to agents. The playground integrates with the Shinkai Node's tool execution engine, allowing real-time invocation of tools with arbitrary parameters. Tool definitions are stored in a registry accessible to all agents, enabling reusable tool libraries.","intents":["I want to create a custom tool and test it before deploying to production","I need to debug a tool's behavior with different input parameters","I want to share tool definitions across multiple agents without duplication"],"best_for":["developers building custom integrations (APIs, databases, webhooks)","teams managing shared tool libraries across multiple agents","iterative tool development with frequent testing cycles"],"limitations":["Tool playground executes against live backends; no sandboxing or dry-run mode","No built-in tool versioning; updates overwrite previous definitions","Tool schema validation relies on JSON Schema; complex conditional logic not fully supported"],"requires":["Shinkai Desktop application","Access to external services the tool integrates with (APIs, databases)","Proper authentication credentials configured in tool definition"],"input_types":["tool schema (JSON Schema format)","tool name and description","execution parameters (arbitrary JSON)"],"output_types":["tool execution result (JSON)","error messages and stack traces","execution logs"],"categories":["tool-use-integration","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-shinkai__cap_10","uri":"capability://automation.workflow.settings.persistence.and.application.configuration","name":"settings persistence and application configuration","description":"Manages application-wide settings (settings.ts) including LLM provider credentials, default agent selection, UI preferences, and node connection details. Settings are persisted to local storage (encrypted for sensitive data) and synchronized across application restarts. The system provides a settings UI (settings.tsx) for user-facing configuration and programmatic APIs for application code to read/write settings.","intents":["I want to save my LLM provider credentials so I don't enter them every time","I need to set application defaults (default agent, default model, etc.)","I want to backup and restore my Shinkai configuration"],"best_for":["users managing multiple Shinkai instances with different configurations","teams standardizing Shinkai settings across users","developers building custom Shinkai extensions"],"limitations":["Settings are stored locally; no cloud sync or multi-device synchronization","Encryption is basic; relies on OS-level file permissions for security","No settings versioning or rollback; updates overwrite previous values"],"requires":["Shinkai Desktop application","Write access to application data directory"],"input_types":["form inputs (text, dropdowns, toggles)","file uploads (for configuration import)"],"output_types":["settings JSON","configuration export files"],"categories":["automation-workflow","configuration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-shinkai__cap_11","uri":"capability://tool.use.integration.galxe.platform.integration.for.credential.and.reputation.management","name":"galxe platform integration for credential and reputation management","description":"Integrates with the Galxe platform for credential verification and reputation tracking, allowing agents to access user credentials and reputation scores during execution. The system implements OAuth-style authentication with Galxe, caches credential data locally, and exposes credentials to agents through the tool execution context. This enables agents to perform reputation-aware actions or access Galxe-protected resources.","intents":["I want agents to verify user credentials via Galxe before executing sensitive actions","I need to track agent execution reputation on the Galxe platform","I want to build agents that access Galxe-protected resources"],"best_for":["organizations using Galxe for credential management","teams building reputation-aware AI agents","developers integrating Shinkai with Galxe ecosystems"],"limitations":["Galxe integration is optional; agents work without it","Credential caching is local; no real-time revocation checks","Reputation tracking is read-only; agents cannot update reputation directly"],"requires":["Galxe account and API credentials","Network connectivity to Galxe API","Shinkai Desktop application"],"input_types":["Galxe API credentials","user credential requests"],"output_types":["credential verification results","reputation scores","credential metadata"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-shinkai__cap_2","uri":"capability://tool.use.integration.mcp.server.exposure.for.agent.and.tool.access","name":"mcp server exposure for agent and tool access","description":"Exposes all created agents and tools as an MCP (Model Context Protocol) server, enabling external clients (Claude, other LLM applications, custom scripts) to discover and invoke agents/tools via standardized MCP endpoints. The system implements MCP resource and tool definitions that map to internal Shinkai agent/tool registries, with request routing handled by the Tauri backend (main.rs, deep_links.rs). This allows Shinkai agents to be consumed by any MCP-compatible client without custom integration code.","intents":["I want Claude or other LLM clients to access my Shinkai agents as tools","I need to expose my agent library to external applications via a standard protocol","I want to build custom MCP clients that orchestrate multiple Shinkai agents"],"best_for":["teams integrating Shinkai with Claude or other MCP-aware LLM clients","developers building multi-agent orchestration systems","organizations standardizing on MCP for AI tool interoperability"],"limitations":["MCP server requires Shinkai Desktop to be running; no standalone server mode","MCP resource discovery is limited to agents/tools defined in the local Shinkai instance","No built-in authentication for MCP endpoints; relies on local network isolation or firewall rules"],"requires":["Shinkai Desktop application running","MCP client application (Claude, custom script, etc.)","Network connectivity between MCP client and Shinkai Desktop (localhost or network)"],"input_types":["MCP resource requests","MCP tool invocation payloads (JSON)"],"output_types":["MCP resource definitions","tool execution results","error responses in MCP format"],"categories":["tool-use-integration","interoperability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-shinkai__cap_3","uri":"capability://text.generation.language.conversational.ai.chat.interface.with.context.management","name":"conversational ai chat interface with context management","description":"Provides a real-time chat UI (chat-conversation.tsx, message-list.tsx) that maintains conversation history, manages context windows, and routes messages to selected agents. The system implements a message system that tracks sender/receiver, timestamps, and message types (user, agent, system), with context set via set-conversation-context.tsx allowing users to bind specific agents, tools, and knowledge bases to a conversation. Messages are persisted and streamed through WebSocket connections to the Shinkai Node backend for real-time response generation.","intents":["I want to have a natural conversation with an AI agent and see responses in real-time","I need to switch agents mid-conversation or add tools to an ongoing chat","I want to maintain conversation history and reference previous messages"],"best_for":["end users interacting with AI agents through a familiar chat interface","teams building internal chatbot applications","developers testing agent behavior through conversational interaction"],"limitations":["Context window management is automatic but not user-configurable; no explicit token counting UI","Message persistence is local to the Shinkai instance; no multi-device sync","No built-in conversation branching or alternative response exploration"],"requires":["Shinkai Desktop application","Active Shinkai Node backend with WebSocket support","Selected agent and optionally bound tools/knowledge bases"],"input_types":["text messages","file attachments (for knowledge base context)"],"output_types":["streamed text responses","agent metadata (name, model used)","tool invocation logs"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-shinkai__cap_4","uri":"capability://memory.knowledge.vector.based.knowledge.base.management.and.search","name":"vector-based knowledge base management and search","description":"Manages a vector file system (vector-fs-context.tsx, all-files-tab.tsx) where documents are indexed and embedded for semantic search. Users can upload files, organize them into knowledge bases, and search using natural language queries (search-node-files.tsx). The system integrates with the Shinkai Node's embedding and vector storage layer, enabling agents to retrieve relevant context from the knowledge base during conversations. Files are chunked, embedded, and stored in a vector database accessible to all agents.","intents":["I want to upload documents and have agents search them semantically during conversations","I need to organize files into knowledge bases and control which agents can access them","I want to search my document library using natural language queries"],"best_for":["teams building RAG (Retrieval-Augmented Generation) applications","organizations managing large document libraries for AI agents","developers building knowledge-intensive chatbots"],"limitations":["Vector search quality depends on embedding model; no built-in embedding model selection UI","File chunking strategy is fixed; no user control over chunk size or overlap","No built-in document versioning; file updates overwrite previous embeddings"],"requires":["Shinkai Desktop application","Shinkai Node with embedding and vector storage support","Supported file formats (PDF, TXT, DOCX, etc.)"],"input_types":["document files (PDF, TXT, DOCX, etc.)","natural language search queries"],"output_types":["ranked document chunks","similarity scores","file metadata"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-shinkai__cap_5","uri":"capability://tool.use.integration.multi.provider.llm.model.management.and.switching","name":"multi-provider llm model management and switching","description":"Provides a settings interface (ais.tsx, default-llm-provider-updater.tsx) for configuring and switching between multiple LLM providers (OpenAI, Anthropic, local models via Ollama, etc.). The system stores provider credentials securely, allows per-agent model selection, and implements a default provider fallback mechanism. Model availability is queried from each provider's API, and the system validates model compatibility with agent requirements before execution.","intents":["I want to use different LLM providers for different agents (e.g., GPT-4 for complex tasks, Claude for writing)","I need to switch between cloud and local models without reconfiguring agents","I want to set a default LLM provider and override it per-agent"],"best_for":["teams evaluating multiple LLM providers","organizations with cost optimization requirements (local vs cloud models)","developers building multi-model agent systems"],"limitations":["Credentials are stored locally; no centralized credential management for teams","Model switching requires agent restart; no mid-conversation model switching","No built-in cost tracking or usage analytics per provider"],"requires":["Shinkai Desktop application","API keys for desired LLM providers (OpenAI, Anthropic, etc.)","Network connectivity to provider APIs or local Ollama instance"],"input_types":["provider API keys","model names/IDs","provider configuration (endpoint URLs, etc.)"],"output_types":["available models list","provider status (connected/disconnected)","model metadata (context window, pricing, etc.)"],"categories":["tool-use-integration","configuration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-shinkai__cap_6","uri":"capability://automation.workflow.task.scheduling.and.automation.workflow.orchestration","name":"task scheduling and automation workflow orchestration","description":"Enables scheduling of agent tasks to run on a recurring basis (via task scheduling in the backend), with support for cron-like expressions and event-based triggers. The system integrates with the Shinkai Node's scheduler to execute agents at specified intervals, capture results, and optionally route outputs to other agents or external systems. Workflow state is persisted, allowing complex multi-step automation sequences.","intents":["I want to run an agent on a schedule (e.g., daily report generation)","I need to chain multiple agents together in a workflow","I want to trigger agent execution based on external events or conditions"],"best_for":["teams automating repetitive AI tasks (data processing, report generation)","organizations building event-driven agent workflows","developers implementing scheduled data pipelines with AI"],"limitations":["Scheduling UI is minimal; complex cron expressions require manual entry","No built-in retry logic or failure handling; failed tasks require manual intervention","Workflow state is not distributed; no multi-instance failover support"],"requires":["Shinkai Desktop application","Shinkai Node with scheduler support","Configured agents and tools"],"input_types":["cron expressions or schedule definitions","agent selection","workflow trigger conditions"],"output_types":["task execution logs","scheduled task results","workflow state snapshots"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-shinkai__cap_7","uri":"capability://automation.workflow.cross.platform.desktop.and.browser.application.deployment","name":"cross-platform desktop and browser application deployment","description":"Provides a Tauri-based desktop application (shinkai-desktop) that runs on Windows, macOS, and Linux, with a companion browser-based interface for remote access. The system uses Tauri's native bridge (main.rs, windows/mod.rs) to expose Shinkai Node functionality to the UI layer, with deep linking support (deep_links.rs) for protocol-based agent invocation. The monorepo structure (NX-based) enables code sharing between desktop and web frontends.","intents":["I want to run Shinkai locally on my desktop without cloud dependencies","I need to access my Shinkai agents from a web browser","I want to integrate Shinkai with native OS features (tray, notifications, file system)"],"best_for":["organizations requiring local-first AI agent management","teams with mixed desktop and web user bases","developers building AI tools with native OS integration"],"limitations":["Desktop app requires installation; no portable single-binary distribution","Web interface requires network connectivity to Shinkai Node; no offline mode","Tauri adds ~50MB to application size compared to pure web apps"],"requires":["Windows 10+, macOS 10.13+, or Linux (Ubuntu 18.04+)","Node.js 18+ for development","Rust toolchain for building from source"],"input_types":["UI interactions (clicks, form submissions)","file system access (file uploads, downloads)","deep links (protocol-based invocations)"],"output_types":["rendered UI","file system writes","native notifications"],"categories":["automation-workflow","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-shinkai__cap_8","uri":"capability://tool.use.integration.real.time.bidirectional.communication.via.websocket","name":"real-time bidirectional communication via websocket","description":"Implements WebSocket-based real-time communication between the Tauri desktop frontend and the Shinkai Node backend, enabling streaming responses, live agent status updates, and bidirectional message flow. The system uses the shinkai-message-ts library to serialize/deserialize messages, with automatic reconnection and message queuing for offline resilience. This allows agents to stream responses character-by-character and tools to report progress in real-time.","intents":["I want to see agent responses stream in real-time instead of waiting for completion","I need live updates on agent status and tool execution progress","I want reliable message delivery even if the connection temporarily drops"],"best_for":["applications requiring low-latency agent interaction","teams building real-time collaborative AI tools","developers implementing streaming LLM responses"],"limitations":["WebSocket connections are not multiplexed; each conversation requires a separate connection","Message queuing is in-memory; no persistent queue for offline scenarios","No built-in message encryption; relies on TLS for security"],"requires":["Shinkai Node with WebSocket support","Network connectivity between client and Shinkai Node","shinkai-message-ts library for message serialization"],"input_types":["user messages","tool invocation requests","context updates"],"output_types":["streamed text responses","status updates","error messages"],"categories":["tool-use-integration","real-time-communication"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-shinkai__cap_9","uri":"capability://automation.workflow.shinkai.node.lifecycle.management.and.local.remote.deployment","name":"shinkai node lifecycle management and local/remote deployment","description":"Provides automated Shinkai Node management (shinkai-node-manager-client.ts) that handles local node startup, shutdown, and health checks, with support for both local and remote node connections. The system can download and manage Shinkai Node binaries, configure node settings, and expose node management APIs through the Tauri backend. Users can toggle between local and remote nodes without restarting the application.","intents":["I want to run a Shinkai Node locally without manual setup","I need to connect to a remote Shinkai Node for team collaboration","I want to monitor Shinkai Node health and restart it if needed"],"best_for":["solo developers running local Shinkai instances","teams deploying shared Shinkai Nodes on servers","organizations managing multiple Shinkai instances"],"limitations":["Local node management is single-instance; no clustering or failover","Node binary downloads are not incremental; full binary is re-downloaded on updates","Health checks are basic (connectivity only); no deep diagnostics"],"requires":["Shinkai Desktop application","Sufficient disk space for Shinkai Node binary (~100-500MB depending on platform)","For remote nodes: network connectivity and authentication credentials"],"input_types":["node configuration (local vs remote)","remote node URL and credentials","node startup parameters"],"output_types":["node status (running/stopped)","health check results","node logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["Shinkai Desktop application running","Active connection to local or remote Shinkai Node","At least one LLM provider configured (OpenAI, Anthropic, etc.)","Shinkai Desktop application","Access to external services the tool integrates with (APIs, databases)","Proper authentication credentials configured in tool definition","Write access to application data directory","Galxe account and API credentials","Network connectivity to Galxe API","MCP client application (Claude, custom script, etc.)"],"failure_modes":["Form-based UI may not expose all advanced Shinkai Node configuration options","No version control or diff-based agent configuration comparison","Agent updates require re-submission through the form; no direct config editing","Tool playground executes against live backends; no sandboxing or dry-run mode","No built-in tool versioning; updates overwrite previous definitions","Tool schema validation relies on JSON Schema; complex conditional logic not fully supported","Settings are stored locally; no cloud sync or multi-device synchronization","Encryption is basic; relies on OS-level file permissions for security","No settings versioning or rollback; updates overwrite previous values","Galxe integration is optional; agents work without it","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.49,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.049Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=shinkai","compare_url":"https://unfragile.ai/compare?artifact=shinkai"}},"signature":"BhO+ZoImPpuAsCDCTVv0Ls2yqLiGywjLUHFEa5rfWhI6GA3MV7ZrkvMNs+DVVtNSWdR7o1X20aOCkNh/rBGJCw==","signedAt":"2026-06-22T04:12:58.572Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/shinkai","artifact":"https://unfragile.ai/shinkai","verify":"https://unfragile.ai/api/v1/verify?slug=shinkai","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"}}